CN115436767A - Transformer partial discharge monitoring and analyzing method and system - Google Patents
Transformer partial discharge monitoring and analyzing method and system Download PDFInfo
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- CN115436767A CN115436767A CN202211381351.2A CN202211381351A CN115436767A CN 115436767 A CN115436767 A CN 115436767A CN 202211381351 A CN202211381351 A CN 202211381351A CN 115436767 A CN115436767 A CN 115436767A
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- G01R31/12—Testing dielectric strength or breakdown voltage ; Testing or monitoring effectiveness or level of insulation, e.g. of a cable or of an apparatus, for example using partial discharge measurements; Electrostatic testing
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Abstract
The invention provides a method and a system for monitoring and analyzing partial discharge of a transformer, and relates to the technical field of transformer monitoring, wherein a discharge monitoring distribution point set is determined based on the type of the transformer to be tested, a plurality of sensors are further assembled, a monitoring data set is obtained, a data spectrogram is generated for characteristic analysis, charge distribution characteristics and discharge signal characteristics are obtained, insulating layer material data are obtained, a partial discharge positioning result is determined based on a partial discharge positioning model, and then partial insulation damage early warning is carried out.
Description
Technical Field
The invention relates to the technical field of transformer monitoring, in particular to a method and a system for monitoring and analyzing partial discharge of a transformer.
Background
Partial discharge is as leading to the transformer to take place insulation breakdown's main factor, also be the main sign of equipment insulation degradation simultaneously, strong partial discharge can lead to dielectric strength to descend very fast, and then cause the equipment insulation to damage, therefore, need strengthen the partial discharge and detect among the transformer operation process, when partial discharge reaches certain limit threshold value, the equipment running state of transformer should in time withdraw from, carry out the overhaul of equipment or change, avoid influencing performance, now, mainly can carry out the operation fault monitoring of transformer through carrying out equipment regular operation and maintenance management and control, can't ensure the real-time of monitoring analysis result, prior art still has certain optimization adjustment space.
In the prior art, when monitoring and analyzing partial discharge of a transformer, due to the fact that a data monitoring mode is not perfect enough to affect accuracy of monitoring data, and meanwhile, analysis steps are not rigorous enough, accuracy of a final partial discharge positioning result is not sufficient, and inconvenience is caused to follow-up operation and maintenance management.
Disclosure of Invention
The application provides a transformer partial discharge monitoring and analyzing method and system, which are used for solving the technical problems that when transformer partial discharge monitoring and analyzing are carried out in the prior art, due to the fact that a data monitoring mode is not perfect enough, accuracy of monitoring data is affected, meanwhile, analyzing steps are not strict enough, accuracy of a final partial discharge positioning result is not enough, and inconvenience is caused for follow-up operation and maintenance management.
In view of the foregoing problems, the present application provides a method and a system for monitoring and analyzing partial discharge of a transformer.
In a first aspect, the present application provides a method for monitoring and analyzing partial discharge of a transformer, where the method includes: acquiring the type of the transformer to be tested according to the data acquisition device; generating a discharge monitoring distribution point set according to the transformer type of the transformer to be detected; assembling the plurality of sensors according to the discharge monitoring distribution point set to obtain a monitoring data set obtained by the plurality of sensors; performing feature analysis on a spectrogram generated by the monitoring data set to obtain charge distribution features and discharge signal features; acquiring insulation layer material data based on the transformer type of the transformer to be tested; inputting the charge distribution characteristics, the discharge signal characteristics and the insulating layer material data into a partial discharge positioning model, and acquiring a partial discharge positioning result according to the partial discharge positioning model; and reminding the transformer to be tested of having local insulation damage by identifying the local discharge positioning result.
