Transformer end electricity consumption monitoring method and system

Abstract

The invention relates to the field of transformer energy consumption monitoring, in particular to a transformer end electricity consumption monitoring method and system. The method comprises the following steps: acquiring real-time operation monitoring parameters of the transformer; carrying out time sequence window segmentation on the real-time operation monitoring parameters of the transformer, and carrying out time sequence load characteristic calculation to obtain transformer load characteristics of a plurality of time periods; constructing a time sequence load characteristic curve based on the transformer load characteristics of the plurality of time periods; performing load distribution trend evolution on the time sequence load characteristic curve to obtain load distribution change trend data; carrying out deep dynamic trend mining on the real-time operation monitoring parameters of the transformer according to the load distribution change trend data so as to obtain a loss power dynamic trend representation; obtaining transformer temperature monitoring data and a transformer surface image; according to transformer temperature monitoring data, multi-dimensional uniform interpolation is carried out on a transformer surface image, and a three-dimensional temperature distribution trend field is constructed. According to the invention, efficient and accurate energy consumption monitoring is realized.

Description

Technical Field

[0001] The present invention relates to the field of transformer energy consumption monitoring, and in particular to a transformer-end power consumption monitoring method and system. Background Art

[0002] With the continuous improvement of the automation and intelligence level of industrial production, the role of transformers in power systems has become increasingly important. Transformers are key energy conversion equipment in power systems, and their operating status directly affects the stability and energy utilization efficiency of the entire power grid. However, in long-term continuous operation, the power consumption of transformers may be affected by various internal and external factors, such as load fluctuations, winding aging, cooling system failures, etc. These factors will cause abnormal energy consumption of transformers, thereby affecting their operating efficiency and economy. Traditional transformer power consumption monitoring methods mainly rely on manual inspections and regular testing, which have problems such as low detection accuracy and low analysis efficiency. Therefore, an intelligent transformer energy consumption monitoring method is needed. Summary of the invention

[0003] In order to solve the above technical problems, the present invention proposes a transformer-end power consumption monitoring method and system to solve at least one of the above technical problems.

[0004] To achieve the above object, the present invention provides a method for monitoring power consumption at a transformer end, comprising the following steps: Step S1: obtaining transformer real-time operation monitoring parameters; performing time series window segmentation on the transformer real-time operation monitoring parameters, and performing time series load characteristic calculation to obtain transformer load characteristics in multiple time periods; Step S2: constructing a time series load characteristic curve based on the transformer load characteristics of multiple time periods; performing load distribution trend evolution on the time series load characteristic curve, thereby obtaining load distribution change trend data; Step S3: conducting deep dynamic trend mining on the real-time operation monitoring parameters of the transformer according to the load distribution change trend data, thereby obtaining a dynamic trend representation of the power loss; Step S4: Obtain transformer temperature monitoring data and transformer surface image; perform multi-dimensional uniform interpolation on the transformer surface image according to the transformer temperature monitoring data, and reconstruct the three-dimensional temperature distribution to construct a three-dimensional temperature distribution trend field; Step S5: performing iterative correlation evolution learning on the three-dimensional temperature distribution trend field based on the dynamic trend characterization of the power loss, and constructing a power-temperature situation map; Step S6: Based on the power-temperature situation map, locate the abnormal energy consumption nodes of the transformer, perform intelligent load parameter fine-tuning, and build an intelligent load fine-tuning engine.

[0005] The present invention helps to capture the dynamic changes of the real-time operating status of the transformer through time-series window segmentation and feature calculation, and provides basic data for subsequent analysis. The transformer load characteristics of multiple time periods can reflect the changes in the load in different time periods, and provide detailed information for energy consumption monitoring. By constructing a time-series load characteristic curve and load distribution trend evolution, the dynamic changes of load distribution can be intuitively understood, providing a basis for energy consumption trend analysis. Obtaining load distribution change trend data helps to discover the regularity of load changes and provide support for subsequent dynamic trend mining. Through deep dynamic trend mining, potential energy consumption trend laws can be discovered, which helps predict future electricity consumption. Obtaining the dynamic trend characterization of loss power can more accurately describe the changes in transformer power loss, and provide more refined data for energy consumption analysis. Multi-dimensional uniform interpolation and three-dimensional temperature distribution reconstruction can accurately reflect the distribution of transformer surface temperature and provide visualization support for temperature monitoring. Constructing a three-dimensional temperature distribution trend field helps track temperature change trends and provide important information for energy consumption and safety analysis. Constructing a power-temperature situation map through iterative correlation evolution learning can comprehensively consider the relationship between power and temperature and help understand the linkage law between the two. Based on the map, the situation changes of power and temperature can be more intuitively displayed, which is helpful to discover abnormal energy consumption nodes and conduct further analysis. By locating abnormal energy consumption nodes and fine-tuning intelligent load parameters, energy consumption anomalies can be found in time and load strategies can be adjusted to effectively reduce energy consumption costs. Building an intelligent load fine-tuning engine can automatically optimize load configuration, improve power efficiency and extend equipment life.

[0006] Preferably, step S1 comprises the following steps: Step S11: continuously monitoring the normal operation status of the transformer based on multiple sensors, and extracting real-time operation monitoring parameters of the transformer; Step S12: performing multi-scale adaptive filtering on the transformer real-time operation monitoring parameters to generate filtering optimized monitoring parameters; Step S13: performing time window segmentation on the filter optimization monitoring parameters, thereby obtaining monitoring parameters for multiple time periods; Step S14: Calculate the time series load characteristics of the monitoring parameters in multiple time periods to obtain the transformer load characteristics in multiple time periods.

[0007] The present invention can fully understand the operating status of the transformer through continuous monitoring by multiple sensors, and the extracted real-time monitoring parameters contain rich information. Data extraction based on multiple sensors helps to accurately capture multiple aspects of the transformer operating status and provide sufficient support for subsequent data processing. Multi-scale adaptive filtering can effectively remove noise and interference in the monitoring parameters and improve the accuracy and reliability of the data. Generating filtering to optimize monitoring parameters helps to improve data quality and provide a more reliable basis for subsequent analysis and decision-making. Time series window segmentation can divide the monitoring parameters by time, making the data easier to process and analyze, while retaining the time series information of the data. Obtaining monitoring parameters for multiple time periods helps to have a more detailed understanding of the changes in the transformer operating status in different time periods, providing more dimensions for subsequent analysis. By calculating the time series load characteristics of the monitoring parameters for multiple time periods, the load situation of the transformer can be more comprehensively understood, and the regularity of load changes can be revealed. Obtaining the transformer load characteristics for multiple time periods can provide more detailed and accurate data support for energy consumption monitoring and analysis, and help discover potential problems and optimize operation.

[0008] Preferably, the specific steps of step S12 are: Calculate the mean value of the real-time operation monitoring parameters of the transformer to obtain the normalized range of the monitoring parameters; Based on the normalized range of monitoring parameters, the real-time operation monitoring parameters of the transformer are subjected to abnormal outlier elimination processing to obtain abnormal value elimination parameters; Perform multi-scale wavelet transform decomposition on the outlier removal parameters to extract detailed feature information in different frequency domain bands; Discrete frequency components are calculated for the detailed feature information of different frequency domain bands to obtain the frequency component value of each frequency band; Adaptively optimizing filter coefficients of detail feature information of different frequency bands based on the frequency component value of each frequency band to generate filter data of multiple frequency bands; The filter data of multiple frequency bands are reconstructed by nonlinear transformation to generate filter optimization monitoring parameters.

