Description:FORM – 2

THE PATENTS ACT, 1970
(39 of 1970)
&
THE PATENTS RULES, 2003

COMPLETE SPECIFICATION
(SEE SECTION 10, RULE 13)

“SYSTEM AND METHOD FOR PREVENTING FAILURE OF ELECTRICAL MACHINES”

BY

KRYFS TECHNOLOGIES PRIVATE LIMITED, A COMPANY REGISTERED UNDER THE LAWS OF INDIA, WHOSE ADDRESS IS AZA HOUSE, 3RD FLOOR, 24 TURNER ROAD, BANDRA (WEST), MUMBAI CITY, MUMBAI-400050, MAHARASHTRA, INDIA

THE FOLLOWING SPECIFICATION PARTICULARLY DESCRIBES THE INVENTION AND THE MANNER IN WHICH IT IS TO BE PERFORMED.

TECHNICAL FIELD OF THE INVENTION
[0001] The present disclosure generally relates to smart grid systems, and more particularly relates to a system and method for preventing failure of electrical machines based on monitoring electrical and physical parameters associated with the electrical machines.
BACKGROUND OF THE INVENTION
[0002] Electricity is the most widely used forms of energy as it is one of the basic requirements in today’s life. Thus, maintaining a continuous supply of electricity is an essential necessity. Electrical power supply systems may include electrical machines (e.g., transformers, alternators, etc.) for transmission and distribution of electricity from electricity generation power plants. Typically, electricity is supplied to, for example, residential and commercial areas by electrical power supply systems.
[0003] However, the electrical machines are prone to some of the common failures such as, winding failure, protection system failure, dielectric faults, prolonged overloading, phase-to-phase faults, and the like. The primary reasons for occurrence of such faults in the electrical machines are improper monitoring of the electrical parameters and poor maintenance of the electrical machines. In addition, failure of the electrical power supply systems (or the electrical machines) may result in interruption of power supply to the consumers. Moreover, servicing and/or replacing the electrical power supply systems and/or electrical machines in case of failure requires huge capital cost.
[0004] In order to overcome the aforesaid limitations, manual operating systems are implemented for monitoring the electrical machines. The manual operating systems include a power carrier communication for transmitting information related to the condition of the electrical machines to an operator in real-time or on a periodic basis (e.g., on a daily basis). This enables the operator to take preventive measures in case of detection of anomalies in the electrical machines. However, the power carrier communication experience frequency interference, signal attenuation, electrical noise due to load changes, and the like. Further, other electronic monitoring systems are implemented to efficiently monitor the electrical machines and transmit the information related to the condition of the electrical machines to the operator. However, the accuracy of the information related to the electrical machines determined by the electronic monitoring systems is low.
[0005] Therefore, there is a need for techniques for determining electrical parameters of the electrical machines with high accuracy to prevent or reduce failure rates of the electrical machines in the electrical power supply systems, in addition to providing other technical advantages.
OBJECTIVE OF THE INVENTION
[0006] The main objective of the present invention is to provide at least an economic, effective and efficient system and a method for real-time monitoring physical and electrical parameters of the electrical machines (e.g., transformer) in an electricity transmission and distribution network, control the operating conditions of the electrical machine and provide preventive actions in order to prevent or reduce failure rates of the electrical machines.
SUMMARY OF THE INVENTION
[0007] An aspect of the present invention is to address at least the above-mentioned problems and/or disadvantages and to provide at least the advantages described below.
[0008] Accordingly, in one aspect of the present invention a system for preventing failure of electrical machines is disclosed. The system includes a monitoring apparatus coupled to an electrical machine. The monitoring apparatus includes a plurality of sensors configured to determine one or more parameters associated with the electrical machine. The one or more parameters includes at least electrical parameters, ambient condition and machine parameters associated with the electrical machine. The system further includes a control device coupled to the monitoring apparatus and the electrical machine. The control device is configured to at least receive the one or more parameters associated with the electrical machine from the monitoring apparatus. Further, the control device determines a plurality of failure conditions associated with the electrical machine based at least on a threshold value associated with each parameter of the electrical parameters, the ambident condition, and the machine parameters of the electrical machine. Further, determining the plurality of conditions includes predicting failure of the electrical machine based at least on machine learning (ML) models and detect anomaly in the operating condition of the electrical machine. The control unit further transmits at least one signal to the electrical machine based at least on determining one parameter of the electrical parameters, the ambient condition, and the machine parameters exceeds their corresponding threshold value, thereby preventing failure of the electrical machine.
[0009] Accordingly, in one aspect of the present invention a method for preventing failure of electrical machines is disclosed. The method performed by the system includes receiving one or more parameters related to an electrical machine from a monitoring apparatus associated with the electrical machine. The one or more parameters includes at least one of electrical parameters, ambient condition and machine parameters associated with the electrical machine. The method further includes determining a plurality of failure conditions associated with the electrical machine based at least on a threshold value associated with each parameter of the electrical parameters, the ambident condition, and the machine parameters associated with the electrical machine. Further, determining the plurality of failure conditions includes predicting failure of the electrical machine based at least on machine learning (ML) models and detecting anomaly in the operating condition of the electrical machine. Further, the method includes transmitting at least one signal to the electrical machine based at least on determining one parameter of the electrical parameters, the ambient condition, and the machine parameters exceeds their corresponding threshold value, thereby preventing failure of the electrical machine.
[0010] 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 ACCOMPANYING DRAWINGS
[0011] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The same numbers are used throughout the drawings to reference like features and modules.
[0012] FIG. 1 illustrates a simplified block diagram representation of an environment, in which at least some embodiments of the present disclosure can be implemented;
[0013] FIG. 2 illustrates a simplified block diagram of a system for preventing failure of the electrical machines, in accordance with an embodiment of the present disclosure;
[0014] FIGS. 3A-3E, collectively, represent user interfaces (UIs) rendered in the application for allowing real-time monitoring and control of the electrical machines, in accordance with an example embodiment of the present disclosure; and
[0015] FIG. 4 is a flowchart depicting a method for providing real-time monitoring of the electrical machines and preventing failure of the electrical machines, in accordance with an embodiment of the present disclosure.
[0016] It should be appreciated by those skilled in the art that any block diagrams herein represent conceptual views of illustrative methods embodying the principles of the present disclosure. Similarly, it will be appreciated that any flow charts, flow diagrams, and the like represent various processes which may be substantially represented in computer readable medium and so executed by a computer or processor, whether or not such computer or processor is explicitly shown.
