Capacitor voltage transformer insulation fault diagnosis and analysis method and system

Abstract

The present invention discloses a method and system for diagnosing and analyzing insulation faults of capacitor voltage transformers, and relates to the technical field of electrical equipment diagnosis. The system obtains key electrical data of capacitor voltage transformers in real time, and transmits these data to a data processing module for preprocessing and feature extraction to obtain an insulation health feature set. The data is stored in a database, and then the insulation health feature set is used to calculate the dielectric loss factor influencing factor Jsh, the dielectric constant influencing factor Jdc, the electromagnetic radiation influencing factor Dem, the electrical noise influencing factor Ned, and the discharge influencing factor Pdd, and the multi-dimensional health index HI is generated by summarizing and calculating, so as to provide a basis for fault diagnosis and early warning of the system. The evaluation and early warning module evaluates according to the preset first fault threshold G1 and the second fault threshold G2, and triggers the corresponding early warning mechanism, completing the complete task chain from data acquisition to fault early warning.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical equipment diagnosis, and in particular to a method and system for diagnosing and analyzing insulation faults of capacitive voltage transformers. Background Art

[0002] The capacitor voltage transformer insulation fault diagnosis and analysis system belongs to the field of power system equipment health monitoring and diagnosis. The voltage transformer in the power system is an important power measurement device. The capacitor voltage transformer has been widely used in high-voltage and ultra-high-voltage transmission networks due to its simple structure, small size, and low cost. The system detects the insulation status of the capacitor voltage transformer and promptly identifies potential fault hazards to ensure the stable operation of the power system.

[0003] In the Chinese invention application with application publication number CN114113734A, a method and device for detecting the insulation charge of a capacitor voltage transformer is disclosed, wherein the method is to collect the current at the end of the C2 end of the capacitor voltage transformer. Current of primary winding of excitation transformer Then, the amplitude and phase of the high-voltage capacitor C1 current are calculated, and a method for judging the insulation state of a single transformer and a method for comparing the insulation states of multiple transformers are proposed. By comparing the relative changes in the currents of various components, the insulation states of various components of the capacitive voltage transformer are accurately obtained. The present invention satisfactorily solves the problem of insensitivity of traditional capacitive voltage transformer live detection technology, and lays a foundation for early detection of defects in capacitive voltage transformers.

[0004] In combination with the prior art, the above application still has the following deficiencies:

[0005] The insulation material of the capacitor voltage transformer will gradually age due to long-term exposure to environmental factors such as electric fields, temperature, and humidity, resulting in a decrease in its insulation performance. If insulation faults are not detected and handled in a timely manner, they may cause equipment failures or even power system interruptions. However, traditional insulation fault diagnosis methods usually rely on a single indicator or simple testing methods, which cannot fully reflect the actual insulation health status of the capacitor voltage transformer. These traditional methods are insufficient in detection sensitivity and accuracy, and often cannot provide effective warnings in the early stages of faults. Summary of the invention

[0006] In view of the deficiencies in the prior art, the present invention provides a method and system for diagnosing and analyzing insulation faults of a capacitive voltage transformer, which solves the problems mentioned in the background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: including an electrical data acquisition module, a data processing module, an insulation health feature analysis module, a multi-dimensional health index analysis module and an evaluation and early warning module;

[0008] The electrical data acquisition module collects real-time electrical data from the capacitive voltage transformer and sends the collected electrical data to the data processing module;

[0009] The data processing module is used to receive electrical data in real time for preprocessing, and to extract features from the preprocessed electrical data and electrical appliance data to obtain an insulation health feature set, and to store the obtained insulation health feature set in a database;

[0010] The insulation health feature analysis module is used to extract the insulation health feature set in the database in real time, and calculate and obtain the dielectric loss factor influence factor Jsh, the dielectric constant influence factor Jdc, the electromagnetic radiation influence factor Dem, the electrical noise influence factor Ned and the discharge influence factor Pdd respectively;

[0011] The multidimensional health index analysis module is used to extract the dielectric loss factor influence factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd obtained in the insulation health feature analysis module, perform correlation calculations, and obtain the multidimensional health index HI;

[0012] The evaluation and early warning module is used to preset the first fault threshold G1 and the second fault threshold G2 and the multi-dimensional health index HI, perform comparative evaluation, and trigger an early warning mechanism according to the evaluation results.

[0013] Preferably, the electrical data acquisition module includes an acquisition unit and a transmission unit;

[0014] The acquisition unit acquires electrical data of the capacitive voltage transformer in real time by using electrical acquisition equipment, wherein the electrical acquisition equipment includes a dielectric loss tester, an LCR meter, a temperature sensor, a humidity sensor, an electromagnetic field probe, a spectrum analyzer, a current probe, a voltage probe, an ultrasonic sensor, an electromagnetic wave sensor, and a discharge analyzer;

[0015] The electrical data package includes loss factor tanδ, capacitance C, temperature T in the environmental chamber, radiation intensity Em, electrical noise N and discharge intensity PD;

[0016] The loss factor tanδ is obtained by connecting a test probe or an electrode to a capacitive voltage transformer, applying a test voltage using a dielectric loss tester, and measuring under high frequency conditions;

[0017] The capacitance C and the temperature T in the environmental chamber are collected and obtained by an LCR meter and a temperature sensor;

[0018] The radiation intensity Em is collected in real time by arranging electromagnetic field probes around the capacitive voltage transformer;

[0019] The electrical noise N is obtained by connecting a current probe and a voltage probe to the input and output ends of the capacitive voltage transformer and using a spectrum analyzer to capture the noise signal;

[0020] The discharge intensity PD is obtained by installing an ultrasonic sensor on the housing and inside of the capacitive voltage transformer, connecting an electromagnetic wave sensor to the output cable of the device to capture the discharge signal, and then processing the signal with a discharge analyzer to obtain the discharge intensity PD;

[0021] The transmission unit is used to encapsulate the acquired electrical data, obtain an electrical data encapsulation package, add a header and perform data link layer processing on the electrical data encapsulation package, and then transmit the electrical data encapsulation package to the data processing module through TCP and UDP protocols.

