Real-time monitoring method and system for power transformer

Abstract

The invention provides a real-time monitoring method and system for a power transformer, and relates to the technical field of intelligent monitoring, and the method comprises the steps: carrying out the noise removal and time synchronization of original data through wavelet transform, and carrying out the recognition and elimination of abnormal data through an isolation forest algorithm, and obtaining the preprocessed data; key features are extracted from the preprocessed data, a weighted average method is adopted for fusion, and a comprehensive state index is generated; based on the comprehensive state index, using a fault classification model constructed based on a deep neural network to identify a fault type, using a long short-term memory network to analyze and predict the future state of the transformer, and calculating a transformer health score according to the comprehensive state index and the future state of the transformer; and according to the transformer health score, setting multi-level alarm thresholds for real-time alarm, and generating a maintenance suggestion in combination with the fault type. According to the invention, the accuracy of fault early warning is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent monitoring technology, and in particular to a method and system for real-time monitoring of power transformers. Background Technology

[0002] In ultra-high voltage transmission systems, voltage fluctuations can reach ±10% or more of the rated voltage. High-frequency voltage surges can accelerate the aging of the insulating medium, leading to hidden dangers such as partial discharge and air gap breakdown. Traditional monitoring relies only on parameters such as oil temperature and winding current, without directly monitoring key state indicators of the insulating medium (such as moisture content in the oil and dissolved gas composition).

[0003] The monitoring of moisture in oil is flawed. When the moisture content of insulating oil in ultra-high voltage transformers exceeds 50 ppm, the insulation breakdown voltage can drop by more than 30%. However, traditional methods use offline sampling and testing, which takes several months and cannot capture the rapid increase in moisture content (such as a sudden surge in moisture content caused by dampness).

[0004] Dissolved gas analysis is delayed. Characteristic gases such as H2 and C2H2 are generated in the early stage of partial discharge. Traditional methods rely on periodic oil sampling and testing, which has a long detection cycle (24-48 hours). However, in ultra-high voltage scenarios, such faults may only take a few hours from the initial stage to breakdown, causing the early warning to fail. Summary of the Invention

[0005] The technical problem to be solved by this invention is to provide a real-time monitoring method for power transformers, which improves the accuracy of fault early warning.

[0006] To solve the above-mentioned technical problems, the technical solution of the present invention is as follows:

[0007] In a first aspect, a method for real-time monitoring of a power transformer, the method comprising:

[0008] Step 1: Install temperature sensors, current sensors, vibration sensors, gas sensors, and humidity sensors on key parts of transformers in ultra-high voltage power transmission projects, and collect raw data once per second.

[0009] Step 2: For the original data, wavelet transform is used to remove noise, time synchronization is performed, and outlier data is identified and removed using the isolated forest algorithm to obtain preprocessed data;

[0010] Step 3: Extract key features from the preprocessed data, and then use a weighted average method to fuse them to generate a comprehensive status index.

[0011] Step 4: Based on the comprehensive status index, identify the fault type using a fault classification model built on a deep neural network, use a long short-term memory network to analyze and predict the future state of the transformer, and calculate the transformer health score based on the comprehensive status index and the future state of the transformer.

[0012] Step 5: Based on the transformer health score, set multi-level alarm thresholds for real-time alarms, and generate maintenance suggestions based on the fault type.

[0013] Furthermore, the raw data includes oil temperature, winding temperature, load current, short-circuit current, core and winding vibration, dissolved gas, and water content in the oil.

[0014] Furthermore, key characteristics include oil temperature change rate, vibration spectrum characteristics, and gas concentration ratio.

[0015] Furthermore, wavelet transform is used to remove noise from the original data, time synchronization is performed, and outlier data is identified and removed using the Isolation Forest algorithm, resulting in preprocessed data, including:

[0016] The collected raw data were decomposed using wavelet decomposition to decompose the temperature time series into approximate components and detail components at different scales.

[0017] Thresholding is performed on the detailed components obtained from the decomposition. After thresholding, wavelet reconstruction is performed using the processed approximate components and detailed components to obtain the denoised data.

[0018] Extract the timestamp of each data point from the collected raw data, and perform time alignment on the denoised data according to a unified time benchmark;

[0019] The data, after denoising and time synchronization processing, are combined into a multidimensional dataset, where each row represents a data sample and each column represents a feature.

[0020] The isolated forest algorithm is used to train the cleaned data. By randomly selecting features and split points, multiple isolated trees are constructed, and the data samples are recursively divided into different nodes.

[0021] For each data sample, calculate its outlier score in the isolated forest. Based on the outlier score, set a threshold and identify data samples with outlier scores higher than the threshold as outliers and remove them to obtain preprocessed data.

[0022] Furthermore, key features are extracted from the preprocessed data, and then a weighted average method is used to fuse them to generate a comprehensive status index, including:

[0023] The importance of each key feature in reflecting the transformer's operating status is assessed to obtain the importance assessment results;

[0024] Historical data is used for analysis, and correlation analysis is used to calculate the correlation between each feature and the actual occurrence of transformer faults.

[0025] Based on the importance assessment results and relevance, determine the weighting coefficient for each key feature;

[0026] The extracted key features are standardized to obtain standardized key feature values;

[0027] The standardized key feature values ​​are multiplied by their corresponding weighting coefficients, and the weighted values ​​of all features are summed to obtain the comprehensive state index.

[0028] Furthermore, based on comprehensive condition indicators, a fault classification model built on a deep neural network is used to identify fault types, and a long short-term memory network is employed to analyze and predict the future state of the transformer. The transformer health score is calculated based on the comprehensive condition indicators and the future state of the transformer, including:

[0029] Extract comprehensive status indicators as input features, organize the comprehensive status indicators into a dataset in chronological order, label the known fault types corresponding to each data sample, and construct training set, validation set and test set;

[0030] The input features are normalized so that all feature values ​​are within the same scale range;

[0031] The architecture for constructing a deep neural network includes an input layer, multiple hidden layers, and an output layer. The number of neurons in the input layer is the same as the number of features in the overall state indicator; there are 2-3 hidden layers; the number of neurons in the output layer equals the number of fault types.

[0032] The ReLU function is selected, and the deep neural network model is trained using the training set. Stochastic gradient descent is used to automatically adjust the parameters of the deep neural network model to obtain the trained DNN model.

[0033] The trained DNN model is used to predict the new comprehensive state index data, and the probability of each fault type is calculated through forward propagation.

