Real-time Visualization Tracking System for Reactive Power Dynamic Compensation of Power Grid Based on Optical Flow Field Analysis

Abstract

The present invention discloses a real-time visualization tracking system for dynamic reactive power compensation of power grids based on optical flow field analysis, which relates to the technical field of power grid compensation visualization. By constructing a multi-module collaborative system including image acquisition, power parameter acquisition, optical flow field calculation, comprehensive analysis, dynamic compensation control, and visualization interaction, accurate identification and response control of the dynamic changes in the reactive power of the power grid are realized. The system intelligently discriminates the disturbance area by fusing the optical flow vector and the power parameter characteristics, guides the compensation device to adjust in real time, and simultaneously displays the operation state of the power grid in a graphical manner, effectively improving the accuracy, real-time performance, and stability of the reactive power compensation and the operation of the system.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid compensation visualization, and particularly to a real-time visualization tracking system for dynamic reactive power compensation of power grid based on optical flow field analysis. Background Art

[0002] Real-time visualization tracking of dynamic reactive power compensation of power grid refers to realizing visual management of the operation state of the power grid by monitoring and displaying the dynamic compensation process of reactive power in the power grid in real time. Specifically, it collects the data of reactive power changes through sensors and control systems, and uses visualization technology to present key parameters such as the working state of compensation equipment, the compensation amount, and the grid voltage in a graphical form on the monitoring platform, helping the operation and maintenance personnel to grasp the power balance of the power grid in real time, adjust the strategy in time, and improve the power quality and system stability.

[0003] The following deficiencies exist in the prior art:

[0004] When deploying cameras in outdoor substations for optical flow analysis in the prior art, due to the rapid change of environmental lighting conditions (such as heavy rain, haze, or day-night alternation), it is easy to cause fluctuations in image quality. Since the optical flow algorithm usually relies on the premise of image brightness continuity, this assumption no longer holds under drastic lighting changes, resulting in distortion of motion vector calculation, which may cause the system to misjudge a large fluctuation in reactive power and wrongly trigger the compensation equipment, interfering with the normal voltage regulation of the power grid and affecting the system stability and control accuracy. Summary of the Invention

[0005] The purpose of the present invention is to provide a real-time visualization tracking system for dynamic reactive power compensation of power grid based on optical flow field analysis to solve the deficiencies in the background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions: A real-time visualization tracking system for dynamic reactive power compensation of power grid based on optical flow field analysis, including an image acquisition module, a power parameter acquisition module, an optical flow field calculation module, a comprehensive analysis module, a dynamic compensation control module, and a visualization interaction module;

[0007] The image acquisition module is used to deploy camera devices in key areas of the power grid to obtain a continuous sequence of monitoring images;

[0008] The power parameter acquisition module is used to collect real-time power grid operation parameters corresponding to the image area, including voltage, current, and reactive power;

[0009] The optical flow field calculation module is used to perform optical flow analysis based on the sequence of monitoring images and calculate the motion vector field of each pixel point;

[0010] A comprehensive analysis module for spatiotemporally corresponding and fusing the optical flow vector field with the power operation parameters to identify the dynamic change trend and spatial distribution of reactive power in the power grid;

[0011] A dynamic compensation control module for generating a compensation control instruction according to the comprehensive analysis result and controlling a static var compensator to perform real-time reactive power compensation on a target area;

[0012] A visualization interaction module for real-time displaying and interacting the power grid state, optical flow change and compensation control result in the form of a graphical interface.

[0013] Preferably, the key areas include the main transformer area, the bus connection point and the reactive power compensation device.

[0014] Preferably, receive continuous frame images from the image acquisition module, uniformly adjust them to grayscale images, preprocess the input images, dynamically switch the enhancement strategy according to different scene conditions, dynamically select a dense or sparse optical flow algorithm according to the computing resources and accuracy requirements; perform pixel-level matching and motion estimation on two consecutive frame images, and output the horizontal and vertical displacements of each pixel; generate a two-dimensional optical flow vector field, that is, each pixel corresponds to a motion vector representing its relative displacement from the previous frame to the current frame.