DETAILED DESCRIPTION OF THE INVENTION
[0017] The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of exemplary embodiments of the invention 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 embodiments described herein can be made without departing from the scope and spirit of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
[0018] The terms and words used in the following description and claims are not limited to the bibliographical meanings but are merely used by the inventor to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention are provided for illustration purpose only and not for the purpose of limiting the invention as defined by the appended claims and their equivalents.
[0019] It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a component surface” includes reference to one or more of such surfaces. References in the specification to “one embodiment” or “an embodiment” mean that a particular feature, structure, characteristic, or function described in connection with the embodiment is included in at least one embodiment of the invention. The appearances of the phrase “in one embodiment” in various places in the specification are not necessarily all referring to the same embodiment.
[0020] By the term “substantially” it is meant that the recited characteristic, parameter, or value need not be achieved exactly, but that deviations or variations, including for example, tolerances, measurement error, measurement accuracy limitations and other factors known to those of skill in the art, may occur in amounts that do not preclude the effect the characteristic was intended to provide.
[0021] Figures discussed below, and the various embodiments used to describe the principles of the present disclosure in this patent document are by way of illustration only and should not be construed in any way that would limit the scope of the disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged communications system. The terms used to describe various embodiments are exemplary. It should be understood that these are provided to merely aid the understanding of the description, and that their use and definitions in no way limit the scope of the invention. Terms first, second, and the like are used to differentiate between objects having the same terminology and are in no way intended to represent a chronological order, unless where explicitly stated otherwise. A set is defined as a non-empty set including at least one element.
[0022] In the following description, for purpose of explanation, specific details are set forth in order to provide an understanding of the present disclosure. It will be apparent, however, to one skilled in the art that the present disclosure may be practiced without these details. One skilled in the art will recognize that embodiments of the present disclosure, some of which are described below, may be incorporated into a number of systems.
[0023] However, the systems and methods are not limited to the specific embodiments described herein. Further, structures and devices shown in the figures are illustrative of exemplary embodiments of the presently disclosure and are meant to avoid obscuring of the presently disclosure.
[0024] Various embodiments of the present disclosure are further described with reference to FIG. 1 to FIG. 4.
[0025] FIG. 1 illustrates a simplified block diagram representation of an environment (100), in accordance with an embodiment of the present disclosure. Although the environment (100) is depicted to include one or few components, modules or devices arranged in a particular arrangement in the present disclosure, it should not be taken to limit the scope of the present disclosure. The environment (100) includes an operator (102) associated with the user device (104) (exemplarily depicted to be a “computer”). Further, the environment (100) includes a plurality of electrical machines in an electricity transmission and distribution network (106). The plurality of electrical machines includes an electrical machine (108a) (represented as EM1), an electrical machine 108b (represented as EM2) and an electrical machine 108c (represented as EM3). For illustration purposes, only three electrical machines are shown in FIG. 1, thus it should not be considered for limiting the scope of the present disclosure. As shown, the electrical machines (108a-108c) are electrically connected to each other via a transmission grid. For example, the electrical machines (108a-108c) may be a transformer. Alternatively, the electrical machines (108a-108c) may be a generator, a motor, an alternator, and the like. The environment (100) further includes a control device (110), a cloud network (112), an interactive platform (114) hosted by the cloud network (112), and a database (122) associated with the control device (110).
[0026] Various entities of the environment (100) are communicably coupled to each other via a network (116). In some embodiments, the network (116) may include wired or wireless communication protocols. In an embodiment, the network (116) may include, but not limited to a local area network LAN, a wide area network WAN (e.g., the Internet), a mobile network (for e.g., GSM (Global System for Mobile Communication), GPRS (General Packet Radio Service), EDGE (Enhanced Data for Global Evolution), etc.) capable of supporting communication among two or more entities illustrated in FIG. 1, or any combination thereof. In an embodiment, the network (116) may include communication protocols such as Wireless Fidelity (Wi-Fi), Ethernet, Radio frequency identification (RFID), Bluetooth, ZigBee, near field communication (NFC), Z-wave, and the like for supporting communication between one or more entities of the environment (100).
[0027] The electrical machines (108a-108c) are equipped with a monitoring apparatus (120). The monitoring apparatus (120) includes a plurality of sensors configured to determine one or more parameters of the respective electrical machines (108a-108c). The one or more parameters may include electrical parameters, ambient condition and machine parameters of the electrical machine 108s. For description purposes, the electrical machine (108a) is taken as reference in the present disclosure for describing the operations performed by each of the entities in FIG. 1 to prevent failure of the electrical machine (108a).
[0028] Further, the monitoring apparatus (120) is communicably coupled to the control device (110). The monitoring apparatus (120) transmits the sensory data (i.e., electrical parameters, ambient condition and machine parameters) to the control device (110) in real-time. The control device (110) may include suitable logics and/or circuitry for monitoring the operating condition of the electrical machine (108a) on a periodic basis to avoid interruption in the power supply and anticipate future problems associated with the electrical machine (108a).
[0029] In an embodiment, the control device (110) may be implemented as an Internet of Things (IoT) based monitoring and control device. Further, implementation of the IoT based monitoring and control device facilitates ease of controlling the power network for optimized output. As such, optimization of the power distribution system will result in reducing energy losses associated with the electrical machine (e.g., transformer), thereby increasing life of the electrical machine which will be explained further in detail.
[0030] In an aspect, the control device (110) is configured to determine a plurality of failure conditions associated with the electrical machine (108a). The failure conditions may include detection of anomaly in the operating condition of the electrical machine, predicting future failures of the electrical machine, and the like. More specifically, the control device (110) may determine the failure conditions based on trained machine learning (ML) models stored in the database (122). It is to be noted that the ML models may be trained with historic data for performing one or more operations (e.g., determining or predicting the plurality of failure conditions) described herein.
