A predictive maintenance method and system for assessing and warning of insulation aging in electrical equipment

Abstract

This invention relates to a predictive maintenance method and system for assessing and warning of insulation aging in electrical equipment, belonging to the field of electrical engineering and automation technology. The method includes: acquiring insulation state data of the electrical equipment; establishing a performance-aging curve; analyzing local aging characteristics through insulation withstand tests to obtain insulation degradation degree data; constructing an insulation degradation prediction framework; generating operation records including insulation steady-state assessment and discharge modes; configuring insulation maintenance information in conjunction with maintenance efficiency indicators to form an insulation degradation assessment scheme; performing spectral analysis, impedance characteristic analysis, and hot zone layout identification on the insulation state; verifying the attenuation cycle of the insulation material using dielectric frequency domain response technology; obtaining equipment aging warning information; and updating insulation attenuation parameters when the predicted trajectory meets maintenance efficiency indicators, thereby achieving adaptive iteration of the insulation degradation prediction framework and improving the prediction accuracy and maintenance timeliness of electrical equipment insulation aging.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of electrical engineering and automation, and particularly relates to an electrical equipment insulation aging evaluation and early warning method and system capable of predictive maintenance. BACKGROUND

[0002] As a core component in the power system, the insulation material of electrical equipment is widely used in the fields of transformers, cables, switch cabinets and generators, and its aging state is directly related to the safety and reliability of equipment operation. Traditional insulation monitoring relies on manual inspection and periodic testing methods, but due to large fluctuations in equipment load, complex working environment, and the influence of multiple factors such as thermal stress, humidity, electric field strength and mechanical vibration on insulation materials, it is often difficult to discover potential aging problems in time, leading to partial discharge, breakdown or insulation failure, and further causing equipment downtime, power interruption or serious safety accidents, resulting in economic losses and social impact. With the popularization of intelligent monitoring technologies such as Internet of Things, artificial intelligence and big data, the level of automatic evaluation of electrical equipment has been significantly improved, but how to monitor, accurately predict and early warning evaluate the insulation aging process is still the focus and difficulty of current research. The existing technology mainly monitors the insulation parameters through single methods such as partial discharge detection, infrared thermal imaging, humidity sensor collection or insulation resistance measurement, lacks joint analysis and fusion of multi-modal data such as temperature and humidity, and the identification of key parameters such as discharge inception voltage, insulation resistance change, hot spot distribution and dielectric loss is not accurate enough, which cannot capture the dynamic characteristics of the aging process. In addition, the existing monitoring methods rely on fixed experience thresholds for judgment, which is difficult to dynamically adapt to the gradual degradation process of insulation materials in long-term operation, especially when the insulation performance gradually decays over time and environment, there is a lack of effective life prediction model and verification means, which cannot realize the transition from reactive maintenance to predictive maintenance. At the same time, the maintenance strategy usually relies on artificial experience to develop, lacks iterative feedback and optimization mechanism based on data-driven, and is easy to cause over-maintenance waste resources or maintenance deficiency to cause fault hidden danger, increase the overall operation cost and equipment risk. Therefore, it is urgent to propose an electrical equipment insulation aging evaluation and early warning method capable of predictive maintenance, which can construct a monitoring grid combining temperature and humidity dual-modal data, extract partial discharge curve features, identify key operating parameters, and realize intelligent diagnosis, dynamic early warning and adaptive maintenance of insulation aging state through feedback optimization and degradation cycle verification. This method not only can significantly improve the reliability and economy of the power system, reduce the probability of failure, but also can promote the sustainable development of smart grid and promote the digital transformation of energy industry. SUMMARY

[0003] To solve the above problems in the prior art, the application provides an electrical equipment insulation aging evaluation and early warning method capable of predictive maintenance,

[0004] The object of the application can be achieved by the following technical solutions:

[0005] S1: obtaining insulation state data of the electrical equipment, creating a performance-aging curve according to the insulation state data, carrying out an insulation resistance test on the electrical equipment, analyzing local aging characteristics in the running stage, and evaluating the resistance capacity of the insulation material according to the local aging characteristics to obtain insulation deterioration degree data;

[0006] S2: constructing an insulation deterioration prediction framework based on the insulation deterioration degree data, importing the insulation deterioration degree data as input parameters into the insulation deterioration prediction framework, generating an operation record containing data of insulation steady state evaluation and discharge form, and integrating maintenance efficiency index to configure insulation maintenance information, the insulation maintenance information being identified by adding maintenance information marks on the performance-aging curve, and formulating an insulation deterioration evaluation scheme;

[0007] S3: executing the insulation deterioration evaluation scheme, analyzing the insulation state data of the electrical equipment, identifying insulation attenuation frequency spectrum and insulation impedance and thermal zone layout data in the insulation maintenance information through spectral analysis extraction technology in the insulation resistance test, and verifying the attenuation period of the insulation material by using dielectric frequency domain response technology to obtain equipment aging early warning information;

[0008] S4: identifying the equipment aging early warning information according to the current health status of insulation attenuation, recording the current insulation deterioration data when the insulation attenuation trend track output by the insulation deterioration prediction framework meets the maintenance efficiency index, and returning the refreshed insulation attenuation parameters to the insulation deterioration prediction framework to update the insulation deterioration evaluation scheme.