In a second aspect, the present application provides a transformer partial discharge monitoring and analyzing system, the system comprising: the type acquisition module is used for acquiring the transformer type of the transformer to be detected according to the data acquisition device; the point set generating module is used for generating a discharge monitoring distribution point set according to the transformer type of the transformer to be tested; the data monitoring module is used for assembling the plurality of sensors according to the discharge monitoring distribution point set to obtain monitoring data sets obtained by the plurality of sensors; the characteristic analysis module is used for carrying out characteristic analysis on a spectrogram generated by the monitoring data set to acquire charge distribution characteristics and discharge signal characteristics; the data acquisition module is used for acquiring insulation layer material data based on the type of the transformer to be detected; the model positioning module is used for inputting the charge distribution characteristics, the discharge signal characteristics and the insulating layer material data into a partial discharge positioning model and acquiring a partial discharge positioning result according to the partial discharge positioning model; and the result warning module is used for reminding the transformer to be tested of local insulation damage through the identification of the local discharge positioning result.
One or more technical solutions provided in the present application have at least the following technical effects or advantages:
according to the monitoring and analyzing method for the partial discharge of the transformer, the type of the transformer to be tested is obtained according to the data acquisition device, a discharge monitoring distribution point set is further generated, the plurality of sensors are assembled according to the discharge monitoring distribution point set, a monitoring data set is obtained, a data spectrogram is generated for characteristic analysis, and charge distribution characteristics and discharge signal characteristics are obtained; the method comprises the steps of obtaining insulation layer material data based on the type of a transformer of the transformer to be detected, inputting the charge distribution characteristics, the discharge signal characteristics and the insulation layer material data into a partial discharge positioning model, obtaining a partial discharge positioning result, and reminding the transformer to be detected of partial insulation damage through identification of the partial discharge positioning result.
Drawings
Fig. 1 is a schematic flow chart of a partial discharge monitoring and analyzing method for a transformer according to the present application;
fig. 2 is a schematic diagram illustrating a process of acquiring the number of sensors in a partial discharge monitoring and analyzing method for a transformer according to the present application;
fig. 3 is a schematic diagram illustrating a partial discharge monitoring and early warning process in a transformer partial discharge monitoring and analyzing method provided by the present application;
fig. 4 is a schematic structural diagram of a partial discharge monitoring and analyzing system for a transformer according to the present application.
Description of reference numerals: the device comprises a type acquisition module 11, a point set generation module 12, a data monitoring module 13, a characteristic analysis module 14, a data acquisition module 15, a model positioning module 16 and a result warning module 17.
Detailed Description
The application provides a method and a system for monitoring and analyzing partial discharge of a transformer, a discharge monitoring distribution point set is determined based on the type of the transformer to be tested, a plurality of sensors are further assembled, a monitoring data set is obtained, a data spectrogram is generated for characteristic analysis, charge distribution characteristics and discharge signal characteristics are obtained, insulation layer material data are obtained, a partial discharge positioning result is determined based on a partial discharge positioning model, and then partial insulation damage early warning is carried out.
Example one
As shown in fig. 1, the present application provides a method for monitoring and analyzing partial discharge of a transformer, where the method is applied to a system for monitoring and analyzing partial discharge of a transformer, the system is communicatively connected to a data acquisition device and a plurality of sensors, and the method includes:
step S100: acquiring the type of the transformer to be tested according to the data acquisition device;
specifically, partial discharge belongs to a main factor causing insulation breakdown of electrical equipment such as a transformer and the like, the partial discharge is usually local overheating and also a sign of aging of electrical elements and mechanical elements, and strong partial discharge can cause the insulation strength of the transformer to be rapidly reduced to cause insulation damage.
The sensors are arranged at a plurality of monitoring points of the transformer and used for monitoring real-time discharge data, the data acquisition device is used for acquiring application parameter information of the transformer, the transformer to be tested is subjected to information acquisition based on the data acquisition device, relevant parameters such as the model, the rated capacity and the load loss of the transformer to be tested are determined, and then the type of the transformer is obtained, wherein due to the difference of the types of the transformer, certain differences exist among corresponding transformer operation parameters, structural characteristics and the like, and the determination of the type of the transformer facilitates the subsequent targeted monitoring and analysis of the transformer to be tested, so that the accuracy of an analysis result is guaranteed.