[0022] Preferably, the data processing module includes a data receiving unit, a feature extraction unit and a data storage unit;

[0023] The data receiving unit is used to receive the transmitted electrical data encapsulation packet in real time, remove the packet header of the electrical data encapsulation packet, and send the electrical data to the feature extraction unit;

[0024] The feature extraction unit is used to extract features from electrical data and summarize them to obtain an insulation health feature set;

[0025] The insulation health feature set includes a loss feature set, a dielectric feature set, an electromagnetic radiation feature set, an electrical noise feature set and a discharge feature set;

[0026] The loss feature set includes the loss factor peak tan δ peak and loss factor harmonic component tanδ n , by using the LCR meter to perform a frequency scan on the loss factor tanδ, measure its response at multiple frequency points, generate spectrum data, perform a fast Fourier transform on the time domain data obtained by the frequency scan, and extract the loss factor peak tanδ peak and loss factor harmonic component tanδ n ;

[0027] The dielectric feature set includes the dielectric constant ε r , dielectric constant change rate Δε r And the temperature change rate △T in the environmental chamber; the specific extraction method is to obtain the dielectric constant by calculating the obtained capacitance C d is the distance between the capacitor plates, A is the plate area, ε0 is the vacuum dielectric constant, and the dielectric constant ε is calculated. r Extract the dielectric constant change rate Δε with the temperature change rate r and the temperature change rate △T in the environmental chamber;

[0028] The electromagnetic radiation feature set includes a radiation intensity peak value Em peak And the radiation spectrum density S(f), by using a spectrum analyzer to detect electromagnetic radiation, generate a spectrum diagram of electromagnetic radiation, and extract the radiation intensity peak Em in the spectrum diagram peak and radiation spectral density S(f);

[0029] The electrical noise feature set includes the electrical noise spectrum density N(f) and the electrical noise peak value N peak , by performing spectrum analysis on the electrical noise N, extracting the electrical noise spectrum density N(f) and the electrical noise peak value N peak ;

[0030] The discharge feature set includes a discharge intensity cumulative value PD cum and discharge intensity peak PD max By using a discharge detector to capture the discharge activity, record the pulse frequency and energy of each discharge, and extract the discharge intensity peak PD max Then process the discharge data, count the pulse frequency and pulse energy, and extract the electric intensity cumulative value PD cum ;

[0031] The storage unit is used to construct a database and store the extracted insulation health feature set in the database.

[0032] Preferably, the insulation health characteristic analysis module includes a dielectric loss analysis unit, a dielectric influence analysis unit, an electromagnetic radiation analysis unit, an electrical noise analysis unit and a discharge influence analysis unit;

[0033] The dielectric loss analysis unit calculates the dielectric loss factor influencing factor Jsh of the capacitor voltage transformer by extracting the loss feature set in the insulation health feature set of the database;

[0034] The dielectric loss factor influencing factor Jsh is calculated and obtained by the following algorithm formula;

[0035]

[0036] Where, tanδ nom It represents the standard loss factor. The specific value is set by the user. log represents the logarithmic function. N represents the total number of frequency points of the dielectric loss signal. Hn represents the harmonic component function. n represents the nth frequency point.

[0037] Preferably, the dielectric influence analysis unit calculates the dielectric constant influence factor Jdc of the capacitive voltage transformer by extracting the dielectric feature set from the insulation health feature set in the database;

[0038] The dielectric constant influencing factor Jdc is calculated and obtained by the following algorithm formula;

[0039]

[0040] In the formula, Tmax represents the peak temperature in the environmental chamber, Tmin represents the valley temperature in the environmental chamber, and ε r，nom Represents the standard dielectric constant. The specific value is set by the user. The significance of the formula is that the dielectric constant ε is taken into account. r The changes at different temperatures and the impact of such changes on the insulation properties of the material can be more accurately evaluated through nonlinear analysis of the temperature change rate. λ represents the temperature-dependent nonlinear influence function.

[0041] Preferably, the electromagnetic radiation analysis unit calculates the electromagnetic radiation influence factor Dem of the capacitor voltage transformer by extracting the electromagnetic radiation feature set from the insulation health feature set in the database;

[0042] The electromagnetic radiation impact factor Dem is calculated and obtained by the following algorithm formula;

[0043]

[0044] In the formula, Em nom represents the standard radiation intensity, f1 represents the lower limit of the electromagnetic radiation frequency, f2 represents the upper limit of the electromagnetic radiation frequency, df represents the integral variable of the electromagnetic radiation frequency, Represents the integral of the radiation spectrum.

[0045] Preferably, the electrical noise analysis unit calculates the electrical noise impact factor Ned of the capacitor voltage transformer by extracting the electrical noise feature set from the insulation health feature set of the database;

[0046] The electrical noise impact factor Ned is calculated and obtained by the following algorithm formula;

[0047]

[0048] Where N nom represents the standard noise level, g represents the weighting function, which is used to weight the noise peak, and f low represents the lower limit of the noise frequency, f high represents the integral upper limit of the noise frequency, and dz represents the integral variable of the noise frequency.

