Insulation state on-line monitoring system for electrical equipment of high-voltage transformer substation

Abstract

The invention relates to the technical field of electrical equipment monitoring, and discloses an insulation state on-line monitoring system for electrical equipment of a high-voltage transformer substation, and the system comprises a data collection module which is used for collecting electric field intensity, partial discharge signals and temperature and humidity data in real time, and transmitting the data to each module through a transmission unit; the multi-physics field coupling modeling module is used for calculating electric field distribution and temperature field distribution in the equipment through electric field simulation and temperature field simulation according to the electric field intensity and temperature and humidity data; and a dielectric loss and partial discharge linkage analysis module. According to the method, electric field intensity, partial discharge signals and temperature and humidity data are comprehensively analyzed by combining multi-physics field coupling modeling and intelligent reasoning technologies, accurate equipment insulation state evaluation and fault prediction are achieved, meanwhile, an electric field-temperature field coupling analysis technology is adopted, partial discharge and a heat effect are combined, and the reliability of equipment insulation state evaluation and fault prediction is improved. And an overheating area and an insulation problem in the equipment can be accurately identified.

Description

Technical Field

[0001] The invention relates to the technical field of electrical equipment monitoring, and in particular to an online monitoring system for insulation status of electrical equipment in a high-voltage substation. Background Art

[0002] In high-voltage substations, the insulation status of electrical equipment is an important factor in ensuring the normal operation of equipment and preventing failures. As the operating time of electrical equipment increases and the external environment changes, the insulation performance will gradually decline, leading to equipment failure or even shutdown, and in severe cases, it may also cause accidents in the power system. Therefore, insulation status monitoring of electrical equipment in high-voltage substations is a key link in ensuring the stable operation of the power system. At present, traditional methods of insulation status monitoring mainly rely on manual inspection, regular testing and single monitoring technology. These methods often cannot reflect the operating status of the equipment in real time and have certain limitations.

[0003] Most existing technical solutions focus on the monitoring of partial discharge signals. Changes in the frequency and amplitude of partial discharge signals are often used to reflect the aging and damage of insulating materials. However, partial discharge signals are not the only factor that determines the insulation performance of equipment. External environmental factors such as electric field strength, temperature and humidity, as well as the multi-physical field coupling effects inside the equipment will affect the state of the insulating material. However, the existing technology usually uses partial discharge signals as the only basis for health assessment, ignoring the interaction of these factors, resulting in the monitoring system being unable to accurately capture early potential problems of the equipment. For example, changes in electric field strength may indicate the occurrence of insulation problems in advance, but traditional monitoring systems fail to effectively combine electric fields and partial discharge signals, missing the opportunity to warn of early faults.

[0004] In addition, existing temperature and humidity monitoring technologies also have certain limitations. Temperature and humidity have a direct impact on the insulation performance of equipment, especially in high temperature and high humidity environments, where the insulation materials of equipment are easily damaged. However, traditional monitoring methods usually only analyze temperature and humidity changes separately, ignoring their coupling relationship with other parameters (such as electric field strength and partial discharge signals). In particular, the impact of temperature changes on electric field distribution and partial discharge phenomena is often not effectively incorporated into the monitoring and analysis process, which makes the monitoring system unable to identify hot spots inside the equipment in a timely manner, delaying the discovery of faults.

[0005] In the existing technology, most fault warning systems rely on a single data source or fixed algorithm to perform equipment health assessment. This method often cannot provide sufficiently accurate fault prediction in an environment with large amounts of data and complex information. Many monitoring systems are unable to dynamically integrate data such as electric field strength, partial discharge signals, temperature and humidity, resulting in the system's judgment of equipment status being too simple and unable to accurately reflect the multi-dimensional operating conditions of the equipment. Traditional fault warning systems lack the ability to comprehensively process multi-dimensional and cross-domain data on the insulation status of equipment, resulting in their inability to achieve timely and accurate fault prediction.

[0006] Based on this, the present invention proposes an online monitoring system for the insulation status of electrical equipment in a high-voltage substation. Summary of the invention

[0007] In view of the deficiencies in the prior art, the present invention provides an online monitoring system for the insulation status of electrical equipment in a high-voltage substation, which solves the problem that the existing insulation status monitoring of electrical equipment in a high-voltage substation cannot integrate multiple data sources, accurately predict faults, and identify overheating hotspots inside the equipment.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an online monitoring system for insulation status of electrical equipment in a high-voltage substation, comprising:

[0009] A data acquisition module, used to collect electric field strength, partial discharge signal, temperature and humidity data in real time and transmit them to each module through a transmission unit;

[0010] A multi-physics field coupling modeling module, used to calculate the electric field distribution and temperature field distribution inside the device through electric field simulation and temperature field simulation according to the electric field strength and temperature and humidity data;

[0011] A dielectric loss and partial discharge linkage analysis module, used to perform linkage analysis based on the electric field strength, partial discharge signal and temperature and humidity data to evaluate the insulation performance of the equipment;

[0012] The inverse calculation and intelligent reasoning module is used to evaluate the insulation status of the equipment through the inverse calculation method based on the physical model and real-time collected data, and to predict faults using the intelligent reasoning algorithm;

[0013] The data display and decision support module is used to visualize data based on the evaluation results of each module and generate a health assessment report to provide decision support for operation and maintenance personnel.

[0014] Preferably, the data acquisition module includes:

[0015] Partial discharge signal acquisition unit, used to monitor partial discharge signals in the equipment in real time;

[0016] Temperature and humidity sensor unit, used to collect temperature and humidity data of the equipment environment in real time;

[0017] An electric field strength sensor unit is used to collect electric field strength data inside the device in real time;

[0018] The data transmission unit is used to transmit the collected data to the central processing system.

