Integrated sensing and monitoring method for transformer partial discharge ultrasonic waves

Abstract

The invention discloses an integrated sensing and monitoring method for transformer partial discharge ultrasonic waves, and belongs to the technical field of transformer detection. In order to solve the problem of inaccurate positioning result caused by limitation of a single sensor in partial discharge positioning, a classification model is established, a characteristic mode in a discharge signal is automatically learned and identified, the discharge intensity is quantified by calculating the energy integral of the signal in a specific time window, the intensity of discharge is objectively reflected, and the positioning accuracy is improved. The level of the discharge intensity is judged by comparing the average amplitude of the signals with the preset threshold value, positioning is performed by using the time difference of the signals received by the plurality of sensors, the limitation of a single sensor on positioning is overcome, the discharge position can be quickly positioned, the maintenance time and cost are reduced, and the working efficiency is improved. A plurality of functional modules such as discharge type identification, intensity evaluation and position positioning are integrated, comprehensive monitoring of partial discharge of the transformer is realized, the operation process is simplified, and the overall performance and stability of the monitoring system are improved.

Description

Technical Field

[0001] The invention relates to the technical field of transformer detection, and in particular to an integrated sensing monitoring method for partial discharge ultrasonic waves of a transformer. Background Art

[0002] Partial discharge in transformers can create tiny channels in the insulating material. Over time, these channels may expand, causing the insulation performance to deteriorate and may eventually lead to insulation breakdown. Partial discharge will produce gas and carbon particles, which will contaminate the transformer oil, reducing its insulation performance and heat dissipation capacity. Partial discharge may develop into an arc fault, which will cause the internal temperature of the transformer to rise sharply, thereby damaging the transformer. Partial discharge will cause additional energy loss, reduce the efficiency of the transformer, and cause transformer failure, which will affect the reliability and stability of the entire power system.

[0003] Therefore, it is necessary to monitor the partial discharge of multiple transformers, but there are still the following problems in actual operation:

[0004] Existing technology relies on manual judgment and experience accumulation to identify discharge types and evaluate discharge intensity. This method is easily affected by subjective factors and has difficulty processing complex and changeable discharge signals. It may lack unified and objective indicators when quantifying discharge intensity, resulting in inconsistent evaluation results. At the same time, a single sensor often has limitations when locating local discharges, such as interference from environmental factors, signal attenuation, etc., resulting in inaccurate positioning results. Summary of the invention

[0005] The object of the present invention is to provide an integrated ultrasonic sensing monitoring method for partial discharge of transformers to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: an integrated ultrasonic sensing monitoring method for partial discharge of transformers, comprising the following steps:

[0007] Sensor selection: select an ultrasonic sensor with a frequency band of 50-200kHz to capture the ultrasonic signal generated by partial discharge inside the transformer, and select an optical fiber sensor as an auxiliary sensor;

[0008] Sensor layout: formulate a sensor layout plan based on the structure and size of the transformer, arrange ultrasonic sensors at key locations on the transformer casing, fully cover possible discharge areas inside the transformer, and install them on site;

[0009] Signal processing: receiving ultrasonic signals from sensors through signal processing units, converting them into electrical signals for amplification and filtering, analyzing the electrical signals and extracting characteristic signals of partial discharge, analyzing and identifying the characteristic signals, and determining the type, intensity and location of partial discharge;

[0010] Remote monitoring: The processed data is transmitted to the remote monitoring center by wireless means. The remote monitoring center monitors and analyzes the partial discharge of the transformer in real time, and displays the real-time data, historical curves and alarm information of partial discharge;

[0011] Fault alarm: set the alarm threshold. When the monitoring data exceeds the threshold, the alarm is automatically triggered. The alarm information includes the discharge type, intensity, location and occurrence time. According to the alarm information and monitoring data, the transformer fault is diagnosed and located.

[0012] System maintenance and optimization: regularly maintain and calibrate sensors and signal processing units, check the stability of the data transmission network and remote monitoring system, and optimize the performance of the monitoring system based on actual operating conditions.

[0013] Furthermore, the sensor selection specifically includes the following steps:

[0014] Determine the primary sensor requirements based on transformer type, size, and intended monitoring environment;

[0015] Select ultrasonic sensors with a frequency band in the range of 50-200kHz, compare the performance parameters of different models, select candidate sensors for field testing, and evaluate their performance in actual environments;

[0016] The selected ultrasonic sensors and optical fiber sensors are integrated into the monitoring system and joint debugging tests are carried out. The detection performance of the system is evaluated by simulating partial discharge experiments.

