Unit coupled electrode thermal steam boiler frequency modulation performance evaluation method, system and equipment

Abstract

The present application belongs to the technical field of power system, and discloses a method, system and equipment for evaluating the frequency modulation performance of a unit-coupled electrode thermal steam boiler, which comprises the following steps: obtaining joint operation data, and generating an effective original data set through screening and preprocessing; establishing a thermal power unit, an electrode thermal steam boiler frequency response model and a joint frequency modulation model, and identifying decoupling parameters; dividing the data set according to the load level and identifying in segments, and constructing a model parameter library for different load segments; simulating and calculating the root mean square error of the actual response to verify and correct the parameter library; when the error is up to the standard, calling the corresponding model and parameters according to the current load, inputting a frequency step signal to simulate and obtaining a joint output response curve; extracting the response time, steady-state regulation error and fluctuation amplitude, calculating the score and outputting the evaluation result. The present application adopts variable decoupling sub-object parameter identification based on a blocking strategy, eliminates coupling interference, and improves the parameter identification accuracy and reliability.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a method, system and equipment for evaluating the frequency regulation performance of a unit-coupled electrode hot steam boiler. Background Technology

[0002] In recent years, the installed capacity of new energy sources such as wind power and photovoltaics in my country has grown rapidly. Their randomness and intermittency pose challenges to the frequency stability of the power system, and thermal power units still bear the core tasks of frequency regulation and load balancing. However, the growth of new energy generation has squeezed the space of thermal power, coupled with power market reforms, resulting in more frequent and longer periods of negative electricity prices. In order to reduce economic losses and lower the lower limit of regulation, thermal power units have been equipped with electrode thermal steam boilers on a large scale to achieve deep peak shaving by consuming plant power and avoiding losses from frequent start-stop operations.

[0003] When a thermal power unit is coupled with an electrode thermal steam boiler, its frequency regulation characteristics change significantly. The power consumption of the electrode thermal steam boiler inversely affects the unit's net output power, and its water level regulation inertia also alters the system's dynamic response. Furthermore, the fluctuations in coal quality caused by the blending of multiple coal types during actual unit operation result in complex operating condition-dependent frequency regulation characteristics of the coupled system. To ensure grid stability, it is necessary to accurately assess the frequency regulation capability of the coupled system and clarify indicators such as response speed and regulation amplitude under different operating conditions, providing a basis for dispatch optimization.

[0004] Currently, traditional frequency regulation capability assessment mainly relies on on-site disturbance tests. This method has obvious drawbacks: it is complex to operate, time-consuming and labor-intensive, and requires the unit to be out of normal operating conditions, which poses safety risks. It can only cover specific operating conditions and cannot adapt to all operating conditions such as deep peak shaving, coal quality fluctuations, and variable load of electrode hot steam boilers, resulting in poor universality. Furthermore, it cannot track changes in operating conditions in real time, and the assessment results are not real-time.

[0005] Therefore, there is an urgent need to develop an evaluation system that does not require on-site disturbance testing, is adaptable to all operating conditions, and can accurately reflect the coupling effect of coal quality and electrode thermal steam boiler. This system would solve the problems of poor adaptability and insufficient accuracy of existing methods and meet the actual needs of evaluating the frequency regulation capability of thermal power units coupled with electrode thermal steam boiler. Summary of the Invention

[0006] The present invention provides a method, system and equipment for evaluating the frequency regulation performance of a unit-coupled electrode hot steam boiler, in order to solve the problems of the above-mentioned technologies in the prior art.

[0007] According to a first aspect of the present invention, a method for evaluating the frequency regulation performance of a unit-coupled electrode hot steam boiler is provided.

[0008] In one embodiment, a method for evaluating the frequency regulation performance of a unit-coupled electrode hot steam boiler includes:

[0009] The acquired operating data of thermal power units and electrode hot steam boilers are subjected to perturbation screening and preprocessing to generate valid raw datasets;

[0010] Based on valid original datasets, establish frequency response models for thermal power units, electrode thermal steam boilers, and joint frequency regulation models, and generate parameter identification processes for the frequency response models of thermal power units and electrode thermal steam boilers.