[0019] Preferably, the multi-physics field coupling modeling module includes:

[0020] The electric field simulation unit is used to calculate the electric field distribution inside the device through Maxwell equations and finite element method to obtain the spatial distribution data of the electric field intensity;

[0021] Temperature field simulation unit, used to simulate the changes in the temperature field inside the equipment, using the heat conduction equation to calculate the temperature distribution, taking into account the partial discharge thermal effect and workload;

[0022] Dielectric loss modeling unit, used to calculate the dielectric loss factor of insulating materials, which represents the energy loss and aging of the material;

[0023] The electric field-temperature field coupling unit is used to couple and analyze the influence of the electric field and the temperature field on the insulation material of the equipment, and comprehensively obtain the interaction results between the electric field and the temperature field;

[0024] Physical field coupling data fusion unit, used to fuse the data of electric field, temperature field and dielectric loss to generate comprehensive evaluation data of equipment insulation status for use by subsequent partial discharge analysis and intelligent reasoning modules;

[0025] A local hot spot identification unit is used to automatically identify possible local overheating areas inside the equipment by coupling and analyzing electric field and temperature field data, and generate corresponding alarm signals;

[0026] The electrical equipment damage assessment unit is used to assess the extent of equipment damage based on coupled analysis of electric field and temperature field combined with historical fault data, and provide corresponding repair or replacement recommendations.

[0027] Preferably, the dielectric loss and partial discharge linkage analysis module includes:

[0028] a dielectric loss factor calculation unit, for calculating a dielectric loss factor based on the frequency response, wherein the dielectric loss factor represents the ability of an insulating material to convert electrical energy into thermal energy;

[0029] The partial discharge and dielectric loss correlation analysis unit is used to analyze the relationship between the frequency, amplitude and dielectric loss factor of the partial discharge signal to evaluate the insulation performance of the equipment.

[0030] Preferably, the dielectric loss factor Calculated by the following formula:

[0031] ;

[0032] in, is the real part of the dielectric constant, is the imaginary part of the dielectric constant, is the dielectric loss factor.

[0033] Preferably, the inversion calculation and intelligent reasoning module includes:

[0034] Inversion calculation unit, used to infer the insulation status of the equipment through the back propagation algorithm based on the electric field, temperature and humidity data and partial discharge signals;

[0035] Intelligent reasoning and decision-making unit, which uses convolutional neural networks or long short-term memory networks to perform intelligent reasoning on the collected data and generate equipment health assessment reports;

[0036] The fault warning unit is used to generate fault warnings based on the reasoning results and make maintenance suggestions.

[0037] Preferably, the data display and decision support module includes:

[0038] Data visualization unit, used to visualize data such as electric field, temperature, partial discharge signal, etc., and generate a graphical representation of the equipment health status;

[0039] A health assessment report generation unit, used to generate a health assessment report of the device based on multi-physics field coupling modeling and intelligent reasoning results;

[0040] Decision support unit, used to provide equipment maintenance priorities, repair recommendations and resource scheduling solutions.

[0041] Preferably, the electric field simulation unit in the multi-physics field coupling modeling module calculates the electric field intensity distribution inside the device by the following formula:

[0042] ;

[0043] in, is the electric displacement vector, is the charge density;

[0044] The electric field strength With electric displacement The relationship is:

[0045] ;

[0046] in, is the dielectric constant, is the electric field strength vector.

[0047] Preferably, the inversion calculation unit in the inversion calculation and intelligent reasoning module calculates the insulation state of the equipment through a back propagation algorithm based on the following mathematical model:

[0048] ;

[0049] in, is the critical voltage for partial discharge to occur, is the electric field strength, and A constant related to the insulation material of the equipment.

[0050] Preferably, the electric field-temperature field coupling unit in the multi-physics field coupling modeling module couples the interaction between the electric field and the temperature field through the following calculation formula:

[0051] ;

[0052] in, is the thermal conductivity of the temperature field, is the temperature gradient, is the heat source term per unit volume, indicating heat generation caused by partial discharge, etc.

[0053] The present invention provides an online monitoring system for the insulation status of electrical equipment in a high-voltage substation. It has the following beneficial effects:

[0054] 1. The present invention adopts a technical solution that combines multi-physical field coupling modeling with intelligent reasoning. It can accurately evaluate the insulation status of the equipment and predict potential faults through comprehensive analysis of real-time electric field strength, local discharge signals and temperature and humidity data, thereby achieving the effect of accurately predicting faults during equipment operation through comprehensive simulation and intelligent reasoning. Compared with the fault warning system in the prior art that only relies on local data or a single model, the present invention solves the shortcomings of the prior art that it is difficult to integrate multiple factors for prediction, resulting in the inability to discover potential problems in advance.

[0055] 2. The present invention adopts electric field-temperature field coupling analysis, combines local discharge and thermal effects to identify hot spots inside the equipment, and generates a health assessment report for the equipment in real time. This solution realizes the coupling analysis and data fusion of electric field strength, temperature field and local discharge signals, and achieves the effect of more accurately locating equipment overheating and insulation problems. Compared with the traditional solutions in the prior art that only focus on electric field strength or temperature changes, the present invention solves the problem that traditional solutions ignore the interaction between temperature and electric field and cannot effectively identify local hot spots. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a framework diagram of the system of the present invention;

[0057] Figure 2 It is a framework diagram of the data acquisition module of the present invention;

[0058] Figure 3 It is a framework diagram of the multi-physics field coupling modeling module of the present invention;

[0059] Figure 4 It is a framework diagram of the dielectric loss and partial discharge linkage analysis module of the present invention;

[0060] Figure 5 It is a framework diagram of the inversion calculation and intelligent reasoning module of the present invention;

[0061] Figure 6 It is a framework diagram of the data display and decision support module of the present invention. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0063] Please see attached Figure 1 The embodiment of the present invention provides an online monitoring system for insulation status of electrical equipment in a high-voltage substation, comprising:

[0064] Please see attached Figure 2 , a data acquisition module, used to collect electric field strength, partial discharge signal, temperature and humidity data in real time and transmit them to each module through a transmission unit;

[0065] The data acquisition module comprises:

[0066] Partial discharge signal acquisition unit, used to monitor partial discharge signals in the equipment in real time;

[0067] Temperature and humidity sensor unit, used to collect temperature and humidity data of the equipment environment in real time;

[0068] An electric field strength sensor unit is used to collect electric field strength data inside the device in real time;

[0069] The data transmission unit is used to transmit the collected data to the central processing system.