[0017] Furthermore, in the sensor arrangement, key positions of the transformer casing include winding ends, insulator surfaces, oil tank bottom, radiator interface, bushing top, positions near the gas relay, and positions near the pressure relief valve.

[0018] Furthermore, the signal processing further includes the following steps:

[0019] Process the received ultrasonic signal, convert the acoustic vibration into a voltage signal through the piezoelectric effect, amplify the voltage signal through the amplifier, retain the signal within the working frequency range of the ultrasonic sensor through the bandpass filter, remove noise and normalize the signal;

[0020] Based on the signal, the characteristic signal of the partial discharge is extracted, and the type, intensity and occurrence location of the partial discharge are determined.

[0021] Furthermore, based on the signal, feature extraction is performed on the discharge characteristic signal of the partial discharge, which specifically includes the following steps:

[0022] Observe the waveform of the signal, calculate the time from the rising edge to the falling edge of the signal to obtain the discharge duration, find the maximum amplitude of the electrical signal and record the peak value;

[0023] The time domain signal is converted into the frequency domain through the fast Fourier transform FFT to analyze the frequency components of the signal;

[0024] Perform full-wave rectification and smoothing on the signal to obtain the envelope of the signal;

[0025] The statistical parameters of peak factor, RMS value and energy spectrum density are extracted as discharge characteristics.

[0026] Further, determining the type, intensity and location of the partial discharge may further include the following steps:

[0027] Identification of discharge types: using machine learning to train the features of known discharge types, establishing a classification model, matching the discharge features of the signal to be identified with the features in the classification model, and determining the discharge type, wherein the discharge types include corona discharge, surface discharge, internal discharge, suspended potential discharge, and discharge in oil;

[0028] The discharge intensity is evaluated by calculating the energy integral of the signal in a specific time window as a quantitative indicator of the discharge intensity, wherein the energy integral is obtained by the following formula:

[0029]

[0030] Where E represents the energy integral at time t, s(t) represents the value of the filtered signal at time t, and t1 and t2 represent the start and end time of the time window;

[0031] The discharge intensity level is determined by comparing the average amplitude of the signal with a preset threshold;

[0032] The discharge position is located by using the time difference TDOA of the signals received by multiple sensors, wherein the TDOA is obtained by the following formula:

[0033] Δt ij =t i -t j

[0034] Among them, Δt ijrepresents the time difference TDOA between the signals received by sensor i and sensor j, t represents the time when the signal arrives at each sensor, and i and j are the numbers of different sensors;

[0035] The discharge position (x, y, z) is determined by triangulation, wherein the discharge position is obtained by the following formula:

[0036]

[0037] Where c represents the speed of sound, (x1,y1,z1), (x2,y2,z2), (x n ,y n ,z n ) represents the position coordinates of each sensor, TDOA n Indicates the time difference of the nth sensor signal.

[0038] Furthermore, the remote monitoring further includes the following steps:

[0039] Data reception and storage: The processed transformer partial discharge data is transmitted to the remote monitoring center in real time through wireless communication, and a database is established in the remote monitoring center to store the received transformer partial discharge data;

[0040] Real-time monitoring and visualization display, through charts, curves, animations to display various data indicators of transformer partial discharge in real time, combined with the transformer three-dimensional model to mark the location information of partial discharge on the map or model.

[0041] Furthermore, the fault alarm further comprises the following steps:

[0042] According to the transformer's operating specifications, historical data and safety standards, alarm thresholds are set for different types of partial discharge characteristics. The set thresholds are integrated into the remote monitoring system to continuously compare the real-time monitoring data of transformer partial discharge. When any item in the monitoring data exceeds the preset threshold, the alarm process is automatically triggered.

[0043] Furthermore, the fault alarm further comprises the following steps:

[0044] When the monitoring data triggers an alarm condition, the system automatically generates an alarm message, which includes the specific type of discharge, intensity level, location, timestamp and fault description. The alarm message is sent instantly to the designated operation and maintenance personnel or management personnel via SMS, email and APP push channels. At the same time, the alarm message is displayed on the interface of the remote monitoring center.

[0045] Furthermore, the system maintenance optimization further includes the following steps:

[0046] Regularly check the installation positions of ultrasonic sensors and fiber optic sensors, perform hardware checks on signal processing units, and calibrate sensors and signal processing units using standard signal sources or known discharge samples;

[0047] Regularly test the stability of the data transmission network, conduct a comprehensive analysis of the performance of the monitoring system based on long-term accumulated monitoring data, and identify potential problems and improvement points.

