Description:
FIELD OF THE INVENTION
The present invention relates generally to the field of electrical power distribution systems, and more particularly to systems and methods for monitoring, assessing, and managing the health of distribution transformers. Specifically, the invention pertains to automated health indexing platforms that utilize real-time data acquisition, weighted parameter analysis, and alert generation to optimize transformer maintenance, reliability, and asset management.

Application
The invention is applicable to the monitoring and management of distribution transformers deployed in electrical utility networks, including both oil type and dry type transformers. It is particularly suited for use by electric utilities, power distribution companies, and industrial facilities responsible for the operation and maintenance of large fleets of distribution transformers. The system can be integrated into smart grid infrastructures, advanced metering systems, and utility asset management platforms to enable predictive maintenance, reduce downtime, improve safety, and ensure regulatory compliance. The invention is also applicable in environments where real-time monitoring, health assessment, and proactive alerting of transformer status are critical for operational efficiency and reliability.

BACKGROUND OF THE INVENTION
Distribution transformers are essential components in electrical power distribution networks, responsible for stepping down high-voltage electricity to levels suitable for residential, commercial, and industrial use. The reliable operation of these transformers is critical for maintaining a stable and efficient power supply. Traditionally, transformer maintenance has relied on periodic inspections, manual data collection, and reactive repairs following failures. As the number of transformers in service increases and grid complexity grows, there is a pressing need for more advanced, data-driven approaches to transformer health management.

Prior Art Problems
Conventional transformer monitoring and maintenance systems suffer from several limitations:
• Manual Data Collection: Many existing systems depend on manual inspection and data logging, which are labor-intensive, prone to human error, and often result in delayed detection of faults.
• Limited Parameter Monitoring: Prior art solutions typically monitor only a limited set of parameters, such as temperature or oil level, failing to provide a comprehensive assessment of transformer health.
• Lack of Real-Time Analysis: Most traditional systems do not support real-time data acquisition or analysis, leading to delayed responses to developing issues.
• Reactive Maintenance: Maintenance is often performed on a fixed schedule or after a failure occurs, rather than being based on the actual condition of the transformer.
• Inadequate Alerting: Existing systems may not provide timely or actionable alerts, resulting in missed opportunities for preventive maintenance.
• No Integrated Health Index: Prior art lacks a unified health index that combines multiple parameters into a single, actionable metric for asset management and decision-making.

Disadvantages of Prior Art
• Increased Downtime: Delayed fault detection and reactive maintenance lead to increased transformer failures and unplanned outages.
• Higher Maintenance Costs: Inefficient maintenance scheduling results in unnecessary servicing of healthy transformers and insufficient attention to at-risk units.
• Reduced Asset Lifespan: Inability to detect and address emerging issues in a timely manner shortens the operational life of transformers.
• Safety Risks: Failure to identify hazardous conditions, such as overheating or oil leaks, can endanger personnel and equipment.
• Lack of Predictive Insights: Absence of comprehensive data analysis prevents utilities from transitioning to predictive maintenance strategies.

Technical Solution of the Present Invention
The present invention provides a comprehensive system and method for health indexing of distribution transformers. The solution includes:
• Automated Data Acquisition: Integration of multiple sensors to continuously monitor a wide range of operational parameters, including temperature, oil level, voltage, load, age, external condition, and electrical phenomena such as partial discharge and hotspots.
• Weighted Parameter Analysis: Assignment of weights to each parameter based on historical failure data and operational significance, enabling a nuanced assessment of transformer health.
• Composite Health Index Calculation: Real-time computation of a composite health index for each transformer, providing a single, actionable metric for asset management.
• Health Classification and Visualization: Classification of transformers into health categories (excellent, good, average, poor, very poor) and visualization through a color-coded dashboard.
• Automated Alerts and Notifications: Generation of real-time alerts when health indices fall below thresholds or when data is missing, with notifications delivered via email or SMS.
• Trend Analysis and Reporting: Display of health trends over time and detailed breakdowns of health index calculations for informed decision-making.
Technical Effect
The technical effects achieved by the present invention include:
• Early Fault Detection: Enables timely identification of anomalies and potential failures, reducing the risk of unplanned outages.
• Optimized Maintenance: Facilitates predictive maintenance scheduling based on actual transformer condition, minimizing unnecessary servicing and reducing costs.
• Improved Asset Management: Provides utilities with actionable insights for repair, replacement, and resource allocation decisions.
• Enhanced Safety: Detects hazardous conditions in real time, improving safety for personnel and equipment.
• Regulatory Compliance: Supports compliance with industry standards and reporting requirements through comprehensive data collection and analysis.

Technical Advancement of the Present Invention
The present invention advances the state of the art by:
• Introducing a Novel Health Indexing Philosophy: Utilizes a unique, in-house developed methodology for parameter selection, weighting, and health index calculation, tailored to the specific needs of distribution transformers.
• Comprehensive Parameter Monitoring: Monitors a broader and more relevant set of parameters than prior art, including both operational and environmental factors.
• Integrated, Automated Platform: Combines data acquisition, analysis, visualization, and alerting in a unified, automated system.
• Customizable and Scalable: Supports different transformer types (oil and dry), adjustable weighting schemes, and scalable deployment across large transformer fleets.
• Real-Time, Actionable Intelligence: Delivers real-time insights and alerts, enabling proactive asset management and rapid response to emerging issues.

Need for the Present Invention
With the increasing complexity of power distribution networks and the growing number of transformers in service, utilities require advanced tools to ensure reliability, efficiency, and safety. The present invention addresses the shortcomings of prior art by providing a comprehensive, automated, and data-driven solution for transformer health management. It empowers utilities to transition from reactive to predictive maintenance, extend asset lifespans, reduce operational costs, and enhance overall grid reliability. The invention fulfills a critical need for intelligent, real-time transformer health indexing and management in modern power distribution systems.

OBJECT OF THE INVENTION
The primary object of the present invention is to provide a comprehensive and automated system and method for health indexing of distribution transformers that enables real-time monitoring, assessment, and management of transformer health.

Another object of the present invention is to facilitate early detection of faults and anomalies in distribution transformers by continuously monitoring a wide range of operational and environmental parameters.

Another object of the present invention is to provide a robust and reliable methodology for calculating a composite health index for each transformer, utilizing weighted analysis of multiple parameters based on their impact on transformer performance and reliability.

Yet another object of the present invention is to classify distribution transformers into distinct health categories, thereby enabling utilities to prioritize maintenance, repairs, and replacements based on actual transformer condition.

Yet another object of the present invention it to generate timely and actionable alerts and notifications when transformer health indices fall below predetermined thresholds or when critical data is missing, thereby reducing the risk of unplanned outages and equipment failures.

A further object of the present invention is to offer an intuitive, color-coded dashboard and user interface that visually presents health status, trends, and alerts for a fleet of distribution transformers, supporting informed decision-making and efficient asset management.