[0070] This embodiment provides a data acquisition module in the online monitoring system of the insulation status of electrical equipment in a high-voltage substation. This module is mainly responsible for real-time acquisition of the electric field strength, partial discharge signal, temperature and humidity data involved in the operation of the equipment, and transmits these data to the central processing system in real time through the transmission unit for subsequent processing, analysis and evaluation. This module is the basic part of the entire monitoring system and provides important input data for subsequent modules such as multi-physics field coupling modeling, partial discharge and dielectric loss linkage analysis, etc.

[0071] In this embodiment, the data acquisition module includes a partial discharge signal acquisition unit, a temperature and humidity sensor unit, an electric field intensity sensor unit, and a data transmission unit. The implementation and functions of these units will be described in detail below.

[0072] In general, the local discharge signal acquisition unit is used to monitor the local discharge signal inside the equipment in real time and obtain important data such as the amplitude, frequency and duration of the local discharge. These data are important for evaluating the insulation performance of the equipment and fault diagnosis. The acquisition of local discharge signals usually uses high-frequency current sensors or electromagnetic wave sensors, which can effectively capture high-frequency signals during the discharge process and avoid conventional electric field interference. In some embodiments, the acquisition unit can remove high-frequency noise signals through a filter and use an amplification circuit to increase the signal strength. Through this processing process, it can be ensured that the collected local discharge signal has a high signal-to-noise ratio, providing a reliable data source for subsequent analysis.

[0073] As an option, the temperature and humidity sensor unit is used to monitor the temperature and humidity data of the environment where the equipment is located in real time, which is crucial for evaluating the insulation performance of the equipment. Ambient temperature and humidity will directly affect the performance of the insulating materials of electrical equipment, especially in high-voltage environments. Changes in temperature and humidity will cause changes in dielectric constants, which in turn affect partial discharge and the speed of equipment aging. Specifically, the temperature and humidity sensor unit can use common digital temperature and humidity sensors, such as DHT11, DHT22, etc. These sensors can provide accurate temperature and humidity readings. Real-time collection of temperature and humidity data provides an important reference for equipment health assessment and predictive maintenance.

[0074] In this embodiment, the electric field strength sensor unit is responsible for collecting the electric field strength data inside the device in real time. Electric field strength is an important factor affecting the insulation performance of electrical equipment. The electric field distribution inside the equipment and the change of electric field strength are often closely related to phenomena such as partial discharge and dielectric loss. In general, the electric field strength sensor adopts an electric field detector or a potential sensor, which can measure the electric field distribution inside the electrical equipment. The measurement results of the sensor provide spatial distribution information of the electric field strength, which can provide key input for the simulation calculation of the electric field inside the device. In some possible implementations, the electric field strength sensor unit can be arranged in the form of a sensor array to monitor the electric field changes in different areas of the equipment in real time, further improving the accuracy and reliability of the system.

[0075] In this embodiment, the data transmission unit is used to transmit all collected data from each sensor unit to the central processing system. In order to ensure real-time performance and reliability of data transmission, the data transmission unit generally adopts wireless communication technology (such as Wi-Fi, ZigBee, LoRa, etc.) or wired communication technology (such as Ethernet, RS485, etc.). Specifically, the data transmission unit uses a suitable communication protocol to package the partial discharge signal, temperature and humidity data, and electric field strength data into a data packet, and transmits it to the central processing system through the communication network. During the transmission process, compression and encryption technology can be used to improve the efficiency and security of data transmission.

[0076] In some embodiments, the local discharge signal acquisition unit may use a high-frequency current sensor or an electromagnetic wave sensor, and the acquired local discharge signal may be processed by the following formula:

[0077] ;

[0078] in, is the instantaneous current of the partial discharge signal, is the signal amplitude, is the frequency of the partial discharge signal, For time, By performing spectrum analysis on the collected partial discharge signal, characteristic parameters such as the frequency and amplitude of partial discharge can be obtained. These parameters play an important role in the subsequent partial discharge analysis and equipment insulation performance evaluation.

[0079] The data acquisition module in this embodiment can acquire and transmit the electric field strength, partial discharge signal, temperature and humidity data of the equipment in real time, providing key data support for subsequent multi-physics field coupling modeling, partial discharge and dielectric loss linkage analysis, intelligent reasoning and decision support. By adopting high-precision sensors and combining reasonable signal processing and data transmission methods, the data acquisition module can ensure the accuracy and real-time nature of monitoring data, providing a reliable basis for equipment health assessment and fault warning.

[0080] Please see attached Figure 3 , a multi-physics field coupling modeling module, used to calculate the electric field distribution and temperature field distribution inside the device through electric field simulation and temperature field simulation according to the electric field strength and temperature and humidity data;

[0081] In this embodiment, the multi-physics field coupling modeling module in the online monitoring system of the insulation status of the electrical equipment of the high-voltage substation mainly evaluates the insulation performance and possible damage of the equipment by comprehensively analyzing the interactive effects of physical fields such as electric field, temperature field, dielectric loss and partial discharge. This module works in coordination with multiple sub-units to provide data support for subsequent partial discharge analysis, intelligent reasoning and decision support modules. Through the coupled calculation of the electric field and the temperature field, the temperature distribution and electric field distribution inside the equipment and their joint influence on the insulation performance can be accurately simulated.