[0009] The present invention can help determine the monitoring parameter range under normal operating conditions by calculating parameter means and establishing normalized ranges, and provide benchmark reference values. Establishing normalized ranges helps identify abnormal values ​​in subsequent monitoring parameters, laying the foundation for abnormal detection and processing. Eliminating abnormal outliers can improve data quality and accuracy, and ensure the effectiveness of subsequent analysis and processing. Through outlier elimination processing, the interference of abnormal data on subsequent feature extraction and analysis can be reduced, making the data more reliable. Wavelet transform can effectively extract the time-frequency characteristics of signals, help capture signal change information at different scales, and extract detailed feature information of different frequency domain bands, which helps to more comprehensively describe the changing laws of monitoring parameters, provide more details for subsequent analysis, and calculate frequency component values. It can help understand the energy distribution of monitoring parameters in different frequency bands and reveal potential frequency domain characteristics. The extraction of frequency component values ​​is helpful for more detailed analysis of the frequency domain characteristics of monitoring parameters and provides important information for subsequent processing. Through adaptive filter coefficient optimization, the filter parameters can be adjusted according to the characteristics of the frequency band to improve the filtering effect. Generating filter data in multiple frequency bands can help to more clearly display the characteristics of monitoring parameters in different frequency domains and provide more dimensional information for subsequent analysis. Through nonlinear transformation reconstruction, the expression of monitoring parameters can be further optimized and the characteristic information of data can be highlighted. Generating filter-optimized monitoring parameters can help reduce noise interference and highlight the potential characteristics of monitoring parameters, providing a more accurate data basis for subsequent analysis and application.

[0010] Preferably, the specific steps of step S2 are: Step S21: performing load serialization fitting on transformer load characteristics in multiple time periods to construct a time series load characteristic curve; Step S22: performing load change analysis on the time-series load characteristic curve to obtain time-series load change data; Step S23: performing load parameter distribution identification on transformer load characteristics in multiple time periods to generate load distribution data in different time periods; Step S24: Based on the load distribution data of different time periods, the load distribution trend of the time series load change data is evolved to obtain load distribution change trend data.

[0011] By serializing and fitting the transformer load characteristics of multiple time periods, the present invention can better understand the regularity of load changes and provide a more accurate data basis for subsequent analysis. Constructing a time-series load characteristic curve is helpful to intuitively display the changing trend of load characteristics over time. By performing load change analysis on the time-series load characteristic curve, the fluctuation of load in different time periods can be deeply understood, providing a basis for abnormal detection and optimization. Obtaining time-series load change data is helpful to discover periodic or sudden changes in load, and help predict future load change trends. Identifying the load parameter distribution in different time periods can reveal the characteristics and changing laws of load in different time periods, and provide important information for load management and prediction. Generating load distribution data for different time periods is helpful to establish a comprehensive load characteristic archive. By performing trend evolution analysis on load distribution data, the evolution of load distribution over time can be understood, providing a basis for formulating long-term energy consumption management strategies. Obtaining load distribution change trend data is helpful to discover the overall change trend of load distribution, and provides support for adjusting load configuration and predicting energy consumption changes.

[0012] Preferably, step S3 specifically comprises the following steps: Step S31: extracting real-time current and voltage data of the transformer based on real-time operation monitoring parameters of the transformer; Step S32: performing multi-time point power calculation on the transformer load characteristics of multiple time periods according to the real-time current and voltage data of the transformer to generate load power parameters of multiple time periods; Step S33: performing reactive power calculation on the load power parameters of multiple time periods to generate load reactive power parameters for each time period; Step S34: performing actual loss calculation on the load power parameters of multiple time periods and the load reactive power parameters of each time period, thereby obtaining load loss power data of different time periods; Step S35: Perform deep dynamic trend mining on the load loss power data of different time periods according to the load distribution change trend data, so as to obtain a dynamic trend representation of the loss power.

[0013] The present invention can monitor the operation status of the power system in real time by extracting the real-time current and voltage data of the transformer, and provide basic data for subsequent energy consumption analysis. The real-time data extraction is helpful to timely discover the abnormal situation of the power system and improve the reliability and safety of equipment operation. The load power calculation of multiple time periods using the real-time current and voltage data can accurately reflect the energy consumption level of the transformer under different load conditions. The generation of load power parameters of multiple time periods is helpful to understand the impact of load changes on energy consumption and provide detailed data support for energy consumption evaluation. The reactive power calculation of the load power parameters of multiple time periods can evaluate the reactive power demand of the system and help optimize the power factor of the power system. The generation of the load reactive power parameters of each time period is helpful to analyze the power factor of the load and provide a basis for energy saving and power quality improvement. The load loss power of different time periods can be accurately evaluated through actual loss calculation, which helps to identify the causes of energy consumption fluctuations and potential energy-saving opportunities. Obtaining load loss power data is helpful to quantify energy consumption losses and provide support for optimizing energy utilization and cost control. Through deep dynamic trend mining of loss power data, the potential laws and changing trends of load loss power can be discovered, which helps to warn energy consumption anomalies in advance.

[0014] Preferably, the specific steps of step S4 are: Step S41: Acquire transformer temperature monitoring data and transformer surface image; Step S42: performing three-dimensional grid division on the transformer surface image to obtain a transformer surface network; Step S43: extracting a plurality of grid points based on the transformer surface network; Step S44: performing multi-dimensional uniform interpolation on multiple grid points according to the transformer temperature monitoring data, thereby obtaining a transformer temperature network; Step S45: performing a three-dimensional temperature distribution analysis on the transformer temperature network to obtain three-dimensional grid temperature distribution data; Step S46: performing multi-time temperature change fluctuation analysis on the three-dimensional grid temperature distribution data to generate three-dimensional temperature change fluctuation data; Step S47: reconstruct the three-dimensional temperature distribution of the three-dimensional temperature change fluctuation data to construct a three-dimensional temperature distribution trend field.

[0015] The present invention can fully understand the working state and heat load of the transformer by acquiring the temperature monitoring data and surface image of the transformer. The temperature monitoring data and surface image provide real-time temperature information, which is helpful to monitor the heat distribution and thermal state change of the transformer. The three-dimensional grid division of the transformer surface image is helpful to more finely describe the structure and characteristics of the transformer surface. The three-dimensional grid division provides a basic grid structure and provides a data basis for subsequent temperature analysis and interpolation. By extracting multiple grid points, representative positions can be selected for temperature monitoring and analysis to accurately reflect the temperature distribution of the entire transformer surface. Multi-dimensional uniform interpolation using the transformer temperature monitoring data can fill in the missing data and obtain a more continuous and accurate temperature network. A comprehensive transformer temperature network can be obtained by interpolation to provide data support for subsequent temperature distribution analysis. The three-dimensional temperature distribution analysis of the transformer temperature network can deeply understand the temperature distribution law and heat transfer inside the transformer. The generated three-dimensional grid temperature distribution data is helpful to evaluate the thermal characteristics of the transformer and provide a basis for temperature control and equipment protection. Through multi-time temperature change fluctuation analysis and three-dimensional temperature distribution reconstruction, the dynamic change law and trend of the transformer temperature over time can be revealed. The construction of a three-dimensional temperature distribution trend field is helpful to predict the temperature change trend, timely discover potential fault risks, and ensure the safe and stable operation of the transformer.