[0031] Additionally, the control device (110) is configured to control the operating conditions of the electrical machines (108a-108c) in the electricity transmission and distribution network (106). Further, the control device (110) may transmit at least one signal to the electrical machine (108a) in case of determining anomaly in the operating conditions or the parameters associated with the electrical machine (108a). In one example scenario, the control device (110) may detect anomaly (e.g., rise in oil temperature) in the electrical machine (108a). In this scenario, the control device (110) may transmit the signal to the electrical machine (108a for operating one or more components of the electrical machine (108a) to prevent failure of the electrical machine (108a).
[0032] Further, the control device (110) is configured to determine loading conditions of the electrical machine (108a). Specifically, the control device (110) may be configured to determine at least off-load condition, on-load condition and peak load condition for the electrical machine (108a). The control device (110) with the data related to loading conditions is configured to determine load distribution among the electrical machines (108a-108c), power usage pattern, and the like. Further, the control device (110) operates the electrical machine (108a) at maximum efficiency, thereby resulting in reduction of wastage of energy.
[0033] The control device (110) further transmits data related to the electrical parameters, the ambient condition, the machine parameters and the plurality of failure conditions associated with the electrical machine (108a) to the interactive platform (114) in real-time. As explained above, the control device (110) is an IoT based monitoring and control device. Further, the control device (110) may utilize a cloud network (112) to store large packets of data (or the IoT data). In general, a cloud server (or the cloud network (112)) is a virtual server running in a cloud computing environment. It is obvious to a person skilled in the art that the cloud network (112) may host a platform (such as the interactive platform (114)) for providing services such as storing and accessing the data remotely. Thus, the control device (110) transmits the data (e.g., the electrical parameters, the ambient condition, the machine parameters, the plurality of failure conditions, etc.) to the cloud network (112) via wireless communication protocols (e.g., Internet). The cloud server (i.e., the cloud network (112)) may include suitable instructions or software(s) required to process the data received from the control device (110).
[0034] The operator (102) may access the data of each electrical machine (108a-108c) in the electricity transmission and distribution network (106) using an application (118). The application (118) may be hosted and managed by the interactive platform (114) of the cloud network (112). In one scenario, the application (118) may be a web-based application. Alternatively, the application (118) may be pre-installed in the user device (104) or may be provided by the cloud network (112) upon receiving a request from the user device (104). Further, the electrical and physical parameters, the operating conditions, etc., associated with the electrical machine (108a) are made available in the application (118). This facilitates real time monitoring of the electrical machine (108a) from the user device (104) associated with the operator (102).
[0035] Further, the control device (110) is configured to determine one or more preventive actions corresponding to each of the plurality of failure conditions based on the trained ML models. The operator (102) may access the preventive actions using the application (118). Additionally, the control device (110) may transmit an alert message to the user device (104) based on detecting anomaly in the operating condition of the electrical machine (108a) and/or predicting the occurrence of failure of the electrical machine (108a). The alert message may be transmitted to the user device (104) of the operator (102) via E-mail, short message services (SMS), or the like. Thus, the operator (102) may control the electrical machine (108a) based on the preventive actions or the alert message to prevent the occurrence of failure of the electrical machine (108a).
[0036] FIG. 2 illustrates a simplified block diagram of a system (200) for preventing failure of the electrical machines, in accordance with an embodiment of the present disclosure. It should be understood that the system (200) as illustrated and hereinafter described is merely illustrative, therefore it should not be taken to limit the scope of the present disclosure. Further, the components of the system (200) provided herein may not be exhaustive, and the system (200) may include more or fewer components than that of depicted in FIG. 2. Further, two or more components may be embodied in one single component, and/or one component may be configured using multiple sub-components to perform the desired functionalities. Some components of the system (200) may be configured using hardware elements, software elements, firmware elements, and/or a combination thereof.
[0037] The system (200) includes various operating modules for monitoring and controlling the electrical machines (108a-108c). In an embodiment, the system (200) includes the control device (110) and the monitoring apparatus (120) (as shown in FIG. 2). In other words, the control device (110) and the monitoring apparatus (120) coupled to each other conforms to a system (such as the system (200)). As such, the system (200) performs one or more operations described herein for preventing the failure of the electrical machine. In an embodiment, few components of the system (200) may be embodied in the cloud network (112).
[0038] The monitoring device (120) includes a plurality of sensors (216) for monitoring electrical and physical parameters of the electrical machine (108a). The sensors (216) may include, but not limited to, oil level sensor, oil temperature sensor, winding temperature sensor, ambient sensor, current sensor, voltage sensor, and the like. Thus, due to operation of each of the above-mentioned sensors, the electrical parameters, machine parameters and the ambient condition associated with the electrical machine (108a) are determined. Further, the electrical parameters may include, but are not limited to, current, voltage, power, energy and harmonics. The machine parameters may include, but are not limited to, oil level, oil temperature and winding temperature. The ambient condition may include weather data around the electrical machine (108a). Furthermore, the electrical parameters, the machine parameters and the ambient condition are transmitted to the control device (110) wirelessly through a communication module (218) via a network (116). The communication module (218) may include suitable transmitter and receiver modules for exchanging information with the control device (110).
[0039] Further, the control device (110) includes various components to perform one or more operations described herein. As shown, the control device (110) includes a processor (202), a memory (204) and a communication interface (206). Further, the one or more components of the control device (110) may communicate with each other via a bus or a centralized circuitry (not shown in figures). It is noted that although the control device (110) is depicted to include only one processor (202), the control device (110) may include a number of processors.
[0040] In an embodiment, the memory (204) is capable of storing machine-executable instructions. The memory (204) may include volatile or non-volatile memories, or a combination thereof. For example, the memory (204) may be a random-access memory (RAM), a read only memory (ROM), flash memory, a hard disk, or any other storage medium.