[0034] Furthermore, long short-term memory network analysis is used to predict the future state of the transformer. Based on the comprehensive state index and the future state of the transformer, a transformer health score is calculated, including:

[0035] The comprehensive state indicators are arranged into time series data in chronological order, and the time series data is divided into multiple input sequences and corresponding target sequences;

[0036] Construct an LSTM model, set up 1-2 LSTM layers in the LSTM model, and add a fully connected layer after the LSTM layers;

[0037] The LSTM model is trained using the constructed time series dataset, and the Adam algorithm is used to update the LSTM model parameters. During the training process, the validation set is used to evaluate and fine-tune the LSTM model. After training is completed, the latest time series data is input into the LSTM model to predict the future state of the transformer and obtain the predicted value of the comprehensive state index in the future period.

[0038] The comprehensive health status index and future health status prediction are divided into different level intervals, and each level interval corresponds to a different health status description and a corresponding scoring range.

[0039] Different weights are assigned to the comprehensive status index and the future status of the transformer. Based on the specific values ​​of the comprehensive status index and the future status of the transformer, their respective scores are determined. The scores are then weighted and summed to obtain the health score of the transformer.

[0040] Furthermore, based on the transformer health score, multi-level alarm thresholds are set for real-time alarms, and maintenance suggestions are generated in conjunction with the fault type, including:

[0041] Based on the transformer's design standards and historical operating data, the transformer's health score is divided into different intervals, each corresponding to a different health status and alarm level, resulting in four intervals:

[0042] A health status score of 90-100 indicates that the transformer is operating normally.

[0043] Note the status; a score of 70-89 indicates a minor abnormality in the transformer, but it does not affect normal operation.

[0044] A warning status, corresponding to a score of 50-69, indicates that the transformer is malfunctioning and has a potential fault.

[0045] A dangerous condition, with a score of 0-49, indicates that the transformer is in a faulty state and a shutdown accident could occur at any time.

[0046] Secondly, a real-time monitoring system for power transformers includes:

[0047] The acquisition module is used to install temperature sensors, current sensors, vibration sensors, gas sensors and humidity sensors at key parts of transformers in ultra-high voltage power transmission projects, and collect raw data once per second.

[0048] The preprocessing module is used to remove noise from the raw data using wavelet transform, synchronize the time, and identify and remove outlier data using the isolated forest algorithm to obtain the preprocessed data.

[0049] The fusion module is used to extract key features from the preprocessed data and then fuse them using a weighted average method to generate a comprehensive status index.

[0050] The evaluation module is used to identify fault types based on comprehensive condition indicators and a fault classification model built on a deep neural network, and to predict the future state of the transformer using long short-term memory network analysis. The transformer health score is calculated based on the comprehensive condition indicators and the future state of the transformer.

[0051] The alarm module is used to set multi-level alarm thresholds based on the transformer health score and to generate real-time alarms, and to generate maintenance suggestions based on the fault type.

[0052] Thirdly, a computing device, comprising:

[0053] One or more processors;

[0054] A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the method.

[0055] Fourthly, a computer-readable storage medium storing a program that, when executed by a processor, implements the method.

[0056] The above-described solution of the present invention has at least the following beneficial effects:

[0057] By deploying five types of sensors—temperature, current, vibration, gas, and humidity—six-dimensional monitoring of the transformer's thermal state (oil temperature / winding temperature), electrical state (load current / short-circuit current), mechanical state (vibration mode), chemical state (dissolved gas components), and insulation state (moisture in oil) can be achieved, thus overcoming the shortcomings of traditional methods that rely solely on single parameters such as oil temperature and current.

[0058] For example, vibration sensors can capture abnormal vibration spectra of 100Hz / 200Hz caused by loose windings, providing early warning of mechanical failures 3-7 days in advance; gas sensors can monitor the concentration of characteristic gases such as H2 and C2H2 in real time, triggering early warnings in the early stages of partial discharge (gas production rate <5mL / day), which is more than 24 hours earlier than traditional offline detection.

[0059] It collects raw data once per second, with a time resolution 60 times higher than traditional minute-level monitoring. It can capture transient anomalies in ultra-high voltage scenarios (such as nanosecond-level vibration pulses caused by lightning overvoltage and millisecond-level temperature surges after short-circuit current impact), providing a valuable time window for pre-fault intervention.

[0060] Wavelet transform can suppress white noise of vibration signals by more than 90%, reducing the error of vibration spectrum feature extraction to ±3%; the isolated forest algorithm can identify anomalies in temperature and current data with an accuracy of 95%, effectively eliminating false data such as sensor drift and communication interference, and avoiding false alarms.

[0061] The weighted average method assigns higher weights to features such as vibration (0.3), gas (0.25), and temperature (0.2) through the analytic hierarchy process, constructing a comprehensive state index that improves the fault identification accuracy by 40% compared to single-parameter judgment. The data dimension compression rate after feature fusion reaches 60%, reducing the computational complexity of subsequent AI models and improving real-time analysis efficiency.

[0062] The DNN model trained on samples achieves a classification accuracy of 92% for seven types of faults, including overheating, discharge, and insulation aging, which is 28% higher than the traditional threshold method. It can identify compound fault modes (such as overheating and discharge) and distinguish different fault types by clustering the feature vector space, thus avoiding the single-factor misjudgment of traditional methods.

[0063] The prediction error for time series such as oil temperature and gas concentration in the next 7 days is less than 5%, which can provide early warning of potential faults (such as predicting that the water content in the oil will exceed the threshold in 7 days, triggering moisture-proof maintenance); the health scoring system (0-100 points) quantifies complex states into a single indicator, improving the decision-making efficiency of operation and maintenance personnel by 50% and reducing the misjudgment rate by 65%.

[0064] By setting thresholds such as health score <70 (warning) and <50 (serious warning), and combining them with fault types (such as abnormal vibration and excessive gas triggering "winding loose warning"), scenario-based alarms are achieved, improving the location accuracy by 70% compared to traditional single-threshold alarms; the alarm response time is <10 seconds, and the alarm is pushed to three levels of maintenance personnel (duty officer → shift leader → technical supervisor) via Web / mobile terminal to ensure rapid handling of emergencies.

[0065] A "fault type - maintenance suggestion" mapping table is generated, with a maintenance plan matching rate of 90%, reducing the blindness of traditional periodic maintenance by 80%. Mechanical loosening is identified in advance through vibration modal analysis (such as winding natural frequency deviation > 5%), and discharge risk is judged by gas concentration ratio (C2H2 / C2H4 > 1.0). A "mechanical-chemical" dual early warning mechanism is constructed, which reduces the insulation breakdown accident rate by 85%. Attached Figure Description

[0066] Figure 1 This is a flowchart illustrating the real-time monitoring method for power transformers provided in an embodiment of the present invention.

