High-voltage power grid voltage quality compensation method based on passive control strategy

Abstract

The application discloses a high-voltage power grid voltage quality compensation method based on a passive control strategy, relates to the technical field of power grid voltage control, and comprises the following steps: reading energy recovery type passive control system data, and constructing a digital twin model; performing control fitting evaluation based on the twin model, distributing monitoring sensors, and establishing mapping in the model; establishing time sequence monitoring data through the monitoring sensors, synchronizing to a short-term power grid state prediction network in the twin model, establishing a prediction data set; analyzing an automatic compensation strategy, performing adjustment analysis of a dynamic voltage regulator, and establishing an adjustment data set; acquiring energy storage data, and establishing a compensation target function; inputting the prediction data set, the adjustment data set, the time sequence monitoring data and the energy storage data into the compensation target function, performing control optimization, and performing power grid voltage quality compensation. In turn, the technical effects of simple control, reduced control cost and improved compensation response efficiency are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of power grid voltage control, and in particular to a high-voltage power grid voltage quality compensation method based on a passive control strategy. Background Art

[0002] The voltage quality of high-voltage power grids is a key factor affecting the stability and reliability of power supply. With the widespread adoption of renewable energy, the volatility and uncertainty faced by power grids are becoming increasingly prominent. Existing compensation control methods, mostly based on PID controllers, require a large number of controllers and complex control parameter settings. These methods suffer from technical issues such as difficult parameter selection, high control costs, and slow compensation response. Summary of the Invention

[0003] The present invention provides a high-voltage power grid voltage quality compensation method based on a passive control strategy to solve the technical problems of difficult parameter selection, high control cost and slow compensation response in the prior art, and achieves the technical effects of simple control, reduced control cost and improved compensation response efficiency.

[0004] The high-voltage grid voltage quality compensation method based on the passive control strategy provided by the present invention is applied to an energy recovery passive control system, wherein the energy recovery passive control system includes an energy storage unit, a dynamic voltage regulator, and a multi-mode controller. The method includes:

[0005] Digital communication with an energy recovery type passive control system is established, system data of the energy recovery type passive control system is read based on the digital communication, and a digital twin model is constructed.

[0006] A control fitting evaluation of the energy recovery passive control system is performed based on the digital twin model, monitoring sensors are distributed according to the control fitting evaluation results, and a mapping of the monitoring sensors within the digital twin model is established.

[0007] Time series monitoring data is established through the monitoring sensor, and the time series monitoring data is synchronized to the short-term power grid state prediction network in the digital twin model through mapping to establish a prediction data set. The prediction data of the short-term power grid state prediction network includes the behavior data of the power grid and the output data of renewable energy.

[0008] The multi-modal controller is analyzed to obtain an automatic compensation strategy of the multi-modal controller, and a regulation analysis of the dynamic voltage regulator is performed based on the automatic compensation strategy to establish a regulation data set.

[0009] Obtain energy storage data of the energy storage unit and establish a compensation objective function.

[0010] The prediction data set, the adjustment data set, the time series monitoring data, and the energy storage data are input into the compensation objective function, the control optimization is performed, the optimization result is established, and the grid voltage quality compensation is performed according to the optimization result.

[0011] In a feasible implementation, inputting the prediction data set, the adjustment data set, the time series monitoring data, and the energy storage data into the compensation objective function further includes:

[0012] The prediction data set is parsed to obtain time series behavior prediction data and time series output prediction data.

[0013] The regulation data set is parsed to obtain a regulation voltage of a dynamic voltage regulator.

[0014] The time series monitoring data is analyzed to obtain the grid demand power, and the time series behavior prediction data and time series output prediction data, regulated voltage, grid demand power, and energy storage data are used as extracted data features and input into the compensation objective function.

[0015] In a feasible implementation, the compensation objective function is as follows:

[0016]

[0017]

[0018] Among them, J represents the compensation objective function, T is the monitoring and evaluation period, and u ref Indicates the maintenance voltage level of the power grid under ideal conditions, u comp (t) is the regulated voltage at time t, P renew,pred (t) is the time series output prediction data at time t, P renew,actual (t) is the actual output data of renewable energy at time t, B represents the closed cycle within the monitoring and evaluation period T, E pred (t) is the predicted energy storage data at time t, E actual (t) is the actual energy storage data at time t, P demand (t) represents the grid power demand at time t, is the time series behavior prediction data, and α, β, γ, δ, and δ are weight factors, which are used to adjust voltage stability, renewable energy data prediction accuracy, energy storage prediction accuracy, the matching degree between grid demand and actual renewable energy output, and the matching degree between predicted demand and predicted renewable energy output, respectively.

