CN115271106A - Intelligent operation and maintenance method and system for transformer based on big data - Google Patents
Intelligent operation and maintenance method and system for transformer based on big data Download PDFInfo
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Abstract
The application provides a transformer intelligent operation and maintenance method and system based on big data, and relates to the field of big data and transformers. The intelligent operation and maintenance method of the transformer based on big data comprises the following steps: acquiring relevant information of various elements in the whole life cycle of the transformer through a big data center; analyzing the influence of disasters on the transformer according to the related information of various factors and the real-time occurrence condition of natural disaster information in the whole life cycle of the transformer, and judging the safety value of the transformer at the next time period; when the safety value exceeds the preset range, the transformer performs power failure action, the big data center automatically stores the data of one second before power failure and uploads the data to the big data center, and the big data center makes a decision on system power maintenance after the data analysis causes. The method can systematically and accurately study and judge the reasons of the problems generated in the operation and maintenance process of the transformer, and is more accurate in problem finding and reason analysis in power grid planning and power grid production operation and maintenance work.
Description
Technical Field
The application relates to the field of big data and transformers, in particular to a transformer intelligent operation and maintenance method and system based on big data.
Background
The power grid planning is the basis for guaranteeing the stable operation of a power grid, the power distribution network is large in scale, multiple in problems, weak in management foundation and weak in professional strength, the problems of power distribution network equipment cannot be found and analyzed properly, the research is insufficient and not deep, the thinking for solving the problems systematically is weak, the problems are often changed, and the huge waste of power grid investment construction is caused.
The section of the power system from the outlet of the step-down distribution substation to the customer premises is called the distribution system. A power distribution system is an electrical power network system that transforms voltage and distributes power directly to end users, consisting of a variety of distribution equipment and distribution facilities. In China, a power distribution system can be divided into a high-voltage power distribution system, a medium-voltage power distribution system and a low-voltage power distribution system. The power distribution system as the last link of the power system is directly oriented to the end users, and the perfection of the power distribution system is directly related to the power utilization reliability and the power utilization quality of the majority of users, so the power distribution system has an important position in the power system.
In the prior art, research data about a power distribution system is single, and a targeted design is not carried out on power system maintenance by relying on the latest product use concept, so that the power maintenance system supported by a power distribution room is slow in response, weak in system maintenance estimation capability and difficult to recover due to accidents.
Therefore, in order to adapt to the current market development situation, an intelligent operation and maintenance system of the transformer utilizing big data is researched.
Disclosure of Invention
The application aims to provide a transformer intelligent operation and maintenance method based on big data, which can systematically and accurately study and judge the reasons of problems generated in the operation and maintenance process of a transformer, and is more accurate in problem finding and reason analysis in power grid planning and power grid production operation and maintenance work.
Another object of the present application is to provide a transformer intelligent operation and maintenance system based on big data, which is capable of operating a transformer intelligent operation and maintenance method based on big data.
The embodiment of the application is realized as follows:
in a first aspect, an embodiment of the application provides an intelligent operation and maintenance method for a transformer based on big data, which includes obtaining information related to various elements in a full life cycle of the transformer through a big data center; analyzing the influence of disasters on the transformer according to the information related to various factors in the whole life cycle of the transformer and the real-time occurrence condition of natural disaster information, and judging the safety value of the transformer in the next time period; when the safety value is detected to exceed the preset range, the transformer performs power failure action, the big data center automatically stores the data of one second before power failure and uploads the data to the big data center, and after the data are analyzed, the big data center makes a decision on system power maintenance.
In some embodiments of the present application, the obtaining of the information related to various elements in the full life cycle of the transformer through the big data center includes: the method comprises the steps of obtaining position information of the transformer, transformer capacity information, load information of the transformer, current information and voltage information of the transformer.
In some embodiments of the present application, the above further includes: the method comprises the steps of collecting position information of the transformer, capacity information of the transformer, load information of the transformer, current information and voltage information of the transformer on line and off line, and storing and managing the collected information.
In some embodiments of the present application, the analyzing the influence of the disaster on the transformer through the occurrence of the information about various elements and the real-time natural disaster information in the full life cycle of the transformer, and determining the safety value of the transformer in the next time period includes: and detecting whether abnormal data, load loss, no-load loss, single-phase impedance, zero-sequence impedance and capacity monitoring data in the transformer line are in a safe numerical range.