[0082] In this embodiment, the electric field simulation unit numerically calculates the electric field inside the device through Maxwell equations and finite element method to obtain the spatial distribution data of the electric field strength. This process can help determine the electric field distribution inside the electrical device, thereby predicting the possible partial discharge area of ​​the device.

[0083] Specifically, the electric field simulation unit performs calculations according to the following Maxwell equations:

[0084] ;

[0085] in, is the electric displacement vector (unit: C / m 2 ), which describes the relationship between the electric field strength and the polarization of the medium, is the charge density (unit: C / m 3 ), represents the charge distribution per unit volume.

[0086] The relationship between electric field strength and electric displacement is:

[0087] ;

[0088] in, is the dielectric constant (unit: F / m), which indicates the ability of electric field to pass through the medium. is the electric field strength vector (unit: V / m), describing the strength and direction of the electric field.

[0089] By calculating the electric displacement vector and electric field strength The electric field simulation unit can accurately predict the distribution of the electric field in the device. The above equations are usually discretized and solved by the finite element method (FEM) to obtain the electric field distribution diagram inside the device.

[0090] The temperature field simulation unit in this embodiment uses the heat conduction equation to simulate the temperature field inside the device. The unit takes into account the thermal effect caused by partial discharge and the load change during the operation of the device, and can generate a temperature distribution diagram to predict the change trend of the device temperature.

[0091] The simulation of the temperature field follows the following heat conduction equation:

[0092] ;

[0093] in, is the temperature (unit: °C), indicating the temperature inside the device. is the thermal diffusion coefficient (unit: m 2 / s), describes the material's ability to conduct heat. is the heat source term (unit: W / m 3 ), represents the heat generated by partial discharge and equipment workload, which comes from the conversion of electrical energy during equipment operation. is the Laplace operator of the temperature field (unit: 1 / m 2 ), which indicates the degree of temperature variation in space.

[0094] This formula is used to simulate the spatial distribution of the temperature field. Specifically, the heat source term Q is usually caused by partial discharge or equipment load. Partial discharge can trigger thermal effects, affect the temperature rise inside the equipment, and further affect the performance of the insulation material.

[0095] The dielectric loss modeling unit is responsible for evaluating the dielectric properties of the equipment's insulation materials and reflects changes in the equipment's insulation performance by calculating the dielectric loss factor of the material. The dielectric loss factor is an important indicator that affects the equipment's insulation performance. As the equipment ages, the dielectric loss factor increases, causing the equipment's insulation performance to deteriorate.

[0096] The calculation formula for dielectric loss factor is:

[0097] ;

[0098] in, is the dielectric loss factor (dimensionless), reflecting the energy loss of the material under the action of the electric field, is the real part of the dielectric constant (unit: F / m), which represents the energy stored in the electric field. It is the imaginary part of the dielectric constant (unit: F / m), which represents the energy loss capacity of the material.

[0099] The calculation method is based on the frequency response of the material. At different frequencies, the size of the dielectric loss factor can reflect the degree of aging of the material. A higher dielectric loss factor means that the material has a greater energy loss in the electric field and poorer insulation performance.

[0100] The electric field-temperature field coupling unit couples the electric field strength with the temperature field to analyze the combined effects of the electric field and temperature on the insulation material of the equipment. The interaction between the electric field and the temperature field is one of the key factors affecting the insulation state of the equipment. The increase in the electric field will lead to an increase in temperature, which in turn affects the dielectric constant of the material, thereby accelerating the aging of the equipment.

[0101] The coupled analysis of the electric field and temperature field is performed using the following formula:

[0102] ;

[0103] in, is the thermal conductivity (unit: W / m·K), which describes the thermal conductivity of the material. is the temperature gradient (unit: K / m), which indicates the spatial rate of temperature change. is the heat source term (unit: W / m 3 ), indicating heat generation caused by partial discharge, etc.

[0104] This formula describes the coupling of electric field and temperature field inside the equipment. It can simulate the impact of electric field and temperature on insulating materials and provide key data for subsequent equipment health assessment and fault prediction.

[0105] The physical field coupling data fusion unit is responsible for integrating the simulation data of the electric field, temperature field and dielectric loss to generate comprehensive evaluation data of the equipment insulation status. Through the joint analysis of multiple physical fields, the insulation status of the equipment can be comprehensively evaluated, providing accurate input data for the partial discharge analysis and intelligent reasoning modules.

[0106] The local hotspot identification unit automatically identifies possible local overheating areas inside the equipment through coupled analysis of the electric field and temperature field. These hotspots usually lead to accelerated aging of the equipment's insulation materials and increase the risk of equipment failure. By timely identifying and generating alarm signals, it can help operation and maintenance personnel take measures in advance to avoid equipment failure.

[0107] The electrical equipment damage assessment unit assesses the damage level of the equipment based on historical fault data by combining changes in the electric field and temperature field. During the long-term operation of the equipment, changes in the electric field and temperature will cause the insulation material to gradually age. Assessing the damage level helps to formulate a reasonable maintenance plan and extend the service life of the equipment.

[0108] The multi-physics coupling modeling module in this embodiment combines the coupling analysis of multiple physical fields such as electric field, temperature field, dielectric loss, etc., and comprehensively analyzes factors such as electric field distribution, temperature change, and dielectric loss inside the equipment through accurate formulas and calculation methods. Through the fusion of multi-physics field data, the insulation status of the equipment can be comprehensively evaluated, and an accurate basis can be provided for the health management and fault prediction of the equipment. This module effectively improves the accuracy of equipment monitoring and can provide strong data support for subsequent partial discharge analysis, intelligent reasoning, and decision support.