[0048] Compared with the prior art, the present invention has the following beneficial effects:

[0049] 1. The present invention trains the features of known discharge types and establishes a classification model. It can automatically learn and identify the characteristic patterns in the discharge signal, and quantify the discharge intensity by calculating the energy integral of the signal in a specific time window. This indicator can objectively reflect the severity of the discharge. At the same time, combined with the comparison between the average amplitude of the signal and the preset threshold, the level of the discharge intensity can be further judged. The time difference of the signals received by multiple sensors is used for positioning, which overcomes the limitations of a single sensor in positioning, improves the accuracy and reliability of positioning, can quickly locate the discharge position, provide accurate fault point information for operation and maintenance personnel, reduce maintenance time and cost, integrate multiple functional modules such as discharge type identification, intensity assessment and position positioning, and realize comprehensive monitoring of partial discharge of transformers, which not only simplifies the operation process, but also improves the overall performance and stability of the monitoring system.

[0050] 2. The present invention displays various data indicators of transformer partial discharge in real time in the form of charts, curves, animations, etc., and marks the location information of partial discharge on the map or model in combination with the three-dimensional model of the transformer, so that the data is more intuitive and easy to understand, which helps operation and maintenance personnel to quickly grasp the operating status of the transformer. According to the operating specifications, historical data and safety standards of the transformer, alarm thresholds are set for different types of partial discharge characteristics. Once any item in the monitoring data exceeds the preset threshold, the system will automatically trigger the alarm process, ensuring the timeliness and accuracy of the fault alarm. When the monitoring data triggers the alarm condition, the system automatically generates alarm information including the specific type of discharge, intensity level, location, timestamp and fault description, which helps operation and maintenance personnel to quickly understand the fault situation and take corresponding treatment measures. The multi-channel notification method ensures the timely transmission of alarm information and the extensiveness of recipients, further improving the efficiency of fault handling. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 It is a schematic diagram of the process of the integrated sensing monitoring method of the present invention. DETAILED DESCRIPTION

[0052] 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.

[0053] In order to solve the technical problems that the existing technology relies on manual judgment and experience accumulation to identify the discharge type and evaluate the discharge intensity, which is easily affected by subjective factors and has difficulty in processing complex and changeable discharge signals, and may lack unified and objective indicators when quantifying the discharge intensity, resulting in inconsistent evaluation results. At the same time, a single sensor often has limitations in locating partial discharges, such as interference from environmental factors, signal attenuation, etc., resulting in inaccurate positioning results, please refer to Figure 1 , the present invention provides the following technical solutions:

[0054] An integrated ultrasonic sensing monitoring method for partial discharge of a transformer comprises the following steps:

[0055] Sensor selection: select an ultrasonic sensor with a frequency band of 50-200kHz to capture the ultrasonic signal generated by partial discharge inside the transformer, and select an optical fiber sensor as an auxiliary sensor;

[0056] Sensor layout: formulate a sensor layout plan based on the structure and size of the transformer, arrange ultrasonic sensors at key locations on the transformer casing, fully cover possible discharge areas inside the transformer, and install them on site;

[0057] Signal processing: receiving ultrasonic signals from sensors through signal processing units, converting them into electrical signals for amplification and filtering, analyzing the electrical signals and extracting characteristic signals of partial discharge, analyzing and identifying the characteristic signals, and determining the type, intensity and location of partial discharge;

[0058] Remote monitoring: The processed data is transmitted to the remote monitoring center by wireless means. The remote monitoring center monitors and analyzes the partial discharge of the transformer in real time, and displays the real-time data, historical curves and alarm information of partial discharge;

[0059] Fault alarm: set the alarm threshold. When the monitoring data exceeds the threshold, the alarm is automatically triggered. The alarm information includes the discharge type, intensity, location and occurrence time. According to the alarm information and monitoring data, the transformer fault is diagnosed and located.

[0060] System maintenance and optimization: regularly maintain and calibrate sensors and signal processing units, check the stability of the data transmission network and remote monitoring system, and optimize the performance of the monitoring system based on actual operating conditions.

[0061] In the above embodiment, by selecting an ultrasonic sensor with a frequency band in the range of 50-200kHz, which is the main concentrated area of ​​the ultrasonic signal of partial discharge of the transformer, the partial discharge phenomenon inside the transformer can be efficiently captured and accurately reflected, thereby improving the sensitivity and accuracy of monitoring. The sensor layout scheme is customized according to the structure and size of the transformer to ensure that ultrasonic sensors are arranged at key positions to fully cover the areas where possible discharge is inside the transformer, thereby avoiding monitoring blind spots and improving the comprehensiveness and reliability of monitoring.