[0015] Preferably, extract the motion features in the optical flow vector field, including the local amplitude mutation value, and the power parameter change in the area, including the reactive power sudden increase value;

[0016] Among them, the method for obtaining the local amplitude mutation value is: perform optical flow calculation on continuous image frames, obtain the horizontal displacement u(x,y) and vertical displacement v(x,y) of each pixel point, and calculate the optical flow amplitude: ; where: M(x,y) is the optical flow amplitude of the pixel point, that is, its motion intensity; divide the region of interest in the image and calculate the average amplitude of each region: ; then calculate the local amplitude change between consecutive frames: ; where: is the average amplitude of the ROI region R at time t; ΔM(R) is the local amplitude mutation value; N is the number of pixel points in the region; when ΔM(R) exceeds the set threshold Tm, it is regarded as the local amplitude mutation value.

[0017] Preferably, among them, the method for obtaining the reactive power sudden increase value is: let the reactive power of the power node corresponding to a certain region at time t be , the sampling period is Δt, and the reactive power sudden increase calculation between two consecutive sampling periods is: ; where: ΔQ is the reactive power sudden increase value.

[0018] Preferably, the local amplitude mutation value and the reactive power mutation value are converted into a comprehensive feature vector, and the comprehensive feature vector is used as the input of a machine learning model. The machine learning model takes the evaluation value label corresponding to the optical flow change and the actual power disturbance predicted by each group of comprehensive feature vectors as the prediction target, and takes minimizing the sum of the prediction errors of all the evaluation value labels corresponding to the optical flow change and the actual power disturbance as the training target to train the machine learning model until the sum of the prediction errors reaches convergence and then stop the model training. The evaluation value corresponding to the optical flow change and the actual power disturbance is determined according to the model output result, wherein the machine learning model is a polynomial regression model.

[0019] Preferably, the evaluation value corresponding to the optical flow change and the actual power disturbance is compared with a predetermined threshold. If the evaluation value corresponding to the optical flow change and the actual power disturbance is greater than the predetermined threshold, it is determined that the optical flow change height is highly correlated with the actual power disturbance event, and it is determined as a region requiring compensation response; if the evaluation value corresponding to the optical flow change and the actual power disturbance is less than or equal to the predetermined threshold, it is considered that there is no obvious disturbance in the region and there is no need to immediately trigger the compensation control.

[0020] Preferably, based on the characteristics of each monitoring region in the current time period: the average value of the optical flow amplitude, the reactive power mutation value, and the duration of the optical flow change; a region feature vector is constructed: all regions are clustered by using the K-means clustering algorithm, and several reactive power disturbance hot spots are automatically divided; each type of region is sorted according to the characteristics of its center point, and different levels of compensation priorities are assigned. The high-priority regions are preferentially matched with compensation devices with fast dynamic response capabilities; the disturbance recognition results in each time period are integrated to generate a dynamic change distribution map; the horizontal axis is the time axis, and the vertical axis is the region number or coordinate; the depth of color or symbol represents the optical flow disturbance intensity or the reactive power mutation degree; the evolution path of the reactive power disturbance in the spatial and temporal dimensions is visually displayed.

[0021] In the above technical solution, the technical effects and advantages provided by the present invention are as follows:

[0022] 1. By constructing a multi-module system integrating image analysis and power data processing, the present invention realizes high-precision recognition and real-time response control of the dynamic change of the reactive power of the power grid. Compared with the problem of easy distortion of the prior art under complex lighting conditions, the present invention introduces a comprehensive feature extraction mechanism combining optical flow amplitude mutation detection and reactive power sudden increase analysis, and realizes intelligent corresponding evaluation of image disturbance and power disturbance with the help of a polynomial regression model, fundamentally improving the recognition accuracy of the system for real disturbances and effectively avoiding the risk of triggering unnecessary compensation due to misjudgment.

[0023] 2. The present invention uses a clustering analysis algorithm to identify hot spots of reactive power disturbances, and combines a dynamic change distribution map to achieve the visualization of evolution in the spatial and temporal dimensions, providing a clear decision-making basis for the compensation strategy. The system outputs control suggestions based on disturbance intensity, duration, and priority, dynamically schedules static var compensators, and achieves fast, accurate, and hierarchical compensation responses. In addition, the system also supports an artificial review mechanism to ensure safety and operation flexibility, and overall significantly improves the intelligent level and operation stability of power grid reactive power control. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention, and those of ordinary skill in the art can also obtain other drawings based on these drawings.