Step S200: generating a discharge monitoring distribution point set according to the transformer type of the transformer to be detected;
step S300: assembling the plurality of sensors according to the discharge monitoring distribution point set to obtain a monitoring data set obtained by the plurality of sensors;
specifically, the type of the transformer to be detected is determined, the types of the transformers are different, corresponding internal structural elements of the transformers are different, corresponding transformer circuits are obtained based on the types of the transformers, circuit intersections and circuit branches are positioned based on the trend of electric signals in the transformer circuits, positioning results are identified, and circuit complexity analysis is performed based on the positioning results, wherein the circuit complexity is proportional to the number of the circuit intersections and the number of the circuit branches, a plurality of monitoring points are determined at proper positions based on the circuit complexity to serve as the discharge monitoring distribution point set, the transformer circuits are subjected to region division based on the discharge monitoring distribution point set, a plurality of divided regions are determined based on the circuit importance, importance levels corresponding to different regions are different, bivalue decision analysis is performed based on the region importance levels and the circuit complexity, and the number of sensors corresponding to the discharge monitoring distribution points is determined, and the number of the sensors is required for ensuring the completeness of data acquisition in the regions, such as signal sensors and temperature sensors, and the number of the sensors is not necessarily consistent.
The discharge monitoring distribution point set and the number of the sensors are correspondingly marked, the sensors are assembled based on the marking results, then the sensors are correspondingly subjected to data monitoring in the corresponding areas of the discharge monitoring distribution points according to the marking results, corresponding collected data are determined based on the sensor types, then the collected data and the discharge monitoring distribution points are correspondingly marked, the monitoring data set is generated, and the monitoring data set serves as a data source to be analyzed, so that basic data basis is provided for the subsequent discharge analysis of the transformer.
Further, as shown in fig. 2, a discharge monitoring distribution point set is generated according to the transformer type of the transformer to be tested, and step S200 of the present application further includes:
step S210: acquiring a transformation circuit according to the type of the transformer to be tested;
step S220: analyzing according to the electric signal guide of the voltage transformation circuit, identifying the circuit cross points and the branch quantity, and obtaining the circuit complexity;
step S230: and configuring the number of sensors of each monitoring node in the discharge monitoring distribution point set according to the circuit complexity.
Specifically, the corresponding transformer circuit is determined based on the transformer type of the transformer to be detected, wherein the transformer to be detected comprises a booster circuit and a step-down circuit, the booster circuit and the step-down circuit are maintained at a required voltage value by adjusting input voltage, the step-down circuit is the most widely used in daily application, the power supply voltage can be adjusted to the equipment operation voltage, in the transformation process of the transformer to be detected, the internal transformer circuit has electric signal flow, a plurality of circuit cross points and a plurality of branches through which the electric signal flows are determined by conducting electric signal guiding analysis, the circuit cross points and the branch number are identified, integral evaluation is conducted based on identification results, the circuit complexity is determined, exemplarily, the circuit complexity grade can be set, the corresponding monitoring point number is determined based on the multi-grade complexity, preferably, the number distribution of a plurality of groups of monitoring points can be determined based on the complexity of different parts of the transformer circuit, the accuracy of subsequent monitoring can be effectively improved, the circuit complexity is proportional to the number of the discharge monitoring points, the discharge monitoring point distribution set is further obtained based on the circuit complexity, the distribution of the monitoring points in the discharge monitoring and the distribution set can determine the distribution positions of the sensors, the distribution of the sensors can be effectively guaranteed, and the accuracy of the distribution of the sensors can be guaranteed, and the distribution of the sensors can be effectively guaranteed.
Specifically, step S230 of the present application further includes:
step S231: analyzing an insulating layer element of the voltage transformation circuit to acquire information of the circuit element;
step S232: analyzing a key area of the circuit according to the information of the circuit element to obtain the grade of the key area;
step S233: and analyzing a binary decision maker according to the grade of the key area and the circuit complexity to obtain a binary decision result, wherein the binary decision result is the number of sensors of each monitoring node in the discharge monitoring distribution point set.
