CN114781165A - Multi-feature information fusion power transformer winding health diagnosis method - Google Patents

Multi-feature information fusion power transformer winding health diagnosis method Download PDF

Info

Publication number
CN114781165A
CN114781165A CN202210453684.5A CN202210453684A CN114781165A CN 114781165 A CN114781165 A CN 114781165A CN 202210453684 A CN202210453684 A CN 202210453684A CN 114781165 A CN114781165 A CN 114781165A
Authority
CN
China
Prior art keywords
power
data
current
voltage
transformer
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
Application number
CN202210453684.5A
Other languages
Chinese (zh)
Inventor
马于贵
王天位
张智豪
吕尧仪
肖豪
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Chongqing College of Electronic Engineering
Original Assignee
Chongqing College of Electronic Engineering
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Chongqing College of Electronic Engineering filed Critical Chongqing College of Electronic Engineering
Priority to CN202210453684.5A priority Critical patent/CN114781165A/en
Publication of CN114781165A publication Critical patent/CN114781165A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
    • G06F2119/02—Reliability analysis or reliability optimisation; Failure analysis, e.g. worst case scenario performance, failure mode and effects analysis [FMEA]

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • General Engineering & Computer Science (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Computational Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Algebra (AREA)
  • Probability & Statistics with Applications (AREA)
  • Operations Research (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Databases & Information Systems (AREA)
  • Software Systems (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computer Hardware Design (AREA)
  • Evolutionary Computation (AREA)
  • Geometry (AREA)
  • Testing Of Short-Circuits, Discontinuities, Leakage, Or Incorrect Line Connections (AREA)

Abstract

The invention provides a multi-feature information fused power transformer winding health diagnosis method, which comprises the following steps: s1, collecting data of current, voltage and output power of the transformer, and carrying out signal offset calculation on the collected data to obtain a linear deviation degree; s2, according to the linear deviation degree in the acquisition period, carrying out error calculation of the data, and calculating the fault probability according to the error function of the data; and S3, calculating the accuracy and the recall rate of the transformer winding data according to the fault probability, and evaluating the health state of the transformer through a clustering algorithm.