[0025] Figure 1 It is a system module mind map of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0027] For the embodiments, please refer to Figure 1 As shown, the real-time visualization tracking system for power grid reactive power dynamic compensation based on optical flow field analysis in this embodiment includes an image acquisition module, a power parameter acquisition module, an optical flow field calculation module, a comprehensive analysis module, a dynamic compensation control module, and a visualization interaction module;

[0028] The image acquisition module is used to deploy camera devices in key areas of the power grid to obtain a continuous sequence of monitoring images;

[0029] The power parameter acquisition module is used to collect real-time power grid operation parameters corresponding to the image area, including voltage, current, and reactive power;

[0030] The optical flow field calculation module is used to perform optical flow analysis based on the monitoring image sequence and calculate the motion vector field of each pixel point;

[0031] The comprehensive analysis module is used to perform spatio-temporal corresponding fusion of the optical flow vector field and the power operation parameters to identify the dynamic change trend and spatial distribution of reactive power in the power grid;

[0032] A dynamic compensation control module, which is used to generate a compensation control instruction according to the comprehensive analysis result and control the static var compensator to implement real-time reactive power compensation for the target area;

[0033] A visualization interaction module, which is used to display and interact with the power grid status, optical flow change and compensation control result in real time in the form of a graphical interface.

[0034] The image acquisition module is the front-end basic component for the system to realize optical flow field analysis and power grid status visualization, and is mainly responsible for acquiring and preliminarily processing continuous image data of key areas of the power grid.

[0035] Deployment of camera equipment: Install high-definition industrial cameras at key nodes of power grid operation (such as main transformer areas, bus access points, and near reactive power compensation devices). It is preferred to use industrial-grade devices with wide dynamic range (WDR), infrared night vision function, and resistance to harsh environments (IP66 or above).

[0036] The deployment angle of the camera is optimized according to the layout of electrical equipment to ensure that the covered area has no obvious occlusion and has a depth perspective effect, so as to more accurately capture the tiny movements of objects on the two-dimensional image plane.

[0037] Frame rate and resolution control: The image acquisition control unit uniformly manages the camera equipment, and preferably sets the image frame rate to ≥30fps to meet the requirement of the optical flow algorithm for time continuity;

[0038] The image resolution is preferably 1080p or higher to enhance the ability to identify equipment movement and vibration and improve the accuracy of optical flow calculation.

[0039] Image synchronization and timestamp management: The collected image frames are automatically marked with a unified timestamp and synchronized with the power data acquisition module (such as SCADA system or PMU system) to ensure that the image information and power parameters can be accurately matched during the analysis stage.

[0040] Image caching and frame loss protection: Configure an edge caching unit (such as an embedded memory or a circular buffer) to temporarily store continuous image sequences; a frame loss monitoring mechanism is provided to ensure image continuity and avoid analysis interruption caused by communication delay or frame loss.

[0041] Image preprocessing unit: Real-time execute denoising, sharpening, brightness equalization and grayscale processing of images to improve image quality and reduce the computational load of the optical flow algorithm;

[0042] In scenes with drastic changes in illumination, the preprocessing unit has functions of adaptive exposure adjustment and gamma correction to enhance the dynamic adaptability of images and reduce errors caused by illumination changes.

[0043] Interface and Data Output: The preprocessed image data is transmitted in real time to the backend optical flow analysis module through a high-speed communication interface (such as Gigabit Ethernet, USB 3.0, or wireless 5G / 6G); it supports the output of standard image formats (such as YUV, RGB, Gray), facilitating the calling and unified processing by algorithm modules.

[0044] Through the design of the above image acquisition module, this system can achieve high-quality and stable image acquisition in complex outdoor substation environments, meeting the stringent requirements of subsequent optical flow field calculation for image continuity, clarity, and time accuracy, and significantly enhancing the robustness and practicality of the overall system.

[0045] The power parameter acquisition module is the core component in this system to realize the digital perception of grid status quantities and the fusion of image analysis information. Its main function is to obtain in real time the key grid operation parameters corresponding to the image acquisition area, including but not limited to voltage, current, and reactive power, etc., to support subsequent dynamic compensation control decisions.