[0041] Further, the processor (202) is capable of executing the machine executable instructions to perform the functions described herein. In an embodiment, the processor (202) may be implemented as a multi-core processor, a single core processor, or a combination of one or more multi-core processors and one or more single core processors. For example, the processor (202) may be embodied as one or more of various processing devices, such as a coprocessor, a microprocessor, a controller, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a microcontroller unit (MCU), or the like.
[0042] In an embodiment, the processor (202) may include one or more modules such as a failure detection module (208), a signaling module (210), an alert generation module (212), and a preventive action determining module (214). Further, the one or more components of the processor (202) are merely illustrative, and the processor (202) may include few or more components.
[0043] The failure detection module (208) includes suitable logic and/or circuitry for determining the plurality of failure conditions of the electrical machine (108a). In particular, the processor (202) of the control device (110) is configured to receive the parameters (electrical parameters, machine parameters and ambient condition) associated with the electrical machine (108a) from the monitoring apparatus (120). Thereafter, the module (208) determines the plurality of failure conditions in real-time based on the parameters of the electrical machine (108a). In particular, the failure conditions are determined based at least on the trained machine learning (ML) models associated with the control device (110).
[0044] In an embodiment, the ML models may be stored in a database (e.g., the database (122)) associated with the control device (110). Prior to performing the operations described above, the ML models may be trained with the training data. The training data (or the historic data) may include, but not limited to, operating conditions, electrical parameters, machine parameters, ambient conditions, threshold values of each parameter, potential failure conditions based on the parameters, and the like. In an embodiment, the ML models may implement supervised machine learning algorithms which involves extracting features from data sets, designing and tuning a model, and training and testing. Thus, in general, such a condition based proactive maintenance model, supported by an IoT-driven smart network, potentially transforms the approach of assessing and managing the operating condition (or health) of the electrical machines (e.g., transformer).
[0045] Thus, the failure detection module (208) determines the failure conditions based on the trained ML models. The failure conditions may be predicting failure of the electrical machine (108a), detect anomaly in the operating condition of the electrical machine (108a), and the like. It is to be noted that the failure detection module (208) determines the failure conditions based at least on a threshold value associated with each parameter of the electrical parameters, the ambident condition, and the machine parameters associated with the electrical machine (108a). Further, determining anomalies and predicting future failures are herein explained below with reference to exemplary parameters of the electrical machine (such as the transformer).
[0046] In an aspect, the processor (202) may receive the parameter pertaining to oil level and temperature of the electrical machine (108a). It is understood that the insulation oil volume plays a major role in the condition of the electrical machine (108a) which is continuously monitored for any abnormality (or anomalies). The failure detection module (208) is configured to monitor in real-time, the temperature rise in oil to detect high currents, continuous overloading or oil decay or quality defects in the electrical machine (108a).
[0047] In another aspect, the processor (202) may receive the parameter pertaining to winding temperature of the electrical machine (108a). In this scenario, the module (208) is configured to monitor drastic variations in the temperatures of transformer windings to detect the operating conditions, life cycle and safety of the electrical machine (108a). In addition, the module (208) may be configured to monitor direct and indirect thermal stress for detecting pre-fault conditions in the electrical machine (108a).
[0048] In another aspect, the processor (202) may receive the parameter pertaining to ambient condition of the electrical machine (108a). It is to be noted that the ambient temperature is taken as a reference by the module (208) for determining the thermal performance of the electrical machine (108a), including top oil temperature rise, operating current, winding hottest spot temperature rise, and the like. Further, the module (208) performs analysis of the correlation between the ambient condition or weather data (wind speeds, ambient & maximum humidity & temperature, precipitation, lighting strikes etc.) and historical operational data of the electrical machines to predict or provide insights of typical failures that occur in the electrical machine (108a) based on location of the electrical machine (108a). In addition, the processor (202) may be configured to perform geo-tagging of the electrical machine (108a).
[0049] In another aspect, the processor (202) may receive the parameter pertaining to current measurement of the electrical machine (108a). The processor (202) may be further configured to determine the loading condition (i.e., off-load, on-load and peak load) of the electrical machine (108a). It is understood by a person skilled in the art that, continuous overloaded machines are subjected to earlier failure. Thus, the processor (202) is configured to plan load distribution based on average loading (%), peak loading (%), and the percent of time the machine is overloaded & counts the number of such instances. Thereafter, the module (208) of the processor (202) may detect the rise in temperature of the electrical machine (108a).
[0050] In another aspect, the processor (202) may receive the parameter pertaining to voltage measurement of the electrical machine (108a). Typically, fluctuating incoming (sags and surges) cause stress on the windings. Further, the load (rectifiers, variable frequency drives (VFD) etc.) may nonlinear causes distortion in supply. Furthermore, the secondary voltage fluctuates to a vast extent endangering failure of machine and at the same time increases the power consumption. Thus, the module (208) detects the tap position of the machine 108a based on the voltage measurements in order to prevent the failure of the machine 108a and reduce power consumption.
[0051] In another aspect, the processor (202) may receive the parameter pertaining to power and energy measurements (KVA, KW, KVAR, and PF) associated with the electrical machine (108a). The processor (202) determines the efficiency, power usage pattern and load distribution based on the power and energy measurements. Further, the processor (202) is configured to compare the utility energy bill with the actual power consumption for over a period of time (e.g., monthly) calculated by the system (200) in order to minimize energy billing or reducing energy wasted in inefficient loads.
[0052] In another aspect, the processor (202) may receive the parameter pertaining to harmonics of the electrical machine (108a). The module (208) determines the harmonics in the electrical machine (108a) due to non-linear loads. It is to be noted that, each of the above-mentioned parameters are associated with a threshold value. Thus, the failure detection module (208) determines the failure conditions of the electrical machine (108a) based on the threshold value associated with the above-mentioned parameters.
