Dynamic power generation control method and control system for generator

Abstract

The invention discloses a dynamic power generation control method and system for a generator, and belongs to the technical field of generator control, and the method comprises the steps: receiving a generator output parameter collected in real time and an operation parameter of first equipment, predicting the load demand of the generator, and obtaining the predicted load power of a period of time in the future; establishing a composite control strategy, taking the predicted load power as the input of feed-forward control, forming a composite control loop with the output power feedback of the generator, and generating an execution instruction; the generator is controlled to operate according to the execution instruction, and self-adaptive fusion execution is carried out; and carrying out statistical analysis on the prediction error sequence and the control response characteristics. In the implementation process of the technical scheme, the prediction load power is obtained, the execution instruction is generated based on the prediction load power, and then adaptive fusion execution is performed in a credibility prediction mode, so that the prediction process participates in the execution process, and the control response speed of the generator is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of motor control, in particular to a dynamic power generation control method and system for a generator. BACKGROUND

[0002] A generator is a core device for converting mechanical energy into electrical energy and is widely used in power systems, new energy and industrial production.

[0003] Stable operation of the generator requires a corresponding control system to adjust the output voltage, frequency and power factor to adapt to load changes. In the prior art, a PID controller is usually used to adjust the output parameters of the generator to achieve stable control of the generator output. In addition, load current is detected for feedforward compensation to improve system response speed. Furthermore, an optimal operating point search strategy based on an efficiency map is used to improve the overall energy efficiency of the generator set. These methods all show good control effects under specific working conditions.

[0004] However, due to the complex and variable use scenarios of the generator and frequent load fluctuations, the traditional PID control cannot balance dynamic response and steady-state accuracy, the feedforward compensation strategy has strong dependence on the model and insufficient adaptability, and the optimal operating point search easily fails under transient conditions, thereby causing energy waste and system stability decline. Especially when the load is suddenly added or removed (for example, in the application scenario of the generator in a new energy vehicle), the system is prone to voltage drop, frequency deviation and other phenomena, which may even affect the normal operation of the electrical equipment.

[0005] Therefore, it is necessary to provide a dynamic power generation control method and system for a generator to solve the above problems.

[0006] It should be noted that the above information disclosed in the background section of the present application is only used to understand the background technology of the concept of the present application, and therefore, it can contain information that does not constitute prior art. SUMMARY

[0007] Based on the above problems existing in the prior art, the present application aims to provide a dynamic power generation control method and system for a generator, which realizes dynamic control of the generator by dynamically monitoring and feedback controlling the real-time load change of the generator.

[0008] The technical solution adopted by the present application to solve the technical problems is: a dynamic power generation control method for a generator, comprising: receiving the real-time collected generator output parameters and the operating parameters of the first device, and predicting the load demand of the generator according to the generator output parameters and the operating parameters of the first device to obtain the predicted load power in the future period of time; A composite control strategy is established, the predicted load power is taken as the input of the feedforward control, and a composite control loop is formed with the output power feedback of the generator to generate an execution instruction; The generator is controlled according to the execution instruction, and adaptive fusion execution is performed based on the prediction credibility; The prediction error sequence and the control response characteristics are statistically analyzed to identify the error distribution law and dynamic characteristics under different working conditions, and then the smoothing parameter and the threshold value of the nonlinear mapping function in the credibility evaluation process are optimized.

[0009] In the implementation process of the technical scheme of the present application, the predicted load power is obtained, and the execution instruction is generated based on the predicted load power, and then adaptive fusion execution is performed in the manner of prediction credibility, so that the prediction process participates in the execution process, and the response speed of the generator control is improved.

[0010] Further, the first device is a core load device or a load management system powered by the generator.

[0011] Further, the implementation of the load prediction is that a sliding time window prediction algorithm based on multi-source information fusion is established, the current and historical generator output power sequence is taken as the basic data, and the planned or predictive load information provided by the first device is taken as the feedforward input, the above two types of data are fused and calculated through a weighted average method with a forgetting factor, and a predicted load power curve in a future adjustable time window is output.

[0012] Further, the core of the weighted average method with a forgetting factor is to assign dynamic changing weights to data at different time points and from different sources, and to fuse the latest power change trend of the generator and the short-term load prediction information provided by the first device.