Specifically, the obtained transformer circuit is subjected to insulation layer element identification, such as winding and the like, the number of turns of the winding, interlayer and turn parameter information, corresponding insulation material and other related information are determined, the information is used as the information of the circuit element, circuit division is further performed based on the information of the circuit element, the insulation between the end part of the winding and the grounding part and the insulation between the windings are used as main insulation, the insulation between parts of the same winding, such as the interlayer, is used as longitudinal insulation, key area analysis of the transformer circuit is performed, illustratively, multi-level area grades can be set based on the influence degree of the circuit element, the transformer circuit is subjected to grade matching respectively, the key area grades are obtained, further, the key area grades and the circuit complexity are subjected to bivalue decision maker analysis, the bivalue decision maker is an auxiliary tool for carrying out a bivalue perception decision, a bivalue decision logic algorithm is embedded, the number of sensors which are arranged in each monitoring node in the discharge monitoring distribution nodes is determined through carrying out multi-dimensional data analysis, monitoring nodes and the number of the monitoring nodes and the sensors are identified as the bivalue decision result, wherein the bivalue decision maker is the most suitable for the arrangement of the transformer circuit.
Step S400: performing characteristic analysis on a spectrogram generated by the monitoring data set to obtain charge distribution characteristics and discharge signal characteristics;
specifically, data monitoring is performed based on the plurality of sensors, the monitoring data set is obtained, each group of data in the monitoring data set corresponds to a monitoring point, data-graph conversion is performed on signal data in the monitoring data set, a signal spectrogram is generated, the signal spectrogram is a visual representation of the monitored signal data, the information representation clarity is improved, data transmission analysis is facilitated, exemplarily, a waveform graph can be drawn based on the monitoring data for data analysis, wherein circuit temperature and the like are influencing factors of signal transmission, regional charge distribution identification is performed based on the signal spectrogram, the charge distribution gradient difference and the distribution uniformity of different regions are determined and are used as the charge distribution characteristics, meanwhile, the signal intensity, the signal period rule and the like of a discharge signal are determined based on the signal spectrogram and are used as the discharge signal characteristics, and the charge distribution characteristics and the discharge signal characteristics are real-time signal fluctuation characteristics of a transformer transformation circuit, and a foundation is laid for subsequent discharge positioning tamping.
Step S500: acquiring insulation layer material data based on the transformer type of the transformer to be detected;
specifically, the types of the transformers are different, the corresponding requirements of insulating layer materials are different, based on the types of the transformers to be tested, main insulating materials inside the transformers to be tested are determined, exemplarily, when the transformers to be tested are oil-immersed transformers, insulating paper and insulating oil are generally used as insulating materials of insulating layers, when the transformers to be tested are dry-type transformers, the corresponding insulating materials of the insulating layers are epoxy resin, insulating paint and the like, the requirement on the insulating performance of insulating equipment is in direct proportion to the voltage level of the transformers, the types, the material quality, the thickness, the insulating wear degree and the like of the insulating layers of the transformers to be tested are determined as the data of the insulating layer materials, meanwhile, the aging degree of the insulation of the transformers is closely related to the service life of the transformers, and the data of the insulating layer materials are used as one of the discharge monitoring elements of the transformers to be tested.
Step S600: inputting the charge distribution characteristics, the discharge signal characteristics and the insulation layer material data into a partial discharge positioning model, and acquiring a partial discharge positioning result according to the partial discharge positioning model;
step S700: and reminding the transformer to be tested of local insulation damage by identifying the local discharge positioning result.
Specifically, the partial discharge positioning model is constructed, the partial discharge positioning model is an auxiliary model for performing discharge positioning analysis, is a multi-level network layer and comprises a data identification layer, an abnormal analysis layer and a positioning output layer, the charge distribution characteristics, the discharge signal characteristics and the insulating layer material are input into the partial discharge positioning model, abnormal discharge causing damage of an insulating layer is positioned by performing multi-level network analysis, the partial discharge positioning result is obtained and model output is performed, the partial discharge positioning result is further identified, the identification result is obtained by including a positioning position and a corresponding damage probability, early warning information is generated based on the identification result, so that early warning of partial insulation damage of the transformer to be detected is performed, wherein the partial insulation damage of the transformer is a main factor influencing the service life of the transformer, and whether maintenance or replacement is needed or not is further judged so as to avoid influencing the operation effect.