[0009] Specifically, the method for obtaining the insulation state data is: analyzing the operation parameters in the running stage of the electrical equipment, performing time sequence calibration and modal integration on the operation parameters, and based on the insulation performance in the running process of the equipment, fusing the operation instructions and feedback signals in the operation parameters to form complete insulation state data.

[0010] Specifically, the performance-aging curve is based on the changes of dielectric strength, insulation impedance, thermal breakdown threshold and local discharge amount of the insulation material of the electrical equipment in different aging stages, and the performance of the insulation aging rate is quantified by fitting the voltage withstand characteristics and loss factor of the insulation material in the accelerated aging test.

[0011] Specifically, the method for generating the insulation deterioration degree data is:

[0012] According to the performance-aging curve, the electrical equipment is subjected to segmented high current stress, and corresponding partial discharge feedback curves are collected at different temperature points. The starting time of the partial discharge curve feedback is recorded at the time of issuing the equipment operation instruction, and the steady state response interval in the insulation conversion operation is obtained;

[0013] The steady state response interval is structured to store the discharge threshold voltage parameter, and the partial discharge feedback curve in the high current stress application process is integrated to form an insulation deterioration degree data set. The insulation deterioration degree data set is used as an input source of the insulation deterioration prediction framework to obtain the insulation deterioration degree data.

[0014] Specifically, the construction method of the insulation deterioration prediction framework is:

[0015] According to the partial discharge and material aging characteristics of the electrical equipment in the running stage, the unstructured insulation state data is converted into a discharge threshold voltage, insulation impedance and thermal zone layout parameter group, and the discharge threshold voltage associated with the insulation deterioration degree in the operation record is fused to form an insulation deterioration prediction characteristic matrix;

[0016] Based on the insulation deterioration prediction characteristic matrix, the voltage, impedance and layout parameter group are received, the insulation prediction monitoring characteristics are analyzed, and the resource configuration result is optimized through the frequency spectrum analysis extraction technology. According to the operation record of the data of the insulation steady state evaluation and discharge mode, a device prediction maintenance scheme is formulated;

[0017] The operation implementation data and feedback results of the maintenance configuration scheme are analyzed in real time, the parameter weight of the insulation deterioration degree prediction is dynamically corrected by gradient descent under the limitation of the performance-aging curve, and the insulation deterioration degree limitation condition is used as an external mapping parameter to construct the insulation deterioration prediction framework.

[0018] Specifically, the insulation deterioration evaluation scheme divides the performance-aging curve according to the aging stage of the insulation material, maps the insulation maintenance information and the insulation attenuation spectrum, generates an evaluation indication including the insulation steady state interval, the partial discharge active interval and the attenuation critical interval, and formulates an insulation inspection cycle, a hot spot temperature measurement cycle and an insulation strengthening treatment strategy based on the evaluation indication.

[0019] Specifically, the insulation attenuation spectrum analyzes the frequency band energy distribution by performing wavelet decomposition on the insulation material response signal, and identifies the insulation attenuation rate and aging type based on the frequency band energy density change, breakdown path characteristics and harmonic distribution structure of the partial discharge signal.

[0020] Specifically, the verification method of the attenuation period of the insulation material is:

[0021] The dielectric frequency domain response test is performed on the electrical equipment to obtain dielectric constant and loss factor change data of the insulating material at different operating frequencies, and spectrum reconstruction and feature decomposition are performed on the dielectric constant and loss factor change data to extract the dielectric spectrum line drift rate and the frequency band energy attenuation coefficient. By comparing the dielectric spectrum patterns of the insulating material at different operating cycles, an insulating performance attenuation characteristic curve is established.

[0022] Based on the insulating performance attenuation characteristic curve, the insulating loss factor growth rate, the dielectric constant change rate and the insulating impedance attenuation rate are solved to obtain the degradation rate index of the insulating material, and the degradation rate index is jointly verified with the insulating hot spot temperature rise rate and the impedance drop characteristic of the insulating operating stage to verify the attenuation cycle of the insulating material.