[0013] Further, the composite control strategy includes the following processes: first, an optimal operating parameter database of the generator under different output powers, speeds and environmental temperatures is established, the storage contents of the optimal operating parameter database include but are not limited to the best excitation current set value, the power factor set value and the cooling system parameter; then the optimal operating parameter database is queried according to the predicted load power, the target working condition point of the generator corresponding to the current predicted load power is matched, and the best excitation current and power factor set value under the working condition are obtained; the load change rate of the current generator is obtained, and when the load change rate exceeds a threshold value, dynamic compensation is performed, the compensation contents include increasing the response slope of the excitation current in advance and adjusting the dynamic gain of the voltage regulator to improve the adaptability of the system to sudden load changes; the predicted load power and the real-time feedback output power are calculated for deviation, and a fuzzy PID controller is introduced to dynamically adjust the deviation, and the fuzzy PID adaptively adjusts the proportional, integral and differential coefficients according to the deviation size.

[0014] Further, the adaptive fusion execution based on the prediction confidence includes: calculating a prediction confidence coefficient based on the short-term prediction error and the smoothed error, the prediction confidence coefficient being between 0 and 1; performing weighted fusion on two control instructions according to the prediction confidence coefficient, the control instructions including a feedforward control instruction and a feedback control instruction; and outputting the fused final execution instruction to a control system of the generator to drive the generator to adjust the output power, and continuously recording the prediction error, the actual load change and the control response data in the execution process.

[0015] Further, the method for obtaining the prediction confidence coefficient includes: periodically obtaining a short-term predicted load power value at a historical time for a current time, and comparing the short-term predicted load power value with an actual measured load power value at the current time to obtain a short-term prediction error e(t), then performing smoothing processing on the error sequence e(t) by using an exponential weighted moving average algorithm to obtain a smoothed error E_smooth(t), and finally mapping E_smooth(t) to the prediction confidence coefficient K through a preset nonlinear mapping function.

[0016] Further, the final execution instruction = K x feedforward instruction + (1-K) x feedback instruction; when K tends to 1, the control is dominated by the feedforward control to improve the response speed, when K tends to 0, the control is dominated by the feedback regulation to ensure the stability of the system, and when K is in the middle range, the feedforward control and the feedback control work together.

[0017] Further, when the system is in a high dynamic load scene, the smoothing parameter is automatically enhanced to suppress the excessive influence of error mutation on the value of K, and when the system is in a steady state, the threshold sensitivity is reduced to improve the participation degree of the feedforward control.

[0018] A generator dynamic power generation control system, comprising: A load power prediction module configured to receive real-time collected generator output parameters and operating parameters of a first device, and predict load demand of the generator according to the generator output parameters and the operating parameters of the first device to obtain predicted load power in a future period of time; A composite control module configured to establish a composite control strategy, take the predicted load power as an input of feedforward control, form a composite control loop with an output power feedback of the generator, and generate an execution instruction; An adaptive execution module configured to control the generator to operate according to the execution instruction, and perform adaptive fusion execution based on a prediction confidence; A closed-loop optimization module configured to statistically analyze a prediction error sequence and a control response characteristic, identify error distribution law and dynamic characteristics in different working conditions, and then optimize a smoothing parameter and a nonlinear mapping function threshold in a confidence evaluation process.

[0019] The beneficial effects of this application are: the generator dynamic power generation control method and control system provided by this application obtains the predicted load power, generates execution commands based on the predicted load power, and then performs adaptive fusion execution through the prediction reliability method, so that the prediction process participates in the execution process, thereby improving the generator control response speed.

[0020] In addition to the purposes, features, and advantages described above, this application has other purposes, features, and advantages. A further detailed description of this application will be provided below with reference to the figures. Attached Figure Description

[0021] The accompanying drawings, which form part of this application, are used to provide a further understanding of this application. The illustrative embodiments and descriptions of this application are used to explain this application and do not constitute an undue limitation of this application. In the drawings: Figure 1 This is an overall schematic diagram of a generator dynamic power generation control method according to this application; Figure 2 This is an overall schematic diagram of a generator dynamic power generation control system according to this application. Detailed Implementation

[0022] It should be noted that, unless otherwise specified, the embodiments and features described in this application can be combined with each other. This application will now be described in detail with reference to the accompanying drawings and embodiments.

