CN111273196A - Health management system and method applied to nuclear power large-scale power transformer - Google Patents
Health management system and method applied to nuclear power large-scale power transformer Download PDFInfo
- Publication number
- CN111273196A CN111273196A CN202010166882.4A CN202010166882A CN111273196A CN 111273196 A CN111273196 A CN 111273196A CN 202010166882 A CN202010166882 A CN 202010166882A CN 111273196 A CN111273196 A CN 111273196A
- Authority
- CN
- China
- Prior art keywords
- transformer
- algorithm
- original data
- data
- cloud server
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D21/00—Measuring or testing not otherwise provided for
- G01D21/02—Measuring two or more variables by means not covered by a single other subclass
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B19/00—Alarms responsive to two or more different undesired or abnormal conditions, e.g. burglary and fire, abnormal temperature and abnormal rate of flow
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Molecular Biology (AREA)
- Biophysics (AREA)
- Computational Linguistics (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Biomedical Technology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Health & Medical Sciences (AREA)
- Testing And Monitoring For Control Systems (AREA)
Abstract
The invention discloses a health management system and method applied to a nuclear power large-scale power transformer, which comprises the following steps: the terminal sensing unit comprises a plurality of acquisition ends and is used for acquiring original data of the transformer; the edge intelligent data acquisition unit receives the original data, cleans and preprocesses the original data through an algorithm, and transmits a calculation result to the cloud server unit in real time; and the cloud server unit is used for receiving the calculation result and the original data, analyzing and calculating according to an expert system algorithm or a neural network learning algorithm, providing the current working health state and the future prediction result of the transformer equipment, and storing the data. The invention can monitor the running state of the transformer in real time, thereby reducing the personnel investment and the errors caused by artificial measurement; the collected original data or characteristic value data are managed in a unified way at the server side, and the safety and the efficiency of data management are guaranteed.
Description
Technical Field
The invention relates to the technical field of transformer monitoring, in particular to a health management system and method applied to a nuclear power large-scale power transformer.
Background
In a nuclear power system, a transformer is a key device. The capacity of a three-phase transformer currently used in most nuclear power plants is 1000MVA or more, the voltage on the low-voltage side is several tens, and the voltage on the high-voltage side is several hundreds KV or so. For the maintenance and management of such important equipment, most of them still adopt the conventional modes of after-repair, regular maintenance and regular inspection by security personnel. The existing monitoring and maintaining mode cannot meet the requirements of key equipment such as a transformer on safety and real-time performance.
The invention as the publication number CN110361088A discloses a transformer mechanical stability fault monitoring and diagnosing system, wherein the sensor layer comprises a plurality of ICP sensors which are adsorbed or fixedly connected on the outer surface of an oil tank, a plurality of load current sensors which are arranged at the load end of a transformer and a neutral point grounding current sensor which is arranged on a neutral point flat iron of the transformer; the data convergence layer comprises an AD data acquisition module and a high-speed signal analysis module; the network layer comprises an edge calculation module; the management layer comprises a transformer fault monitoring platform; the ICP sensor, the load current sensor and the neutral point grounding current sensor are respectively connected with the AD data acquisition module, and the high-speed signal analysis module is respectively connected with the AD data acquisition module and the edge calculation module through signals; and the edge calculation module is in communication connection with the transformer fault monitoring platform.
According to the requirement of practical nuclear power field application, the existing maintenance scheme cannot acquire equipment state data in real time. Monitoring of the equipment is a discrete, non-real-time manner, and potential safety problems of the equipment are difficult to analyze and predict. The collected data are difficult to form a uniform and effective database for management. The operation condition of the transformer cannot be managed.
Disclosure of Invention
Aiming at the problem that the running state of a transformer cannot be managed in the prior art, the invention provides a health management system applied to a nuclear power large-scale power transformer, which monitors the health state of the transformer in real time through acquisition and analysis of various parameters, even if abnormal information is known, and improves the safety and the reliability.
The technical scheme of the invention is as follows.
A health management system applied to a nuclear power large-scale power transformer comprises: the terminal sensing unit comprises a plurality of acquisition ends and is used for acquiring original data of the transformer; the edge intelligent data acquisition unit receives the original data, cleans and preprocesses the original data through an algorithm, and transmits a calculation result to the cloud server unit in real time; and the cloud server unit is used for receiving the calculation result and the original data, analyzing and calculating according to an expert system algorithm or a neural network learning algorithm, providing the current working health state and the future prediction result of the transformer equipment, and storing the data.
