DESC:BACKGROUND
Technical Field
[0001] The embodiments herein generally relate to electrical systems, and more particularly, to a system and method for analyzing and visualizing electrical systems using voice-enabled artificial intelligence and identifying a fault condition based on a status of the electrical systems to enable a user to rectify the fault condition.
Description of the Related Art
[0002] An electrical system is a complex network of system components, sensors, and communication devices. The system components, the sensors, and the communication devices collect data in real-time and the data is further processed for obtaining a status of health and reliability of the electrical system.
[0003] The electrical system that can synchronize itself in real-time with an actual facility's operating conditions is critical which makes it difficult to obtain predictions that are reflective of the electrical system's reliability, availability, health and performance in relation to a life cycle of the electrical system. Static systems cannot adjust to many daily changes of the electrical system that occur at a facility (For example motors and pumps switching on or off, changes to on-site generation status, changes to utility electrical feed and the like). For example, mission-critical electrical systems, such as data centers or nuclear power facilities, must be designed to ensure that power is always available. Thus, the electrical system must be failure proof. Traditionally, data mining consumes time and also requires a professional to write queries to extract required information for further analysis of the electrical system.
[0004] Accordingly, there remains a system and method for analyzing and visualizing electrical systems using voice-enabled artificial intelligence and identifying a fault condition based on a status of the electrical systems to enable a user to rectify the fault condition.
SUMMARY
[0005] In view of the foregoing, an embodiment herein provides a system comprising an artificial intelligence model for extracting a status of one or more electrical systems using a voice command received from a user, identifying a fault condition based on the status of the one or more electrical systems and enabling the user to rectify the fault condition of the one or more electrical systems. The system comprises a memory unit and a processor. The memory unit stores a database and a set of instructions. The database comprises the status of one or more electrical systems in real time which are collected from at least one of a sensor or protection equipment’s. The processor executes the set of instructions and is configured to (i) obtain a voice command of a user, (ii) transform the voice command into a text by parsing the voice command of the user, (iii) transform the text into a database query using a query builder, (iv) extract a status comprising a health and reliability of the one or more electrical systems associated with the database query by analyzing the database using the artificial intelligence model, (v) generate a graphical representation comprising the status of health and reliability of the one or more electrical systems based on the extracted status of the one or more electrical systems using the artificial intelligence model, (vi) identify the fault condition of the one or more electrical systems based on the status of the health and reliability of the one or more electrical systems using the artificial intelligence model, and (vii) enable the user to rectify the fault condition to improve the efficiency of the one or more electrical systems.
[0006] In some embodiments, the processor is configured to provide the health and reliability of the one or more electrical systems to the artificial intelligence model as training data to generate the artificial intelligence model.
[0007] In some embodiments, the system comprises a voice to text conversion module that employs a natural language processing technique to transform the voice command of the user into a text.
[0008] In some embodiments, the system comprises a visualization module that displays the graphical representation comprising the status of health and reliability of the one or more electrical systems in a graphical display.
[0009] In some embodiments, the system comprises a text to voice conversion model that transforms the status comprising the health and reliability of the one or more electrical systems texts into a voice output. The voice output comprising the status of the one or more electrical systems that is queried is provided to the user using a speaker.
[0010] In some embodiments, the protection equipment’s are programmed to access the fault condition and take action in terms of network isolation. The protection equipment’s comprises protection relays and circuit breakers.
[0011] In some embodiments, the user rectifying the fault condition is stored as fault signatures. The fault signatures comprise data points of the rectified fault condition. The artificial intelligence model identifies the fault condition of the one or more electrical systems by analyzing the fault signatures to reduce the faults before occurring.
[0012] In some embodiments, the fault condition comprises one or more fault trends and patterns. The one or more fault trends and patterns is based on historical data of the fault conditions. The artificial intelligence model identifies the fault condition of the one or more electrical systems by analysing the one or more fault trends and patterns.
[0013] In an aspect, an embodiment herein provides a method for extracting a status of one or more electrical systems using a voice command received from a user, identifying a fault condition based on the status of the one or more electrical systems and enabling the user to rectify the fault condition of the one or more electrical systems with an artificial intelligence model using the system. The method comprises, (i) obtaining a voice command from a user, (ii) transforming, using a voice to text conversion module, the voice command into a text by parsing the voice command of the user; transforming, using a query builder, the text into a database query, (iii) extracting, using the artificial intelligence model, a status comprising a health and reliability of the one or more electrical systems associated with the database query by analysing the database, (iv) generating, using the artificial intelligence model, a graphical representation comprising the status of health and reliability of the one or more electrical systems based on the extracted status of the one or more electrical systems, (v) identifying, using the artificial intelligence model, the fault condition of the one or more electrical systems based on the status of the health and reliability of the one or more electrical systems and (vi) enabling the user to rectify the fault condition to improve the efficiency of the one or more electrical systems.
BRIEF DESCRIPTION OF THE DRAWINGS