Further, step S600 of the present application further includes:
step S610: inputting the charge distribution characteristics, the discharge signal characteristics and the insulating layer material data into the partial discharge positioning model, wherein the partial discharge positioning model comprises a data identification layer, an anomaly analysis layer and a positioning output layer;
step S620: and performing model network layer analysis according to the data identification layer, the abnormal analysis layer and the positioning output layer to obtain the partial discharge positioning result, wherein the partial discharge positioning result is an abnormal positioning result with damaged insulating layer.
Specifically, the partial discharge positioning model is built based on a machine learning algorithm, the partial discharge positioning model is a multi-level network layer and comprises the data identification layer, the abnormal discharge layer and the positioning output layer, various kinds of transformation circuit information of different transformers are further called based on big data and used as sample data, the sample data are further divided into a training set and a verification set, the training set and the verification set are input into the partial discharge positioning model, model optimization is carried out through model training and verification until the analysis precision of the partial discharge positioning model reaches a preset standard, and model training is stopped.
The charge distribution characteristics, the discharge signal characteristics and the insulation layer material data are further input into the partial discharge positioning model, data type identification matching is conducted on input data based on the data identification layer, a matching result is transmitted to the abnormal analysis layer, whether the insulation layer is damaged or not is determined according to index data, multiple damage probability calculation results are determined through data analysis and further transmitted to the positioning output layer, a transformation circuit position corresponding to abnormal data is determined through abnormal positioning, the abnormal data and the circuit position are correspondingly marked, the partial discharge positioning result is obtained and output, the partial discharge positioning result is an abnormal positioning result of the insulation layer damage, positioning analysis is conducted through the model, and accuracy and objectivity of the analysis result can be effectively guaranteed.
Further, step S620 of the present application further includes:
step S621: performing feature matching on the charge distribution features and the discharge signal features according to the data identification layer to obtain coincidence features;
step S622: inputting the superposition characteristics and the insulating layer material data into the abnormal analysis layer, and performing insulating damage probability calculation to obtain a probability calculation result;
step S623: and judging the probability calculation result through the positioning output layer to obtain N positioning results which are more than or equal to a preset damage probability, and outputting the N positioning results as the partial discharge positioning results.
Specifically, the charge distribution characteristic, the discharge signal characteristic and the insulation layer material data are input into the partial discharge positioning model, the charge distribution characteristic and the discharge signal characteristic are subjected to characteristic matching based on the data identification layer, wherein the charge distribution characteristic and the discharge signal characteristic of the same voltage transformation circuit are synchronized to obtain the coincidence characteristic, the coincidence characteristic indicates that the accuracy of data parameters is higher, the coincidence characteristic and the insulation layer material data are input into the anomaly analysis layer, insulation damage probabilities corresponding to the monitoring points in the discharge monitoring distribution point set are determined, probability calculation results are obtained, the probability calculation results correspond to the discharge monitoring distribution point set one by one, the probability calculation results are further input into the positioning output layer and are judged according to the preset damage probabilities, the preset damage probabilities are critical values limited by the preset damage probabilities, the probability calculation results are respectively judged based on the preset damage probabilities, the probability calculation results larger than or equal to the preset damage probabilities are subjected to position positioning, N positioning results are obtained, the N positioning results are used as the partial discharge positioning model, and the effective positioning efficiency is improved.
Further, as shown in fig. 3, step S700 of the present application further includes:
step S710: obtaining a working data set of the transformer to be tested, wherein the working data set comprises working voltage data, working period data and working frequency data;
step S720: analyzing the working voltage data, the working period data and the working frequency data to obtain a working characteristic data set;
step S730: acquiring the insulation performance index of the transformer to be tested according to the working characteristic data set;
step S740: and carrying out partial discharge monitoring and early warning according to the insulation performance index.