[0023] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0024] Example 1: As Figure 1 As shown, this application provides a dynamic power generation control method for a generator. This control method is applied during the operation of a generator, which can be used in scenarios including but not limited to grid peak shaving, emergency power supply, new energy grid connection, and continuous industrial load power supply. By dynamically monitoring and feedback control of the generator's real-time load changes, dynamic control of the generator is achieved. Specifically, the control method includes the following steps: Step A: Receive the real-time collected generator output parameters and the operating parameters of the first device, and predict the generator load demand based on the generator output parameters and the operating parameters of the first device to obtain the predicted load power for a period of time in the future. The dynamic generation control of the generator needs to be adjusted around the load demand, and therefore, accurately predicting the load power is a prerequisite for efficient control. In predicting the load power, the output parameters of the generator need to be obtained, including but not limited to voltage, current, frequency, power factor and output power. Meanwhile, to improve the predictability and accuracy, the operating parameters of the first device also need to be introduced. Specifically, the first device refers to the core load device or load management system supplied with power by the generator. For example, in the data center backup power supply scenario, the first device can be an energy management unit of a server cluster, and its operating parameters include the computing load rate of the cluster and the preset charging and discharging plan. In the new energy grid-connected scenario, the first device can be a wind-solar prediction system, and its operating parameters include the wind-solar output prediction data in the next 15 minutes. In the new energy vehicle scenario, the first device can be a battery management system of a vehicle, and its operating parameters include the state of charge of the battery, the charging power demand and the driving plan. The specific implementation of the load prediction is to establish a sliding time window prediction algorithm based on multi-source information fusion. This algorithm takes the current and historical generator output power sequence P(t), P(t-1),... P(t-n) as the basic data, and takes the planned or predicted load information provided by the first device as the feedforward input. Through a weighted average method with a forgetting factor, the above two types of data are fused and calculated to output the predicted load power curve in a adjustable time window (such as 30 seconds to 5 minutes) in the future. The advantage of this algorithm is that it can respond to the real-time change trend of the generator output power and effectively overcome the lag of relying solely on historical data on the generator side for prediction by utilizing the prior information provided by the first device. Specifically, the core of the weighted average method with a forgetting factor is to assign dynamic changing weights to data at different time points and from different sources to more accurately reflect the future load trend of the system. This method fuses the latest power change trend of the generator with the short-term load prediction information provided by the first device through a configurable weight distribution strategy. In the calculation, the system assigns higher weights to "recent data" and "short-term prediction", and balances the proportion of the two in the final prediction result through a fusion coefficient, so as to quickly and prospectively obtain the predicted load power. For example, in the new energy vehicle scenario, when the battery management system (as the first device) detects that the vehicle is about to enter a long uphill section or the driver has an intention of sudden acceleration, it will generate a prediction that the charging power demand will significantly increase within the next 30 seconds. At the same time, the generator controller monitors the current output power in real time. At this time, the prediction algorithm calculates as follows: a higher weight is given to the information that the future power demand provided by the battery management system is increasing, because this information directly reflects the explicit intention of load change. Then a relatively low weight is given to the historical data sequence of the generator's current smooth output power, because its trend will be covered by the new driving instruction. Then through weighted average, a comprehensive predicted load power value is obtained, which indicates that the generator needs to increase the output in advance, so that when the battery actually starts high-power charging, the generator has already been adjusted in place, effectively avoiding voltage fluctuations caused by sudden load changes, and achieving smooth power connection and energy management. The advantage of the above method is that it is no longer a passive response to the power change that has occurred at the generator end, but can actively control in advance according to the plan and intention of the load end, significantly improving the dynamic response speed of the system. Step B: Establish a composite control strategy, use the predicted load power as the input of the feedforward control, form a composite control loop with the output power feedback of the generator, and generate an execution instruction; After obtaining the predicted load power, it needs to be converted into accurate control instructions for the generator system, so as to realize predictive regulation of the generator output power. Specifically, by establishing a composite control strategy, the predicted load power is used as a feedforward control variable, which is superimposed on the traditional feedback control loop to form a composite control strategy, solving the problem of response lag and regulation delay of the traditional control algorithm. Specifically, the composite control strategy includes the following processes: First, an optimal operating parameter database of the generator under different output powers, speeds and environmental temperatures is established. The storage content of the optimal operating parameter database includes but is not limited to the best excitation current set value, power factor set value and cooling system parameters. The optimal operating parameter database is calibrated by offline simulation and measured data, and the parameters stored in it are continuously updated as the operation data is updated, the generator's service life decays and the environmental adaptability changes. The optimal operating parameter database stores various parameters of the generator under different operating conditions, providing dynamic optimization basis for the composite control strategy. Then, according to the predicted load power, the optimal operating parameter database is queried to match the target operating point of the generator corresponding to the predicted