Generator excitation control optimization system based on model prediction

Abstract

The present invention provides a generator excitation control optimization system based on model prediction, which relates to the field of data processing technology. The system includes: a data acquisition module for collecting operating parameters of the generator; a state estimation module for calculating state variables of the generator excitation system and constructing a state space model of the generator excitation system; a model prediction control module for calculating excitation current prediction values at multiple moments in the future using a rolling time domain optimization method, and dynamically correcting them based on a prediction error correction mechanism; an optimization calculation module for constructing a target optimization function, including a generator voltage stability target item and an excitation current regulation smoothness target item, and calculating the optimal excitation current adjustment amount using a constrained optimization method; and an excitation regulation module for controlling the generator excitation system to adjust the excitation current. The present invention improves the autonomy and accuracy of the generator excitation control optimization system.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a generator excitation control optimization system based on model prediction. Background Art

[0002] In existing technologies, generator excitation control is typically adjusted using traditional PID (proportional-integral-derivative) control or fuzzy control methods. Specifically, PID control measures the deviation between the generator terminal voltage and the set value and calculates the corresponding adjustment variable to adjust the excitation current to maintain system stability. Fuzzy control methods, based on expert experience and rules, use a fuzzy inference system to adjust the excitation current to adapt to different operating conditions. These methods can, to a certain extent, improve the dynamic response capability of the generator and the power quality of the power grid. In addition, in recent years, some research has introduced neural networks or genetic algorithms to optimize excitation control parameters to improve system adaptability and regulation accuracy. However, these methods still have certain limitations and cannot meet the optimization needs of complex power grid environments.

[0003] In practical applications, existing excitation control methods may experience a dynamic response lag when grid faults or severe load fluctuations occur. For example, in the case of large-scale new energy grid integration, the uncertainty of renewable energy output such as wind power and photovoltaics causes large voltage fluctuations in the grid. Traditional excitation control methods struggle to adapt to these changes in real time, resulting in a lag in generator voltage regulation. During the process of connecting a hydropower station to the main grid, if the water velocity changes suddenly within a short period of time, traditional PID control may be unable to adjust quickly due to fixed parameters, causing the generator voltage to overshoot or undervoltage, thereby affecting the stability of the entire grid. In addition, fuzzy control based on expert experience may have problems with unreasonable rule settings when dealing with complex operating conditions, which reduces the accuracy of excitation regulation and further increases the uncertainty of grid operation. Summary of the Invention

[0004] The purpose of the present invention is to provide a generator excitation control optimization system based on model prediction, aiming to solve the problems mentioned in the background technology.

[0005] In order to solve the above technical problems, the technical solutions of the present invention are as follows:

[0006] A generator excitation control optimization system based on model prediction, the system comprising:

[0007] The data acquisition module is used to collect the operating parameters of the generator, including the generator terminal voltage, stator current, rotor current, speed and grid frequency, and filter the operating parameters to obtain pre-processed data;

[0008] The state estimation module is used to calculate the state variables of the generator excitation system based on the preprocessed data, including voltage deviation, excitation current change rate, flux change rate and speed deviation, and construct the state space model of the generator excitation system based on the state variables;

[0009] The model predictive control module is used to calculate the excitation current prediction values at multiple moments in the future based on the state space model using the rolling horizon optimization method to obtain the excitation current prediction sequence, and dynamically correct the excitation current prediction sequence based on the prediction error correction mechanism to obtain the excitation current prediction correction sequence;

[0010] The optimization calculation module is used to construct the target optimization function based on the excitation current prediction correction sequence, including the generator voltage stability target term and the excitation current regulation smoothness target term, and use the constrained optimization method to calculate the optimal excitation current adjustment amount to obtain the optimal excitation control signal;

[0011] The excitation regulation module is used to control the generator excitation system according to the optimal excitation control signal to adjust the excitation current.

[0012] Preferably, the state estimation module includes:

[0013] A data correction unit, used to suppress noise on pre-processed data based on a dynamic adaptive filtering method, and calculate correction data in combination with historical operating data;

[0014] The state variable calculation unit is used to calculate the state variables of the excitation system according to the correction data, including voltage deviation, excitation current change rate, flux change rate and speed deviation, and to construct the initial estimate of the state space model based on the state variable dynamic fitting method;

[0015] The error compensation unit is used to calculate the state prediction error according to the initial estimated value of the state space model, and dynamically adjust the state variables using an incremental correction strategy to obtain a corrected state space model.

[0016] Preferably, the model predictive control module includes:

[0017] The future time domain prediction unit is used to calculate the excitation system state variables at multiple future moments based on the modified state space model, and calculate the excitation current prediction values at multiple future moments using a sliding window strategy to obtain an excitation current prediction sequence;

[0018] The prediction error correction unit is used to calculate the excitation current prediction error term based on the prediction error cumulative trend analysis method, and adopt a dynamic error correction mechanism to adjust the excitation current prediction sequence to obtain the excitation current prediction correction sequence.

[0019] Preferably, the optimization calculation module includes:

[0020] An objective function construction unit is used to construct an optimization objective function of the excitation system based on the excitation current prediction correction sequence, wherein the optimization objective function includes a generator voltage stability objective term and an excitation current regulation smoothness objective term, and calculate an initial weight value of the optimization objective function based on a weighted cumulative error analysis method;

[0021] The target weight adjustment unit is used to adjust the initial weight value of the optimization objective function according to the dynamic load characteristics of the power grid and generate an optimization weight parameter set;

[0022] The constrained optimization calculation unit is used to calculate the optimal excitation current adjustment amount using a constrained optimization solution method based on the optimization objective function and the optimization weight parameter set.

[0023] Preferably, the excitation system state variables, including voltage deviation, excitation current change rate, flux change rate, and speed deviation, are calculated based on the correction data, and the initial estimate of the state space model is constructed based on the state variable dynamic fitting method, including:

[0024] According to the correction data, the voltage deviation, excitation current change rate, flux linkage change rate and speed deviation are calculated, and the variable weight adaptive normalization method is used for different state variables to obtain the normalized state variables;

[0025] According to the normalized state variables, the initial state fitting model is constructed using the nonlinear multi-order curve fitting method;

[0026] According to the initial state fitting model, the time trend of the state variable is calculated, and the initial estimate of the state space model is constructed in combination with the historical state data.

[0027] Preferably, the state prediction error is calculated based on the initial estimated value of the state space model, and the state variable is dynamically adjusted using an incremental correction strategy to obtain a corrected state space model, including:

[0028] According to the initial estimate of the state space model, the state prediction error is calculated based on the actual state variables and the predicted state variables at the current moment;

[0029] According to the state prediction error, the state increment correction term is calculated using the dynamic error compensation method;

[0030] According to the state increment correction term, the initial estimated value of the state space model is dynamically adjusted to obtain a corrected state space model.

[0031] Preferably, the method of calculating the excitation system state variables at multiple future moments according to the modified state space model and calculating the excitation current prediction values at multiple future moments using a sliding window strategy to obtain the excitation current prediction sequence includes:

[0032] According to the modified state space model and based on the recursive state update method, the state variables of the excitation system at multiple moments in the future are calculated;

[0033] According to the state variables of the excitation system at multiple future moments, a sliding window strategy is adopted to calculate the predicted values of the excitation current at multiple future moments;

[0034] An excitation current prediction sequence is constructed based on the excitation current prediction values at multiple future moments.

[0035] Preferably, the method of calculating the excitation current prediction error term based on the prediction error cumulative trend analysis method and adjusting the excitation current prediction sequence using a dynamic error correction mechanism to obtain the excitation current prediction correction sequence includes:

[0036] According to the excitation current prediction sequence and based on the historical prediction error data, the excitation current prediction error term is calculated;

[0037] According to the excitation current prediction error term, a dynamic error correction mechanism is used to calculate the excitation current adjustment correction value;

[0038] According to the excitation current adjustment correction value, the excitation current prediction sequence is error compensated to obtain the excitation current prediction correction sequence.