[0016] Preferably, the specific steps of step S5 are: Step S51: mining implicit correlation features of the three-dimensional temperature distribution trend field based on the power loss dynamic trend characterization to obtain power loss-temperature distribution correlation data; Step S52: discretizing the time series temperature characteristics of the three-dimensional temperature distribution trend field to extract the three-dimensional temperature distribution vector; Step S53: performing feature space mapping processing on the dynamic trend representation of power loss and the three-dimensional temperature distribution vector to construct a potential feature space model; Step S54: performing iterative correlation evolution learning on the potential feature space model according to the loss power-temperature distribution correlation data to construct a power-temperature situation map. The present invention can deeply mine the potential correlation features between the dynamic trend of power loss and the three-dimensional temperature distribution through implicit correlation feature mining, and obtain the power loss-temperature distribution correlation data, which is helpful to understand the relationship between power loss and temperature distribution. By discretizing the three-dimensional temperature distribution and extracting the three-dimensional temperature distribution vector, the complex temperature data can be converted into an easy-to-process feature vector. The three-dimensional temperature distribution vector extraction helps to more effectively describe and analyze the temperature characteristics, and provides a data basis for subsequent feature space mapping processing. Through feature space mapping processing, the power loss dynamic trend characterization and the three-dimensional temperature distribution vector can be mapped to the potential feature space. The construction of the potential feature space model helps to convert the correlation relationship between different features into a visual and analytical form, and helps to understand the complex relationship between power and temperature. Through iterative correlation evolution learning, a power-temperature situation map can be constructed based on the power loss-temperature distribution correlation data. The construction of the power-temperature situation map helps to reveal the complex relationship and change law between power and temperature, and provides a deeper understanding and analysis for transformer-end power consumption monitoring.

[0017] Preferably, the specific steps of step S6 are: Step S61: monitoring the abnormal energy consumption parameter deviation of the transformer based on the power-temperature situation map, and marking the abnormal energy consumption deviation parameter; Step S62: locating abnormal nodes on the transformer surface network according to the abnormal energy consumption deviation parameter, and extracting abnormal energy consumption grid points; Step S63: performing abnormal type attribution identification on the abnormal energy consumption grid point to obtain abnormal type attribution data; Step S64: Perform intelligent load parameter fine-tuning based on the abnormal type attribution data and build an intelligent load fine-tuning engine.

[0018] The present invention can monitor the abnormal energy consumption parameter deviation of the transformer through the power-temperature situation map, timely identify potential energy consumption abnormalities, mark the abnormal energy consumption deviation parameters to help accurately capture the abnormal energy consumption of the transformer, and provide important information for subsequent abnormal problem diagnosis and processing. According to the abnormal energy consumption deviation parameters, the abnormal nodes of the transformer surface network are located, the abnormal energy consumption grid points are extracted, and the abnormal energy consumption points are accurately located. The extraction of abnormal energy consumption grid points helps to focus on the specific location where the abnormality exists, and provides positioning support for further abnormal type attribution identification and processing. Abnormal type attribution identification of abnormal energy consumption grid points can analyze the root cause of the abnormality, help understand the specific type and characteristics of the abnormal situation, and obtain abnormal type attribution data to help deeply understand the abnormal situation, and provide a basis for identifying abnormalities for subsequent intelligent load parameter fine-tuning. Based on the abnormal type attribution data, an intelligent load parameter fine-tuning engine can be constructed to realize intelligent fine-tuning and optimization of transformer load parameters. The intelligent load fine-tuning engine helps to automatically adjust load parameters according to abnormal situations, improve energy utilization efficiency, reduce energy consumption losses, and ensure equipment operation stability.

[0019] In this specification, a transformer-end power consumption monitoring system is provided, which is used to execute the transformer-end power consumption monitoring method as described above, including: The time series segmentation module is used to obtain the real-time operation monitoring parameters of the transformer; the real-time operation monitoring parameters of the transformer are segmented into time series windows, and the time series load characteristics are calculated to obtain the transformer load characteristics of multiple time periods; The load distribution module is used to construct a time-series load characteristic curve based on the transformer load characteristics of multiple time periods; the load distribution trend of the time-series load characteristic curve is evolved to obtain load distribution change trend data; Dynamic trend mining module, used to conduct deep dynamic trend mining of transformer real-time operation monitoring parameters according to load distribution change trend data, so as to obtain dynamic trend representation of power loss; The three-dimensional temperature distribution module is used to obtain transformer temperature monitoring data and transformer surface images; perform multi-dimensional uniform interpolation on the transformer surface image according to the transformer temperature monitoring data, reconstruct the three-dimensional temperature distribution, and construct a three-dimensional temperature distribution trend field; The situation map module is used to perform iterative correlation evolution learning on the three-dimensional temperature distribution trend field based on the dynamic trend representation of power loss, and construct a power-temperature situation map; The parameter fine-tuning module is used to locate abnormal energy consumption nodes of the transformer based on the power-temperature situation map, and to perform intelligent load parameter fine-tuning to build an intelligent load fine-tuning engine.

[0020] The present invention helps to capture the time series characteristics of the transformer operating status by acquiring real-time monitoring parameters and performing time series window segmentation, providing a real-time data basis. Time series load characteristic calculation can help analyze the load characteristics of different time periods, and provide key data for subsequent load distribution analysis and dynamic trend mining. Constructing a time series load characteristic curve based on the load characteristics of multiple time periods helps to reveal the changing trend of load characteristics over time, and provides visualization support for the evolution of load distribution trends. Analyzing the load distribution change trend data helps to understand the laws and trends of load changes, and provides important clues for subsequent dynamic trend mining. Through deep dynamic trend mining, the dynamic trends of the transformer real-time monitoring parameters can be revealed according to the load distribution change trend data, including the dynamic characterization of power loss. Obtaining the dynamic trend characterization of power loss helps to understand the laws and trends of power changes, and provides in-depth information for energy consumption monitoring and analysis. Insight: Through multi-dimensional uniform interpolation and three-dimensional temperature distribution reconstruction, an accurate three-dimensional temperature distribution trend field can be constructed to reveal the spatial distribution characteristics of the transformer surface temperature. Constructing a three-dimensional temperature distribution trend field helps to understand the dynamic changes of temperature distribution and provides a basis for power-temperature correlation analysis and situation map construction. Through iterative correlation evolution learning, a power-temperature situation map is constructed to reveal the correlation between power and temperature, providing a comprehensive perspective for energy consumption monitoring and anomaly detection. Constructing a power-temperature situation map helps to fully understand the dynamic situation of power consumption at the transformer end and provide a basis for subsequent parameter fine-tuning. Through abnormal energy consumption node positioning and intelligent load parameter fine-tuning, intelligent adjustment of the transformer can be achieved, energy utilization efficiency can be improved, and energy consumption losses can be reduced. Building an intelligent load fine-tuning engine helps to adjust load parameters in real time, ensure safe and stable operation of equipment, and improve equipment life and performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A schematic diagram of the steps of a transformer-end power consumption monitoring method according to the present invention; Figure 2 Detailed implementation flow chart of step S1; Figure 3 Detailed implementation flow chart of step S2; Figure 4 Detailed implementation flow chart of step S3. DETAILED DESCRIPTION

[0022] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0023] The example of this application provides a method and system. The execution subjects of the method and system include but are not limited to: mechanical equipment, data processing platform, cloud server node, network upload device, etc. equipped with the system can be regarded as the general computing node of this application, and the data processing platform includes but is not limited to: at least one of audio and image management system, information management system, and cloud data management system.

