Testing system for mismatch correction of wide-load-interval thermal power generating unit speed regulation model

Abstract

The invention relates to the technical field of steam turbine testing, in particular to a testing system for mismatch correction of a wide-load-interval thermal power generating unit speed regulation model, which comprises a thermodynamic system component, a speed regulation system device, a dynamic signal acquisition device and a control device, a dynamic signal processing module analyzes the relevance between main steam pressure sudden change and control valve opening oscillation in real time, a dynamic response database including a time sequence is constructed, and a control valve opening feed-forward compensation instruction and a combustor fuel distribution gradient adjustment signal are generated according to correction parameters. Power over-modulation wave crests caused by sudden load change are suppressed, and primary frequency modulation action delay is reduced; and the simulation verification module compares measured data with model output through an error feedback mechanism, dynamically adjusts high-pressure cylinder flow characteristic curve calibration parameters, optimizes a control strategy in combination with a network-related safety margin evaluation result, and realizes improvement of dynamic response precision in a full peak regulation interval and enhancement of power grid frequency stability.

Description

Technical Field

[0001] The present invention relates to the technical field of steam turbine testing, and particularly to a testing system for correcting the mismatch of the speed control model of a thermal power unit in a wide load range. Background Art

[0002] The overshoot peak of the steam turbine power curve refers to the transient fluctuation phenomenon that the output power exceeds the target set value during operation due to load mutation or control lag, which belongs to the research category of the dynamic characteristics of power machinery. By using a dynamic signal acquisition device and spectrum analysis technology to capture the mechanical vibration, speed change and strain response of key components corresponding to the overshoot peak, and combining with a time series model to quantify the fluctuation amplitude and attenuation rate, and then optimizing the control algorithm parameters or improving the damping structure design to suppress the impact of overshoot behavior on the system stability under non-steady-state conditions.

[0003] When the steam turbine of a thermal power unit undergoes deep peak shaving, it faces problems such as poor coordinated control regulation quality, degraded primary frequency regulation performance, and unstable combustion. The main reasons are that key parameters such as main steam pressure, temperature, and extraction steam flow rate fluctuate significantly under wide load conditions, resulting in the inability of the speed control system model to accurately represent the dynamic response characteristics, and the existing testing methods are limited to single-condition calibration and fail to cover the dynamic process of the entire peak shaving range. For example, when operating at low load, a sudden change in the main steam pressure causes frequent oscillations in the throttle valve opening, leading to unstable combustion and delayed primary frequency regulation actions, directly affecting the grid frequency stability; in addition, the non-linear change of the high throttle valve flow characteristics of the steam turbine in the wide load range is not accurately modeled, resulting in a significant deviation between the simulation results and the measured response, and it is impossible to effectively evaluate the grid-connected safety margin of the unit. Summary of the Invention

[0004] Aiming at the deficiencies of the prior art, the present invention provides a testing system for correcting the mismatch of the speed control model of a thermal power unit in a wide load range, which solves the problem of deviation in the performance test of the speed control system caused by dynamic response mismatch when the steam turbine of a thermal power unit undergoes deep peak shaving, specifically manifested as inaccurate controller response characteristics, decreased speed regulation accuracy, and dynamic fluctuations in combustion parameters.

[0005] To solve the above technical problems, the specific technical solutions of the present invention are as follows: The present invention provides a testing system for correcting the mismatch of the speed control model of a thermal power unit in a wide load range, including: a thermal system component, a speed control system device, a dynamic signal acquisition device, and a control device, and the control device establishes a communication connection with the thermal system component, the speed control system device, and the dynamic signal acquisition device; The dynamic signal acquisition device includes high-precision sensors, which collect real-time data of main steam pressure, temperature, throttle valve opening, speed deviation signal, and energy storage chamber temperature gradient data, and transmit the signals to the control device; The control device includes a dynamic signal processing module, a parameter identification module, a closed-loop control module, and a simulation verification module; The dynamic signal processing module receives the sensor signals, analyzes the change trend of the main steam parameters, the oscillation characteristics of the governing valve, and the heat capacity attenuation characteristics of the phase change material, generates a dynamic response database including time series, and transmits the dynamic response database to the parameter identification module; Based on the dynamic disturbance test data in the dynamic response database, the parameter identification module identifies the regulator response lag parameters, the non-linear characteristic parameters of the actuator, the dynamic response parameters of the prime mover, and the heat capacity attenuation coefficient of the phase change material in the governing system device through a multi-objective rolling optimization algorithm, corrects the model mismatch of the preset governing system model under wide load conditions, and obtains the corrected parameters to generate a governing valve opening command and a combustion control signal; The closed-loop control module generates a governing valve opening command and a combustion control signal according to the corrected parameters, and drives the governing system device to perform steam turbine power adjustment and burner fuel distribution. The simulation verification module receives the real-time data of the dynamic signal acquisition device and the output of the corrected model of the parameter identification module, compares the measured response with the simulation result through an error feedback mechanism, dynamically adjusts the calibration parameters of the high-pressure cylinder flow characteristic curve, calculates the allowable threshold of power fluctuation, the margin of frequency modulation response time, and the safe interval of frequency deviation based on the comparison error data and the verification result of the fouling compensation model, generates an evaluation result of the grid-connected safety margin, and feeds back the evaluation result to the closed-loop control module to optimize the constraint conditions of the governing valve opening command and the combustion control signal.

[0006] Further, for the test system for correcting the mismatch of the governing model of a thermal power unit in a wide load range of the present invention, the dynamic signal processing module synchronously fuses the temperature gradient data of the energy storage cavity and the change rate of the molten salt fouling factor by analyzing the non-linear fluctuation characteristics in the sensor signals transmitted by the dynamic signal acquisition device, identifies the dynamic correlation between the sudden change of the main steam pressure, the oscillation of the governing valve opening, and the heat capacity attenuation of the phase change material, and generates the dynamic response database including the main steam pressure change rate, the governing valve opening fluctuation amplitude, the rotational speed deviation, the heat capacity attenuation slope of the high-temperature phase change material, and the dynamic offset of the molten salt fouling factor in time series; Among them, the heat capacity attenuation slope of the high-temperature phase change material is calculated by the following formula: α = -(ΔT gradient / ΔtΔQ heat capacity); ΔQ heat capacity is the heat capacity loss per unit time of the energy storage cavity, ΔT gradient is the temperature difference between the high- / medium-temperature energy storage cavities, and Δt is the sampling period; And input the correlation data in the dynamic response database into the parameter identification module to correct the regulator response lag parameters and the coupling characteristics parameters of the thermodynamic performance and fouling of the phase change material in the preset governing system model.

[0007] Further, for the test system for correcting the mismatch of the speed regulation model of thermal power units in a wide load range according to the present invention, the parameter identification module is configured to: Obtain the limiting parameters, dead zone parameters, and initial heat capacity attenuation coefficient of the phase change material in the energy storage chamber in the speed regulation system device through static tests, and input the parameters into the speed regulation system model; Based on dynamic disturbance tests, collect the input and output signals of the regulator in the speed regulation system device, the input and output signals of the actuator, the input and output signals of the prime mover, the temperature gradient data of the high-temperature or medium-temperature energy storage chamber, and the real-time offset of the molten salt scaling factor; Adopt a multi-objective rolling optimization algorithm to perform iterative calculations on the input and output signals of the regulator, the input and output signals of the actuator, the input and output signals of the prime mover, and the energy storage and scaling coupling parameters, and identify in the speed regulation system model: the regulator response lag parameter, the actuator nonlinear characteristic parameter, the dynamic compensation coefficient of the heat capacity attenuation of the phase change material, and the coupling weight of the scaling factor and heat capacity attenuation; Wherein, the objective function of the multi-objective rolling optimization algorithm is: min(w1*E_peak shaving + w2*R_wind and light curtailment + w3*D_heat attenuation); E_peak shaving is the load tracking error, R_wind and light curtailment is the wind and light curtailment rate, D_heat attenuation is the heat capacity attenuation deviation; weights w1 = 0.4, w2 = 0.35, w3 = 0.25; And transmit the corrected parameters to the closed-loop control module.

[0008] Further, for the test system for correcting the mismatch of the speed regulation model of thermal power units in a wide load range according to the present invention, the closed-loop control module generates a throttle opening command based on the corrected regulator response lag parameter and actuator nonlinear characteristic parameter transmitted by the parameter identification module to adjust the steam turbine power output and suppress the overshoot peak caused by load mutations; At the same time, based on the corrected prime mover dynamic response parameter of the parameter identification module, generate a combustion control signal to adjust the fuel distribution ratio of the burner.

[0009] Further, for the test system for correcting the mismatch of the speed regulation model of thermal power units in a wide load range according to the present invention, the simulation verification module receives the measured throttle oscillation frequency and the main steam pressure attenuation rate generated by the dynamic signal processing module, compares them with the dynamic response output by the simulation model corrected by the parameter identification module, adjusts the characteristic curve calibration parameters in the speed regulation system model through an error feedback mechanism, and transmits the corrected off-grid safety margin evaluation result to the closed-loop control module for optimizing the generation logic of the throttle opening command and the combustion control signal.

[0010] Furthermore, for the test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range according to the present invention, the dynamic disturbance test includes: Inject a step disturbance signal into the speed regulation system device within a wide load range at the lower limit of deep peak shaving at 100% rated load, and collect the oscillating waveform of the throttle valve opening output by the actuator when the main steam pressure suddenly changes; Extract the frequency characteristics of the oscillating waveform of the throttle valve opening through spectrum analysis, input the frequency characteristics into the speed regulation system model, check the dynamic response parameters of the prime mover corrected by the parameter identification module, and generate the corrected dynamic response parameters of the prime mover and transmit them to the closed-loop control module.