Specifically, a preset time interval is determined, historical working data of the transformer to be tested is called based on the preset time interval, data identification and analysis are further carried out, and a working time period of the transformer to be tested is determined, for example, equipment operation is carried out on a working day; in the operation process of the voltage device, potential difference exists in the circuit, the potential difference is used as working voltage of the voltage device, and the voltage range in which the voltage device to be detected can normally operate is determined based on historical working data and used as the working voltage data; the relation between the loss of an iron core in the transformer and the working frequency is large, the working frequency of the transformer to be tested is calculated based on historical working data, the working frequency data is obtained, the working voltage data, the working period data and the working frequency data are used as a working data set of the transformer to be tested, then the working data set is comprehensively evaluated, the working state, the performance gradient efficiency and the like of the transformer to be tested are determined and used as the working characteristic data set, the insulation performance of the transformer to be tested is evaluated based on the working characteristic data set, insulation performance indexes such as overvoltage effect, temperature and humidity and the like are obtained, during the working process of the transformer, the working overvoltage can cause insulation degradation or insulation damage, the insulation performance indexes can be evaluated based on related parameter data, and insulation early warning is carried out based on the insulation performance indexes, so that the regulation and the replacement of the transformer can be carried out in time.
Further, according to the partial discharge monitoring and early warning performed according to the insulation performance index, step S740 of the present application further includes:
step S741: carrying out transformer load analysis according to the insulation performance index to obtain a loadable voltage, wherein the loadable voltage is the maximum voltage which can be loaded by the transformer to be tested under the condition of meeting the preset safe insulation performance index;
step S742: generating a monitoring constraint condition with the loadable voltage;
step S743: taking the monitoring constraint condition as an early warning condition for monitoring by the plurality of sensors, and judging whether the monitored real-time voltage is greater than the loadable voltage;
step S744: and if the monitored real-time voltage is greater than the loadable voltage, generating early warning information.
Specifically, in the normal operation process of the transformer to be detected, an affordable load voltage needs to be within a certain voltage interval, if the affordable load voltage exceeds the certain voltage interval, the overload voltage is obtained, load analysis is performed on the transformer to be detected based on the insulation performance index, preset safe insulation performance index conditions, such as operation voltage and temperature and humidity, are obtained, wherein changes of the temperature and humidity can have certain influences on the state of an insulation material, when the transformer to be detected meets the preset safe insulation performance index conditions, the maximum voltage capable of being loaded is used as the loadable voltage, detection constraint conditions, such as a set voltage threshold value, are generated based on the loadable voltage, the monitoring constraint conditions are used as monitoring and early warning conditions of the sensors, whether the real-time voltage monitored by the sensors is larger than the loadable voltage or not is judged, when the maximum voltage is larger than the preset safe insulation performance index conditions, it is shown that the monitored real-time voltage is in an abnormal state, certain risk exists, early warning information is generated, and early warning is carried out, so that timely adjustment is carried out, and the performance of the transformer is prevented from being influenced by overload, and even equipment faults are even caused.
Example two
Based on the same inventive concept as the partial discharge monitoring and analyzing method of the transformer in the foregoing embodiment, as shown in fig. 4, the present application provides a partial discharge monitoring and analyzing system of a transformer, the system including:
the type obtaining module 11 is used for obtaining the transformer type of the transformer to be tested according to the data acquisition device by the type obtaining module 11;
the point set generating module 12 is configured to generate a discharge monitoring distribution point set according to the transformer type of the transformer to be tested;
the data monitoring module 13 is configured to assemble the plurality of sensors according to the set of discharge monitoring distribution points to obtain monitoring data sets obtained by the plurality of sensors;
a feature analysis module 14, wherein the feature analysis module 14 is configured to perform feature analysis on a spectrogram generated by the monitoring data set, so as to obtain a charge distribution feature and a discharge signal feature;
the data acquisition module 15 is used for acquiring insulation layer material data based on the transformer type of the transformer to be detected;
the model positioning module 16 is configured to input the charge distribution characteristic, the discharge signal characteristic, and the insulation layer material data into a partial discharge positioning model, and obtain a partial discharge positioning result according to the partial discharge positioning model;
and the result warning module 17 is used for reminding the transformer to be tested of local insulation damage through the identification of the local discharge positioning result.