Yet further object of the present invention is to support predictive maintenance strategies, optimize maintenance schedules, and reduce unnecessary servicing and operational costs.

Yet another object of the present invention is to enhance the safety, reliability, and operational efficiency of electrical power distribution networks by enabling proactive transformer health management.

The other object of the present invention is to provide a scalable and customizable solution that can be adapted to different types of distribution transformers and integrated into existing utility infrastructure and smart grid systems.

SUMMARY OF THE INVENTION
The following disclosure presents a simplified summary of the invention in order to provide a basic understanding of some aspects of the invention. This summary is not an extensive overview of the present invention. It is not intended to identify the key/critical elements of the invention or to delineate the scope of the invention. Its sole purpose is to present some concept of the invention in a simplified form as a prelude to a more detailed description of the invention presented later.

The present invention provides a comprehensive system and method for health indexing of distribution transformers, designed to enhance the reliability, safety, and efficiency of electrical power distribution networks. The invention leverages real-time data acquisition, advanced analytics, and automated alerting to enable utilities and asset managers to proactively monitor and manage the health of both oil type and dry type distribution transformers.

At the core of the invention is a network of sensors deployed on each distribution transformer. These sensors continuously monitor a wide range of operational and environmental parameters, including but not limited to temperature, oil level, voltage, load, age, external condition, and electrical phenomena such as partial discharge and hotspot detection. The collected data is transmitted via a communication module to a central processing unit for analysis.

The central processing unit is configured to assign a weight to each monitored parameter based on its historical impact on transformer reliability and failure rates. Using these weighted values, the system calculates a composite health index for each transformer. This health index provides a single, actionable metric that reflects the overall condition of the transformer, allowing for easy comparison and prioritization across a fleet of assets.

Based on the calculated health index, each transformer is classified into one of several health categories, such as excellent, good, average, poor, or very poor. These categories are visually represented on a user-friendly dashboard using a color-coded scheme, enabling operators to quickly assess the status of all monitored transformers at a glance.

The dashboard further provides trend analysis, displaying changes in health index over time for each transformer. This feature allows utilities to identify deteriorating conditions early and to track the effectiveness of maintenance actions. The dashboard also includes a summary section that presents the distribution of transformers across the various health categories, as well as a detailed breakdown of the health index calculation for each unit.

A key feature of the invention is its automated alert and notification system. When a transformer’s health index falls below a predetermined threshold, or when critical data is missing, the system generates real-time alerts. These alerts are displayed on the dashboard and can also be transmitted to designated personnel via email or SMS, ensuring that maintenance teams are promptly informed of emerging issues.

The invention supports both oil type and dry type distribution transformers, with customizable parameter sets and weighting schemes for each type. This flexibility allows the system to be tailored to the specific needs and operational contexts of different utilities and transformer fleets.

By integrating comprehensive data acquisition, advanced analytics, intuitive visualization, and automated alerting, the present invention enables utilities to transition from reactive to predictive maintenance strategies. This results in reduced downtime, optimized maintenance schedules, extended asset lifespans, and improved safety for personnel and equipment. The invention thus represents a significant advancement in the field of transformer asset management and power distribution reliability.

Other aspects, advantages, and salient features of the invention will become apparent to those skilled in the art from the following detailed description, which, taken in conjunction with the annexed drawings, discloses exemplary embodiments of the invention.

BRIEF DESCRIPTION OF THE ACCOMPANYING DRAWINGS
The above and other aspects, features and advantages of the embodiments of the present disclosure will be more apparent in the following description taken in conjunction with the accompanying drawings, in which:

FIGURE 1 illustrates a system architecture diagram of the health indexing platform for distribution transformers, showing the arrangement of sensors, communication modules, central processing unit, dashboard interface, and network connections.

FIGURE 2 depicts a sensor placement diagram for oil type distribution transformers, highlighting the locations of temperature sensors, oil level gauges, voltage sensors, and other monitoring devices on the transformer body.

FIGURE 3 shows a sensor placement diagram for dry type distribution transformers, indicating the positions of sensors for temperature, voltage, explosion vent pipe condition, and other relevant parameters.

FIGURE 4 presents a flowchart of the data acquisition and transmission process, detailing the steps from sensor data collection to communication with the central processing unit.

FIGURE 5 provides a flowchart of the health index calculation algorithm, illustrating the process of parameter weighting, data processing, and composite health index computation.

FIGURE 6 displays a flowchart for the alert generation and notification process, outlining the detection of abnormal health indices or missing data and the subsequent alert transmission to designated personnel.

FIGURE 7 shows a main dashboard interface, featuring color-coded health status indicators for a fleet of distribution transformers, summary statistics, and active alerts.

FIGURE 8 illustrates an alert pop-up window or notification panel, demonstrating how real-time alerts are presented to users when transformer health indices fall below thresholds or data is missing.

FIGURE 9 presents a trend analysis graph for an individual transformer, showing changes in health index over a selected time period to facilitate monitoring of condition deterioration or improvement.

FIGURE 10 depicts a detailed parameter breakdown view for a selected transformer, displaying individual parameter values, assigned weights, and their contributions to the overall health index.

FIGURE 11 shows a configuration or settings screen, allowing users to adjust alert thresholds, parameter weighting schemes, and notification preferences.

FIGURE 12 illustrates a health category visualization legend, demonstrating the color-coding scheme used to represent health categories such as excellent, good, average, poor, and very poor.

FIGURE 13 presents a population-wise status chart of oil and dry type distribution transformers, showing the distribution of health categories across the monitored fleet.

FIGURE 14 displays a report or summary screen, providing health-wise variation for a particular transformer over the last 30 days, including scoring details and system-generated maintenance suggestions or abnormalities.

Persons skilled in the art will appreciate that elements in the figures are illustrated for simplicity and clarity and may not have been drawn to scale. For example, the dimensions of some of the elements in the figure may be exaggerated relative to other elements to help to improve understanding of various exemplary embodiments of the present disclosure. Throughout the drawings, it should be noted that like reference numbers are used to depict the same or similar elements, features, and structures.

DETAILED DESCRITION OF THE INVENTION
The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of various embodiments of the present disclosure as defined by the claims and their equivalents. It includes various specific details to assist in that understanding, but these are to be regarded as merely exemplary. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the various embodiments described herein can be made without departing from the scope and spirit of the present disclosure. In addition, descriptions of well-known functions and constructions may be omitted for clarity and conciseness.

All terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which various embodiments belong. Further, the meaning of terms or words used in the specification and the claims should not be limited to the literal or commonly employed sense but should be construed in accordance with the spirit of the disclosure to most properly describe the present disclosure.