[0067] Figure 2 This is a schematic diagram of a real-time monitoring system for power transformers provided in an embodiment of the present invention. Detailed Implementation

[0068] Exemplary embodiments of the present disclosure will now be described in more detail with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

[0069] like Figure 1 As shown, an embodiment of the present invention proposes a real-time monitoring method for power transformers, the method comprising the following steps:

[0070] Step 1: Install temperature sensors, current sensors, vibration sensors, gas sensors, and humidity sensors on key parts of transformers in ultra-high voltage power transmission projects. Collect raw data once per second. The raw data includes oil temperature, winding temperature, load current, short-circuit current, core and winding vibration, dissolved gas, and oil moisture content.

[0071] Step 2: For the original data, wavelet transform is used to remove noise, time synchronization is performed, and outlier data is identified and removed using the isolated forest algorithm to obtain preprocessed data;

[0072] Step 3: Extract key features from the preprocessed data, and then use the weighted average method to fuse them to generate a comprehensive state index. Key features include oil temperature change rate, vibration spectrum characteristics, and gas concentration ratio.

[0073] Step 4: Based on the comprehensive status index, identify the fault type using a fault classification model built on a deep neural network, use a long short-term memory network to analyze and predict the future state of the transformer, and calculate the transformer health score based on the comprehensive status index and the future state of the transformer.

[0074] Step 5: Based on the transformer health score, set multi-level alarm thresholds for real-time alarms, and generate maintenance suggestions based on the fault type.

[0075] In this embodiment of the invention, multiple types of sensors are installed at key parts of the transformer, collecting raw data covering multiple dimensions such as oil temperature, winding temperature, and load current once per second. This allows for comprehensive and high-frequency monitoring of the transformer's operating status, effectively avoiding monitoring loopholes caused by incomplete or untimely data collection. Wavelet transform is used to remove noise, effectively filtering out interference signals in the raw data and improving data quality. Time synchronization ensures the consistency of data collected by different sensors in the time dimension, facilitating comprehensive analysis. The isolated forest algorithm accurately identifies and removes abnormal data, preventing abnormal data from misleading subsequent analysis results and making the preprocessed data more reliable.

[0076] Key features such as oil temperature change rate, vibration spectrum characteristics, and gas concentration ratio are extracted from the preprocessed data. These features reflect the transformer's operating status from different perspectives. For example, the oil temperature change rate reflects the transformer's heat generation, the vibration spectrum characteristics reflect the operating status of mechanical components, and the gas concentration ratio can be used to detect internal faults such as discharge or overheating, helping to more accurately assess the transformer's condition. A weighted average method is used to fuse the extracted key features, generating a comprehensive condition index. By rationally allocating the weights of each feature, multiple aspects of information can be comprehensively considered, avoiding the limitations of single features. This allows the comprehensive condition index to more comprehensively and accurately reflect the overall operating status of the transformer, improving the accuracy of fault diagnosis and condition assessment.

[0077] Based on comprehensive status indicators, a fault classification model constructed using deep neural networks can automatically learn complex patterns and features in the data, accurately identifying transformer fault types. Deep neural networks possess powerful nonlinear fitting capabilities, handling high-dimensional and complex feature data, and achieving higher fault diagnosis accuracy compared to traditional methods. This helps maintenance personnel understand transformer fault conditions in a timely manner and take targeted maintenance measures. Long Short-Term Memory (LSTM) networks are used to predict the future state of transformers. This network can process time-series data, capture long-term dependencies in the data, and accurately predict the future operating trends of transformers. By predicting the future state of transformers, maintenance personnel can formulate maintenance plans in advance, rationally allocate maintenance resources, avoid power outages caused by sudden transformer failures, and improve the reliability and stability of the power system. A transformer health score is calculated based on comprehensive status indicators and the future state of the transformer, providing a clear reflection of the overall health status of the transformer. The health score provides maintenance personnel with a quantitative assessment indicator, facilitating a comprehensive evaluation of the transformer's operating status.

[0078] Based on the transformer health score, multi-level alarm thresholds are set for real-time alarms. The system can promptly issue alarm signals of corresponding levels according to different transformer health conditions, enabling maintenance personnel to quickly understand the abnormal situation of the transformer and take timely measures to deal with it, preventing the fault from escalating further. Based on the fault type, maintenance suggestions are generated, which can provide specific maintenance guidance for maintenance personnel. Different fault types require different maintenance measures. Targeted maintenance suggestions can improve maintenance efficiency, reduce maintenance costs, and extend the service life of the transformer.

[0079] Step 1 above may include: using a fiber optic grating temperature sensor (FBG) with a measurement accuracy of ±0.2℃, a withstand voltage level of ≥1100kV, and an electromagnetic interference resistance of 100dB to meet the requirements for strong magnetic field suppression under ultra-high voltage environments.

[0080] Installation location:

[0081] High voltage winding (3 points per phase): Wound around the outer insulation layer of the winding and fixed with high temperature resistant glue (temperature resistance 200℃) to monitor hot spot temperature; Iron core clamp (2 points): Embedded in the groove on the surface of the iron core to measure the eddy current loss heat generation of the iron core; Oil tank temperature (1 point each at the top and bottom): Inserted into the oil body through a stainless steel sleeve to avoid the influence of oil flow disturbance.

[0082] Current sensor

[0083] Selection: Low-power Rogowski coil (bandwidth DC-100kHz, measurement range 0-5000A), non-contact measurement avoids primary circuit modification, linearity error <0.5%. High-voltage side incoming bushing (A / B / C three phases): The coil surrounds the root of the bushing to collect load current and short-circuit current; monitors zero-sequence current to identify asymmetrical faults such as inter-turn short circuits in the winding.

[0084] Vibration sensor

[0085] Selection: Triaxial piezoelectric accelerometer (sensitivity 100mV / g, frequency range 0.1-10kHz), with matching charge amplifier noise immunity >80dB.

[0086] Installation location:

[0087] Iron core housing (3 points): 1 point each in the vertical, horizontal and axial directions to monitor the magnetostrictive vibration of the iron core; Winding pressure plate (2 points): fixed to the top pressure plate of the winding through a magnetic base to capture the vibration of the winding displacement; Cooler bracket (1 point): monitors the vibration of the fan / oil pump and distinguishes between mechanical faults and interference from the cooling system.

[0088] Gas sensor

[0089] Selection: Miniature gas chromatograph sensor with built-in pretreatment module to filter oil mist, response time <30 seconds.

[0090] Installation location:

[0091] Oil pillow vent: Real-time monitoring of gas composition above the oil surface, reflecting oil decomposition products;

[0092] Gas relay gas sampling port: connected via stainless steel pipeline, to collect sudden gas generation during faults;

[0093] Bushing end screen lead-down line: used to assist in monitoring the gas generated by internal discharge of the bushing.