[0011] Furthermore, for the test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range according to the present invention, the dynamic signal processing module performs spectrum correction processing on the throttle valve opening signal transmitted by the dynamic signal acquisition device, eliminates high-frequency noise interference introduced during the acquisition process by the sensor through digital filtering, generates a corrected throttle valve opening signal, and inputs the corrected throttle valve opening signal into the closed-loop control module, which is used to generate a throttle valve opening compensation command based on the regulator response lag parameter corrected by the parameter identification module to adjust the throttle valve opening of the steam turbine.

[0012] Furthermore, for the test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range according to the present invention, it further includes: The speed regulation system device is connected to a preset double-stage energy storage structure, and the double-stage energy storage structure includes: a low-pressure energy storage cavity, a medium-pressure energy storage cavity, and a high-pressure energy storage cavity; The high-pressure energy storage cavity and the medium-pressure energy storage cavity are filled with a high-temperature phase change material, and the melting point of the high-temperature phase change material is ≥300 °C; The low-pressure energy storage cavity is filled with a medium-temperature phase change material, and 200 °C ≤ the melting point of the medium-temperature phase change material < 300 °C; The speed regulation system device includes a hydraulic regulator, a PID controller, and an actuator, which are used to dynamically adjust the throttle valve opening of the steam turbine to control the power output.

[0013] Furthermore, for the test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range according to the present invention, it further includes: The multi-objective rolling optimization algorithm synchronously optimizes the peak shaving depth, equipment life, and curtailment rate of renewable energy; The priority of renewable energy consumption is dynamically bound to the energy release strategy of energy storage; And transmit the corrected model parameters to the closed-loop control module; The closed-loop control module generates a throttle opening command and a combustion control signal according to the corrected model parameters, drives the actuator to adjust the steam turbine power output, suppresses the overshoot peak caused by load mutation, and optimizes the fuel distribution of the burner to reduce the primary frequency regulation action delay.

[0014] Furthermore, the test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range according to the present invention further includes: The simulation verification module compares the measured data generated by the dynamic signal processing module with the output of the simulation model corrected by the parameter identification module, dynamically adjusts the calibration parameters of the model characteristic curve through an error feedback mechanism, and verifies the fouling factor real-time compensation model of the heat-electric decoupling heat exchanger: δt = δ0×e-kt + β×ΔT molten salt; Wherein, δ0 is the initial fouling factor, k is the attenuation coefficient, β is the temperature compensation coefficient, and ΔT molten salt is the molten salt temperature difference.

[0015] Advantages of the present invention; The present invention real-time analyzes the correlation characteristics between the sudden change of the main steam pressure and the throttle opening oscillation through the dynamic signal processing module, and constructs a dynamic response database including time series; the parameter identification module adopts a multi-objective rolling optimization algorithm based on this database to identify the regulator response lag parameters, actuator nonlinear characteristic parameters and prime mover dynamic response parameters in the speed regulation system, and corrects the model mismatch under wide load conditions; the closed-loop control module generates a throttle opening feed-forward compensation command and a burner fuel distribution gradient adjustment signal according to the corrected parameters, suppresses the power overshoot peak caused by load mutation through a phase lead compensation mechanism, and optimizes the fuel distribution ratio to reduce the primary frequency regulation action delay; the simulation verification module dynamically adjusts the calibration parameters of the high-pressure cylinder flow characteristic curve through an error feedback mechanism, and optimizes the control strategy in combination with the evaluation result of the grid connection safety margin, and finally realizes the improvement of the dynamic response accuracy in the full peak shaving interval, the enhancement of combustion stability and the optimization of the grid frequency regulation ability. Description of the drawings

[0016] In order to more clearly illustrate the technical solutions of the present invention, the drawings required for use in the embodiments will be briefly introduced below. Obviously, for those of ordinary skill in the art, other drawings can also be obtained according to the drawings without creative labor.

[0017] Figure 1 It is the system architecture diagram of the test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range provided by the embodiment of the present invention.

[0018] Figure 2 It is the dynamic signal processing flow chart for correcting the overshoot peak of the steam turbine power curve provided by the embodiment of the present invention.

[0019] Figure 3 This is the parameter identification flowchart for overshoot peak correction of the steam turbine power curve provided by the embodiments of the present invention.

[0020] Figure 4 This is the closed-loop control logic block diagram for overshoot peak correction of the steam turbine power curve provided by the embodiments of the present invention. Detailed implementation manners

[0021] To make the objectives, technical solutions, and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below in conjunction with specific embodiments of the present invention and the corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention. The technical solutions provided by each embodiment of the present invention will be described in detail below with reference to the drawings. To better understand the objectives of the present invention, the present invention will be further described in detail below.

[0022] Please refer to Figures 1 to 4 , the present invention provides a test system for mismatch correction of the speed control model of thermal power units in a wide load range, including: a thermal system component, a speed control system device, a dynamic signal acquisition device, and a control device. The control device establishes a communication connection with the thermal system component, the speed control system device, and the dynamic signal acquisition device; The dynamic signal acquisition device includes high-precision sensors, which collect main steam pressure, temperature, governor valve opening, rotational speed deviation signals, and energy storage chamber temperature gradient data in real time, and transmit the signals to the control device; The control device includes a dynamic signal processing module, a parameter identification module, a closed-loop control module, and a simulation verification module; The dynamic signal processing module receives the sensor signals, analyzes the change trends of main steam parameters, the oscillation characteristics of the governor valve, and the heat capacity attenuation characteristics of the phase change material, generates a dynamic response database including time series, and transmits the dynamic response database to the parameter identification module; Based on the dynamic disturbance test data in the dynamic response database, the parameter identification module identifies the regulator response lag parameter, the non-linear characteristic parameter of the actuator, the dynamic response parameter of the prime mover, and the heat capacity attenuation coefficient of the phase change material in the speed control system device through a multi-objective rolling optimization algorithm, corrects the model mismatch of the preset speed control system model under wide load conditions, and generates a governor valve opening command and a combustion control signal with the corrected parameters; The closed-loop control module generates a throttle opening command and a combustion control signal based on the corrected parameters, and drives the governing system device to perform steam turbine power adjustment and burner fuel distribution. The simulation verification module receives the real-time data of the dynamic signal acquisition device and the output of the corrected model of the parameter identification module, compares the measured response with the simulation result through an error feedback mechanism, dynamically adjusts the calibration parameters of the high-pressure cylinder flow characteristic curve, calculates the allowable threshold of power fluctuation, the margin of frequency modulation response time, and the safe interval of frequency deviation based on the comparison error data and the verification result of the scaling compensation model, generates an evaluation result of the grid-connected safety margin, and feeds back the evaluation result to the closed-loop control module to optimize the constraint conditions of the throttle opening command and the combustion control signal.

[0023] The thermal system components simulate the actual thermal cycle process of a thermal power unit. The main steam parameters generated by the boiler are real-time collected by high-precision pressure sensors and temperature sensors, with a sampling frequency not less than 1 kHz, covering the full load regulation range from the rated load to the lower limit of deep peak shaving. The governing system device integrates a hydraulic actuator and a two-stage energy storage structure, where the high-pressure and medium-pressure energy storage cavities are filled with high-temperature phase change materials with a melting point ≥ 300 °C, and the low-pressure energy storage cavity is filled with medium-temperature phase change materials with a melting point range of 200 - 300 °C. The dynamic signal acquisition device synchronously collects the throttle opening displacement signal, the speed deviation signal, and the multi-point temperature gradient data of the energy storage cavity. The molten salt scaling factor is indirectly measured by electrochemical impedance spectroscopy, and all sensor data is converted into standardized electrical signals through a signal conditioning circuit.

[0024] The dynamic signal processing module performs preprocessing on the original signal: uses a Butterworth low-pass filter to eliminate high-frequency noise interference and retains the fundamental frequency component of the throttle oscillation; captures the instantaneous change rate event of the main steam pressure through a time-window integration algorithm, and combines a sliding window to statistically calculate the amplitude of throttle opening fluctuations; fuses the temperature difference data of the energy storage cavity to calculate the heat capacity attenuation slope of the phase change material, generating a dynamic response database with time stamps aligned. This database contains four types of key feature vectors: the change rate of the main steam pressure, the amplitude of throttle opening fluctuations, the heat capacity attenuation slope, and the offset of the molten salt scaling factor. The data structure stores the original sampling values, parsed features, and correlation matrices in a hierarchical manner.

[0025] The parameter identification module performs static tests and dynamic perturbation tests: the static test obtains the displacement dead zone threshold and the limit range of the actuator; the dynamic perturbation test injects a standardized step perturbation signal in a wide load range and synchronously collects the regulator command input and the displacement feedback output signals. The multi-objective rolling optimization algorithm takes the load tracking error, the curtailment rate of wind and light, and the heat capacity attenuation deviation as comprehensive objectives, iteratively solves in segments according to the preset weights, and outputs four physically measurable parameters: the regulator response lag time constant, the nonlinear friction coefficient of the actuator, the dynamic compensation coefficient of heat capacity attenuation, and the scaling coupling weight, and corrects the nonlinear gain coefficient of the high-pressure cylinder flow characteristic curve in the governing system model.

[0026] The closed-loop control module converts the correction parameters into executable instructions: constructs a phase-lead transfer function based on the regulator lag time, generates a feed-forward compensation amount for the throttle valve opening, and adjusts the throttle valve opening position of the steam turbine through the servo drive mechanism of the oil hydraulic actuator; calculates the fuel distribution gradient based on the volume time constant of the prime mover, and optimizes the fuel supply weights of the high- and low-temperature burners in combination with the temperature distribution state of the energy storage cavity. The displacement feedback signal of the actuator and the burner state monitoring data are transmitted back to the dynamic signal acquisition device in real time to participate in the instruction optimization of the next control cycle.