[0053] Further, the signaling module (210) includes suitable logic and/or circuitry for transmitting the at least one signal (analog or digital signals) to the electrical machine (108a) based on detecting the failure condition. It is to be noted that the processor (202) triggers the signaling module (210) in case of immediate actions to be taken to prevent failure of the electrical machine (108a). The module (208) is configured to determine if at least one parameter of the electrical machine (108a) exceed their corresponding threshold value. In one example scenario, the module (208) may determine high voltage transmission (exceeding the voltage threshold) in the electrical machine (108a). In this scenario, the signaling module (210) may transmit a first signal to one or more protection devices (e.g., relays, circuit breakers, isolators, etc.,) of the electrical machine (108a). The first signal operates the one or more protection devices from a connected state to a disconnected state in order to prevent failure of the electrical machine (108a).
[0054] In another example scenario, the module (208) may determine the electrical machine (108a) is overloaded or operated in the peak load condition greater than a threshold time defined for the electrical machine (108a). In this scenario, the processor (202) may identify another electrical machine (such as the electrical machine (108b)) in the electricity transmission and distribution network (106) for load distribution. Further, the signaling module (210) transmits a second signal to the electrical machine (108a) and the other electrical machine (108b) electrically connected to the electrical machine (108a). The second signal facilitates load distribution between the electrical machine (108a) the other electrical machine (108b) to prevent failure of the electrical machine (108a).
[0055] Additionally, the processor (202) is configured to operate the electrical machine (108a) at maximum efficiency based at least on the parameters of the electrical machine (108a), thereby minimizing wastage of energy associated with the electrical machine (108a). For example, the module (208) may determine wastage of energy in the electrical machine (108a) due to incorrect tap position settings (i.e., on load tap changer (OLTC)) in the electrical machine (108a). In this scenario, the signaling module (210) may transmit control signals to change the tap position of the electrical machine (108a) in order to reduce wastage of energy. Moreover, the processor (202) computes Health Index (HI) indicative of health status of the electrical machine (108a) based on the parameters of the electrical machine (108a). Further, parameter such as humming noise of the electrical machine (108a) may also be used for HI calculation.
[0056] The alert generation module (212) includes suitable logic and/or circuitry for transmitting an alert message to the user device (104) associated with the operator (102) through the communication interface (206). The communication interface (206) may facilitate wireless transmission of the alert message to the operator (102) as explained with reference to FIG. 1. More specifically, the alert generation module (212) transmits the alert message to the user device (104) of the operator (102) based on determining the plurality of failure conditions. The alert message indicates the operator (102) to take preventive actions to prevent the failure of the electrical machine (108a). In an embodiment, the alert message may provide information regarding necessary actions taken on the electrical machine (108a) in emergency faulty conditions (e.g., high voltage transmission).
[0057] The preventive action determining module (214) includes suitable logic and/or circuitry for determining one or more preventive actions corresponding to each of the failure conditions of the electrical machine (108a). More specifically, the module (214) along with the trained ML models performs a comparative analysis of the failure conditions and thereafter provides suitable preventive actions corresponding to the failure conditions. The preventive actions may include maintenance actions, or prognostic and diagnostic measures to solve problems, and the like. Thus, the operator (102) may either access the preventive actions from the application (118) and act accordingly to prevent failure of the electrical machine (108a).
[0058] In an embodiment, the electrical machine (108a) may be a standalone equipment (operating independently or not connected in the electricity transmission and distribution network (106)). Without loss of generality, the system (200) including the monitoring apparatus (120) and the control device (110) can be implemented in the electrical machine (108a) operating standalone for preventing the failure of the electrical machine (108a).
[0059] Additionally, the system (200) may be configured to include specific firmware (such as Firmware Over the Air (FOTA)). As such, FOTA is configured to update the firmware and configuration of the system (200) with prior approval of the customer. Further, FOTA facilitates the system (200) to add/rectify configuration files of electrical machines (i.e., the electrical machines 108a-108c), change data formats, change communication destination locations, troubleshoot software bugs (if any) in a secured manner.
[0060] Further, the system (200) may perform backup (or store) of the required data in a local storage (not shown in figures) in case of connectivity failure issues. For instance, in case of failure of General Packet Radio Service (GPRS) connectivity, the system (200) may store the required data in an SD card, which serves as its internal memory storage. Moreover, once the GPRS connectivity is restored the system (200) shall synchronize with the cloud server and communicate all the missing data. In an embodiment, the internal memory storage may be able to store data upto 200 parameters periodically from each electrical machine at an interval of 15 minutes with a local timestamp for at least 5 years.
[0061] The system (200) may be configured with a built-in real time clock (RTC) along with an auxiliary power source. This facilitates a self-diagnostic feature for RTC, memory, battery, communication module, etc., and obtain instantaneous data with a timestamp from each electrical machine. Furthermore, the system (200) may also detect various configurable alarm or event conditions such as Contactor ON/ OFF Status, Grid ON/OFF status, Fault or Trip status, etc. and transmit data immediately to the server (or the control device 110).
[0062] Further, the data (i.e., the electrical parameters, ambient condition, machine parameters, failure conditions, preventive actions, etc.) is transmitted by the control device (110) to the cloud network (112) as explained above. The cloud network (112) along with the interactive platform (114) may include the software(s) and/or executable instructions required to run and function as independent units. The application (118) may render a plurality of user interfaces (UIs) for allowing the operator (102) to monitor the operating conditions of the electrical machine (108a) in real-time and control the electrical machine (108a). Some exemplary UIs rendered in the application (118) are explained with references to FIGS. 3A-3C.
[0063] FIGS. 3A-3C, collectively, represent user interfaces (UIs) rendered in the application (118) for allowing real-time monitoring and control of the electrical machines, in accordance with an example embodiment of the present disclosure.
[0064] Referring to FIG. 3A, the application (118) renders a user interface (UI) (300) in the user device (104) associated with the operator (102). The UI (300) corresponds to a home page (or a dashboard) of the application (118). The UI is depicted to include information related to total number of electrical machines (such as the electrical machines (108a-108c)) and their operating status (offline, online and normal), total KVA, KW and KWH of all the machines and their corresponding minimum, maximum and average range values, and the like. Further, the UI (300) depicts the geo-location of each machine. The operator (102) may provide inputs in the application (118) for selecting the transformer to be monitored and controlled.