[0023] Specifically, the device aging warning information includes insulating temperature rise abnormality prompt, insulating impedance drop rate exceeding standard alarm and insulating aging index exceeding limit reminder. By consistently comparing the above warning information with the aging trend trajectory output by the insulating deterioration prediction framework, a dynamic aging warning result is generated.

[0024] Specifically, the insulating attenuation trend trajectory is based on the deterioration characteristic points of the insulating material at different operating cycles, and a visual attenuation path is established by time series regression and multi-point extrapolation technology to analyze the insulating aging direction and speed.

[0025] Specifically, the method of returning the insulating attenuation parameters to the insulating deterioration prediction framework is: obtaining the latest insulating deterioration evaluation result and comparing it with the expected attenuation trajectory of the prediction framework, converting the deviation result into an insulating state correction parameter, generating a new set of deterioration parameters, and returning them to the insulating deterioration prediction framework as iterative input parameters.

[0026] Specifically, a predictable maintenance electrical equipment insulating aging evaluation and warning system comprises:

[0027] An insulating state acquisition module: obtains insulating state data of an electrical equipment, creates a performance-aging curve using the insulating state data, carries out an insulating endurance test on the electrical equipment, analyzes local aging characteristics at the operating stage, and evaluates the endurance capability of the insulating material based on the local aging characteristics to obtain insulating deterioration degree data;

[0028] A deterioration prediction framework construction module: constructs an insulating deterioration prediction framework based on the insulating deterioration degree data, imports the insulating deterioration degree data as input parameters into the insulating deterioration prediction framework, generates an operation record containing data of insulating steady state evaluation and discharge mode, integrates maintenance efficiency index to configure insulating maintenance information, and formulates an insulating deterioration evaluation scheme.

[0029] insulation attenuation period verification module: performing the insulation deterioration evaluation scheme, analyzing the insulation state data of the electrical equipment, identifying the insulation attenuation frequency spectrum by the spectrum analysis extraction technology, and verifying the insulation attenuation period of the insulation material by the dielectric frequency domain response technology, and obtaining the equipment aging early warning information;

[0030] insulation aging early warning identification module: identifying the equipment aging early warning information according to the current insulation attenuation health status, recording the current insulation deterioration data when detecting that the insulation attenuation trend track output by the insulation deterioration prediction framework meets the maintenance efficiency index, and returning the refreshed insulation attenuation parameters to the insulation deterioration prediction framework to update the insulation deterioration evaluation scheme.

[0031] The beneficial effects of the present application are:

[0032] The electrical equipment insulation aging evaluation and early warning method provided by the present application realizes the whole life cycle evaluation and dynamic prediction of the insulation state of the electrical equipment by introducing the performance-aging curve, the insulation deterioration prediction framework and the dielectric frequency domain response verification mechanism. Compared with the traditional insulation monitoring method which relies on single-point detection or fixed threshold judgment, the present application uses multi-modal parameters such as local aging characteristics, dielectric frequency response characteristics and partial discharge morphology to construct the insulation deterioration trend track, which can accurately reflect the real attenuation rate and aging stage of the insulation material, and improve the insulation state recognition accuracy and trend extrapolation ability.

[0033] The insulation attenuation frequency spectrum generated based on the spectrum analysis extraction technology of the present application can identify the development path and energy distribution change of the internal defects of the insulation material, realize the visualization of the insulation degradation characteristics, verify the insulation attenuation period by the dielectric frequency domain response technology, and periodically quantitatively judge the potential breakdown path, dielectric constant drift and loss factor improvement, which helps to identify the critical stage of insulation deterioration in advance. Combined with the labeling and binding mode of the insulation maintenance information in the performance-aging curve, the system can dynamically generate the insulation deterioration evaluation scheme, realize the adaptive adjustment of the maintenance strategy, and avoid the problems of excessive maintenance or insufficient maintenance.

[0034] In addition, the present application uses the recursive parameter return mechanism to continuously correct the insulation deterioration prediction framework with the updated insulation attenuation parameters, so that the insulation deterioration prediction framework has the model self-learning and self-iteration ability, and significantly improves the prediction accuracy and universality. When the running environment of the electrical equipment changes or the aging rate accelerates, the system can still output high-reliability early warning information in real time. In summary, the present application can effectively improve the accuracy, predictability and response speed of the insulation diagnosis of the electrical equipment, reduce the operation and maintenance cost, enhance the safe operation level of the equipment, and has important significance for the stability of the power system. BRIEF DESCRIPTION OF DRAWINGS

[0035] For the convenience of those skilled in the art to understand, the present application is further described below in conjunction with the drawings.