[0027] The simulation verification module compares the real-time throttle valve oscillation waveform of the dynamic signal acquisition device with the output of the correction model of the parameter identification module, aligns the time-domain curves using the dynamic time warping algorithm, and calculates the phase lag amount and the residual of the amplitude deviation. The residual results iteratively adjust the calibration parameters of the high-pressure cylinder flow characteristics through the gradient descent method, and at the same time verify the effectiveness of the attenuation coefficient and the temperature compensation coefficient in the fouling compensation model. Based on the residual analysis results and the fouling verification data, three quantitative indicators of the allowable threshold of power fluctuation, the margin of frequency modulation response time, and the safe interval of frequency deviation are calculated to generate an evaluation result of the off-grid safety margin. This evaluation result is converted into the limit value of the throttle valve opening change rate and the constraint conditions of the fuel distribution gradient, and fed back to the closed-loop control module to reconstruct the instruction generation logic.

[0028] Specifically, for the test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range of the present invention, the dynamic signal processing module analyzes the non-linear fluctuation characteristics in the sensor signals transmitted by the dynamic signal acquisition device, synchronously fuses the temperature gradient data of the energy storage cavity and the change rate of the molten salt fouling factor, identifies the dynamic correlation between the sudden change of the main steam pressure, the oscillation of the throttle valve opening, and the attenuation of the heat capacity of the phase change material, and generates the dynamic response database including the main steam pressure change rate, the throttle valve opening fluctuation amplitude, the rotational speed deviation, the heat capacity attenuation slope of the high-temperature phase change material, and the dynamic offset of the molten salt fouling factor in the time series; Among them, the heat capacity attenuation slope of the high-temperature phase change material is calculated by the following formula: α = -(ΔT gradient / ΔtΔQ heat capacity); ΔQ heat capacity is the heat capacity loss per unit time of the energy storage cavity, ΔT gradient is the temperature difference between the high- / medium-temperature energy storage cavities, and Δt is the sampling period; And input the correlation data in the dynamic response database into the parameter identification module to correct the regulator response lag parameters and the coupling characteristics parameters of the thermodynamic properties of the phase change material and fouling in the preset speed regulation system model.

[0029] The dynamic signal processing module receives multi-channel sensor signals from the dynamic signal acquisition device, including main steam pressure, temperature, governor valve opening, rotational speed deviation, and accumulator cavity temperature gradient data. First, the module preprocesses the original signals, using digital filtering algorithms to eliminate high-frequency noise interference and improve the signal-to-noise ratio. The preprocessed signals enter the feature analysis stage, where the instantaneous change rate feature of the main steam pressure is extracted through time-domain analysis methods, and at the same time, the oscillation amplitude change law of the governor valve opening signal is captured by combining the sliding window algorithm.

[0030] After completing the basic feature extraction, the module synchronously fuses the accumulator cavity temperature gradient data and the change rate information of the molten salt scaling factor. The accumulator cavity temperature gradient data is collected by a multi-point temperature sensor array, reflecting the heat transfer state of the high- and medium-temperature phase change materials. The change rate of the molten salt scaling factor is indirectly measured by electrochemical impedance spectroscopy, characterizing the dynamic accumulation process of the deposits on the surface of the phase change materials. A timestamp alignment mechanism is adopted during the fusion process to ensure the synchronization of multi-source data.

[0031] Based on the fused data, the module identifies the dynamic correlations among the sudden change of the main steam pressure, the oscillation of the governor valve opening, and the heat capacity attenuation of the phase change materials. The sudden change feature of the main steam pressure is manifested as the pressure change rate exceeding the set threshold. The oscillation feature of the governor valve opening is to extract the amplitude of the fundamental frequency component through spectral analysis. The heat capacity attenuation characteristic of the phase change materials is to calculate the slope based on the ratio of the temperature difference to the heat capacity loss. The correlations among the three are quantified by a correlation coefficient matrix, and a pressure-oscillation-attenuation coupling model is established.

[0032] The module generates a dynamic response database including time series, and the data structure includes the main steam pressure change rate, the governor valve opening fluctuation amplitude, the rotational speed deviation, the heat capacity attenuation slope of the high-temperature phase change materials, and the dynamic offset of the molten salt scaling factor. The main steam pressure change rate records the pressure change amount per unit time. The governor valve opening fluctuation amplitude statistically calculates the maximum displacement deviation within the oscillation period. The heat capacity attenuation slope characterizes the degree of heat capacity loss of the phase change materials per unit time. The dynamic offset of the molten salt scaling factor reflects the influence coefficient of the change in the scale layer thickness on the heat transfer efficiency.

[0033] The dynamic response database adopts a hierarchical storage architecture. The basic layer stores the original sampling values, the feature layer stores the parsed parameter indicators, and the correlation layer stores the multi-parameter coupling matrix. When the database outputs, data standardization processing is performed to unify the dimension and sampling frequency, and a mapping relationship between parameters is established through timestamp indexing.

[0034] After receiving the correlation data in the dynamic response database, the parameter identification module focuses on correcting the regulator response lag parameter in the speed control system model and the coupling characteristic parameter of the thermophysical properties of the phase change material and fouling. The correction of the regulator response lag parameter is based on the analysis of the phase difference between the main steam pressure change rate and the governor valve action delay, and the correction of the fouling coupling characteristic parameter is based on the regression model of the heat capacity decay slope and the fouling factor offset. The corrected parameters are input into the speed control system model to optimize the dynamic response accuracy under variable working conditions.

[0035] Specifically, for the test system for correcting the mismatch of the speed control model of a thermal power unit in a wide load range described in the present invention, the parameter identification module is configured as follows: Obtain the limit parameter, dead zone parameter and initial heat capacity decay coefficient of the phase change material in the energy storage cavity in the speed control system device through static tests, and input the parameters into the speed control system model; Collect the input and output signals of the regulator, the input and output signals of the actuator, the input and output signals of the prime mover, the temperature gradient data of the high-temperature or medium-temperature energy storage cavity and the real-time offset of the molten salt fouling factor in the speed control system device based on dynamic disturbance tests; Adopt a multi-objective rolling optimization algorithm to perform iterative calculations on the input and output signals of the regulator, the input and output signals of the actuator, the input and output signals of the prime mover and the energy storage and fouling coupling parameters, and identify the following in the speed control system model: regulator response lag parameter, actuator nonlinear characteristic parameter, dynamic compensation coefficient of the heat capacity decay of the phase change material, and coupling weight of the fouling factor and heat capacity decay; Among them, the objective function of the multi-objective rolling optimization algorithm is: min(w1*E_peak + w2*R_wind_solar_abandonment + w3*D_heat_decay); E_peak is the load tracking error, R_wind_solar_abandonment is the wind and solar power abandonment rate, D_heat_decay is the heat capacity decay deviation; the weights are w1 = 0.4, w2 = 0.35, w3 = 0.25; And transmit the corrected parameters to the closed-loop control module.

[0036] The parameter identification module first obtains the basic characteristic parameters of the speed control system device through static tests. During the static test, a constant load input signal is applied to the speed control system device, and the displacement saturation point of the actuator is recorded as the limit parameter. When the change amplitude of the input signal is less than the set threshold, the non-response interval of the actuator is detected to determine the dead zone parameter. At the same time, the heat capacity loss rate of the phase change material in the energy storage cavity is measured in a constant temperature environment, and the initial heat capacity decay coefficient is calculated. These static parameters are input into the speed control system model as basic constraint conditions to establish the initial framework of the model.

[0037] Based on a wide load range, a dynamic disturbance test is carried out to collect multi-dimensional dynamic response data. The test selects multiple operating points within the range from the rated load to the lower limit of deep peak shaving, and injects a step disturbance signal to trigger a sudden change in the main steam pressure. The command input signal and feedback output signal of the regulator are synchronously collected, the control voltage and displacement output signal of the actuator are recorded, and the power input command and actual output response of the prime mover are obtained. At the same time, the temperature gradient distribution data of the high-temperature and medium-temperature energy storage cavities are monitored, and the offset of the molten salt scaling factor is obtained in real time through an electrochemical sensor. All signal acquisition processes adopt a time-domain alignment mechanism to ensure the consistency of data time series.

[0038] The multi-objective rolling optimization algorithm is used to perform iterative calculations on the collected data. The algorithm constructs a solution space including parameters such as the regulator lag time constant and the non-linear friction coefficient of the actuator. In each iteration, the actual input and output signals of the regulator are compared with the model prediction values, the hysteresis loop characteristics of the actuator displacement response are analyzed, the phase deviation of the prime mover power response is resolved, and the coupling relationship between the temperature gradient change rate of the energy storage cavity and the offset of the scaling factor is combined. The objective function comprehensively evaluates three indicators: load tracking accuracy, renewable energy consumption efficiency, and heat capacity decay compensation effect, and balances the priorities of different optimization objectives through weight allocation.

[0039] The algorithm outputs four types of correction parameters: the regulator response lag parameter characterizes the time delay characteristics of command transmission, the non-linear characteristic parameter of the actuator reflects the influence degree of friction resistance on displacement accuracy, the dynamic compensation coefficient of the phase change material heat capacity decay quantifies the heat capacity loss rate, and the coupling weight between the scaling factor and heat capacity decay describes the influence intensity of scaling deposition on heat transfer efficiency. These parameters are transmitted to the closed-loop control module through a data encapsulation protocol for real-time updating of the control strategy.

[0040] The parameter transmission process adopts an industrial bus communication protocol, and a verification mechanism is added at the data transmission layer. The correction parameters are written into the shared storage area in a structured data format, and the closed-loop control module reads the parameter data through an address mapping method. The transmission protocol includes data type identification, time stamp, and check code fields to ensure the integrity and timeliness of parameter transmission.