[0024] See also Figures 1 to 4 The present invention provides a transformer-side power consumption monitoring method, the transformer-side power consumption monitoring method comprising the following steps: Step S1: obtaining transformer real-time operation monitoring parameters; performing time series window segmentation on the transformer real-time operation monitoring parameters, and performing time series load characteristic calculation to obtain transformer load characteristics in multiple time periods; Step S2: constructing a time series load characteristic curve based on the transformer load characteristics of multiple time periods; performing load distribution trend evolution on the time series load characteristic curve, thereby obtaining load distribution change trend data; Step S3: conducting deep dynamic trend mining on the real-time operation monitoring parameters of the transformer according to the load distribution change trend data, thereby obtaining a dynamic trend representation of the power loss; Step S4: Obtain transformer temperature monitoring data and transformer surface image; perform multi-dimensional uniform interpolation on the transformer surface image according to the transformer temperature monitoring data, and reconstruct the three-dimensional temperature distribution to construct a three-dimensional temperature distribution trend field; Step S5: performing iterative correlation evolution learning on the three-dimensional temperature distribution trend field based on the dynamic trend characterization of the power loss, and constructing a power-temperature situation map; Step S6: Based on the power-temperature situation map, locate the abnormal energy consumption nodes of the transformer, perform intelligent load parameter fine-tuning, and build an intelligent load fine-tuning engine.

[0025] The present invention helps to capture the dynamic changes of the real-time operating status of the transformer through time-series window segmentation and feature calculation, and provides basic data for subsequent analysis. The transformer load characteristics of multiple time periods can reflect the changes in the load in different time periods, and provide detailed information for energy consumption monitoring. By constructing a time-series load characteristic curve and load distribution trend evolution, the dynamic changes of load distribution can be intuitively understood, providing a basis for energy consumption trend analysis. Obtaining load distribution change trend data helps to discover the regularity of load changes and provide support for subsequent dynamic trend mining. Through deep dynamic trend mining, potential energy consumption trend laws can be discovered, which helps predict future electricity consumption. Obtaining the dynamic trend characterization of loss power can more accurately describe the changes in transformer power loss, and provide more refined data for energy consumption analysis. Multi-dimensional uniform interpolation and three-dimensional temperature distribution reconstruction can accurately reflect the distribution of transformer surface temperature and provide visualization support for temperature monitoring. Constructing a three-dimensional temperature distribution trend field helps track temperature change trends and provide important information for energy consumption and safety analysis. Constructing a power-temperature situation map through iterative correlation evolution learning can comprehensively consider the relationship between power and temperature and help understand the linkage law between the two. Based on the map, the situation changes of power and temperature can be more intuitively displayed, which is helpful to discover abnormal energy consumption nodes and conduct further analysis. By locating abnormal energy consumption nodes and fine-tuning intelligent load parameters, energy consumption anomalies can be found in time and load strategies can be adjusted to effectively reduce energy consumption costs. Building an intelligent load fine-tuning engine can automatically optimize load configuration, improve power efficiency and extend equipment life.

[0026] In the embodiment of the present invention, refer to Figure 1 , is a schematic flow chart of the steps of a transformer-end power consumption monitoring method of the present invention. In this example, the steps of the transformer-end power consumption monitoring method include: Step S1: obtaining transformer real-time operation monitoring parameters; performing time series window segmentation on the transformer real-time operation monitoring parameters, and performing time series load characteristic calculation to obtain transformer load characteristics in multiple time periods; In this embodiment, sensors (such as current sensors, temperature sensors and voltage sensors) are installed at key parts of the transformer, and the sampling frequency of the sensors is configured to ensure that the operating status of the transformer can be monitored in real time. Sensor data is regularly acquired through a data acquisition system (such as a PLC or SCADA system) to ensure the real-time and accuracy of data acquisition. The monitoring parameters (such as current, temperature, voltage, etc.) at each time point are recorded, and the size of the time series window (such as every hour, every minute, etc.) and the overlapping step (such as 0, 50%, etc.) are determined. These parameters will affect the accuracy and time resolution of the analysis. The real-time monitoring parameters are segmented using a sliding window technology, and the continuous time series data is divided into multiple time series windows. Each window contains a certain number of time point data for load feature calculation, and the load feature indicators that need to be calculated are determined, such as average load, maximum load, load factor, power factor, etc., and load feature calculation is performed on the data of each time series window to generate a load feature value for each window. The load feature data of each time period is recorded to form a complete data set for subsequent analysis.

[0027] Step S2: constructing a time series load characteristic curve based on the transformer load characteristics of multiple time periods; performing load distribution trend evolution on the time series load characteristic curve, thereby obtaining load distribution change trend data; In this embodiment, the load characteristic data (such as average load, maximum load, etc.) of multiple time periods are sorted into a table to ensure that the data of each time period is clearly visible and the load characteristics are arranged in chronological order. A suitable drawing tool or library (such as Matplotlib, Seaborn or Excel) is selected to draw a time series load characteristic curve. The time series load characteristic curve is drawn according to the sorted load characteristic data. Usually, the X-axis is time and the Y-axis is the load characteristic value. The drawn curve is annotated, and legends, titles and axis labels are added to improve readability. The load distribution characteristics that need to be analyzed are determined, such as mean, variance, skewness, kurtosis, etc. These characteristics can reflect the change trend of the load. Select Appropriate statistical analysis methods (such as moving average, trend line regression analysis, time series analysis, etc.) are used to analyze changes in load distribution. According to the data characteristics, a suitable model (such as linear regression, exponential smoothing or ARIMA model) is selected for trend analysis. The time series load characteristic curve is analyzed to identify the evolution trend of the load distribution. The selected model is used to fit the data, extract trend information, and calculate the rate of change of load characteristics over time, such as growth rate or fluctuation range, to reflect the dynamic changes of load distribution. The load distribution change trend data obtained from the analysis is organized into tables or charts, and the changes in characteristics such as mean and variance over time are recorded. The load distribution change trend data is plotted into a curve to facilitate the intuitive display of the evolution process of the load distribution.

[0028] Step S3: conducting deep dynamic trend mining on the real-time operation monitoring parameters of the transformer according to the load distribution change trend data, thereby obtaining a dynamic trend representation of the power loss; In this embodiment, a suitable dynamic trend mining algorithm is selected according to the data characteristics, such as time series analysis (ARIMA, SARIMA), machine learning method (LSTM, random forest), or deep learning model, and the dynamic correlation characteristics between the two are analyzed to identify monitoring parameters closely related to transformer power loss, such as load current, winding temperature and other indicators, as the main features of subsequent power loss dynamic trend mining. In combination with the physical characteristics and loss mechanism of the transformer, a mathematical model for calculating transformer power loss is established to provide a calculation basis for subsequent dynamic trend analysis. The selected model is trained using load distribution change trend data and extracted real-time monitoring parameter features, and the training set and test set are divided to ensure the generalization ability of the model. The model is evaluated using appropriate evaluation indicators (such as mean square error MSE, root mean square error RMSE) to ensure the prediction accuracy of the model. The trained model is used to predict the real-time monitoring parameters, identify the dynamic trend of power loss, perform time series prediction on power loss, generate power loss dynamic trend data for a period of time in the future, calculate the rate of change of power loss, identify mutation points and abnormal fluctuations, and provide in-depth analysis.