[0036] Figure 1 The structural schematic diagram of the electrical equipment insulation aging evaluation and early warning method and system according to the present application.

[0037] Figure 2 The overall technical flow schematic diagram of the electrical equipment insulation aging evaluation and early warning method and system according to the present application. DETAILED DESCRIPTION

[0038] In order to further illustrate the technical means and effects adopted by the present application to achieve the predetermined application purposes, the specific embodiments, structures, features and effects according to the present application are described in detail below in conjunction with the drawings and preferred embodiments.

[0039] Embodiment one

[0040] Please refer to Figure 1 An electrical equipment insulation aging evaluation and early warning method with predictable maintenance:

[0041] S1: Obtain insulation state data of electrical equipment, create a performance-aging curve according to the insulation state data, carry out insulation resistance test on the electrical equipment, analyze local aging characteristics in the running stage, and evaluate the resistance capacity of the insulation material according to the local aging characteristics to obtain insulation deterioration degree data;

[0042] S2: Construct an insulation deterioration prediction framework based on the insulation deterioration degree data, import the insulation deterioration degree data as input parameters into the insulation deterioration prediction framework, generate an operation record containing data of insulation steady state evaluation and discharge form, and integrate maintenance efficiency index to configure insulation maintenance information, the insulation maintenance information is identified by adding maintenance information mark on the performance-aging curve, and an insulation deterioration evaluation scheme is developed;

[0043] S3: Execute the insulation deterioration evaluation scheme, analyze the insulation state data of the electrical equipment, identify insulation attenuation frequency spectrum graph and insulation impedance and hot area layout data in the insulation maintenance information through spectrum analysis extraction technology in the insulation resistance test, and verify the attenuation period of the insulation material by using dielectric frequency domain response technology to obtain equipment aging early warning information;

[0044] S4: According to the current health status of insulation attenuation, identify the equipment aging early warning information, when the insulation attenuation trend track output by the insulation deterioration prediction framework meets the maintenance efficiency index, record the current insulation deterioration data, and return the refreshed insulation attenuation parameters to the insulation deterioration prediction framework to update the insulation deterioration evaluation scheme.

[0045] In this embodiment, the insulation state data acquisition method is to analyze the operation parameters in the running stage of the electrical equipment, perform time series calibration and modal integration on the operation parameters, and based on the insulation performance in the running process of the equipment, fuse the operation instructions and feedback signals in the operation parameters to establish complete insulation state data.

[0046] In this embodiment, the performance-aging curve is based on the changes of dielectric strength, insulation impedance, thermal breakdown threshold and partial discharge amount of the insulation material of the electrical equipment in different aging stages, and the performance of the insulation aging rate is quantified by fitting the withstand voltage characteristics and loss factor of the insulation material in the accelerated aging test.

[0047] In this embodiment, the method for generating insulation deterioration degree data is:

[0048] According to the performance-aging curve, the electrical equipment is subjected to segmented high current pressure, and the corresponding partial discharge feedback curve is collected at different temperature points, the starting time of the partial discharge curve feedback is recorded at the time of issuing the equipment operation instruction, and the steady state response interval in the insulation conversion operation is obtained;

[0049] The steady state response interval is structured to store the discharge threshold voltage parameter, and the partial discharge feedback curve in the high current pressure application process is integrated to establish the insulation deterioration degree data set, and the insulation deterioration degree data set is used as the input source of the insulation deterioration prediction framework to obtain the insulation deterioration degree data.

[0050] In this embodiment, based on the insulation running data of 10kV transformer equipment, the insulation parameters in the equipment running cycle are continuously collected, including dielectric constant ε(t), loss factor tanδ(t), partial discharge pulse number PD(t), insulation impedance Z(t) and the like. According to the insulation resistance test results, a segmented exponential decay model is used to fit the performance-aging curve:

[0051] ,

[0052] ,

[0053] Wherein: P(t) is an insulation performance index, is the initial insulation performance value, α is the aging decay coefficient, β is used to represent the weight of the performance decay of partial discharge, the local aging feature vector is extracted according to the change of the curve slope, and the insulation material resistance is judged according to L(t) to obtain the insulation deterioration degree data D(t).

[0054] The D(t) is input into the insulation deterioration prediction framework, in the implementation of the insulation deterioration evaluation scheme, the insulation signal is subjected to FFT transformation through the spectrum analysis extraction technology, the insulation attenuation spectrum F(omega) is obtained, which is used for identifying the local discharge energy concentrated frequency band, and the impedance Z(omega) under different frequencies is measured by using the dielectric frequency domain response technology:

[0055] ,

[0056] ,

[0057] The predicted trajectory D^(t) is compared with the actual deterioration index D(t), and the deviation Delta is calculated:

[0058] ,

[0059] ,

[0060] When |Delta(t)| exceeds the preset threshold theta, the system generates the aging warning information, and the updated insulation attenuation parameters are returned to the prediction framework in a recursive manner, wherein eta is an adaptive learning rate, through iterative updating, the dynamic self-calibration of the prediction framework in different aging stages is realized, and the warning accuracy is improved.