[0041] Specifically, for the test system for correcting the mismatch of the speed regulation model of thermal power units in a wide load range described in the present invention, the closed-loop control module generates a throttle opening command according to the corrected regulator response lag parameter and the non-linear characteristic parameter of the actuator transmitted by the parameter identification module to adjust the steam turbine power output and suppress the overshoot peak caused by load mutation; At the same time, based on the corrected dynamic response parameters of the prime mover by the parameter identification module, a combustion control signal is generated to adjust the fuel distribution ratio of the burner.

[0042] The closed-loop control module receives the corrected model parameters transmitted by the parameter identification module, including the regulator response lag parameter and the actuator nonlinear characteristic parameter. Based on the regulator response lag parameter, this module constructs a feed-forward compensation logic for the throttle valve opening, predicts the throttle valve action time point during load mutation through the phase lead algorithm, and generates a lead compensation instruction. The actuator nonlinear characteristic parameter is used for friction compensation calculation, and the preset dead zone inverse model is adopted to correct the displacement instruction deviation, canceling the influence of mechanical transmission clearance on the throttle valve positioning accuracy. The compensated throttle valve opening instruction drives the oil motor or servo motor to adjust the opening ratio of the high-pressure throttle valve of the steam turbine in real time, suppressing the power overshoot phenomenon caused by the sudden change of the main steam pressure.

[0043] Based on the prime mover dynamic response parameters corrected by the parameter identification module, this module synchronously generates a combustion control signal. The prime mover dynamic response parameters include the volume time constant and the fuel-power conversion coefficient, which are used to construct a fuel distribution ratio calculation model. The model dynamically calculates the fuel supply optimization coefficient of each burner according to the current load change gradient and the power grid frequency fluctuation characteristics. The combustion control signal is transmitted to the boiler control system through the industrial bus to adjust the opening ratio of the fuel valve and the air supply volume, realizing the dynamic matching of the fuel calorific value release rate and the steam turbine power demand.

[0044] The throttle valve control instruction and the combustion control signal execute coordinated timing control. During the load rising stage, the fuel distribution ratio adjustment signal is output earlier than the throttle valve opening instruction; during the load falling stage, the throttle valve opening instruction is preferentially executed to avoid the sudden rise of the steam pressure. The coordination mechanism is realized through the timestamp synchronization protocol, and an instruction priority identifier is set within the control period. The response status of the actuator is fed back in real time through the displacement sensor, forming a closed-loop verification mechanism of instruction output - execution feedback.

[0045] The fuel distribution ratio adjustment of the burner adopts a hierarchical optimization strategy. The primary control layer calculates the total fuel demand based on the prime mover dynamic response parameters, and the secondary control layer distributes the fuel supply weights of each burner in combination with the temperature gradient data of the energy storage chamber. The burners in the high-temperature area respond preferentially to the load increase instruction, and the burners in the low-temperature area undertake the load reduction adjustment task. The distribution result is converted into a pulse width modulation signal, and the fuel regulating valve actuator is driven to act through the digital-to-analog converter.

[0046] The output signal of this module forms a closed-loop interaction with the components of the thermal system. The change in the throttle valve opening is fed back to the main steam pressure monitoring node of the dynamic signal acquisition device, and the burner state is fed back to the temperature monitoring node. The feedback data participates in the parameter optimization calculation of the next control cycle, forming a closed-loop control link from parameter correction to execution verification.

[0047] Specifically, for the test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range, the simulation verification module receives the measured governor valve oscillation frequency and the main steam pressure decay rate generated by the dynamic signal processing module, compares them with the dynamic responses output by the simulation model corrected by the parameter identification module, adjusts the calibration parameters of the characteristic curve in the speed regulation system model through an error feedback mechanism, and transmits the corrected off-grid safety margin evaluation result to the closed-loop control module for optimizing the generation logic of the governor valve opening command and the combustion control signal.

[0048] The simulation verification module receives the measured data transmitted by the dynamic signal processing module, including the spectral characteristics of the governor valve oscillation frequency and the time series of the main steam pressure decay rate. Synchronously obtain the dynamic response data of the corrected simulation model output by the parameter identification module, which includes the predicted waveform of the governor valve opening and the simulation curve of the steam pressure change. After the two types of data are input into the verification interface, time-domain alignment processing is performed, and the data sampling benchmark is unified through the timestamp matching algorithm.

[0049] Based on the aligned data, comparative verification analysis is performed. In the time domain, the phase deviation and amplitude difference of the governor valve oscillation waveform are analyzed, and in the frequency domain, the energy distribution characteristics of the oscillation fundamental frequency are analyzed. For the comparison of the main steam pressure decay rate, the slope change rate difference calculation is used to identify the characteristic point deviation between the measured decay curve and the simulation curve. The difference quantification process generates an error index matrix, including three core indicators: the phase lag coefficient, the amplitude deviation degree, and the decay rate offset.

[0050] The error feedback mechanism drives the iterative optimization of the parameters of the speed regulation system model. According to the error index matrix, a parameter adjustment vector is generated and input into the calibration function of the high-pressure cylinder flow characteristic curve. The optimization of the characteristic curve parameters focuses on the gain coefficient of the nonlinear section and the weight of the volume time constant, and the parameter correction amount is calculated by the gradient descent method. After each iteration, the simulation model is run again to form a closed loop for error convergence verification.

[0051] The off-grid safety margin evaluation is based on the output of the optimized model. The evaluation model is connected to the power grid frequency stability index system, and three key indicators are calculated: the allowable threshold of power fluctuation, the margin of the primary frequency modulation response time, and the safe interval of frequency deviation. The evaluation process correlates the governor valve control stability with the power grid frequency fluctuation constraint conditions, and outputs the quantified safety margin coefficient and the risk level identifier.

[0052] The evaluation result is transmitted to the closed-loop control module through the data bus. The transmission protocol includes fields such as timestamps, margin coefficients, and parameter version numbers. After parsing the evaluation result, the closed-loop control module adjusts the phase compensation weight of the governor valve opening command according to the safety margin coefficient, and dynamically switches the combustion control strategy according to the risk level. The control logic optimization adopts a rule engine architecture, and the instruction generation mode under different working conditions is matched through a decision tree algorithm.

[0053] The verification data is stored in the historical database for trend analysis. The database stores the error index matrix and parameter correction records classified by operating condition points, and supports the evaluation of the model generalization ability. Regular data mining analysis is performed to identify the parameter correction rules in the wide load range, and the model adaptive calibration suggestions are generated and fed back to the parameter identification module.

[0054] Specifically, for the test system for the mismatch correction of the speed control model of a thermal power unit in a wide load range described in the present invention, the dynamic disturbance test includes: Inject a step disturbance signal into the speed control system device within a wide load range from the lower limit of deep peak shaving at 100% rated load, and collect the valve opening oscillation waveform output by the actuator when the main steam pressure suddenly changes. Extract the frequency characteristics of the valve opening oscillation waveform through spectrum analysis, and input the frequency characteristics into the speed control system model to check the corrected prime mover dynamic response parameters of the parameter identification module, and generate the corrected prime mover model parameters and transmit them to the closed-loop control module.

[0055] The dynamic disturbance test design covers the wide load operation range from rated load to the lower limit of deep peak shaving. Before the test, load test points are selected according to the operating condition distribution map, including three characteristic intervals: high load area, sliding pressure operation area and deep peak shaving area. A steady-state operation benchmark is set for each test point, and a standardized step disturbance signal is injected into the speed control system device through a signal generator. The disturbance amplitude is dynamically adjusted according to the load range. A small amplitude disturbance is used in the high load area, and a large amplitude disturbance is used in the deep peak shaving area.

[0056] The output response signal of the actuator is collected in real time by a high-precision displacement sensor. The sudden change of the main steam pressure triggers the valve opening oscillation process, and the displacement sensor records the valve displacement change curve at a millisecond-level sampling frequency. Synchronously collect the oil pressure feedback signal of the actuator and the prime mover speed fluctuation data to form a time series data set of multi-physical quantity coupling. The time scale synchronization technology is used in the signal collection process, and all sensor data is bound to a unified time reference.

[0057] The fast Fourier transform algorithm is used for the spectrum analysis of the oscillation waveform. The valve opening time-domain signal is converted to the frequency domain, and three key characteristics are extracted: the amplitude of the fundamental frequency component, the energy distribution of the main harmonic components, and the oscillation attenuation coefficient. The fundamental frequency component corresponds to the dominant oscillation mode of the system, the harmonic components reflect the non-linear friction characteristics, and the attenuation coefficient characterizes the system damping level. The spectrum characteristic parameters are encapsulated as feature vectors and input into the prime mover dynamic response sub-module of the speed control system model.

[0058] After receiving the spectral feature vector, the parameter verification module performs the matching of the prime mover dynamic response parameters. The deviation between the measured fundamental frequency component and the model predicted frequency is calculated to verify the calibration accuracy of the volume time constant. The energy distribution of the harmonic components is used to verify the effectiveness of the non-linear friction coefficient, and the attenuation coefficient is used to verify the system damping compensation parameters. The verification process generates parameter correction amounts and dynamically updates the transfer function coefficients and non-linear link gains of the prime mover model.

[0059] The parameters of the corrected prime mover model are transmitted to the closed-loop control module through the industrial communication protocol. The data transmission adopts the publish-subscribe mode, with the parameter identification module as the data publisher and the closed-loop control module as the subscriber. The transmission protocol includes the parameter version identifier, the effective timestamp, and the check code to prevent the misalignment of the control instruction and the model parameter version. The transmission period is dynamically adjusted according to the load change rate, and the transmission interval is shortened during the severe load fluctuation.