[0019] In a feasible implementation, synchronizing the time series monitoring data to a short-term power grid state prediction network in a digital twin model through mapping to establish a prediction data set further includes:

[0020] The renewable energy analysis sub-network of the short-term power grid state prediction network is called, and weather data is read through the renewable energy analysis sub-network to establish auxiliary input data.

[0021] Through the renewable energy analysis sub-network, auxiliary input data and time series monitoring data are analyzed and predicted, the output data of renewable energy is established, and a prediction data set is established based on the output data of renewable energy.

[0022] In a feasible implementation, establishing a prediction data set based on the output data of renewable energy further includes:

[0023] The behavior prediction subnetwork of the short-term power grid state prediction network is called, and after receiving the time series monitoring data through the behavior prediction subnetwork, the time series monitoring data is time-series segmented to establish a time series segmentation result.

[0024] An adaptive feature extraction window is configured based on the time series segmentation results, long and short time series features of the time series segmentation results are extracted through the adaptive feature extraction window, behavior prediction is performed based on the long and short time series feature extraction results, the behavior data of the power grid is established, and a prediction data set is established based on the behavior data of the power grid and the output data of renewable energy.

[0025] In a feasible implementation, the method further includes:

[0026] A time-series electricity price set is established, and a balancing model is configured, wherein the balancing model is used to balance voltage stability and cost efficiency.

[0027] The time-series electricity price set and the optimization result are used as input data, and a balance analysis is performed through a balance model to establish a balance analysis result.

[0028] The optimization results are adjusted according to the balance analysis results, and the adjusted optimization results are used to compensate for the grid voltage quality.

[0029] In a feasible implementation, the method further includes:

[0030] The optimization result is monitored after compensation to establish a monitoring result.

[0031] The monitoring results are optimized and controlled, and control feedback data is established based on the optimized control and authentication results.

[0032] Self-learning feedback optimization of the multi-modal controller is performed using the control feedback data.

[0033] In a feasible implementation, establishing the monitoring results further includes:

[0034] Determine whether any of the monitoring results meets a preset abnormality threshold.

[0035] If any of the monitoring results meets a preset abnormality threshold, a control abnormality is reported.

[0036] The present invention discloses a method for compensating high-voltage power grid voltage quality based on a passive control strategy. The method comprises: reading data from an energy-recovery passive control system via digital communication to construct a digital twin model. Control fit evaluation is performed based on the digital twin model, and monitoring sensors are distributed based on the results, with sensor locations mapped within the model. Time-series monitoring data is established from the monitoring sensors and synchronized to the short-term power grid state prediction network of the digital twin model to generate a prediction dataset, including power grid behavior data and renewable energy output data. A multimodal controller is analyzed to obtain an automatic compensation strategy, and based on this, a dynamic voltage regulator analysis is performed to establish a regulation dataset. Storage data from energy storage units is obtained to establish a compensation objective function. The prediction dataset, regulation dataset, time-series monitoring data, and energy storage data are input into the compensation objective function, control optimization is performed, optimization results are generated, and power grid voltage quality compensation is performed. The method for compensating high-voltage power grid voltage quality based on a passive control strategy disclosed in the present invention solves the technical problems of difficult parameter selection, high control costs, and slow compensation response, achieving the technical advantages of simplified control, reduced control costs, and improved compensation response efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 Schematic diagram of the flow of the high-voltage power grid voltage quality compensation method based on the passive control strategy of the present invention;

[0038] Figure 2 The present invention is a flow chart of establishing a prediction data set in a high-voltage power grid voltage quality compensation method based on a passive control strategy. DETAILED DESCRIPTION

[0039] The technical solutions provided in the embodiments of the present invention are designed to solve the technical problems of difficult parameter selection, high control costs, and slow compensation response in the prior art. The overall approach adopted is as follows:

[0040] First, digital communication with the energy recovery type passive control system is established, and system data of the energy recovery type passive control system is read based on the digital communication to build a digital twin model.