[0061] In the embodiment, the construction method of the insulation deterioration prediction framework is:

[0062] According to the local discharge and material aging characteristics in the operation stage of the electrical equipment, the unstructured insulation state data is converted into the extracted discharge threshold voltage, insulation impedance and thermal zone layout parameter group, and the discharge threshold voltage in the operation record is fused to associate the insulation deterioration degree, and the insulation deterioration prediction characteristic matrix is established;

[0063] Based on the insulation deterioration prediction characteristic matrix, the voltage, impedance and layout parameter group are received, the insulation prediction monitoring characteristics are analyzed, and the resource configuration result is optimized through the spectrum analysis extraction technology, and the equipment prediction maintenance scheme is formulated according to the operation record of the data of the insulation steady state evaluation and discharge mode;

[0064] The equipment operation implementation data and feedback results collected in real time according to the maintenance configuration scheme are analyzed, the parameter weight of the insulation deterioration degree prediction is dynamically corrected through the gradient descent under the limitation of the performance-aging curve, and the insulation deterioration degree limitation condition is taken as an external mapping parameter to construct the insulation deterioration prediction framework.

[0065] In this embodiment, the insulation deterioration evaluation scheme is segmented according to the aging stage of the insulation material, the insulation maintenance information is mapped with the insulation attenuation spectrum, the evaluation indication containing the insulation steady state interval, the partial discharge active interval and the attenuation critical interval is generated, and the insulation inspection cycle, the hot spot temperature measurement cycle and the insulation strengthening treatment strategy are formulated based on the evaluation indication.

[0066] The embodiment provides an electrical equipment insulation aging evaluation and early warning system capable of predicting maintenance, which is deployed in a cloud computing node of a power equipment operation and maintenance platform and a field edge diagnosis device, realizes real-time evaluation, trend prediction and maintenance strategy output on an electrical equipment insulation aging process through insulation state data acquisition, attenuation feature analysis, aging trend evaluation modeling, dielectric performance verification reasoning and dynamic update of early warning strategy.

[0067] The overall architecture of the system is shown in Figure 2 The system includes a data acquisition module, a spectrum analysis module, an attenuation feature analysis module, an aging trend prediction module, a dielectric cycle verification module and an early warning generation module, forming a complete "data acquisition → feature extraction → aging identification → trend prediction → cycle verification → early warning output" closed-loop technology flow.

[0068] Firstly, the system continuously acquires the insulation state data of the equipment through the field data access interface, including insulation impedance, dielectric loss factor, hot zone temperature distribution, partial discharge pulse sequence and operating current waveform. The data preprocessing engine performs format analysis, time series reconstruction, outlier removal and noise suppression on the original acquisition stream, and realizes traceability management of data in different operation cycles and frequency intervals through the unique identification of the equipment and the working condition label.

[0069] Subsequently, the spectrum analysis module utilizes multi-scale spectrum decomposition and feature band extraction technology to deeply process the frequency domain response of the insulation material. This module uses a segmented spectrum analysis method based on STFT / wavelet packet to analyze the insulation attenuation spectrum, extracts key features such as partial discharge energy cluster distribution, frequency band energy migration path and insulation dielectric spectrum drift, and synchronously aligns the frequency domain results with the operating condition features to generate an insulation spectrum feature set.

[0070] The attenuation feature analysis module performs multi-modal fusion on the collected time series features and spectrum features. This module constructs an insulation deterioration feature vector based on a feature weight network, dynamically aggregates parameters such as insulation impedance attenuation rate, partial discharge activity, hot spot temperature trend, dielectric loss change, and distinguishes between steady-state parameters, early aging parameters and rapid attenuation parameters through a feature importance identification mechanism, thereby forming a high-dimensional degradation feature stream that can be used for trend modeling.

[0071] In the aging trend prediction stage, the system constructs an insulation aging trend trajectory through a time series extrapolation model and a degradation stage discrimination model. The model adopts an attention mechanism-based time series feature fusion framework to assign weights to different degradation features, identify whether the insulation state enters a sub-health, rapid degradation, or critical aging stage, and generate a deterioration trend prediction curve for the future time window of the insulation state to realize predictive maintenance capability.