According to the invention, an edge and cloud two-stage data management and analysis system is constructed, so that the sensing and real-time analysis of key operation parameters of the transformer are realized, and the workload and the working difficulty of daily data acquisition and analysis of workers are greatly reduced. The edge intelligent data acquisition unit acquires data of the terminal sensor unit in real time, a data analysis mining platform is constructed based on the cloud server unit, an expert system algorithm or a neural network learning algorithm is deployed, and predictive maintenance can be performed on the transformer equipment by comprehensively analyzing the data of each sensor. And monitoring the state change of the transformer equipment, predicting the operation life track of the transformer equipment, and analyzing to obtain the health degree of the transformer.
Preferably, the cloud server unit is used for analyzing and calculating the calculation result by using an expert system algorithm or a neural network learning algorithm, and providing the current working health state and the future prediction result of the transformer equipment; and using the original data, the current working health state of the transformer equipment and a future prediction result for training and perfecting the algorithm, and issuing the latest algorithm to the edge intelligent data acquisition unit. The algorithm of the massive measurement data is continuously optimized based on an expert system or a neural network learning system, the system can be continuously upgraded, and the accuracy of the transformer health state prediction is improved.
Preferably, the edge intelligent data acquisition unit executes: alarming by parameter threshold; counting under severe working conditions; hot spot calculation and insulation aging calculation; detecting the abnormality of the original spectrum peak of the gas dissolved in the oil; detecting the abnormity of the dissolved gas in the oil and judging the fault based on the guide rule; extracting partial discharge signal characteristics; monitoring the working state of the cooling system; and counting the running time of the fan. And after receiving the data, the cloud server unit carries out further diagnosis and analysis on the transformer, mainly focuses on cluster management of equipment and modeling based on learning, and calculates and provides the health state and future result prediction of the current transformer based on a fault diagnosis algorithm and each state parameter of the transformer. And the visual interface displays the fault type and corresponding solution strategy if the equipment state is abnormal, and the working personnel can make corresponding judgment and implement subsequent operation according to the result and the prompt information.
Preferably, the edge intelligent data acquisition unit comprises an interface module, and the interface module provides a communication interface for external detection equipment.
Preferably, the acquisition and transmission of data among the terminal sensing unit, the edge intelligent data acquisition unit and the cloud server unit are encrypted through an algorithm. And the data security is ensured.
The scheme also comprises a health management method applied to the nuclear power large-scale power transformer, and the health management system applied to the nuclear power large-scale power transformer comprises the following steps: a terminal sensing unit acquires original data of a transformer; the edge intelligent data acquisition unit receives the original data, performs cleaning and preprocessing through an algorithm, and transmits a calculation result to the cloud server unit in real time; the cloud server unit receives the calculation result and the original data, analyzes and calculates according to an expert system algorithm or a neural network learning algorithm, and provides the current working health state and the future prediction result of the transformer equipment; the cloud server unit uses the original data, the current working health state of the transformer equipment and the future prediction result for training and perfecting the algorithm, and sends the latest algorithm to the edge intelligent data acquisition unit.
Preferably, the calculation content of the edge intelligent data acquisition unit includes: alarming by parameter threshold; counting under severe working conditions; hot spot calculation and insulation aging calculation; detecting the abnormality of the original spectrum peak of the gas dissolved in the oil; detecting the abnormity of the dissolved gas in the oil and judging the fault based on the guide rule; extracting partial discharge signal characteristics; monitoring the working state of the cooling system; and counting the running time of the fan. The specific process comprises the following steps:
1) cleaning original data: calculating a differential sequence of the monitoring sequence; eliminating abnormal sampling points by adopting a loeda rule; filling missing data by spline interpolation; the setting time is not long, and the time alignment of different monitoring data is realized.
2) Hot spot calculation and insulation aging calculation: based on GB/T1094.7 part 7 of the power transformer: the method for calculating the real-time hot spot temperature and the insulation life loss of the transformer is specified in the load guide rule of the oil-immersed power transformer, wherein the input quantity is the load rate (load current/rated current) and the ambient temperature, and the output quantity is the hot spot temperature and the life loss.