[0094] Humidity sensor

[0095] Selection: Polymer capacitive humidity probe (accuracy ±2% RH, oil corrosion resistant), with built-in temperature compensation circuit to eliminate the influence of oil temperature on humidity measurement.

[0096] Installation location: Lowest point at the bottom of the oil tank (5cm from the bottom of the tank), installed via flange, ensuring the probe is submerged in the oil to avoid measurement deviations caused by moisture deposition.

[0097] The sensor cable uses double-layer shielding (aluminum foil and copper mesh), with the shielding layer grounded at one end (industrial computer end), and the grounding resistance <1Ω; an insulating gasket (0.5mm thick) is placed between the vibration sensor and the metal mounting surface to prevent ground loop interference. The sensor mounting bolts are secured with anti-loosening adhesive (Loctite 243), and the torque wrench is calibrated to 8-10 N·m; the gas pipeline uses stainless steel compression fittings with a pressure resistance ≥2MPa to avoid gas leakage caused by vibration.

[0098] It employs a PCIe-6366 high-speed acquisition card with 16-bit resolution, 8-channel synchronous sampling, and a maximum sampling rate of 1MS / s, meeting the requirement of synchronous acquisition once per second (actual configuration is 1Hz sampling, with reserved bandwidth to handle sudden high-frequency sampling). An optical fiber demodulator (wavelength accuracy ±1pm) converts FBG wavelength drift into a temperature signal; a charge amplifier (1000x gain) converts piezoelectric signals into voltage signals (0-10V); and a 4-20mA to voltage converter (accuracy 0.1% FS) adapts to the sensor output signal.

[0099] Read the sensor configuration file (calibration coefficients, installation location code); synchronize the GPS clock, trigger multi-channel synchronous sampling once per second to acquire raw data (including raw values ​​of temperature, current, vibration acceleration, raw gas concentration count, humidity and voltage values), pre-filter the vibration signal (50Hz notch filter to remove power frequency interference), and perform nonlinear correction on the temperature signal (based on the FBG temperature-wavelength calibration curve).

[0100] In a preferred embodiment of the present invention, the original data is subjected to wavelet transform to remove noise, time synchronization is performed, and outlier data is identified and removed using the isolated forest algorithm to obtain preprocessed data, including:

[0101] The collected raw data were decomposed using wavelet decomposition to decompose the temperature time series into approximate components and detail components at different scales.

[0102] Thresholding is performed on the detailed components obtained from the decomposition. After thresholding, wavelet reconstruction is performed using the processed approximate components and detailed components to obtain the denoised data.

[0103] Extract the timestamp of each data point from the collected raw data, and perform time alignment on the denoised data according to a unified time base;

[0104] The data, after denoising and time synchronization processing, are combined into a multidimensional dataset, where each row represents a data sample and each column represents a feature.

[0105] The isolated forest algorithm is used to train the cleaned data. By randomly selecting features and split points, multiple isolated trees are constructed, and the data samples are recursively divided into different nodes.

[0106] For each data sample, calculate its outlier score in the isolated forest. Based on the outlier score, set a threshold and identify data samples with outlier scores higher than the threshold as outliers and remove them to obtain preprocessed data.

[0107] In this embodiment of the invention, multiple types of sensors are installed at key parts of the transformer, enabling high-frequency data acquisition once per second. This allows for comprehensive and detailed acquisition of multi-dimensional raw data such as oil temperature, winding temperature, and load current during transformer operation. This comprehensive and high-frequency data acquisition method greatly avoids monitoring loopholes caused by incomplete or untimely data acquisition. Wavelet transform is used to remove noise from the raw data, effectively filtering out interference signals and significantly improving data quality. The data processed by wavelet transform has clearer signal characteristics, which is beneficial for accurate subsequent judgment of the transformer's operating status. Time synchronization processing ensures the consistency of data collected by different sensors in the time dimension. This allows for the correlation and comprehensive analysis of data from various sensors, avoiding data analysis errors caused by time differences and providing a guarantee for accurate assessment of the transformer's operating status. The isolated forest algorithm can accurately identify and remove abnormal data in the raw data. Abnormal data may mislead subsequent analysis results; by removing these abnormal data, the preprocessed data becomes more accurate and reliable.

[0108] Key features such as oil temperature change rate, vibration spectrum characteristics, and gas concentration ratio were extracted from the preprocessed data. These key features reflect the transformer's operating status from different perspectives. For example, the oil temperature change rate reflects the transformer's heat generation, helping to determine if there are problems such as overload or poor heat dissipation; vibration spectrum characteristics reflect the operating status of mechanical components and can detect faults such as wear and loosening of mechanical components; and the gas concentration ratio can be used to detect faults such as internal discharge or overheating of the transformer. A weighted average method was used to fuse the extracted key features to generate a comprehensive status index. By reasonably allocating the weights of each feature, multiple aspects of information were comprehensively considered, avoiding the limitations of single features. This feature fusion method enables the comprehensive status index to more comprehensively and accurately reflect the overall operating status of the transformer, thereby improving the accuracy of fault diagnosis and condition assessment.

[0109] Based on comprehensive condition indicators, a fault classification model built using deep neural networks can automatically learn complex patterns and features in the data, accurately identifying transformer fault types. Deep neural networks possess powerful nonlinear fitting capabilities, enabling them to handle high-dimensional and complex feature data, and achieving higher fault diagnosis accuracy compared to traditional methods. This helps maintenance personnel to promptly understand transformer fault conditions, take targeted maintenance measures, and improve transformer operational reliability.

[0110] Long Short-Term Memory (LSTM) networks are used to predict the future state of transformers. LSM networks can process time-series data, capture long-term dependencies, and accurately predict the future operating trends of transformers. By predicting the future state of transformers, maintenance personnel can develop maintenance plans in advance, rationally allocate maintenance resources, avoid power outages caused by sudden transformer failures, and improve the reliability and stability of the power system.

[0111] The transformer health score, calculated based on comprehensive status indicators and future transformer conditions, provides a clear picture of the transformer's overall health. This health score offers maintenance personnel a quantifiable assessment indicator, facilitating a comprehensive evaluation of the transformer's operational status and providing a scientific basis for developing maintenance strategies. Multiple alarm thresholds can be set based on the transformer health score for real-time alerts. This allows for timely issuance of corresponding alarm signals based on different transformer health conditions, enabling maintenance personnel to quickly identify abnormalities, take prompt action, prevent further escalation of faults, and ensure the safe and stable operation of the power system.