[0039] Preferably, the optimization objective function of the excitation system is constructed according to the excitation current prediction correction sequence, the optimization objective function including the generator voltage stability objective term and the excitation current regulation smoothness objective term, and the initial weight value of the optimization objective function is calculated based on the weighted cumulative error analysis method, including:

[0040] Calculate the generator voltage deviation and the excitation current regulation change rate according to the excitation current prediction correction sequence, and construct an optimization objective function, which includes a generator voltage stability objective term and an excitation current regulation smoothness objective term;

[0041] ,in, To optimize the objective function, is the prediction time step, and To optimize the weight coefficient of the objective function, For the Voltage deviation at the moment, The set voltage indicates the target voltage that the generator should maintain. For the The actual voltage at the moment, For the The rate of change of the excitation current at the moment For the The excitation current at time For the The excitation current at the moment;

[0042] According to historical operation data, the weighted cumulative error analysis method is used to calculate the initial weight value of the optimization objective function;

[0043] According to the initial weight value of the optimization objective function, the optimization objective constraint relationship is established.

[0044] Preferably, the calculation of the optimal excitation current adjustment amount using a constrained optimization solution method according to the optimization objective function and the optimization weight parameter set includes:

[0045] Constructing an optimal excitation current adjustment calculation model according to the optimization objective function and the optimization weight parameter set, wherein the calculation model includes excitation current adjustment constraint conditions;

[0046] According to the dynamic working range of the excitation system, the excitation current change rate threshold and voltage stability threshold are set, and the calculation model is solved based on the constrained optimization solution method to obtain the optimal excitation current adjustment;

[0047] The calculation formula of the optimal excitation current adjustment amount is:

[0048] ,in, is the optimal excitation current adjustment, is the excitation current adjustment, is an operator, which represents the variable value that makes the objective function achieve the minimum value;

[0049] Constraints ( ) ,in, and are the minimum and maximum values of the excitation current, respectively. is the upper limit of the excitation current change rate;

[0050] An optimal excitation control signal is generated according to the optimal excitation current adjustment amount.

[0051] The above solution of the present invention includes at least the following beneficial effects:

[0052] This system adopts an excitation control optimization method based on model prediction, and through the collaborative work of multiple functional modules, it significantly improves the accuracy, real-time performance and stability of excitation regulation.

[0053] The system first uses a data acquisition module to collect real-time generator operating parameters, including generator terminal voltage, stator current, rotor current, speed, and grid frequency, and then filters these parameters. Compared to traditional control methods that rely on a single variable (such as voltage deviation), this system integrates multiple key variables, improving its perception of grid status and ensuring the accuracy of data input, thereby providing more stable input for subsequent control calculations.

[0054] Based on data acquisition, the state estimation module calculates the state of the generator excitation system and constructs a state-space model. Traditional methods typically use fixed control parameters or empirically defined control rules, which make it difficult to accurately describe the dynamic characteristics of the excitation system under different operating conditions. This system calculates multiple key variables, such as voltage deviation, excitation current rate of change, flux linkage rate of change, and speed deviation, and combines them with a state-space model to establish a more accurate description of the system's dynamic characteristics. This allows the control strategy to adapt to grid changes at any time, avoiding the regulation lag caused by fixed parameters.

[0055] Based on the state-space model, the model predictive control module utilizes a rolling horizon optimization method to calculate excitation current forecasts for multiple future moments and generate an excitation current forecast sequence. Unlike existing technologies that rely on historical data for control, this system can predict excitation demand at future moments and make adjustments in advance, avoiding the problem of delayed excitation regulation caused by sudden changes in grid conditions. Furthermore, this system employs a prediction error correction mechanism to dynamically adjust the excitation current forecast, reducing the cumulative impact of prediction errors and ensuring more precise excitation regulation.

[0056] In terms of optimization calculations, the optimization calculation module constructs an optimization objective function, which includes generator voltage stability and excitation current regulation smoothness. Compared to traditional PID control, which focuses solely on voltage stability, this system considers the smoothness of excitation current variation during the optimization process, avoiding large fluctuations in excitation current and thus reducing the impact on grid stability. Furthermore, this system uses a constrained optimization method to ensure that the optimal excitation current adjustment is within a reasonable range, avoiding excitation current exceeding the equipment's load capacity or causing regulation overshoot, thereby improving the robustness of the control strategy.

[0057] Finally, the excitation regulation module controls the generator excitation system based on the optimal excitation control signal, ensuring that the excitation current can quickly respond to grid changes and ensure voltage stability. Traditional excitation control methods can lead to large voltage fluctuations when renewable energy sources such as wind and photovoltaic power are connected to the grid due to output uncertainty, making it difficult for the regulation system to adapt in real time. This system, through model prediction and optimized calculations, achieves more accurate and rapid excitation regulation, reducing the impact of renewable energy integration on the grid and improving overall system stability.

[0058] In summary, this system overcomes the shortcomings of existing excitation control methods through the coordinated work of data acquisition, state estimation, predictive control, optimization calculation and excitation regulation, and significantly improves the accuracy, real-time performance and stability of excitation regulation. It is particularly suitable for applications in environments with large load fluctuations, frequent grid connection of new energy sources, and complex power grids. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 It is an architecture diagram of a generator excitation control optimization system based on model prediction provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0061] like Figure 1 As shown, an embodiment of the present invention proposes a generator excitation control optimization system based on model prediction, the system comprising:

[0062] The data acquisition module is used to collect the operating parameters of the generator, including the generator terminal voltage, stator current, rotor current, speed and grid frequency, and filter the operating parameters to obtain pre-processed data;

[0063] The state estimation module is used to calculate the state variables of the generator excitation system based on the preprocessed data, including voltage deviation, excitation current change rate, flux change rate and speed deviation, and construct the state space model of the generator excitation system based on the state variables;

[0064] The model predictive control module is used to calculate the excitation current prediction values at multiple moments in the future based on the state space model using the rolling horizon optimization method to obtain the excitation current prediction sequence, and dynamically correct the excitation current prediction sequence based on the prediction error correction mechanism to obtain the excitation current prediction correction sequence;

[0065] The optimization calculation module is used to construct the target optimization function based on the excitation current prediction correction sequence, including the generator voltage stability target term and the excitation current regulation smoothness target term, and use the constrained optimization method to calculate the optimal excitation current adjustment amount to obtain the optimal excitation control signal;

[0066] The excitation regulation module is used to control the generator excitation system according to the optimal excitation control signal to adjust the excitation current.

[0067] In embodiments of the present invention, during the generator excitation control process, traditional excitation control methods struggle to meet the dynamic adjustment requirements of complex power grid environments. Conventional fixed-parameter control strategies, especially during periods of drastic load fluctuations or grid failures, can lead to increased voltage fluctuations and affect grid stability. A model-based prediction-based generator excitation control optimization system achieves precise optimization and control of the excitation current through the collaborative operation of multiple modules, including data acquisition, state estimation, model prediction, optimization calculation, and excitation regulation, thereby improving the dynamic response capability and operational stability of the power system.

[0068] During excitation system operation, the data acquisition module collects real-time generator operating parameters, including generator terminal voltage, stator current, rotor current, speed, and grid frequency. This data, in its raw state, may contain measurement errors and noise, so it requires filtering to generate preprocessed data to ensure the accuracy of subsequent calculations. Effective data processing improves the system's ability to perceive actual grid conditions and provides reliable input for subsequent state estimation.

[0069] Based on preprocessed data, the state estimation module calculates key state variables of the generator excitation system, including voltage deviation, excitation current rate of change, flux linkage rate of change, and speed deviation. These variables accurately characterize the operating state of the excitation system. Using state-space modeling, a state-space model of the system is constructed. This model describes the dynamic behavior of the excitation system under different control inputs, providing a mathematical foundation for subsequent prediction and optimization calculations.

[0070] Based on the state-space model, the model predictive control module uses a rolling horizon optimization method to calculate excitation current predictions for multiple future moments, forming an excitation current prediction sequence. Traditional excitation control methods struggle to adapt to rapid changes in grid operating conditions. However, this module predicts future excitation regulation requirements based on the system's real-time state, improving adaptability to grid disturbances. Furthermore, this module incorporates a prediction error correction mechanism that dynamically adjusts the excitation current prediction sequence, reducing the impact of accumulated prediction errors on system control accuracy.