[0049] Preferably, the discharge impact analysis unit calculates the discharge impact factor Pdd of the capacitor voltage transformer by extracting the discharge feature set from the insulation health feature set in the database;

[0050] The discharge influence factor Pdd is calculated and obtained by the following algorithm formula:

[0051]

[0052] In the formula, PD nom Indicates standard discharge intensity, PD th represents the discharge intensity threshold, k represents the adjustment factor, and exp represents the exponential function.

[0053] Preferably, the multidimensional health index analysis module is used to perform multidimensional data fusion calculation on the obtained dielectric loss factor influence factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd to obtain a multidimensional health index HI, and to perform a comprehensive analysis on the insulation health status of the capacitive voltage transformer;

[0054] The multidimensional health index HI is calculated and obtained by the following algorithm formula;

[0055] HI=[(a1·Jsh)+(a2·Jdc)+(a3·Dem)+(a4·Ned)+(a5·Pdd)]+A;

[0056] Wherein, a1, a2, a3, a4 and a5 respectively represent the preset weight values ​​of dielectric loss factor influence factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd, and the specific values ​​are set by the user, and a1+a2+a3+a4+a5=1, A represents the correction constant;

[0057] The evaluation and early warning module is used to preset a first fault threshold G1 and a second fault threshold G2 based on the normal state of the capacitor voltage transformer insulation, and compare and evaluate the obtained multi-dimensional health index HI to diagnose the capacitor voltage transformer insulation fault and generate an early warning mechanism;

[0058] When the multi-dimensional health index HI ≥ the first fault threshold G1, the insulation health of the current capacitor voltage transformer is diagnosed, and monitoring continues without generating early warning information;

[0059] When the second fault threshold G2 is less than the multi-dimensional health index HI and less than the first fault threshold G1, it is diagnosed that the insulation of the current capacitor voltage transformer is in an abnormal state, and a second-level warning message is generated to prompt maintenance;

[0060] When the multi-dimensional health index HI ≤ the first fault threshold G2, the insulation of the current capacitor voltage transformer is diagnosed to be in an abnormal state, a first-level warning message is generated, and a command is sent to control the transformer to shut down;

[0061] Level 1 warning information > Level 2 warning information.

[0062] The insulation fault diagnosis and analysis method of a capacitor voltage transformer comprises the following steps:

[0063] S1, first collects real-time electrical data from the capacitor voltage transformer and sends the collected electrical data to the data processing module;

[0064] S2, receiving electrical data in real time for preprocessing, extracting features from the preprocessed electrical data and electrical appliance data, obtaining an insulation health feature set, and storing the obtained insulation health feature set in a database;

[0065] S3, extract the insulation health feature set in the database in real time, and calculate and obtain the dielectric loss factor influence factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd respectively;

[0066] S4, performing correlation calculations based on the obtained dielectric loss factor influence factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd to obtain a multidimensional health index HI;

[0067] S5. Finally, the first fault threshold G1 and the second fault threshold G2 are preset and compared with the multi-dimensional health index HI, and an early warning mechanism is triggered according to the evaluation results.

[0068] The present invention provides a method and system for diagnosing and analyzing insulation faults of capacitor voltage transformers. It has the following beneficial effects:

[0069] (1) The system achieves an accurate assessment of the insulation health status of the capacitive voltage transformer by calculating the multi-dimensional health index HI. By combining the dielectric loss factor influencing factor Jsh, the dielectric constant influencing factor Jdc, the electromagnetic radiation influencing factor Dem, the electrical noise influencing factor Ned, and the discharge influencing factor Pdd, the system can comprehensively and meticulously analyze the insulation status, taking into account the complexity of various environmental and operating conditions. Compared with the traditional single-indicator monitoring method, this multi-dimensional fusion evaluation method can more accurately reflect the actual working status of the equipment, thereby improving the reliability and accuracy of the diagnosis.

[0070] (2) The system is equipped with an evaluation and early warning module, which can diagnose the insulation fault status of the capacitive voltage transformer in real time by comparing it with the preset first fault threshold G1 and second fault threshold G2, and trigger the corresponding early warning mechanism. The mechanism includes two levels of early warning: when the HI value reaches the first-level early warning threshold, the system will immediately generate a first-level early warning message and control the transformer to shut down to prevent further damage to the equipment or the spread of the fault; when the HI value reaches the second-level early warning threshold, the system will generate a second-level early warning message to prompt maintenance personnel to inform the maintenance personnel that there is a potential risk to the equipment. Through this real-time early warning system, equipment damage and downtime caused by insulation failure can be greatly reduced, thereby improving the safety and stability of equipment operation.

[0071] (3) The system constructs an intelligent analysis module by extracting features from the collected electrical data and integrating different types of features into the database. This module can calculate various influencing factors based on different feature sets and algorithm formulas, and generate a comprehensive health index through multidimensional data fusion technology. This technology not only improves the intelligence level of insulation fault diagnosis, but also enhances the system's ability to identify and analyze complex fault types. Compared with traditional single-dimensional analysis methods, this multidimensional data fusion technology can better cope with complex scenarios in practical applications, making fault diagnosis more accurate and effective. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 It is a schematic diagram of the process flow of the insulation fault diagnosis and analysis system of the capacitor voltage transformer of the present invention;

[0073] Figure 2 It is a schematic diagram of the steps of the method for diagnosing and analyzing insulation faults of a capacitor voltage transformer of the present invention. DETAILED DESCRIPTION

[0074] The following will be combined with the drawings in the embodiments 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.