Description

Multi-feature information fusion power transformer winding health diagnosis method
Technical Field
The invention relates to the field of data analysis, in particular to a multi-feature information fused power transformer winding health diagnosis method.
Background
The transformer is one of the most important and critical electrical devices in the power system, and the safety and reliability of the operation of the transformer are directly related to the safety and stability of the power system. In the long-term operation process of the power transformer, various short-circuit current surges often occur, and particularly when short-circuit faults occur at the outlet and near area of the transformer, the transformer winding is heated rapidly by tens of times or even hundreds of times of electrodynamic force generated in normal operation due to the huge short-circuit surge current, and the transformer winding is easy to damage or deform under the condition. During transportation, installation or cover hanging, power transformers are often subjected to accidental jostling and vibration, which also tend to cause the windings to deform. Proved by a large amount of experimental research and practice, transformer winding deformation has obvious accumulative effect, after transformer winding deformation accumulates to a certain degree, transformer winding's dynamic stability will receive destruction, anti short circuit ability descends by a wide margin, will lose dynamic balance when transformer winding suffers the short circuit impact once more, thereby lead to the emergence of major accident, current monitoring method has the short circuit impedance method, the voiceprint monitoring method, the pulse injection method, these methods have the fault discovery untimely, the monitoring is sensitive inadequately, the injected energy produces the influence to the winding, need the monitoring of having a power failure, therefore, but a real-time high accuracy on-line monitoring non-invasive monitoring method is needed urgently.
Disclosure of Invention
The invention aims to at least solve the technical problems in the prior art, and particularly creatively provides a multi-characteristic information fused power transformer winding health diagnosis method.
In order to achieve the above object, the present invention provides a method for diagnosing health of a power transformer winding with multi-feature information fusion, comprising the following steps:
s1, collecting data of current, voltage and output power of the transformer, and carrying out signal offset calculation on the collected data to obtain a linear deviation degree;
s2, according to the linear deviation degree in the acquisition period, carrying out error calculation of the data, and calculating the fault probability according to the error function of the data;
and S3, calculating the accuracy and the recall rate of the transformer winding data according to the fault probability, and evaluating the health state of the transformer through a clustering algorithm.
Preferably, the S1 includes:
s1-1, collecting the current value of the transformer winding, and in one collection period, obtaining a current data set of { I }iWhere 1,2,3,4,5, …, n, the acquired voltage dataset is { V }jWhere j is 1,2,3,4,5, …, n, the acquired power dataset is { P }k}(k＝1,2,3,4,5,…,n)，
State vector, T, in the course of the acquired current data setICurrent overload record, current transmission temperature record, transformer temperature record; state vector, T, in the course of the acquired voltage data setV(voltage load, voltage fluctuation record); state vector, T, in the course of the acquired power data setPPower distortion recording, power regulation recording, power output recording.
Preferably, the S1 further includes:
s1-2, according to the linear trend of the collected data of the state vector, the data deviation degree is calculated,
when the influence of the interference threshold μ increases, the linear deviation thereof increases in order to equalize the equations; the interference threshold value has a small proportion for evaluating the deviation of current, voltage and power in the transformer, so that the health condition result of the transformer is subjected to preliminary deviation judgment, the calculation describes the actual running state of the transformer, the preliminary state evaluation of the transformer is reflected by the threshold value calculation,
Figure BDA0003617951820000031
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003617951820000032
is the amplitude of the signal for the change in current,
Figure BDA0003617951820000033
the characteristic value is reconstructed for the current signal,
Figure BDA0003617951820000034
is the amplitude of the signal for the voltage change,
Figure BDA0003617951820000035
the characteristic value is reconstructed for the voltage signal,
Figure BDA0003617951820000036
is the amplitude of the signal for the power change,
Figure BDA0003617951820000037
the eigenvalues are reconstructed for the power signal.
Preferably, the S1 further includes:
s1-3, traversing the amplitudes of the current, voltage and power data in the sampling period, and distinguishing and identifying the current, voltage and power data through a linear deviation degree calculation formula according to the amplitudes as reference values of transformer health detection;