[0046] The data acquisition unit is installed at the power equipment access points corresponding to the camera monitoring area, such as the feeder outgoing end, the secondary side of the transformer, the capacitor bank, or the busbar end of the static var compensator; high-precision sensors are used to collect the real-time voltage (U) and current (I) waveforms of the three-phase AC system; the data sampling frequency is preferably ≥10 kHz to meet the needs of grid dynamic change analysis and retain information characteristics such as harmonics and transients; it supports the simultaneous acquisition of derived parameters such as active power, reactive power, power factor, and frequency.

[0047] The synchronized measurement terminal (PMU) supports GPS or IEEE 1588 time synchronization protocols to ensure the synchronization of power parameters and image frames to achieve the time consistency of multi-source data; it has phasor measurement functions and can obtain the instantaneous voltage and current amplitudes and phase information of each phase; it marks the sampling data timestamps in real time and supports data collaboration with the SCADA system or the edge control unit.

[0048] Perform preprocessing operations such as filtering, denoising, normalization, and short-time Fourier transform (STFT) on the original power data locally;

[0049] Realize the preliminary identification of the dynamic trend of reactive power, such as short-period reactive power fluctuations, load mutations, or compensation device response delays, etc.;

[0050] It can calculate event characteristics such as the change of regional power flow direction, reactive power mutation points, and frequency offset, etc., providing parameter support for subsequent optical flow fusion analysis.

[0051] Upload the real-time power parameters to the comprehensive analysis module through standard industrial communication protocols (such as Modbus-TCP, IEC 61850, DNP3.0);

[0052] Support data breakpoint resumption and exception retransmission mechanisms to ensure data integrity under edge network fluctuations;

[0053] Work in coordination with the timestamp synchronization mechanism of the image acquisition module to ensure a one-to-one correspondence between images and power parameters, forming a spatio-temporal linked data foundation.

[0054] Through the above-mentioned structured design of the power parameter acquisition module, this system can achieve high-precision, high-efficiency, and high-reliability multi-parameter power grid operation state perception, support the accurate positioning and rapid response to reactive power changes, and provide key support for the integrated control of power grid dynamic compensation and image intelligent tracking.

[0055] The optical flow field calculation module is used to perform pixel-level motion estimation on the collected continuous image sequence, thereby generating an optical flow vector field reflecting the dynamic changes of the scene. This module is a key component for the system to achieve dynamic vision analysis and reactive power compensation tracking.

[0056] Receive continuous frame images (such as the t-th frame and the (t + 1)-th frame) from the image acquisition module and uniformly adjust them to grayscale images or a unified resolution;

[0057] Perform denoising, edge enhancement, and brightness normalization on the input images to improve the robustness of subsequent optical flow calculations;

[0058] Dynamically switch enhancement strategies (such as gamma correction or adaptive histogram equalization) for different scene conditions (night, shadows, etc.).

[0059] The system dynamically selects dense or sparse optical flow algorithms according to computing resources and accuracy requirements;

[0060] Dense optical flow (such as the Gunnar Farneback method): Used to generate the motion vector of each pixel for the entire image area;

[0061] Sparse optical flow (such as the Lucas-Kanade pyramid method): Used for local motion estimation of selected feature points to improve computing efficiency;

[0062] Support deep learning-assisted optical flow (such as PWC-Net or RAFT) to improve robustness and accuracy in complex environments.

[0063] Perform pixel-level matching and motion estimation on two consecutive frames of images, and output the horizontal and vertical displacements (u, v) of each pixel;

[0064] Generate a two-dimensional optical flow vector field, that is, each pixel corresponds to a motion vector representing its relative displacement from the previous frame to the current frame;

[0065] The optical flow magnitude (|V|) and direction angle (θ) can be further calculated to form the basis for visualizing the vector field.

[0066] Using the velocity threshold method or the motion consistency method, abnormal optical flow vectors in the calculation results are removed;

[0067] Applying median filtering or Gaussian smoothing to improve the smoothness and continuity of the overall vector field;

[0068] If there is occlusion or image perturbation (such as sudden illumination change), the system can trigger a redundancy detection mechanism for inter-frame correction.

[0069] Divide the key equipment areas of the power grid into ROIs (Regions of Interest), and calculate the average motion intensity and main direction of each region;

[0070] Combined with the spatial position mapping of the power parameter acquisition module, mark the areas where corresponding reactive power changes may occur;

[0071] Extract typical features such as: local violently moving areas, areas with sudden changes in motion direction, for subsequent comprehensive judgment and compensation control decisions.