[0071] The optimization calculation module constructs an optimization objective function based on the excitation current prediction and correction sequence. This function includes the generator voltage stability objective and the excitation current regulation smoothness objective. By using a constrained optimization method to calculate the optimal excitation current adjustment, the excitation system is ensured to maintain generator terminal voltage stability while avoiding severe excitation current fluctuations, thereby improving the accuracy and robustness of regulation.

[0072] The excitation regulation module ultimately adjusts the excitation current in real time based on the optimal excitation control signal, enabling stable system operation under varying load and grid conditions. Compared to traditional methods, this system responds more quickly to grid disturbances, reduces voltage fluctuations, and improves power quality. It also reduces excitation system control errors, making generator operation more efficient and safer.

[0073] In a preferred embodiment of the present invention, the state estimation module includes:

[0074] A data correction unit, used to suppress noise on pre-processed data based on a dynamic adaptive filtering method, and calculate correction data in combination with historical operating data;

[0075] The state variable calculation unit is used to calculate the state variables of the excitation system according to the correction data, including voltage deviation, excitation current change rate, flux change rate and speed deviation, and to construct the initial estimate of the state space model based on the state variable dynamic fitting method;

[0076] The error compensation unit is used to calculate the state prediction error according to the initial estimated value of the state space model, and dynamically adjust the state variables using an incremental correction strategy to obtain a corrected state space model.

[0077] In the embodiments of the present invention, during the excitation control process, directly using collected operating parameters can lead to reduced control accuracy due to measurement errors, data noise, and environmental changes, affecting the regulation of the excitation system. To address this issue, the state estimation module uses multiple units, including data correction, state variable calculation, and error compensation, to work together to improve state estimation accuracy, thereby optimizing subsequent excitation control calculations.

[0078] The data correction unit uses dynamic adaptive filtering to suppress noise in preprocessed data and then applies corrections based on historical operating data. Given the complex power grid operating environment, traditional static filtering methods can cause signal distortion when responding to sudden load changes or short-term grid disturbances. However, dynamic adaptive filtering adaptively adjusts filtering parameters based on data characteristics, ensuring that the extracted data is more representative. Combining this correction with historical operating data eliminates long-term drift errors and makes state estimation more reliable.

[0079] Based on data correction, the state variable calculation unit calculates key state variables of the excitation system, including voltage deviation, excitation current rate of change, flux linkage rate of change, and speed deviation. Using dynamic fitting methods, it constructs an initial estimate of the state-space model. This estimate provides a relatively accurate initial state, providing more precise input for subsequent model predictive control.

[0080] The error compensation unit further optimizes the state estimation results. Because errors may exist in the calculation of the state-space model, this unit uses an incremental correction strategy to dynamically adjust the state variables. This allows the state estimation results to be continuously optimized during actual operation, ensuring more accurate control calculations for the excitation system.

[0081] The implementation of the data correction unit mainly includes three stages: noise suppression, outlier detection and data compensation.

[0082] 1. Noise suppression:

[0083] The operating data of the excitation system mainly includes the generator terminal voltage, stator current, rotor current, speed and grid frequency. These data may be subject to various interferences, such as:

[0084] Electromagnetic noise: interference from electrical equipment or high-frequency signals around the generator.

[0085] Measurement error: Insufficient sensor accuracy or aging leads to data deviation.

[0086] Signal jitter: Due to sampling frequency limitations, the data may have high-frequency fluctuations.

[0087] To address the noise problem, the data correction unit uses a dynamic adaptive filtering method, which automatically adjusts the filtering parameters according to the fluctuation of the data, so that the system can effectively filter out high-frequency noise without weakening the changing characteristics of the actual signal. For example:

[0088] When the power grid is stable, the filtering parameters are small, preserving the original characteristics of the data to facilitate the detection of small changes.

[0089] When the power grid fluctuates greatly, the filtering parameters are automatically increased to reduce noise interference and improve signal quality.

[0090] 2. Outlier Detection

[0091] Outliers are usually caused by sensor failures, communication errors, or sudden changes in the environment, such as:

[0092] Sudden voltage or current jumps far beyond the normal physical range.

[0093] The data fluctuates dramatically in a short period of time, but does not correspond to the adjustment of the load or excitation system.

[0094] The data correction unit performs real-time detection using a sliding window method. This involves analyzing historical data over a period of time and calculating the deviation between the data point and the historical data. If the deviation exceeds a set threshold, the data point is considered a possible outlier and is marked for processing.

[0095] 3. Data compensation:

[0096] When abnormal data is detected, compensation is required to ensure the continuity and accuracy of the data. Compensation methods usually use:

[0097] Interpolation method: If data is missing or there are few outliers, use the previous and next data to interpolate and supplement reasonable values.

[0098] Historical data fitting: If the anomaly lasts for a long time, a trend model is established using historical normal data, and reasonable values are predicted based on the trend model for replacement.

[0099] Multi-sensor fusion: If the system has redundant sensors, such as multiple measurement points, abnormal data can be corrected by taking a weighted average of the different sensors.

[0100] Through the above correction steps, the data correction unit can effectively reduce measurement errors and noise impact, improve data quality, and make subsequent state estimation and control calculations more accurate.

[0101] In a preferred embodiment of the present invention, the model predictive control module includes:

[0102] The future time domain prediction unit is used to calculate the excitation system state variables at multiple future moments based on the modified state space model, and calculate the excitation current prediction values at multiple future moments using a sliding window strategy to obtain an excitation current prediction sequence;

[0103] The prediction error correction unit is used to calculate the excitation current prediction error term based on the prediction error cumulative trend analysis method, and adopt a dynamic error correction mechanism to adjust the excitation current prediction sequence to obtain the excitation current prediction correction sequence.

[0104] In this embodiment of the present invention, accurate prediction of the excitation system state at future moments in model predictive control is crucial for optimizing the control strategy. This system improves the accuracy of excitation current prediction through future time-domain prediction and error correction mechanisms, enabling excitation regulation to more precisely adapt to grid state changes.

[0105] The future time-domain prediction unit calculates the excitation system state variables for multiple future moments based on a modified state-space model and employs a sliding window strategy to calculate predicted excitation current values for these multiple future moments. This sliding window strategy updates the predicted data at each time step, avoiding the lag effect caused by fixed window lengths and improving the prediction accuracy. Compared with traditional fixed-parameter prediction methods, this method is more flexible in handling grid load variations, enabling timely adjustment of the prediction range and improving the accuracy of excitation current calculations.

[0106] The prediction error correction unit further optimizes the excitation current prediction based on future time-domain predictions. It uses a cumulative trend analysis method to calculate the excitation current prediction error term and adjusts the excitation current prediction sequence through a dynamic error correction mechanism. Traditional methods often rely on fixed error correction parameters and are difficult to adapt to dynamically changing systems. This method, however, adaptively adjusts the correction parameters to gradually converge the prediction error and improve excitation control accuracy.

[0107] In a preferred embodiment of the present invention, the optimization calculation module includes:

[0108] An objective function construction unit is used to construct an optimization objective function of the excitation system based on the excitation current prediction correction sequence, wherein the optimization objective function includes a generator voltage stability objective term and an excitation current regulation smoothness objective term, and calculate an initial weight value of the optimization objective function based on a weighted cumulative error analysis method;

[0109] The target weight adjustment unit is used to adjust the initial weight value of the optimization objective function according to the dynamic load characteristics of the power grid and generate an optimization weight parameter set;

[0110] The constrained optimization calculation unit is used to calculate the optimal excitation current adjustment amount using a constrained optimization solution method based on the optimization objective function and the optimization weight parameter set.

[0111] In this embodiment of the present invention, during the excitation system optimization calculation process, the construction of the optimization objective function and the dynamic adjustment of optimization parameters play a key role in ensuring the stable operation of the system. Through units such as objective function construction, objective weight adjustment, and constrained optimization calculation, this optimization calculation module ensures that excitation current adjustment not only ensures generator voltage stability but also reduces unnecessary excitation current fluctuations, thereby improving the overall performance of the system.