obtaining the linear deviation degree A according to the ratio of the peak value of the corresponding data to the average value of the amplitude valuesI，AV，AP；
Figure BDA0003617951820000038
Figure BDA0003617951820000039
Figure BDA0003617951820000041
Preferably, the S2 includes:
s2-1, carrying out error operation on the linear deviation value to obtain reconstructed characteristic value average values AVEI, AVEV and AVEP of the current, voltage and power data; obtaining deviation values of the current, voltage and power data according to the difference of the reconstructed characteristic values of the current, voltage and power data, obtaining a standardized characteristic value according to the ratio of the deviation values to the absolute deviation values,
Figure BDA0003617951820000042
Figure BDA0003617951820000043
Figure BDA0003617951820000044
preferably, the S2 further includes:
s2-2, according to the normalized eigenvalue and the sum result after the absolute value of the reconstructed eigenvalue of current, voltage and power is squared,
Figure BDA0003617951820000051
performing normalization calculation
Figure BDA0003617951820000052
According to the result of the normalized calculation, the current, voltage and power values of the transformer are judged to be within
0＜UI,UV,UPWhen the value is less than or equal to 0.4, the health condition of the transformer winding is good,
if 0.4 < UI,UV,UPWhen the fault risk is less than or equal to 0.8, the fault risk exists;
if 0.8 < UI,UV,UPIf < 1, there is a significant risk.
Preferably, the S2 further includes:
s2-3, carrying out average fault probability analysis on current, voltage and power in the existing fault risk interval to ensure the accuracy of data;
lambda is an empirical judgment threshold, characteristic sorting is carried out according to data of the characteristic vector, the average fault probability of a fault risk period existing in the acquisition cycle is determined and solved for current, voltage and power data,
the mean failure probability is calculated as
Figure BDA0003617951820000053
n is the total period of time for collecting current-voltage-power data, and f (i) + f (v) + f (p) is the total number of failure risk periods.
Preferably, the S3 includes:
s3-1, after obtaining the average fault probability, calculating the accuracy,
Figure BDA0003617951820000054
s3-2, calculating the recall rate,
Figure BDA0003617951820000061
preferably, S3-3, the calculated score is
Figure BDA0003617951820000062
Wherein n ' is the number of reference samples for obtaining abnormal time periods in the acquisition cycle, n ' is the number of actual abnormal time periods extracted after the average fault probability calculation is carried out, and n ' is a standard value of the number of abnormal time periods in the acquisition cycle obtained from historical data.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
the method can judge the current, voltage or power deviation of the transformer winding in certain time periods, calculate and grade the extracted time periods, realize the health safety check of the transformer winding in the whole time period according to corresponding supervision calculation, and provide data reference for users.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a general schematic of the present invention;
FIG. 2 is a schematic diagram of the working method of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As shown in fig. 1 and 2, the invention discloses a multi-feature information fused power transformer winding health diagnosis method, which comprises the following steps:
s1, collecting data of current, voltage and output power of the transformer, and carrying out signal offset calculation on the collected data to obtain a linear deviation degree;
s2, calculating the error of the data according to the linear deviation degree in the acquisition period, and calculating the fault probability according to the error function of the data;
and S3, calculating the accuracy and the recall rate of the transformer winding data according to the fault probability, and evaluating the health state of the transformer through a clustering algorithm.
And data collection is carried out through the current sensor, the voltage sensor and the power sensor, and finally data analysis operation is carried out through the processor.
The S1 includes:
s1-1, collecting the current value of the transformer winding, and in a collecting period, obtaining a current data set as { I }iGet the number of voltages (i ═ 1,2,3,4,5, …, n), getThe data set is { VjJ ═ 1,2,3,4,5, …, n), the acquired power data set being { Pk}(k＝1,2,3,4,5,…,n)，
State vector, T, in the course of the acquired current data setICurrent overload record, current transmission temperature record, transformer temperature record; state vector, T, in the course of the acquired voltage data setV(voltage load, voltage fluctuation record); state vector, T, in the course of the acquired power data setPPower distortion recording, power regulation recording, power output recording;
s1-2, according to the linear trend of the collected data of the state vector, the data deviation degree is calculated,
when the influence of the interference threshold μ increases, the linear deviation thereof increases in order to equalize the equations; the interference threshold value has a small proportion for evaluating the deviation of current, voltage and power in the transformer, so that the health condition result of the transformer is subjected to preliminary deviation judgment, the calculation describes the actual running state of the transformer, the preliminary state evaluation of the transformer is reflected by the threshold value calculation,