[0109] Please refer to the attached Figure 4 , a dielectric loss and partial discharge linkage analysis module, used to perform linkage analysis based on the electric field strength, partial discharge signal and temperature and humidity data to evaluate the insulation performance of the equipment;

[0110] The dielectric loss and partial discharge linkage analysis module includes:

[0111] a dielectric loss factor calculation unit, for calculating a dielectric loss factor based on the frequency response, wherein the dielectric loss factor represents the ability of an insulating material to convert electrical energy into thermal energy;

[0112] The partial discharge and dielectric loss correlation analysis unit is used to analyze the relationship between the frequency, amplitude and dielectric loss factor of the partial discharge signal to evaluate the insulation performance of the equipment.

[0113] The dielectric loss factor Calculated by the following formula:

[0114] ;

[0115] in, is the real part of the dielectric constant, is the imaginary part of the dielectric constant, is the dielectric loss factor.

[0116] Please refer to the attached Figure 5 , the inverse calculation and intelligent reasoning module is used to evaluate the insulation status of the equipment through the inverse calculation method based on the physical model and real-time collected data, and to predict faults using the intelligent reasoning algorithm;

[0117] The inverse calculation and intelligent reasoning module in this embodiment plays a vital role in the online monitoring system of the insulation status of electrical equipment in high-voltage substations. The module combines the physical model with the real-time collected data, uses the inverse calculation method to evaluate the insulation status of the equipment, and uses the intelligent reasoning algorithm to predict possible failures of the equipment. The inverse calculation and intelligent reasoning module can calculate the health status of the equipment based on various data during the operation of the equipment, issue fault warnings in time, and ensure the stable operation of the equipment.

[0118] This module consists of three main sub-units: inversion calculation unit, intelligent reasoning and decision-making unit, and fault warning unit. Through the collaboration of these three units, the system can achieve accurate analysis and prediction in the equipment health assessment process, providing a decision-making basis for equipment maintenance and management.

[0119] The inverse calculation unit uses the back propagation algorithm to infer the insulation status of the equipment based on the physical model and real-time data collection. The application of the back propagation algorithm in the neural network can effectively adjust the parameters of the model and reduce the error between the model prediction results and the actual results, thereby optimizing the health assessment of the equipment.

[0120] Specifically, the inverse calculation unit calculates the insulation status of the equipment based on the following mathematical model:

[0121] ;

[0122] In this formula: is the critical voltage of partial discharge (unit: V). Partial discharge is a precursor to the deterioration of the insulation state of electrical equipment, and the change in the critical voltage value is crucial to evaluating the insulation performance of the equipment. The electric field strength (unit: V / m) indicates the electric field strength inside the equipment. The electric field strength directly affects the polarization and partial discharge of the insulating material. Too high an electric field strength will increase the risk of partial discharge of the equipment. and are constants that are usually related to the physical characteristics of the device, such as its geometry and material properties. Through experiments and historical data, the constants and Calibration will be performed based on different equipment and environmental factors.

[0123] During the training process of the back propagation algorithm, based on the electric field strength, temperature and humidity data and partial discharge signals, the inversion calculation unit gradually optimizes the model parameters to more accurately infer the insulation status of the equipment. This process helps to identify potential equipment failures in advance and evaluate the degradation of insulation materials.

[0124] The intelligent reasoning and decision-making unit uses deep learning algorithms (such as convolutional neural networks (CNN) or long short-term memory networks (LSTM)) to perform intelligent reasoning on the collected electric field strength, temperature and humidity data, and partial discharge signals. Through in-depth analysis of the data, the unit can identify the changing trend of the equipment insulation status, timely predict possible failures, and provide corresponding maintenance decisions.

[0125] Specifically, the intelligent reasoning and decision-making unit evaluates the health status of the equipment through the following formula:

[0126] ;

[0127] in: The equipment health assessment result (dimensionless), that is, the current health status of the equipment. The assessment result can be "healthy", "minor fault" or "requires maintenance", etc. is the input feature vector, which contains monitoring data such as electric field strength, frequency and amplitude of partial discharge signals, temperature and humidity, etc. After preprocessing, these data are used as input features of the neural network; The parameters of the neural network, including the weights and biases in the network, are optimized through the training algorithm to improve the prediction accuracy.

[0128] Specifically, convolutional neural networks (CNNs) are used to extract time-frequency features from partial discharge signals to capture the spatial and temporal patterns of electric fields and discharge signals. Long short-term memory networks (LSTMs) can process time series data and analyze time-related equipment states such as temperature and humidity changes and electric field fluctuations. Through deep learning algorithms, intelligent reasoning and decision-making units can accurately infer the insulation status and health of equipment.

[0129] The goal of intelligent reasoning:

[0130] Through intelligent analysis of input data, an equipment health assessment report is generated to provide health checks and maintenance recommendations to operation and maintenance personnel.

[0131] In some cases, combined with historical data, intelligent reasoning can predict the future health of equipment, enabling more efficient preventive maintenance.

[0132] The fault warning unit generates a fault warning signal based on the reasoning results of the intelligent reasoning and decision-making unit, and provides relevant suggestions for equipment maintenance. When the equipment health assessment value is lower than a certain set threshold, the system will issue a fault warning signal. The fault warning unit usually relies on key indicators such as the equipment health assessment value, changes in partial discharge signals, temperature and humidity fluctuations, etc. to monitor the operating status of the equipment in real time to ensure that an alarm can be issued in time when a problem occurs in the equipment.

[0133] The fault warning unit generates warning signals according to the following criteria:

[0134] Health assessment value threshold: When the health assessment value of the device is lower than the preset threshold, the system automatically generates a fault warning. For example, when the health assessment value is lower than 0.5, the device may be at risk of failure.

[0135] Changes in partial discharge signals: When the amplitude of the partial discharge signal exceeds the predetermined safety range, it indicates that the insulation material of the equipment may be damaged or aged, and the system will issue a warning signal.

