An online monitoring system for insulation status of electrical equipment in high-voltage substations

Abstract

The present invention relates to the field of electrical equipment monitoring technology, and discloses an online insulation status monitoring system for high-voltage substation electrical equipment, comprising: a data acquisition module for collecting electric field strength, partial discharge signals, and temperature and humidity data in real time and transmitting them to various modules via a transmission unit; a multi-physics field coupling modeling module for calculating the electric field and temperature field distribution within the equipment through electric field simulation and temperature field simulation based on the electric field strength and temperature and humidity data; and a dielectric loss and partial discharge linkage analysis module. By combining multi-physics field coupling modeling with intelligent reasoning technology, a comprehensive analysis of electric field strength, partial discharge signals, and temperature and humidity data is performed, enabling accurate equipment insulation status assessment and fault prediction. Furthermore, the use of electric field-temperature field coupling analysis technology, combined with partial discharge and thermal effects, enables accurate identification of overheating areas and insulation problems within the equipment.

Description

Technical Field

[0001] The present 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 condition of electrical equipment is a crucial factor in ensuring proper operation and preventing failures. As equipment ages and the external environment changes, insulation performance gradually deteriorates, leading to equipment failure or even downtime. In severe cases, this can even cause power system accidents. Therefore, monitoring the insulation condition of high-voltage substation electrical equipment is crucial for ensuring stable power system operation. Currently, traditional methods for insulation condition monitoring rely primarily on manual inspections, periodic testing, and single monitoring technologies. These methods often fail to reflect the equipment's operating status in real time and exhibit certain limitations.

[0003] Most existing technical solutions focus on monitoring 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 within the equipment, can affect the state of the insulating material. However, existing technologies often use partial discharge signals as the sole basis for health assessment, ignoring the interaction of these factors, resulting in the monitoring system being unable to accurately capture early potential problems with 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 field and partial discharge signals, missing the opportunity to warn of early faults.

[0004] Furthermore, existing temperature and humidity monitoring technologies have limitations. Temperature and humidity have a direct impact on the insulation performance of equipment, particularly in high-temperature, high-humidity environments, where insulation materials are susceptible to damage. However, traditional monitoring methods typically analyze temperature and humidity changes in isolation, overlooking their coupling 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. This prevents the monitoring system from promptly identifying hotspots within the equipment, delaying fault detection.

[0005] Existing fault warning systems often rely on a single data source or fixed algorithm to assess equipment health. This approach often fails to provide sufficiently accurate fault predictions in environments 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 overly simplistic judgments about equipment status and an inability to accurately reflect the equipment's multi-dimensional operating conditions. Traditional fault warning systems lack the ability to comprehensively process multi-dimensional, cross-domain data on equipment insulation status, resulting in an inability to provide timely and accurate fault predictions.

[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 response to the shortcomings of the existing technology, the present invention provides an online insulation status monitoring system for high-voltage substation electrical equipment, which solves the problem that the existing insulation status monitoring of high-voltage substation electrical equipment 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] 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 the transmission unit;

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

[0011] A dielectric loss and partial discharge linkage analysis module is 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 inverse calculation methods based on physical models and real-time collected data, and to predict faults using intelligent reasoning algorithms;

[0013] The data display and decision support module is used to visualize data based on the evaluation results of each module and generate health assessment reports 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] Electric field strength sensor unit, 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's equations and the 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 effects of the electric field and temperature field on the insulation material of the equipment, and comprehensively obtain the interaction results of the electric field and temperature field;

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

[0025] The local hotspot identification unit is used to automatically identify possible local overheating areas inside the equipment through coupled analysis of electric field and temperature field data, and generate corresponding alarm signals;

[0026] The electrical equipment damage assessment unit is used to evaluate the extent of equipment damage based on coupled analysis of electric and temperature fields, 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, configured to calculate a dielectric loss factor based on the frequency response, wherein the dielectric loss factor represents an 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 and amplitude of the partial discharge signal and the dielectric loss factor 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 calculate 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 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, and partial discharge signals, and generate a graphical representation of the equipment health status;

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

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

[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 and 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, representing 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-physics 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, partial discharge signals, and temperature and humidity data. It achieves 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 detect potential problems in advance.