[0065] Referring to FIG. 3B, the application (118) rendered a user interface (UI) (310) in the user device (104). The UI (310) corresponds to an alerts log. As shown, the UI (300) depicts information related to the potential failure conditions of the machines, parameters causing the potential failures, and their actual and threshold values, and the time stamp of detecting the potential failure condition. As explained above, the control device (110) may operate the electrical machine (108a) or the operator (102) may take preventive actions for preventing the failure of the electrical machines. In an embodiment, the application (118) may be configured to render the potential failure conditions of a particular machine in a graphical representation (see, UI (320)). Additionally, the application (118) may render interactive trending curves for analysis which is explained with reference to FIG. 3D.
[0066] Referring to FIG. 3D, represents an UI (330) in the application (118). The UI (330) is depicted to include a graphical representation of variation of voltage in each phase with respect to time of an electrical machine. As such, the operator (102) may provide selection input in the UI (300) for viewing the status and/or performance of the electrical machine over the time. Based on the selection input, the UI (330) depicts the information such as, but not limited to, identifier (ID) of the electrical machine, age of the electrical machine, parameters (exemplarily depicted as “voltage”). Further, the operator (102) may also provide inputs related to the time slot for monitoring the selected parameters of the electrical machine.
[0067] Referring to FIG. 3E, an UI (340) depicting a graphical representation of down time analysis of an electrical machine is illustrated. As shown, the graphical representation provides information related to total online time and offline time associated with the electrical machine at each time stamp (e.g., daily).
[0068] FIG. 4 is a flowchart depicting a method (400) for providing real-time monitoring of the electrical machines and preventing failure of the electrical machines, in accordance with an embodiment of the present disclosure. The method (400) depicted in the flowchart may be executed by the system (200). The method (400) starts at operation (402).
[0069] At operation (402), the method (400) includes receiving, by a system (200), one or more parameters related to an electrical machine of an electricity transmission and distribution network from the monitoring apparatus (120) associated with the electrical machine. The one or more parameters includes at least one of electrical parameters, ambient condition and machine parameters associated with the electrical machine.
[0070] At operation (404), the method (400) includes determining, by the system (200), a plurality of failure conditions associated with the electrical machine based at least on a threshold value associated with each parameter of the electrical parameters, the ambident condition, and the machine parameters associated with the electrical machine. Further determining the plurality of failure conditions includes at least predicting failure of the electrical machine based at least on machine learning (ML) models and detecting anomaly in the operating condition of the electrical machine.
[0071] At operation (406), the method (400) includes transmitting, by the system (200), at least one signal to the electrical machine based at least on determining one parameter of the electrical parameters, the ambient condition, and the machine parameters exceeds their corresponding threshold value, thereby preventing failure of the electrical machine. Further, the one or more operations performed herein for preventing failure of the electrical machines are explained with references to FIGS. 1 and 2, therefore they are not reiterated herein for the sake of brevity.
ADVANTAGES
[0072] In an advantageous aspect, the present disclosure provides an economic, accurate and efficient system to predict failure conditions of the electrical machines and prevent failure rate of the electrical machines.
[0073] In another advantageous aspect, the present disclosure prevents interruption of power supply to consumers.
[0074] In another advantageous aspect, the present disclosure provides efficient systems that serve as utility asset register with a repository of data cloud and eliminate manual maintenance log sheets and provide energy efficiency insights.
[0075] The various embodiments described above are specific examples of a single broader invention. Any modifications, alterations or the equivalents of the above-mentioned embodiments are pertaining to the same invention as long as they are not falling beyond the scope of the invention as defined by the appended claims. It will be apparent to a person skilled in the art that the system and method for autonomous recovery of space based or terrestrial objects may be provided using some or many of the above-mentioned features or components without departing from the scope of the invention. It will be also apparent to a skilled person that the embodiments described above are specific examples of a single broader invention which may have greater scope than any of the singular descriptions taught. There may be many alterations made in the invention without departing from the spirit and scope of the invention.
[0076] Figures are merely representational and are not drawn to scale. Certain portions thereof may be exaggerated, while others may be minimized. Figures illustrate various embodiments of the invention that can be understood and appropriately carried out by those of ordinary skill in the art.
[0077] In the foregoing detailed description of embodiments of the invention, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting an intention that the claimed embodiments of the invention require more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus, the following claims are hereby incorporated into the detailed description of embodiments of the invention, with each claim standing on its own as a separate embodiment.
[0078] It is understood that the above description is intended to be illustrative, and not restrictive. It is intended to cover all alternatives, modifications and equivalents as may be included within the scope of the invention as defined in the appended claims. Many other embodiments will be apparent to those of skill in the art upon reviewing the above description. The scope of the invention should, therefore, be determined with reference to the appended claims, along with the full scope of equivalents to which such claims are entitled. In the appended claims, the terms “including” and “in which” are used as the plain-English equivalents of the respective terms “comprising” and “wherein,” respectively.
, Claims:
1. A system (200) for preventing failure of electrical machines (108a-108c), comprising:
a monitoring apparatus (120) coupled to an electrical machine (108a), the monitoring apparatus (120) comprising a plurality of sensors (216) configured to determine one or more parameters associated with the electrical machine (108a), the one or more parameters comprising at least electrical parameters, ambient condition and machine parameters associated with the electrical machine (108a); and
a control device (110) coupled to the monitoring apparatus (120) and the electrical machine (108a), the control device (110) configured to at least:
receive the one or more parameters associated with the electrical machine (108a) from the monitoring apparatus (120),
determine a plurality of failure conditions associated with the electrical machine (108a) based at least on a threshold value associated with each parameter of the electrical parameters, the ambident condition, and the machine parameters of the electrical machine (108a), wherein determining the plurality of conditions comprises:
predict failure of the electrical machine (108a) based at least on machine learning (ML) models,
detect anomaly in the operating condition of the electrical machine (108a); and
transmit at least one signal to the electrical machine (108a) based at least on determining one parameter of the electrical parameters, the ambient condition, and the machine parameters exceeds their corresponding threshold value, thereby preventing failure of the electrical machine (108a).