[0029] Step S4: Obtain transformer temperature monitoring data and transformer surface image; perform multi-dimensional uniform interpolation on the transformer surface image according to the transformer temperature monitoring data, and reconstruct the three-dimensional temperature distribution to construct a three-dimensional temperature distribution trend field; In this embodiment, real-time temperature data is regularly acquired from the temperature sensor device of the transformer to ensure that the temperature information at different locations is accurately recorded. The data includes but is not limited to the oil temperature and winding temperature of the transformer. A high-resolution camera is used to photograph the surface of the transformer to ensure that the image is clear and covers the entire transformer surface. The timestamp of the image shooting is recorded for subsequent alignment with the temperature data. The acquired temperature monitoring data is organized into a structured format (such as CSV or Excel) to ensure that the temperature data of each sensor has a clear timestamp. The surface image is named and stored in an easily accessible folder. A suitable interpolation method is selected, such as inverse distance weighted (IDW), Kriging interpolation, or spline interpolation, to interpolate the temperature distribution on the surface image. The transformer surface image is generated. A three-dimensional grid is formed, and the resolution of the grid is ensured to be high enough to capture the temperature changes more accurately. Each grid point will be used for subsequent temperature interpolation calculations. The selected interpolation algorithm is used to interpolate the temperature of each grid point in the three-dimensional grid according to the temperature monitoring data. The interpolation result of each grid point is recorded to form a complete three-dimensional temperature distribution data set. A three-dimensional temperature distribution model is constructed based on the temperature grid point data obtained by interpolation. A three-dimensional visualization tool (such as Matplotlib, ParaView or Mayavi) is used for three-dimensional reconstruction to display the temperature distribution on the surface and inside of the transformer. A trend analysis is performed on the reconstructed three-dimensional temperature distribution to identify the temperature change trend and hot spots, and record the key features of the temperature distribution, such as the highest temperature point and temperature gradient.

[0030] Step S5: performing iterative correlation evolution learning on the three-dimensional temperature distribution trend field based on the dynamic trend characterization of the power loss, and constructing a power-temperature situation map; In this embodiment, deep learning and other methods are used to mine the implicit correlation features between the two, establish correlation data reflecting the relationship between power loss and temperature distribution, discretize temperature features in the time dimension, convert them into three-dimensional temperature distribution vectors, provide input data for subsequent feature space mapping processing, divide the integrated data into training sets and test sets to ensure the effectiveness of model training and evaluation, use the training set to train the selected model, optimize the model parameters by iteration, reduce the loss function (such as mean square error), adjust the model's hyperparameters (such as learning rate, batch size, number of layers, etc.) according to the performance during the training process to optimize the model performance, use the test set to evaluate the trained model, calculate the accuracy of the predicted power loss and temperature distribution, analyze the difference between the model prediction results and the actual values, identify the performance of the model under specific conditions, use the trained model to predict the power loss and temperature distribution in the future period, generate a power-temperature situation map, and use visualization tools (such as Matplotlib, Plotly or Tableau) to visualize the power-temperature situation map to show the relationship between power and temperature under different conditions.

[0031] Step S6: Based on the power-temperature situation map, locate the abnormal energy consumption nodes of the transformer, perform intelligent load parameter fine-tuning, and build an intelligent load fine-tuning engine.

[0032] In this embodiment, power-temperature situation map data and real-time monitoring parameters (such as current, voltage, temperature, etc.) are collected to ensure that the timestamps of the data are consistent for subsequent analysis, and a suitable anomaly detection method is selected, such as Z-score, IQR (interquartile range) method or machine learning method (such as isolation forest or LOF). The power-temperature situation map is analyzed using the selected algorithm to identify abnormal energy consumption nodes (such as nodes that are significantly higher than the normal range), formulate an intelligent load fine-tuning strategy, and combine machine learning or optimization algorithms (such as genetic algorithms, particle swarm optimization) to determine the optimal load parameters. According to the characteristics of the abnormal energy consumption nodes, the load parameter values ​​that need to be fine-tuned are calculated to ensure that they return to the normal range, and the calculated The load parameters are applied to the control system of the transformer, the fine-tuning effect is monitored in real time, the load parameters and energy consumption data after fine-tuning are observed to ensure that the energy consumption returns to normal levels, the changes before and after fine-tuning are recorded, and the architecture of the intelligent load fine-tuning engine is designed, including the data input module, analysis and decision module, execution module and feedback module. The various modules of the fine-tuning engine are developed to ensure that they can work together efficiently to achieve automated load fine-tuning. The fine-tuning engine is comprehensively tested to ensure its stability and reliability under different conditions. The intelligent load fine-tuning engine is deployed in the production environment and monitored in real time to ensure its effective operation. Based on actual operation data and feedback, the algorithms and strategies of the fine-tuning engine are continuously optimized to improve its intelligence level.

[0033] In this embodiment, refer to Figure 2 , is a flowchart of detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include: Step S11: continuously monitoring the normal operation status of the transformer based on multiple sensors, and extracting real-time operation monitoring parameters of the transformer; Step S12: performing multi-scale adaptive filtering on the transformer real-time operation monitoring parameters to generate filtering optimized monitoring parameters; Step S13: performing time window segmentation on the filter optimization monitoring parameters, thereby obtaining monitoring parameters for multiple time periods; Step S14: Calculate the time series load characteristics of the monitoring parameters in multiple time periods to obtain the transformer load characteristics in multiple time periods.

[0034] In this embodiment, a variety of sensors are deployed in the transformer system, including current sensors, temperature sensors, vibration sensors, etc., to continuously collect multi-dimensional operating parameter data of the transformer under normal operating conditions, such as current, temperature, vibration, etc., and various important operating status parameter indicators are extracted from the collected original monitoring data as real-time operating monitoring parameters of the transformer. The parameters of the filter (such as thresholds, scale factors, etc.) are adjusted according to the characteristics of the monitoring parameters, and the real-time operating monitoring parameters are filtered to eliminate noise, extract more accurate monitoring parameters, determine the size of the timing window and the overlapping step length, select a suitable time period (such as every hour, every minute, etc.), use the sliding window method to segment the monitoring parameters after filtering optimization, generate monitoring parameter data sets for multiple time periods, determine the load characteristic indicators that need to be calculated, such as average load, maximum load, load factor and load fluctuation, etc., calculate the statistical characteristics of the transformer load in the corresponding time period, including indicators such as average value, variance, peak factor, fluctuation range, etc., so as to obtain the load characteristic parameters of the transformer in different time periods.

[0035] In this embodiment, the specific steps of step S12 are: Calculate the mean value of the real-time operation monitoring parameters of the transformer to obtain the normalized range of the monitoring parameters; Based on the normalized range of monitoring parameters, the real-time operation monitoring parameters of the transformer are subjected to abnormal outlier elimination processing to obtain abnormal value elimination parameters; Perform multi-scale wavelet transform decomposition on the outlier removal parameters to extract detailed feature information in different frequency domain bands; Discrete frequency components are calculated for the detailed feature information of different frequency domain bands to obtain the frequency component value of each frequency band; Adaptively optimizing filter coefficients of detail feature information of different frequency bands based on the frequency component value of each frequency band to generate filter data of multiple frequency bands; The filter data of multiple frequency bands are reconstructed by nonlinear transformation to generate filter optimization monitoring parameters.