[0055] 2. The present invention uses electric field-temperature field coupling analysis, combined with partial discharge and thermal effects to identify hotspots inside the equipment and generate a real-time health assessment report for the equipment. This solution realizes the coupling analysis and data fusion of electric field strength, temperature field and partial discharge signals, achieving the effect of more accurately locating equipment overheating and insulation problems. Compared with 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, resulting in the inability to effectively identify local hotspots. BRIEF DESCRIPTION OF THE DRAWINGS

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

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

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

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

[0060] Figure 5 This 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 clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the present specification. 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 making creative efforts are within the scope of protection of the present invention.

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

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

[0065] The data acquisition module includes:

[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] Electric field strength sensor unit, 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 for an online insulation condition monitoring system for high-voltage substation electrical equipment. This module is primarily responsible for collecting real-time data on electric field strength, partial discharge signals, and temperature and humidity during equipment operation. This data is then transmitted to a central processing system via a transmission unit for subsequent processing, analysis, and evaluation. This module is a fundamental component of the entire monitoring system, providing important input data for subsequent modules such as multi-physics field coupling modeling and partial discharge and dielectric loss linkage analysis.

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

[0072] Typically, a partial discharge signal acquisition unit monitors the partial discharge signal within the equipment in real time, acquiring key data such as the amplitude, frequency, and duration of the partial discharge. This data is crucial for evaluating the equipment's insulation performance and diagnosing faults. Partial discharge signals are typically acquired using high-frequency current sensors or electromagnetic wave sensors, which effectively capture the high-frequency signals during the discharge process and avoid interference from conventional electric fields. In some embodiments, the acquisition unit can remove high-frequency noise signals through a filter and enhance signal strength through an amplifier circuit. This process ensures a high signal-to-noise ratio for the acquired partial discharge signal, providing a reliable data source for subsequent analysis.

[0073] As an option, a temperature and humidity sensor unit is used to monitor the temperature and humidity data of the equipment's environment in real time, which is crucial for evaluating the equipment's insulation performance. Ambient temperature and humidity directly affect the performance of the insulation materials of electrical equipment. Especially in high-voltage environments, changes in temperature and humidity can cause changes in the dielectric constant, which in turn affects partial discharge and the rate of equipment aging. Specifically, the temperature and humidity sensor unit can use common digital temperature and humidity sensors such as DHT11 and DHT22, which 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 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 and changes in electric field strength inside the equipment are often closely related to phenomena such as partial discharge and dielectric loss. Generally, the electric field strength sensor uses 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 changes in the electric field 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 a central processing system. To ensure real-time transmission and reliable data transmission, the data transmission unit generally utilizes wireless communication technologies (such as Wi-Fi, ZigBee, LoRa, etc.) or wired communication technologies (such as Ethernet, RS485, etc.). Specifically, the data transmission unit uses a suitable communication protocol to package partial discharge signals, temperature and humidity data, and electric field strength data into data packets and transmits them to the central processing system via a communication network. Compression and encryption technologies can be used during transmission to improve data transmission efficiency and security.

[0076] In some embodiments, the partial discharge signal acquisition unit may be a high-frequency current sensor or an electromagnetic wave sensor, and the acquired partial discharge signal may be processed using 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 the partial discharge can be obtained. These parameters play an important role in subsequent partial discharge analysis and equipment insulation performance evaluation.

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

[0080] Please see the 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 within the online insulation condition monitoring system for high-voltage substation electrical equipment primarily assesses the equipment's insulation performance and potential damage by comprehensively analyzing the interactions among physical fields such as the electric field, temperature field, dielectric loss, and partial discharge. This module, through the collaborative operation of multiple subunits, provides data support for subsequent partial discharge analysis, intelligent reasoning, and decision support modules. By coupling the electric and temperature fields, it accurately simulates the internal temperature and electric field distributions of the equipment, as well as their combined impact on insulation performance.

[0082] In this embodiment, the electric field simulation unit uses Maxwell's equations and the finite element method to numerically calculate the electric field inside the device, obtaining spatial distribution data of the electric field intensity. This process can help determine the electric field distribution inside the electrical device and thus predict possible partial discharge areas in the device.

[0083] Specifically, the electric field simulation unit performs calculations based on 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 ), which 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 the electric field to pass through the medium. is the electric field strength vector (unit: V / m), which describes 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 using the finite element method (FEM) to obtain the electric field distribution map 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. This unit takes into account the thermal effects caused by partial discharge and the load changes during device operation, and can generate a temperature distribution map to predict the temperature change trend of the device.