[0062] In the above embodiment, the ultrasonic signal is converted into an electrical signal by the signal processing unit and amplified and filtered, so that the characteristic signal of partial discharge can be quickly extracted and identified, and then transmitted to the remote monitoring center in real time by wireless means, thereby realizing real-time monitoring and remote data analysis of partial discharge of the transformer, improving the fault response speed and processing efficiency, setting the alarm threshold, and automatically triggering the alarm, providing detailed information on the discharge type, intensity, location and occurrence time, providing strong support for fault diagnosis and positioning, and helping operation and maintenance personnel to quickly determine the cause of the fault, take corresponding measures, and reduce fault losses.

[0063] In the above embodiment, the sensors, signal processing units and data transmission networks are regularly maintained and calibrated to ensure the stability and accuracy of the monitoring system. At the same time, the monitoring system is optimized according to the actual operating conditions to improve the long-term operating efficiency and reliability of the system. Through early warning and timely intervention, the power outage time and maintenance costs caused by transformer failures are reduced, and the stability and economy of the power system are improved. The real-time monitoring and rapid response mechanism helps to promptly discover and deal with potential safety hazards, avoid more serious equipment failures or accidents caused by partial discharge, and improve the overall safety of the power system.

[0064] Sensor selection also includes the following steps:

[0065] Determine the primary sensor requirements based on transformer type, size, and intended monitoring environment;

[0066] Select ultrasonic sensors with a frequency band in the range of 50-200kHz, compare the performance parameters of different models, select candidate sensors for field testing, and evaluate their performance in actual environments;

[0067] The selected ultrasonic sensors and optical fiber sensors are integrated into the monitoring system and joint debugging tests are carried out. The detection performance of the system is evaluated by simulating partial discharge experiments.

[0068] In the above embodiment, by customizing the sensor requirements according to the specific type, size and expected monitoring environment of the transformer, it is possible to ensure that the selected sensor is more suitable for the actual application scenario, thereby improving the accuracy of the monitoring data, and by testing the candidate sensors on site and evaluating their performance in the actual environment, it is possible to screen out those sensors that are insensitive to interference signals, have good stability and respond quickly. Such sensors can better adapt to the complex and changeable transformer operating environment and improve the reliability of the monitoring system.

[0069] In the above embodiment, the ultrasonic sensor and the optical fiber sensor are integrated into the same monitoring system to realize multi-parameter monitoring of transformer partial discharge. The optical fiber sensor may be used to measure other physical quantities related to partial discharge, which complements the ultrasonic sensor and provides more comprehensive and accurate discharge information. Through joint debugging and simulated partial discharge experiments, not only the detection performance of the system is evaluated, but also data support is provided for subsequent intelligent processing.

[0070] In the sensor arrangement, the key positions of the transformer casing include the winding ends, the insulator surface, the bottom of the oil tank, the radiator interface, the top of the bushing, the position near the gas relay, and the position near the pressure relief valve.

[0071] In the above embodiments, these positions are sensitive areas inside or outside the transformer where partial discharge is prone to occur. By arranging sensors at these positions, all-round and multi-angle monitoring of the transformer partial discharge phenomenon can be achieved to ensure that no potential discharge activities are missed. The sensors are placed at positions where partial discharge signals are easy to propagate or concentrate. The ultrasonic signals at these positions are usually strong and easy to capture, which helps to timely discover and locate the discharge source. By real-time monitoring of the ultrasonic signals at these key positions, the system can quickly capture early signs of partial discharge and issue an early warning before the fault develops further, which helps the operation and maintenance personnel to take timely measures to prevent the fault from expanding and reduce losses.

[0072] The signal processing specifically includes the following steps:

[0073] Process the received ultrasonic signal, convert the acoustic vibration into a voltage signal through the piezoelectric effect, amplify the voltage signal through the amplifier, retain the signal within the working frequency range of the ultrasonic sensor through the bandpass filter, remove noise and normalize the signal;

[0074] Based on the signal, the characteristic signal of the partial discharge is extracted, and the type, intensity and occurrence location of the partial discharge are determined.