[0041] Then, a control fitting evaluation of the energy recovery passive control system is performed based on the digital twin model, monitoring sensors are distributed according to the control fitting evaluation results, and a mapping of the monitoring sensors within the digital twin model is established.

[0042] Then, the monitoring sensors are used to generate time-series monitoring data, which is then mapped and synchronized to a short-term grid state prediction network within the digital twin model to create a prediction dataset. The prediction data from the short-term grid state prediction network includes grid behavior data and renewable energy output data.

[0043] Next, the multi-modal controller is analyzed to obtain an automatic compensation strategy of the multi-modal controller, and a regulation analysis of the dynamic voltage regulator is performed based on the automatic compensation strategy to establish a regulation data set.

[0044] Furthermore, energy storage data of the energy storage unit is obtained, and a compensation objective function is established.

[0045] Finally, the prediction data set, the adjustment data set, the time series monitoring data, and the energy storage data are input into the compensation objective function, the control optimization is performed, the optimization result is established, and the grid voltage quality compensation is performed according to the optimization result.

[0046] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention, rather than all, are shown in the drawings.

[0047] Example 1

[0048] Figure 1 This is a flow chart of a high-voltage grid voltage quality compensation method based on a passive control strategy according to the present invention. The method is applied to an energy recovery passive control system, which includes an energy storage unit, a dynamic voltage regulator, and a multi-modal controller. The method includes:

[0049] Digital communication with an energy recovery type passive control system is established, system data of the energy recovery type passive control system is read based on the digital communication, and a digital twin model is constructed.

[0050] Specifically, digital communication with the energy recovery passive control system is established, including determining the communication protocol and interface type (such as Modbus, CAN, Ethernet, etc.), and configuring communication parameters (such as baud rate, data bits, stop bits, check mode, etc.), thereby establishing a communication connection with the energy recovery passive control system.

[0051] Specifically, the system data of the energy recovery passive control system is read, including hardware configuration, operating parameters, system status, historical performance, etc. Exemplarily, the system data is obtained from sources such as sensors, controllers, and log files of the energy recovery passive control system.

[0052] Among them, the energy storage unit includes multiple energy storage elements (such as capacitors) for storing and releasing electrical energy. When the power grid experiences voltage fluctuations or short voltage interruptions, the energy storage unit can quickly release electrical energy to maintain the stability of the load-side voltage. The dynamic voltage regulator (DVR) can dynamically adjust the voltage to offset the voltage disturbance in the power grid, and is used to improve the voltage quality in the power system and ensure the stability of the load-side voltage. The multimodal controller is the control center of the energy recovery passive control system, which is used to control the operation of the entire system. The multimodal controller adjusts the action of the energy storage unit and the DVR according to the real-time status of the power grid and the load to ensure voltage stability and efficient use of electrical energy.

[0053] Specifically, to build a digital twin model, a physical model of the energy-recovery passive control system is first created to accurately describe the system's physical characteristics, connectivity, and component configuration. Then, using data analysis and machine learning techniques, a data-driven model is constructed to update the state of the physical model based on real-time data. Finally, these physical and data-driven models are integrated to form a digital twin model, which can update its state based on incoming real-time data.

[0054] A control fitting evaluation of the energy recovery passive control system is performed based on the digital twin model, monitoring sensors are distributed according to the control fitting evaluation results, and a mapping of the monitoring sensors within the digital twin model is established.

[0055] Specifically, the control fit evaluation of an energy-recovery passive control system based on a digital twin model begins by using the digital twin model to simulate the actual operation of the control system and generate predicted system states and performance. These predicted results are then compared with ideal system states and performance (such as grid voltage amplitude and harmonic content) to calculate the fit error. This fit error reflects the performance of the evaluated control system and the accuracy of the digital twin model.

[0056] Furthermore, a control fit evaluation is performed based on the fitting error. Based on the results of the control fit evaluation, the system's performance components are determined to be strong and weak. Furthermore, the sensor distribution is adaptively adjusted to more effectively and specifically monitor the system's status and performance. Examples include increasing the density of monitoring sensors in areas with significant voltage fluctuations and increasing the frequency of sensor acquisition.