In some embodiments of the present application, the above further includes: analyzing and processing the information related to various factors and the real-time natural disaster information occurrence condition in the whole life cycle of the transformer, and optimizing the analyzed and processed data to obtain the influence result and the operation and maintenance optimization suggestion of the disaster on the transformer.
In some embodiments of the application, when it is detected that the safety value exceeds the preset range, the transformer performs a power failure operation, the big data center automatically stores data of one second before power failure and uploads the data to the big data center, and after the data is analyzed for reasons, the big data center makes a decision on system power maintenance, including: and comparing the safety value for detecting the operation of the transformer in real time with a preset threshold value so as to judge the real-time state of the current transformer.
In some embodiments of the present application, the above further includes: and comprehensively evaluating the real-time state of the current transformer to obtain an evaluation result, analyzing the cause of the transformer when the evaluation result is a fault, and making a decision on system power maintenance by the big data center after analyzing the cause.
In a second aspect, an embodiment of the application provides an intelligent operation and maintenance system for a transformer based on big data, which includes an information acquisition module, configured to acquire information related to various elements in a whole life cycle of the transformer through a big data center;
the analysis module is used for analyzing the influence of disasters on the transformer according to the information related to various elements in the whole life cycle of the transformer and the occurrence condition of real-time natural disaster information and judging the safety value of the transformer at the next time period;
and the operation and maintenance module is used for performing power failure action on the transformer when the safety value is detected to exceed the preset range, automatically storing the data of one second before power failure by the big data center, uploading the data to the big data center, and making a decision on system power maintenance by the big data center after the data analysis causes.
In some embodiments of the present application, the above includes: at least one memory for storing computer instructions; at least one processor in communication with the memory, wherein the at least one processor, when executing the computer instructions, causes the system to: the system comprises an information acquisition module, an analysis module and an operation and maintenance module.
In a third aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements a method as any one of big data based transformer intelligent operation and maintenance methods.
Compared with the prior art, the embodiment of the application has at least the following advantages or beneficial effects:
the operation data of the transformer equipment is obtained by constructing an analysis model based on the power big data, quantitative analysis and judgment are automatically and intelligently carried out on the problems existing in the transformer equipment from multiple dimensions by applying a computer program, and finally, the reasons for the problems are accurately identified. The computer algorithm is used for automatically, quantitatively and accurately identifying, so that the reason of the power grid equipment problem is accurately found, and the situation can be determined. The problem of transformer operation and maintenance problem cause is subjected to automatic intelligent quantitative analysis in power grid planning and production operation and maintenance work, and scientific support is provided for power grid planning construction and production operation and maintenance decisions. The intelligent transformer operation and maintenance system can also help operation and maintenance personnel to control the operation state of the transformer in real time, timely push alarm information, diagnosis results of intelligent monitoring and early warning and operation and maintenance optimization suggestions, count fault information and operation records, generate an intelligent report, facilitate development of operation and maintenance work of the transformer, and has revolutionary significance for promoting development of the field of intelligent transformers.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a schematic diagram illustrating steps of an intelligent operation and maintenance method for a transformer based on big data according to an embodiment of the present application;
fig. 2 is a detailed step schematic diagram of a transformer intelligent operation and maintenance method based on big data according to an embodiment of the present application;
fig. 3 is a schematic diagram of a transformer intelligent operation and maintenance system module based on big data according to an embodiment of the present application;
fig. 4 is an electronic device according to an embodiment of the present disclosure.
An icon: 10-an information acquisition module; 20-an analysis module; 30-an operation and maintenance module; 101-a memory; 102-a processor; 103-a communication interface.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, as generally described and illustrated in the figures herein, could be arranged and designed in a wide variety of different configurations.
Thus, the following detailed description of the embodiments of the present application, presented in the accompanying drawings, is not intended to limit the scope of the claimed application, but is merely representative of selected embodiments of the application. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, it need not be further defined and explained in subsequent figures.
It is to be noted that the term "comprises," "comprising," or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
Some embodiments of the present application will be described in detail below with reference to the accompanying drawings. The embodiments and features of the embodiments described below can be combined with one another without conflict.
Example 1
Referring to fig. 1, fig. 1 is a schematic diagram illustrating steps of an intelligent operation and maintenance method for a transformer based on big data according to an embodiment of the present application, which is shown as follows:
s100, acquiring relevant information of various elements in the whole life cycle of the transformer through a big data center;
in some embodiments, the information related to various elements in the full life cycle of the transformer comprises on-line collected data of the operating state parameters of the transformer, such as oil color spectrum, partial discharge, infrared temperature measurement, oil surface/winding temperature, iron core/clamp grounding current, vibration, noise, oil tank pressure, voltage regulating switch motor current, casing dielectric loss, casing capacitance and the like.