[0112] The objective function construction unit establishes an optimization objective function based on the excitation current prediction and correction sequence, including the generator voltage stability objective and the excitation current regulation smoothness objective. The voltage stability objective ensures minimal voltage fluctuations at the generator terminal, while the excitation current regulation smoothness objective constrains the excitation current variation to prevent system oscillations caused by frequent adjustments.

[0113] The target weight adjustment unit adjusts the initial weights of the optimization objective function based on the dynamic load characteristics of the power grid, generating a set of optimized weight parameters. Power grid load characteristics are time-varying, and the requirements for excitation regulation vary at different load levels. By dynamically adjusting the weights of the target function, the optimization calculation can be made more consistent with actual operational requirements.

[0114] Based on the target weight adjustment, the constrained optimization calculation unit uses the constrained optimization solution method to calculate the optimal excitation current adjustment amount, ensuring that the optimization result makes the excitation current change smooth and reasonable while meeting the stability of the power grid, thereby improving the overall control effect.

[0115] In a preferred embodiment of the present invention, the excitation system state variables, including voltage deviation, excitation current change rate, flux change rate, and speed deviation, are calculated based on the correction data, and the initial estimate of the state space model is constructed based on the state variable dynamic fitting method, including:

[0116] According to the correction data, the voltage deviation, excitation current change rate, flux linkage change rate and speed deviation are calculated, and the variable weight adaptive normalization method is used for different state variables to obtain the normalized state variables;

[0117] According to the normalized state variables, the initial state fitting model is constructed using the nonlinear multi-order curve fitting method;

[0118] According to the initial state fitting model, the time trend of the state variable is calculated, and the initial estimate of the state space model is constructed in combination with the historical state data.

[0119] In this embodiment of the present invention, the accuracy of state estimation plays a crucial role in the overall system control during generator excitation control. Traditional state estimation methods, due to their poor adaptability to system parameters, are susceptible to external interference, resulting in large errors in the calculated results. This embodiment, through steps such as data correction, state variable calculation, and model construction, achieves more accurate state estimation results, providing high-quality input for subsequent excitation current prediction and optimization.

[0120] During the data correction phase, the operating parameters acquired by the system are often affected by noise or measurement errors. Directly using these parameters for calculations can lead to reduced system regulation accuracy. Therefore, a dynamic adaptive filtering method is employed to suppress noise on parameters at different time scales and to compensate for errors by combining historical data. This method effectively distinguishes true signals from noise components in the data, improving the credibility of the correction data.

[0121] During the state variable calculation process, voltage deviation, excitation current change rate, flux change rate, and speed deviation are calculated based on the corrected data. These variables accurately represent the operating state of the generator excitation system. A variable-weight adaptive normalization method is used during the calculation process to ensure consistent numerical scales across different variables, thus avoiding weight imbalances caused by varying numerical scales in subsequent calculations. This method automatically adjusts the influence weights of variables under different operating conditions, making state variable calculations more stable and reliable.

[0122] During the model construction phase, a nonlinear multi-order curve fitting method is used to establish an initial state fitting model based on historical data of state variables. Traditional linear fitting methods often struggle to accurately describe the dynamic changes of state variables when dealing with complex power grid operating environments. Multi-order curve fitting, however, can better capture the nonlinear characteristics of the system and improve the accuracy of the state-space model. Finally, based on the initial state fitting model, the temporal trends of the state variables are calculated in combination with historical state data, and the initial estimates of the state-space model are constructed. This makes the state estimation results more accurate and provides reliable input for subsequent model predictive control.

[0123] The method of constructing an initial state fitting model based on the normalized state variables and adopting a nonlinear multi-order curve fitting method specifically includes:

[0124] 1. Fitting method selection:

[0125] The nonlinear multi-order curve fitting method usually adopts the following steps:

[0126] Data acquisition: Collect state variable data over a period of time, such as voltage deviation, excitation current change rate, flux change rate and speed deviation.

[0127] Model selection: Select the appropriate curve type based on the changing trend of the data, for example:

[0128] Quadratic curve fitting: Suitable for slowly changing state variables, such as excitation current during steady-state operation.

[0129] Cubic or higher-order curve fitting: Suitable for rapidly changing state variables, such as voltage deviation during sudden load changes.

[0130] Calculate fitting parameters: Use historical data to calculate the best fitting curve with the minimum error.

[0131] Data verification: Compare the fitted model with the actual data to ensure that the fitting accuracy meets the requirements.

[0132] 2. Application of fitting results:

[0133] The fitted state variable model can be used to:

[0134] Short-term prediction: Determine the changing trend of state variables in the future in advance, so that the control system can be adjusted more accurately.

[0135] Anomaly detection: If the actual data deviates significantly from the fitted model, it may indicate a system anomaly, such as a sensor failure or abnormal power grid fluctuations.

[0136] Optimization calculation input: serves as the basic data for subsequent optimization calculations to improve the accuracy of optimization calculations.

[0137] The method of fitting the model according to the initial state, calculating the time variation trend of the state variable, and constructing the initial estimate of the state space model in combination with the historical state data specifically includes:

[0138] 1. Calculation of time-varying trends of state variables:

[0139] The time variation trend of the state variable reflects the rate of change of the system at different times, that is:

[0140] Changing trend of voltage deviation: reflects the response speed of generator voltage to excitation current adjustment.

[0141] Trend of excitation current change rate: used to evaluate the dynamic adjustment capability of the excitation system.

[0142] Trend of flux change rate: reflects the rate of change of the generator rotor magnetic field.

[0143] Methods for calculating trends include:

[0144] Sliding window analysis: Analyze the rate of change of state variables within a certain time window to avoid the impact of short-term mutations.

[0145] Trend fitting: Through curve fitting methods such as multi-order curve fitting, the growth or decay trend of state variables is analyzed, enabling the system to predict state changes in advance.

[0146] Weighted cumulative analysis: Combines historical data to give higher weight to recent changes to improve the timeliness of trend calculations.

[0147] 2. Construction of initial estimates of the state space model:

[0148] After obtaining the temporal trend of the state variables, it is necessary to combine the historical state data to construct the initial estimate of the state space model. This process includes:

[0149] Extracting initial state based on historical data: By analyzing historical operating data, the steady-state parameters of the system are extracted to serve as the reference point of the state space model.

[0150] Use trend information to adjust initial estimates: If trend analysis indicates that the state variables are changing rapidly, the initial estimates are adjusted to suit the current system state.

[0151] Dynamic update: As the system runs, the initial estimate of the state space model needs to be continuously updated to reflect the latest state changes.

[0152] Through the above method, the system can build a more accurate state-space model, making subsequent control calculations and optimization adjustments more accurate, thereby improving the efficiency and stability of excitation regulation.

[0153] In a preferred embodiment of the present invention, the state prediction error is calculated based on the initial estimated value of the state space model, and the state variable is dynamically adjusted using an incremental correction strategy to obtain a corrected state space model, including:

[0154] According to the initial estimate of the state space model, the state prediction error is calculated based on the actual state variables and the predicted state variables at the current moment;

[0155] According to the state prediction error, the state increment correction term is calculated using the dynamic error compensation method;

[0156] According to the state increment correction term, the initial estimated value of the state space model is dynamically adjusted to obtain a corrected state space model.

[0157] In an embodiment of the present invention, the accuracy of state estimation is crucial to effective control in an excitation control system. However, due to the complexity of power grid operating conditions, traditional fixed-parameter models struggle to adapt to dynamically changing environments, leading to biased state estimation. This embodiment improves state estimation accuracy through state prediction error calculation and a dynamic correction strategy, enabling the control system to better adapt to power grid changes.

[0158] First, the state prediction error is calculated based on the actual and predicted state variables at the current moment. Due to the nonlinear characteristics of the excitation system, even with highly accurate state modeling, it is difficult to completely eliminate the prediction error. Therefore, the error calculation process not only considers the current deviation value but also analyzes historical error trends to determine whether the error has a cumulative effect or a cyclical trend. This error analysis method can more comprehensively reflect the uncertainty of the state estimate, providing a basis for subsequent corrections.