[0057] Specifically, after adjusting the sensor distribution, a sensor mapping is established to update the location and function of multiple sensors in the digital twin model. The sensor mapping describes the location and function of each sensor in the model. Exemplarily, the sensor mapping is determined based on the unique device identifier of the monitoring sensor (such as identity ID, SN, IMEI, etc.), the layout location of the monitoring sensor (physical address and logical address), etc., and the sensor mapping is represented as a database or structured text information. The establishment of the sensor mapping helps to efficiently find relevant sensor information when maintaining and debugging the system, performing data analysis, and updating the model.

[0058] Time series monitoring data is established through the monitoring sensor, and the time series monitoring data is synchronized to the short-term power grid state prediction network in the digital twin model through mapping to establish a prediction data set. The prediction data of the short-term power grid state prediction network includes the behavior data of the power grid and the output data of renewable energy.

[0059] Specifically, multiple monitoring sensors distributed across the target power grid are activated to collect information such as voltage, frequency, and phase, as well as data related to renewable energy output, such as wind speed, light intensity, and temperature. These data are stored as time-series monitoring data. This collected time-series monitoring data is then mapped and synchronized to the short-term power grid state prediction network within the digital twin model using a constructed sensor map, linking each sensor's data with the corresponding component of the model.

[0060] Among them, the prediction network is used to predict the short-term state of the power grid, and the output prediction data includes the behavior data of the power grid (such as the predicted values ​​of voltage, current, frequency, etc.) and the output data of renewable energy (such as the predicted output of wind turbines or solar panels). Exemplarily, the prediction network is a lightweight prediction network built based on neural networks and knowledge distillation. It learns the historical time series monitoring data of the target power grid through a large, highly complex neural network model, learns the behavior pattern of the target power grid and the output characteristics of renewable energy, and builds a smaller and lighter neural network model. The lightweight model imitates the output of the large model to learn some internal representations and decision-making processes of the teacher model, and then obtains short-term prediction capabilities. In this way, the accuracy of predictions can be achieved on devices with limited resources.

[0061] In some embodiments, as Figure 2 As shown, the time series monitoring data is synchronized to the short-term power grid state prediction network in the digital twin model by mapping to establish a prediction data set, and further includes:

[0062] The renewable energy analysis sub-network of the short-term power grid state prediction network is called, and weather data is read through the renewable energy analysis sub-network to establish auxiliary input data.

[0063] Through the renewable energy analysis sub-network, auxiliary input data and time series monitoring data are analyzed and predicted, the output data of renewable energy is established, and a prediction data set is established based on the output data of renewable energy.

[0064] Specifically, the renewable energy analysis subnetwork within the short-term grid state prediction network is first invoked. This subnetwork is used to read and analyze weather data, such as wind speed, light intensity, and temperature, to generate auxiliary input data. Exemplarily, the renewable energy analysis subnetwork connects to a weather data source or platform to obtain real-time weather data for the target grid's environment.

[0065] Furthermore, the renewable energy analysis subnetwork uses the auxiliary input data and time-series monitoring data as input to perform analysis and prediction, establishing renewable energy output data. This renewable energy output data reflects the expected output of renewable energy devices (such as wind turbines or solar panels) under given weather conditions. The renewable energy analysis subnetwork includes a neural network-based analysis model and a mathematical prediction model based on the principles of renewable energy regeneration.

[0066] For example, neural network-based analytical models learn complex nonlinear relationships from large amounts of training data to predict future energy output. Specifically, the training data includes weather data, equipment status data, historical energy output data, etc. Mathematical prediction models based on renewable energy regeneration principles are constructed based on the physical principles of renewable energy, such as the photovoltaic effect of solar cells and the aerodynamics of wind turbines. They can provide physically interpretable predictions and use weather data (such as light intensity and wind speed) to calculate expected energy output.

[0067] In some implementations, establishing a prediction dataset based on the output data of renewable energy sources further includes:

[0068] The behavior prediction subnetwork of the short-term power grid state prediction network is called, and after receiving the time series monitoring data through the behavior prediction subnetwork, the time series monitoring data is time-series segmented to establish a time series segmentation result.