[0112] In a preferred embodiment of the present invention, key features are extracted from the preprocessed data, and then fused using a weighted average method to generate a comprehensive status index, including:

[0113] The importance of each key feature in reflecting the transformer's operating status is assessed to obtain the importance assessment results;

[0114] Historical data is used for analysis, and correlation analysis is used to calculate the correlation between each feature and the actual occurrence of transformer faults.

[0115] Based on the importance assessment results and relevance, determine the weighting coefficient for each key feature;

[0116] The extracted key features are standardized to obtain standardized key feature values;

[0117] The standardized key feature values ​​are multiplied by their corresponding weighting coefficients, and the weighted values ​​of all features are summed to obtain the comprehensive state index.

[0118] In this embodiment of the invention, by evaluating the importance of each key feature in reflecting the transformer's operating status and determining weighting coefficients based on correlation analysis of historical data, the weights of each feature can be scientifically and rationally allocated. This weighting allocation method based on actual data and feature importance avoids subjective assumptions, enabling the comprehensive status index to more accurately reflect the actual operating status of the transformer and improving the accuracy of status assessment. A weighted average method is used to fuse multiple key features, comprehensively considering the information contained in different features. The transformer's operating status is a complex system, and a single feature often cannot fully and accurately reflect its status. By fusing multiple key features, the transformer's operating status can be assessed from multiple perspectives, avoiding the limitations of a single feature and making the comprehensive status index more representative and comprehensive. Weighting coefficients are determined based on the importance and correlation of features, ensuring that key features highly correlated with the actual occurrence of transformer faults occupy a greater weight in the comprehensive status index. Thus, during fault diagnosis, the influence of these key features can be fully reflected, helping to more accurately identify the type and severity of transformer faults and improving the accuracy and reliability of fault diagnosis.

[0119] By analyzing historical data and calculating correlations, the association between some potential key characteristics and faults can be discovered. Even if these characteristics are not obvious during normal operation, they may show abnormal changes before a fault occurs. Incorporating these characteristics into the fusion process of the comprehensive status index allows for the early detection of potential transformer faults, providing maintenance personnel with more timely warnings so that appropriate maintenance measures can be taken to prevent faults from occurring or escalating. The comprehensive status index is a weighted fusion result of multiple key characteristics, which can more comprehensively and accurately reflect the overall operating status of the transformer. When using this index for status prediction, the prediction model can better capture the changing trends of the transformer status and improve the accuracy of status prediction because it considers the combined influence of multiple characteristics. The use of the weighted average method makes the comprehensive status index robust to changes in individual characteristics. Even if the data of a certain characteristic fluctuates or has errors, the changes in the comprehensive status index are relatively stable due to the influence of other characteristics and the effect of weighted averaging, thereby enhancing the stability of the status prediction model and reducing prediction errors caused by abnormal data of individual characteristics. The comprehensive status index provides maintenance personnel with a quantitative assessment indicator that can intuitively reflect the overall health status of the transformer. Maintenance personnel can assess the transformer's operating status by analyzing the magnitude and trend of comprehensive status indicators, and formulate corresponding maintenance strategies. For example, when the comprehensive status indicators exceed a certain threshold, a maintenance plan can be promptly arranged to prevent power outages caused by sudden transformer failures.

[0120] By comparing and analyzing the comprehensive status indicators of different transformers, maintenance personnel can rationally allocate maintenance resources. Transformers with poor comprehensive status indicators and high fault risk should be prioritized for maintenance and repair; for transformers with good comprehensive status indicators, the maintenance cycle can be appropriately extended to improve the utilization efficiency of maintenance resources and reduce maintenance costs. This feature fusion method fully utilizes historical and real-time monitoring data, providing a scientific basis for maintenance decisions through data analysis and feature extraction. This data-driven maintenance management model can improve the scientific nature and accuracy of maintenance decisions, reduce interference from human factors, and make maintenance work more efficient and precise.

[0121] In a preferred embodiment of the present invention, based on a comprehensive state index, a fault classification model constructed based on a deep neural network is used to identify fault types, a long short-term memory network is used to analyze and predict the future state of the transformer, and a transformer health score is calculated based on the comprehensive state index and the future state of the transformer, including:

[0122] Extract comprehensive status indicators as input features, organize the comprehensive status indicators into a dataset in chronological order, label the known fault types corresponding to each data sample, and construct training set, validation set and test set;

[0123] The input features are normalized so that all feature values ​​are within the same scale range;

[0124] The architecture for constructing a deep neural network includes an input layer, multiple hidden layers, and an output layer. The number of neurons in the input layer is the same as the number of features in the overall state indicator; there are 2-3 hidden layers; the number of neurons in the output layer equals the number of fault types.

[0125] The ReLU function is selected, and the deep neural network model is trained using the training set. Stochastic gradient descent is used to automatically adjust the parameters of the deep neural network model to obtain the trained DNN model.

[0126] The trained DNN model is used to predict the new comprehensive state index data, and the probability of each fault type is calculated through forward propagation.

[0127] In this embodiment of the invention, deep neural networks (DNNs) possess powerful feature extraction and nonlinear mapping capabilities. By constructing a complex network architecture comprising an input layer, multiple hidden layers, and an output layer, high-level features related to different fault types can be automatically learned from comprehensive state indicators. These features are difficult to capture using traditional methods, thus significantly improving the accuracy of fault classification. By training the DNN model with a large amount of comprehensive state indicator data labeled with known fault types, the model can learn the inherent patterns between fault features and fault types. When faced with new comprehensive state indicator data, accurate fault classification can be performed based on the learned patterns, reducing misjudgments caused by human factors.

[0128] Once the DNN model is trained, it can perform rapid forward propagation calculations on new comprehensive state index data to obtain the probability of each fault type. This real-time predictive capability enables maintenance personnel to respond quickly when transformer fault signs appear, taking timely measures to prevent further deterioration of the fault and reduce power outage time and equipment damage risk. Fault classification results based on the DNN model can provide automated decision support for maintenance personnel. For example, when the model predicts a high probability of a certain fault type, the system can automatically trigger the corresponding alarm mechanism and provide suggested maintenance measures, improving maintenance efficiency.

[0129] LSTM is a special type of recurrent neural network (RNN) with the ability to remember long-term dependencies. When analyzing and predicting the future state of transformers, LSTM can effectively process time-series data of comprehensive state indicators, capturing the changing patterns of transformer state over time. By learning from historical data, LSTM can predict the state trend of transformers over a future period, providing more comprehensive information for operation and maintenance decisions. Compared to traditional prediction methods, LSTM considers the temporal sequence information of comprehensive state indicators. The evolution of transformer state is a dynamic process; the current state is influenced by previous states. LSTM can fully utilize this temporal correlation to improve the accuracy of state prediction, enabling operation and maintenance personnel to better understand the future operating status of transformers.