load power, and the best excitation current and power factor set value under this operating condition are obtained. After obtaining the predicted load power, the optimal operating parameter combination matching the predicted load power, in particular, the target excitation current and power factor setting value, can be retrieved from the optimal operating parameter database, so as to provide accurate setting targets for the advance adjustment of the generator and avoid output power fluctuation caused by the delay of real-time feedback control; The load change rate of the current generator is obtained, and when the load change rate exceeds a threshold value, dynamic compensation is performed, and the compensation content includes increasing the response slope of the excitation current in advance and adjusting the dynamic gain of the voltage regulator, so as to improve the adaptability of the system to sudden load changes; In actual application, the load change rate of the generator is affected by different working conditions and power demand. For example, in the new energy vehicle scene, the load power may fluctuate sharply within milliseconds in the scenarios of sudden acceleration and uphill driving. At this time, the system monitors the load change rate in real time, and once the load change rate exceeds the preset threshold value, the dynamic compensation mechanism is started immediately to increase the excitation current rise rate in advance and simultaneously increase the dynamic gain of the voltage regulator, so as to enhance the response speed of the generator front end; At the same time, the predicted load power and the real-time feedback output power are calculated for deviation, and a fuzzy PID controller is introduced to dynamically adjust the deviation. The fuzzy PID adjusts the proportional, integral and differential coefficients adaptively according to the deviation size; The fuzzy PID is an adaptive control algorithm based on fuzzy logic, which can effectively handle nonlinear, time-varying and uncertain factors. By setting the input variable as the power deviation and its change rate and the output variable as the correction amount of the PID parameters, the proportional, integral and differential coefficients are dynamically adjusted in real time to improve the adjustment accuracy and response speed when the load changes suddenly or the operating environment fluctuates. When the power deviation is large, the fuzzy rule enhances the proportional action to speed up the response, while suppressing the integral saturation. When the deviation tends to zero, the integral adjustment is strengthened to eliminate the steady-state error. Step C: Control the generator operation according to the execution instruction, and perform adaptive fusion execution based on the prediction confidence; Since the foregoing process involves load prediction, in actual application, there will inevitably be some errors in the prediction results, which may be caused by the randomness of load fluctuation, model precision limitation or external interference. Therefore, in the execution process, adaptive fusion execution based on the prediction confidence is needed to balance the weights of the prediction control and the real-time feedback. Specifically, the adaptive fusion execution based on the prediction confidence includes: Step C1: Calculate the prediction confidence coefficient based on the short-term prediction error and the smoothing error, and the prediction confidence coefficient is between 0 and 1; Specifically, the method for obtaining the prediction confidence coefficient is: periodically obtaining the short-term predicted load power value at the historical time for the current time, and comparing it with the actual measured load power value at the current time to obtain the short-term prediction error e(t), then smoothing the error sequence e(t) by using the exponential weighted moving average algorithm to obtain the smoothed error E_smooth(t), finally, through a pre-set nonlinear mapping function, E_smooth(t) is mapped to the prediction confidence coefficient K, when the smoothed error is small, K tends to 1, indicating that the prediction is highly reliable; when the smoothed error is large, K tends to 0, indicating that the prediction is unreliable, wherein the nonlinear mapping function can adopt the form of S-shaped function or inverse proportional function, etc., to ensure that K value quickly decays with the increase of the smoothed error; Step C2: weighting and fusing two control instructions according to the prediction confidence coefficient, the control instructions including feedforward control instructions and feedback control instructions, the feedforward control instructions being generated based on the predicted load power value and used for responding to the expected load change in advance, and the feedback control instructions being generated according to the deviation between the real-time detected output power and the actual load; Then, the two instructions are weighted and fused according to the prediction confidence coefficient K, that is, the final execution instruction = K × feedforward instruction + (1 - K) × feedback instruction; when K tends to 1, the control is dominated by feedforward, which improves the response speed; when K tends to 0, the control is dominated by feedback, which ensures the stability of the system; when K is in the middle range, the feedforward and feedback work together to achieve the balance between fast response and steady-state accuracy, thereby effectively dealing with the influence of prediction uncertainty on the control performance of the system, improving the stability and energy efficiency of the overall operation, dynamically adjusting the weight distribution of feedforward and feedback, so that the control system has good adaptability under different working conditions, especially in the case of load mutation or prediction model misalignment, the system can still maintain small power deviation and fast regulation response, thereby realizing high-precision tracking and optimal control of the generator output power; Step C3: outputting the fused final execution instruction to the control system of the generator to drive the generator to adjust the output power, and continuously recording the prediction error, actual load change and control response data in the execution process; After dynamically weighting and fusing the feedforward and feedback control instructions through the prediction confidence coefficient, the fused final execution instruction can be output to the generator control system to real-time drive the generator to adjust the output power, so as to accurately track the load demand, while the actual operation data is collected in a closed loop for subsequent optimization of the confidence model parameters and control strategy; Step D: statistically analyzing the prediction error sequence and control response characteristics to identify the error distribution law and dynamic characteristics under different working conditions, and then optimizing the smoothing parameters and nonlinear mapping function thresholds in the confidence evaluation process.