[0069] An adaptive feature extraction window is configured based on the time series segmentation results, long and short time series features of the time series segmentation results are extracted through the adaptive feature extraction window, behavior prediction is performed based on the long and short time series feature extraction results, the behavior data of the power grid is established, and a prediction data set is established based on the behavior data of the power grid and the output data of renewable energy.

[0070] Specifically, the behavior prediction subnetwork of the short-term power grid state prediction network reflects the periodic variation pattern of the target power grid's power consumption behavior characteristics. In other words, the behavior prediction subnetwork is used to describe how the target power grid's power consumption behavior fluctuates over time. For example, the behavior prediction subnetwork learns the periodic patterns of power grid power consumption behavior by analyzing historical power grid power consumption data. This includes daily cycles (e.g., power consumption behavior during the day and at night may be very different), weekly cycles (e.g., power consumption behavior during weekdays and weekends may be very different), and annual cycles (e.g., power consumption behavior in summer and winter may be very different).

[0071] Specifically, after receiving time-series monitoring data, the behavior prediction subnetwork performs time-series segmentation on the data, dividing the continuous data stream into a series of time windows, where each window contains data from a certain period of time. An adaptive feature extraction window is then configured based on the time-series segmentation results. This adaptive feature extraction window automatically adjusts its size to best adapt to data changes.

[0072] Exemplarily, the size of the adaptive feature extraction window is determined based on the information entropy of the time series monitoring data. First, the information entropy of the time series monitoring data is calculated based on the voltage distribution or the distribution of the harmonic proportion. Then, the size of the adaptive feature extraction window is determined based on the information entropy. If the information entropy is high (the data complexity or uncertainty is large), a larger window size is selected to capture more data features. Conversely, if the information entropy is low (the data complexity or uncertainty is small), a smaller window size is selected to adapt to data changes more quickly.

[0073] Specifically, the adaptive feature extraction window is used to extract long and short time series features from the time series segmentation results to extract key features from the data. The extracted long and short time series features can reflect both long-term and short-term trends in the data. Furthermore, the extracted long and short time series features are input into the behavior prediction subnetwork for behavior prediction, obtaining grid behavior data. This data is then used to predict the future behavior and state of the target grid, including load, voltage, harmonics, frequency, and more.

[0074] Exemplarily, long and short time series features are extracted from the results of time series segmentation, including extracting various statistical features (such as mean value, variance, etc.), trend features (such as rising or falling trends), periodic features (such as daily cycle, weekly cycle, annual cycle), etc.

[0075] The multi-modal controller is analyzed to obtain an automatic compensation strategy of the multi-modal controller, and a regulation analysis of the dynamic voltage regulator is performed based on the automatic compensation strategy to establish a regulation data set.

[0076] Specifically, analyzing the multimodal controller involves obtaining its internal control logic and strategy, such as analyzing the controller's control algorithm, parameter settings, mode switching logic, and determining the automatic compensation strategy.

[0077] Exemplarily, the automatic compensation strategy is a passive control strategy based on the EL model. The EL model is an energy and delay model used to describe the energy consumption and delay of a passive control system. Factors involved in the EL model include the system's operating mode, workload, and environmental conditions.

[0078] Furthermore, based on the automatic compensation strategy, a dynamic voltage regulator regulation analysis is performed. This involves simulating the controller's response, analyzing its compensation effect on voltage fluctuations, and evaluating its performance under different conditions. This process then creates a regulation dataset, which includes multiple sets of correlated and stored regulation effects, regulation control parameters, and system status data. This provides a set of compensation solutions with clear performance and can be quickly accessed for subsequent compensation, helping to improve the response speed and quality of subsequent compensation.

[0079] Obtain energy storage data of the energy storage unit and establish a compensation objective function.

[0080] Specifically, the energy storage data of an energy storage unit refers to the amount and distribution of electrical energy stored in multiple capacitors within the energy storage unit. The compensation objective function optimizes the voltage quality compensation effect and includes multiple optimization objectives. Exemplary optimization objectives include maximizing energy utilization, minimizing energy consumption, and minimizing latency.