Step S110, analyzing the influence of disasters on the transformer according to the related information of various elements in the whole life cycle of the transformer and the occurrence condition of real-time natural disaster information, and judging the safety value of the transformer at the next time period;
in some embodiments, when detecting that any value in the natural disaster information reaches or exceeds a system set safety value, the system continuously detects the value condition within 2min, if the value continuously reaches the safety value, an internal exploration system or an external exploration system of the active defense exploration system is started, an unmanned vehicle is used for approaching an abnormal point for detection, meanwhile, the rotor unmanned aerial vehicle takes off for multi-angle data acquisition, and when the value is confirmed to reach the set safety value, the data is immediately sent to a big data center and an alarm is sent, and an operator makes a decision; when the confirmed value is lower than the set safety value, the system backs up the data and sends the data to a big data center; when the system monitors that any data in the sensors exceeds a safety value, the system directly alarms and sends the data to the big data center.
And step S120, when the safety value is detected to exceed the preset range, the transformer performs power failure action, the big data center automatically stores the data of one second before power failure and uploads the data to the big data center, and the big data center makes a decision on system power maintenance after the data analysis reason.
In some embodiments, after a serious natural disaster occurs, the system controls an external probing system to recheck environmental data, corrects the safety value of a fixed sensor in the system, and protects weak parts for detailed monitoring to evaluate the influence of the disaster; when an accident occurs and the power distribution room is completely powered off, the system automatically stores the data of one second before power failure and uploads the data to the cloud data computing processor, and after the data are analyzed for reasons, the big data center makes a decision on system power maintenance
Example 2
Referring to fig. 2, fig. 2 is a detailed step diagram of a transformer intelligent operation and maintenance method based on big data according to an embodiment of the present application, which is shown as follows:
step S200, acquiring position information, transformer capacity information, load information, current information and voltage information of the transformer.
And step S210, carrying out online acquisition and offline acquisition on the position information of the transformer, the capacity information of the transformer, the load information of the transformer, the current information and the voltage information of the transformer, and storing and managing the acquired information.
Step S220, detecting whether the abnormal data, the load loss, the no-load loss, the single-phase impedance, the zero-sequence impedance and the capacity monitoring data in the transformer line are in a safe numerical value range.
And step S230, analyzing and processing the information related to various elements in the whole life cycle of the transformer and the occurrence condition of real-time natural disaster information, and optimizing the analyzed and processed data to obtain the influence result of the disaster on the transformer and an operation and maintenance optimization suggestion.
Step S240, comparing the safety value of the real-time detection of the operation of the transformer with a preset threshold, so as to determine the real-time status of the current transformer.
And S250, comprehensively evaluating the real-time state of the current transformer to obtain an evaluation result, analyzing the cause of the transformer when the evaluation result is a fault, and making a decision on system power maintenance by the big data center after analyzing the cause.
In some embodiments, a transformer analysis model is established, and causes, analysis rules and parameters of transformer problems are configured. Acquiring distribution transformer account parameters and operation data, and acquiring related data from a power big data center according to required calculation parameters in transformer analysis model analysis rules; analyzing whether the power supply radius is too long, calculating the power supply distance from the transformer substation to the current transformer according to the acquired data, comparing the result with an analysis rule to determine whether the transformer capacity is insufficient, calculating the transformer capacity load value according to the acquired rated capacity and load data of the transformer, analyzing whether distribution transformer three phases are unbalanced, analyzing whether the distribution transformer tap position is improper according to the acquired three-phase current data of the transformer in a specified time period, judging the tap position according to the acquired voltage value of the high-voltage side of the transformer through the voltage value, judging the tap position according to the current position, judging whether the current position is in a low gear or a middle gear, judging whether the current position is in an up-regulation space under the condition of abnormal operation and maintenance, and judging that the reason of the improper tap position of the transformer is true, otherwise, judging that the current position is not true.
Example 3
Referring to fig. 3, fig. 3 is a schematic diagram of a transformer intelligent operation and maintenance system module based on big data according to an embodiment of the present application, which is as follows:
the information acquisition module 10 is used for acquiring information related to various elements in the whole life cycle of the transformer through a big data center;
the analysis module 20 is configured to analyze the influence of a disaster on the transformer according to the information related to various elements in the full life cycle of the transformer and the occurrence condition of real-time natural disaster information, and determine a safety value of the transformer at a next time period;
and the operation and maintenance module 30 is used for performing power failure action on the transformer when the safety value is detected to exceed the preset range, automatically storing the data of one second before power failure in the big data center, uploading the data to the big data center, and making a decision on system power maintenance by the big data center after the data analysis reason.