[0159] During the error correction phase, a dynamic error compensation method is employed to calculate incremental state corrections based on the error trend. Compared to traditional fixed-increment correction methods, this method dynamically adjusts the correction amplitude under varying operating conditions, resulting in more accurate corrections. When the error is small, the correction amount can be small to avoid unnecessary parameter perturbations. When the error is large, the system automatically increases the correction force to accelerate the convergence of the state estimate.

[0160] Finally, based on the state increment correction term, the initial estimate of the state-space model is dynamically adjusted to obtain a revised state-space model. This model can reflect the latest operating status of the excitation system in real time, improving the accuracy of state estimation and providing a reliable foundation for subsequent excitation current prediction and optimization calculations. Compared with traditional methods, this technology not only improves the accuracy of state estimation but also reduces the impact on system stability, enabling more efficient and stable operation of the excitation control system.

[0161] The calculation of the state prediction error based on the initial estimated value of the state space model and the actual state variables and the predicted state variables at the current moment specifically includes:

[0162] 1. Data preparation:

[0163] Actual state variables at the current moment: These data come from the real-time operating parameters of the generator, including voltage, excitation current, speed, etc. After being processed by the data correction unit, they can more accurately reflect the current state of the generator.

[0164] Predicted state variables: These are calculated based on the initial estimates of the state-space model, that is, the state that the system should theoretically reach if it develops according to the trends calculated by the model.

[0165] 2. Calculate the state prediction error:

[0166] For example, if the state space model predicts that the voltage deviation at a certain moment in the future should gradually decrease, but the actual measured data shows that the voltage deviation increases instead of decreasing, it indicates that there is an error in the state prediction and correction is needed.

[0167] In the process of calculating the state prediction error, in addition to the simple comparison between the current state and the predicted value, the following methods can also be combined:

[0168] Time series analysis: Consider the error trend over a period of time to determine whether the error is a short-term fluctuation or a long-term accumulation.

[0169] Error distribution analysis: Determine the distribution characteristics of the error. If the error continues to deviate in a certain direction, it may indicate that the model has systematic deviations and requires overall adjustment.

[0170] Dynamic weight adjustment: Different weights are assigned to error calculations under different operating conditions. For example, during a sudden load change, the short-term error may be large, but the impact on the overall system is small, so a lower weight can be assigned. However, during steady-state operation, small changes in the error may affect control accuracy, so a higher weight should be assigned.

[0171] By calculating the state prediction error in the above manner, the system can more accurately evaluate the accuracy of the model prediction and provide a basis for subsequent error compensation and dynamic correction.

[0172] The calculation of the state increment correction term by using a dynamic error compensation method based on the state prediction error specifically includes:

[0173] 1. Error classification:

[0174] Random Error: Short-term errors fluctuate widely, but overall there is no obvious bias. This may be caused by measurement errors or short-term external interference. For this type of error, the system can use a small correction to avoid over-adjustment that affects system stability.

[0175] Systematic Error: A persistent accumulation of errors in a certain direction indicates a shift in the state-space model, such as when model parameters fail to accurately reflect the system's actual characteristics. In this case, the system needs to increase correction efforts to quickly eliminate the impact of the errors on the control strategy.

[0176] Sudden error: A short-term error caused by a sudden change in the external environment, such as a sudden load change or a short-term disturbance in the power grid. This error may be temporary, so it is necessary to determine whether to correct it immediately or wait for the system to recover on its own.

[0177] 2. Error trend analysis:

[0178] Calculate the rate of change of the error at different time points. If the error continues to increase, it means that the correction force needs to be increased; if the error is decreasing, it means that the system has a certain degree of self-adjustment ability and the correction force can be appropriately reduced.

[0179] 3. Calculation of state increment correction item:

[0180] Based on the type and trend of the error, appropriate corrections are calculated and the state-space model is adjusted. For example, when the error is small, the correction term can be set to a smaller increment to avoid unnecessary adjustments to the system. When the error is large and continues to grow, the magnitude of the correction term can be increased to accelerate error convergence.

[0181] The method of dynamically adjusting the initial estimated value of the state space model according to the state increment correction term to obtain the corrected state space model specifically includes:

[0182] 1. Adjust the weight of state variables:

[0183] Under certain operating conditions, some state variables may have a greater impact on the system than others. For example, during the integration of renewable energy, voltage fluctuations may be significant. Therefore, it is necessary to increase the weight of voltage deviation in the model calculation to ensure that the control strategy pays more attention to voltage stability.

[0184] 2. Adjust model parameters:

[0185] By analyzing historical data, key parameters of the state-space model are updated, such as adjusting the model's time constant and gain coefficient, to better reflect the current system state. For example, in the case of rapid load changes, the system's response sensitivity can be increased to improve control accuracy.

[0186] 3. Dynamically modify the state transfer relationship:

[0187] Traditional state-space models are typically based on a fixed state transition matrix, but the system's dynamic characteristics may change under different grid operating conditions. For example, under low-load operation, the excitation system's adjustment response is slow, while under high-load operation, the response speed may be faster. Therefore, it is necessary to dynamically adjust the state transition relationship based on the results of error analysis to enable the model to better predict future system states.

[0188] 4. Real-time update of state space model:

[0189] During operation, the system continuously monitors error changes and periodically updates the state space model so that it can adapt to changes in the power grid environment and improve the overall stability of the control system.

[0190] Through the above method, the system can automatically adjust the model parameters in a complex power grid environment to make the state estimation more accurate, thereby improving the regulation capability of the excitation system, reducing the lag of the excitation current adjustment, improving the stability of voltage regulation, and enhancing the generator's adaptability to load fluctuations.

[0191] In a preferred embodiment of the present invention, the excitation system state variables at multiple future moments are calculated based on the modified state space model, and the excitation current prediction values at multiple future moments are calculated using a sliding window strategy to obtain the excitation current prediction sequence, including:

[0192] According to the modified state space model and based on the recursive state update method, the state variables of the excitation system at multiple moments in the future are calculated;

[0193] in, , For the The state variables of the excitation system at time , For the The state variable vector of the excitation system at time , is the state transfer matrix, which describes the time evolution relationship of the excitation system state. is the control input matrix, describing the effect of excitation current adjustment on state variables, For the The excitation current control input at the moment;

[0194] According to the state variables of the excitation system at multiple future moments, a sliding window strategy is adopted to calculate the predicted values of the excitation current at multiple future moments;

[0195] in, , For the The predicted value of the excitation current at time is the output matrix, describing the state variables The influence on the excitation current, is the control input influence matrix, describing the excitation current control input Direct effect on the excitation current;

[0196] An excitation current prediction sequence is constructed based on the excitation current prediction values at multiple future moments.

[0197] In an embodiment of the present invention, predicting future system states is crucial for optimizing control schemes during excitation system control. Traditional excitation control methods, often based on fixed-parameter rule-based control, struggle to adapt to dynamic changes in complex power grid environments in real time. This can lead to delayed or excessive excitation current adjustments, impacting system stability. This embodiment utilizes future-time prediction and a sliding window calculation method to achieve more accurate excitation current prediction and enhance the flexibility of excitation control.

[0198] During the future time-domain prediction phase, the excitation system state variables are calculated for multiple future moments based on the revised state-space model. A recursive state update method enables the system to leverage current state information to predict future excitation system operating trends. Traditional static prediction methods can only perform simple extrapolations based on the current state, making them difficult to adapt to complex grid dynamics. However, the recursive state update method updates the predicted values at each time step, resulting in more accurate predictions.

[0199] When calculating the excitation current prediction values for multiple future moments, a sliding window strategy is used. This strategy dynamically adjusts the prediction data for multiple future moments to accommodate different control requirements. Compared to fixed window prediction methods, the sliding window strategy allows for more flexible adjustment of the prediction interval length. This allows the system to quickly adjust the excitation current prediction range in the face of sudden load changes or fault disturbances, improving the reliability of the prediction results.