[0075] Example 1

[0076] See also Figure 1 , the present invention provides a capacitive voltage transformer insulation fault diagnosis and analysis system. To achieve the above purpose, the present invention is implemented through the following technical solutions: including an electrical data acquisition module, a data processing module, an insulation health feature analysis module, a multi-dimensional health index analysis module and an evaluation and early warning module;

[0077] The electrical data acquisition module collects real-time electrical data from the capacitor voltage transformer and sends the collected electrical data to the data processing module;

[0078] The data processing module is used to receive electrical data in real time for preprocessing, and to extract features from the preprocessed electrical data and electrical appliance data to obtain an insulation health feature set, and to store the obtained insulation health feature set in a database;

[0079] The insulation health feature analysis module is used to extract the insulation health feature set in the database in real time, and calculate and obtain the dielectric loss factor influence factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd respectively;

[0080] The multidimensional health index analysis module is used to extract the dielectric loss factor influence factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd obtained in the insulation health feature analysis module, perform correlation calculations, and obtain the multidimensional health index HI;

[0081] The evaluation and early warning module is used to preset the first fault threshold G1 and the second fault threshold G2 and the multi-dimensional health index HI, perform comparative evaluation, and trigger the early warning mechanism according to the evaluation results.

[0082] In this embodiment, the system realizes comprehensive monitoring and analysis of insulation faults of capacitor voltage transformers through the collaborative work of five modules. The electrical data acquisition module can obtain the key electrical data of the capacitor voltage transformer in real time, and transmit these data to the data processing module for preprocessing and feature extraction. The data processing module further stores the extracted insulation health features in the database to form a complete feature set. The insulation health feature analysis module uses these feature data to calculate the dielectric loss factor influence factor Jsh, the dielectric constant influence factor Jdc, the electromagnetic radiation influence factor Dem, the electrical noise influence factor Ned and the discharge influence factor Pdd, providing an in-depth analysis of the insulation state. Finally, the multidimensional health index analysis module generates a multidimensional health index HI by comprehensively calculating these influencing factors, providing a basis for the fault diagnosis and early warning of the system. The evaluation and early warning module evaluates the multidimensional health index HI according to the preset first fault threshold G1 and the second fault threshold G2, and triggers the corresponding early warning mechanism, completing the complete task chain from data acquisition to fault early warning. Through this multi-dimensional and multi-level fault diagnosis system, the system significantly improves the insulation health management level of the capacitor voltage transformer. Its improved technical means not only improve the accuracy and timeliness of diagnosis, but also enhance the system's ability to identify and respond to complex faults, thereby reducing equipment downtime and maintenance costs. Ultimately, the system ensures the safe operation of capacitor voltage transformers through comprehensive monitoring, in-depth analysis and effective early warning mechanisms, improves the overall stability and reliability of the power system, and achieves the ultimate goal of intelligent management of equipment operating status.

[0083] Example 2

[0084] This embodiment is explained in Example 1, please refer to Figure 1 ,Specifically: the electrical data acquisition module includes an acquisition unit and a transmission unit;

[0085] The collection unit collects electrical data of the capacitive voltage transformer in real time by using electrical collection equipment, which includes a dielectric loss tester, an LCR meter, a temperature sensor, a humidity sensor, an electromagnetic field probe, a spectrum analyzer, a current probe, a voltage probe, an ultrasonic sensor, an electromagnetic wave sensor, and a discharge analyzer;

[0086] The electrical data package includes loss factor tanδ, capacitance C, temperature in the environmental chamber T, radiation intensity Em, electrical noise N and discharge intensity PD;

[0087] The loss factor tanδ is obtained by connecting a test probe or electrode to a capacitive voltage transformer, applying a test voltage using a dielectric loss tester, and measuring under high frequency conditions;

[0088] The capacitance C and the temperature T in the environmental chamber are acquired through the LCR meter and the temperature sensor;

[0089] The radiation intensity Em is collected in real time by arranging electromagnetic field probes around the capacitive voltage transformer;

[0090] The electrical noise N is obtained by connecting a current probe and a voltage probe to the input and output of the CVT and using a spectrum analyzer to capture the noise signal.

[0091] The discharge intensity PD is obtained by installing ultrasonic sensors on the shell and inside of the capacitive voltage transformer, connecting the electromagnetic wave sensor to the output cable of the equipment to capture the discharge signal, and then using the discharge analyzer to process the signal to obtain the discharge intensity PD;

[0092] The transmission unit is used to encapsulate the acquired electrical data, obtain an electrical data encapsulation package, add a header and perform data link layer processing on the electrical data encapsulation package, and then transmit the electrical data encapsulation package to the data processing module through TCP and UDP protocols.

[0093] In this embodiment, the system integrates a variety of electrical acquisition devices through the electrical data acquisition module, and realizes comprehensive real-time acquisition of the electrical data of the capacitive voltage transformer. Compared with the single parameter acquisition method, the multi-sensor fusion technology of this module makes the data acquisition coverage wider and higher, ensuring a comprehensive grasp of the equipment operation status. The design of the transmission unit uses TCP and UDP protocols to achieve efficient and reliable data transmission, further enhancing the real-time performance and stability of the system. Through this improvement, the system has achieved significant improvements in the integrity and response speed of equipment status monitoring, ultimately improving the fault prediction and prevention capabilities of the capacitive voltage transformer, and ensuring the safety and reliability of equipment operation.