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

[0092] ;

[0093] in, is the temperature (unit: °C), which indicates the temperature inside the device. is the thermal diffusivity (unit: m 2 / s), describes the material's ability to conduct heat, is the heat source term (unit: W / m 3 ), which represents the heat generated by partial discharge and equipment workload, and is derived 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, causing the temperature inside the equipment to rise, and further affecting the performance of the insulation material.

[0095] The dielectric loss modeling unit evaluates the dielectric properties of the device's insulation materials. It reflects changes in the device's insulation performance by calculating the material's dielectric loss factor. The dielectric loss factor is a key indicator of device insulation performance. As the device ages, the dielectric loss factor increases, leading to a decrease in insulation performance.

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

[0097] ;

[0098] in, is the dielectric loss factor (dimensionless), which reflects 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 dissipation capacity of the material.

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

[0100] The Electric Field-Temperature Field Coupling Unit couples the electric field strength with the temperature field to analyze changes in the temperature field, evaluating the combined effects of the electric field and temperature on the insulation material of a device. The interaction between the electric and temperature fields is a key factor affecting the insulation state of a device. An increase in the electric field causes a rise in temperature, which in turn affects the dielectric constant of the material, accelerating device aging.

[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 and temperature fields inside the device. It can simulate the impact of electric and temperature on insulating materials, providing key data for subsequent equipment health assessment and fault prediction.

[0105] The physical field coupling data fusion unit integrates simulation data from the electric field, temperature field, and dielectric loss to generate comprehensive assessment data for the device's insulation condition. This combined analysis of multiple physical fields enables a comprehensive assessment of the device's insulation condition, providing accurate input data for the partial discharge analysis and intelligent reasoning modules.

[0106] The local hotspot identification unit automatically identifies potential localized overheating areas within the equipment through coupled analysis of electric and temperature fields. These hotspots often accelerate the aging of insulation materials and increase the risk of equipment failure. By promptly identifying and generating an alarm signal, maintenance personnel can take proactive measures to prevent equipment failure.

[0107] The electrical equipment damage assessment unit assesses the extent of equipment damage based on historical fault data by combining changes in the electric and temperature fields. Over long-term operation, changes in the electric and temperature fields can cause insulation materials to gradually age. Assessing the extent of this damage helps develop appropriate maintenance plans and extend equipment life.

[0108] The multi-physics coupling modeling module in this embodiment combines coupled analysis of multiple physical fields, including electric field, temperature field, and dielectric loss, to comprehensively analyze factors such as the electric field distribution, temperature changes, and dielectric loss within the device through accurate formulas and calculation methods. By integrating multi-physics field data, the insulation status of the device can be comprehensively assessed, providing an accurate basis for device health management and fault prediction. This module effectively improves the accuracy of device monitoring and can provide strong data support for subsequent partial discharge analysis, intelligent reasoning, and decision support.

[0109] Please see 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, configured to calculate a dielectric loss factor based on the frequency response, wherein the dielectric loss factor represents an 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 and amplitude of the partial discharge signal and the dielectric loss factor 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 see the attached Figure 5 ,Inverse calculation and intelligent reasoning module, which is used to evaluate the insulation status of the equipment through inverse calculation method based on physical models and real-time collected data, and use intelligent reasoning algorithm to predict faults;

[0117] The inverse calculation and intelligent reasoning module in this embodiment plays a crucial role in the online insulation condition monitoring system for high-voltage substation electrical equipment. By combining physical models with real-time data, this module uses inverse calculation methods to assess the insulation condition of equipment and employs intelligent reasoning algorithms to predict potential equipment failures. Based on various operational data, the inverse calculation and intelligent reasoning module can estimate the equipment's health and issue timely fault warnings, ensuring stable operation.

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

[0119] The inverse calculation unit uses a backpropagation algorithm to infer the insulation status of the equipment based on a physical model and real-time data. The application of the backpropagation algorithm in a neural network effectively adjusts the model's parameters, reducing the error between the model's predictions and actual results, thereby optimizing equipment health assessments.

[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 condition of electrical equipment. Changes in the critical voltage value are crucial for evaluating the insulation performance of the equipment. The electric field strength (V / m) indicates the electric field strength inside the device. The electric field strength directly affects the polarization of the insulating material and partial discharge. Excessive electric field strength increases the risk of partial discharge in the device. and are constants that are usually related to the physical characteristics of the device, such as its geometry and material properties. and Calibration will be performed based on different equipment and environmental factors.