[0200] Ultimately, an excitation current prediction sequence is constructed, which provides a basis for subsequent optimization calculations and control signal generation. Compared with traditional methods, this technology can more accurately predict future excitation current trends, making excitation control more precise, reducing the problem of over- or under-adjustment of excitation current due to prediction errors, and improving system stability and adaptability.

[0201] Among them, the matrix 、 、 、 Used to describe the dynamic characteristics of a system, they can be obtained through theoretical modeling or system identification.

[0202] Theoretical modeling is to build a mathematical model based on the physical characteristics of the excitation system, thereby deriving the matrix 、 、 、 The excitation system mainly consists of the excitation winding, armature winding, voltage regulator, etc. Its core function is to control the excitation current of the generator to maintain a stable generator terminal voltage.

[0203] The dynamic behavior of the excitation system can usually be mathematically related by the law of electromagnetic induction, Kirchhoff's circuit law, etc. For example:

[0204] The generator terminal voltage is mainly affected by the rotor flux, which is determined by the excitation current.

[0205] The change of excitation current is affected by the inductance, resistance and input excitation voltage of the excitation winding.

[0206] The voltage regulator is responsible for adjusting the excitation current to maintain the generator terminal voltage at the set value.

[0207] The mathematical model of the excitation system can be divided into state variables such as voltage deviation, excitation current change rate, etc., control input such as excitation current adjustment amount, and system output such as generator terminal voltage.

[0208] In order to convert the excitation system into a state space representation, it is necessary to define the state variables and control input U:

[0209] State variables X: generator terminal voltage (indicating dynamic voltage changes), excitation current (indicating flux changes), speed deviation (affecting excitation current regulation);

[0210] Control input U: excitation voltage (signal generated by the excitation regulator), compensation of the voltage regulator

[0211] After determining these variables, the matrix 、 Used to describe how state variables evolve over time, and the matrix 、 Used to describe how state variables affect output.

[0212] Based on the electromagnetic equations of the generator and excitation system, the state equation can be established. For example:

[0213] The relationship between the voltage and current of the excitation winding is determined by the inductance, resistance and input voltage.

[0214] The generator terminal voltage is affected by the flux linkage and rotor current.

[0215] After converting the electromagnetic equation into a discrete time system, we can get the matrix 、 、 、 .

[0216] Practical application: If the system parameters, such as inductance, resistance, and time constant are known, the matrix can be directly calculated using the standard mathematical model of the excitation system. 、 、 、 This method is suitable for excitation systems with detailed physical parameters, such as those available under laboratory test conditions.

[0217] When the system parameters cannot be obtained directly, the system identification method can be used to estimate the matrix through experimental data. 、 、 、 This method is suitable for scenarios where accurate modeling is not possible or where the excitation system parameters are significantly affected by the environment.

[0218] First, the following data needs to be collected in the actual operating environment of the excitation system:

[0219] Input data U: control signal applied to the excitation system (excitation current adjustment);

[0220] Output data Y: records the real-time changes of voltage and current at the generator terminal;

[0221] Status data X: records excitation current, voltage deviation, flux change, etc.;

[0222] Data collection can be completed through power monitoring systems or data acquisition equipment and stored as time series data.

[0223] Based on the input-output data of the excitation system, the matrix can be estimated using the following method 、 、 、 :

[0224] The least squares method is applicable to linear systems and fits the state equation by minimizing the prediction error. It requires a large amount of experimental data and is suitable for modeling large-scale excitation systems.

[0225] The recursive least squares method is applicable to the situation where the excitation system parameters change with time, and the matrix parameters can be updated dynamically.

[0226] Kalman filtering is suitable for system identification containing noise, and it estimates the system state and corrects the matrix parameters through recursion.

[0227] Deep learning methods, such as LSTM neural networks, are suitable for complex nonlinear excitation systems but require a large amount of data for training.

[0228] After obtaining the input U and output Y of the excitation system, the linear regression method can be used to calculate the matrix 、 、 、 :

[0229] Establish an input-output mapping relationship and find the relationship between the control input U and the state variable X.

[0230] Apply the least squares method to fit the state space equation and obtain the best estimate matrix 、 、 、 .

[0231] In a preferred embodiment of the present invention, the method of calculating the excitation current prediction error term based on the prediction error cumulative trend analysis method and adjusting the excitation current prediction sequence using a dynamic error correction mechanism to obtain the excitation current prediction correction sequence includes:

[0232] According to the excitation current prediction sequence and based on the historical prediction error data, the excitation current prediction error term is calculated;

[0233] According to the excitation current prediction error term, a dynamic error correction mechanism is used to calculate the excitation current adjustment correction value;

[0234] in, , For the The excitation current adjustment correction value at the moment, is the error correction coefficient, is the error compensation term, For the The actual excitation current value measured at all times;

[0235] According to the excitation current adjustment correction value, the excitation current prediction sequence is error compensated to obtain the excitation current prediction correction sequence.

[0236] In an embodiment of the present invention, during the predictive control of the excitation system, errors may occur in the excitation current prediction due to the uncertainty of the grid's operating state. Traditional methods typically use fixed error correction parameters for adjustment, which makes it difficult to effectively suppress the accumulation of prediction errors, thereby affecting control accuracy. This embodiment, through prediction error calculation and a dynamic error correction mechanism, makes excitation current prediction more accurate and improves the stability of excitation control.

[0237] First, the excitation current prediction error term is calculated based on historical prediction error data. Since the excitation system is affected by various factors during actual operation, including load fluctuations and system parameter drift, there may be a certain deviation between the predicted and actual excitation current values. Therefore, by collecting prediction error data at multiple times and applying error accumulation trend analysis, we can identify the error variation pattern and determine whether the error is random, systematic, or periodic.

[0238] During the error correction phase, a dynamic error correction mechanism is employed to calculate the excitation current adjustment correction value based on the cumulative error trend. Compared to traditional fixed correction methods, this method can maintain a smaller correction amount when the error is small, thus avoiding over-adjustment of the system. When the error is large, the system can adaptively increase the correction amplitude, accelerating error convergence and improving the accuracy of excitation current prediction.

[0239] Finally, based on the excitation current adjustment correction value, the excitation current prediction sequence is error-compensated to obtain a more accurate excitation current prediction correction sequence. Compared to traditional methods, this technology can adaptively adjust the excitation current prediction value, allowing the excitation control system to maintain high control accuracy under different load conditions, improving the overall stability and robustness of the system.

[0240] In a preferred embodiment of the present invention, the optimization objective function of the excitation system is constructed based on the excitation current prediction correction sequence. The optimization objective function includes a generator voltage stability objective term and an excitation current regulation smoothness objective term, and the initial weight value of the optimization objective function is calculated based on a weighted cumulative error analysis method, including:

[0241] Calculate the generator voltage deviation and the excitation current regulation change rate according to the excitation current prediction correction sequence, and construct an optimization objective function, which includes a generator voltage stability objective term and an excitation current regulation smoothness objective term;

[0242] ,in, To optimize the objective function, is the prediction time step, and To optimize the weight coefficient of the objective function, For the Voltage deviation at the moment, The set voltage indicates the target voltage that the generator should maintain. For the The actual voltage at the moment, For the The rate of change of the excitation current at the moment For the The excitation current at time For the The excitation current at the moment;

[0243] According to historical operation data, the weighted cumulative error analysis method is used to calculate the initial weight value of the optimization objective function;

[0244] According to the initial weight value of the optimization objective function, the optimization objective constraint relationship is established.

[0245] In this embodiment of the present invention, during the excitation system optimization calculation process, the rational construction of the optimization objective function directly determines the accuracy and stability of the control strategy. To ensure that the excitation system can maintain stable generator terminal voltage while avoiding large fluctuations in excitation current, this embodiment constructs an optimization objective function and calculates its initial weights based on historical operating data, enabling the optimization calculation to more accurately meet grid operation requirements.

[0246] During the optimization objective function construction phase, the generator voltage deviation and excitation current regulation rate of change are calculated based on the excitation current prediction and correction sequence, and these are used as the core parameters of the optimization objective. Traditional excitation control methods often focus on a single objective, such as optimizing only voltage stability while ignoring the smoothness of excitation current adjustment. This can cause large current fluctuations in the excitation system during the adjustment process, thus affecting system stability. This invention introduces an excitation current regulation smoothness objective term into the optimization objective, enabling the optimization calculation to suppress large excitation current variations while ensuring voltage stability, thereby improving the smoothness and robustness of the control.