Further, the system further comprises:
the transformer testing device comprises a data set acquisition module, a data processing module and a data processing module, wherein the data set acquisition module is used for acquiring a working data set of the transformer to be tested, and the working data set comprises working voltage data, working period data and working frequency data;
the data analysis module is used for analyzing the working voltage data, the working period data and the working frequency data to obtain a working characteristic data set;
the index acquisition module is used for acquiring the insulation performance index of the transformer to be tested according to the working characteristic data set;
and the local early warning module is used for carrying out local discharge monitoring and early warning according to the insulation performance index.
Further, the system further comprises:
the load voltage acquisition module is used for carrying out transformer load analysis according to the insulation performance index to acquire loadable voltage, wherein the loadable voltage is the maximum voltage which can be loaded by the transformer to be tested under the condition of meeting the preset safe insulation performance index;
a constraint generating module for generating a monitoring constraint with the loadable voltage;
the voltage judgment module is used for taking the monitoring constraint condition as an early warning condition for monitoring the sensors and judging whether the monitored real-time voltage is greater than the loadable voltage or not;
and the early warning information generation module is used for generating early warning information if the monitored real-time voltage is greater than the loadable voltage.
Further, the system further comprises:
the circuit acquisition module is used for acquiring a transformation circuit according to the type of the transformer to be tested;
the circuit complexity analysis module is used for analyzing according to the electric signal guide of the voltage transformation circuit, identifying the circuit cross points and the branch number and acquiring the circuit complexity;
a sensor configuration module to configure a number of sensors of each monitoring node in the set of discharge monitoring distribution points with the circuit complexity.
Further, the system further comprises:
the component analysis module is used for analyzing the insulating layer component of the voltage transformation circuit to acquire the information of the circuit component;
the grade acquisition module is used for analyzing a key area of the circuit according to the information of the circuit element to acquire the grade of the key area;
and the dual-value decision module is used for analyzing a dual-value decision device according to the key region grade and the circuit complexity to obtain a dual-value decision result, wherein the dual-value decision result is the number of sensors of each monitoring node in the discharge monitoring distribution point set.
Further, the system further comprises:
the information input module is used for inputting the charge distribution characteristics, the discharge signal characteristics and the insulating layer material data into the partial discharge positioning model, wherein the partial discharge positioning model comprises a data identification layer, an anomaly analysis layer and a positioning output layer;
and the local discharge positioning module is used for performing model network layer analysis according to the data identification layer, the abnormal analysis layer and the positioning output layer to obtain a local discharge positioning result, wherein the local discharge positioning result is an abnormal positioning result of the damaged insulating layer.
Further, the system further comprises:
the characteristic matching module is used for carrying out characteristic matching on the charge distribution characteristics and the discharge signal characteristics according to the data identification layer to obtain coincidence characteristics;
the probability calculation module is used for inputting the coincidence characteristics and the insulation layer material data into the abnormal analysis layer, performing insulation damage probability calculation and acquiring a probability calculation result;
and the result output module is used for judging the probability calculation result through the positioning output layer to obtain N positioning results which are more than or equal to the preset damage probability, and outputting the N positioning results as the partial discharge positioning results.
In the present specification, through the foregoing detailed description of the transformer partial discharge monitoring and analyzing method, it is clear to those skilled in the art that the transformer partial discharge monitoring and analyzing method and system in the present embodiment are disclosed.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. The partial discharge monitoring and analyzing method of the transformer is applied to a partial discharge monitoring and analyzing system of the transformer, the system is in communication connection with a data acquisition device and a plurality of sensors, and the method comprises the following steps:
acquiring the type of the transformer to be tested according to the data acquisition device;
generating a discharge monitoring distribution point set according to the transformer type of the transformer to be detected;
assembling the plurality of sensors according to the discharge monitoring distribution point set to obtain a monitoring data set obtained by the plurality of sensors;
performing characteristic analysis on a spectrogram generated by the monitoring data set to obtain charge distribution characteristics and discharge signal characteristics;
acquiring insulation layer material data based on the transformer type of the transformer to be detected;
inputting the charge distribution characteristics, the discharge signal characteristics and the insulation layer material data into a partial discharge positioning model, and acquiring a partial discharge positioning result according to the partial discharge positioning model;
and reminding the transformer to be tested of local insulation damage by identifying the local discharge positioning result.