As shown in fig. 4, an embodiment of the present application provides an electronic device, which includes a memory 101 for storing one or more programs; a processor 102. The one or more programs, when executed by the processor 102, implement the method of any of the first aspects as described above.
Also included is a communication interface 103, and the memory 101, processor 102 and communication interface 103 are electrically connected to each other, directly or indirectly, to enable transfer or interaction of data. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory 101 may be used to store software programs and modules, and the processor 102 executes the software programs and modules stored in the memory 101 to thereby execute various functional applications and data processing. The communication interface 103 may be used for communicating signaling or data with other node devices.
The Memory 101 may be, but is not limited to, a random access Memory 101 (RAM), a Read Only Memory 101 (ROM), a Programmable Read Only Memory 101 (PROM), an Erasable Read Only Memory 101 (EPROM), an electrically Erasable Read Only Memory 101 (EEPROM), and the like.
The processor 102 may be an integrated circuit chip having signal processing capabilities. The Processor 102 may be a general-purpose Processor 102, including a Central Processing Unit (CPU) 102, a Network Processor (NP) 102, and the like; but may also be a Digital Signal processor 102 (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware components.
In the embodiments provided in the present application, it should be understood that the disclosed method and system may be implemented in other ways. The method and system embodiments described above are merely illustrative, for example, the flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of methods and systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
In addition, functional modules in the embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.
In another aspect, embodiments of the present application provide a computer-readable storage medium, on which a computer program is stored, which, when executed by the processor 102, implements the method according to any one of the first aspect described above. The functions, if implemented in the form of software functional modules and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory 101 (ROM), a Random Access Memory 101 (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In summary, according to the transformer intelligent operation and maintenance method and system based on big data provided by the embodiment of the application, the operation data of the transformer equipment is obtained by constructing the analysis model based on the big data of the power, quantitative analysis and judgment are automatically and intelligently performed from multiple dimensions by applying a computer program aiming at the existing problems, and finally, the reasons for the problems are accurately identified. The computer algorithm is used for automatically, quantitatively and accurately identifying, so that the reason of the power grid equipment problem is accurately found, and the situation can be determined. The problem of transformer operation and maintenance problem cause is subjected to automatic intelligent quantitative analysis in power grid planning and production operation and maintenance work, and scientific support is provided for power grid planning construction and production operation and maintenance decisions. The intelligent transformer operation and maintenance system can also help operation and maintenance personnel to control the operation state of the transformer in real time, timely push alarm information, diagnosis results of intelligent monitoring and early warning and operation and maintenance optimization suggestions, count fault information and operation records, generate an intelligent report, facilitate development of operation and maintenance work of the transformer, and has revolutionary significance for promoting development of the field of intelligent transformers.
The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
It will be evident to those skilled in the art that the present application is not limited to the details of the foregoing illustrative embodiments, and that the present application may be embodied in other specific forms without departing from the spirit or essential attributes thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference sign in a claim should not be construed as limiting the claim concerned.
Claims (10)
1. An intelligent operation and maintenance method for a transformer based on big data is characterized by comprising the following steps:
acquiring relevant information of various elements in the whole life cycle of the transformer through a big data center;
analyzing the influence of disasters on the transformer according to the information related to various factors in the whole life cycle of the transformer and the real-time occurrence condition of natural disaster information, and judging the safety value of the transformer in the next time period;
when the safety value is detected to exceed the preset range, the transformer performs power failure action, the big data center automatically stores the data of one second before power failure and uploads the data to the big data center, and after the data are analyzed, the big data center makes a decision on system power maintenance.
2. The intelligent operation and maintenance method of the big data-based transformer according to claim 1, wherein the obtaining of the information related to various elements in the whole life cycle of the transformer through the big data center comprises:
the method comprises the steps of obtaining position information of the transformer, transformer capacity information, load information of the transformer, current information and voltage information of the transformer.
3. The intelligent operation and maintenance method of the transformer based on the big data as claimed in claim 2, further comprising:
the method comprises the steps of collecting position information of the transformer, capacity information of the transformer, load information of the transformer, current information and voltage information of the transformer on line and off line, and storing and managing the collected information.