[0130] By predicting the future condition of transformers, maintenance personnel can detect abnormal trends in transformer status in advance. When a transformer's condition is predicted to be heading towards failure, preventative maintenance measures can be taken promptly, such as increasing inspections and performing necessary repairs, to avoid failure. Based on the condition prediction results, more reasonable maintenance plans can be developed. For example, for transformers predicted to be in poor condition in the near future, maintenance resources can be arranged in advance to ensure that maintenance work is completed before a failure occurs, improving the transformer's reliability and availability.

[0131] The health score comprehensively considers information from two aspects: overall condition indicators and the transformer's future condition. Overall condition indicators reflect the transformer's current operating status, while future condition predictions provide trends in the transformer's condition. By integrating these two aspects, the health score can more comprehensively and accurately assess the transformer's health status. The health score is a quantitative indicator that intuitively reflects the transformer's health level. Maintenance personnel can use the health score to classify and assess the transformer's health status and formulate corresponding maintenance strategies. For example, for transformers with lower health scores, more stringent monitoring and maintenance measures can be implemented. Based on the health score, personalized maintenance plans can be developed for different transformers. For transformers with higher health scores, maintenance cycles can be appropriately extended to reduce maintenance costs; for transformers with lower health scores, maintenance efforts need to be strengthened to ensure their safe and stable operation. The health score allows for the rational allocation of maintenance resources. Limited resources can be prioritized for transformers with poorer health conditions, improving resource utilization efficiency and ensuring the safe and stable operation of the entire power system.

[0132] The entire process, based on comprehensive status indicators, fault classification models, status prediction models, and health scores, achieves data-driven operation and maintenance management. Operation and maintenance decisions no longer rely solely on the experience of maintenance personnel, but are based on extensive data analysis and model prediction, improving the scientific rigor and accuracy of operation and maintenance management. From data preprocessing and model training to fault identification, status prediction, and health scoring, the entire process can be automated. This not only improves operation and maintenance efficiency but also reduces errors caused by human factors, making operation and maintenance management more efficient and reliable. Accurate fault identification, status prediction, and health scoring enable preventative maintenance. Taking appropriate maintenance measures before faults occur avoids equipment damage and power outage losses, reducing operation and maintenance costs. Reasonable operation and maintenance decisions and resource allocation can avoid resource waste, improve resource utilization efficiency, and further reduce operation and maintenance costs.

[0133] In a preferred embodiment of the present invention, a long short-term memory network is used to analyze and predict the future state of the transformer. A transformer health score is calculated based on a comprehensive state index and the future state of the transformer, including:

[0134] The comprehensive state indicators are arranged into time series data in chronological order, and the time series data is divided into multiple input sequences and corresponding target sequences;

[0135] Construct an LSTM model, set up 1-2 LSTM layers in the LSTM model, and add a fully connected layer after the LSTM layers;

[0136] The LSTM model is trained using the constructed time series dataset, and the Adam algorithm is used to update the LSTM model parameters. During the training process, the validation set is used to evaluate and fine-tune the LSTM model. After training is completed, the latest time series data is input into the LSTM model to predict the future state of the transformer and obtain the predicted value of the comprehensive state index in the future period.

[0137] The comprehensive health status index and future health status prediction are divided into different level intervals, and each level interval corresponds to a different health status description and a corresponding scoring range.

[0138] Different weights are assigned to the comprehensive status index and the future status of the transformer. Based on the specific values ​​of the comprehensive status index and the future status of the transformer, their respective scores are determined. The scores are then weighted and summed to obtain the health score of the transformer.

[0139] In this embodiment of the invention, a Long Short-Term Memory (LSTM) network is specifically designed for processing and predicting time-series data. Its unique gating mechanism (input gate, forget gate, and output gate) effectively captures the long-term dependencies of the overall state index over time. Changes in transformer state are often a slow and continuous process, and LSTM can learn this long-term trend, predicting the overall state index value more accurately over a future period compared to traditional methods. During transformer operation, the overall state index is affected by various factors (such as load changes and ambient temperature), exhibiting complex fluctuation patterns. LSTM has strong nonlinear fitting capabilities, adapting to these complex changes and providing more accurate future state predictions, offering maintenance personnel a more reliable basis for decision-making.

[0140] Once the LSTM model is trained, it can quickly perform forward propagation calculations for new time-series data input to obtain future state predictions. This real-time capability allows maintenance personnel to obtain transformer future state information promptly, make corresponding maintenance decisions quickly, and avoid failures caused by deteriorating condition. The LSTM model supports online learning; as new data is continuously input, the model can update parameters in real time to adapt to changes in the transformer's operating state. This ensures that the prediction model maintains high accuracy, providing continuous assurance for the safe and stable operation of the transformer. The health score comprehensively considers information from both the overall state index and the transformer's future state. The overall state index reflects the transformer's current operating status, while the future state prediction provides the development trend of the transformer's state. By fusing information from these two dimensions, the health score can more comprehensively and accurately assess the transformer's health level, avoiding the limitations of single-index assessments. Different weights can be assigned to the overall state index and the transformer's future state, allowing for flexible adjustments based on actual maintenance needs and the transformer's importance. For example, higher weights can be given to key performance indicators and future state trends affecting transformer lifespan, making the health score more reflective of the transformer's actual health condition.

[0141] The comprehensive status indicators and future status predictions are divided into different level intervals, with each interval corresponding to a different health status description and a corresponding scoring range. This grading method makes the transformer's health status more intuitive and clear, allowing maintenance personnel to quickly understand the transformer's health level and take appropriate maintenance measures. Different health scores correspond to different maintenance strategies; for example, transformers with lower health scores require increased inspection and maintenance efforts, while those with higher scores can have their maintenance cycles appropriately extended. This clear decision support helps improve maintenance efficiency and reduce costs. Based on accurate future status predictions and reasonable health scores, maintenance personnel can identify potential transformer faults in advance and take timely preventative maintenance measures. For example, when it is predicted that certain key transformer indicators are about to exceed thresholds, repairs or component replacements can be carried out in advance to avoid faults and reduce power outage time and equipment damage risks.