[0036] In this embodiment, the monitoring parameter data is filtered and optimized, and the statistical mean and standard deviation of various monitoring parameters are calculated to determine the normalization range of the monitoring parameters under normal operating conditions. The monitoring parameters are detected for abnormal values ​​to determine whether each data point is outside the normalization range. The monitoring parameters that exceed the normalization range are regarded as abnormal values ​​and are eliminated, and normal data are retained. A suitable mother wavelet (such as Haar wavelet and Daubechies wavelet) is selected, and wavelet transform is performed according to data characteristics. The monitoring parameters after eliminating abnormal values ​​are subjected to wavelet transform to extract detail feature information at different scales, and a suitable adaptive filtering method (such as LMS algorithm or RLS algorithm) is selected to optimize the filtering coefficient. According to the frequency component value of each frequency band, the detail features of different frequency domain bands are adaptively filtered to optimize the signal, and the optimized filtered data of multiple frequency bands are organized into a data set. Data fusion is performed through nonlinear transformation (such as wavelet reconstruction, etc.), and the final filtering optimization monitoring parameters are reconstructed and generated. These monitoring parameters that have undergone multiple optimizations will provide a high-quality data basis for subsequent abnormality detection.

[0037] In this embodiment, refer to Figure 3 , is a flowchart of detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include: Step S21: performing load serialization fitting on transformer load characteristics in multiple time periods to construct a time series load characteristic curve; Step S22: performing load change analysis on the time-series load characteristic curve to obtain time-series load change data; Step S23: performing load parameter distribution identification on transformer load characteristics in multiple time periods to generate load distribution data in different time periods; Step S24: Based on the load distribution data of different time periods, the load distribution trend of the time series load change data is evolved to obtain load distribution change trend data.

[0038] In this embodiment, the load characteristic data of the transformer is extracted from multiple time periods to ensure the integrity and continuity of the data, and a suitable serialization method (such as linear interpolation, spline interpolation or polynomial fitting) is selected to convert the load characteristic data into a time series format, fit the serialized load data, generate a time series load characteristic curve, use the least square method and other techniques to optimize the fitting effect, determine a suitable load change analysis method, such as a difference method, a moving average or an exponential smoothing method, use the selected analysis method to process the time series load characteristic curve, calculate the load change rate or change amount, organize the obtained time series load change data into a structured format, and record the load changes in different time periods. Changes in the load parameters are determined, such as mean, variance, skewness and kurtosis, etc., and appropriate statistical distribution models (such as normal distribution, Log-Normal distribution or Gamma distribution) are selected to identify the load parameter distribution. The load characteristic data of each time period is analyzed to identify the corresponding load distribution, and load distribution data of different time periods are generated. Appropriate trend analysis methods are selected, such as time series analysis, regression analysis or machine learning algorithms, and the load distribution data of different time periods are integrated with the time series load change data in preparation for trend evolution analysis. The integrated data is analyzed to identify the evolution law of the load distribution over time and record the change trend.

[0039] In this embodiment, refer to Figure 4 , is a flowchart of detailed implementation steps of step S3. In this embodiment, the detailed implementation steps of step S3 include: Step S31: extracting real-time current and voltage data of the transformer based on real-time operation monitoring parameters of the transformer; Step S32: performing multi-time point power calculation on the transformer load characteristics of multiple time periods according to the real-time current and voltage data of the transformer to generate load power parameters of multiple time periods; Step S33: performing reactive power calculation on the load power parameters of multiple time periods to generate load reactive power parameters for each time period; Step S34: performing actual loss calculation on the load power parameters of multiple time periods and the load reactive power parameters of each time period, thereby obtaining load loss power data of different time periods; Step S35: Perform deep dynamic trend mining on the load loss power data of different time periods according to the load distribution change trend data, so as to obtain a dynamic trend representation of the loss power.

[0040] In this embodiment, current and voltage data are extracted from the real-time monitoring system of the transformer to ensure the real-time and accuracy of the data, the extracted current and voltage data are verified to ensure that they are within the normal range and to exclude abnormal values, power calculation is performed on the current and voltage data of each time period to generate corresponding load power parameters, reactive power calculation is performed on the current and voltage data of each time period to generate corresponding reactive power parameters, and the actual power loss of the transformer in different time periods is calculated in combination with the loss characteristics of the transformer to obtain the load loss power data of each time period, and a suitable dynamic trend mining method is selected, such as time series analysis, machine learning algorithm (such as LSTM) or regression analysis, and the load loss power data is integrated with the load distribution change trend data in preparation for in-depth analysis, the integrated data is analyzed to identify the dynamic change law of the power loss and record the trend characteristics.

[0041] In this embodiment, step S4 includes the following steps: Step S41: Acquire transformer temperature monitoring data and transformer surface image; Step S42: performing three-dimensional grid division on the transformer surface image to obtain a transformer surface network; Step S43: extracting a plurality of grid points based on the transformer surface network; Step S44: performing multi-dimensional uniform interpolation on multiple grid points according to the transformer temperature monitoring data, thereby obtaining a transformer temperature network; Step S45: performing a three-dimensional temperature distribution analysis on the transformer temperature network to obtain three-dimensional grid temperature distribution data; Step S46: performing multi-time temperature change fluctuation analysis on the three-dimensional grid temperature distribution data to generate three-dimensional temperature change fluctuation data; Step S47: reconstruct the three-dimensional temperature distribution of the three-dimensional temperature change fluctuation data to construct a three-dimensional temperature distribution trend field.

[0042] In this embodiment, temperature sensors are installed at key positions of the transformer, temperature monitoring data is collected regularly, and the surface of the transformer is photographed using a high-resolution camera to ensure the clarity and integrity of the image. The acquired surface image is preprocessed, including denoising, contrast enhancement, etc., to improve the subsequent processing effect. A suitable meshing algorithm is selected, such as Delaunay triangulation or Voronoi diagram, and the preprocessed surface image is three-dimensionally modeled and meshed to generate a three-dimensional grid structure covering the transformer surface. The standard for extracting grid points is determined, such as evenly spaced points or points within a specific area. Multiple grid points are extracted from the generated surface network, and their three-dimensional coordinates are recorded. A suitable interpolation algorithm is selected, such as Kriging interpolation, inverse distance weighted (IDW), or spline interpolation. , use the temperature monitoring data to perform multi-dimensional uniform interpolation on the extracted grid points to generate the temperature network on the transformer surface, select a suitable three-dimensional temperature distribution analysis method, such as heat map generation or three-dimensional visualization tools (such as Matplotlib, ParaView), analyze the generated temperature network, generate temperature distribution data of the three-dimensional grid, analyze the changes in temperature distribution between different times, including indicators such as temperature fluctuation amplitude and rate, generate fluctuation data describing the three-dimensional temperature change trend, select a suitable reconstruction algorithm, such as inverse distance weighted or wavelet reconstruction, use the three-dimensional temperature change fluctuation data to reconstruct the temperature distribution, generate a three-dimensional temperature distribution trend field, visualize the reconstructed three-dimensional temperature distribution trend field, and show the dynamic change trend of the temperature distribution.