The terminology used herein is for the purpose of describing particular various embodiments only and is not intended to be limiting of various embodiments. As used herein, the singular forms "a," "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising" used herein specify the presence of stated features, integers, steps, operations, members, components, and/or groups thereof, but do not preclude the presence or addition of one or more other features, integers, steps, operations, members, components, and/or groups thereof.

The present disclosure will now be described more fully with reference to the accompanying drawings, in which various embodiments of the present disclosure are shown.

The present invention provides a comprehensive system and method for health indexing of distribution transformers, enabling real-time monitoring, assessment, and management of transformer health. The invention is designed to address the limitations of conventional transformer maintenance practices by leveraging advanced sensor technology, data analytics, and automated alerting to facilitate predictive maintenance and asset optimization.

System Overview
In one embodiment, the system comprises a plurality of sensors installed on each distribution transformer. These sensors are configured to continuously monitor a range of operational and environmental parameters critical to transformer health. The monitored parameters include, but are not limited to, temperature (top oil temperature and oil temperature profile), oil level, voltage, load patterns, transformer age, external condition, and electrical phenomena such as partial discharge (PD) and hotspot detection.

The sensors are connected to a communication module, which may utilize wired or wireless communication protocols to transmit the collected data to a central processing unit (CPU) or server. The communication module ensures reliable and secure data transfer, supporting real-time or near-real-time monitoring of transformer status.

Data Acquisition and Parameter Monitoring
The system is designed to support both oil type and dry type distribution transformers. For oil type transformers, the monitored parameters include oil level in the conservator tank, condition of the conservator tank, condition of the silica gel breather, and status of high-tension (HT) and low-tension (LT) chambers. For dry type transformers, the system monitors the condition of the explosion vent pipe and other relevant components.

The sensors may include temperature sensors, oil level gauges, voltage sensors, current sensors, humidity sensors, and specialized sensors for detecting partial discharge and hotspots. The system is capable of integrating with existing remote monitoring infrastructure or advanced metering infrastructure (AMI) to enhance data collection capabilities.

Parameter Weighting and Data Processing
Upon receipt of the operational data, the central processing unit assigns a weight to each parameter. The weighting scheme is based on the historical impact of each parameter on transformer reliability and failure rates, as determined from failure data and expert knowledge. For example, parameters such as age and loading may be assigned higher weights due to their significant influence on transformer health.

The weighting scheme is customizable and can be adjusted based on transformer type, operational environment, and utility-specific requirements. The system supports the use of different weighting algorithms for oil type and dry type transformers, ensuring accurate and relevant health assessments.

Health Index Calculation
The CPU processes the weighted parameters to calculate a composite health index for each transformer. The health index is typically expressed as a value between 0 (poor condition) and 10 (excellent condition), although other scales may be used. The calculation involves combining the weighted values of each parameter using a predetermined algorithm, such as a weighted average or a more complex mathematical model.

The health index provides a single, actionable metric that reflects the overall condition of the transformer. This enables utilities to easily compare and prioritize maintenance actions across a large fleet of transformers.

Health Classification
Based on the calculated health index, each transformer is classified into one of several health categories. In one embodiment, the categories include “Excellent,” “Good,” “Average,” “Poor,” and “Very Poor.” The classification thresholds are configurable and may be set according to utility policy or industry standards.

The health categories are used to guide maintenance and replacement decisions. For example, transformers classified as “Very Poor” may be scheduled for immediate replacement, while those in the “Average” category may be subject to close monitoring.

Dashboard and Visualization
The system includes a dashboard interface that provides a comprehensive view of transformer health across the network. The dashboard displays the health index and health category for each transformer, using a color-coded visualization scheme (e.g., deep green for excellent, light green for good, yellow for average, amber for poor, and red for very poor).

The dashboard also presents a summary of the health status distribution among all monitored transformers, such as a pie chart showing the proportion of transformers in each health category. Users can filter the display by location, transformer type, or health category to focus on specific assets.

Trend Analysis and Historical Data
A key feature of the dashboard is the ability to display trends in health index over time for each transformer. The system stores historical health index data, allowing users to view changes in transformer condition over selected time intervals (e.g., 30 days, 6 months, 1 year). This trend analysis helps utilities identify deteriorating conditions early and evaluate the effectiveness of maintenance actions.

Detailed Calculation Breakdown
For each transformer (Oil Type or Dry Tyep), the dashboard provides a detailed breakdown of the health index calculation. Referring table 1 (Oil Type DTR) and table 2 (Dry Type DTR) below users can view the individual parameter values, assigned weights, and the contribution of each parameter to the overall health index. This transparency supports informed decision-making and facilitates root cause analysis in the event of declining transformer health.

Oil Type DTR

SI. NO. PARAMETERS DATA SOURCE WEIGHTAGE LOGIC
1 Age DTLMS 20 0- Age>20 years
5- Age-10-20 years
10- Age<10 years
2 Loading TD 24*7 20 0- Loading>130%
5- Loading>80% & <130%
10- Loading<80%
3 Voltage TD 24*7 10 0- Voltage>110%
5- Voltage>100% & <110%
10- Voltage=100%
4 Oil level Remote Monitoring// Dreams 10 0- oil level below conservator or any cooling circuit choked case or profuse leakage cases reported
10- oil level ok
5 Status of HT FFA chamber Remote Monitoring// Dreams 5 0- HT door open/rusted
10- ok
6 Status of LT CFS chamber Remote Monitoring// Dreams
5 0- LT door open/rusted
10- ok
7 Condition of conservator tank (for oil type DTR) Dreams 10 0- conservator tank rusted or holes in it
10- ok
8 Condition of silica gel breather Dreams 5 0- saturated
10- ok
9 Hotspot detected Dreams 5 0- detected
10- not detected
10 PD detected Dreams 5 0- detected
10- not detected
11 Fault in LT network Mains software 5 0- No. >110% of average
5- No.->100% & <110 % of average
10- < 100% of average

Table 1

The data collected for oil type DTRs is critical for accurately assessing transformer health, predicting failures, and optimizing maintenance schedules. The following parameters are specifically monitored for oil type DTRs:

1. Age: The operational age of the transformer is a key indicator of its expected remaining life. Older transformers are more prone to failures due to insulation degradation and wear of internal components.
2. Loading: The load on the transformer, measured as a percentage of its rated capacity, is continuously monitored. Overloading can cause excessive heating, accelerate insulation breakdown, and lead to premature failure.
3. Voltage: Monitoring the voltage, especially on the low-tension (LT) side, helps detect abnormal fluctuations that may indicate grid instability or transformer malfunction.
4. Oil Level: The oil in the conservator tank serves as both a coolant and an insulating medium. A low oil level, oil leakage, or a choked cooling circuit can result in inadequate cooling, increased risk of insulation failure, and ultimately transformer breakdown.
5. Status of HT FFA Chamber: The condition of the high-tension chamber, including whether the door is open or rusted, is monitored to ensure the integrity and safety of the transformer.
6. Status of LT CFS Chamber: Similar to the HT chamber, the low-tension chamber’s condition is checked for rust, damage, or improper closure, which could compromise transformer performance and safety.
7. Condition of Conservator Tank: The conservator tank’s physical state is assessed for rust or holes, as deterioration can lead to oil contamination, leakage, and reduced transformer life.
8. Condition of Silica Gel Breather: The silica gel breather is monitored for saturation. A saturated breather cannot effectively remove moisture from the air entering the transformer, increasing the risk of insulation degradation.
9. Hotspot Detection: The presence of hotspots within the transformer is a critical indicator of localized overheating, which can rapidly deteriorate insulation and lead to catastrophic failure.
10. Partial Discharge (PD) Detection: PD activity is a sign of insulation breakdown or defects. Early detection allows for timely intervention before major faults occur.
11. Faults in LT Network: The frequency and severity of faults in the LT network connected to the transformer are tracked, as persistent faults can stress the transformer and reduce its lifespan.

Relevance of Oil Type DTR Data:
• Comprehensive Health Assessment: By monitoring these parameters, the system provides a holistic view of the transformer’s operational state and physical condition, enabling accurate health indexing.
• Predictive Maintenance: The data allows for early detection of emerging issues, supporting a shift from reactive to predictive maintenance strategies.
• Risk Mitigation: Timely identification of oil leaks, overheating, or insulation problems helps prevent catastrophic failures, reducing downtime and associated costs.
• Asset Management: The weighted analysis of these parameters informs maintenance prioritization, replacement planning, and resource allocation.
• Safety and Compliance: Monitoring critical safety-related parameters ensures compliance with industry standards and enhances the safety of personnel and equipment.
• Data-Driven Decision Making: The integration of real-time and historical data supports informed operational and strategic decisions for utility asset managers.
Dry Type DTR

SI. NO. PARAMETERS DATA SOURCE WEIGHTAGE LOGIC
1 Age DTLMS 20 0- Age>20 years
5- Age-10-20 years
10- Age<10 years
2 Loading TD 24*7 20 0- Loading>130%
5- Loading>80% & <130%
10- Loading<80%
3 Voltage TD 24*7 10 0- Voltage>110%
5- Voltage>100% & <110%
10- Voltage=100%
4 Status of HT FFA chamber Remote Monitoring// Dreams 5 0- HT door open/rusted
10- ok
5 Status of LT CFS chamber Remote Monitoring// Dreams 5 0- LT door open/rusted
10- ok
6 Condition of explosion vent pipe (for dry type DTR mainly) Dreams 20 0- vent open/rusted
10- ok
7 Hotspot detected Dreams 5 0- detected
10- not detected
8 PD detected Dreams 5 0- detected
10- not detected
9 Fault in LT network Mains software 10 0- No. >110% of average
5- No.->100% & <110 % of average
10- < 100% of average

Table 2
The data collected for dry type DTRs is essential for ensuring reliable operation, early fault detection, and effective asset management. The following parameters are specifically monitored for dry type DTRs:

1. Age: The operational age of the transformer is a primary factor in assessing its health and predicting its remaining useful life. As with oil type DTRs, older dry type transformers are more susceptible to insulation breakdown and mechanical wear.
2. Loading: Continuous monitoring of the load, expressed as a percentage of the transformer’s rated capacity, is crucial. Overloading can cause excessive heating, leading to insulation degradation and reduced transformer lifespan.
3. Voltage: Monitoring the voltage, particularly on the LT side, helps identify abnormal voltage conditions that may indicate grid issues or transformer malfunction.
4. Status of HT FFA Chamber: The condition of the high-tension chamber, including whether the door is open or rusted, is checked to ensure the transformer’s safety and operational integrity.
5. Status of LT CFS Chamber: The low-tension chamber is similarly monitored for rust, damage, or improper closure, as these issues can compromise transformer performance and safety.
6. Condition of Explosion Vent Pipe: Unique to dry type DTRs, the explosion vent pipe is monitored for rust or whether it is open. A compromised vent pipe can pose a safety risk and indicate potential internal pressure issues.
7. Hotspot Detection: The presence of hotspots is a critical indicator of localized overheating, which can quickly damage insulation and lead to transformer failure.
8. Partial Discharge (PD) Detection: PD activity is monitored as it signals insulation defects or breakdown. Early detection of PD allows for timely maintenance and prevention of major faults.
9. Faults in LT Network: The system tracks the frequency and severity of faults in the LT network connected to the transformer. Persistent faults can stress the transformer and accelerate aging.

Relevance of Dry Type DTR Data:
• Targeted Health Assessment: The selected parameters provide a comprehensive and accurate assessment of the unique risks and failure modes associated with dry type transformers.
• Predictive Maintenance: Real-time monitoring of these parameters enables early detection of issues such as overheating, insulation degradation, or mechanical failures, supporting a predictive maintenance approach.
• Safety Assurance: Monitoring the condition of the explosion vent pipe and electrical chambers is vital for preventing hazardous situations and ensuring safe operation.
• Asset Optimization: Weighted analysis of these parameters allows utilities to prioritize maintenance and replacement, optimize resource allocation, and extend transformer service life.
• Operational Reliability: By tracking load, voltage, and network faults, the system helps maintain stable transformer operation and reduces the risk of unexpected outages.
• Data-Driven Decision Making: The integration of real-time and historical data for dry type DTRs supports informed decisions regarding maintenance scheduling, asset replacement, and network planning.

Automated Alerts and Notifications
The system includes an automated alert and notification module. When a transformer’s health index falls below a predetermined threshold, or when critical data is missing from any sensor, the system generates a real-time alert. Alerts are displayed on the dashboard and can be transmitted to designated personnel via email or SMS.

The alert module supports multiple alert modes, such as alerts for missing data, alerts for transformers in “Very Poor” health, and alerts for transformers that have shifted from a higher to a lower health category. The system can escalate alerts if no response is received within a specified time frame.

Maintenance Recommendations
In some embodiments, the system is configured to generate maintenance or replacement recommendations based on the classified health category of each transformer. For example, transformers in the “Poor” category may be recommended for replacement within two years, while those in the “Average” category may be flagged for close monitoring.