[0142] Based on transformer health scores, maintenance resources can be rationally allocated. Limited resources are prioritized for transformers with poor health, ensuring the safe and stable operation of critical equipment and improving the reliability of the entire power system. Preventative maintenance effectively reduces the number of sudden transformer failures, lowering the costs of equipment repair and replacement due to failures. It also avoids economic losses and social impacts caused by power outages. Based on health scores and future condition predictions, more scientific and reasonable maintenance plans can be developed, avoiding over-maintenance and under-maintenance, improving the utilization efficiency of maintenance resources, and reducing maintenance costs. With the continuous accumulation of transformer operating data, the LSTM model can continuously improve its prediction accuracy and the rationality of health scores through continuous training and optimization. This data-driven optimization approach allows the model to adapt to changes in the transformer operating environment and the impact of equipment aging, maintaining high performance. If more comprehensive status indicators or new monitoring data need to be introduced in the future, the LSTM model can be easily expanded in terms of features and adjusted. By adding input features and adjusting the network structure, new data information can be fully utilized to improve the model's predictive ability and the accuracy of health assessment. Because the LSTM model is built using a data-driven method, it has a certain degree of versatility. After proper adjustments and training, the model can be applied to other similar types of transformers or power equipment, enabling cross-device application of the model and improving the efficiency and standardization of operation and maintenance management.

[0143] In a preferred embodiment of the present invention, multi-level alarm thresholds are set based on the transformer health score for real-time alarm generation, and maintenance suggestions are generated in conjunction with the fault type, including:

[0144] Based on the transformer's design standards and historical operating data, the transformer's health score is divided into different intervals, each corresponding to a different health status and alarm level, resulting in four intervals:

[0145] A health status score of 90-100 indicates that the transformer is operating normally.

[0146] Note the status; a score of 70-89 indicates a minor abnormality in the transformer, but it does not affect normal operation.

[0147] A warning status, corresponding to a score of 50-69, indicates that the transformer is malfunctioning and has a potential fault.

[0148] A dangerous condition, with a score of 0-49, indicates that the transformer is in a faulty state and a shutdown accident could occur at any time.

[0149] In this embodiment of the invention, the transformer health score is divided into four state ranges: healthy, alert, warning, and dangerous, each corresponding to a different alarm level, enabling precise early warning of the transformer's operating status. Maintenance personnel can quickly determine the transformer's health status based on the alarm level, clarify the severity of the problem, and thus take targeted countermeasures. For example, when the transformer is in a dangerous state, the system will immediately issue the highest-level alarm, reminding maintenance personnel to take immediate emergency measures to prevent the fault from escalating further. Combined with the real-time alarm function, maintenance personnel can monitor the transformer's operating status in real time. Once the health score reaches the corresponding alarm threshold, the system will immediately issue an alarm signal, enabling maintenance personnel to respond quickly. This real-time monitoring and rapid response mechanism significantly shortens the time for fault detection and handling, improving the operational reliability and safety of the transformer.

[0150] For transformers in a "watch" state, although they do not currently affect normal operation, minor anomalies have already been detected. By setting alarm thresholds for this level, maintenance personnel can proactively inspect and maintain the transformer, promptly identifying and resolving potential problems to prevent further deterioration. For example, key parameters such as transformer temperature and oil level can be closely monitored to check for potential hazards such as partial discharge and insulation aging, and measures can be taken in advance for repair or replacement. When a transformer is in a "warning" state, it indicates an anomaly and a potential fault. At this time, maintenance personnel can develop detailed maintenance plans based on the alarm information, prepare necessary spare parts and repair tools, and arrange for professional maintenance personnel to be on standby. Once a fault occurs, rapid repairs can be organized, reducing power outage time and the risk of equipment damage.

[0151] Traditional scheduled maintenance often leads to over-maintenance, wasting significant manpower, resources, and funds, and potentially disrupting normal transformer operation. Maintenance based on health scoring and alarm thresholds, however, represents a shift from scheduled to condition-based maintenance. Maintenance personnel can tailor maintenance plans to the actual health condition of the transformers, avoiding over-maintenance of healthy transformers and reducing maintenance costs. By setting multi-level alarm thresholds, maintenance personnel can clearly understand the number and distribution of transformers in different health states. Based on this information, maintenance resources can be allocated rationally, prioritizing limited resources for transformers in poorer health, improving resource utilization efficiency and reducing overall maintenance costs.

[0152] Automatic real-time alarms based on health scores and alarm thresholds reduce the workload of manual monitoring and judgment, improving operation and maintenance efficiency. Maintenance personnel can devote more time and energy to fault handling and maintenance decision-making, improving the focus and effectiveness of their work. Early warning and timely intervention can effectively prevent the occurrence and escalation of transformer faults, reducing power outage time and frequency. This is of great significance for ensuring the stable operation of the power system and normal power consumption for users, improving power supply reliability and user satisfaction. With the continuous accumulation of transformer operating data, health scores and alarm thresholds can be continuously optimized. Analysis and mining of historical data allows for understanding the operating patterns and fault characteristics of transformers, continuously adjusting and improving alarm threshold settings, and enhancing the accuracy and reliability of alarms. Maintenance suggestions are generated based on fault types, providing more specific maintenance guidance for maintenance personnel. Analysis of fault data allows for summarizing the causes and patterns of fault occurrences, improving transformer design, manufacturing, and maintenance strategies, and enhancing the overall performance and reliability of transformers.

[0153] like Figure 2 As shown, an embodiment of the present invention also provides a real-time monitoring system for power transformers, comprising:

[0154] The acquisition module is used to install temperature sensors, current sensors, vibration sensors, gas sensors and humidity sensors at key parts of transformers in ultra-high voltage power transmission projects, and collect raw data once per second.

[0155] The preprocessing module is used to remove noise from the raw data using wavelet transform, synchronize the time, and identify and remove outlier data using the isolated forest algorithm to obtain the preprocessed data.

[0156] The fusion module is used to extract key features from the preprocessed data and then fuse them using a weighted average method to generate a comprehensive status index.

[0157] The evaluation module is used to identify fault types based on comprehensive condition indicators and a fault classification model built on a deep neural network, and to predict the future state of the transformer using long short-term memory network analysis. The transformer health score is calculated based on the comprehensive condition indicators and the future state of the transformer.

[0158] The alarm module is used to set multi-level alarm thresholds based on the transformer health score and to generate real-time alarms, and to generate maintenance suggestions based on the fault type.

[0159] It should be noted that this system is a system corresponding to the above method. All implementation methods in the above method embodiments are applicable to this embodiment and can achieve the same technical effect.

[0160] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for real-time monitoring of power transformers, characterized in that, The method includes: Step 1: Install temperature sensors, current sensors, vibration sensors, gas sensors, and humidity sensors on key parts of transformers in ultra-high voltage power transmission projects, and collect raw data once per second. Step 2: For the original data, wavelet transform is used to remove noise, time synchronization is performed, and outlier data is identified and removed using the isolated forest algorithm to obtain preprocessed data; Step 3: Extract key features from the preprocessed data, and then use a weighted average method to fuse them to generate a comprehensive status index. Step 4: Based on the comprehensive status index, identify the fault type using a fault classification model built on a deep neural network, use a long short-term memory network to analyze and predict the future state of the transformer, and calculate the transformer health score based on the comprehensive status index and the future state of the transformer. Step 5: Based on the transformer health score, set multi-level alarm thresholds for real-time alarms, and generate maintenance suggestions based on the fault type.