[0075] In the above embodiment, ultrasonic vibrations are converted into voltage signals through the piezoelectric effect, thereby achieving accurate conversion from physical signals to electrical signals, providing high-quality raw data for subsequent processing. The amplifier amplifies the voltage signal to enhance the signal strength, making subsequent signal processing easier and more accurate. The bandpass filter is used to retain the signal within the operating frequency range of the ultrasonic sensor, filtering out unnecessary noise and interference signals, further improving the signal quality. De-noising and normalizing the signal can effectively reduce the impact of random noise and interference on the monitoring results, making the signal more stable and reliable.

[0076] In the above embodiment, feature extraction of characteristic signals of partial discharge is performed based on the signal, which can accurately capture the key features of the discharge signal, and provide strong support for subsequent analysis and judgment. By analyzing the characteristic signal, the type and intensity of partial discharge can be determined, which provides an important basis for evaluating the health status of the transformer. Combined with signal processing technology and sensor layout, the location of partial discharge can be determined more accurately, which helps operation and maintenance personnel to quickly locate the fault point and take corresponding measures.

[0077] Extracting the characteristic signal of the partial discharge based on the signal specifically includes the following steps:

[0078] Observe the waveform of the signal, calculate the time from the rising edge to the falling edge of the signal to obtain the discharge duration, find the maximum amplitude of the electrical signal and record the peak value;

[0079] The time domain signal is converted into the frequency domain through the fast Fourier transform FFT to analyze the frequency components of the signal;

[0080] Perform full-wave rectification and smoothing on the signal to obtain the envelope of the signal;

[0081] The statistical parameters of peak factor, RMS value and energy spectrum density are extracted as discharge characteristics.

[0082] In the above embodiment, by observing the waveform of the signal and calculating the time from the rising edge to the falling edge of the signal to obtain the discharge duration, and finding the maximum amplitude of the electrical signal to record the peak value, the basic characteristics of the discharge signal are quantified to make the feature extraction more accurate, and the time domain signal is converted into the frequency domain by fast FFT, and the frequency components of the signal are analyzed, which helps to understand the spectral characteristics of the discharge signal and further reveal the complexity and diversity of the discharge. The signal is full-wave rectified and smoothed to obtain the envelope of the signal, which helps to remove high-frequency noise and interference in the signal and extract more stable and smooth discharge feature information. Statistical parameters such as peak factor, root mean square value, energy spectrum density, etc. are extracted as discharge features. These parameters reflect the intensity and characteristics of the discharge signal from different angles, making the feature extraction more comprehensive.

[0083] In the above embodiment, each step in the above technical solution is based on mature signal processing methods and statistical theories, and has high reliability and stability. Applying these methods to the integrated sensing monitoring of transformer partial discharge ultrasonic waves can ensure the long-term stable operation of the monitoring system. By denoising and filtering the signal, and extracting stable statistical parameters as features, the impact of external interference on the monitoring results can be effectively reduced, and the anti-interference ability of the monitoring system can be improved.

[0084] Determine the type, intensity and location of partial discharge, including the following steps:

[0085] Identification of discharge types: using machine learning to train the features of known discharge types, establishing a classification model, matching the discharge features of the signal to be identified with the features in the classification model, and determining the discharge type, wherein the discharge types include corona discharge, surface discharge, internal discharge, suspended potential discharge, and discharge in oil;

[0086] The discharge intensity is evaluated by calculating the energy integral of the signal in a specific time window as a quantitative indicator of the discharge intensity, wherein the energy integral is obtained by the following formula:

[0087]

[0088] Where E represents the energy integral at time t, s(t) represents the value of the filtered signal at time t, and t1 and t2 represent the start and end time of the time window;

[0089] The discharge intensity level is determined by comparing the average amplitude of the signal with a preset threshold;

[0090] The discharge position is located by using the time difference TDOA of the signals received by multiple sensors, wherein the TDOA is obtained by the following formula:

[0091] Δt ij =t i -t j

[0092] Among them, Δt ij represents the time difference TDOA between the signals received by sensor i and sensor j, t represents the time when the signal arrives at each sensor, and i and j are the numbers of different sensors;

[0093] The discharge position (x, y, z) is determined by triangulation, wherein the discharge position is obtained by the following formula:

[0094]

[0095] Where c represents the speed of sound, (x1,y1,z1), (x2,y2,z2), (x n ,y n ,z n ) represents the position coordinates of each sensor, TDOA n Indicates the time difference of the nth sensor signal.