2. The method of claim 1, wherein the method further comprises:
obtaining a working data set of the transformer to be tested, wherein the working data set comprises working voltage data, working period data and working frequency data;
analyzing the working voltage data, the working period data and the working frequency data to obtain a working characteristic data set;
acquiring the insulation performance index of the transformer to be tested according to the working characteristic data set;
and carrying out partial discharge monitoring and early warning according to the insulation performance index.
3. The method of claim 2, wherein the partial discharge monitoring and early warning is performed according to the insulation performance index, and the method comprises the following steps:
carrying out transformer load analysis according to the insulation performance index to obtain a loadable voltage, wherein the loadable voltage is the maximum voltage which can be loaded by the transformer to be tested under the condition of meeting the preset safe insulation performance index;
generating a monitoring constraint condition according to the loadable voltage;
taking the monitoring constraint condition as an early warning condition for monitoring by the plurality of sensors, and judging whether the monitored real-time voltage is greater than the loadable voltage;
and if the monitored real-time voltage is greater than the loadable voltage, generating early warning information.
4. The method of claim 1, wherein a set of discharge monitoring distribution points is generated according to a transformer type of the transformer under test, the method further comprising:
acquiring a voltage transformation circuit according to the type of the transformer to be tested;
analyzing according to the electric signal guide of the voltage transformation circuit, identifying the circuit cross points and the branch quantity, and obtaining the circuit complexity;
and configuring the number of sensors of each monitoring node in the discharge monitoring distribution point set according to the circuit complexity.
5. The method of claim 4, wherein the method further comprises:
analyzing an insulating layer element of the voltage transformation circuit to acquire information of the circuit element;
analyzing a key area of the circuit according to the information of the circuit element to obtain the grade of the key area;
and analyzing a binary decision maker according to the grade of the key area and the circuit complexity to obtain a binary decision result, wherein the binary decision result is the number of sensors of each monitoring node in the discharge monitoring distribution point set.
6. The method of claim 1, wherein the method further comprises:
inputting the charge distribution characteristics, the discharge signal characteristics and the insulating layer material data into the partial discharge positioning model, wherein the partial discharge positioning model comprises a data identification layer, an anomaly analysis layer and a positioning output layer;
and performing model network layer analysis according to the data identification layer, the abnormal analysis layer and the positioning output layer to obtain the partial discharge positioning result, wherein the partial discharge positioning result is an abnormal positioning result with damaged insulating layer.
7. The method of claim 6, wherein the method further comprises:
performing feature matching on the charge distribution features and the discharge signal features according to the data identification layer to obtain coincidence features;
inputting the superposition characteristics and the insulating layer material data into the abnormal analysis layer, and performing insulating damage probability calculation to obtain a probability calculation result;
and judging the probability calculation result through the positioning output layer to obtain N positioning results which are more than or equal to a preset damage probability, and outputting the N positioning results as the partial discharge positioning results.
8. A partial discharge monitoring and analyzing system for a transformer, wherein the system is in communication connection with a data acquisition device and a plurality of sensors, and the system comprises:
the type acquisition module is used for acquiring the transformer type of the transformer to be detected according to the data acquisition device;
the point set generating module is used for generating a discharge monitoring distribution point set according to the type of the transformer to be tested;
the data monitoring module is used for assembling the plurality of sensors according to the discharge monitoring distribution point set to obtain monitoring data sets obtained by the plurality of sensors;
the characteristic analysis module is used for carrying out characteristic analysis on a spectrogram generated by the monitoring data set to acquire charge distribution characteristics and discharge signal characteristics;
the data acquisition module is used for acquiring insulation layer material data based on the type of the transformer to be detected;
the model positioning module is used for inputting the charge distribution characteristics, the discharge signal characteristics and the insulating layer material data into a partial discharge positioning model and acquiring a partial discharge positioning result according to the partial discharge positioning model;
and the result warning module is used for reminding the transformer to be tested of local insulation damage through the identification of the local discharge positioning result.
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