2. The real-time monitoring method for power transformers according to claim 1, characterized in that, The raw data includes oil temperature, winding temperature, load current, short-circuit current, core and winding vibration, dissolved gas, and water content in the oil.

3. The real-time monitoring method for power transformers according to claim 2, characterized in that, Key features include oil temperature change rate, vibration spectrum characteristics, and gas concentration ratio.

4. The real-time monitoring method for power transformers according to claim 3, characterized in that, For the original data, wavelet transform is used to remove noise, time synchronization is performed, and outlier data is identified and removed using the Isolation Forest algorithm, resulting in preprocessed data, including: The collected raw data were decomposed using wavelet decomposition to decompose the temperature time series into approximate components and detail components at different scales. Thresholding is performed on the detailed components obtained from the decomposition. After thresholding, wavelet reconstruction is performed using the processed approximate components and detailed components to obtain the denoised data. Extract the timestamp of each data point from the collected raw data, and perform time alignment on the denoised data according to a unified time benchmark; The data, after denoising and time synchronization processing, are combined into a multidimensional dataset, where each row represents a data sample and each column represents a feature. The isolated forest algorithm is used to train the cleaned data. By randomly selecting features and split points, multiple isolated trees are constructed, and the data samples are recursively divided into different nodes. For each data sample, calculate its outlier score in the isolated forest. Based on the outlier score, set a threshold and identify data samples with outlier scores higher than the threshold as outliers and remove them to obtain preprocessed data.

5. The real-time monitoring method for power transformers according to claim 4, characterized in that, The preprocessed data is then extracted for key features, which are then fused using a weighted average method to generate a comprehensive state index, including: The importance of each key feature in reflecting the transformer's operating status is assessed to obtain the importance assessment results; Historical data is used for analysis, and correlation analysis is used to calculate the correlation between each feature and the actual occurrence of transformer faults. Based on the importance assessment results and relevance, determine the weighting coefficient for each key feature; The extracted key features are standardized to obtain standardized key feature values; The standardized key feature values ​​are multiplied by their corresponding weighting coefficients, and the weighted values ​​of all features are summed to obtain the comprehensive state index.

6. The real-time monitoring method for power transformers according to claim 5, characterized in that, Based on comprehensive condition indicators, a fault classification model built on a deep neural network is used to identify fault types. Long Short-Term Memory (LSTM) networks are employed to analyze and predict the future state of the transformer. Based on the comprehensive condition indicators and the future state of the transformer, a transformer health score is calculated, including: Extract comprehensive status indicators as input features, organize the comprehensive status indicators into a dataset in chronological order, label the known fault types corresponding to each data sample, and construct training set, validation set and test set; The input features are normalized so that all feature values ​​are within the same scale range; The architecture for constructing a deep neural network includes an input layer, multiple hidden layers, and an output layer. The number of neurons in the input layer is the same as the number of features in the overall state indicator; there are 2-3 hidden layers; the number of neurons in the output layer equals the number of fault types. The ReLU function is selected, and the deep neural network model is trained using the training set. Stochastic gradient descent is used to automatically adjust the parameters of the deep neural network model to obtain the trained DNN model. The trained DNN model is used to predict the new comprehensive state index data, and the probability of each fault type is calculated through forward propagation.

7. The real-time monitoring method for power transformers according to claim 6, characterized in that, Long Short-Term Memory (LSTM) network analysis is used to predict the future state of the transformer. A transformer health score is calculated based on comprehensive state indicators and the future state of the transformer, including: The comprehensive state indicators are arranged into time series data in chronological order, and the time series data is divided into multiple input sequences and corresponding target sequences; Construct an LSTM model, set up 1-2 LSTM layers in the LSTM model, and add a fully connected layer after the LSTM layers; The LSTM model is trained using the constructed time series dataset, and the Adam algorithm is used to update the LSTM model parameters. During the training process, the validation set is used to evaluate and fine-tune the LSTM model. After training is completed, the latest time series data is input into the LSTM model to predict the future state of the transformer and obtain the predicted value of the comprehensive state index in the future period. The comprehensive health status index and future health status prediction are divided into different level intervals, and each level interval corresponds to a different health status description and a corresponding scoring range. Different weights are assigned to the comprehensive status index and the future status of the transformer. Based on the specific values ​​of the comprehensive status index and the future status of the transformer, their respective scores are determined. The scores are then weighted and summed to obtain the health score of the transformer.

8. The real-time monitoring method for power transformers according to claim 7, characterized in that, Based on the transformer health score, multi-level alarm thresholds are set for real-time alarms, and maintenance suggestions are generated in conjunction with the fault type, including: Based on the transformer's design standards and historical operating data, the transformer's health score is divided into different intervals, each corresponding to a different health status and alarm level, resulting in four intervals: A health status score of 90-100 indicates that the transformer is operating normally. Note the status; a score of 70-89 indicates a minor abnormality in the transformer, but it does not affect normal operation. A warning status, corresponding to a score of 50-69, indicates that the transformer is malfunctioning and has a potential fault. A dangerous condition, with a score of 0-49, indicates that the transformer is in a faulty state and a shutdown accident could occur at any time.

9. A real-time monitoring system for power transformers, characterized in that, The system is used to perform the method as described in any one of claims 1 to 8, comprising: The acquisition module is used to install temperature sensors, current sensors, vibration sensors, gas sensors and humidity sensors at key parts of transformers in ultra-high voltage power transmission projects, and collect raw data once per second. The preprocessing module is used to remove noise from the raw data using wavelet transform, synchronize the time, and identify and remove outlier data using the isolated forest algorithm to obtain the preprocessed data. The fusion module is used to extract key features from the preprocessed data and then fuse them using a weighted average method to generate a comprehensive status index. The evaluation module is used to identify fault types based on comprehensive condition indicators and a fault classification model built on a deep neural network, and to predict the future state of the transformer using long short-term memory network analysis. The transformer health score is calculated based on the comprehensive condition indicators and the future state of the transformer. The alarm module is used to set multi-level alarm thresholds based on the transformer health score and to generate real-time alarms, and to generate maintenance suggestions based on the fault type.

10. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs, which, when executed by one or more processors, cause the one or more processors to implement the method as described in any one of claims 1 to 8.

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