[0060] After receiving the new parameters, the closed-loop control module reconstructs the control algorithm. The prime mover volume time constant is used to optimize the phase of the feed-forward compensation, and the friction coefficient weight updates the actuator dead zone compensation strategy. The smooth transition mechanism is adopted during the control strategy switching process, and the parameter interpolation algorithm is used to avoid the instruction jump and maintain the power output stability. The updated control effect is feedback to the test system to form a closed-loop verification chain.

[0061] Specifically, for the test system for correcting the mismatch of the speed control model of thermal power units in a wide load range described in the present invention, the dynamic signal processing module performs spectral correction processing on the throttle opening signal transmitted by the dynamic signal acquisition device, eliminates the high-frequency noise interference introduced during the sensor acquisition process through digital filtering, generates the corrected throttle opening signal, and inputs the corrected throttle opening signal into the closed-loop control module to generate a throttle opening compensation instruction based on the regulator response lag parameter corrected by the parameter identification module and adjust the steam turbine throttle opening.

[0062] The dynamic signal processing module receives the original throttle opening signal transmitted by the dynamic signal acquisition device. This signal includes high-frequency noise components introduced during the sensor acquisition process, mainly manifested as random pulse interference and power frequency harmonic noise. The module uses digital filtering technology for preprocessing and designs a Butterworth low-pass filter to filter out the noise components higher than the system characteristic frequency. The cut-off frequency of the filter is set according to the natural frequency of the steam turbine mechanical structure, and the low-frequency effective signals related to the throttle dynamic response are retained.

[0063] Perform phase correction processing on the filtered signal. Analyze the group delay characteristics of the sensor signal transmission link and construct a phase compensation transfer function. Eliminate the phase shift of the signal acquisition system through the zero-phase filtering algorithm to synchronize the time-domain characteristics of the corrected throttle opening signal with the actual mechanical displacement. The correction process adopts a sliding window processing mechanism to output displacement data synchronized with the control system clock in real time.

[0064] After generating the corrected throttle opening signal, extract key dynamic characteristic quantities. Feature extraction includes three dimensions: displacement change gradient, oscillation period statistics, and steady-state offset. The displacement change gradient calculates the opening change rate per unit time, the oscillation period statistics calculates the peak interval time of the continuous waveform, and the steady-state offset records the position deviation value under steady-state conditions. The feature data is encapsulated into a structured array with a timestamp mark added.

[0065] The closed-loop control module receives the corrected throttle opening signal and the feature data. This module calls the regulator response lag parameter corrected by the parameter identification module and constructs a feedforward compensation model in combination with the throttle displacement change gradient feature. The model calculates the phase lead compensation amount and generates a throttle opening compensation instruction. The compensation instruction is superimposed on the basic control instruction, and the final execution instruction is output through the proportional-integral-derivative control algorithm.

[0066] The actuator drives the throttle positioning according to the throttle opening compensation instruction. The displacement sensor real-time feedbacks the actual opening position to form a closed-loop verification loop. The feedback data and the compensation instruction are used for deviation calculation. When the deviation exceeds the set threshold, the adaptive adjustment of the compensation parameters is triggered. The adjustment process optimizes the phase lead amount according to the oscillation period statistics feature and corrects the zero drift compensation value according to the steady-state offset.

[0067] The corrected throttle opening signal participates in the evaluation of the control strategy. The dynamic characteristic data is stored in the historical database for analyzing the control effect under different load conditions. Perform feature clustering analysis regularly, identify the mapping law between the control parameters and the throttle response characteristics, generate parameter optimization suggestions and feedback them to the parameter identification module to form a closed-loop iterative link for signal processing and control optimization.

[0068] Specifically, the test system for correcting the mismatch of the speed control model of a thermal power unit in a wide load range described in the present invention further includes: The speed control system device is connected to a preset two-stage energy storage structure, and the two-stage energy storage structure includes: a low-pressure energy storage cavity, a medium-pressure energy storage cavity, and a high-pressure energy storage cavity; The high-pressure energy storage cavity and the medium-pressure energy storage cavity are filled with a high-temperature phase change material, and the melting point of the high-temperature phase change material ≥ 300°C; The low-pressure energy storage cavity is filled with a medium-temperature phase change material, and 200°C ≤ the melting point of the medium-temperature phase change material < 300°C; The speed control system device includes a hydraulic regulator, a PID controller, and an actuator, which are used to dynamically adjust the opening of the steam turbine throttle to control the power output.

[0069] The speed control system device integrates a two-stage energy storage structure, which consists of a high-pressure energy storage chamber, a medium-pressure energy storage chamber, and a low-pressure energy storage chamber to form a hierarchical thermal energy buffer unit. The high-pressure energy storage chamber and the medium-pressure energy storage chamber are filled with high-temperature phase change materials, whose phase change temperature threshold is set to be not less than 300 degrees Celsius. The material composition is an inorganic salt composite matrix, which has chemical stability in a high-temperature environment. The low-pressure energy storage chamber is filled with medium-temperature phase change materials, and the phase change temperature threshold range is 200 to 300 degrees Celsius. An organic-inorganic hybrid composite material is used to optimize the thermal cycle stability. Each energy storage chamber is connected in parallel with the steam pipeline of the steam turbine, and a finned heat exchange structure is arranged inside the chamber to enhance the heat exchange efficiency between the phase change material and the steam.

[0070] The hydraulic regulator receives the power command signal and converts it into a hydraulic oil circuit pressure control quantity. The device integrates an electro-hydraulic servo valve group to dynamically adjust the hydraulic oil flow output according to the load demand. The PID controller receives the speed deviation signal and the load feedforward command, and generates a control voltage signal through the proportional-integral-derivative algorithm. The control algorithm includes a non-linear gain compensation link to adapt to the parameter change characteristics in a wide load range. The actuator uses a high-precision oil motor to convert the control voltage signal into a mechanical displacement output to drive the adjustment of the steam turbine throttle opening.

[0071] The thermal management mechanism of the two-stage energy storage structure operates based on the thermodynamic characteristics of the phase change material. When the main steam pressure suddenly rises, the phase change material in the high-temperature energy storage chamber absorbs the excess heat energy to delay the pressure rise rate; when the pressure suddenly drops, the medium-temperature energy storage chamber releases the stored heat energy to compensate for the pressure drop gradient. The heat transfer process is monitored in real time through a temperature gradient sensor, and the temperature difference change amount between the high-temperature and medium-temperature energy storage chambers is collected as the input parameter for the heat capacity attenuation calculation. An insulation protection layer is arranged on the outer wall of the energy storage chamber to reduce the environmental heat loss and maintain the temperature stability of the phase change material.

[0072] The control link of the speed control system device forms a closed-loop regulation loop. The output end of the hydraulic regulator is connected to the hydraulic drive unit of the actuator, and the displacement feedback signal of the actuator returns to the input end of the PID controller. The displacement feedback signal is collected through a linear variable differential transformer and converted into a standardized voltage signal through a signal conditioning circuit. The control loop is built-in with a limit protection module to constrain the output command based on the displacement limit value obtained from the static test to prevent mechanical overload. The dead zone compensation module dynamically adjusts the control gain based on the non-linear characteristic parameters to eliminate the adjustment lag caused by the transmission gap.

[0073] This structure optimizes the dynamic characteristics of power output through thermo-mechanical coupling. The thermal buffering effect of the phase change material smooths the steam pressure fluctuations and reduces the regulation frequency of the throttle valve opening; the precise displacement control of the mechanical actuator synchronously responds to the load change requirements. The temperature gradient data of the double-stage energy storage cavity is input into the dynamic signal processing module to participate in the calculation of the heat capacity attenuation slope, forming a closed-loop of thermal energy management. The state parameters of the actuator are fed back to the parameter identification module to continuously optimize the control algorithm parameters and maintain the speed regulation stability within the full load range. Specifically, the test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range according to the present invention further includes: The multi-objective rolling optimization algorithm synchronously optimizes the peak shaving depth, equipment life, and curtailment rate of renewable energy; The priority of renewable energy consumption is dynamically bound to the energy storage and release strategy; And transmits the corrected model parameters to the closed-loop control module; The closed-loop control module generates a throttle valve opening command and a combustion control signal according to the corrected model parameters, drives the actuator to adjust the steam turbine power output, suppresses the overshoot peak caused by load mutation, and optimizes the fuel distribution of the burner to reduce the primary frequency modulation action delay.

[0074] The multi-objective rolling optimization algorithm constructs a three-dimensional solution space including the peak shaving depth, equipment life, and curtailment rate of renewable energy. The peak shaving depth index is quantified by a load tracking ability evaluation model, and the deviation integral value between the actual load and the target load is calculated. The equipment life index is based on the material fatigue accumulation model, and the stress cycle times of key components under variable working conditions are analyzed. The curtailment rate index of renewable energy is associated with the power grid dispatching instruction, and the proportion of the time period when the output of renewable energy is limited is statistically calculated. The algorithm adopts a rolling time domain optimization framework, dynamically adjusts the target weight coefficient within each optimization period, and generates a parameter correction vector.

[0075] A dynamic binding mechanism is established between the priority of renewable energy consumption and the energy storage and release strategy. When the power grid issues a high proportion of new energy consumption instructions, the optimization algorithm increases the weight coefficient of the curtailment rate index of renewable energy and triggers the adjustment of the energy storage cavity release strategy. This strategy changes the heat storage capacity distribution ratio of the energy storage cavity by adjusting the working temperature range of the phase change material. The high-pressure energy storage cavity preferentially responds to the release demand, and the medium-pressure energy storage cavity undertakes the task of energy storage balance, forming a thermal energy scheduling logic matching the consumption priority.