3) And (3) detecting abnormal peaks of the chromatographic spectrum: the chromatogram sampling spectrogram is a time sequence, a large number of normal spectrogram samples and abnormal spectrogram sampling samples are collected, and the abnormal detection of a spectrogram peak is realized by adopting a long-short term memory neural network (LSTM).
4) And (3) detecting the abnormality of the dissolved gas in the oil: and determining the fault type of the transformer by weighting voting based on the guide rule of GB/T7252 'guide rule for analyzing and judging dissolved gas in transformer oil', by integrating a three-ratio method, a great-satellite trigonometry method and a stereographic method.
5) Ultrasonic partial discharge signal feature extraction: and extracting the discharging times in the partial discharge power frequency period and the phase position of the discharging event as the core characteristic of the partial discharge so as to effectively distinguish noise and a discharging signal and judge the severity of the partial discharge.
6) Monitoring the working state of the cooling system: and establishing a model of the power supply current and the cooling rate of the cooling system through a neural network so as to realize abnormal monitoring of the cooling system.
The substantial effects of the invention include: the running state of the transformer can be monitored in real time, and the personnel investment and errors caused by artificial measurement are reduced; the algorithm of the massive measurement data is continuously optimized based on an expert system or a neural network learning system, the system can be continuously upgraded, and the accuracy of the transformer health state prediction is improved; the collected original data or characteristic value data are managed in a unified way at the server side, so that the safety and the efficiency of data management are ensured; data algorithm encryption is adopted in data acquisition and transmission, and data security is guaranteed.
Drawings
FIG. 1 is a schematic diagram of an embodiment of the present invention;
the figure includes: the system comprises a terminal sensing unit, a 2-edge intelligent data acquisition unit, a 3-cloud server unit and a 4-display device.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. In addition, numerous specific details are set forth below in order to provide a better understanding of the present invention. It will be understood by those skilled in the art that the present invention may be practiced without some of these specific details. In some instances, methods, means, elements and circuits that are well known to those skilled in the art have not been described in detail so as not to obscure the present invention.
Example (b):
as shown in fig. 1, a health management system applied to a large power transformer for nuclear power includes: the terminal sensing unit 1 comprises a plurality of acquisition ends and is used for acquiring original data of the transformer; the edge intelligent data acquisition unit 2 receives the original data, cleans and preprocesses the original data through an algorithm, and transmits a calculation result to the cloud server unit 3 in real time; the cloud server unit 3 is used for receiving the calculation result and the original data, analyzing and calculating according to an expert system algorithm or a neural network learning algorithm, providing the current working health state and the future prediction result of the transformer equipment, and storing the data; and a display device 4 for displaying various data.
Wherein the terminal sensing unit 1 mainly includes, for example, a temperature sensor, a vibration sensor, a noise sensor, and the like.
The cloud server unit 3 is used for analyzing and calculating the calculation result in an expert system algorithm or a neural network learning algorithm, and providing the current working health state and a future prediction result of the transformer equipment; and using the original data, the current working health state of the transformer equipment and the future prediction result for training and perfecting the algorithm, and sending the latest algorithm to the edge intelligent data acquisition unit 2. The algorithm of the massive measurement data is continuously optimized based on an expert system or a neural network learning system, the system can be continuously upgraded, and the accuracy of the transformer health state prediction is improved.
The calculation content of the edge intelligent data acquisition unit 2 comprises: alarming by parameter threshold; counting under severe working conditions; hot spot calculation and insulation aging calculation; detecting the abnormality of the original spectrum peak of the gas dissolved in the oil; detecting the abnormity of the dissolved gas in the oil and judging the fault based on the guide rule; extracting partial discharge signal characteristics; monitoring the working state of the cooling system; and counting the running time of the fan. After receiving the data, the cloud server unit 3 further diagnoses and analyzes the transformer, mainly focuses on cluster management of equipment and modeling based on learning, and calculates and provides a current health state and future result prediction of the transformer based on a fault diagnosis algorithm and various state parameters of the transformer. And the visual interface displays the fault type and corresponding solution strategy if the equipment state is abnormal, and the working personnel can make corresponding judgment and implement subsequent operation according to the result and the prompt information.