Figure BDA0003617951820000081
wherein, the first and the second end of the pipe are connected with each other,
Figure BDA0003617951820000082
is the amplitude of the signal for the change in current,
Figure BDA0003617951820000083
the characteristic value is reconstructed for the current signal,
Figure BDA0003617951820000084
is the amplitude of the signal for the voltage change,
Figure BDA0003617951820000085
the characteristic value is reconstructed for the voltage signal,
Figure BDA0003617951820000086
is the amplitude of the signal for the power change,
Figure BDA0003617951820000087
reconstructing the characteristic value for the power signal, and adjusting the corresponding reconstructed characteristic value according to the signal amplitude changes of current, voltage and power so as to ensure equality of equations and extract the signal abnormality of the corresponding acquisition point;
s1-3, traversing the amplitude of the current, voltage and power data in the sampling period, and distinguishing and identifying the current, voltage and power data through a linear deviation degree calculation formula according to the amplitude as a reference value for the health detection of the transformer;
obtaining the linear deviation degree A according to the ratio of the peak value of the corresponding data to the average value of the amplitudeI，AV，AP；
Figure BDA0003617951820000088
Figure BDA0003617951820000089
Figure BDA0003617951820000091
If the deviation degree difference is small, the corresponding data stability is good, otherwise, the stability is poor; and carrying out fault probability analysis on the data with good stability.
The S2 includes:
s2-1, carrying out error operation on the linear deviation value to obtain the reconstructed eigenvalue average value AVE of the current, voltage and power dataI，AVEV，AVEP(ii) a And the difference of the reconstructed characteristic values of the current, voltage and power data, obtaining deviation values of the current, voltage and power data, obtaining a standardized characteristic value according to the ratio of the deviation values to the absolute deviation values,
Figure BDA0003617951820000092
Figure BDA0003617951820000093
Figure BDA0003617951820000094
s2-2, according to the normalized eigenvalue and the sum result after the absolute value of the reconstructed eigenvalue of current, voltage and power is squared,
Figure BDA0003617951820000101
performing normalization calculation
Figure BDA0003617951820000102
According to the result of the normalized calculation, the current, voltage and power values of the transformer are judged to be within
0＜UI,UV,UPWhen the value is less than or equal to 0.4, the health condition of the transformer winding is good,
if 0.4 < UI,UV,UPWhen the fault risk is less than or equal to 0.8, the fault risk exists;
if 0.8 < UI,UV,UPIf the number is less than 1, serious risks exist;
s2-3, for the transformer winding with good state and great risk, corresponding data can be directly screened out, but U is more than 0.4I,UV,UPWhen the current, the voltage and the power in the existing fault risk interval are less than or equal to 0.8, the average fault probability analysis is required to be carried out, and the accuracy of data is ensured;
lambda is an empirical judgment threshold, characteristic sorting is carried out according to data of the characteristic vectors, the average fault probability of fault risk periods in the acquisition cycle is determined and solved for current, voltage and power data,
the mean failure probability is calculated as
Figure BDA0003617951820000103
n is the total period of time over which current-voltage-power data is collected, f (I) + f (V) + f (P) is the total number of fault-risk periods,
the S3 includes:
s3-1, after obtaining the average fault probability, calculating the accuracy,
Figure BDA0003617951820000111
s3-2, calculating the recall rate,
Figure BDA0003617951820000112
s3-3, calculating the score
Figure BDA0003617951820000113
Wherein n ' is the number of reference samples for obtaining abnormal time periods in the acquisition cycle, n ' is the number of actual abnormal time periods extracted after the average fault probability calculation is carried out, and n ' is a standard value of the number of abnormal time periods in the acquisition cycle obtained from historical data; in practical cases, the number of reference samples and the standard value have a certain deviation, which has an influence on the calculated score, but the deviation is negligible for a large amount of collected data.
According to the calculation scores, the deviation of the current, the voltage or the power of the transformer winding in certain time periods is judged, the calculation scores are carried out on the extracted time periods, the health safety check of the transformer winding in all time periods can be realized according to corresponding supervision calculation, data reference is provided for users, abnormal time period characteristics are calibrated, and the abnormal time period characteristics are pushed to a cloud platform.
While embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that: various changes, modifications, substitutions and alterations can be made to the embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims (9)