[0072] In the dielectric cycle verification stage, the system introduces a dielectric frequency domain response knowledge graph, including dielectric constant evolution nodes, insulation decay type classification nodes, partial discharge mode nodes, and thermal zone coupling relationship edges. The system verifies the current insulation state through a dielectric response graph reasoning engine, identifies whether there are signs of accelerated decay of the insulation material, and generates insulation decay cycle, deterioration source path, and aging mode determination results.

[0073] When the trend prediction curve or the cycle verification result triggers the risk threshold, the early warning generation module starts the aging early warning process, judges the risk level of the insulation state, and outputs the corresponding maintenance strategy, including partial discharge monitoring encryption, local reinforcement processing, dielectric performance re-examination, or advance maintenance plan. At the same time, the system writes the latest decay characteristics, prediction deviation, and verification results back to the model parameter library through a dynamic feedback mechanism, and continuously improves the stability and accuracy of the prediction framework through incremental updating and weight optimization mechanism.

[0074] Through the above technical flow design, the embodiment realizes an intelligent insulation aging evaluation and prediction system that integrates data acquisition, decay characteristic analysis, trend prediction, cycle verification, and dynamic early warning. It can continuously provide high-precision aging state monitoring, early warning, and maintenance decision support throughout the life cycle of electrical equipment, greatly improving the safety, stability, and intelligent level of power equipment operation.

[0075] In the embodiment, the insulation decay frequency spectrum is obtained by performing wavelet decomposition on the response signal of the insulation material to analyze the energy distribution of the frequency band, and identifying the insulation decay rate and aging type based on the frequency band energy density change, breakdown path characteristics, and harmonic distribution structure of the partial discharge signal.

[0076] In the embodiment, the verification method of the decay cycle of the insulation material is as follows:

[0077] Perform a dielectric frequency domain response test on the electrical equipment to obtain the dielectric constant and loss factor change data of the insulation material under different operating frequencies, and perform frequency spectrum reconstruction and feature decomposition on the dielectric constant and loss factor change data to extract the dielectric spectrum line drift rate and frequency band energy attenuation coefficient. By comparing the dielectric spectrum morphology of the insulation material at different operating cycles, an insulation performance decay characteristic curve is established.

[0078] Solving is performed on the insulation loss factor growth rate, dielectric constant change rate and insulation impedance decay rate based on the insulation performance decay characteristic curve to obtain a degradation rate index of the insulation material, and the degradation rate index is jointly checked with an insulation hotspot temperature rise rate and an impedance drop characteristic in an insulation operation stage to verify the decay period of the insulation material.

[0079] In the embodiment, the device aging early warning information includes insulation temperature rise abnormality prompt, insulation impedance drop rate exceeding standard alarm and insulation aging index exceeding limit reminder, and a dynamic aging early warning result is generated by consistent comparison of the early warning information and an aging trend track output by the insulation deterioration prediction framework.

[0080] In the embodiment, the insulation decay trend track is based on deterioration characteristic points of the insulation material in different operation periods, and a visual decay path is established by time series regression and multi-point extrapolation technology to analyze the insulation aging direction and speed.

[0081] In the embodiment, the method for returning the insulation decay parameters to the insulation deterioration prediction framework is: obtaining the latest insulation deterioration evaluation result and performing deviation comparison with the expected decay track of the prediction framework, converting the deviation result into insulation state correction parameters, generating a new deterioration parameter set, and returning the new deterioration parameter set as an iterative input parameter to the insulation deterioration prediction framework.

[0082] Embodiment two

[0083] In the embodiment of the electrical equipment insulation aging evaluation early warning system for predictable maintenance, the system specifically comprises:

[0084] The insulation state acquisition module: obtains insulation state data of the electrical equipment, creates a performance-aging curve using the insulation state data, carries out insulation endurance test on the electrical equipment, analyzes local aging characteristics in the operation stage, and evaluates the endurance capability of the insulation material according to the local aging characteristics to obtain insulation deterioration degree data;

[0085] The deterioration prediction framework construction module: constructs an insulation deterioration prediction framework based on the insulation deterioration degree data, imports the insulation deterioration degree data as input parameters into the insulation deterioration prediction framework, generates an operation record containing data of insulation steady state evaluation and discharge mode, integrates maintenance efficiency index to configure insulation maintenance information, the insulation maintenance information is identified by adding maintenance information marks on the performance-aging curve, and an insulation deterioration evaluation scheme is developed;

[0086] The insulation attenuation period verification module: performs the insulation deterioration evaluation scheme, analyzes the insulation state data of the electrical equipment, identifies the insulation attenuation frequency spectrum by the spectrum analysis extraction technology in the insulation resistance test, and verifies the attenuation period of the insulation material by the dielectric frequency domain response technology, and obtains the equipment aging early warning information;

[0087] The insulation aging early warning identification module: according to the current insulation attenuation health status, identifies the equipment aging early warning information, when detecting that the insulation attenuation trend track output by the insulation deterioration prediction framework meets the maintenance efficiency index, records the current insulation deterioration data, and returns the refreshed insulation attenuation parameters to the insulation deterioration prediction framework, and updates the insulation deterioration evaluation scheme.