The edge intelligent data acquisition unit 2 comprises a measurement module, a bus module and a hardware platform, wherein the hardware platform comprises an edge end communication module, a central processing module, a cloud end communication module and a data encryption module. The edge intelligent data acquisition unit 2 adopts a modular design. According to the interface type of the terminal sensor unit, a plurality of interface modes such as a BNC connector, a two-wire system and a three-wire system can be configured. The types of signals that can be collected include thermocouple temperature data, IEPE type vibration signals, and ultrasonic signals in addition to the basic voltage and current signals. The data acquisition unit is also provided with serial port modules such as 232 and 485 and the like for communicating with other detection equipment.
The acquisition and transmission of data among the terminal sensing unit 1, the edge intelligent data acquisition unit 2 and the cloud server unit 3 are encrypted through an algorithm. And the data security is ensured.
According to the embodiment, an edge and cloud two-stage data management and analysis system is constructed, sensing and real-time analysis of key operation parameters of the transformer are achieved, and workload and working difficulty of daily data acquisition and analysis of workers are greatly reduced. The edge intelligent data acquisition unit 2 acquires data of the terminal sensor unit in real time, a data analysis mining platform is constructed based on the cloud server unit 3, an expert system algorithm or a neural network learning algorithm is deployed, and predictive maintenance can be performed on the transformer equipment by comprehensively analyzing the data of each sensor. And monitoring the state change of the transformer equipment, predicting the operation life track of the transformer equipment, and analyzing to obtain the health degree of the transformer.
The embodiment also comprises a health management method applied to the nuclear power large-scale power transformer, which comprises the following steps: the terminal sensing unit 1 acquires original data of a transformer; the edge intelligent data acquisition unit 2 receives the original data, carries out cleaning and preprocessing through an algorithm, and transmits a calculation result to the cloud server unit 3 in real time; the cloud server unit 3 receives the calculation result and the original data, analyzes and calculates according to an expert system algorithm or a neural network learning algorithm, and provides the current working health state and the future prediction result of the transformer equipment; the cloud server unit 3 uses the original data, the current working health state of the transformer equipment and the future prediction result for training and perfecting the algorithm, and sends the latest algorithm to the edge intelligent data acquisition unit 2.
The calculation content of the edge intelligent data acquisition unit 2 comprises: alarming by parameter threshold; counting under severe working conditions; hot spot calculation and insulation aging calculation; detecting the abnormality of the original spectrum peak of the gas dissolved in the oil; detecting the abnormity of the dissolved gas in the oil and judging the fault based on the guide rule; extracting partial discharge signal characteristics; monitoring the working state of the cooling system; and counting the running time of the fan.
The specific process comprises the following steps:
1) cleaning original data: calculating a differential sequence of the monitoring sequence; eliminating abnormal sampling points by adopting a loeda rule; filling missing data by spline interpolation; the setting time is not long, and the time alignment of different monitoring data is realized.
2) Hot spot calculation and insulation aging calculation: based on GB/T1094.7 part 7 of the power transformer: the method for calculating the real-time hot spot temperature and the insulation life loss of the transformer is specified in the load guide rule of the oil-immersed power transformer, wherein the input quantity is the load rate (load current/rated current) and the ambient temperature, and the output quantity is the hot spot temperature and the life loss.
3) And (3) detecting abnormal peaks of the chromatographic spectrum: the chromatogram sampling spectrogram is a time sequence, a large number of normal spectrogram samples and abnormal spectrogram sampling samples are collected, and the abnormal detection of a spectrogram peak is realized by adopting a long-short term memory neural network (LSTM).
4) And (3) detecting the abnormality of the dissolved gas in the oil: and determining the fault type of the transformer by weighting voting based on the guide rule of GB/T7252 'guide rule for analyzing and judging dissolved gas in transformer oil', by integrating a three-ratio method, a great-satellite trigonometry method and a stereographic method.
5) Ultrasonic partial discharge signal feature extraction: and extracting the discharging times in the partial discharge power frequency period and the phase position of the discharging event as the core characteristic of the partial discharge so as to effectively distinguish noise and a discharging signal and judge the severity of the partial discharge.