[0096] In the above embodiment, the features of known discharge types are trained and a classification model is established, which can automatically learn and identify the characteristic patterns in the discharge signal. Compared with traditional manual judgment, it has higher accuracy and efficiency. The discharge type identification covers multiple types such as corona discharge, surface discharge, internal discharge, suspended potential discharge and oil discharge, which can meet the identification needs of transformer partial discharge in different scenarios.

[0097] In the above embodiment, the discharge intensity is quantified by calculating the energy integral of the signal within a specific time window. This indicator can objectively reflect the severity of the discharge. At the same time, combined with the comparison between the average amplitude of the signal and the preset threshold, the level of the discharge intensity can be further judged, providing an intuitive reference for operation and maintenance personnel, and being able to monitor and evaluate the changes in discharge intensity in real time, which helps operation and maintenance personnel to promptly discover and deal with potential fault hazards.

[0098] In the above embodiment, the time difference of signals (TDOA) received by multiple sensors is used for positioning, and the discharge position is accurately determined by triangulation, thereby overcoming the limitations of a single sensor in positioning, improving the accuracy and reliability of positioning, being able to quickly locate the discharge position, and providing accurate fault point information to operation and maintenance personnel, thereby reducing maintenance time and costs.

[0099] In the above embodiment, multiple functional modules such as discharge type identification, intensity assessment and position positioning are integrated into one to realize comprehensive monitoring of partial discharge of transformer, which not only simplifies the operation process, but also improves the overall performance and stability of the monitoring system. Through real-time monitoring and data analysis, it provides scientific decision-making basis for operation and maintenance personnel. Operation and maintenance personnel can adjust the operation and maintenance strategy in time according to the monitoring results to ensure the safe and stable operation of the transformer.

[0100] Remote monitoring also includes the following steps:

[0101] Data reception and storage: The processed transformer partial discharge data is transmitted to the remote monitoring center in real time through wireless communication, and a database is established in the remote monitoring center to store the received transformer partial discharge data;

[0102] Real-time monitoring and visualization display, through charts, curves, animations to display various data indicators of transformer partial discharge in real time, combined with the transformer three-dimensional model to mark the location information of partial discharge on the map or model.

[0103] Fault alarm, specifically also includes the following steps:

[0104] According to the transformer's operating specifications, historical data and safety standards, alarm thresholds are set for different types of partial discharge characteristics. The set thresholds are integrated into the remote monitoring system to continuously compare the real-time monitoring data of transformer partial discharge. When any item in the monitoring data exceeds the preset threshold, the alarm process is automatically triggered.

[0105] When the monitoring data triggers an alarm condition, the system automatically generates an alarm message, which includes the specific type of discharge, intensity level, location, timestamp and fault description. The alarm message is sent instantly to the designated operation and maintenance personnel or management personnel via SMS, email and APP push channels. At the same time, the alarm message is displayed on the interface of the remote monitoring center.

[0106] In the above embodiment, the processed transformer partial discharge data is transmitted to the remote monitoring center in real time through wireless communication technology, ensuring the timeliness and accuracy of the data, so that the operation and maintenance personnel can timely understand the operating status of the transformer and discover potential problems in time. A database is established in the remote monitoring center to store the received transformer partial discharge data, which not only facilitates data management and query, but also provides a basis for subsequent data analysis and mining. Various data indicators of transformer partial discharge are displayed in real time in the form of charts, curves, animations, etc., and the location information of partial discharge is marked on the map or model in combination with the transformer three-dimensional model, making the data more intuitive and easy to understand, which helps the operation and maintenance personnel to quickly grasp the operating status of the transformer.

[0107] In the above embodiment, alarm thresholds are set for different types of partial discharge characteristics according to the operating specifications, historical data and safety standards of the transformer. Once any item in the monitoring data exceeds the preset threshold, the system will automatically trigger the alarm process, ensuring the timeliness and accuracy of the fault alarm. When the monitoring data triggers the alarm condition, the system automatically generates alarm information including the specific type of discharge, intensity level, location, timestamp and fault description, which helps the operation and maintenance personnel to quickly understand the fault situation and take corresponding treatment measures. The multi-channel notification method ensures the timely transmission of the alarm information and the wide range of recipients, further improving the efficiency of fault handling.

[0108] In the above embodiment, the entire monitoring and alarm process is highly automated, which reduces the possibility of manual intervention and misjudgment, not only improves work efficiency, but also reduces operation and maintenance costs. Due to the characteristics of real-time data transmission and instant alarm notification, operation and maintenance personnel can respond and handle faults quickly to prevent faults from expanding and worsening. Through the application of real-time monitoring and fault alarm technology, potential problems can be discovered and handled in a timely manner, thereby improving the reliability and stability of the transformer and the power system in which it is located.