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

[0124] The intelligent reasoning and decision-making unit uses deep learning algorithms (such as convolutional neural networks (CNNs) or long short-term memory (LSTMs)) to perform intelligent reasoning on collected data on electric field strength, temperature and humidity, and partial discharge signals. Through in-depth analysis of this data, the unit can identify trends in the equipment's insulation status, promptly predict potential failures, and provide appropriate maintenance decisions.

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

[0126] ;

[0127] in: The device health assessment result (dimensionless), that is, the current health status of the device. The assessment result can be "healthy", "minor fault", or "requires maintenance", etc. The input feature vector contains monitoring data such as electric field strength, frequency and amplitude of partial discharge signals, temperature and humidity. After preprocessing, these data serve 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 prediction accuracy.

[0128] Specifically, a convolutional neural network (CNN) is used to extract time-frequency features from partial discharge signals to capture the spatial and temporal patterns of the electric field and discharge signals. A long short-term memory (LSTM) network processes time series data and analyzes time-dependent device states, such as temperature and humidity changes and electric field fluctuations. Using deep learning algorithms, the intelligent reasoning and decision-making unit accurately infers the insulation status and health of the 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] Based on the inference results of the intelligent reasoning and decision-making unit, the fault warning unit generates a fault warning signal and provides relevant maintenance recommendations. When the equipment health assessment value falls below a set threshold, the system issues a fault warning signal. The fault warning unit typically relies on key indicators such as the equipment health assessment value, changes in partial discharge signals, and temperature and humidity fluctuations to monitor the equipment's operating status in real time, ensuring timely alerts when equipment problems occur.

[0133] The fault warning unit generates warning signals based on the following criteria:

[0134] Health Assessment Value Threshold: When the device health assessment value falls below the preset threshold, the system automatically generates a fault warning. For example, when the health assessment value falls below 0.5, the device may be at risk of failure.

[0135] Partial discharge signal changes: When the amplitude of the local 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 temperature and humidity change dramatically, the performance of the insulation material may deteriorate. The system will provide 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, the data acquisition module acquires real-time electric field strength, partial discharge signals, and temperature and humidity data, and then performs data preprocessing. Preprocessing steps include denoising and normalization to ensure data quality and consistency.

[0139] Inverse Calculation: The inverse calculation unit uses a backpropagation algorithm to optimize model parameters and continuously adjust network weights to accurately infer the insulation status of the equipment. The backpropagation training process continuously minimizes the error between the equipment health assessment results and the actual observations, thereby improving the accuracy of the inverse calculation.

[0140] Intelligent Reasoning and Decision-Making: The intelligent reasoning and decision-making unit uses convolutional neural networks (CNNs) or long short-term memory networks (LSTMs) to perform intelligent reasoning on collected data, generating equipment health assessment reports. This intelligent reasoning identifies potential faults based on historical and real-time data and provides proactive maintenance recommendations.

[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 repair or replacement.

[0142] The inverse calculation and intelligent reasoning module in this embodiment, based on physical models and deep learning algorithms, enables efficient and accurate equipment health assessment and fault prediction. By combining backpropagation algorithms with deep learning networks, 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 approach to fault prediction and health assessment provides a powerful technical foundation for the intelligent operation and maintenance of electrical equipment in high-voltage substations.

[0143] Please see the attached Figure 6,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.

[0144] The data display and decision support module in this embodiment is a key component of the online insulation condition monitoring system for high-voltage substation electrical equipment. It aims to visualize the evaluation results of each module and generate health assessment reports, providing comprehensive and scientific decision support for operations and maintenance personnel. This module provides intuitive display of equipment health status, fault warnings, maintenance recommendations, and resource scheduling plans, helping operators optimize equipment management, reduce downtime, and improve equipment reliability.

[0145] The Data Display and Decision Support Module consists of three sub-units: Data Visualization, Health Assessment Report Generation, and Decision Support. Each unit complements the others, collaboratively generating comprehensive reports and providing maintenance priorities and decision-making 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 excessively high electric field intensity and temperature are identified as hot spots, helping to identify potential equipment failure points.