6) Monitoring the working state of the cooling system: and establishing a model of the power supply current and the cooling rate of the cooling system through a neural network so as to realize abnormal monitoring of the cooling system.
Through the description of the above embodiments, those skilled in the art will understand that, for convenience and simplicity of description, only the division of the above functional modules is used as an example, and in practical applications, the above function distribution may be completed by different functional modules according to needs, that is, the internal structure of a specific device is divided into different functional modules to complete all or part of the above described functions.
In the embodiments provided in this application, it should be understood that the disclosed structures and methods may be implemented in other ways. For example, a module or element may be partitioned into only one logical function, and may be physically implemented in another way, such as by combining multiple elements or components, or by integrating multiple elements or components into another structure, or by omitting some features or by performing none of the features. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, structures or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may be one physical unit or a plurality of physical units, may be located in one place, or may be distributed to a plurality of different places. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially or partially contributed to by the prior art, or all or part of the technical solutions may be embodied in the form of a software product, where the software product is stored in a storage medium and includes several instructions to enable a device (which may be a single chip, a chip, or the like) or a processor (processor) to execute all or part of the steps of the methods of the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk, or an optical disk.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (7)
1. A health management system applied to a nuclear power large-scale power transformer is characterized by comprising:
the terminal sensing unit comprises a plurality of acquisition ends and is used for acquiring original data of the transformer;
the edge intelligent data acquisition unit receives the original data, cleans and preprocesses the original data through an algorithm, and transmits a calculation result to the cloud server unit in real time;
and the cloud server unit is used for receiving the calculation result and the original data, analyzing and calculating according to an expert system algorithm or a neural network learning algorithm, providing the current working health state and the future prediction result of the transformer equipment, and storing the data.
2. The health management system applied to the nuclear large-scale power transformer of claim 1, wherein the cloud server unit is used for analyzing and calculating a calculation result in an expert system algorithm or a neural network learning algorithm to provide a current working health state and a future prediction result of transformer equipment; and using the original data, the current working health state of the transformer equipment and a future prediction result for training and perfecting the algorithm, and issuing the latest algorithm to the edge intelligent data acquisition unit.
3. The health management system applied to the nuclear large-scale power transformer according to claim 1 or 2, wherein the edge intelligent data acquisition unit executes: alarming by parameter threshold; counting under severe working conditions; hot spot calculation and insulation aging calculation; detecting the abnormality of the original spectrum peak of the gas dissolved in the oil; detecting the abnormity of the dissolved gas in the oil and judging the fault based on the guide rule; extracting partial discharge signal characteristics; monitoring the working state of the cooling system; and counting the running time of the fan.
4. The health management system applied to the nuclear large-scale power transformer of claim 1, wherein the edge intelligent data acquisition unit comprises an interface module, and the interface module provides a communication interface for external detection equipment.
5. The health management system applied to the nuclear large-scale power transformer according to claim 1, wherein the acquisition and transmission of data among the terminal sensing unit, the edge intelligent data acquisition unit and the cloud server unit are encrypted through an algorithm.
6. The health management method applied to the nuclear large-scale power transformer uses the health management system applied to the nuclear large-scale power transformer in claim 1, and is characterized by comprising the following steps of:
a terminal sensing unit acquires original data of a transformer;
the edge intelligent data acquisition unit receives the original data, performs cleaning and preprocessing through an algorithm, and transmits a calculation result to the cloud server unit in real time;
the cloud server unit receives the calculation result and the original data, analyzes and calculates according to an expert system algorithm or a neural network learning algorithm, and provides the current working health state and the future prediction result of the transformer equipment;
the cloud server unit uses the original data, the current working health state of the transformer equipment and the future prediction result for training and perfecting the algorithm, and sends the latest algorithm to the edge intelligent data acquisition unit.