[0081] In some embodiments, the compensation objective function is as follows:

[0082]

[0083] Among them, J represents the compensation objective function, T is the monitoring and evaluation period, and u ref Indicates the maintenance voltage level of the power grid under ideal conditions, u comp (t) is the regulated voltage at time t, P renew,pred (t) is the time series output prediction data at time t, P renew,actual (t) is the actual output data of renewable energy at time t, B represents the closed cycle within the monitoring and evaluation period T, E pred (t) is the predicted energy storage data at time t, E actual (t) is the actual energy storage data at time t, P demand (t) represents the grid power demand at time t, is the time series behavior prediction data, α, β, γ, δ, ∈ are weight factors, which are used to adjust voltage stability, renewable energy data prediction accuracy, energy storage prediction accuracy, the matching degree between grid demand and actual renewable energy output, and the matching degree between predicted demand and predicted renewable energy output, respectively.

[0084] Specifically, the compensation objective function is a composite objective function that takes into account multiple factors such as voltage stability, renewable energy data prediction accuracy, energy storage prediction accuracy, the matching degree between grid demand and actual renewable energy output, and the matching degree between predicted demand and predicted renewable energy output, so as to evaluate and optimize the performance of voltage quality compensation from multiple perspectives.

[0085] Specifically, the compensation objective function optimizes voltage quality compensation performance by minimizing the sum of squares of various errors (the differences between multiple grid voltage indicators and their ideal values). Each error corresponds to a specific performance indicator, such as voltage stability or prediction accuracy. Furthermore, each error is assigned a weighting factor to reflect the importance of the different performance indicators.

[0086] The prediction data set, the adjustment data set, the time series monitoring data, and the energy storage data are input into the compensation objective function, the control optimization is performed, the optimization result is established, and the grid voltage quality compensation is performed according to the optimization result.

[0087] In some embodiments, inputting the prediction data set, the adjustment data set, the time series monitoring data, and the energy storage data into the compensation objective function further includes:

[0088] The prediction data set is parsed to obtain time series behavior prediction data and time series output prediction data.

[0089] The regulation data set is parsed to obtain a regulation voltage of a dynamic voltage regulator.

[0090] The time series monitoring data is analyzed to obtain the grid demand power, and the time series behavior prediction data and time series output prediction data, regulated voltage, grid demand power, and energy storage data are used as extracted data features and input into the compensation objective function.

[0091] Specifically, the control strategy can be adjusted to minimize the value of the compensation objective function, which may include adjusting the energy storage unit charging / discharging strategy, adjusting the system's operating mode, adjusting the parameters of the prediction model, etc.

[0092] Optionally, the adjustment data set is used as the parameter space for optimization, and through optimization algorithms (such as gradient descent, genetic algorithm, simulated annealing, etc.), the adjustment data set is traversed to iterate the adjustment voltages of multiple dynamic voltage regulators, and combined with the timing behavior prediction data and timing output prediction data, grid demand power, and energy storage data, the compensation objective function is input to calculate the function value, and the control strategy that minimizes the compensation objective function is found, and the output is the optimization result.

[0093] Furthermore, the method further comprises:

[0094] A time-series electricity price set is established, and a balancing model is configured, wherein the balancing model is used to balance voltage stability and cost efficiency.

[0095] The time-series electricity price set and the optimization result are used as input data, and a balance analysis is performed through a balance model to establish a balance analysis result.

[0096] The optimization results are adjusted according to the balance analysis results, and the adjusted optimization results are used to compensate for the grid voltage quality.

[0097] Specifically, in actual grid operation, voltage stability must be ensured while also considering operational costs. Therefore, a balance model is configured to find a balance between voltage stability and cost efficiency. The time-series electricity price set is obtained from the grid operator or the market, including the electricity price for each time period.

[0098] For example, the configuration balancing model is a multi-objective optimization model, where one objective is voltage stability and the other is cost efficiency. The two objectives are assigned corresponding weighting coefficients to adjust their importance. Specifically, the weighting coefficients are determined based on the target grid's operational requirements. For example, if the target grid prioritizes reducing operating costs, a higher weighting coefficient is assigned to cost efficiency.

[0099] Specifically, the time-series electricity price set and optimization results are used as input data for analysis using a balance model. Based on the model's output, a balance analysis result is generated. This balance analysis result is a numerical analysis result used to quantitatively evaluate the degree to which the optimization results balance the target grid voltage stability and cost efficiency requirements.