[0043] In this embodiment, step S5 includes the following steps: Step S51: mining implicit correlation features of the three-dimensional temperature distribution trend field based on the power loss dynamic trend characterization to obtain power loss-temperature distribution correlation data; Step S52: discretizing the time series temperature characteristics of the three-dimensional temperature distribution trend field to extract the three-dimensional temperature distribution vector; Step S53: performing feature space mapping processing on the dynamic trend representation of power loss and the three-dimensional temperature distribution vector to construct a potential feature space model; Step S54: performing iterative correlation evolution learning on the potential feature space model according to the loss power-temperature distribution correlation data to construct a power-temperature situation map. In this embodiment, a suitable association mining method is selected, such as Pearson correlation coefficient, Spearman rank correlation or multivariate regression analysis, and the selected method is used to analyze the implicit association between loss power and temperature distribution, generate loss power-temperature distribution association data, discretize temperature features from the time dimension, discretize the three-dimensional temperature distribution trend field, extract representative three-dimensional temperature distribution vectors, select a suitable feature space mapping method, such as principal component analysis (PCA), t-SNE or autoencoder, map the loss power dynamic trend representation and the three-dimensional temperature distribution vector, construct a potential feature space model, select a suitable iterative learning algorithm, such as gradient descent, back propagation algorithm or reinforcement learning method, use the loss power-temperature distribution association data to train the potential feature space model, update model parameters, optimize association features, generate a power-temperature situation map based on the updated model, show the dynamic relationship between loss power and temperature distribution, and visualize the constructed power-temperature situation map to ensure that the information is clear and easy to understand.

[0044] In this embodiment, step S6 includes the following steps: Step S61: monitoring the abnormal energy consumption parameter deviation of the transformer based on the power-temperature situation map, and marking the abnormal energy consumption deviation parameter; Step S62: locating abnormal nodes on the transformer surface network according to the abnormal energy consumption deviation parameter, and extracting abnormal energy consumption grid points; Step S63: performing abnormal type attribution identification on the abnormal energy consumption grid point to obtain abnormal type attribution data; Step S64: Perform intelligent load parameter fine-tuning based on the abnormal type attribution data and build an intelligent load fine-tuning engine.

[0045] In this embodiment, the power loss and temperature distribution data of the transformer are monitored in real time, compared with the normal operating range in the situation map, and abnormal power loss and temperature deviation parameters are identified. The marked abnormal energy consumption deviation parameters are integrated with the transformer surface network data to ensure data consistency, and a suitable abnormal node positioning method is selected, such as distance-based clustering analysis or threshold-based screening. According to the abnormal energy consumption deviation parameters, the abnormal nodes are located in the surface network, and the corresponding abnormal energy consumption grid points are extracted. A suitable attribution identification method is selected, such as a decision tree, a random forest or a neural network, and the feature data of the abnormal energy consumption grid points are sorted to prepare for attribution analysis. The abnormal energy consumption grid points are analyzed using the selected attribution identification method to identify the abnormal type and its cause. According to the abnormal type attribution data, the load parameters that need to be fine-tuned, such as current, voltage or power settings, are determined, and an intelligent load fine-tuning strategy is formulated. The load parameters are optimized in combination with machine learning algorithms (such as reinforcement learning). The formulated strategy is applied to fine-tune the load parameters, and the fine-tuning effect is monitored in real time. The fine-tuning process and the algorithm are integrated to build an intelligent load fine-tuning engine to ensure subsequent automatic execution.

[0046] In this embodiment, a transformer-end power consumption monitoring system is provided, which is used to execute the transformer-end power consumption monitoring method as described above, including: The time series segmentation module is used to obtain the real-time operation monitoring parameters of the transformer; the real-time operation monitoring parameters of the transformer are segmented into time series windows, and the time series load characteristics are calculated to obtain the transformer load characteristics of multiple time periods; The load distribution module is used to construct a time-series load characteristic curve based on the transformer load characteristics of multiple time periods; the load distribution trend of the time-series load characteristic curve is evolved to obtain load distribution change trend data; Dynamic trend mining module, used to conduct deep dynamic trend mining of transformer real-time operation monitoring parameters according to load distribution change trend data, so as to obtain dynamic trend representation of power loss; The three-dimensional temperature distribution module is used to obtain transformer temperature monitoring data and transformer surface images; perform multi-dimensional uniform interpolation on the transformer surface image according to the transformer temperature monitoring data, reconstruct the three-dimensional temperature distribution, and construct a three-dimensional temperature distribution trend field; The situation map module is used to perform iterative correlation evolution learning on the three-dimensional temperature distribution trend field based on the dynamic trend representation of power loss, and construct a power-temperature situation map; The parameter fine-tuning module is used to locate abnormal energy consumption nodes of the transformer based on the power-temperature situation map, and to perform intelligent load parameter fine-tuning to build an intelligent load fine-tuning engine.

[0047] The present invention helps to capture the time series characteristics of the transformer operating status by acquiring real-time monitoring parameters and performing time series window segmentation, providing a real-time data basis. Time series load characteristic calculation can help analyze the load characteristics of different time periods, and provide key data for subsequent load distribution analysis and dynamic trend mining. Constructing a time series load characteristic curve based on the load characteristics of multiple time periods helps to reveal the changing trend of load characteristics over time, and provides visualization support for the evolution of load distribution trends. Analyzing the load distribution change trend data helps to understand the laws and trends of load changes, and provides important clues for subsequent dynamic trend mining. Through deep dynamic trend mining, the dynamic trends of the transformer real-time monitoring parameters can be revealed according to the load distribution change trend data, including the dynamic characterization of power loss. Obtaining the dynamic trend characterization of power loss helps to understand the laws and trends of power changes, and provides in-depth information for energy consumption monitoring and analysis. Insight: Through multi-dimensional uniform interpolation and three-dimensional temperature distribution reconstruction, an accurate three-dimensional temperature distribution trend field can be constructed to reveal the spatial distribution characteristics of the transformer surface temperature. Constructing a three-dimensional temperature distribution trend field helps to understand the dynamic changes of temperature distribution and provides a basis for power-temperature correlation analysis and situation map construction. Through iterative correlation evolution learning, a power-temperature situation map is constructed to reveal the correlation between power and temperature, providing a comprehensive perspective for energy consumption monitoring and anomaly detection. Constructing a power-temperature situation map helps to fully understand the dynamic situation of power consumption at the transformer end and provide a basis for subsequent parameter fine-tuning. Through abnormal energy consumption node positioning and intelligent load parameter fine-tuning, intelligent adjustment of the transformer can be achieved, energy utilization efficiency can be improved, and energy consumption losses can be reduced. Building an intelligent load fine-tuning engine helps to adjust load parameters in real time, ensure safe and stable operation of equipment, and improve equipment life and performance.