[0247] During the weight calculation phase, a weighted cumulative error analysis method is used to calculate the initial weights of the optimization objective function to ensure that the optimization objective function has reasonable weight distribution under different operating conditions. Due to the influence of grid load changes and voltage disturbances, voltage deviations and excitation current adjustment requirements may have different importance in different time periods. Traditional fixed-weight optimization methods are difficult to adapt to dynamic grid changes. However, the weighted cumulative error analysis method dynamically calculates the initial weights of the voltage stability objective term and the excitation current adjustment objective term by analyzing historical error data, making the optimization objective function more consistent with actual needs during the optimization calculation.

[0248] Finally, based on the calculated initial weights, an optimization objective constraint relationship is established to ensure that the optimization results can meet the requirements of grid stability while improving the regulation accuracy of the excitation system. Compared with traditional methods, the construction of this optimization objective function not only improves the accuracy of voltage control but also makes the excitation current regulation smoother, reducing grid fluctuations caused by frequent adjustments and improving overall operational stability.

[0249] In a preferred embodiment of the present invention, adjusting the initial weight value of the optimization objective function according to the dynamic load characteristics of the power grid to generate an optimization weight parameter set includes:

[0250] Calculate the optimization target weight adjustment coefficient based on the current grid load level, load change rate and system operation stability;

[0251] According to the initial weight value of the optimization objective function and the optimization objective weight adjustment coefficient, the optimization objective weight is adaptively adjusted to generate an optimization weight parameter set;

[0252] The optimized weight parameter set includes weight coefficients and ,in,

[0253] ,

[0254] in, is the initial weight value of the generator voltage stability objective term, is the time window for cumulative calculation, For the The actual voltage at the moment, is the absolute value of the voltage error, indicating the voltage deviation of the generator. is the attenuation coefficient, is the weight adjustment coefficient, is the active power change rate, which indicates the rate of change of grid load;

[0255] ,

[0256] in, is the initial weight value of the excitation current smoothness objective term, For the The excitation current at time For the The excitation current at time is the excitation current variation amplitude, is the attenuation coefficient, is the weight adjustment factor, is the reactive power change rate, which indicates the fluctuation of reactive power in the power grid;

[0257] According to the optimization weight parameter set, the optimization priority of the optimization objective function under different operating conditions is dynamically adjusted.

[0258] In this embodiment of the present invention, during the optimization calculation process, the initial weight values of the optimization objective function can be calculated using historical error data. However, due to the dynamic changes in power grid load levels and operating conditions, fixed initial weight values may not fully meet system requirements during actual operation. To further improve the adaptability of the optimization calculation, this embodiment adjusts the initial weight values of the optimization objective function to generate a set of optimized weight parameters, so that the optimization calculation results can better adapt to the dynamic characteristics of the power grid.

[0259] During the weight adjustment phase, the optimization objective weight adjustment coefficient is first calculated based on the current grid load level, load change rate, and system operational stability. The grid load change rate directly affects the regulation requirements for the excitation current. For example, in the event of a sudden large load change, voltage stability is far more important than the smoothness of excitation current regulation. However, when the grid is operating smoothly, the smoothness of excitation current regulation is even more critical. Therefore, by monitoring the load level and change rate in real time and calculating the optimization objective weight adjustment coefficient, we can ensure that the weights of different objectives are properly distributed during the optimization calculation process.

[0260] During the generation of the optimization weight parameter set, the initial weight values of the optimization objective function are adaptively adjusted in combination with the calculated optimization target weight adjustment coefficient to form a new optimization weight parameter set. This method avoids the limitations of traditional fixed weight calculation methods, enabling the optimization calculation to more flexibly adapt to the needs of different operating conditions. For example, during operating phases with large load fluctuations, the voltage stability target weight will be automatically increased to ensure that voltage fluctuations do not affect grid stability. During operating phases with smaller load changes, the smoothness weight of the excitation current regulation will be appropriately increased to reduce the impact of frequent adjustments on system equipment.

[0261] Ultimately, based on a set of optimized weight parameters, the optimization objective function's optimization priority is dynamically adjusted under different operating conditions, enabling the excitation control strategy to more accurately adapt to changes in the grid's operating state. Compared to traditional methods, this solution adaptively adjusts the weights of the optimization objectives, enabling the optimization calculation to achieve better regulation results under different grid operating conditions, improving the response speed and stability of the excitation system.

[0262] In a preferred embodiment of the present invention, the method of calculating the optimal excitation current adjustment amount using a constrained optimization solution method based on the optimization objective function and the optimization weight parameter set includes:

[0263] Constructing an optimal excitation current adjustment calculation model according to the optimization objective function and the optimization weight parameter set, wherein the calculation model includes excitation current adjustment constraint conditions;

[0264] According to the dynamic working range of the excitation system, the excitation current change rate threshold and voltage stability threshold are set, and the calculation model is solved based on the constrained optimization solution method to obtain the optimal excitation current adjustment;

[0265] The calculation formula of the optimal excitation current adjustment amount is:

[0266] ,in, is the optimal excitation current adjustment, is the excitation current adjustment, is an operator, which represents the variable value that makes the objective function achieve the minimum value;

[0267] Constraints ( ) ,in, and are the minimum and maximum values of the excitation current, respectively. is the upper limit of the excitation current change rate;

[0268] An optimal excitation control signal is generated according to the optimal excitation current adjustment amount.

[0269] In the optimization calculation of the excitation system in the embodiments of the present invention, simply dynamically adjusting the weight of the optimization objective is not sufficient to ensure a globally optimal optimization result. During the actual execution of the optimization calculation, it is also necessary to combine the constraints of the excitation system and use a constrained optimization solution method to calculate the optimal excitation current adjustment. This ensures that the final control signal calculation result not only meets the optimization objective but also operates within the physical constraints, preventing abnormal changes in the excitation current from affecting the system.

[0270] During the optimization phase, an optimal excitation current adjustment calculation model is first constructed based on the optimization objective function and a set of optimization weight parameters. Excitation current adjustment constraints are then introduced into this model. The excitation current adjustment range is often limited by the physical characteristics of the equipment, such as the maximum current carrying capacity of the excitation winding and the voltage regulation range of the generator. Therefore, reasonable constraints on the excitation current adjustment must be imposed during the optimization calculation process to ensure that the calculation results remain within a physically safe range.

[0271] Subsequently, based on the dynamic operating range of the excitation system, the excitation current change rate and voltage stability threshold are set, and the computational model is solved using a constrained optimization solution to obtain the optimal excitation current adjustment. Traditional optimization methods often use fixed thresholds for calculations and are unable to dynamically adapt to changes in the system's operating state. However, this solution dynamically constrains the excitation current adjustment range by combining the system's real-time operating data, making the optimization results more consistent with actual operating requirements. For example, when the load suddenly changes within a short period of time, the optimization calculation will allow the excitation current adjustment range to be appropriately increased to ensure grid voltage stability. However, when the grid is operating stably, the excitation current adjustment range is automatically reduced to reduce unnecessary power loss.

[0272] Finally, based on the calculated optimal excitation current adjustment, an optimal excitation control signal is generated and sent to the excitation regulation module to achieve dynamic optimization of the excitation current. Compared to traditional methods, this solution not only improves the accuracy of excitation current calculation through optimization, but also introduces dynamic constraints, ensuring that the optimization results are more consistent with the operating constraints of the excitation system, thereby improving the safety and stability of system operation.