[0088] The above is only a preferred embodiment of the present application, not any form of limitation on the present application, although the present application has been disclosed as above with a preferred embodiment, however, not to limit the present application, any person skilled in the art, without departing from the scope of the technical scheme of the present application, can make some changes or modifications to the above disclosed technical content to make equivalent embodiments with equivalent changes, but as long as it does not deviate from the technical scheme content of the present application, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present application, all still belong to the scope of the technical scheme of the present application.

Claims

1. A method for predicting maintenance of an electrical equipment insulation aging evaluation early warning, characterized in that, The method comprises the following steps: S1: obtaining insulation state data of the electrical equipment, creating a performance-aging curve according to the insulation state data, carrying out an insulation resistance test on the electrical equipment, analyzing local aging characteristics in the running stage, and evaluating the resistance capacity of the insulation material according to the local aging characteristics to obtain insulation deterioration degree data; S2: constructing an insulation deterioration prediction framework based on the insulation deterioration degree data, importing the insulation deterioration degree data as input parameters into the insulation deterioration prediction framework, generating an operation record containing data of insulation steady-state evaluation and discharge mode, and configuring insulation maintenance information by integrating maintenance efficiency indicators, the insulation maintenance information being identified by adding maintenance information markers on the performance-aging curve, and formulating an insulation deterioration evaluation scheme; S3: executing the insulation deterioration evaluation scheme, analyzing the insulation state data of the electrical equipment, identifying insulation attenuation frequency spectrum and insulation impedance and thermal zone layout data in the insulation maintenance information by using a spectrum analysis extraction technology in the insulation resistance test, and verifying the attenuation period of the insulation material by using a dielectric frequency domain response technology to obtain equipment aging early warning information; S4: identifying the equipment aging early warning information according to the current health status of the insulation attenuation, recording the current insulation deterioration data when the insulation attenuation trend track output by the insulation deterioration prediction framework meets the maintenance efficiency indicators, and returning the refreshed insulation attenuation parameters to the insulation deterioration prediction framework to update the insulation deterioration evaluation scheme.

2. The method of claim 1, wherein, The method for obtaining the insulation state data comprises the following steps: analyzing operation parameters in the running stage of the electrical equipment, performing time sequence calibration and modal integration on the operation parameters, and fusing operation instructions and feedback signals in the operation parameters based on insulation performance in the running process of the equipment to form complete insulation state data.

3. The method of claim 1, wherein, The performance-aging curve is based on changes of dielectric strength, insulation impedance, thermal breakdown threshold and local discharge amount of the insulation material of the electrical equipment in different aging stages, and the performance of the insulation aging rate is quantified by fitting the voltage withstand characteristics and loss factor of the insulation material in the accelerated aging test.

4. The method of claim 1, wherein, The method for generating the insulation deterioration degree data comprises the following steps: applying segmented high-current pressure to the electrical equipment according to the performance-aging curve, collecting corresponding local discharge feedback curves at different temperature points, recording the starting time of the local discharge curve feedback at the time of issuing the equipment operation instruction, and obtaining a steady-state response interval in the insulation conversion operation; structurally storing the discharge threshold voltage parameter in the steady-state response interval, integrating the local discharge feedback curve in the high-current pressure application process, forming an insulation deterioration degree data set, taking the insulation deterioration degree data set as an input source of the insulation deterioration prediction framework, and obtaining the insulation deterioration degree data.

5. The method of claim 2, wherein, The method for constructing the insulation deterioration prediction framework comprises the following steps: according to local discharge and material aging characteristics in the running stage of the electrical equipment, converting unstructured insulation state data into a set of extraction discharge threshold voltage, insulation impedance and thermal zone layout parameters, and fusing the discharge threshold voltage associated insulation deterioration degree in the operation record to form an insulation deterioration prediction characteristic matrix; Receive the voltage, impedance and layout parameter group based on the insulation deterioration prediction characteristic matrix, analyze the insulation prediction monitoring characteristics, and perform optimization on the resource configuration result through spectral analysis extraction technology, and formulate the equipment maintenance configuration scheme according to the operation record of the data of the insulation steady state evaluation and the discharge form; Analyze the equipment operation implementation data and feedback results collected in real time according to the maintenance configuration scheme, dynamically correct the parameter weight of the insulation deterioration degree prediction under the limitation of the performance-aging curve through gradient descent, and take the insulation deterioration degree limitation condition as an external mapping parameter to construct an insulation deterioration prediction framework.