DTR Assets
Health Status Count (Dry) Percentage (Dry) Count (Oil) Percentage (Oil)
Very Poor (Immediate Replacement) 62 2.41% 318 4.78%
Poor (Close Monitoring) 69 2.69% 711 10.68%
Average 306 11.92% 2070 31.09%
Good 530 20.64% 1492 22.41%
Excellent 1601 62.34% 2068 31.06%
Total 2568 100% 6659 100%

Mode-1: If data is not coming from any field

Health Status Count (Dry) Percentage (Dry) Count (Oil) Percentage (Oil)
Very Poor (Immediate Replacement) 62 2.41% 318 4.78%
Poor (Close Monitoring) 69 2.69% 711 10.68%
Average 306 11.92% 2070 31.09%
Good 530 20.64% 1492 22.41%
Excellent 1601 62.34% 2068 31.06%
Total 2568 100% 6659 100%

Mode-2: Count of DTRs with very poor health

Integration with Utility Systems
The invention is designed for seamless integration with existing utility infrastructure, including asset management systems, geographic information systems (GIS), and smart grid platforms. The system can import and export data to and from other utility databases, supporting comprehensive asset management and regulatory compliance.

Scalability and Customization
The system is highly scalable, capable of monitoring thousands of transformers across a wide geographic area. The parameter sets, weighting schemes, health index calculation algorithms, and alert thresholds are all customizable to meet the specific needs of different utilities and operational environments.

Security and Data Integrity
Data security and integrity are critical considerations in the design of the system. The communication module employs encryption and authentication protocols to protect data in transit. The central processing unit includes data validation and error-checking routines to ensure the accuracy and reliability of health index calculations.

User Access and Permissions
The dashboard interface supports multiple user roles and access levels. Administrators can configure system settings, adjust weighting schemes, and manage user accounts. Maintenance personnel can view alerts, access detailed calculation breakdowns, and update maintenance records.

Reporting and Analytics
The system includes reporting tools that generate periodic summaries of transformer health, alert history, and maintenance actions. Reports can be customized and exported in various formats for regulatory compliance, management review, and operational planning.

Embodiment for Oil Type Transformers
In one embodiment, the system is specifically configured for oil type distribution transformers. The monitored parameters include oil level, condition of the conservator tank, condition of the silica gel breather, and status of HT and LT chambers. The weighting scheme reflects the criticality of oil-related parameters, and the health index calculation algorithm is tailored to the operational characteristics of oil type transformers.

Embodiment for Dry Type Transformers
In another embodiment, the system is adapted for dry type distribution transformers. The monitored parameters include the condition of the explosion vent pipe and other components unique to dry type transformers. The system applies a different set of weights and logic to accurately assess the health of dry type units.

Partial Discharge and Hotspot Detection
The system supports advanced monitoring of electrical phenomena such as partial discharge and hotspot detection. Specialized sensors detect abnormal electrical activity and temperature rises, which are early indicators of insulation breakdown and impending failure. These parameters are given significant weight in the health index calculation.

External Condition Monitoring
The system also monitors the external condition of transformers, including signs of rust, damage, or moisture ingress. Visual inspection data can be integrated into the system, either manually or via image recognition technology, to provide a comprehensive assessment of transformer health.

Fault Detection in LT Network
The system is capable of detecting faults in the low-tension (LT) network connected to each transformer. By analyzing load patterns and fault data, the system can identify transformers operating under abnormal conditions and adjust the health index accordingly.

Handling Missing Data
If data from any sensor is missing or unavailable, the system is configured to exclude the missing parameter from the health index calculation and generate a corresponding alert. This ensures that the health index remains accurate and that maintenance personnel are informed of potential sensor or communication issues.

Color-Coded Visualization
The dashboard’s color-coded visualization scheme provides an intuitive representation of transformer health. Each health category is associated with a distinct color, enabling users to quickly identify transformers in need of attention.

User Interaction and Customization
Users can interact with the dashboard to filter transformers by location, type, or health category. The system supports customization of display settings, alert preferences, and reporting formats to suit individual user needs.

Trend and Shift Analysis
The system tracks changes in health category status over time, enabling utilities to identify transformers that are deteriorating or improving. This shift analysis supports proactive maintenance planning and resource allocation.

Summary and Alert Overview
The dashboard includes a summary section that presents the total number of transformers monitored, the distribution of health categories, and a list of active alerts. This overview supports high-level management and operational decision-making.

Audit Trail and Compliance
All system actions, including data collection, health index calculations, alert generation, and user interactions, are logged to provide a complete audit trail. This supports regulatory compliance and quality assurance.

Benefits and Technical Effects
The invention enables utilities to transition from reactive to predictive maintenance strategies, reducing downtime, optimizing maintenance schedules, and extending transformer lifespans. The system enhances safety by detecting hazardous conditions in real time and supports regulatory compliance through comprehensive data collection and reporting.

In one embodiment, the method for health indexing of distribution transformers begins with the collection of operational data from a plurality of transformers deployed across an electrical utility network. This data acquisition is performed using a network of sensors installed on each transformer, which continuously monitor key operational and environmental parameters. For oil type transformers, the sensors measure parameters such as top oil temperature, oil level in the conservator tank, voltage, load, transformer age, condition of the conservator tank, silica gel breather status, and the state of high-tension and low-tension chambers. For dry type transformers, the sensors monitor temperature, voltage, load, age, condition of the explosion vent pipe, and the status of electrical chambers. Additionally, specialized sensors are used to detect electrical phenomena such as partial discharge and hotspots, which are critical indicators of insulation breakdown and localized overheating.

Once the operational data is collected, the method proceeds to the assignment of weights to each parameter. The weighting scheme is determined based on the historical impact of each parameter on transformer reliability and failure rates, as well as expert knowledge and utility-specific requirements. For example, parameters such as transformer age and loading may be assigned higher weights due to their significant influence on health and expected lifespan. The weighting algorithm is customizable and can be adjusted for different transformer types and operational environments.

The next step involves the calculation of a composite health index for each transformer. The central processing unit receives the weighted parameter data and applies a predetermined algorithm, such as a weighted average or a more advanced mathematical model, to compute the health index. The health index is typically expressed on a scale from 0 (poor condition) to 10 (excellent condition), providing a single, actionable metric that reflects the overall health of the transformer.

Following the calculation, each transformer is classified into a health category based on its health index. The categories include excellent, good, average, poor, and very poor, with configurable thresholds for each category. This classification enables utilities to prioritize maintenance, repairs, and replacements according to the actual condition of each transformer.

The method further includes the generation of alerts when a transformer’s health index falls below a predetermined threshold or when operational data from any parameter is missing. The alert system is designed to notify designated personnel in real time via dashboard notifications, email, or SMS, ensuring prompt response to emerging issues. Alerts may also be generated when a transformer shifts from a higher to a lower health category or when abnormal trends are detected in the health index over time.

The health index, health category, and alert status for each transformer are displayed on a dashboard interface. The dashboard provides a color-coded visualization of health status, trend analysis graphs showing changes in health index over selected time periods, and a summary section presenting the distribution of health categories across the transformer fleet. Users can interact with the dashboard to filter transformers by location, type, or health category, and access detailed breakdowns of health index calculations for individual units.