[0100] Specifically, if the balance analysis results show that the optimization results do not meet the balance analysis threshold corresponding to the target grid demand, the optimization results are adjusted based on the contribution ratio of the grid voltage stability and cost efficiency to the balance analysis results in the analysis results. For example, if the contribution ratio of cost efficiency to the balance analysis results is low, it means that the operating cost is significantly different from the expected value of the target grid, and the voltage regulation strategy is adjusted to reduce costs. Exemplary methods include reducing the operating frequency of the voltage regulator, optimizing the working mode of the voltage regulator, etc. The method of adjusting the optimization results based on the balance analysis results can ensure that the grid can effectively control operating costs while ensuring voltage stability.

[0101] Furthermore, the method further comprises:

[0102] The optimization result is monitored after compensation to establish a monitoring result.

[0103] The monitoring results are optimized and controlled, and control feedback data is established based on the optimized control and authentication results.

[0104] Specifically, the target power grid is continuously monitored based on monitoring sensors, the compensation effect data of the target power grid is obtained, and monitoring results are established. Exemplarily, the monitoring results include the voltage amplitude, voltage stability, harmonic ratio, etc. of the target power grid after compensation. Then, through in-depth analysis and evaluation of the monitoring results, the effectiveness and accuracy of the optimization results are verified to perform optimization control certification, and control feedback data is established. Optimization control certification is used to quantitatively evaluate the voltage quality compensation efficiency of the optimization results. Through the above-mentioned method steps, the voltage quality of the power grid can be further ensured, and possible problems in compensation can be discovered in a timely manner.

[0105] In some implementations, establishing the monitoring results further includes:

[0106] Determine whether any of the monitoring results meets a preset abnormality threshold.

[0107] If any of the monitoring results meets a preset abnormality threshold, a control abnormality is reported.

[0108] Furthermore, the system determines whether any of the monitoring results meet a preset abnormality threshold. If so, a control abnormality is reported and a control abnormality response is implemented based on the reported control abnormality. This may include suspending compensation or adjusting the compensation strategy.

[0109] In summary, the high-voltage grid voltage quality compensation method based on the passive control strategy provided by the present invention has the following technical effects:

[0110] Data from the energy-recovery passive control system is read via digital communication to construct a digital twin model. Control fit evaluation is performed based on this digital twin model, and monitoring sensors are distributed based on the results, with sensor locations mapped within the model. Time-series monitoring data is generated from the monitoring sensors and synchronized to the digital twin model's short-term grid state prediction network to generate a prediction dataset, including grid behavior data and renewable energy output data. The multimodal controller is analyzed to obtain an automatic compensation strategy, which is then used to analyze the dynamic voltage regulator and establish a regulation dataset. Energy storage unit storage data is obtained to establish a compensation objective function. The prediction dataset, regulation dataset, time-series monitoring data, and energy storage data are input into the compensation objective function. Control optimization is performed, resulting in optimized results and grid voltage quality compensation. This results in simplified control, reduced control costs, and improved compensation response efficiency.

[0111] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the embodiments mentioned above. It should be understood that those skilled in the art can still modify the technical solutions described in the above embodiments or replace some of the technical features therein with equivalents; and such modifications or replacements do not deviate from the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention and are all included in the scope of protection of the present invention.

Claims

1. A high-voltage power grid voltage quality compensation method based on a passive control strategy, characterized in that: The method is applied to an energy recovery passive control system, which includes an energy storage unit, a dynamic voltage regulator, and a multi-mode controller. The method includes: Establishing digital communication with the energy recovery type passive control system, reading system data of the energy recovery type passive control system based on the digital communication, and building a digital twin model; performing a control fit evaluation of the energy recovery passive control system based on the digital twin model, distributing monitoring sensors according to the control fit evaluation results, and establishing a mapping of the monitoring sensors within the digital twin model; Establishing time series monitoring data through the monitoring sensor, synchronizing the time series monitoring data to a short-term power grid state prediction network within the digital twin model through mapping, and establishing a prediction data set, wherein the prediction data of the short-term power grid state prediction network includes behavior data of the power grid and output data of renewable energy; Analyzing the multi-modal controller, obtaining an automatic compensation strategy of the multi-modal controller, and performing regulation analysis of the dynamic voltage regulator based on the automatic compensation strategy to establish a regulation data set; Obtaining energy storage data of the energy storage unit and establishing a compensation objective function; The prediction data set, the adjustment data set, the time series monitoring data, and the energy storage data are input into the compensation objective function, the control optimization is performed, the optimization result is established, and the grid voltage quality compensation is performed according to the optimization result.