[0094] Example 3

[0095] This embodiment is explained in Example 2. Please refer to Figure 1 ,Specifically: the data processing module includes a data receiving unit, a feature extraction unit and a data storage unit;

[0096] The data receiving unit is used to receive the transmitted electrical data encapsulation package in real time, remove the header of the electrical data encapsulation package, and send the electrical data to the feature extraction unit;

[0097] The feature extraction unit is used to extract features from electrical data and summarize them to obtain an insulation health feature set;

[0098] The insulation health feature set includes the loss feature set, dielectric feature set, electromagnetic radiation feature set, electrical noise feature set and discharge feature set;

[0099] The loss feature set includes the loss factor peak tanδ peak and loss factor harmonic component tanδ n , by using the LCR meter to perform a frequency scan on the loss factor tanδ, measure its response at multiple frequency points, generate spectrum data, perform a fast Fourier transform on the time domain data obtained by the frequency scan, and extract the loss factor peak tanδ peak and loss factor harmonic component tanδ n ;

[0100] The dielectric feature set includes the dielectric constant ε r , dielectric constant change rate Δε r And the temperature change rate △T in the environmental chamber; the specific extraction method is to obtain the dielectric constant by calculating the obtained capacitance C d is the distance between the capacitor plates, A is the plate area, ε0 is the vacuum dielectric constant, and the dielectric constant ε is calculated. r Extract the dielectric constant change rate Δε with the temperature change rate r and the temperature change rate △T in the environmental chamber;

[0101] The electromagnetic radiation feature set includes the radiation intensity peak Em peak And the radiation spectrum density S(f), by using a spectrum analyzer to detect electromagnetic radiation, generate a spectrum diagram of electromagnetic radiation, and extract the radiation intensity peak Em in the spectrum diagram peak and radiation spectral density S(f);

[0102] The electrical noise signature set includes the electrical noise spectral density N(f) and the electrical noise peak value N peak , by performing spectrum analysis on the electrical noise N, extracting the electrical noise spectrum density N(f) and the electrical noise peak value N peak ;

[0103] The discharge feature set includes the discharge intensity cumulative value PD cum and discharge intensity peak PD max By using a discharge detector to capture the discharge activity, record the pulse frequency and energy of each discharge, and extract the discharge intensity peak PD max Then process the discharge data, count the pulse frequency and pulse energy, and extract the electric intensity cumulative value PD cum ;

[0104] The storage unit is used to construct a database and store the extracted insulation health feature set in the database.

[0105] In this embodiment, the data processing module of the system realizes efficient processing and in-depth analysis of electrical data through the organic combination of its data receiving unit, feature extraction unit and data storage unit. The data receiving unit ensures the smooth transmission of real-time data, and the feature extraction unit extracts the key health characteristics of the capacitive voltage transformer from multiple dimensions through advanced spectrum analysis and fast Fourier transform technology, and constructs a loss feature set, a dielectric feature set, an electromagnetic radiation feature set, an electrical noise feature set and a discharge feature set. Compared with a single or limited data processing method, this module provides a more comprehensive and accurate feature extraction capability, and systematically saves these features in the database through a storage unit, providing a solid data foundation for subsequent health status assessment and fault diagnosis. Ultimately, these improvements effectively improve the diagnostic accuracy and reliability of the system, and can detect potential faults earlier and more accurately, thereby significantly enhancing the safety and long-term effectiveness of equipment operation.

[0106] Example 4

[0107] This embodiment is explained in Example 3, please refer to Figure 1 ,Specifically: the insulation health feature analysis module includes a dielectric loss analysis unit, a dielectric ,impact analysis unit, an electromagnetic radiation analysis unit, an electrical noise analysis unit and a discharge ,impact analysis unit;

[0108] The dielectric loss analysis unit calculates the dielectric loss factor influencing factor Jsh of the capacitor voltage transformer by extracting the loss feature set from the insulation health feature set of the database;

[0109] The dielectric loss factor influencing factor Jsh is calculated by the following algorithm formula;

[0110]

[0111] Where, tanδ nom It represents the standard loss factor. The specific value is set by the user. log represents the logarithmic function. N represents the total number of frequency points of the dielectric loss signal. Hn represents the harmonic component function. n represents the nth frequency point.

[0112] The dielectric influence analysis unit calculates the dielectric constant influence factor Jdc of the capacitive voltage transformer by extracting the dielectric feature set from the insulation health feature set of the database;

[0113] The dielectric constant influence factor Jdc is calculated by the following algorithm formula;

[0114]

[0115] In the formula, Tmax represents the peak temperature in the environmental chamber, Tmin represents the valley temperature in the environmental chamber, and ε r，nomRepresents the standard dielectric constant. The specific value is set by the user. The significance of the formula is that the dielectric constant ε is taken into account. r The changes at different temperatures and the impact of such changes on the insulation properties of the material can be more accurately evaluated through nonlinear analysis of the temperature change rate. λ represents the temperature-dependent nonlinear influence function.

[0116] The electromagnetic radiation analysis unit calculates the electromagnetic radiation influence factor Dem of the capacitor voltage transformer by extracting the electromagnetic radiation feature set from the insulation health feature set of the database;

[0117] The electromagnetic radiation impact factor Dem is calculated by the following algorithm formula;

[0118]

[0119] In the formula, Em nom represents the standard radiation intensity, f1 represents the lower limit of the electromagnetic radiation frequency, f2 represents the upper limit of the electromagnetic radiation frequency, df represents the integral variable of the electromagnetic radiation frequency, Represents the integral of the radiation spectrum.