[0076] The corrected model parameters are transmitted to the closed-loop control module via an industrial bus. The data transmission adopts the publish / subscribe mode. The parameter identification module acts as the data producer to publish parameter update messages, and the closed-loop control module acts as the consumer to subscribe to the updates. The message structure includes a parameter version identifier, an effective timestamp, and a data verification field to prevent the inconsistency between the control instruction and the model parameter version. The transmission period is adaptively adjusted according to the load change rate, and the transmission interval is shortened when the load fluctuates violently.

[0077] After parsing the model parameters, the closed-loop control module reconstructs the control strategy. The regulator response lag parameter is used to construct a feedforward compensation model for the throttle valve opening, and a compensation instruction is generated through a phase lead algorithm. The non-linear characteristic parameter of the actuator is input into the friction compensation calculation unit, and the displacement deviation is corrected by using a preset dead zone inverse model. The compensated throttle valve opening command drives the servo actuator, and the opening of the high-pressure throttle valve of the steam turbine is adjusted through a mechanical transmission device to suppress the power overshoot peak caused by the sudden change of the main steam pressure.

[0078] The combustion control signal is generated based on the prime mover dynamic response parameters. The volume time constant output by the parameter identification module is used to calculate the fuel supply gradient. Combining the load change rate and the steam pressure decay rate, the fuel ratio of each burner is dynamically allocated. The signal output adopts a hierarchical control architecture: the primary control layer generates the total fuel demand command, and the secondary control layer optimizes the fuel distribution weight according to the energy storage chamber temperature distribution. The control command is converted into a valve position opening signal and transmitted to the combustion actuator via a field bus to execute fuel adjustment, shortening the primary frequency modulation action delay time.

[0079] This module establishes a control effect feedback mechanism. The throttle valve displacement sensor real-time collects the actual opening position, and the burner status monitoring unit feeds back the fuel distribution data. The feedback signal is input into the dynamic signal acquisition device and participates in the parameter optimization calculation of the next control cycle. The control strategy version and the model parameter version are verified by timestamp matching, forming a closed-loop control link from parameter update to execution verification.

[0080] Specifically, the test system for the mismatch correction of the speed regulation model of a thermal power unit in a wide load range described in the present invention further includes: The simulation verification module compares the measured data generated by the dynamic signal processing module with the output of the simulation model corrected by the parameter identification module, dynamically adjusts the calibration parameters of the model characteristic curve through an error feedback mechanism, and verifies the real-time compensation model of the fouling factor of the thermoelectric decoupling heat exchanger: δt=δ0×e-kt+β×ΔT molten salt; Where, δ0 is the initial fouling factor, k is the attenuation coefficient, β is the temperature compensation coefficient, and ΔT molten salt is the molten salt temperature difference.

[0081] The simulation verification module receives the measured data set transmitted by the dynamic signal processing module, including the time-domain signal of the governor valve opening oscillation waveform and the main steam pressure decay rate curve. Synchronously obtain the dynamic response data of the simulation model output by the parameter identification module, including the governor valve opening prediction curve and the simulated steam pressure change rate. The two types of data are input into the comparative analysis interface, and time-domain synchronization alignment processing is performed. The time base is unified through the feature point matching algorithm to eliminate the phase deviation caused by the sampling frequency difference.

[0082] The comparative analysis process uses a multi-dimensional error quantification method. The comparison of the governor valve oscillation characteristics focuses on three core indicators: the amplitude deviation of the fundamental frequency component, the harmonic distortion degree, and the decay time constant. The comparison of the main steam pressure change characteristics uses the dynamic time warping algorithm to calculate the morphological similarity between the measured curve and the simulated curve. The error quantification results generate a feature difference matrix, including three types of evaluation parameters: the amplitude deviation coefficient, the phase lag amount, and the morphological distortion degree.

[0083] The error feedback mechanism drives the iterative optimization of the model parameters. The feature difference matrix is input into the high-pressure cylinder flow characteristic curve parameter adjustment module, which includes a nonlinear gain coefficient calibration function and a volume time constant weight distribution algorithm. The parameter adjustment uses the adaptive gradient descent method to correct the slope of the gain curve according to the amplitude deviation coefficient and optimize the time constant weight distribution ratio according to the phase lag amount. After each parameter update, the simulation model is re-run to form an error convergence verification closed-loop.

[0084] The verification of the fouling factor compensation model for the thermoelectric decoupling heat exchanger is performed independently. This verification process constructs a fouling factor change trend analysis model based on the molten salt temperature difference monitoring data and the historical fouling deposition records. The verification module compares the theoretically calculated fouling factor value with the measured offset in real time and evaluates the effectiveness of the compensation model through residual analysis. When the residual exceeds the allowable threshold, the adaptive adjustment mechanism of the compensation coefficient is triggered to update the attenuation coefficient and the temperature compensation coefficient.

[0085] The evaluation of the grid-connected safety margin is based on the output results of the optimized model. The evaluation model is connected to the grid frequency stability analysis framework to calculate three indicators: the allowable range of power fluctuation, the frequency modulation response time window, and the frequency deviation safety interval. The evaluation process correlates the governor valve control stability with the grid frequency constraint conditions and outputs the quantified safety level coefficient and the risk identification code. The safety level coefficient reflects the grid adaptation ability of the unit in the wide load range, and the risk identification code indicates the type of operation risk under critical conditions.

[0086] The evaluation results are transmitted to the closed-loop control module through the industrial communication protocol. The data transmission adopts a message structure with timestamps, including a safety level coefficient, a risk identification code, and a field for the effective operating condition range. After parsing the message, the closed-loop control module adjusts the feedforward compensation intensity of the throttle valve according to the safety level coefficient and switches the combustion control strategy based on the risk identification code. The control logic reconstruction uses a rule engine architecture, and the instruction generation mode under different safety levels is matched through a decision tree model.

[0087] The historical verification data is stored in the knowledge base system. The knowledge base classifies and stores error quantization records, parameter adjustment trajectories, and safety assessment results according to load intervals, supporting data trend mining and analysis. The clustering analysis algorithm is executed regularly to identify the mapping relationship between parameter correction rules and operating condition characteristics, generate an evaluation report on the model generalization ability, and feedback it to the parameter identification module to form a closed-loop iterative system for verification and optimization.

[0088] The technical features involved in the technical solution of the present invention are explained as follows: The correlation analysis logic of the dynamic signal processing module: This module realizes multi-parameter coupling analysis through the signal feature hierarchical extraction method: Recognition of sudden change in main steam pressure: Adopt a pressure change rate threshold determination mechanism, combined with a time window integration algorithm to filter out instantaneous interference, and accurately capture effective pressure mutation events.

[0089] Quantification of throttle valve oscillation characteristics: Perform spectral energy analysis on the filtered displacement signal, extract the fundamental frequency amplitude and attenuation coefficient, and eliminate the interference of mechanical transmission noise.

[0090] Heat capacity attenuation correlation modeling: Generate dynamic attenuation parameters based on the proportional relationship between the temperature difference in the energy storage cavity and the heat capacity loss, reflecting the change trend of the energy storage / discharge efficiency of the phase change material.

[0091] The multi-objective optimization mechanism of the parameter identification module: This module adopts a constraint hierarchical solution strategy to realize model mismatch correction: Design of the objective function: Combine the load tracking error, the curtailment rate of wind and light, and the heat capacity attenuation deviation into a comprehensive optimization objective according to preset weights, and the weight distribution reflects the balance between the power grid frequency regulation demand and the equipment life.

[0092] Rolling horizon optimization: Solve the parameters in segments during the dynamic disturbance test, and use the optimization results of the previous segment as the constraint conditions for the next segment to adapt to the time-varying characteristics of the parameters under variable operating conditions.

[0093] Output of physical parameters: The identification results directly point to measurable physical quantities (such as the lag time of the regulator and the friction coefficient of the actuator), avoiding abstract mathematical descriptions.

[0094] The physical compensation logic of the closed-loop control module: Governor opening feedforward compensation: Convert the governor lag time into a phase lead amount, generate a compensation command through transfer function reconstruction, and convert it into a difference equation executable by a PID controller during specific implementation.

[0095] Dynamic adjustment of fuel distribution: Set the fuel supply gradient based on the prime mover volume time constant, and control the opening ratio of the burner valve to match the change rate of power demand.

[0096] Engineering verification path of the simulation verification module: Dynamic response comparison method: Use the waveform shape similarity algorithm to align the measured and simulated curves, and the residual calculation results drive the iteration of the high-pressure cylinder flow characteristic parameters.

[0097] Implementation of the scaling compensation model: The initial scaling factor is obtained through off-line calibration, the attenuation coefficient and temperature compensation coefficient are regression-fitted based on long-term operation data, and the model output is an executable compensation command.

[0098] Conversion of grid-connected security constraints: Convert indicators such as power fluctuation threshold and frequency regulation time margin into rate limit parameters of control commands.

[0099] Description of the feasibility of the technical solution: Parameter acquisition path: The governor lag time is measured through a step response test; the molten salt scaling factor is monitored online using an industrial-grade electrochemical impedance spectrometer; Engineering implementation of the algorithm: The signal processing module can be deployed on a DSP chip; the optimization algorithm runs on an industrial computer; the control logic is embedded in the unit DCS system; Verification of technical effects: Indicators such as load tracking error and primary frequency regulation delay are tested according to the DL / T standard, and the results are reproducible.

[0100] Embodiments of the present invention are as follows: The test system of the present invention is applied to the deep peak shaving scenario of a 600MW coal-fired unit, and the mismatch correction of the speed control model is realized through the following steps: System construction: The thermal system components simulate the thermal cycle of the boiler and steam turbine. The main steam pressure sensor (range 0-25MPa, accuracy ±0.1%) and temperature sensor (range 0-600°C, accuracy ±1°C) collect steam parameters in real time, with a sampling frequency of 1kHz.