[0025] Based on historically accumulated prediction error and control response data, statistical analysis is performed to identify the error fluctuation range and response delay characteristics under typical operating conditions. This allows for adaptive adjustment of the smoothing parameter and the threshold parameter of the nonlinear mapping function, making the generated reliability coefficient K more closely reflect changes in the actual operating environment. Specifically, when the system is under high dynamic load scenarios, the smoothing parameter is automatically enhanced to suppress the excessive influence of sudden error changes on the K value, while under steady-state conditions, the threshold sensitivity is reduced to increase the participation of feedforward control. Through a combination of offline training and online updates, the morphological characteristics of the nonlinear mapping function are continuously optimized to ensure that the reliability assessment has good discriminative ability across different time scales and load modes, thereby improving the adaptability of the K value under complex operating conditions.

[0026] Example 2: Figure 2 As shown, this application proposes a generator dynamic power generation control system. This system operates the power generation control method described in Embodiment 1 to achieve precise dynamic adjustment of the generator output power. The system includes: The load power prediction module is used to receive the real-time collected generator output parameters and the operating parameters of the first device, and predict the load demand of the generator based on the generator output parameters and the operating parameters of the first device to obtain the predicted load power for a period of time in the future. The composite control module is used to establish a composite control strategy, taking the predicted load power as the input of the feedforward control, forming a composite control loop with the generator's output power feedback, and generating execution commands. The adaptive execution module is used to control the generator operation according to the execution instructions and to perform adaptive fusion execution based on the prediction confidence level. The closed-loop optimization module is used to perform statistical analysis on the predicted error sequence and control response characteristics, identify the error distribution patterns and dynamic characteristics under different operating conditions, and then optimize the smoothing parameters and nonlinear mapping function thresholds in the credibility assessment process.

[0027] The above description is merely a preferred embodiment of this application and is not intended to limit this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A dynamic power generation control method for a generator, characterized in that: include: The generator receives real-time collected generator output parameters and the operating parameters of the first device, and predicts the generator load demand based on the generator output parameters and the operating parameters of the first device to obtain the predicted load power for a period of time in the future. A composite control strategy is established, which uses the predicted load power as the input to the feedforward control, forms a composite control loop with the generator's output power feedback, and generates execution commands. The generator is controlled to operate according to the execution instructions, and adaptive fusion execution is performed based on the prediction reliability. Statistical analysis is performed on the prediction error sequence and control response characteristics to identify the error distribution patterns and dynamic characteristics under different operating conditions, thereby optimizing the smoothing parameters and nonlinear mapping function thresholds in the credibility assessment process.