[0120] The electrical noise analysis unit calculates the electrical noise impact factor Ned of the capacitor voltage transformer by extracting the electrical noise feature set from the insulation health feature set of the database;

[0121] The electrical noise impact factor Ned is calculated using the following algorithm formula:

[0122]

[0123] Where N nom represents the standard noise level, g represents the weighting function, which is used to weight the noise peak, and f low represents the lower limit of the noise frequency, f high represents the integral upper limit of the noise frequency, and dz represents the integral variable of the noise frequency.

[0124] The discharge impact analysis unit calculates the discharge impact factor Pdd of the capacitor voltage transformer by extracting the discharge feature set from the insulation health feature set in the database;

[0125] The discharge influence factor Pdd is calculated by the following algorithm formula;

[0126]

[0127] In the formula, PD nom Indicates standard discharge intensity, PD th represents the discharge intensity threshold, k represents the adjustment factor, and exp represents the exponential function.

[0128] In this embodiment, the insulation health characteristic analysis module of the system conducts a comprehensive and detailed evaluation of the insulation state of the capacitive voltage transformer through a multi-dimensional analysis unit. The dielectric loss analysis unit, dielectric influence analysis unit, electromagnetic radiation analysis unit, electrical noise analysis unit and discharge influence analysis unit respectively use algorithm formulas to quantify various influencing factors such as dielectric loss factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd. These analyses not only achieve in-depth analysis of various insulation characteristics, but also improve the accuracy and reliability of the evaluation through comprehensive consideration of different physical quantities. This module comprehensively evaluates the various factors that affect insulation performance through complex mathematical modeling, thereby providing a more detailed basis for predicting potential fault risks.

[0129] Example 5

[0130] This embodiment is explained in Example 1, please refer to Figure 1 Specifically: the multidimensional health index analysis module is used to obtain the dielectric loss factor influence factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd, perform multidimensional data fusion calculation to obtain the multidimensional health index HI, and conduct a comprehensive analysis of the insulation health status of the capacitive voltage transformer;

[0131] The multidimensional health index HI is calculated by the following algorithm formula;

[0132] HI=[(a1·Jsh)+(a2·Jdc)+(a3·Dem)+(a4·Ned)+(a5·Pdd)]+A;

[0133] Wherein, a1, a2, a3, a4 and a5 respectively represent the preset weight values ​​of dielectric loss factor influence factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd, and the specific values ​​are set by the user, and a1+a2+a3+a4+a5=1, A represents the correction constant;

[0134] The evaluation and early warning module is used to preset a first fault threshold value G1 and a second fault threshold value G2 based on the normal state of the insulation of the capacitor voltage transformer, and compare and evaluate the obtained multi-dimensional health index HI to diagnose the insulation fault of the capacitor voltage transformer and generate an early warning mechanism;

[0135] When the multi-dimensional health index HI ≥ the first fault threshold G1, the insulation health of the current capacitor voltage transformer is diagnosed, and monitoring continues without generating early warning information;

[0136] When the second fault threshold G2 is less than the multi-dimensional health index HI and less than the first fault threshold G1, it is diagnosed that the insulation of the current capacitor voltage transformer is in an abnormal state, and a second-level warning message is generated to prompt maintenance;

[0137] When the multi-dimensional health index HI ≤ the first fault threshold G2, the insulation of the current capacitor voltage transformer is diagnosed to be in an abnormal state, a first-level warning message is generated, and a command is sent to control the transformer to shut down;

[0138] Level 1 warning information > Level 2 warning information.

[0139] In this embodiment, the introduction of the system's multidimensional health index analysis module and evaluation and early warning module makes the insulation health status monitoring of the capacitive voltage transformer more accurate and intelligent. Through multidimensional data fusion calculation, the module can integrate the dielectric loss factor influencing factor Jsh, dielectric constant influencing factor Jdc, electromagnetic radiation influencing factor Dem, electrical noise influencing factor Ned and discharge influencing factor Pdd to generate a unified multidimensional health index HI, which provides a scientific basis for the comprehensive evaluation of the insulation condition of the transformer. The evaluation and early warning module realizes real-time diagnosis and early warning classification of transformer insulation faults by presetting fault thresholds, ensuring timely response when abnormalities occur to avoid potential risks. Compared with the single parameter monitoring method, this combined module not only improves the accuracy and sensitivity of monitoring, but also greatly reduces the complexity of equipment maintenance and the risk of sudden failures through an automated early warning mechanism, significantly improving the overall operational safety and reliability of the system.

[0140] Example 6

[0141] See also Figure 1 and Figure 2 , a capacitive voltage transformer insulation fault diagnosis and analysis method comprises the following steps:

[0142] S1, first collects real-time electrical data from the capacitor voltage transformer and sends the collected electrical data to the data processing module;

[0143] S2, receiving electrical data in real time for preprocessing, extracting features from the preprocessed electrical data and electrical appliance data, obtaining an insulation health feature set, and storing the obtained insulation health feature set in a database;

[0144] S3, extract the insulation health feature set in the database in real time, and calculate and obtain the dielectric loss factor influence factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd respectively;

[0145] S4, performing correlation calculations based on the obtained dielectric loss factor influence factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd to obtain a multidimensional health index HI;

[0146] S5. Finally, the first fault threshold G1 and the second fault threshold G2 are preset and compared with the multi-dimensional health index HI, and an early warning mechanism is triggered according to the evaluation results.