[0101] The speed control system device is equipped with an electro-hydraulic actuator (stroke 0-300mm, resolution 0.01mm) to drive the steam turbine governor valve, and the accuracy of the speed measurement module is ±0.05% of the rated speed.

[0102] The double-stage energy storage structure includes: The high-pressure / intermediate-pressure energy storage cavity is filled with a high-temperature nitrate phase change material with a melting point of 320°C (heat capacity ≥1.5kJ / kg·K); The low-pressure energy storage cavity is filled with a medium-temperature fatty acid-based phase change material with a melting point of 250°C (heat capacity ≥ 2.0 kJ / kg·K); The temperature difference sensor in the energy storage cavity (range 0 - 200°C, accuracy ±0.5°C) monitors the temperature gradient.

[0103] Dynamic signal processing: The dynamic signal acquisition device collects the oscillating signal of the throttle valve opening (bandwidth 0 - 50 Hz), and eliminates high-frequency noise through a Butterworth low-pass filter (cut-off frequency 15 Hz).

[0104] Identify the event of sudden change in main steam pressure (>1.2 MPa / s), and correlate the amplitude of throttle valve opening fluctuation (>6%) and the heat capacity decay slope of the high-temperature energy storage cavity (calculation period 100 ms).

[0105] The molten salt scaling factor is monitored in real time by an impedance analyzer (frequency range 10 Hz - 100 kHz), and fused with the heat capacity decay data to generate a dynamic response database.

[0106] Parameter identification and correction: The dead zone (1.2 mm) and the limit range (0 - 100% opening) of the actuator are calibrated through static tests.

[0107] Inject a step disturbance (amplitude 8% of the rated load) in the 40% - 100% load range, and collect the regulator response lag data (typical value 120 ms).

[0108] The multi-objective optimization algorithm iteratively calculates with weights (w1 = 0.4, w2 = 0.35, w3 = 0.25), and outputs: The compensation amount of the non-linear friction coefficient of the actuator (0.15 - 0.35); The dynamic compensation coefficient of heat capacity decay (1.05 - 1.25); The coupling weight of scaling and heat capacity (0.45).

[0109] Closed-loop control execution: Feed-forward compensation for throttle valve opening: Based on the regulator lag time (95 ms after correction), generate a compensation command with a 25° phase lead, reducing the power overshoot peak by 45% during load mutation (the measured overshoot is reduced from 8.5% to 4.7%).

[0110] Combustion control optimization: Based on the prime mover volume time constant (12 s after correction), adjust the fuel distribution ratio (the fuel ratio of the high-temperature zone burner is increased to 75%), and shorten the primary frequency modulation action delay to 1.8 s (original value 4.5 s).

[0111] Simulation verification and safety enhancement: Compare the measured main steam pressure decay rate (0.85 MPa / s) with the model output (0.82 MPa / s), and optimize the gain coefficient of the high-pressure cylinder flow characteristic curve (adjustment amount +8%) through the sum of squared residuals.

[0112] Scaling compensation model verification: The initial scaling factor δ0 = 1.25, the decay coefficient k = 0.003 s⁻¹, the temperature compensation coefficient β = 0.02, and the compensation error ≤ 5% when the molten salt temperature difference ΔT = 35°C.

[0113] Grid-connected safety margin assessment: The power fluctuation threshold is ±12 MW (±2% of the rated value), the frequency modulation response time margin is 1.6 s, and the control module restricts the change rate of the throttle valve opening ≤ 3% / s accordingly.

[0114] The test system of the present invention is designed for the wide-load peak shaving scenario of thermal power units. By integrating the thermal system components, speed control system devices, dynamic signal acquisition devices, and control devices, it solves the problem of test deviation of the speed control system performance caused by dynamic response mismatch during deep peak shaving of steam turbines in thermal power units in the prior art, specifically manifested as inaccurate controller response characteristics, decreased speed regulation accuracy, and dynamic fluctuations of combustion parameters. The control device establishes a real-time communication link with each component to form a closed-loop test and correction architecture.

[0115] The thermal system components simulate the actual thermal cycle process of thermal power units. The main steam parameters generated by the boiler are collected in real time by high-precision sensors with a sampling frequency of 1 kHz, covering the full peak shaving range from the rated load to the lower limit of deep peak shaving (such as 30% of the rated load). The speed control system device integrates a hydraulic regulator, a PID controller, and an actuator. The actuator drives the throttle valve opening of the steam turbine to control the power output. The displacement sensor collects the throttle valve opening signal (resolution 0.01 mm), and synchronously records the speed deviation signal (accuracy ±0.1 rpm). The speed control system device is connected to a preset two-stage energy storage structure: the high-pressure energy storage cavity and the medium-pressure energy storage cavity are filled with high-temperature phase change materials with a melting point ≥ 300°C (such as nitrate composites), and the low-pressure energy storage cavity is filled with medium-temperature phase change materials with a melting point of 200 - 300°C (such as organic-inorganic hybrid materials). The temperature gradient of the energy storage cavity is collected by a multi-point temperature sensor array (temperature difference measurement accuracy ±0.5°C), and the molten salt scaling factor is indirectly measured by electrochemical impedance spectroscopy with a sampling period of 100 ms.

[0116] The dynamic signal processing module performs preprocessing on the original sensor signals: a Butterworth low-pass filter with a cut-off frequency of 50 Hz is used to eliminate high-frequency noise and retain the fundamental frequency component of the governor oscillation (typical range 2 - 15 Hz); the rate of change of the main steam pressure (pressure change per unit time dP / dt), the amplitude of the governor valve opening fluctuation (maximum displacement deviation within the oscillation period), and the attenuation slope of the heat capacity of the energy storage cavity (calculated based on the ratio of the temperature difference ΔT to the heat capacity loss ΔQ) are analyzed; the rate of change of the molten salt fouling factor is fused to identify the dynamic correlations between sudden changes in the main steam pressure (threshold > 1 MPa / s), governor valve opening oscillation (amplitude > 5%), and heat capacity attenuation, and a time-series dynamic response database is generated.

[0117] The parameter identification module obtains the displacement dead zone (0.5 - 2 mm), the limit range (0 - 100% opening), and the initial heat capacity attenuation coefficient of the phase change material of the actuator through static tests. In the dynamic perturbation test, a step perturbation (amplitude 5 - 10% of the rated load) is injected in a wide load range, and the regulator input / output signals, the actuator displacement response, and the temperature gradient of the energy storage cavity are collected. Using a multi-objective rolling optimization algorithm, iterative calculations are performed with the objective function min(0.4 load tracking error + 0.35 curtailment rate of wind and solar power + 0.25 * heat capacity attenuation deviation) to identify the regulator response lag time (50 - 200 ms), the non-linear friction coefficient of the actuator, the dynamic compensation coefficient of the heat capacity attenuation of the phase change material, and the coupling weight of fouling and heat capacity attenuation (0.2 - 0.8), and the modified speed control model parameters are output.

[0118] The closed-loop control module generates control commands based on the modified parameters: a phase lead algorithm (lead angle 10° - 30°) is used to generate a feed-forward compensation command for the governor valve opening according to the regulator lag time to suppress the power overshoot peak caused by load mutations (overshoot reduced by more than 40%); the fuel distribution ratio is dynamically adjusted according to the dynamic response parameters of the prime mover (such as the weight ratio of high / low temperature burners 3:1), and combined with the air supply ratio signal, the primary frequency modulation action delay is shortened to within 2 s; the governor valve displacement feedback and the burner state feedback participate in the optimization of the next cycle command to form a closed-loop control.

[0119] The simulation verification module compares the measured governor oscillation frequency and the main steam pressure attenuation rate with the output of the modified model, and dynamically adjusts the parameters of the high-pressure cylinder flow characteristic curve through an error feedback mechanism (minimization of the sum of squared residuals). The effectiveness of the fouling compensation model of the heat-electric decoupling heat exchanger is verified, and the real-time compensation amount is output based on the initial fouling factor δ0, the attenuation coefficient k, and the molten salt temperature difference ΔT. The grid-connected safety margin is evaluated by calculating the allowable threshold of power fluctuation (±2% of the rated power), the frequency modulation response time margin (> 1.5 s), and the frequency deviation safety interval (49.8 - 50.2 Hz), and the evaluation results are fed back to the closed-loop control module to optimize the command logic.

[0120] Through dynamic signal correlation analysis, multi-objective parameter identification, and closed-loop verification mechanism, this system realizes the real-time correction of the mismatch of the speed regulation model under wide load conditions, reduces the load tracking error in the full peak shaving range to less than 1%, and reduces the amplitude of furnace pressure fluctuations by more than 50%, meeting the requirements of dynamic response accuracy and grid safety for deep peak shaving.

[0121] Aiming at the problem of poor coordination control regulation quality, the present invention uses a dynamic signal processing module to analyze the correlation characteristics of sudden changes in main steam pressure and governor valve opening oscillation in real time, and constructs a dynamic response database including time series. The parameter identification module uses a multi-objective rolling optimization algorithm based on this database to identify the regulator response lag parameters and actuator nonlinear characteristic parameters in the speed regulation system, and corrects the model mismatch under wide load conditions. The closed-loop control module generates a feedforward compensation command for the governor valve opening according to the corrected parameters, and cancels the control lag effect through a phase lead compensation mechanism, suppressing the power overshoot peak caused by load mutation, and realizing accurate load tracking in the full peak shaving range.

[0122] For the defect of the decline in primary frequency regulation performance, the system uses the thermal buffering characteristics of the double-stage energy storage structure to smooth the steam pressure fluctuations. The high-pressure and medium-pressure energy storage chambers are filled with high-temperature phase change materials, and the low-pressure energy storage chamber is filled with medium-temperature phase change materials, which absorb / release heat energy through the phase change process. The parameter identification module combines the temperature gradient data of the energy storage chamber to correct the dynamic response parameters and heat capacity attenuation coefficient of the prime mover. The closed-loop control module generates a combustion control signal based on the corrected parameters, dynamically optimizes the fuel distribution ratio and air supply ratio, makes the combustion response rate match the grid frequency fluctuation requirements, and reduces the primary frequency regulation action delay time.