[0072] Encode the final optical flow vector field into formats such as color maps, arrow maps, or heat maps;

[0073] Output to the comprehensive analysis module and the visualization interface to provide graphical support for the evolution of the power grid state;

[0074] Support structured data export, such as vector field matrices, regional motion feature tables, etc., for further use by algorithm modules.

[0075] Through the above step-by-step processing, the optical flow field calculation module can achieve high-precision perception of fine-grained dynamic changes in images, especially suitable for identifying small displacements, vibrations, abnormal fluctuations, etc. caused by changes in the power grid state, providing intuitive and quantifiable visual input for reactive power dynamic compensation.

[0076] The comprehensive analysis module is used to perform spatio-temporal correspondence and fusion calculation on the optical flow vector field and power operation parameters, so as to accurately identify the dynamic change trend and spatial distribution area of reactive power in the power grid.

[0077] Based on the unified timestamp mechanism, align the power parameter data with the image frames at the corresponding time points in the time domain;

[0078] Combined with the power grid GIS information and the camera deployment angle, establish the correspondence between the image pixel space and the physical space of electrical equipment;

[0079] Map each monitoring area (ROI) in the image to the corresponding power grid node or device one by one to form an image-power grid area correspondence table.

[0080] Extract the motion features in the optical flow vector field, including the local amplitude mutation value and the change of power parameters in this area, including the sudden rise value of reactive power, and conduct a joint analysis;

[0081] Among them, the method for obtaining the local amplitude mutation value is as follows: conduct optical flow calculation on consecutive image frames (the t-th frame and the (t + 1)-th frame), obtain the horizontal displacement u(x, y) and vertical displacement v(x, y) of each pixel point, and calculate the optical flow amplitude: ; where: M(x, y) is the optical flow amplitude of the pixel point, that is, its motion intensity.

[0082] Divide the region of interest (ROI) in the image, such as a 10x10 pixel grid, and calculate the average amplitude of each region: ; then calculate the local amplitude change between consecutive frames: ; where: is the average amplitude of the ROI region R at time t; ΔM(R) is the local amplitude mutation value; N is the number of pixel points in the region.

[0083] When ΔM(R) exceeds the set threshold Tm (empirical value or set based on historical statistics), it is regarded as the local amplitude mutation value. The larger the local amplitude mutation value, the more significant the dynamic change in this area.

[0084] Among them, the method for obtaining the sudden rise value of reactive power is as follows: Let the reactive power of the power node corresponding to a certain region at time t be , and the sampling period is Δt. The sudden rise calculation of reactive power between two consecutive sampling periods is: ; where: ΔQ is the sudden rise value of reactive power; the larger the sudden rise value of reactive power, the more it is considered that there is an electric power disturbance event.

[0085] Convert the local amplitude mutation value and the sudden rise value of reactive power into a comprehensive feature vector, use the comprehensive feature vector as the input of the machine learning model, take the evaluation value label corresponding to the prediction of optical flow change and actual power disturbance by the machine learning model for each group of comprehensive feature vectors as the prediction target, and take minimizing the sum of prediction errors for all evaluation value labels corresponding to optical flow change and actual power disturbance as the training target, train the machine learning model until the sum of prediction errors reaches convergence and then stop the model training, and determine the evaluation value corresponding to the optical flow change and actual power disturbance according to the model output result. Among them, the machine learning model is a polynomial regression model.

[0086] Compare the evaluation value corresponding to the optical flow change and the actual power disturbance with a predetermined threshold. If the evaluation value corresponding to the optical flow change and the actual power disturbance is greater than the predetermined threshold, it is determined that the optical flow change is highly likely to be related to the actual power disturbance event, and it is judged as a region requiring compensation response; if the evaluation value corresponding to the optical flow change and the actual power disturbance is less than or equal to the predetermined threshold, it is considered that the change in this region is small or there is no obvious disturbance, and there is no need to immediately trigger the compensation control.

[0087] The system is based on the following characteristics of each monitored area in the current period:

[0088] The mean value of the optical flow amplitude, the sudden change value of the reactive power, and the duration of the optical flow change;

[0089] Construct its regional feature vector:

[0090] Use the K-means clustering algorithm to cluster all areas and automatically divide several reactive power disturbance hotspots;

[0091] Each type of area is sorted according to the characteristics of its center point, and different levels of compensation priorities are assigned, such as level 1 (high), level 2 (medium), level 3 (low), etc.