[0149] Line charts: These charts display fluctuations in temperature and humidity, electric field strength, and partial discharge signals during equipment operation. These trends help operators monitor equipment health and adjust operating conditions in a timely manner.

[0150] Partial discharge signal spectrum: Displays the frequency, amplitude, and changing trend of the partial discharge signal inside the equipment. Changes in partial discharge signal strength are directly related to the health of the equipment's insulation materials.

[0151] Specifically, the data visualization unit integrates multiple data sources (such as electric field, temperature and humidity, and partial discharge) on a single interface. By displaying different parameters in various formats, such as charts, graphs, bar charts, real-time data strips, and dashboards, operators can clearly view the health of the equipment.

[0152] Furthermore, the data visualization unit can display historical trends and compare real-time data for various monitoring data, helping operators identify potential equipment risks. For example, indicators such as excessive temperature and abnormal partial discharge signals can be highlighted in the visualization chart through color or shape changes, allowing for prompt maintenance measures.

[0153] The health assessment report generation unit automatically generates a device health assessment report based on the analysis results of the multi-physics coupling modeling and intelligent reasoning module. This report details the device's health status, the impact of various key data, and provides repair and maintenance recommendations based on device health trends.

[0154] A health assessment report typically includes the following:

[0155] Equipment Health Overview: Provides a brief overview of the current health status of the equipment and provides a rating (such as "Healthy," "Minor Fault," "Repair Required," etc.) based on the equipment's health assessment score. This rating is based on a comprehensive analysis using multi-physics coupled 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 equipment operation. This data reflects the equipment's operating environment and status, and can intuitively demonstrate potential equipment failure risks.

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

[0158] Fault prediction and repair recommendations: Based on multiphysics simulation results, partial discharge analysis, and intelligent reasoning, the report provides equipment repair recommendations. For example, if the partial discharge signal is abnormal, the report may recommend inspecting 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 every operation and maintenance personnel can obtain the health status of the equipment and relevant recommendations in a timely manner.

[0160] The decision support unit intelligently provides maintenance priorities, repair recommendations, and resource scheduling solutions based on equipment health assessments, fault warnings, and operating environment data. Based on the actual equipment operating conditions and historical data, the unit automatically generates targeted operational decisions, helping operators efficiently schedule repairs and resources, reducing equipment downtime.

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

[0162] Equipment Maintenance Priority: Intelligently assigns equipment maintenance priorities by analyzing data such as equipment health, partial discharge intensity, electric field strength, and temperature and humidity fluctuations. For example, when a device's health assessment falls below a set threshold, its maintenance priority automatically increases, prompting operators to conduct an inspection or shut down the device for maintenance as soon as possible.

[0163] Maintenance recommendations: Combining health assessment reports and fault warning information, the decision support unit provides specific maintenance recommendations for the equipment. For example, if an abnormally high electric field strength is detected, the system may recommend checking the equipment's insulation for damage.

[0164] Resource Scheduling: The decision support unit automatically dispatches maintenance personnel, tools, and spare parts based on equipment maintenance priorities and available resources, optimizing the equipment maintenance process. The unit also rationally schedules maintenance windows and allocates human resources based on equipment maintenance needs, ensuring efficient maintenance.

[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 operating 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, and partial discharge signals from the data acquisition module, and performs preprocessing operations such as data cleaning, denoising, and standardization to ensure data quality.

[0168] Data visualization: A graphical interface displays key parameters such as temperature, partial discharge, and fault warnings. Operations and maintenance personnel can use the visualization interface to view the health status of equipment and identify potential fault points.

[0169] Generate health assessment reports: Based on the assessment results of each module, a detailed health assessment report is automatically generated, providing specific repair recommendations to operators. Health assessment reports are output using standardized templates, making it easy for operators to quickly obtain and understand device 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-making support platform for the online insulation condition monitoring system of high-voltage substation electrical equipment. Through data visualization, health assessment report generation, and intelligent decision support, the module provides operators with a comprehensive view of equipment health and helps formulate appropriate maintenance plans and resource scheduling. This module not only improves equipment monitoring efficiency but also helps operators quickly identify potential issues and address them promptly, reducing downtime and improving equipment reliability.

[0172] Working Principle: When the system is running, the data acquisition module begins operation. Sensors collect real-time data on the device's electric field strength, partial discharge signals, and temperature and humidity. This data reflects the device's real-time status and helps quickly identify potential problems. This data does not remain at the acquisition end but is immediately transmitted to the system's core via the data transmission unit, facilitating subsequent processing and analysis.