[0109] System maintenance and optimization also includes the following steps:

[0110] Regularly check the installation positions of ultrasonic sensors and fiber optic sensors, perform hardware checks on signal processing units, and calibrate sensors and signal processing units using standard signal sources or known discharge samples;

[0111] Regularly test the stability of the data transmission network, conduct a comprehensive analysis of the performance of the monitoring system based on long-term accumulated monitoring data, and identify potential problems and improvement points.

[0112] In the above embodiments, the installation positions of the ultrasonic sensors and the optical fiber sensors are regularly checked, as well as the hardware inspection of the signal processing unit is performed, so as to ensure that the sensors and the signal processing unit are always in good working condition. Calibration using a standard signal source or a known discharge sample can further verify and adjust the output of the monitoring system, thereby improving the accuracy of the monitoring data. Through calibration, the monitoring errors caused by factors such as sensor offset, signal attenuation or degradation of the signal processing unit performance can be reduced, thereby ensuring that the monitoring results are more reliable.

[0113] In the above embodiment, the stability of the data transmission network is tested regularly, so that problems such as network delay and packet loss can be discovered and resolved in a timely manner, ensuring that the monitoring data can be transmitted to the remote monitoring center in real time and in full. A stable data transmission network can shorten the data transmission time, improve the response speed of the monitoring system, and help operation and maintenance personnel understand the transformer operation status more promptly and take corresponding measures.

[0114] In the above embodiment, by regularly inspecting and maintaining sensors, signal processing units, and data transmission networks, potential problems can be discovered and resolved in a timely manner, reducing the possibility of failures. Good maintenance can extend the service life of key equipment such as sensors and signal processing units, and reduce the frequency and cost of equipment replacement.

[0115] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any technician familiar with the technical field can make equivalent replacements or changes according to the technical scheme and inventive concept of the present invention within the technical scope disclosed by the present invention, which should be covered by the protection scope of the present invention.

Claims

1. An integrated ultrasonic sensing monitoring method for partial discharge of transformers, characterized in that: The following steps are involved: Select an ultrasonic sensor with a frequency band of 50-200kHz to capture the ultrasonic signal generated by partial discharge inside the transformer, and select an optical fiber sensor as an auxiliary sensor; According to the structure and size of the transformer, a sensor layout plan is formulated, ultrasonic sensors are arranged at key positions of the transformer casing to fully cover the possible discharge areas inside the transformer, and then installed on site; The ultrasonic signal from the sensor is received by the signal processing unit, and converted into an electrical signal for amplification and filtering, the electrical signal is analyzed and the characteristic signal of partial discharge is extracted, the characteristic signal is analyzed and identified, and the type, intensity and occurrence location of partial discharge are determined; The processed data is transmitted to the remote monitoring center by wireless means, and the real-time monitoring and data analysis of partial discharge of transformers are carried out in the remote monitoring center, and the real-time data, historical curves and alarm information of partial discharge are displayed; Set the alarm threshold. When the monitoring data exceeds the threshold, the alarm is automatically triggered. The alarm information includes the discharge type, intensity, location and occurrence time. According to the alarm information and monitoring data, the transformer fault is diagnosed and located. Regularly maintain and calibrate sensors and signal processing units, check the stability of the data transmission network and remote monitoring system, and optimize the performance of the monitoring system based on actual operating conditions.

2. The integrated ultrasonic sensing monitoring method for partial discharge of transformer according to claim 1, characterized in that: The sensor selection specifically further includes the following steps: Determine the primary sensor requirements based on transformer type, size, and intended monitoring environment; Select ultrasonic sensors with a frequency band in the range of 50-200kHz, compare the performance parameters of different models, select candidate sensors for field testing, and evaluate their performance in actual environments; The selected ultrasonic sensors and optical fiber sensors are integrated into the monitoring system and joint debugging tests are carried out. The detection performance of the system is evaluated by simulating partial discharge experiments.

3. The integrated ultrasonic sensing monitoring method for partial discharge of transformer according to claim 1, characterized in that: In the sensor arrangement, key positions of the transformer housing include winding ends, insulator surfaces, oil tank bottom, radiator interface, bushing top, positions near the gas relay, and positions near the pressure relief valve.