In another embodiment, the method supports the exclusion of parameters with missing data from the health index calculation, ensuring that the health assessment remains accurate and reliable. Corresponding alerts are generated to inform maintenance personnel of potential sensor or communication issues.

The method also includes the capability to recommend maintenance or replacement actions based on the classified health category of each transformer. For example, transformers in the “very poor” category may be recommended for immediate replacement, while those in the “average” category may be flagged for close monitoring.

Additional embodiments provide for the integration of the health indexing method with existing utility asset management systems, geographic information systems, and smart grid platforms. The method supports the import and export of data to and from other utility databases, facilitating comprehensive asset management and regulatory compliance.

The method is scalable and customizable, capable of monitoring thousands of transformers across wide geographic areas. The parameter sets, weighting schemes, health index calculation algorithms, and alert thresholds can be tailored to meet the specific needs of different utilities and operational environments.

In summary, the method embodiments encompass the steps of real-time data collection, parameter weighting, health index calculation, health classification, alert generation, dashboard visualization, exclusion of missing data, maintenance recommendation, and integration with utility systems. These embodiments collectively support the method claims and enable utilities to transition from reactive to predictive maintenance strategies, optimize asset management, and enhance the reliability and safety of power distribution networks.

In summary, the present invention provides a robust and flexible system and method for health indexing of distribution transformers. By integrating advanced sensor technology, data analytics, and automated alerting, the invention empowers utilities to proactively manage transformer health, improve operational efficiency, and ensure the reliability and safety of power distribution networks.

DETAILED DESCRIPTION OF THE FIGURES:
FIGURE 1 illustrates the detailed system architecture of the health indexing platform for distribution transformers. The diagram presents the interconnection of hardware and software components, including a plurality of sensors installed on each transformer, a communication gateway for data transmission, a central processing unit (CPU) for data analysis, and a dashboard interface for visualization. The sensors are shown monitoring various operational and environmental parameters such as temperature, oil level, voltage, load, age, and external condition. The communication module is depicted as supporting both wired and wireless protocols, ensuring secure and reliable data transfer to the CPU. The CPU is represented as the core analytical engine, responsible for parameter weighting, health index calculation, health classification, and alert generation. The dashboard is shown as the user-facing component, providing real-time visualization, trend analysis, and alert management. Network connections to utility asset management systems, geographic information systems (GIS), and smart grid platforms are also illustrated, highlighting the system’s integration capabilities.

FIGURE 2 provides a sensor placement diagram for oil type distribution transformers. The figure details the physical locations of various sensors on the transformer, including top oil temperature sensors, oil level gauges in the conservator tank, voltage and current sensors on the high-tension (HT) and low-tension (LT) sides, and specialized sensors for partial discharge and hotspot detection. The diagram also indicates the placement of sensors for monitoring the condition of the conservator tank, silica gel breather, HT FFA chamber, and LT CFS chamber. Each sensor is labeled according to its function, and arrows illustrate the flow of data from the sensors to the communication module. The figure emphasizes the comprehensive coverage of operational and safety-critical parameters for oil type transformers.

FIGURE 3 presents a sensor placement diagram for dry type distribution transformers. The illustration highlights the locations of temperature sensors, voltage sensors, and current sensors, as well as sensors dedicated to monitoring the condition of the explosion vent pipe, HT FFA chamber, and LT CFS chamber. Specialized sensors for hotspot and partial discharge detection are also shown. The diagram demonstrates how the system adapts to the unique structural and operational characteristics of dry type transformers, ensuring targeted monitoring of parameters relevant to their health and safety.

FIGURE 4 depicts an expanded flowchart of the data acquisition and transmission process. The flowchart begins with the continuous collection of operational and environmental data by the installed sensors. The data is then transmitted via the communication module, which may utilize protocols such as cellular, Wi-Fi, or Ethernet. The flowchart details steps for data validation, error checking, and encryption to ensure data integrity and security. The validated data is forwarded to the central processing unit, where it is stored and prepared for analysis. The flowchart also includes provisions for handling missing or corrupted data, with corresponding alerts generated for maintenance personnel.

FIGURE 5 provides a detailed flowchart of the health index calculation algorithm. The process starts with the receipt of operational data from the sensors. Each parameter is assigned a weight based on its historical impact on transformer reliability and failure rates. The flowchart illustrates the mathematical combination of weighted parameters, typically using a weighted average or a more complex model, to compute the composite health index. The calculated health index is then compared against predefined thresholds to classify the transformer into health categories such as excellent, good, average, poor, or very poor. The flowchart includes steps for excluding parameters with missing data and generating alerts when necessary.

FIGURE 6 displays a flowchart for the alert generation and notification process. The flowchart begins with the continuous monitoring of health indices and sensor data. When a health index falls below a predetermined threshold or when data from any sensor is missing, the system triggers an alert. The alert is displayed on the dashboard and transmitted to designated personnel via email or SMS. The flowchart includes escalation procedures if no response is received within a specified time frame, as well as logging of all alerts and responses for audit and compliance purposes.

FIGURE 7 shows the main dashboard interface of the health indexing platform. The dashboard provides a comprehensive, color-coded visualization of the health status of all monitored distribution transformers. Each transformer is represented by an icon or row, with its health category indicated by a distinct color (deep green for excellent, light green for good, yellow for average, amber for poor, and red for very poor). The dashboard includes summary statistics, such as the total number of transformers in each health category, and a list of active alerts. Users can filter the display by location, transformer type, or health category. The dashboard also features interactive elements for accessing detailed parameter breakdowns, trend analysis, and maintenance recommendations.

FIGURE 8 illustrates an alert pop-up window or notification panel within the dashboard interface. The figure shows how real-time alerts are presented to users when a transformer’s health index falls below a threshold or when critical data is missing. The alert window includes information such as the affected transformer’s identification, the type of alert (e.g., very poor health, missing data), the time of occurrence, and recommended actions. Options for acknowledging the alert, viewing detailed diagnostic information, and sending notifications to maintenance teams are also depicted.

FIGURE 9 presents a trend analysis graph for an individual transformer. The graph displays changes in the health index over a selected time period, such as the last 30 days, 6 months, or 1 year. The figure includes markers for significant events, such as maintenance actions or fault occurrences, allowing users to correlate health index trends with operational history. The trend analysis supports early identification of deteriorating conditions and evaluation of maintenance effectiveness.

FIGURE 10 depicts a detailed parameter breakdown view for a selected transformer. The figure shows a table or chart listing each monitored parameter, its current value, assigned weight, and contribution to the overall health index. The breakdown provides transparency into the health assessment process and supports root cause analysis in the event of declining transformer health. Users can access historical parameter data and compare current values against baseline or threshold levels.

FIGURE 11 shows a configuration or settings screen for the health indexing platform. The figure illustrates user options for adjusting alert thresholds, customizing parameter weighting schemes, and setting notification preferences (e.g., email, SMS, escalation rules). The settings screen supports system administrators in tailoring the platform to the specific needs of their utility network and operational environment.