[0136] Changes in ambient temperature and humidity: When the temperature and humidity change dramatically, the performance of the insulation material may deteriorate. The system will make corresponding maintenance recommendations based on the degree of temperature and humidity changes.

[0137] The implementation details of the inversion calculation and intelligent reasoning module include the following key steps:

[0138] Data acquisition and preprocessing: First, obtain real-time electric field strength, partial discharge signal, temperature and humidity data from the data acquisition module, and perform data preprocessing. The preprocessing steps include denoising and normalization to ensure data quality and consistency.

[0139] Inversion calculation: The inversion calculation unit uses the back propagation algorithm to optimize the model parameters and accurately calculates the insulation status of the equipment by continuously adjusting the network weights. The back propagation training process continuously minimizes the error between the equipment health assessment results and the actual observed values, thereby improving the accuracy of the inversion calculation.

[0140] Intelligent reasoning and decision-making: The intelligent reasoning and decision-making unit uses convolutional neural networks (CNN) or long short-term memory networks (LSTM) to perform intelligent reasoning on the collected data to generate a health assessment report for the equipment. Intelligent reasoning can identify potential faults based on historical data and real-time data and provide maintenance suggestions in advance.

[0141] Fault warning and maintenance suggestions: Based on the health assessment results, the fault warning unit generates a warning signal and provides suggestions for equipment maintenance or replacement.

[0142] The inverse calculation and intelligent reasoning module in this embodiment, based on physical models and deep learning algorithms, can achieve efficient and accurate equipment health assessment and fault prediction. Through the joint application of back propagation algorithm and deep learning network, the system can process multi-source data in real time and provide data support for subsequent intelligent decision-making and equipment management. This data-driven fault prediction and health assessment method provides a strong technical guarantee for the intelligent operation and maintenance of electrical equipment in high-voltage substations.

[0143] Please see attached Figure 6,The data display and decision support module is used to visualize the data based on the ,evaluation results of each module and generate a health assessment report to ,provide decision support for operation and maintenance personnel.

[0144] The data display and decision support module in this embodiment is a key part of the online monitoring system for the insulation status of electrical equipment in high-voltage substations. It aims to visualize the evaluation results of each module and generate a health assessment report to provide comprehensive and scientific decision support for operation and maintenance personnel. Through this module, the health status, fault warning, maintenance suggestions and resource scheduling plans of the equipment can be intuitively displayed, helping operation and maintenance personnel to optimize equipment management, reduce downtime and improve equipment reliability.

[0145] The data display and decision support module consists of three sub-units: data visualization unit, health assessment report generation unit and decision support unit. Each unit complements each other and collaborates to generate comprehensive reports and provide equipment maintenance priorities and decision solutions.

[0146] The data visualization unit is responsible for displaying the collected data from multiple monitoring modules (including electric field strength, temperature and humidity data, partial discharge signals, etc.) to operation and maintenance personnel in a visual form, simplifying the monitoring process of equipment operation status and improving monitoring efficiency.

[0147] Generally, the data visualization unit presents the device status through the following graphical display methods:

[0148] Heat map: Visualizes the spatial distribution of electric and temperature fields. Areas with high electric field strength and temperature are marked as hot spots, helping to identify potential failure points of equipment.

[0149] Line chart: used to show the changes in temperature and humidity, electric field strength, and fluctuations in partial discharge signals during equipment operation. These changing trends help maintenance personnel monitor the health of equipment and adjust operating conditions in a timely manner.

[0150] Partial discharge signal spectrum: displays the frequency, amplitude and change trend of the partial discharge signal inside the equipment. The change of the partial discharge signal intensity is directly related to the health of the equipment's insulation materials.

[0151] Specifically, the data visualization unit can integrate multiple data sources (such as electric field, temperature and humidity, partial discharge, etc.) on the same interface. Different parameters are displayed in various forms such as charts, graphs, bar charts, real-time data bars, and dashboards, so that operation and maintenance personnel can view the health status of the equipment at a glance.

[0152] In addition, the data visualization unit can display the historical trends of various monitoring data of the equipment and compare them with real-time data, helping operation and maintenance personnel to identify potential risks of the equipment. For example, indicators such as excessive temperature and abnormal partial discharge signals can be highlighted in the visualization chart through color changes or shape changes, so that corresponding maintenance measures can be taken quickly.

[0153] The health assessment report generation unit automatically generates a health assessment report for the equipment based on the analysis results of the multi-physics field coupling modeling and intelligent reasoning module. This report describes in detail the health status of the equipment, the impact of various key data, and provides repair and maintenance suggestions based on the health trend of the equipment.

[0154] A health assessment report usually contains the following:

[0155] Equipment Health Overview: Provides a brief overview of the current health status of the equipment and provides a corresponding rating (such as "healthy", "minor failure", "requires repair", etc.) based on the equipment's health assessment score. The rating is based on comprehensive analysis of multi-physics field coupling modeling and intelligent reasoning modules.

[0156] Key monitoring data analysis: Displays the changing trends of parameters such as electric field strength, temperature and humidity, and partial discharge signals during the operation of the equipment. These data reflect the operating environment and status of the equipment and can directly reflect the potential failure risks of the equipment.

[0157] Historical data comparison and trend prediction: By comparing historical data with current monitoring data, the health trend of the equipment is displayed. Combined with the long-term operation data of the equipment, the report can predict the future health status of the equipment and provide a basis for subsequent maintenance.

[0158] Fault prediction and maintenance recommendations: Based on multi-physics simulation results, partial discharge analysis, and intelligent reasoning results, the report provides maintenance recommendations for the equipment. For example, if the partial discharge signal is abnormal, the report may recommend checking the insulation layer of the electrical equipment or shutting down the equipment for inspection.

[0159] As an option, health assessment reports can also be generated through an automated system and sent to the operation and maintenance team's management platform or personal terminal, ensuring that each operation and maintenance personnel can obtain the health status of the equipment and related recommendations in a timely manner.