1. A multi-feature information fused power transformer winding health diagnosis method is characterized by comprising the following steps:
s1, collecting data of current, voltage and output power of the transformer, and carrying out signal offset calculation on the collected data to obtain a linear deviation degree;
s2, calculating the error of the data according to the linear deviation degree in the acquisition period, and calculating the fault probability according to the error function of the data;
and S3, calculating the accuracy and the recall rate of the transformer winding data according to the fault probability, and evaluating the health state of the transformer through a clustering algorithm.
2. The multi-signature information fusion power transformer winding health diagnosis method according to claim 1, wherein the S1 includes:
s1-1, collecting the current value of the transformer winding, and in a collecting period, obtaining a current data set as { I }iWhere 1,2,3,4,5, …, n, the acquired voltage dataset is { V }jJ ═ 1,2,3,4,5, …, n), the acquired power data set being { Pk}(k＝1,2,3,4,5,…,n)，
State vector, T, in the course of the acquired current data setICurrent overload record, current transmission temperature record, transformer temperature record; state vector, T, in the course of the acquired voltage data setV(voltage load, voltage fluctuation record); state vector, T, in the course of the acquired power data setPPower distortion recording, power regulation recording, power output recording).
3. The multi-signature information fusion power transformer winding health diagnosis method of claim 2, wherein the S1 further comprises:
s1-2, according to the linear trend of the collected data of the state vector, the data deviation degree is calculated,
when the influence of the interference threshold μ increases, the linear deviation thereof increases in order to equalize the equations; the interference threshold value has a small proportion for evaluating the deviation of current, voltage and power in the transformer, so that the health condition result of the transformer is subjected to preliminary deviation judgment, the calculation describes the actual running state of the transformer, the preliminary state evaluation of the transformer is reflected by the threshold value calculation,
Figure FDA0003617951810000021
wherein the content of the first and second substances,
Figure FDA0003617951810000022
is the amplitude of the signal for the change in current,
Figure FDA0003617951810000023
the characteristic value is reconstructed for the current signal,
Figure FDA0003617951810000024
is the amplitude of the signal for the voltage change,
Figure FDA0003617951810000025
the characteristic value is reconstructed for the voltage signal,
Figure FDA0003617951810000026
is the amplitude of the signal for the power change,
Figure FDA0003617951810000027
the eigenvalues are reconstructed for the power signal.
4. The multi-signature information fusion power transformer winding health diagnosis method of claim 2, wherein the S1 further comprises:
s1-3, traversing the amplitude of the current, voltage and power data in the sampling period, and distinguishing and identifying the current, voltage and power data through a linear deviation degree calculation formula according to the amplitude as a reference value for the health detection of the transformer;
obtaining the linear deviation degree A according to the ratio of the peak value of the corresponding data to the average value of the amplitude valuesI，AV，AP；
Figure FDA0003617951810000028
Figure FDA0003617951810000031
Figure FDA0003617951810000032
5. The multi-signature information fusion power transformer winding health diagnosis method according to claim 1, wherein the S2 comprises:
s2-1, carrying out error operation on the linear deviation value to obtain the reconstructed eigenvalue average value AVE of the current, voltage and power dataI，AVEV，AVEP(ii) a And the difference of the reconstructed characteristic values of the current, voltage and power data, obtaining deviation values of the current, voltage and power data, obtaining a standardized characteristic value according to the ratio of the deviation values to the absolute deviation values,
Figure FDA0003617951810000033
Figure FDA0003617951810000034
Figure FDA0003617951810000041
6. the multi-signature information fusion power transformer winding health diagnosis method according to claim 1, wherein the S2 further comprises:
s2-2, according to the normalized eigenvalue and the sum result after the absolute value of the reconstructed eigenvalue of current, voltage and power is squared,
Figure FDA0003617951810000042
performing normalization calculation
Figure FDA0003617951810000043
According to the result of the normalization calculation, the current, voltage and power values of the transformer are judged to be in
0＜UI,UV,UPWhen the value is less than or equal to 0.4, the health condition of the transformer winding is good,
if 0.4 < UI,UV,UPWhen the fault risk is less than or equal to 0.8, the fault risk exists;
if 0.8 < UI,UV,UPIf < 1, there is a significant risk.
7. The multi-signature information fusion power transformer winding health diagnosis method of claim 1, wherein the S2 further comprises:
s2-3, carrying out average fault probability analysis on current, voltage and power in the existing fault risk interval to ensure the accuracy of data;
lambda is an empirical judgment threshold, characteristic sorting is carried out according to data of the characteristic vector, the average fault probability of a fault risk period existing in the acquisition cycle is determined and solved for current, voltage and power data,
the mean failure probability is calculated as
Figure FDA0003617951810000051
n is the total period of time for collecting current-voltage-power data, and f (i) + f (v) + f (p) is the total number of failure risk periods.
8. The multi-signature information fusion power transformer winding health diagnosis method according to claim 1, wherein the S3 comprises:
s3-1, after obtaining the average fault probability, calculating the accuracy,
Figure FDA0003617951810000052
s3-2, calculating the recall rate,
Figure FDA0003617951810000053
9. the multi-signature information fusion power transformer winding health diagnosis method according to claim 8, wherein the S3 further comprises:
s3-3, calculating the score
Figure FDA0003617951810000055
Wherein n ' is the number of reference samples for obtaining abnormal time periods in the acquisition cycle, n ' is the number of actual abnormal time periods extracted after the average fault probability calculation is carried out, and n ' is a standard value of the number of abnormal time periods in the acquisition cycle obtained from historical data.
CN202210453684.5A 2022-04-24 2022-04-24 Multi-feature information fusion power transformer winding health diagnosis method Pending CN114781165A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210453684.5A CN114781165A (en) 2022-04-24 2022-04-24 Multi-feature information fusion power transformer winding health diagnosis method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210453684.5A CN114781165A (en) 2022-04-24 2022-04-24 Multi-feature information fusion power transformer winding health diagnosis method