[0092] The system integrates the disturbance identification results within each time period to generate a dynamic change distribution map;

[0093] The horizontal axis is the time axis, and the vertical axis is the area number or coordinates;

[0094] The color depth or symbol represents the optical flow disturbance intensity or the degree of reactive power mutation;

[0095] Visualize the evolution path of reactive power disturbance in the spatial and temporal dimensions, which helps to observe the disturbance propagation trend and source.

[0096] High-priority areas are preferentially matched with compensation devices (such as STATCOM) with fast dynamic response capabilities.

[0097] Evaluate the required reactive power compensation amount according to the comprehensive characteristics of each area (including optical flow intensity, reactive power mutation amplitude, disturbance duration);

[0098] Determine the required for each area: target compensation capacity (such as 300 kVar, 500 kVar, etc.); response time level (such as fast <1s, medium speed <5s); action duration (such as maintaining for more than 10 seconds or dynamically adjusting according to the change trend).

[0099] Convert the control recommended value into a structured control criterion, which clearly includes: the number of the compensation target area; the required reactive power capacity; the response level (such as "fast", "standard"); the action priority; the control criterion is transmitted to the dynamic compensation execution module to trigger the action of physical devices.

[0100] Control the output adjustment of the matching SVC or STATCOM device according to the signal content: change the capacitance / inductance access ratio; adjust the inverter output current; dynamically adjust the reactive power injection into the power grid to achieve voltage stability and reactive power balance; the response strategy is adjusted in real time according to the priority to ensure that the key area responds first.

[0101] After the compensation device is executed, the voltage and reactive power changes in the target area are monitored in real time; if the disturbance has not been eliminated, the system can continue or add compensation; if the disturbance has disappeared or the change has shifted, the response is terminated in time; all compensation response data are recorded and transmitted back to the system database for subsequent model training and strategy optimization.

[0102] If the system identifies a high-risk or repeatedly disturbed area, an early warning message is automatically sent to the operation and maintenance monitoring platform; the operation and maintenance personnel can review the control criteria and manually intervene whether to execute the compensation action; the system supports manual switching to the "manual confirmation mode" to ensure the safety and controllability of the compensation response.

[0103] In this embodiment, through the collaborative work of modules such as image acquisition, power parameter perception, optical flow calculation, comprehensive analysis, intelligent discrimination and dynamic control, accurate identification and rapid response to dynamic reactive power disturbances in the power grid are realized. The system integrates the motion characteristics in the image and the power operation parameters, uses machine learning and clustering algorithms to identify the compensation hot spots, generates the compensation control strategy and drives the reactive power compensation device to adjust in real time, and at the same time provides a visual monitoring interface, effectively improving the intelligence, stability and compensation efficiency of the power grid operation.

[0104] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0105] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that includes one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, hard disk, or magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0106] As described above, the specific implementation manners of the present application are only described, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all of them should be covered by the protection scope of the present application.