[0273] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. Generator excitation control optimization system based on model prediction, characterized by: The system comprises: The data acquisition module is used to collect the operating parameters of the generator, including the generator terminal voltage, stator current, rotor current, speed and grid frequency, and filter the operating parameters to obtain pre-processed data; The state estimation module is used to calculate the state variables of the generator excitation system based on the preprocessed data, including voltage deviation, excitation current change rate, flux change rate and speed deviation, and construct the state space model of the generator excitation system based on the state variables; The model predictive control module is used to calculate the excitation current prediction values at multiple moments in the future based on the state space model using the rolling horizon optimization method to obtain the excitation current prediction sequence, and dynamically correct the excitation current prediction sequence based on the prediction error correction mechanism to obtain the excitation current prediction correction sequence; The optimization calculation module is used to construct the target optimization function based on the excitation current prediction correction sequence, including the generator voltage stability target term and the excitation current regulation smoothness target term, and use the constrained optimization method to calculate the optimal excitation current adjustment amount to obtain the optimal excitation control signal; The excitation regulation module is used to control the generator excitation system according to the optimal excitation control signal to adjust the excitation current; The optimization calculation module includes: An objective function construction unit is used to construct an optimization objective function of the excitation system based on the excitation current prediction correction sequence, wherein the optimization objective function includes a generator voltage stability objective term and an excitation current regulation smoothness objective term, and calculate an initial weight value of the optimization objective function based on a weighted cumulative error analysis method; The target weight adjustment unit is used to adjust the initial weight value of the optimization objective function according to the dynamic load characteristics of the power grid and generate an optimization weight parameter set; A constrained optimization calculation unit is used to calculate the optimal excitation current adjustment amount using a constrained optimization solution method according to an optimization objective function and an optimization weight parameter set; The method of adjusting the initial weight value of the optimization objective function according to the dynamic load characteristics of the power grid to generate an optimization weight parameter set includes: Calculate the optimization target weight adjustment coefficient based on the current grid load level, load change rate and system operation stability; According to the initial weight value of the optimization objective function and the optimization objective weight adjustment coefficient, the optimization objective weight is adaptively adjusted to generate an optimization weight parameter set; The optimized weight parameter set includes weight coefficients and ,in, ， in, is the initial weight value of the generator voltage stability objective term, is the time window for cumulative calculation, For the The actual voltage at the moment, is the absolute value of the voltage error, indicating the voltage deviation of the generator. is the attenuation coefficient, is the weight adjustment coefficient, is the active power change rate, which indicates the rate of change of the grid load. The set voltage indicates the target voltage that the generator should maintain; ， in, is the initial weight value of the excitation current smoothness objective term, For the The excitation current at time For the The excitation current at time is the excitation current variation amplitude, is the attenuation coefficient, is the weight adjustment factor, is the reactive power change rate, which indicates the fluctuation of reactive power in the power grid; According to the optimization weight parameter set, the optimization priority of the optimization objective function under different operating conditions is dynamically adjusted.

2. The generator excitation control optimization system based on model prediction according to claim 1 is characterized in that: The state estimation module includes: A data correction unit, used to suppress noise on pre-processed data based on a dynamic adaptive filtering method, and calculate correction data in combination with historical operating data; The state variable calculation unit is used to calculate the state variables of the excitation system according to the correction data, including voltage deviation, excitation current change rate, flux change rate and speed deviation, and to construct the initial estimate of the state space model based on the state variable dynamic fitting method; The error compensation unit is used to calculate the state prediction error according to the initial estimated value of the state space model, and dynamically adjust the state variables using an incremental correction strategy to obtain a corrected state space model.

3. The generator excitation control optimization system based on model prediction according to claim 2 is characterized in that: The model predictive control module includes: The future time domain prediction unit is used to calculate the excitation system state variables at multiple future moments based on the modified state space model, and calculate the excitation current prediction values at multiple future moments using a sliding window strategy to obtain an excitation current prediction sequence; The prediction error correction unit is used to calculate the excitation current prediction error term based on the prediction error cumulative trend analysis method, and adopt a dynamic error correction mechanism to adjust the excitation current prediction sequence to obtain the excitation current prediction correction sequence.

4. The generator excitation control optimization system based on model prediction according to claim 3 is characterized in that: The excitation system state variables are calculated based on the correction data, including voltage deviation, excitation current change rate, flux change rate and speed deviation, and the initial estimate of the state space model is constructed based on the state variable dynamic fitting method, including: According to the correction data, the voltage deviation, excitation current change rate, flux linkage change rate and speed deviation are calculated, and the variable weight adaptive normalization method is used for different state variables to obtain the normalized state variables; According to the normalized state variables, the initial state fitting model is constructed using the nonlinear multi-order curve fitting method; According to the initial state fitting model, the time trend of the state variable is calculated, and the initial estimate of the state space model is constructed in combination with the historical state data.

5. The generator excitation control optimization system based on model prediction according to claim 4 is characterized in that: The state prediction error is calculated based on the initial estimated value of the state space model, and the state variable is dynamically adjusted using an incremental correction strategy to obtain a corrected state space model, including: Calculate the state prediction error based on the actual state variables and predicted state variables at the current moment according to the initial estimate of the state space model; According to the state prediction error, the state increment correction term is calculated using the dynamic error compensation method; According to the state increment correction term, the initial estimated value of the state space model is dynamically adjusted to obtain a corrected state space model.

6. The generator excitation control optimization system based on model prediction according to claim 5, characterized in that: The method of calculating the excitation system state variables at multiple future moments based on the modified state space model and calculating the excitation current prediction values at multiple future moments using a sliding window strategy to obtain an excitation current prediction sequence includes: According to the modified state space model and based on the recursive state update method, the state variables of the excitation system at multiple moments in the future are calculated; According to the state variables of the excitation system at multiple future moments, a sliding window strategy is adopted to calculate the predicted values of the excitation current at multiple future moments; An excitation current prediction sequence is constructed based on the excitation current prediction values at multiple future moments.

7. The generator excitation control optimization system based on model prediction according to claim 6, characterized in that: The method for analyzing the cumulative trend of prediction errors is used to calculate the excitation current prediction error term, and a dynamic error correction mechanism is used to adjust the excitation current prediction sequence to obtain the excitation current prediction correction sequence, including: According to the excitation current prediction sequence and based on the historical prediction error data, the excitation current prediction error term is calculated; According to the excitation current prediction error term, a dynamic error correction mechanism is used to calculate the excitation current adjustment correction value; According to the excitation current adjustment correction value, the excitation current prediction sequence is error compensated to obtain the excitation current prediction correction sequence.

8. The generator excitation control optimization system based on model prediction according to claim 1, characterized in that: The optimization objective function of the excitation system is constructed based on the excitation current prediction correction sequence. The optimization objective function includes a generator voltage stability objective term and an excitation current regulation smoothness objective term, and the initial weight value of the optimization objective function is calculated based on a weighted cumulative error analysis method, including: Calculate the generator voltage deviation and the excitation current regulation change rate according to the excitation current prediction correction sequence, and construct an optimization objective function, which includes a generator voltage stability objective term and an excitation current regulation smoothness objective term; ,in, To optimize the objective function, is the prediction time step, and To optimize the weight coefficient of the objective function, For the Voltage deviation at the moment, For the The actual voltage at the moment, For the The rate of change of the excitation current at the moment For the The excitation current at time For the The excitation current at the moment; According to historical operation data, the weighted cumulative error analysis method is used to calculate the initial weight value of the optimization objective function; According to the initial weight value of the optimization objective function, the optimization objective constraint relationship is established.

9. The generator excitation control optimization system based on model prediction according to claim 8, characterized in that: The method of calculating the optimal excitation current adjustment amount using a constrained optimization solution method according to the optimization objective function and the optimization weight parameter set includes: Constructing an optimal excitation current adjustment calculation model according to the optimization objective function and the optimization weight parameter set, wherein the calculation model includes excitation current adjustment constraint conditions; According to the dynamic working range of the excitation system, the excitation current change rate threshold and voltage stability threshold are set, and the calculation model is solved based on the constrained optimization solution method to obtain the optimal excitation current adjustment; The calculation formula of the optimal excitation current adjustment amount is: ,in, is the optimal excitation current adjustment, is an operator, which represents the variable value that makes the objective function achieve the minimum value; and satisfy ,in, and are the minimum and maximum values of the excitation current, respectively. is the upper limit of the excitation current change rate; An optimal excitation control signal is generated according to the optimal excitation current adjustment amount.

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