[0160] The decision support unit intelligently provides equipment maintenance priorities, repair suggestions, and resource scheduling solutions based on the equipment's health assessment results, fault warning information, and equipment operating environment data. The unit automatically generates targeted operational decisions based on the actual operating conditions and historical data of the equipment, helping operation and maintenance personnel to efficiently arrange maintenance and resource scheduling and reduce equipment downtime.

[0161] The main functions of the decision support unit include:

[0162] Equipment maintenance priority: Intelligently assign equipment maintenance priority by intelligently analyzing data such as equipment health status, partial discharge intensity, electric field intensity, and temperature and humidity fluctuations. For example, when the health assessment value of a certain device is lower than the set threshold, the maintenance priority of the device will automatically increase, and the system will prompt the operation and maintenance personnel to conduct inspections or shut down the device for maintenance as soon as possible.

[0163] Maintenance recommendations: Based on the health assessment report and fault warning information, the decision support unit provides specific maintenance recommendations for the equipment. For example, when an abnormal increase in electric field strength is detected, the system may recommend checking whether the insulation layer of the equipment is damaged.

[0164] Resource Scheduling Solution: The decision support unit automatically dispatches maintenance personnel, tools and spare parts according to the equipment maintenance priority and existing resources to optimize the equipment maintenance process. The unit can also reasonably arrange maintenance time and dispatch human resources according to the maintenance needs of the equipment to ensure the efficient implementation of equipment maintenance work.

[0165] In this embodiment, the decision support unit can not only help operation and maintenance personnel identify potential faults in a timely manner through real-time monitoring and historical data analysis, but also reduce the downtime of equipment due to faults and improve the overall operation efficiency of the equipment through intelligent optimization suggestions.

[0166] The implementation process of the data display and decision support module is as follows:

[0167] Data acquisition and preprocessing: The system obtains raw data such as electric field, temperature and humidity, partial discharge signals, etc. from the data acquisition module, and performs preprocessing operations such as data cleaning, denoising, and standardization to ensure data quality.

[0168] Data visualization: Display key parameters such as temperature, partial discharge, fault warning, etc. through a graphical interface. Operation and maintenance personnel can view the health status of the equipment and identify potential fault points through the visualization interface.

[0169] Generate health assessment report: According to the assessment results of each module, a detailed health assessment report is automatically generated, and specific maintenance suggestions are provided to operation and maintenance personnel. The health assessment report is output through a standardized template, which makes it easy for operation and maintenance personnel to quickly obtain and understand equipment health information.

[0170] Decision support and scheduling: The decision support unit intelligently analyzes the maintenance priority of equipment based on health assessment and fault warning information, and automatically generates resource scheduling plans to optimize the equipment maintenance process.

[0171] The data display and decision support module provides an efficient decision support platform for the online monitoring system of the insulation status of electrical equipment in high-voltage substations. Through data visualization, health assessment report generation and intelligent decision support, the module provides operation and maintenance personnel with a comprehensive view of the health status of the equipment and helps to formulate reasonable maintenance plans and resource scheduling plans. This module not only improves the efficiency of equipment monitoring, but also helps operation and maintenance personnel quickly identify potential problems and deal with them in a timely manner, reducing downtime and improving the operational reliability of equipment.

[0172] Working principle: When the system of the present invention is running, first, the data acquisition module starts working. The electric field strength, partial discharge signal and temperature and humidity data in the equipment are collected in real time through sensors. These data reflect the real-time status of the equipment and can help us quickly identify possible problems. These data will not stay at the acquisition end, but will be immediately transmitted to the core of the system through the data transmission unit, which is convenient for subsequent processing and analysis.

[0173] Next, the data enters the multi-physics field coupling modeling module. Through electric field simulation and temperature field simulation, we can accurately understand the electric field distribution and temperature changes inside the device. The spatial distribution of electric field strength can tell us which places may be subjected to excessive voltage, and temperature changes reveal which areas may overheat. In particular, the generation of partial discharge is a direct signal of equipment aging. Here, the simulation of the temperature field takes into account the workload and partial discharge effects, making the simulation of temperature distribution more accurate.

[0174] Then, the dielectric loss and partial discharge linkage analysis module comes into play. The correlation between partial discharge and dielectric loss factor reveals changes in the insulation performance of the equipment. By calculating the dielectric loss factor through frequency response, we can see the energy loss of the material and then determine the degree of aging of the equipment. More importantly, this module can also analyze the relationship between changes in partial discharge signals and material loss, providing more intuitive feedback on the health status of the equipment.

[0175] Next, the inverse calculation and intelligent reasoning module begins to conduct in-depth analysis of these real-time data. Through the back-propagation algorithm, the system converts real-time electric field data, temperature and humidity, and partial discharge signals into the estimated results of the equipment insulation status. Through this calculation, we can evaluate whether the equipment is in a dangerous state or whether there is a risk of potential failure. Furthermore, the system uses intelligent algorithms such as convolutional neural networks (CNN) or long short-term memory networks (LSTM) to infer the historical data of the equipment, predict possible failures, and provide specific health assessment reports.

[0176] Finally, the data display and decision support module integrates all the analysis results and displays them visually. Through charts and graphs, operation and maintenance personnel can quickly understand the health status of the equipment and take action. For example, the electric field strength and partial discharge signal can be presented through a thermal map, the temperature data can be displayed through a line graph, and the changes in the health status of the equipment can also be clearly presented in the chart. If the equipment is in a healthy state, the display will be green; if there is a problem with the equipment, the system will promptly remind, warn and generate maintenance suggestions.

[0177] The entire system process is not simply divided into separate modules, but these modules work closely together. After the data is collected from the sensor and transmitted, different modules conduct in-depth analysis. The output of each module provides support for the next step of reasoning and decision-making. Ultimately, the decision support module helps operation and maintenance personnel make correct maintenance decisions to ensure that the equipment is always in the best working condition.