2. The high-voltage power grid voltage quality compensation method based on the passive control strategy according to claim 1, characterized in that: The step of inputting the prediction data set, the adjustment data set, the time series monitoring data, and the energy storage data into the compensation objective function further includes: Parsing the prediction data set to obtain time series behavior prediction data and time series output prediction data; parsing the regulation data set to obtain a regulation voltage of a dynamic voltage regulator; The time series monitoring data is analyzed to obtain the grid demand power, and the time series behavior prediction data and time series output prediction data, regulated voltage, grid demand power, and energy storage data are used as extracted data features and input into the compensation objective function.

3. The high-voltage power grid voltage quality compensation method based on passive control strategy according to claim 2, characterized in that: The compensation objective function is as follows: Among them, J represents the compensation objective function, T is the monitoring and evaluation period, and u ref Indicates the maintenance voltage level of the power grid under ideal conditions, u comp (t) is the regulated voltage at time t, P renew,pred (t) is the time series output prediction data at time t, P renew,actual (t) is the actual output data of renewable energy at time t, B represents the closed cycle within the monitoring and evaluation period T, E pred (t) is the predicted energy storage data at time t, E actual (t) is the actual energy storage data at time t, P demand (t) represents the grid power demand at time t, is the time series behavior prediction data, α, β, γ, δ, ∈ are weight factors, which are used to adjust voltage stability, renewable energy data prediction accuracy, energy storage prediction accuracy, the matching degree between grid demand and actual renewable energy output, and the matching degree between predicted demand and predicted renewable energy output, respectively.

4. The high-voltage power grid voltage quality compensation method based on passive control strategy according to claim 1, characterized in that: The step of synchronizing the time series monitoring data to a short-term power grid state prediction network within the digital twin model through mapping to establish a prediction data set further includes: Invoking the renewable energy analysis subnetwork of the short-term power grid state prediction network, reading weather data through the renewable energy analysis subnetwork, and establishing auxiliary input data; Through the renewable energy analysis sub-network, auxiliary input data and time series monitoring data are analyzed and predicted, the output data of renewable energy is established, and a prediction data set is established based on the output data of renewable energy.

5. The high-voltage power grid voltage quality compensation method based on passive control strategy according to claim 4, characterized in that: The step of establishing a prediction data set based on the output data of renewable energy further includes: calling a behavior prediction subnetwork of a short-term power grid state prediction network, and after receiving the time series monitoring data through the behavior prediction subnetwork, performing time series segmentation on the time series monitoring data to establish a time series segmentation result; An adaptive feature extraction window is configured based on the time series segmentation results, long and short time series features of the time series segmentation results are extracted through the adaptive feature extraction window, behavior prediction is performed based on the long and short time series feature extraction results, the behavior data of the power grid is established, and a prediction data set is established based on the behavior data of the power grid and the output data of renewable energy.

6. The high-voltage power grid voltage quality compensation method based on passive control strategy according to claim 1, characterized in that: The method further comprises: Establishing a time-series electricity price set and configuring a balancing model, wherein the balancing model is used to balance voltage stability and cost efficiency; Using the time-series electricity price set and the optimization result as input data, performing a balance analysis through a balance model, and establishing a balance analysis result; The optimization results are adjusted according to the balance analysis results, and the adjusted optimization results are used to compensate for the grid voltage quality.

7. The high-voltage power grid voltage quality compensation method based on passive control strategy according to claim 1, characterized in that: The method further comprises: monitoring the optimization results after compensation and establishing monitoring results; Performing optimized control authentication on the monitoring results, and establishing control feedback data based on the optimized control authentication results; Self-learning feedback optimization of the multi-modal controller is performed using the control feedback data.

8. The high-voltage power grid voltage quality compensation method based on passive control strategy according to claim 7, characterized in that: The establishing of monitoring results further includes: Determine whether the monitoring results meet a preset abnormality threshold; If any of the monitoring results meets a preset abnormality threshold, a control abnormality is reported.

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