FIGURE 12 illustrates a health category visualization legend. The figure presents the color-coding scheme used throughout the dashboard and reports, with each health category represented by a distinct color: deep green for excellent, light green for good, yellow for average, amber for poor, and red for very poor. The legend provides users with a quick reference for interpreting health status indicators across the platform.

FIGURE 13 presents a population-wise status chart of oil and dry type distribution transformers. The figure displays the distribution of health categories across the monitored fleet, using bar graphs, pie charts, or tables. Separate sections for oil type and dry type transformers are included, showing the count and percentage of units in each health category. The chart supports high-level asset management and strategic planning by providing an overview of fleet health.

FIGURE 14 displays a report or summary screen for individual transformers. The figure provides health-wise variation for a particular transformer over the last 30 days, including a time-series graph of health index values, a scoring breakdown based on parameter weights, and system-generated suggestions for maintenance or identification of abnormalities. The report includes options for exporting data, scheduling maintenance actions, and viewing historical alert history, supporting comprehensive asset management and regulatory compliance.

Advantages of the Invention
1. Comprehensive Health Assessment:
The invention enables real-time monitoring of a wide range of operational and environmental parameters, providing a holistic and accurate assessment of each distribution transformer’s health.

2. Early Fault Detection:
By continuously tracking critical indicators such as temperature, oil level, load, voltage, partial discharge, and hotspots, the system facilitates early identification of anomalies and emerging faults, allowing for timely intervention before catastrophic failures occur.

3. Predictive Maintenance Enablement:
The health indexing methodology supports a shift from reactive or scheduled maintenance to predictive maintenance, optimizing maintenance schedules, reducing unnecessary servicing, and minimizing transformer downtime.

4. Automated Alerts and Notifications:
The system generates real-time alerts and notifications when health indices fall below thresholds or when critical data is missing, ensuring that maintenance teams are promptly informed and can take immediate action.

5. Data-Driven Decision Making:
The invention provides actionable insights through weighted analysis and health classification, empowering utilities to prioritize maintenance, plan replacements, and allocate resources more effectively.

6. Enhanced Asset Management:
By classifying transformers into health categories and tracking trends over time, the system supports strategic asset management, helping utilities extend transformer lifespans and optimize capital investments.

7. Improved Safety:
Continuous monitoring and timely alerts for hazardous conditions, such as overheating, oil leaks, or compromised explosion vent pipes, enhance the safety of personnel and equipment.

8. Customizable and Scalable:
The system is adaptable to both oil type and dry type transformers, with customizable parameter sets, weighting schemes, and alert thresholds, making it suitable for utilities of any size and network complexity.

9. User-Friendly Dashboard:
The intuitive, color-coded dashboard interface provides clear visualization of transformer health, trends, and alerts, supporting quick assessment and efficient operational management.

10. Regulatory Compliance and Reporting:
The system’s comprehensive data collection, audit trail, and reporting capabilities facilitate compliance with industry standards and regulatory requirements.

11. Seamless Integration:
The invention is designed for easy integration with existing utility infrastructure, including asset management systems, GIS, and smart grid platforms.

12. Reduction in Operational Costs:
By enabling targeted maintenance and reducing the incidence of unexpected failures, the system helps lower overall operational and maintenance costs.

13. Scalable Fleet Management:
The platform supports monitoring and management of large fleets of transformers across wide geographic areas, making it ideal for utilities with extensive distribution networks.

14. Transparency and Accountability:
Detailed calculation breakdowns and audit trails ensure transparency in health assessments and maintenance actions, supporting accountability and continuous improvement.

15. Technical Advancement:
The invention introduces a novel, in-house developed health indexing philosophy and analytics, representing a significant advancement over conventional transformer monitoring and maintenance solutions.

In summary, the invention delivers significant operational, economic, and safety benefits, transforming the way utilities monitor, maintain, and manage distribution transformers.

The embodiments, examples, and descriptions provided herein are intended to illustrate the principles and features of the present invention and should not be construed as limiting the scope of the invention in any way. Variations, modifications, substitutions, and equivalents may be made by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims. The specific parameters, configurations, and features described for oil type and dry type distribution transformers are exemplary and may be adapted or altered to suit different transformer types, operational environments, or utility requirements.

The present invention is not limited to the precise arrangements, components, or methods disclosed, and encompasses all such modifications and improvements that fall within the true scope and spirit of the invention. Any reference to specific standards, protocols, or commercial products is for illustrative purposes only and does not imply any limitation or endorsement. The invention may be practiced in various forms and should be interpreted broadly to include all novel and non-obvious features and combinations thereof as claimed.
, Claims:
1. A system for health indexing of distribution transformers, comprising:
a plurality of sensors configured to monitor operational parameters of each distribution transformer, the operational parameters including at least temperature, oil level, voltage, load, age, and external condition;
a communication module operatively coupled to the sensors and configured to transmit the monitored operational parameters to a central processing unit;
a central processing unit configured to:
o receive the operational parameters from the communication module;
o assign a weight to each operational parameter based on its impact on transformer health;
o calculate a composite health index for each distribution transformer using the weighted operational parameters;
o classify each distribution transformer into a health category based on the calculated health index, the health categories including at least: excellent, good, average, poor, and very poor;
o generate an alert if the health index of a distribution transformer falls below a predetermined threshold or if data from any sensor is missing;
a dashboard operatively connected to the central processing unit and configured to:
o display the health index and health category of each distribution transformer;
o provide a visual indication of health status using color-coded categories;
o display trends in health index over a selected time period;
o present alerts generated by the central processing unit.

2. The system of claim 1, wherein the plurality of sensors further includes sensors configured to detect electrical phenomena in the transformer, including partial discharge and hotspot temperature.

3. The system of claim 1, wherein the central processing unit is further configured to assign weights to the operational parameters based on historical failure data of distribution transformers.

4. The system of claim 1, wherein the communication module is configured to transmit the monitored operational parameters via a wireless network.

5. The system of claim 1, wherein the dashboard is further configured to generate and transmit alerts via at least one of email or SMS to designated personnel.

6. The system of claim 1, wherein the dashboard displays a pie chart representing the distribution of health categories among a plurality of distribution transformers.

7. The system of claim 1, wherein the health index is calculated as a weighted average of the operational parameters, and the weights are adjustable based on transformer type.

8. The system of claim 1, wherein the health categories are visually indicated on the dashboard using at least five distinct colors.

9. The system of claim 1, wherein the central processing unit is further configured to exclude parameters with missing data from the health index calculation and to generate a corresponding alert.

10. The system of claim 1, wherein the dashboard is further configured to display a detailed calculation breakdown for the health index of each distribution transformer.