[0178] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An online monitoring system for insulation status of electrical equipment in a high-voltage substation, characterized in that: include: A data acquisition module, used to collect electric field strength, partial discharge signal, temperature and humidity data in real time and transmit them to each module through a transmission unit; A multi-physics field coupling modeling module, used to calculate the electric field distribution and temperature field distribution inside the device through electric field simulation and temperature field simulation according to the electric field strength and temperature and humidity data; A dielectric loss and partial discharge linkage analysis module, used to perform linkage analysis based on the electric field strength, partial discharge signal and temperature and humidity data to evaluate the insulation performance of the equipment; The inverse calculation and intelligent reasoning module is used to evaluate the insulation status of the equipment through the inverse calculation method based on the physical model and real-time collected data, and to predict faults using the intelligent reasoning algorithm; The data display and decision support module is used to visualize data based on the evaluation results of each module and generate a health assessment report to provide decision support for operation and maintenance personnel.

2. The online monitoring system for insulation status of electrical equipment in a high-voltage substation according to claim 1 is characterized in that: The data acquisition module comprises: Partial discharge signal acquisition unit, used to monitor partial discharge signals in the equipment in real time; Temperature and humidity sensor unit, used to collect temperature and humidity data of the equipment environment in real time; An electric field strength sensor unit is used to collect electric field strength data inside the device in real time; The data transmission unit is used to transmit the collected data to the central processing system.

3. The online monitoring system for insulation status of electrical equipment in a high-voltage substation according to claim 1 is characterized in that: The multi-physics coupling modeling module includes: The electric field simulation unit is used to calculate the electric field distribution inside the device through Maxwell equations and finite element method to obtain the spatial distribution data of the electric field intensity; Temperature field simulation unit, used to simulate the changes in the temperature field inside the equipment, using the heat conduction equation to calculate the temperature distribution, taking into account the partial discharge thermal effect and workload; Dielectric loss modeling unit, used to calculate the dielectric loss factor of insulating materials, which represents the energy loss and aging of the material; The electric field-temperature field coupling unit is used to couple and analyze the influence of the electric field and the temperature field on the insulation material of the equipment, and comprehensively obtain the interaction results between the electric field and the temperature field; Physical field coupling data fusion unit, used to fuse the data of electric field, temperature field and dielectric loss to generate comprehensive evaluation data of equipment insulation status for use by subsequent partial discharge analysis and intelligent reasoning modules; A local hot spot identification unit is used to automatically identify possible local overheating areas inside the equipment by coupling and analyzing electric field and temperature field data, and generate corresponding alarm signals; The electrical equipment damage assessment unit is used to assess the extent of equipment damage based on coupled analysis of electric field and temperature field combined with historical fault data, and provide corresponding repair or replacement recommendations.

4. The online monitoring system for insulation status of electrical equipment in a high-voltage substation according to claim 1 is characterized in that: The dielectric loss and partial discharge linkage analysis module includes: a dielectric loss factor calculation unit, for calculating a dielectric loss factor based on the frequency response, wherein the dielectric loss factor represents the ability of an insulating material to convert electrical energy into thermal energy; The partial discharge and dielectric loss correlation analysis unit is used to analyze the relationship between the frequency, amplitude and dielectric loss factor of the partial discharge signal to evaluate the insulation performance of the equipment.

5. The online monitoring system for insulation status of electrical equipment in a high-voltage substation according to claim 4 is characterized in that: The dielectric loss factor Calculated by the following formula: ; in, is the real part of the dielectric constant, is the imaginary part of the dielectric constant, is the dielectric loss factor.

6. The online monitoring system for insulation status of electrical equipment in a high-voltage substation according to claim 1 is characterized in that: The inversion calculation and intelligent reasoning module includes: Inversion calculation unit, used to infer the insulation status of the equipment through the back propagation algorithm based on the electric field, temperature and humidity data and partial discharge signals; Intelligent reasoning and decision-making unit, which uses convolutional neural networks or long short-term memory networks to perform intelligent reasoning on the collected data and generate equipment health assessment reports; The fault warning unit is used to generate fault warnings based on the reasoning results and make maintenance suggestions.

7. The online monitoring system for insulation status of electrical equipment in a high-voltage substation according to claim 1 is characterized in that: The data display and decision support module includes: Data visualization unit, used to visualize data such as electric field, temperature, partial discharge signal, etc., and generate a graphical representation of the equipment health status; A health assessment report generation unit, used to generate a health assessment report of the device based on multi-physics field coupling modeling and intelligent reasoning results; Decision support unit, used to provide equipment maintenance priorities, repair recommendations and resource scheduling solutions.

8. The online monitoring system for insulation status of electrical equipment in a high-voltage substation according to claim 3 is characterized in that: The electric field simulation unit in the multi-physics field coupling modeling module calculates the electric field intensity distribution inside the device through the following formula: ; in, is the electric displacement vector, is the charge density; The electric field strength With electric displacement The relationship is: ; in, is the dielectric constant, is the electric field strength vector.

9. The online monitoring system for insulation status of electrical equipment in a high-voltage substation according to claim 6, characterized in that: The inversion calculation unit in the inversion calculation and intelligent reasoning module calculates the insulation state of the equipment through the back propagation algorithm based on the following mathematical model: ; in, is the critical voltage for partial discharge to occur, is the electric field strength, and A constant related to the insulation material of the equipment.

10. The online monitoring system for insulation status of electrical equipment in a high-voltage substation according to claim 3, characterized in that: The electric field-temperature field coupling unit in the multi-physics field coupling modeling module couples the interaction between the electric field and the temperature field through the following calculation formula: ; in, is the thermal conductivity of the temperature field, is the temperature gradient, is the heat source term per unit volume, indicating heat generation caused by partial discharge, etc.

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