4. The integrated ultrasonic sensing monitoring method for partial discharge of transformer according to claim 1, characterized in that: The signal processing further comprises the following steps: Process the received ultrasonic signal, convert the acoustic vibration into a voltage signal through the piezoelectric effect, amplify the voltage signal through the amplifier, retain the signal within the working frequency range of the ultrasonic sensor through the bandpass filter, remove noise and normalize the signal; Based on the signal, the characteristic signal of the partial discharge is extracted, and the type, intensity and occurrence location of the partial discharge are determined.

5. The integrated ultrasonic sensing monitoring method for partial discharge of transformers as claimed in claim 4 is characterized in that: Extracting the characteristic signal of the partial discharge based on the signal specifically includes the following steps: Observe the waveform of the signal, calculate the time from the rising edge to the falling edge of the signal to obtain the discharge duration, find the maximum amplitude of the electrical signal and record the peak value; The time domain signal is converted into the frequency domain through the fast Fourier transform FFT to analyze the frequency components of the signal; Perform full-wave rectification and smoothing on the signal to obtain the envelope of the signal; The statistical parameters of peak factor, RMS value and energy spectrum density are extracted as discharge characteristics.

6. The integrated ultrasonic sensing monitoring method for partial discharge of transformers as claimed in claim 4, characterized in that: Determine the type, intensity and location of partial discharge, including the following steps: Identification of discharge types: using machine learning to train the features of known discharge types, establishing a classification model, matching the discharge features of the signal to be identified with the features in the classification model, and determining the discharge type, wherein the discharge types include corona discharge, surface discharge, internal discharge, suspended potential discharge, and discharge in oil; The discharge intensity is evaluated by calculating the energy integral of the signal in a specific time window as a quantitative indicator of the discharge intensity, wherein the energy integral is obtained by the following formula: Where E represents the energy integral at time t, s(t) represents the value of the filtered signal at time t, and t1 and t2 represent the start and end time of the time window; The discharge intensity level is determined by comparing the average amplitude of the signal with a preset threshold; The discharge position is located by using the time difference TDOA of the signals received by multiple sensors, wherein the TDOA is obtained by the following formula: Δt ij =t i -t j Among them, Δt ij represents the time difference TDOA between the signals received by sensor i and sensor j, t represents the time when the signal arrives at each sensor, and i and j are the numbers of different sensors; The discharge position (x, y, z) is determined by triangulation, wherein the discharge position is obtained by the following formula: Where c represents the speed of sound, (x1,y1,z1), (x2,y2,z2), (x n ,y n ,z n ) represents the position coordinates of each sensor, TDOA n Indicates the time difference of the nth sensor signal.

7. The integrated ultrasonic sensing monitoring method for partial discharge of transformers according to claim 1, characterized in that: The remote monitoring further comprises the following steps: Data reception and storage: The processed transformer partial discharge data is transmitted to the remote monitoring center in real time through wireless communication, and a database is established in the remote monitoring center to store the received transformer partial discharge data; Real-time monitoring and visualization display, through charts, curves, animations to display various data indicators of transformer partial discharge in real time, combined with the transformer three-dimensional model to mark the location information of partial discharge on the map or model.

8. The integrated ultrasonic sensing monitoring method for partial discharge of transformers according to claim 1, characterized in that: The fault alarm further comprises the following steps: According to the transformer's operating specifications, historical data and safety standards, alarm thresholds are set for different types of partial discharge characteristics. The set thresholds are integrated into the remote monitoring system to continuously compare the real-time monitoring data of transformer partial discharge. When any item in the monitoring data exceeds the preset threshold, the alarm process is automatically triggered.

9. The integrated ultrasonic sensing monitoring method for partial discharge of transformers as claimed in claim 8, characterized in that: The fault alarm further comprises the following steps: When the monitoring data triggers an alarm condition, an alarm message is automatically generated. The alarm message includes the specific type of discharge, intensity level, location, timestamp, and fault description. The alarm message is instantly sent to designated operation and maintenance personnel or management personnel via SMS, email, or APP push channels. At the same time, the alarm message is displayed on the interface of the remote monitoring center.

10. The integrated ultrasonic sensing monitoring method for partial discharge of transformer according to claim 1, characterized in that: The system maintenance optimization specifically further includes the following steps: Regularly check the installation positions of ultrasonic sensors and fiber optic sensors, perform hardware checks on signal processing units, and calibrate sensors and signal processing units using standard signal sources or known discharge samples; Regularly test the stability of the data transmission network, conduct a comprehensive analysis of the performance of the monitoring system based on long-term accumulated monitoring data, and identify potential problems and improvement points.

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