11. The system of claim 1, wherein the system is configured to monitor both oil type and dry type distribution transformers, and to apply different parameter sets and weighting schemes for each type.

12. The system of claim 1, wherein the central processing unit is further configured to track and display changes in health category status for each distribution transformer over a predetermined time period.

13. The system of claim 1, wherein the dashboard is further configured to provide a summary of all active alerts and their corresponding distribution transformers.

14. The system of claim 1, wherein the operational parameters further comprise the status of at least one of a high-tension chamber, a low-tension chamber, a conservator tank, a silica gel breather, or an explosion vent pipe.

15. The system of claim 1, wherein the system is further configured to recommend maintenance or replacement actions based on the classified health category of each distribution transformer.

16. A method for health indexing of distribution transformers, comprising:
i) collecting operational data from a plurality of distribution transformers, the operational data including at least temperature, oil level, voltage, load, age, and external condition;
ii) assigning a weight to each operational parameter based on its impact on transformer health;
iii) calculating, by a processor, a composite health index for each distribution transformer using the weighted operational parameters;
iv) classifying each distribution transformer into a health category based on the calculated health index, the health categories including at least: excellent, good, average, poor, and very poor;
v) generating an alert if the health index of a distribution transformer falls below a predetermined threshold or if operational data from any parameter is missing;
vi) displaying, on a dashboard, the health index and health category of each distribution transformer, including a visual indication of health status using color-coded categories and a trend of health index over a selected time period; and
vii) providing, on the dashboard, alerts and a summary of health status for the plurality of distribution transformers.

17. The method of claim 16, further comprising collecting operational data related to electrical phenomena in the transformer, including partial discharge and hotspot temperature.

18. The method of claim 16, wherein assigning a weight to each operational parameter comprises determining the weight based on historical failure data of distribution transformers.

19. The method of claim 16, wherein collecting operational data comprises receiving data from sensors via a wireless communication network.

20. The method of claim 16, further comprising transmitting alerts via at least one of email or SMS to designated personnel when an alert is generated.

21. The method of claim 16, wherein displaying the health index and health category comprises presenting a pie chart representing the distribution of health categories among the plurality of distribution transformers.

22. The method of claim 16, wherein calculating the composite health index comprises computing a weighted average of the operational parameters, and wherein the weights are adjustable based on transformer type.

23. The method of claim 16, wherein displaying the health category comprises using at least five distinct colors to visually indicate the health status of each distribution transformer.

24. The method of claim 16, further comprising excluding parameters with missing data from the health index calculation and generating a corresponding alert.

25. The method of claim 16, further comprising displaying a detailed calculation breakdown for the health index of each distribution transformer on the dashboard.

26. The method of claim 16, further comprising applying different parameter sets and weighting schemes for oil type and dry type distribution transformers.

27. The method of claim 16, further comprising tracking and displaying changes in health category status for each distribution transformer over a predetermined time period.

28. The method of claim 16, further comprising providing a summary of all active alerts and their corresponding distribution transformers on the dashboard.

29. The method of claim 16, wherein collecting operational data further comprises monitoring the status of at least one of a high-tension chamber, a low-tension chamber, a conservator tank, a silica gel breather, or an explosion vent pipe.

30. The method of claim 16, further comprising recommending maintenance or replacement actions based on the classified health category of each distribution transformer.

31. A user interface for monitoring the health of distribution transformers, comprising:
i) a graphical display configured to present, for each distribution transformer, a health index and a corresponding health category, the health categories including at least: excellent, good, average, poor, and very poor;
ii) a color-coded visualization scheme wherein each health category is represented by a distinct color;
iii) a trend display configured to show changes in the health index of each distribution transformer over a selected time period;
iv) an alert section configured to display real-time notifications when a health index falls below a predetermined threshold or when operational data is missing;
v) a summary section configured to present an overview of the health status distribution among a plurality of distribution transformers.

32. The user interface of claim 31, wherein the graphical display further comprises a pie chart representing the proportion of distribution transformers in each health category.

33. The user interface of claim 31, wherein the alert section is further configured to display the type of alert, the affected transformer, and the time of occurrence.

34. The user interface of claim 31, wherein the trend display is interactive and allows a user to select different time intervals for viewing historical health index data.

35. The user interface of claim 31, wherein the summary section further comprises a table listing each distribution transformer, its health index, health category, and recommended maintenance action.

36. The user interface of claim 31, wherein the color-coded visualization scheme comprises at least five distinct colors corresponding to the health categories: deep green for excellent, light green for good, yellow for average, amber for poor, and red for very poor.

37. The user interface of claim 31, further comprising a detailed calculation view accessible for each distribution transformer, the detailed calculation view displaying the weighted values of each operational parameter used in the health index calculation.

38. The user interface of claim 31, wherein the alert section is further configured to provide options for sending notifications via email or SMS to designated personnel.

39. The user interface of claim 31, wherein the summary section further displays the total number of distribution transformers monitored and the percentage of transformers in each health category.

40. The user interface of claim 31, wherein the graphical display further comprises a filter function allowing a user to view transformers by health category, location, or type.

41. An alert and notification system for a distribution transformer health indexing platform, comprising:
i) a monitoring module configured to receive operational data from a plurality of distribution transformers;
ii) an analysis module configured to calculate a health index for each distribution transformer and to detect when the health index falls below a predetermined threshold or when operational data is missing;
iii) an alert generation module configured to generate an alert in response to the detection of a health index below the threshold or missing operational data;
iv) a notification module configured to transmit the generated alert to designated personnel via at least one of email or SMS;
v) an alert dashboard configured to display active alerts, including the affected transformer, the type of alert, and the time of occurrence.

42. The alert and notification system of claim 41, wherein the alert generation module is further configured to generate alerts when a distribution transformer shifts from a higher health category to a lower health category.

43. The alert and notification system of claim 41, wherein the notification module is further configured to escalate alerts to additional personnel if a response is not received within a predetermined time period.

44. The alert and notification system of claim 41, wherein the alert dashboard further comprises a summary section displaying the total number of active alerts and their distribution by alert type.

45. The alert and notification system of claim 41, wherein the alert generation module is further configured to generate alerts in response to the detection of abnormal trends in the health index over a selected time period.

46. The alert and notification system of claim 41, wherein the notification module is further configured to provide a user interface for configuring alert thresholds and notification preferences.

47. The alert and notification system of claim 41, wherein the alert dashboard further displays the recommended maintenance or replacement action associated with each alert.

48. The alert and notification system of claim 41, wherein the alert generation module is further configured to generate alerts when data from any sensor is not received for a predetermined duration.

49. The alert and notification system of claim 41, wherein the notification module is further configured to log all transmitted alerts and responses for audit and compliance purposes.

50. The alert and notification system of claim 41, wherein the alert dashboard further provides filtering options to view alerts by transformer location, health category, or alert type.