7. The health management method applied to the nuclear large-scale power transformer according to claim 6, wherein the calculation content of the edge intelligent data acquisition unit comprises: alarming by parameter threshold; counting under severe working conditions; hot spot calculation and insulation aging calculation; detecting the abnormality of the original spectrum peak of the gas dissolved in the oil; detecting the abnormity of the dissolved gas in the oil and judging the fault based on the guide rule; extracting partial discharge signal characteristics; monitoring the working state of the cooling system; and counting the running time of the fan.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010166882.4A CN111273196A (en) | 2020-03-11 | 2020-03-11 | Health management system and method applied to nuclear power large-scale power transformer |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010166882.4A CN111273196A (en) | 2020-03-11 | 2020-03-11 | Health management system and method applied to nuclear power large-scale power transformer |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111273196A true CN111273196A (en) | 2020-06-12 |
Family
ID=70997730
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010166882.4A Pending CN111273196A (en) | 2020-03-11 | 2020-03-11 | Health management system and method applied to nuclear power large-scale power transformer |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111273196A (en) |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111769556A (en) * | 2020-07-09 | 2020-10-13 | 石家庄科林云能信息科技有限公司 | Health assessment method for low-voltage power distribution system |
| CN111783880A (en) * | 2020-06-30 | 2020-10-16 | 清华大学天津高端装备研究院洛阳先进制造产业研发基地 | Complicated transmission chain predictive maintenance system and method based on industrial Internet of things |
| CN112087731A (en) * | 2020-09-11 | 2020-12-15 | 山东润一智能科技有限公司 | Edge processing host, system and method for information acquisition, processing and transmission of Internet of things |
| CN112163297A (en) * | 2020-09-30 | 2021-01-01 | 厦门科灿信息技术有限公司 | Equipment health prediction system |
| CN112214553A (en) * | 2020-11-26 | 2021-01-12 | 震坤行网络技术(南京)有限公司 | Method, computing device and computer storage medium for transmitting measurement data |
| CN112685949A (en) * | 2020-11-25 | 2021-04-20 | 江苏科技大学 | Transformer health prediction method based on digital twinning |
| CN112834224A (en) * | 2021-01-05 | 2021-05-25 | 广东核电合营有限公司 | Method and system for evaluating health state of nuclear power steam turbine generator |
| CN113030666A (en) * | 2021-03-22 | 2021-06-25 | 三门核电有限公司 | Large-scale transformer discharge fault diagnosis method and device |
| CN113553927A (en) * | 2021-07-08 | 2021-10-26 | 国网福建省电力有限公司福州供电公司 | Running state analysis method, system, server and medium of dry-type transformer |
| CN114111902A (en) * | 2021-11-24 | 2022-03-01 | 保定天威保变电气股份有限公司 | Intelligent monitoring system for natural ester transformer |
| CN114444734A (en) * | 2022-01-27 | 2022-05-06 | 山东电工电气集团有限公司 | Transformer multi-mode fault diagnosis method based on edge calculation |
| CN115129479A (en) * | 2022-08-24 | 2022-09-30 | 南方电网数字电网研究院有限公司 | Method and device for processing edge data of power chip in partition mode and computer equipment |
| CN117670304A (en) * | 2023-12-11 | 2024-03-08 | 承德神源太阳能发电有限公司 | Photovoltaic power station transformer state monitoring and failure early warning system and method |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110830943A (en) * | 2019-11-06 | 2020-02-21 | 湖南银河电气有限公司 | Equipment state monitoring system based on edge calculation and big data analysis |
-
2020
- 2020-03-11 CN CN202010166882.4A patent/CN111273196A/en active Pending
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110830943A (en) * | 2019-11-06 | 2020-02-21 | 湖南银河电气有限公司 | Equipment state monitoring system based on edge calculation and big data analysis |
Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111783880A (en) * | 2020-06-30 | 2020-10-16 | 清华大学天津高端装备研究院洛阳先进制造产业研发基地 | Complicated transmission chain predictive maintenance system and method based on industrial Internet of things |
| CN111769556A (en) * | 2020-07-09 | 2020-10-13 | 石家庄科林云能信息科技有限公司 | Health assessment method for low-voltage power distribution system |
| CN112087731A (en) * | 2020-09-11 | 2020-12-15 | 山东润一智能科技有限公司 | Edge processing host, system and method for information acquisition, processing and transmission of Internet of things |
| CN112163297B (en) * | 2020-09-30 | 2023-07-18 | 厦门科灿信息技术有限公司 | Equipment health prediction system |