[0014] The embodiments herein will be better understood from the following detailed description with reference to the drawings, in which:
[0015] FIG. 1 is a block diagram of a system for analyzing and visualizing one or more electrical systems and identifying a fault condition based on a status of the one or more electrical systems to enable a user to rectify the fault condition according to an embodiment herein;
[0016] FIG. 2 illustrates an internal view of an artificial intelligence engine of the system according to an embodiment herein; and
[0017] FIG. 3 illustrates a method for analyzing and visualizing one or more electrical systems and identifying a fault condition based on a status of the one or more electrical systems to enable a user to rectify the fault condition according to an embodiment herein.
DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS
[0018] The embodiments herein and the various features and advantageous details thereof are explained more fully with reference to the non-limiting embodiments that are illustrated in the accompanying drawings and detailed in the following description. Descriptions of well-known components and processing techniques are omitted so as to not unnecessarily obscure the embodiments herein. The examples used herein are intended merely to facilitate an understanding of ways in which the embodiments herein may be practiced and to further enable those of skill in the art to practice the embodiments herein. Accordingly, the examples should not be construed as limiting the scope of the embodiments herein.
[0019] As mentioned, there remains a need for a system and method for analyzing and visualizing electrical systems using voice-enabled artificial intelligence and identifying a fault condition based on a status of the electrical systems to enable a user to rectify the fault condition. The embodiments herein achieve this by proposing an artificial intelligence engine to perform specific data mining for an electrical system (power system). Referring now to the drawings, and more particularly to FIGS. 1 through 3, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments.
[0020] FIG. 1 is a block diagram of a system for analyzing and visualizing one or more electrical systems 104A-N and identifying a fault condition based on a status of the one or more electrical systems 104A-N to enable a user to rectify the fault condition according to an embodiment herein. The system includes an analyzing and visualizing tool 102 that provides analytics and data visualization of one or more electrical systems 104A-N to a user. In an embodiment, the one or more electrical systems 104A-N may include a power distribution system, data centers, a nuclear power plant, a circulation pumps, compressors, manufacturing systems, a refrigeration plant and the like. The analyzing and visualizing tool 102 allows the user to extract a required information from a database by issuing voice commands to an artificial intelligence engine of the system. The artificial intelligence engine understands the voice commands made by the user. The voice commands are translated into a database query to obtain a status of health and reliability of the one or more electrical systems 104A-N. The analyzing and visualizing tool 102 presents the status of health and reliability to the user in a form of a voice output or a graphical output.
[0021] In some embodiments, the system extracts the status of the one or more electrical systems using the voice command received from the user, identifying a fault condition based on the status of the one or more electrical systems and enabling the user to rectify the fault condition of the one or more electrical systems. In some embodiments, the system comprises a memory unit and a processor. The memory unit stores a database and a set of instructions. The database comprises the status of the one or more electrical systems in real time that are collected from at least one of a sensor or protection equipment’s.
[0022] In some embodiments, the processor executes the set of instructions and is configured to (i) obtain a voice command of a user, (ii) transform the voice command into a text by parsing the voice command of the user, (iii) transform the text into a database query using a query builder, (iv) extract a status comprising a health and reliability of the one or more electrical systems associated with the database query by analyzing the database using the artificial intelligence model, (v) generate a graphical representation comprising the status of health and reliability of the one or more electrical systems based on the extracted status of the one or more electrical systems using the artificial intelligence model, (vi) identify the fault condition of the one or more electrical systems based on the status of the health and reliability of the one or more electrical systems using the artificial intelligence model, and (vii) enable the user to rectify the fault condition to improve the efficiency of the one or more electrical systems.
[0023] In some embodiments, the voice command of the user is captured using a microphone. In some embodiments, the voice to text conversion module employs a natural language processing technique to transform the voice command of the user into the text. In some embodiments, the voice command includes a query about a status of the one or more electrical systems. In some embodiments, the query includes at least one of, but not limited to, (i) show the list of lines where 3 phase to ground fault has occurred, (ii) how successful is auto closure, (iii) list all the lines whenever the fault current is more than 10000 amps, or (iv) show the count of stations and regions where the fault current is more than 8000 amps.
[0024] In some example embodiments, the graphical representation is generated as a pie chart when the query is, show the count of stations and regions where the fault current is more than 8000 amps and fault type is line to ground fault and group by region.
[0025] In some embodiments, the protection equipment’s are programmed to access the fault condition and take action in terms of network isolation. In some embodiments, the protection equipment’s include protection relays and circuit breakers. In some embodiments, the user rectifying the fault condition is stored as fault signatures. The fault signatures include data points of the rectifies fault condition. The artificial intelligence model identifies the fault condition of the one or more electrical systems by analyzing the fault signatures to reduce the faults before occurring.
[0026] In some embodiments, the fault condition includes one or more fault trends and patterns. The one or more fault trends and patterns is based on historical data of the fault conditions. The artificial intelligence model identifies the fault condition of the one or more electrical systems by analyzing the one or more fault trends and patterns.