[0173] Next, the data enters the multiphysics coupling modeling module. Through electric and temperature field simulations, we can accurately understand the electric field distribution and temperature changes within the device. The spatial distribution of electric field intensity can indicate where excessive voltage may be present, while temperature changes reveal areas that may be overheating. Partial discharge, in particular, is a direct indicator of device aging. Here, the temperature field simulation takes into account the workload and partial discharge effects, resulting in a more accurate simulation of the temperature distribution.

[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 device's insulation performance. By calculating the dielectric loss factor based on frequency response, we can see the energy loss of the material and, in turn, determine the degree of device aging. 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 of the device.

[0175] Next, the inverse calculation and intelligent reasoning module begins in-depth analysis of this real-time data. Using a backpropagation algorithm, the system converts real-time electric field data, temperature and humidity, and partial discharge signals into an estimated result of the equipment's insulation condition. This calculation allows us to assess whether the equipment is in a dangerous state or at risk of potential failure. Furthermore, the system utilizes intelligent algorithms such as convolutional neural networks (CNNs) or long short-term memory networks (LSTMs) to reason about historical equipment data, predict potential failures, and provide a detailed health assessment report.

[0176] Finally, the Data Display and Decision Support module integrates and visualizes all analysis results. Through charts and graphs, operations and maintenance personnel can quickly understand the health status of the equipment and take action. For example, electric field strength and partial discharge signals can be displayed as heat maps, temperature data can be displayed as line graphs, and changes in equipment health status can also be clearly displayed in charts. 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 notify, issue an early warning, and generate maintenance recommendations.

[0177] The entire system's workflow isn't simply divided into separate modules; instead, these modules collaborate closely. After data is collected and transmitted from sensors, it undergoes in-depth analysis by different modules. The output of each module supports further reasoning and decision-making. Ultimately, the decision-support module helps operators make informed maintenance decisions, ensuring equipment remains in optimal working condition.

[0178] While 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 these embodiments without departing from the principles and spirit of the invention, and that the scope of the 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: 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 the transmission unit; A multi-physics field coupling modeling module is used to calculate the electric field distribution and temperature field distribution inside the device through electric field simulation and temperature field simulation based on the electric field strength and temperature and humidity data; A dielectric loss and partial discharge linkage analysis module is 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 inverse calculation methods based on physical models and real-time collected data, and to predict faults using intelligent reasoning algorithms; The data display and decision support module is used to visualize data based on the evaluation results of each module and generate health assessment reports 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, characterized in that: The data acquisition module includes: 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; Electric field strength sensor unit, 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, 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's equations and the 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 effects of the electric field and temperature field on the insulation material of the equipment, and comprehensively obtain the interaction results of the electric field and temperature field; The physical field coupling data fusion unit is used to fuse the data of electric field, temperature field and dielectric loss to generate comprehensive evaluation data of the equipment insulation status for use in subsequent partial discharge analysis and intelligent reasoning modules; The local hotspot identification unit is used to automatically identify possible local overheating areas inside the equipment through coupled analysis of electric field and temperature field data, and generate corresponding alarm signals; The electrical equipment damage assessment unit is used to evaluate the extent of equipment damage based on coupled analysis of electric and temperature fields, 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, characterized in that: The dielectric loss and partial discharge linkage analysis module includes: a dielectric loss factor calculation unit, configured to calculate a dielectric loss factor based on the frequency response, wherein the dielectric loss factor represents an 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 and amplitude of the partial discharge signal and the dielectric loss factor 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, 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, characterized in that: The inversion calculation and intelligent reasoning module includes: Inversion calculation unit, used to calculate 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 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, characterized in that: The data display and decision support module includes: Data visualization unit, used to visualize electric field, temperature, and partial discharge signal data to generate a graphical representation of the equipment health status; A health assessment report generation unit is used to generate a health assessment report for the device based on the multi-physics field coupling modeling and intelligent reasoning results; Decision support unit, used to provide equipment maintenance priorities, repair recommendations and resource scheduling plans.

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 using the following formula: ; in, is the electric displacement vector, is the charge density; The electric field strength and 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 inverse calculation unit in the inverse calculation and intelligent reasoning module calculates the insulation status 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, representing the heat generation caused by partial discharge.

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