[0147] While the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that many changes, modifications, substitutions and variations can be made to the embodiments without departing from the principles and spirit of the invention.

Claims

1. Capacitive voltage transformer insulation fault diagnosis and analysis system, characterized by: It includes electrical data acquisition module, data processing module, insulation health characteristic analysis module, multi-dimensional health index analysis module and evaluation and early warning module; The electrical data acquisition module collects real-time electrical data from the capacitive voltage transformer and sends the collected electrical data to the data processing module; The data processing module is used to receive electrical data in real time for preprocessing, and to extract features from the preprocessed electrical data and electrical appliance data to obtain an insulation health feature set, and to store the obtained insulation health feature set in a database; The insulation health feature analysis module is used to extract the insulation health feature set in the database in real time, and calculate and obtain the dielectric loss factor influence factor Jsh, the dielectric constant influence factor Jdc, the electromagnetic radiation influence factor Dem, the electrical noise influence factor Ned and the discharge influence factor Pdd respectively; The dielectric loss factor influencing factor Jsh is calculated and obtained by the following algorithm formula; ； In the formula, represents the peak value of the loss factor, It represents the standard loss factor. The specific value is set by the user. log represents the logarithmic function. N represents the total number of frequency points of the dielectric loss signal. Hn represents the harmonic component function. n represents the nth frequency point. Indicates the harmonic component of loss factor; The dielectric constant influencing factor Jdc is calculated and obtained by the following algorithm formula; ； In the formula, represents the dielectric constant, Tmax represents the peak temperature in the environmental chamber, and Tmin represents the valley temperature in the environmental chamber. Indicates the standard dielectric constant. The specific value is set by the user. represents the temperature-dependent nonlinear influence function, △T represents the temperature change rate in the environmental chamber, , represents the rate of change of dielectric constant; The electromagnetic radiation impact factor Dem is calculated and obtained by the following algorithm formula; ； In the formula, Em peak Indicates the peak radiation intensity, Em nom represents the standard radiation intensity, f1 represents the lower limit of the electromagnetic radiation frequency, f2 represents the upper limit of the electromagnetic radiation frequency, df represents the integral variable of the electromagnetic radiation frequency, S(f) represents the radiation spectrum density, represents the integral of the radiation spectrum; The electrical noise impact factor Ned is calculated and obtained by the following algorithm formula; ； Where N(f) represents the electrical noise spectrum density, N nom Indicates the standard noise level, N peak represents the peak value of electrical noise, g represents the weighting function, and f low represents the lower limit of the noise frequency, f high represents the integral upper limit of the noise frequency, and dz represents the integral variable of the noise frequency; The discharge influence factor Pdd is calculated and obtained by the following algorithm formula: ； Where, PD cum Indicates the accumulated value of electric strength, PD nom Indicates standard discharge intensity, PD max Indicates the peak value of electric intensity, PD th represents the discharge intensity threshold, k represents the adjustment factor, and exp represents the exponential function; The multidimensional health index analysis module is used to extract the dielectric loss factor influence factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd obtained in the insulation health feature analysis module, perform correlation calculations, and obtain the multidimensional health index HI; The multidimensional health index HI is calculated and obtained by the following algorithm formula; ； Wherein, a1, a2, a3, a4 and a5 respectively represent the preset weight values ​​of dielectric loss factor influence factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd, and the specific values ​​are set by the user, and a1+a2+a3+a4+a5=1, and A represents the correction constant; The evaluation and early warning module is used to preset the first fault threshold G1 and the second fault threshold G2 and the multi-dimensional health index HI, perform comparative evaluation, and trigger an early warning mechanism according to the evaluation results.

2. The capacitive voltage transformer insulation fault diagnosis and analysis system according to claim 1, characterized in that: The electrical data acquisition module includes an acquisition unit and a transmission unit; The acquisition unit acquires electrical data of the capacitive voltage transformer in real time by using electrical acquisition equipment, wherein the electrical acquisition equipment includes a dielectric loss tester, an LCR meter, a temperature sensor, a humidity sensor, an electromagnetic field probe, a spectrum analyzer, a current probe, a voltage probe, an ultrasonic sensor, an electromagnetic wave sensor, and a discharge analyzer; The electrical data package includes the loss factor , capacitance C, temperature in the environmental chamber T, radiation intensity Em, electrical noise N and discharge intensity PD; The transmission unit is used to encapsulate the acquired electrical data, obtain an electrical data encapsulation package, add a header and perform data link layer processing on the electrical data encapsulation package, and then transmit the electrical data encapsulation package to the data processing module through TCP and UDP protocols.