[0027] FIG. 2 illustrates an internal view of an artificial intelligence engine of the system according to an embodiment herein. The artificial intelligence engine 222 includes a voice to text conversion module 204, a text to voice conversion module 206, a graphical display 210, a parser module 212, a visualization module 214, a query builder module 216, and a data extractor module 218. A user 202 issues voice commands to the artificial intelligence engine 222 to obtain the status of health and reliability of the one or more electrical systems 104A-N. The artificial intelligence engine 222 converts the voice commands into texts using the voice to text conversion module 204. The parser module 212 converts the texts into a database query using the query builder module 216 and the data extractor module 218. The data extractor module 218 extracts the status of health and reliability of the one or more electrical systems 104A-N from the database 220 according to the database query. The database 220 includes one or more information on the health and reliability of the one or more electrical systems 104A-N. The status of health and reliability of the one or more electrical systems 104A-N are given to the visualization module 214 to obtain a graphical representation of the status of health and reliability of the one or more electrical systems 104A-N to be displayed on the graphical display 210. The status of health and reliability of the one or more electrical systems 104A-N are given to the text to voice conversion module 206 to obtain a voice output of the status of health and reliability of the one or more electrical systems 104A-N through a speaker 208.
[0028] The artificial intelligence engine 222 extracts statistics with enhanced visualization within a second by constantly learning based on a data which is collected by sensors and protection equipment (For example protection relays, and circuit breakers). The artificial intelligence engine 222 classifying various fault conditions of the one or more electrical systems 104A-N based on past observations. The artificial intelligence engine 222 converts reactive monitoring of the one or more electrical systems 104A-N to pro-active monitoring of the one or more electrical systems 104A-N. The artificial intelligence engine 222 improves the overall efficiency of operating staff and also decision makers. The analyzing and visualizing tool 102 is capable of performing data mining tasks on any data set as the analyzing and visualizing tool 102 may perform tasks of translating human speech into a structured query on a database.
[0029] FIG. 3 illustrates a method for analyzing and visualizing one or more electrical systems 104A-N and identifying a fault condition based on a status of the one or more electrical systems 104A-N to enable a user to rectify the fault condition according to an embodiment herein. At step 302 for analyzing and visualizing the one or more electrical systems 104A-N, a voice command is obtained from a user 202. At step 304 for analyzing and visualizing the one or more electrical systems 104A-N, the voice command is transformed into a text by parsing the voice command of the user using a voice to text conversion module. At step 306 for analyzing and visualizing the one or more electrical systems 104A-N, the text is transformed into a database query using a query builder. At step 308 for analyzing and visualizing the one or more electrical systems 104A-N, a status comprising a health and reliability of the one or more electrical systems 104A-N associated with the database query is extracted by analyzing the database using the artificial intelligence model. At step 310 for analyzing and visualizing the one or more electrical systems 104A-N, a graphical representation comprising the status of health and reliability of the one or more electrical systems 104A-N based on the extracted status of the one or more electrical systems 104A-N is generated using the artificial intelligence model. At step 312 for analyzing and visualizing the one or more electrical systems 104A-N, the fault condition of the one or more electrical systems 104A-N based on the status of the health and reliability of the one or more electrical systems 104A-N is identified using the artificial intelligence model. At step 314 for analyzing and visualizing the one or more electrical systems 104A-N, enables the user to rectify the fault condition to improve the efficiency of the one or more electrical systems 104A-N.
[0030] Foregoing description of the specific embodiments will so fully reveal the general nature of the embodiments herein that others can, by applying current knowledge, readily modify and/or adapt for various applications such specific embodiments without departing from the generic concept, and, therefore, such adaptations and modifications should and are intended to be comprehended within the meaning and range of equivalents of the disclosed embodiments. It is to be understood that the phraseology or terminology employed herein is for the purpose of description and not of limitation. Therefore, while the embodiments herein have been described in terms of preferred embodiments, those skilled in the art will recognize that the embodiments herein can be practiced with modification within the spirit and scope of the appended claims. ,CLAIMS:I/We Claim:
1. A system comprising an artificial intelligence model for extracting a status of one or more electrical systems (104A-N) using a voice command received from a user, identifying a fault condition based on the status of the one or more electrical systems (104A-N) and enabling the user to rectify the fault condition of the one or more electrical systems (104A-N), wherein the system comprises,
a memory unit that stores a database and a set of instructions, wherein the database comprises the status of one or more electrical systems (104A-N) in real time which are collected from at least one of a sensor or protection equipment’s;
a processor that executes the set of instructions and is configured to:
obtain a voice command of a user, wherein the voice command of the user is captured using a microphone, wherein the voice command comprises a query about a status of the one or more electrical systems (104A-N);
transform the voice command into a text by parsing the voice command of the user;
transform the text into a database query using a query builder;
extract, using the artificial intelligence model, a status comprising a health and reliability of the one or more electrical systems (104A-N) associated with the database query by analyzing the database;
generate, using the artificial intelligence model, a graphical representation comprising the status of health and reliability of the one or more electrical systems (104A-N) based on the extracted status of the one or more electrical systems (104A-N);
identifying, using the artificial intelligence model, the fault condition of the one or more electrical systems (104A-N) based on the status of the health and reliability of the one or more electrical systems (104A-N); and
enabling the user to rectify the fault condition to improve the efficiency of the one or more electrical systems (104A-N).