6. The method of claim 5, wherein, The insulation deterioration evaluation scheme divides the performance-aging curve according to the aging stage of the insulation material, maps the insulation maintenance information and the insulation attenuation frequency spectrum, generates an evaluation indication including the insulation steady state interval, the partial discharge active interval and the attenuation critical interval, and formulates the insulation inspection cycle, the hot spot temperature measurement cycle and the insulation strengthening treatment strategy based on the evaluation indication.

7. The method of claim 4, wherein, The insulation attenuation frequency spectrum analyzes the energy distribution of the frequency band by performing wavelet decomposition on the response signal of the insulation material, and identifies the insulation attenuation rate and the aging type based on the frequency band energy density change, the breakdown path characteristic and the harmonic distribution structure of the partial discharge signal.

8. The method of claim 2, wherein, The verification method of the attenuation period of the insulation material is: Perform dielectric frequency domain response test on the electrical equipment to obtain the dielectric constant and loss factor change data of the insulation material under different operating frequencies, and perform frequency spectrum reconstruction and feature decomposition on the dielectric constant and loss factor change data to extract the dielectric spectrum line drift rate and the frequency band energy attenuation coefficient, and establish the insulation performance attenuation characteristic curve by comparing the dielectric spectrum form of the insulation material at different running periods; Solve the insulation loss factor growth rate, the dielectric constant change rate and the insulation impedance attenuation rate based on the insulation performance attenuation characteristic curve to obtain the degradation rate index of the insulation material, and jointly verify the degradation rate index, the insulation hot spot temperature rise rate and the insulation impedance drop characteristic of the insulation running stage to verify the attenuation period of the insulation material.

9. The method of claim 4, wherein, The device aging early warning information includes insulation temperature rise abnormality prompt, insulation impedance drop rate exceeding standard alarm and insulation aging index exceeding limit reminder, and the dynamic aging early warning result is generated by consistent comparison of the early warning information and the aging trend track output by the insulation deterioration prediction framework.

10. The method of claim 4, wherein, The insulation attenuation trend track is based on the deterioration characteristic points of the insulation material at different running periods, and a visual attenuation path is established through time series regression and multi-point extrapolation technology to analyze the insulation aging direction and speed.

11. The method of claim 7, wherein, The method for returning the insulation attenuation parameter to the insulation deterioration prediction framework is: obtaining the latest insulation deterioration evaluation result and comparing the deviation with the expected attenuation track of the prediction framework, converting the deviation result into insulation state correction parameters, generating a new set of deterioration parameters, and returning them to the insulation deterioration prediction framework as iterative input parameters.

12. A pre-emptive maintenance electrical equipment insulation ageing assessment warning system for performing the method of any one of claims 1-11, characterised in that, It includes: The insulation state acquisition module: obtains the insulation state data of the electrical equipment, creates a performance-aging curve using the insulation state data, carries out an insulation endurance test on the electrical equipment, analyzes the local aging characteristics in the running stage, and evaluates the endurance capacity of the insulation material according to the local aging characteristics to obtain insulation deterioration degree data; The deterioration prediction framework construction module: constructs an insulation deterioration prediction framework based on the insulation deterioration degree data, imports the insulation deterioration degree data into the insulation deterioration prediction framework as input parameters, generates an operation record containing data of insulation steady-state evaluation and discharge mode, and configures insulation maintenance information by integrating maintenance efficiency indicators, the insulation maintenance information is identified by adding maintenance information markers on the performance-aging curve, and an insulation deterioration evaluation scheme is developed; The insulation attenuation period verification module: executes the insulation deterioration evaluation scheme, analyzes the insulation state data of the electrical equipment, identifies insulation attenuation frequency spectrum and insulation impedance and thermal zone layout data in the insulation maintenance information through spectral analysis extraction technology in the insulation endurance test, and verifies the attenuation period of the insulation material using dielectric frequency domain response technology to obtain equipment aging early warning information; The insulation aging early warning identification module: identifies the equipment aging early warning information according to the current health status of the insulation attenuation, records the current insulation deterioration data when the insulation attenuation trend trajectory output by the insulation deterioration prediction framework meets the maintenance efficiency indicators, and returns the refreshed insulation attenuation parameters to the insulation deterioration prediction framework to update the insulation deterioration evaluation scheme.

Images