| CN112163297A (en) * | 2020-09-30 | 2021-01-01 | 厦门科灿信息技术有限公司 | Equipment health prediction system |
| CN112685949A (en) * | 2020-11-25 | 2021-04-20 | 江苏科技大学 | Transformer health prediction method based on digital twinning |
| CN112214553A (en) * | 2020-11-26 | 2021-01-12 | 震坤行网络技术(南京)有限公司 | Method, computing device and computer storage medium for transmitting measurement data |
| CN112214553B (en) * | 2020-11-26 | 2021-03-16 | 震坤行网络技术(南京)有限公司 | Method, computing device and computer storage medium for transmitting measurement data |
| CN112834224A (en) * | 2021-01-05 | 2021-05-25 | 广东核电合营有限公司 | Method and system for evaluating health state of nuclear power steam turbine generator |
| CN113030666A (en) * | 2021-03-22 | 2021-06-25 | 三门核电有限公司 | Large-scale transformer discharge fault diagnosis method and device |
| CN113030666B (en) * | 2021-03-22 | 2024-06-11 | 三门核电有限公司 | Method and device for diagnosing discharge faults of large transformer |
| CN113553927A (en) * | 2021-07-08 | 2021-10-26 | 国网福建省电力有限公司福州供电公司 | Running state analysis method, system, server and medium of dry-type transformer |
| CN114111902A (en) * | 2021-11-24 | 2022-03-01 | 保定天威保变电气股份有限公司 | Intelligent monitoring system for natural ester transformer |
| CN114111902B (en) * | 2021-11-24 | 2024-04-05 | 保定天威保变电气股份有限公司 | Intelligent monitoring system for natural ester transformer |
| CN114444734A (en) * | 2022-01-27 | 2022-05-06 | 山东电工电气集团有限公司 | Transformer multi-mode fault diagnosis method based on edge calculation |
| CN115129479A (en) * | 2022-08-24 | 2022-09-30 | 南方电网数字电网研究院有限公司 | Method and device for processing edge data of power chip in partition mode and computer equipment |
| CN117670304A (en) * | 2023-12-11 | 2024-03-08 | 承德神源太阳能发电有限公司 | Photovoltaic power station transformer state monitoring and failure early warning system and method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111273196A (en) | Health management system and method applied to nuclear power large-scale power transformer | |
| CN107831422B (en) | GIS equipment partial discharge diagnosis method and system | |
| CN106199305B (en) | Underground coal mine power supply system dry-type transformer insulation health state evaluation method | |
| CN109886475B (en) | Information security situation perception system of measurement automation system based on AI | |
| CN104990629B (en) | A kind of electrical equipment fault automatic early-warning system of infrared imaging temperature measuring | |
| CN206311708U (en) | On-line monitoring of cable device and its system | |
| CN102221651A (en) | Fault on-line diagnosis and early warning method of flameproof dry-type transformer for mine | |
| CN110647093B (en) | Intelligent monitoring system and monitoring method for power system based on big data analysis | |
| CN115566804B (en) | Electric power monitoring system based on distributed optical fiber sensing technology | |
| CN204064509U (en) | A kind of temperature of high-tension switch cabinet on-line monitoring system | |
| CN105571638A (en) | Machinery device fault combination prediction system and method | |
| CN110299758A (en) | A kind of comprehensive on-line monitoring and diagnosis system of substation | |
| CN115792724A (en) | Uninterrupted diagnosis method for transformer bushing fault | |
| CN117152925B (en) | Big data-based power communication data processing system | |
| CN109324255A (en) | Transformer turn-to-turn short circuit on-line monitoring system and method based on electric energy loss | |
| CN117406047A (en) | Partial discharge state on-line monitoring system of power equipment | |
| CN113159517A (en) | Three-dimensional visual power grid operation data analysis system | |
| CN203164360U (en) | Transformer device insulation online monitoring system | |
| CN116070140B (en) | Power distribution substation safe operation state monitoring system and method | |
| CN106383509A (en) | High-power locomotive fault early-warning analysis method | |
| CN109030976B (en) | Power electronic equipment monitoring and diagnosing system and method | |
| CN116264417A (en) | Power-saving visualization system and method | |
| CN114069865A (en) | Converter station operation monitoring system, method and device, computer equipment and medium | |
| CN115524569A (en) | Fault diagnosis method, device and system | |
| CN108680841A (en) | A kind of Operation Condition of Power Transformers comprehensive monitor system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200612 |
|
| RJ01 | Rejection of invention patent application after publication |