2. The system as claimed in claim 1, wherein the processor is configured to provide the health and reliability of the one or more electrical systems (104A-N) to the artificial intelligence model as training data to generate the artificial intelligence model.


3. The system as claimed in claim 1, wherein the system comprises a voice to text conversion module (204) that employs a natural language processing technique to transform the voice command of the user into the text.


4. The system as claimed in claim 1, wherein the system comprises a visualization module (214) that displays the graphical representation comprising the status of health and reliability of the one or more electrical systems (104A-N) in a graphical display (210).


5. The system as claimed in claim 1, wherein the system comprises a text to voice conversion module (206) that transforms the status comprising the health and reliability of the one or more electrical systems (104A-N) texts into a voice output , wherein the voice output comprising the status of the one or more electrical systems (104A-N) that is queried is provided to the user using a speaker (208).

6. The system as claimed in claim 1, wherein the protection equipment’s are programmed to access the fault condition and take action in terms of network isolation, wherein the protection equipment’s comprises protection relays and circuit breakers.

7. The system as claimed in claim 1, wherein the user rectifying the fault condition is stored as fault signatures, wherein the fault signatures comprise data points of the rectified fault condition, wherein the artificial intelligence model identifies the fault condition of the one or more electrical systems (104A-N) by analyzing the fault signatures to reduce the faults before occurring.

8. The system as claimed in claim 1, wherein the fault condition comprises one or more fault trends and patterns, wherein the one or more fault trends and patterns is based on historical data of the fault conditions, wherein the artificial intelligence model identifies the fault condition of the one or more electrical systems (104A-N) by analyzing the one or more fault trends and patterns.

9. A method for extracting a status of one or more electrical systems (104A-N) using a voice command received from a user (202), identifying a fault condition based on the status of the one or more electrical systems (104A-N) and enabling the user to rectify the fault condition of the one or more electrical systems (104A-N) with an artificial intelligence model using the system, wherein the method comprises,
obtaining a voice command from a user (202);
transforming, using a voice to text conversion module (204), the voice command into a text by parsing the voice command of the user;
transforming, using a query builder, the text into a database query;
extracting, using the artificial intelligence model, a status comprising a health and reliability of the one or more electrical systems (104A-N) associated with the database query by analyzing the database;
generating, using the artificial intelligence model, a graphical representation comprising the status of health and reliability of the one or more electrical systems (104A-N) based on the extracted status of the one or more electrical systems (104A-N);
identifying, using the artificial intelligence model, the fault condition of the one or more electrical systems (104A-N) based on the status of the health and reliability of the one or more electrical systems (104A-N); and
enabling the user (202) to rectify the fault condition to improve the efficiency of the one or more electrical systems (104A-N).