Claims

1. A real-time visualization tracking system for reactive power dynamic compensation of power grids based on optical flow field analysis, characterized in that: It includes an image acquisition module, a power parameter acquisition module, an optical flow field calculation module, a comprehensive analysis module, a dynamic compensation control module, and a visualization interaction module; The image acquisition module is used to deploy camera devices in key areas of the power grid to obtain a continuous sequence of monitoring images; The power parameter acquisition module is used to acquire real-time power grid operation parameters corresponding to the image area, including voltage, current, and reactive power; The optical flow field calculation module is used to perform optical flow analysis based on the monitoring image sequence and calculate the motion vector field of each pixel point; The comprehensive analysis module is used to perform spatio-temporal corresponding fusion of the optical flow vector field and power operation parameters to identify the dynamic change trend and spatial distribution of reactive power in the power grid; specifically including: Extract the motion features in the optical flow vector field, including the local amplitude mutation value, and the change of the electrical parameters in the region, including the sudden rise value of reactive power; among them, the method for obtaining the local amplitude mutation value is: perform optical flow calculation on consecutive image frames, obtain the horizontal displacement u(x, y) and vertical displacement v(x, y) of each pixel point, and calculate the optical flow amplitude: ; where: M(x, y) is the optical flow amplitude of the pixel point, that is, its motion intensity; divide the region of interest in the image and calculate the average amplitude of each region: ; then calculate the local amplitude change between consecutive frames: ; where: is the average amplitude of the ROI region R at time t; ΔM(R) is the local amplitude mutation value; N is the number of pixel points in the region; when ΔM(R) exceeds the set threshold Tm, it is regarded as the local amplitude mutation value; Extract the motion features in the optical flow vector field, including the sudden increase value of reactive power. The acquisition method is as follows: Let the reactive power of the power node corresponding to a certain area at time t be , the sampling period is Δt, and the sudden increase in reactive power between two consecutive sampling periods is calculated as: ; where: ΔQ is the sudden increase value of reactive power; The dynamic compensation control module is used to generate a compensation control instruction according to the comprehensive analysis result and control the static var compensator to implement real-time reactive power compensation for the target area; The visualization interaction module is used to display and interact with the power grid status, optical flow changes, and compensation control results in the form of a graphical interface in real time.

2. The real-time visualization tracking system for reactive power dynamic compensation of power grid based on optical flow field analysis according to claim 1, wherein: The key areas include the main transformer area, the bus connection point, and the reactive power compensation device.

3. The real-time visualization tracking system for reactive power dynamic compensation of power grid based on optical flow field analysis according to claim 1, characterized in that: Receive continuous frame images from the image acquisition module, uniformly adjust them to grayscale images, preprocess the input images, dynamically switch the enhancement strategy according to different scene conditions, and dynamically select dense or sparse optical flow algorithms according to the computing resources and accuracy requirements; Perform pixel-level matching and motion estimation on two consecutive frames of images, and output the horizontal and vertical displacements of each pixel; generate a two-dimensional optical flow vector field, that is, each pixel corresponds to a motion vector, indicating its relative displacement from the previous frame to the current frame.

4. The real-time visualization tracking system for reactive power dynamic compensation of power grid based on optical flow field analysis according to claim 1, wherein: Convert the local amplitude mutation value and the reactive power mutation value into a comprehensive feature vector, use the comprehensive feature vector as the input of the machine learning model, use the machine learning model to predict the evaluation value label corresponding to the optical flow change and the actual power disturbance for each group of comprehensive feature vectors as the prediction target, and use minimizing the sum of the prediction errors of all evaluation value labels corresponding to the optical flow change and the actual power disturbance as the training target to train the machine learning model until the sum of the prediction errors reaches convergence and then stop the model training, and determine the evaluation value corresponding to the optical flow change and the actual power disturbance according to the model output result, where the machine learning model is a polynomial regression model.

5. The real-time visualization tracking system for reactive power dynamic compensation of power grid based on optical flow field analysis according to claim 4, characterized in that: Compare the evaluation value corresponding to the optical flow change and the actual power disturbance with a predetermined threshold. If the evaluation value corresponding to the optical flow change and the actual power disturbance is greater than the predetermined threshold, it is determined that the optical flow change height is highly correlated with the actual power disturbance event, and it is determined as the area requiring compensation response; if the evaluation value corresponding to the optical flow change and the actual power disturbance is less than or equal to the predetermined threshold, it is considered that there is no obvious disturbance in the area and there is no need to immediately trigger compensation control.

6. The real-time visualization tracking system for reactive power dynamic compensation of power grid based on optical flow field analysis according to claim 5, characterized in that: Based on the characteristics of each monitoring area in the current period: the mean value of the optical flow amplitude, the mutation value of the reactive power, and the duration of the optical flow change; construct its regional feature vector: use the K-means clustering algorithm to cluster all areas, and automatically divide several reactive power disturbance hot spots; each type of area is sorted according to the characteristics of its center point, and different levels of compensation priorities are assigned. Areas with high priorities are preferentially matched with compensation devices with fast dynamic response capabilities; integrate the disturbance recognition results in each time period to generate a dynamic change distribution map; the horizontal axis is the time axis, and the vertical axis is the area number or coordinates; the depth of color or symbols represent the intensity of optical flow disturbance or the degree of reactive power mutation; visually display the evolution path of reactive power disturbance in the spatial and temporal dimensions.

Images