Publications (1)

Publication Number Publication Date
CN114781165A true CN114781165A (en) 2022-07-22

Family

ID=82433469

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210453684.5A Pending CN114781165A (en) 2022-04-24 2022-04-24 Multi-feature information fusion power transformer winding health diagnosis method

Country Status (1)

Country Link
CN (1) CN114781165A (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117214592A (en) * 2023-11-09 2023-12-12 国网甘肃省电力公司白银供电公司 Fault monitoring management system and method for power transformer

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117214592A (en) * 2023-11-09 2023-12-12 国网甘肃省电力公司白银供电公司 Fault monitoring management system and method for power transformer
CN117214592B (en) * 2023-11-09 2024-03-15 国网甘肃省电力公司白银供电公司 Fault monitoring management system and method for power transformer

Similar Documents

Publication Publication Date Title
CN103490511B (en) A kind of power distribution network communication terminal detection system and method
CN107037279B (en) A kind of shunt capacitor monitoring system and method
CN102707238A (en) Storage battery performance on-line monitoring system and monitoring method
CN105866638A (en) City network cable joint insulation state online monitoring apparatus early warning apparatus and method
CN109670242B (en) Transformer winding deformation unsupervised online monitoring method based on elliptical envelope curve
CN115115282B (en) Data analysis method for high-voltage transformer area power system
CN102944811B (en) Detection method of transformer winding deformation
CN107247204B (en) State monitoring system and monitoring method for voltage limiters in ultra-high and extra-high voltage series compensation device
CN108879654B (en) Remote diagnosis method based on abnormal remote measurement of abnormal equipment
CN108828406A (en) The fault recognition method and its system of non-intrusion type user power utilization
CN112345955B (en) Multi-fault online diagnosis method and system for power battery
CN112600306A (en) Distribution transformer monitoring system
CN117002309B (en) Intelligent fault early warning method and system for charging pile
CN109765440A (en) The sub- lightning current monitoring device of Intelligent insulation
CN113533910A (en) Method and system suitable for converter transformer partial discharge early warning
CN114781165A (en) Multi-feature information fusion power transformer winding health diagnosis method
CN111797533B (en) Nuclear power device operation parameter abnormity detection method and system
CN209400633U (en) Transformer turn-to-turn short circuit on-line monitoring system based on electric energy loss
CN202661606U (en) On-line performance monitoring system for storage batteries
CN109270383A (en) A kind of non-intrusion type charging pile automatic testing method
CN105242167B (en) Transformer station&#39;s routine sampling instrument transformer secondary circuit broken string online test method
CN117491813A (en) Insulation abnormality detection method for power battery system of new energy automobile
WO2022082339A1 (en) Intelligent insulator lightning current monitoring device and intelligent insulator fault determination method
CN109324255A (en) Transformer turn-to-turn short circuit on-line monitoring system and method based on electric energy loss
CN117239942A (en) Ammeter with monitoring function

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