2. The system (200) as claimed in claim 1, wherein the control device (110) is further configured to at least:
transmit a first signal to one or more protection devices of the electrical machine (108a) based on determining the at least one parameter of the electrical parameters, the ambient condition, and the machine parameters exceeds their corresponding threshold value, wherein the first signal operates the one or more protection devices from a connected state to a disconnected state to prevent failure of the electrical machine (108a).

3. The system (200) as claimed in claim 2, wherein the electrical machine (108a) is located in an electricity transmission and distribution network (106), and wherein the control device (110) is further configured to at least:
selectively operate, the electrical machine (108a) based at least on an off-load condition, on-load condition and peak load condition; and
transmit a second signal to the electrical machine (108a) and another electrical machine (108b) electrically connected to the electrical machine (108a) in the electricity transmission and distribution network (106),
wherein the second signal is transmitted based at least on determining the electrical machine (108a) being operated in the peak load condition greater than a threshold time and the electrical machine (108a) being overloaded, and
wherein the second signal facilitates load distribution between the electrical machine (108a) the other electrical machine (108b) to prevent failure of the electrical machine (108a).

4. The system (200) as claimed in claim 3, wherein the control device (110) is further configured to at least:
operate the electrical machine (108a) at maximum efficiency based at least on the one or more parameters, thereby minimizing wastage of energy associated with the electrical machine (108a); and
determine a health index (HI) indicative of a health status of the electrical machine (108a) based at least on the electrical parameters, the machine parameters and humming noise of the electrical machine (108a).