[0048] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is therefore intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0049] The above is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for monitoring power consumption at a transformer end, characterized in that: The following steps are involved: Step S1: Obtaining real-time operation monitoring parameters of the transformer; Perform time series window segmentation on the real-time operation monitoring parameters of the transformer and perform time series load characteristic calculation to obtain transformer load characteristics in multiple time periods; Step S2: constructing a time series load characteristic curve based on transformer load characteristics in multiple time periods; Perform load distribution trend evolution on the time series load characteristic curve to obtain load distribution change trend data; Step S3: conducting deep dynamic trend mining on the real-time operation monitoring parameters of the transformer according to the load distribution change trend data, thereby obtaining a dynamic trend representation of the power loss; Step S4: Obtain transformer temperature monitoring data and transformer surface image; According to the transformer temperature monitoring data, the transformer surface image is interpolated in multiple dimensions, and the three-dimensional temperature distribution is reconstructed to construct the three-dimensional temperature distribution trend field; Step S5: performing iterative correlation evolution learning on the three-dimensional temperature distribution trend field based on the dynamic trend characterization of the power loss, and constructing a power-temperature situation map; Step S6: Based on the power-temperature situation map, locate the abnormal energy consumption nodes of the transformer, perform intelligent load parameter fine-tuning, and build an intelligent load fine-tuning engine.

2. The transformer-end power consumption monitoring method according to claim 1, characterized in that: The specific steps of step S1 are: Step S11: continuously monitoring the normal operation status of the transformer based on multiple sensors, and extracting real-time operation monitoring parameters of the transformer; Step S12: performing multi-scale adaptive filtering on the transformer real-time operation monitoring parameters to generate filtering optimized monitoring parameters; Step S13: performing time window segmentation on the filter optimization monitoring parameters, thereby obtaining monitoring parameters for multiple time periods; Step S14: Calculate the time series load characteristics of the monitoring parameters in multiple time periods to obtain the transformer load characteristics in multiple time periods.

3. The transformer-end power consumption monitoring method according to claim 2, characterized in that: The specific steps of step S12 are: Calculate the mean value of the real-time operation monitoring parameters of the transformer to obtain the normalized range of the monitoring parameters; Based on the normalized range of monitoring parameters, the real-time operation monitoring parameters of the transformer are subjected to abnormal outlier elimination processing to obtain abnormal value elimination parameters; Perform multi-scale wavelet transform decomposition on the outlier removal parameters to extract detailed feature information in different frequency domain bands; Discrete frequency components are calculated for the detailed feature information of different frequency domain bands to obtain the frequency component value of each frequency band; Adaptively optimizing filter coefficients of detail feature information of different frequency bands based on the frequency component value of each frequency band to generate filter data of multiple frequency bands; The filter data of multiple frequency bands are reconstructed by nonlinear transformation to generate filter optimization monitoring parameters.

4. The transformer-end power consumption monitoring method according to claim 1, characterized in that: The specific steps of step S2 are: Step S21: performing load serialization fitting on transformer load characteristics in multiple time periods to construct a time series load characteristic curve; Step S22: performing load change analysis on the time-series load characteristic curve to obtain time-series load change data; Step S23: performing load parameter distribution identification on transformer load characteristics in multiple time periods to generate load distribution data in different time periods; Step S24: Based on the load distribution data of different time periods, the load distribution trend of the time series load change data is evolved to obtain load distribution change trend data.

5. The method according to claim 1, characterized in that The specific steps of step S3 are: Step S31: extracting real-time current and voltage data of the transformer based on real-time operation monitoring parameters of the transformer; Step S32: performing multi-time point power calculation on the transformer load characteristics of multiple time periods according to the real-time current and voltage data of the transformer to generate load power parameters of multiple time periods; Step S33: performing reactive power calculation on the load power parameters of multiple time periods to generate load reactive power parameters for each time period; Step S34: performing actual loss calculation on the load power parameters of multiple time periods and the load reactive power parameters of each time period, thereby obtaining load loss power data of different time periods; Step S35: Perform deep dynamic trend mining on the load loss power data of different time periods according to the load distribution change trend data, so as to obtain a dynamic trend representation of the loss power.

6. The transformer-end power consumption monitoring method according to claim 1, characterized in that: The specific steps of step S4 are: Step S41: Acquire transformer temperature monitoring data and transformer surface image; Step S42: performing three-dimensional grid division on the transformer surface image to obtain a transformer surface network; Step S43: extracting a plurality of grid points based on the transformer surface network; Step S44: performing multi-dimensional uniform interpolation on multiple grid points according to the transformer temperature monitoring data, thereby obtaining a transformer temperature network; Step S45: performing a three-dimensional temperature distribution analysis on the transformer temperature network to obtain three-dimensional grid temperature distribution data; Step S46: performing multi-time temperature change fluctuation analysis on the three-dimensional grid temperature distribution data to generate three-dimensional temperature change fluctuation data; Step S47: reconstruct the three-dimensional temperature distribution of the three-dimensional temperature change fluctuation data to construct a three-dimensional temperature distribution trend field.

7. The transformer-end power consumption monitoring method according to claim 1, characterized in that: The specific steps of step S5 are: Step S51: mining implicit correlation features of the three-dimensional temperature distribution trend field based on the power loss dynamic trend characterization to obtain power loss-temperature distribution correlation data; Step S52: discretizing the time series temperature characteristics of the three-dimensional temperature distribution trend field to extract the three-dimensional temperature distribution vector; Step S53: performing feature space mapping processing on the dynamic trend representation of power loss and the three-dimensional temperature distribution vector to construct a potential feature space model; Step S54: performing iterative correlation evolution learning on the potential feature space model according to the loss power-temperature distribution correlation data to construct a power-temperature situation map.

8. The transformer-end power consumption monitoring method according to claim 1, characterized in that: The specific steps of step S6 are: Step S61: monitoring the abnormal energy consumption parameter deviation of the transformer based on the power-temperature situation map, and marking the abnormal energy consumption deviation parameter; Step S62: locating abnormal nodes on the transformer surface network according to the abnormal energy consumption deviation parameter, and extracting abnormal energy consumption grid points; Step S63: performing abnormal type attribution identification on the abnormal energy consumption grid point to obtain abnormal type attribution data; Step S64: Perform intelligent load parameter fine-tuning based on the abnormal type attribution data and build an intelligent load fine-tuning engine.

9. A transformer-end power consumption monitoring system, characterized in that: The method for monitoring power consumption at the transformer end according to claim 1 comprises: The time series segmentation module is used to obtain the real-time operation monitoring parameters of the transformer; the real-time operation monitoring parameters of the transformer are segmented into time series windows, and the time series load characteristics are calculated to obtain the transformer load characteristics of multiple time periods; The load distribution module is used to construct a time-series load characteristic curve based on the transformer load characteristics of multiple time periods; the load distribution trend of the time-series load characteristic curve is evolved to obtain load distribution change trend data; Dynamic trend mining module, used to conduct deep dynamic trend mining of transformer real-time operation monitoring parameters according to load distribution change trend data, so as to obtain dynamic trend representation of power loss; The three-dimensional temperature distribution module is used to obtain transformer temperature monitoring data and transformer surface images; perform multi-dimensional uniform interpolation on the transformer surface image according to the transformer temperature monitoring data, reconstruct the three-dimensional temperature distribution, and construct a three-dimensional temperature distribution trend field; The situation map module is used to perform iterative correlation evolution learning on the three-dimensional temperature distribution trend field based on the dynamic trend representation of power loss, and construct a power-temperature situation map; The parameter fine-tuning module is used to locate abnormal energy consumption nodes of the transformer based on the power-temperature situation map, and to perform intelligent load parameter fine-tuning to build an intelligent load fine-tuning engine.

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