[0123] Aiming at the combustion stability problem, the simulation verification module compares the measured data with the model output, and dynamically adjusts the calibration parameters of the high-pressure cylinder flow characteristic curve through an error feedback mechanism. At the same time, the fouling factor compensation model of the thermal electrolytic decoupling heat exchanger is verified. This model comprehensively considers the initial fouling factor, attenuation coefficient, and molten salt temperature difference compensation amount, and corrects the heat transfer efficiency parameters in real time. The optimized evaluation result of the grid-connected safety margin is fed back to the closed-loop control module to reconstruct the governor valve opening command and fuel distribution strategy, maintaining the furnace pressure stability under different load conditions and eliminating the combustion fluctuations caused by sudden changes in main steam parameters.

Claims

1. A test system for mismatch correction of the speed control model of a thermal power unit in a wide load range, characterized in that, Including: A thermal system component, a speed control system device, a dynamic signal acquisition device, and a control device. The control device establishes communication connections with the thermal system component, the speed control system device, and the dynamic signal acquisition device; The dynamic signal acquisition device includes a high-precision sensor, which collects main steam pressure, temperature, governor valve opening, rotational speed deviation signal, and energy storage cavity temperature gradient data in real time and transmits them to the control device; The control device includes a dynamic signal processing module, a parameter identification module, a closed-loop control module, and a simulation verification module; The dynamic signal processing module receives the sensor signals, analyzes the change trends of main steam parameters, the oscillation characteristics of the governor valve, and the heat capacity attenuation characteristics of the phase change material, generates a dynamic response database including time series, and transmits the dynamic response database to the parameter identification module; Based on the dynamic disturbance test data in the dynamic response database, the parameter identification module identifies the regulator response lag parameter, the non-linear characteristic parameter of the actuator, the dynamic response parameter of the prime mover, and the heat capacity attenuation coefficient of the phase change material in the speed control system device through a multi-objective rolling optimization algorithm, corrects the model mismatch of the preset speed control system model under wide load conditions, and generates a governor valve opening command and a combustion control signal with the corrected parameters; The closed-loop control module generates a governor valve opening command and a combustion control signal according to the corrected parameters, drives the speed control system device to perform steam turbine power adjustment and burner fuel distribution. The simulation verification module receives the real-time data of the dynamic signal acquisition device and the output of the corrected model of the parameter identification module, compares the measured response with the simulation result through an error feedback mechanism, dynamically adjusts the calibration parameters of the high-pressure cylinder flow characteristic curve, calculates the allowable threshold of power fluctuation, the time margin of frequency modulation response, and the safe interval of frequency deviation based on the comparison error data and the verification result of the fouling compensation model, generates a grid-connected safety margin evaluation result, and feeds back the evaluation result to the closed-loop control module to optimize the constraint conditions of the governor valve opening command and the combustion control signal.

2. The test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range according to claim 1, wherein, The dynamic signal processing module synchronously fuses the energy storage cavity temperature gradient data and the change rate of the molten salt fouling factor by analyzing the non-linear fluctuation characteristics in the sensor signals transmitted by the dynamic signal acquisition device, identifies the dynamic correlation between the sudden change of main steam pressure, the oscillation of the governor valve opening, and the heat capacity attenuation of the phase change material, and generates the dynamic response database including the main steam pressure change rate, the governor valve opening fluctuation amplitude, the rotational speed deviation, the heat capacity attenuation slope of the high-temperature phase change material, and the dynamic offset of the molten salt fouling factor in time series; Wherein, the heat capacity attenuation slope of the high-temperature phase change material is calculated by the following formula: α = -(ΔT gradient / ΔtΔQ heat capacity); ΔQ heat capacity is the heat capacity loss per unit time of the energy storage cavity, ΔT gradient is the temperature difference between the high- / medium-temperature energy storage cavities, and Δt is the sampling period; And input the correlation data in the dynamic response database into the parameter identification module to correct the regulator response lag parameter and the coupling characteristics parameter of the thermodynamics performance and fouling of the phase change material in the preset speed control system model.

3. The test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range according to claim 2, wherein, The parameter identification module is configured as: Obtain the amplitude limiting parameters, dead zone parameters, and initial heat capacity attenuation coefficient of the phase change material in the energy storage cavity in the speed regulation system device through static tests, and input them into the speed regulation system model; Collect the input and output signals of the regulator, the input and output signals of the actuator, the input and output signals of the prime mover, the temperature gradient data of the high-temperature or medium-temperature energy storage cavity, and the real-time offset of the molten salt scaling factor in the speed regulation system device based on dynamic disturbance tests; Adopt a multi-objective rolling optimization algorithm to perform iterative calculations on the input and output signals of the regulator, the input and output signals of the actuator, the input and output signals of the prime mover, and the energy storage and scaling coupling parameters, and identify in the speed regulation system model: the regulator response lag parameter, the actuator nonlinear characteristic parameter, the dynamic compensation coefficient of the heat capacity attenuation of the phase change material, and the coupling weight between the scaling factor and the heat capacity attenuation; Among them, the objective function of the multi-objective rolling optimization algorithm is: min(w1*E_peak regulation + w2*R_power curtailment + w3*D_heat attenuation); E_peak regulation is the load tracking error, R_power curtailment is the wind and light curtailment rate, D_heat attenuation is the heat capacity attenuation deviation; the weights w1 = 0.4, w2 = 0.35, w3 = 0.25; And transmit the corrected parameters to the closed-loop control module.

4. The test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range according to claim 3, wherein The closed-loop control module generates a throttle opening command based on the corrected regulator response lag parameter and actuator nonlinear characteristic parameter transmitted by the parameter identification module to adjust the steam turbine power output and suppress the overshoot peak caused by load mutation; At the same time, based on the corrected dynamic response parameter of the prime mover by the parameter identification module, generate a combustion control signal to adjust the fuel distribution ratio of the burner.

5. The test system for mismatch correction of the speed regulation model of a thermal power unit in a wide load range according to claim 4, characterized in that The simulation verification module receives the measured throttle oscillation frequency and the main steam pressure attenuation rate generated by the dynamic signal processing module, compares them with the dynamic response output by the simulation model corrected by the parameter identification module, adjusts the characteristic curve calibration parameters in the speed regulation system model through an error feedback mechanism, and transmits the corrected off-grid safety margin evaluation result to the closed-loop control module for optimizing the generation logic of the throttle opening command and the combustion control signal.

6. The test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range according to claim 5, characterized in that, The dynamic disturbance test includes: Inject a step disturbance signal into the speed regulation system device within a wide load range at the lower limit of deep peak shaving at 100% rated load, and collect the throttle opening oscillation waveform output by the actuator when the main steam pressure suddenly changes; Extract the frequency characteristics of the throttle opening oscillation waveform through spectrum analysis, input the frequency characteristics into the speed regulation system model, check the corrected dynamic response parameter of the prime mover by the parameter identification module, and generate the corrected prime mover model parameter and transmit it to the closed-loop control module.

7. The test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range according to claim 6, wherein The dynamic signal processing module performs spectrum correction processing on the throttle opening signal transmitted by the dynamic signal acquisition device, eliminates the high-frequency noise interference introduced during the sensor acquisition process through digital filtering, generates a corrected throttle opening signal, and inputs the corrected throttle opening signal into the closed-loop control module for generating a throttle opening compensation command based on the corrected regulator response lag parameter by the parameter identification module to adjust the steam turbine throttle opening.

8. The test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range according to claim 7, characterized in that, Also include: The speed control system device is connected to a preset two-stage energy storage structure, and the two-stage energy storage structure includes: a low-pressure energy storage chamber, a medium-pressure energy storage chamber, and a high-pressure energy storage chamber; The high-pressure energy storage chamber and the medium-pressure energy storage chamber are filled with a high-temperature phase change material, and the melting point of the high-temperature phase change material is ≥300 °C; The low-pressure energy storage chamber is filled with a medium-temperature phase change material, and 200 °C ≤ the melting point of the medium-temperature phase change material < 300 °C; The speed control system device includes a hydraulic regulator, a PID controller, and an actuator, which are used to dynamically adjust the opening of the steam turbine control valve to control the power output.

9. The test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range according to claim 8, characterized in that, It also includes: The multi-objective rolling optimization algorithm synchronously optimizes the peak shaving depth, equipment life, and curtailment rate of wind and solar power; The priority of renewable energy consumption is dynamically bound to the energy release strategy of energy storage; And transmit the corrected model parameters to the closed-loop control module; The closed-loop control module generates a control valve opening command and a combustion control signal according to the corrected model parameters, drives the actuator to adjust the steam turbine power output, suppresses the overshoot peak caused by load mutation, and optimizes the fuel distribution of the burner to reduce the primary frequency modulation action delay.

10. The test system for correcting the mismatch of the speed regulation model of a thermal power unit in a wide load range according to claim 9, characterized in that, It also includes: The simulation verification module compares the measured data generated by the dynamic signal processing module with the output of the simulation model corrected by the parameter identification module, dynamically adjusts the calibration parameters of the model characteristic curve through an error feedback mechanism, and verifies the real-time compensation model of the fouling factor of the thermoelectric decoupling heat exchanger: δt = δ0 × e-kt + β × ΔT molten salt; Wherein, δ0 is the initial fouling factor, k is the attenuation coefficient, β is the temperature compensation coefficient, and ΔT molten salt is the temperature difference of the molten salt.

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