2. The generator dynamic power generation control method according to claim 1, characterized in that: The first device is the core load device or load management system powered by the generator.

3. The generator dynamic power generation control method according to claim 1, characterized in that: The load forecasting is implemented by establishing a sliding time window forecasting algorithm based on multi-source information fusion. This algorithm uses the current and historical generator output power sequences as basic data, and the planned or predictive load information provided by the first device as feedforward input. The two types of data are fused and calculated using a weighted average method with a forgetting factor, and the predicted load power curve for an adjustable time window in the future is output.

4. The generator dynamic power generation control method according to claim 3, characterized in that: The core of the weighted average method with forgetting factor lies in assigning dynamically changing weights to data from different time points and different sources, and integrating the latest power change trend of the generator with the short-term load forecast information provided by the first device.

5. The generator dynamic power generation control method according to claim 1, characterized in that: Composite control strategy The process includes the following steps: First, establish a database of optimal operating parameters for the generator under different output power, speed, and ambient temperature. This database stores, but is not limited to, optimal excitation current setpoint, power factor setpoint, and cooling system parameters. Then, query the database based on the predicted load power, match the generator target operating point corresponding to the current predicted load power, and obtain the optimal excitation current and power factor setpoint under that operating condition. Obtain the current generator load change rate. When the load change rate exceeds a threshold, perform dynamic compensation. The compensation includes increasing the response slope of the excitation current in advance and adjusting the dynamic gain of the voltage regulator to improve the system's adaptability to sudden load changes. Calculate the deviation between the predicted load power and the real-time feedback output power, and introduce a fuzzy PID controller to dynamically adjust the deviation. The fuzzy PID controller adaptively adjusts the proportional, integral, and derivative coefficients according to the magnitude of the deviation.

6. The generator dynamic power generation control method according to claim 1, characterized in that: Adaptive fusion execution based on prediction reliability includes: calculating a prediction reliability coefficient based on short-term prediction error and smoothing error, where the prediction reliability coefficient is between 0 and 1; weighting and fusing two control commands, including feedforward control commands and feedback control commands, according to the prediction reliability coefficient; outputting the fused final execution command to the generator's control system to drive the generator to adjust its output power, and continuously recording prediction errors, actual load changes, and control response data during the execution process.

7. The generator dynamic power generation control method according to claim 6, characterized in that: The method for obtaining the prediction confidence coefficient is as follows: periodically obtain the short-term predicted load power value of the current moment at a historical time, and compare it with the actual measured load power value at the current moment to obtain the short-term prediction error e(t). Then, use the exponential weighted moving average algorithm to smooth the error sequence e(t) to obtain the smoothing error E_smooth(t). Finally, use a preset nonlinear mapping function to map E_smooth(t) to the prediction confidence coefficient K.

8. The generator dynamic power generation control method according to claim 6, characterized in that: The final execution instruction = K × feedforward instruction + (1 - K) × feedback instruction; when K approaches 1, the control is dominated by feedforward to improve the response speed; when K approaches 0, the control is dominated by feedback adjustment to ensure system stability; when K is in the middle range, feedforward and feedback work together.

9. The generator dynamic power generation control method according to claim 7, characterized in that: When the system is under high dynamic load, the smoothing parameter is automatically enhanced to suppress the excessive influence of error mutation on the K value, while under steady-state conditions, the threshold sensitivity is reduced and the participation of feedforward control is increased.

10. A generator dynamic power generation control system, characterized in that: include: The load power prediction module is used to receive the real-time collected generator output parameters and the operating parameters of the first device, and predict the load demand of the generator based on the generator output parameters and the operating parameters of the first device to obtain the predicted load power for a period of time in the future. The composite control module is used to establish a composite control strategy, taking the predicted load power as the input of the feedforward control, forming a composite control loop with the generator's output power feedback, and generating execution commands. The adaptive execution module is used to control the generator operation according to the execution instructions and to perform adaptive fusion execution based on the prediction confidence level. The closed-loop optimization module is used to perform statistical analysis on the predicted error sequence and control response characteristics, identify the error distribution patterns and dynamic characteristics under different operating conditions, and then optimize the smoothing parameters and nonlinear mapping function thresholds in the credibility assessment process.

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