4. The intelligent operation and maintenance method of the transformer based on the big data as claimed in claim 1, wherein the analyzing the influence of the disaster on the transformer through the information related to various elements in the whole life cycle of the transformer and the occurrence condition of the real-time natural disaster information, and the determining the safety value of the transformer in the next time period comprises:
and detecting whether abnormal data and load loss, no-load loss, single-phase impedance, zero-sequence impedance and capacity monitoring data in the transformer line are in a safe numerical range or not.
5. The intelligent operation and maintenance method of the transformer based on the big data as claimed in claim 4, further comprising:
analyzing and processing the information related to various factors and the real-time natural disaster information occurrence condition in the whole life cycle of the transformer, and optimizing the analyzed and processed data to obtain the influence result of the disaster on the transformer and the operation and maintenance optimization suggestion.
6. The intelligent operation and maintenance method of the big data based transformer as claimed in claim 1, wherein when it is detected that the safety value exceeds the preset range, the transformer performs a power failure action, the big data center automatically stores the data of one second before the power failure and uploads the data to the big data center, and after the data analysis reason, the big data center makes a decision on the system power maintenance, including:
and comparing the safety value for detecting the operation of the transformer in real time with a preset threshold value so as to judge the real-time state of the current transformer.
7. The intelligent operation and maintenance method of the transformer based on the big data as claimed in claim 6, further comprising:
and comprehensively evaluating the real-time state of the current transformer to obtain an evaluation result, analyzing the cause of the transformer when the evaluation result is a fault, and making a decision on system power maintenance by the big data center after analyzing the cause.
8. The utility model provides a transformer intelligence fortune dimension system based on big data which characterized in that includes:
the information acquisition module is used for acquiring relevant information of various elements in the whole life cycle of the transformer through the big data center;
the analysis module is used for analyzing the influence of disasters on the transformer according to the information related to various elements in the whole life cycle of the transformer and the occurrence condition of real-time natural disaster information and judging the safety value of the transformer at the next time period;
and the operation and maintenance module is used for performing power failure action on the transformer when the safety value is detected to exceed the preset range, automatically storing the data of one second before power failure by the big data center, uploading the data to the big data center, and making a decision on system power maintenance by the big data center after the data analysis causes.
9. The intelligent operation and maintenance system for the big data-based transformer according to claim 8, comprising:
at least one memory for storing computer instructions;
at least one processor in communication with the memory, wherein the at least one processor, when executing the computer instructions, causes the system to perform: the system comprises an information acquisition module, an analysis module and an operation and maintenance module.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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| CN202210623188.XA CN115271106A (en) | 2022-06-02 | 2022-06-02 | Intelligent operation and maintenance method and system for transformer based on big data |
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| CN202210623188.XA CN115271106A (en) | 2022-06-02 | 2022-06-02 | Intelligent operation and maintenance method and system for transformer based on big data |
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108388950A (en) * | 2018-01-29 | 2018-08-10 | 杭州安脉盛智能技术有限公司 | Intelligent transformer O＆M method and system based on big data |
| WO2020147349A1 (en) * | 2019-01-14 | 2020-07-23 | 中国电力科学研究院有限公司 | Power distribution network operation aided decision-making analysis system and method |
| CN113452139A (en) * | 2021-05-14 | 2021-09-28 | 贵州正航众联电力建设有限公司 | Power distribution room power operation and maintenance system using big data |
| CN114358564A (en) * | 2021-12-30 | 2022-04-15 | 云南电网有限责任公司信息中心 | Intelligent analysis method for low-voltage fault of distribution transformer based on electric power big data |
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- 2022-06-02 CN CN202210623188.XA patent/CN115271106A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108388950A (en) * | 2018-01-29 | 2018-08-10 | 杭州安脉盛智能技术有限公司 | Intelligent transformer O＆M method and system based on big data |
| WO2020147349A1 (en) * | 2019-01-14 | 2020-07-23 | 中国电力科学研究院有限公司 | Power distribution network operation aided decision-making analysis system and method |
| CN113452139A (en) * | 2021-05-14 | 2021-09-28 | 贵州正航众联电力建设有限公司 | Power distribution room power operation and maintenance system using big data |
| CN114358564A (en) * | 2021-12-30 | 2022-04-15 | 云南电网有限责任公司信息中心 | Intelligent analysis method for low-voltage fault of distribution transformer based on electric power big data |
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Application publication date: 20221101 |