3. The capacitive voltage transformer insulation fault diagnosis and analysis system according to claim 2, characterized in that: The data processing module includes a data receiving unit, a feature extraction unit and a data storage unit; The data receiving unit is used to receive the transmitted electrical data encapsulation packet in real time, remove the packet header of the electrical data encapsulation packet, and send the electrical data to the feature extraction unit; The feature extraction unit is used to extract features from electrical data and summarize them to obtain an insulation health feature set; The insulation health feature set includes a loss feature set, a dielectric feature set, an electromagnetic radiation feature set, an electrical noise feature set and a discharge feature set; The loss characteristic set includes a loss factor peak and loss factor harmonic components , by using an LCR meter to measure the loss factor Perform frequency scanning, measure its response at multiple frequency points, generate spectrum data, perform fast Fourier transform on the time domain data obtained by frequency scanning, and extract the loss factor peak value and loss factor harmonic components ; The dielectric feature set includes a dielectric constant , Dielectric constant change rate And the temperature change rate △T in the environmental chamber; the specific extraction method is to obtain the dielectric constant by calculating the obtained capacitance C , d is the distance between the capacitor plates, A is the plate area, is the dielectric constant of vacuum, and calculates the dielectric constant Rate of change with temperature , extract the rate of change of dielectric constant and the rate of change of temperature in the environmental chamber △T; The electromagnetic radiation feature set includes a radiation intensity peak value Em peak The spectrum analyzer is used to detect electromagnetic radiation and generate a spectrum diagram of electromagnetic radiation. The peak value Em in the spectrum diagram is extracted. peak and radiation spectral density S(f); The electrical noise feature set includes the electrical noise spectral density N(f) and the electrical noise peak value N peak , by performing spectrum analysis on the electrical noise N, extracting the electrical noise spectrum density N (f) and the electrical noise peak value N peak ; The discharge feature set includes a discharge intensity cumulative value PD cum and discharge intensity peak PD max By using a discharge detector to capture the discharge activity, record the pulse frequency and energy of each discharge, and extract the discharge intensity peak PD max Then process the discharge data, count the pulse frequency and pulse energy, and extract the electric intensity cumulative value PD cum ; The storage unit is used to construct a database and store the extracted insulation health feature set in the database.

4. The capacitive voltage transformer insulation fault diagnosis and analysis system according to claim 3, characterized in that: The insulation health feature analysis module includes a dielectric loss analysis unit, a dielectric influence analysis unit, an electromagnetic radiation analysis unit, an electrical noise analysis unit and a discharge influence analysis unit; The dielectric loss analysis unit calculates the dielectric loss factor influence factor Jsh of the capacitive voltage transformer by extracting the loss feature set from the insulation health feature set in the database.

5. The capacitive voltage transformer insulation fault diagnosis and analysis system according to claim 4, characterized in that: The dielectric influence analysis unit calculates the dielectric constant influence factor Jdc of the capacitive voltage transformer by extracting the dielectric feature set from the insulation health feature set in the database.

6. The capacitive voltage transformer insulation fault diagnosis and analysis system according to claim 4, characterized in that: The electromagnetic radiation analysis unit calculates the electromagnetic radiation influence factor Dem of the capacitive voltage transformer by extracting the electromagnetic radiation feature set from the insulation health feature set in the database.

7. The capacitive voltage transformer insulation fault diagnosis and analysis system according to claim 4, characterized in that: The electrical noise analysis unit calculates the electrical noise impact factor Ned of the capacitive voltage transformer by extracting the electrical noise feature set from the insulation health feature set in the database.

8. The capacitive voltage transformer insulation fault diagnosis and analysis system according to claim 4, characterized in that: The discharge impact analysis unit calculates the discharge impact factor Pdd of the capacitor voltage transformer by extracting the discharge feature set from the insulation health feature set in the database.

9. The capacitive voltage transformer insulation fault diagnosis and analysis system according to claim 1, characterized in that: The multidimensional health index analysis module is used to perform multidimensional data fusion calculation on the obtained dielectric loss factor influence factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd to obtain a multidimensional health index HI, and to comprehensively analyze the insulation health status of the capacitive voltage transformer; The evaluation and early warning module is used to preset a first fault threshold G1 and a second fault threshold G2 based on the normal state of the capacitor voltage transformer insulation, and compare and evaluate the obtained multi-dimensional health index HI to diagnose the capacitor voltage transformer insulation fault and generate an early warning mechanism; When the multi-dimensional health index HI ≥ the first fault threshold G1, the insulation health of the current capacitor voltage transformer is diagnosed, and monitoring continues without generating early warning information; When the second fault threshold G2 is less than the multi-dimensional health index HI and less than the first fault threshold G1, it is diagnosed that the insulation of the current capacitor voltage transformer is in an abnormal state, and a second-level warning message is generated to prompt maintenance; When the multi-dimensional health index HI ≤ the first fault threshold G2, the insulation of the current capacitor voltage transformer is diagnosed to be in an abnormal state, a first-level warning message is generated, and a command is sent to control the transformer to shut down; Level 1 warning information > Level 2 warning information.

10. A capacitor voltage transformer insulation fault diagnosis and analysis method, applied to a capacitor voltage transformer insulation fault diagnosis and analysis system according to any one of claims 1 to 9, characterized in that: The following steps are involved: S1, first collects real-time electrical data from the capacitor voltage transformer and sends the collected electrical data to the data processing module; S2, receiving electrical data in real time for preprocessing, extracting features from the preprocessed electrical data and electrical appliance data, obtaining an insulation health feature set, and storing the obtained insulation health feature set in a database; S3, extract the insulation health feature set in the database in real time, and calculate and obtain the dielectric loss factor influence factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd respectively; S4, performing correlation calculations based on the obtained dielectric loss factor influence factor Jsh, dielectric constant influence factor Jdc, electromagnetic radiation influence factor Dem, electrical noise influence factor Ned and discharge influence factor Pdd to obtain a multidimensional health index HI; S5. Finally, the first fault threshold G1 and the second fault threshold G2 are preset and compared with the multi-dimensional health index HI, and an early warning mechanism is triggered according to the evaluation results.

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