5. The system (200) as claimed in claim 1, wherein the control device (110) is further configured to determine one or more preventive actions corresponding to each of the plurality of failure conditions of the electrical machine (108a) based at least on the machine learning (ML) models implementing supervised machine learning.

6. The system (200) as claimed in claim 1, wherein the control device (110) is further configured to at least:
transmit via wireless communication protocols, an alert message to a user device (104) associated with an operator (102) of the electrical machine (108a) based at least on detecting anomaly in the operating condition of the electrical machine (108a) and predicting the occurrence of failure of the electrical machine (108a); and
transmit data related to the electrical parameters, the ambient condition, the machine parameters, and the plurality of failure conditions associated with the electrical machine (108a) to an interactive platform (114) in real-time.

7. The system (200) as claimed in claim 1, wherein the electrical machine (108a) is a transformer.

8. The system (200) as claimed in claim 1, wherein the electrical parameters associated with the electrical machine (108a) comprise at least current, voltage, power, energy, and harmonics, and
wherein the machine parameters associated with the electrical machine (108a) comprise at least oil level, oil temperature and winding temperature, and
wherein the ambient condition comprises weather data around the electrical machine (108a).

9. A method (400) for preventing failure of electrical machines (108a-108c), the method (400) comprising:
receiving (402), by a system (200), one or more parameters related to an electrical machine (108a) from a monitoring apparatus (120) associated with the electrical machine (108a), wherein the one or more parameters comprises at least one of electrical parameters, ambient condition and machine parameters associated with the electrical machine (108a);
determining (404), by the system (200), a plurality of failure conditions associated with the electrical machine (108a) based at least on a threshold value associated with each parameter of the electrical parameters, the ambident condition, and the machine parameters associated with the electrical machine (108a), wherein determining the plurality of failure conditions comprises:
predicting failure of the electrical machine (108a) based at least on machine learning (ML) models, and
detecting anomaly in the operating condition of the electrical machine (108a); and
transmitting (406), by the system (200), at least one signal to the electrical machine (108a) based at least on determining one parameter of the electrical parameters, the ambient condition, and the machine parameters exceeds their corresponding threshold value, thereby preventing failure of the electrical machine (108a).

10. The method (400) as claimed in claim 9, wherein transmitting the at least one signal to the electrical machine (108a) comprises:
transmitting, by the system (200), a first signal to one or more protection devices of the electrical machine (108a) based on determining the at least one parameter of the electrical parameters, the ambient condition, and the machine parameters exceeds their corresponding threshold value, wherein the first signal operates the one or more protection devices from a connected state to a disconnected state to prevent failure of the electrical machine (108a).

11. The method (400) as claimed in claim 10, wherein the electrical machine (108a) is located in an electricity transmission and distribution network (106), and wherein transmitting the at least one signal further comprises:
selectively operating, by the system (200), the electrical machine (108a) based at least on an off-load condition, on-load condition and peak load condition; and
transmitting, by the system (200), a second signal to the electrical machine (108a) and another electrical machine (108b) electrically connected to the electrical machine (108a) in the electricity transmission and distribution network (106),
wherein the second signal is transmitted based at least on determining the electrical machine (108a) being operated in a peak load condition greater than a threshold time and the electrical machine (108a) being overloaded, and
wherein the second signal facilitates load distribution between the electrical machine (108a) and the other electrical machine (108b) to prevent failure of the electrical machine (108a).

12. The method (400) as claimed in claim 11, further comprises:
operating, by the system (200), the electrical machine (108a) at maximum efficiency based at least on the one or more parameters, thereby minimizing wastage of energy associated with the electrical machine (108a); and
determining, by the system (200), one or more preventive actions corresponding to each of the plurality of failure conditions of the electrical machine (108a) based at least on the machine learning (ML) models implementing supervised machine learning.

13. The method (400) as claimed in claim 9, further comprising:
transmitting via wireless communication protocols, by the system (200), an alert message to a user device (104) associated with an operator (102) of the electrical machine (108a) based at least on detecting anomaly in the operating condition of the electrical machine (108a) and predicting the occurrence of failure of the electrical machine (108a); and
transmitting, by the system (200), data related to the electrical parameters, the ambient condition, the machine parameters, and the plurality of failure conditions associated with the electrical machine (108a) to an interactive platform (114) in real-time.