[0011] The effective raw dataset is divided into load segments based on the load level of the thermal power units, and a parameter identification process is performed based on the data corresponding to each load segment to obtain the identification parameter set corresponding to each load segment; a load segment model parameter library is constructed based on the identification parameter set.

[0012] Joint frequency modulation simulation is performed on the identified parameter set in the load segment model parameter library to obtain the simulation response curve. The simulation response curve is then compared with the actual response curve, and the root mean square error between the simulation response curve and the actual response curve is calculated to verify and correct the identified parameters in the load segment model parameter library, thus obtaining the corrected identified parameter set.

[0013] When the root mean square error meets the preset requirements, the joint frequency modulation model of the corresponding load segment and the corrected identification parameter set are called according to the current operating load. The frequency step signal is input to perform frequency modulation response simulation and obtain the joint output response curve.

[0014] The response time, steady-state adjustment error, and steady-state fluctuation amplitude are extracted from the joint output response curve. The frequency modulation performance score is calculated based on the response time, steady-state adjustment error, and steady-state fluctuation amplitude according to the preset evaluation rules, and the frequency modulation performance evaluation result is output.

[0015] In one embodiment, the process of perturbation filtering and preprocessing of the acquired operating data of thermal power units and electrode hot steam boilers to generate a valid raw dataset includes the following steps:

[0016] Acquire real-time frequency data from the PMU measurement device;

[0017] Calculate the absolute value of the system frequency deviation, and identify data with an absolute value of frequency deviation greater than a preset disturbance threshold as large disturbance event data and remove them;

[0018] Data with absolute frequency deviations less than or equal to the perturbation threshold are retained as small perturbation event data; outlier removal, time axis alignment, and data format standardization are performed on the small perturbation event data to generate a valid original dataset.

[0019] In one embodiment, when establishing the joint frequency modulation model, the joint power formula is as follows:

[0020] ;

[0021] in, This represents the combined power change when the response frequency is adjusted. This represents the change in power output of thermal power units. The power change represents the change in power of the electrode-heated steam boiler; the combined power change is used as the output of the combined frequency regulation model for subsequent parameter identification and frequency regulation performance evaluation.

[0022] In one embodiment, establishing a frequency response model for a thermal power unit, a frequency response model for an electrode thermal steam boiler, and a joint frequency regulation model based on a valid original dataset, and generating a parameter identification process for the frequency response models of the thermal power unit and the electrode thermal steam boiler, includes the following steps:

[0023] The primary frequency regulation function of the thermal power unit is locked to maintain the constant output operation of the thermal power unit; the parameters of the frequency response model of the electrode hot steam boiler are identified using effective original datasets to obtain the controller time constant and water level regulation time constant.

[0024] The power frequency regulation function of the electrode hot steam boiler is locked to keep the electrode hot steam boiler in a constant power operation state; using effective original datasets, the parameters of the frequency response model of the thermal power unit are identified to obtain the governor gain, governor time constant, reheater time constant, boiler gain, and boiler time constant.

[0025] In one embodiment, parameter identification is achieved using a particle swarm optimization algorithm. The mean square error between the model output power response curve and the actual power response curve is used as the objective function. The minimum value of the objective function is searched iteratively through particle velocity update and particle position update. The parameter set corresponding to the minimum objective function value is determined as the identification parameter set.

[0026] In one embodiment, the effective raw dataset is divided into load segments according to the load level of the thermal power unit, and a parameter identification process is performed based on the data corresponding to each load segment to obtain the identification parameter set corresponding to each load segment; the construction of a load segment model parameter library based on the identification parameter set includes the following steps:

[0027] The load range is divided according to the proportion of the thermal power unit load to the rated load;

[0028] For each load interval, a parameter identification process is executed to obtain the corresponding set of identification parameters;

[0029] The load range information, equipment type information, parameter name, parameter value, and unit are associated and stored to build a load segment model parameter library.

[0030] In one embodiment, joint frequency modulation simulation is performed on the identified parameter set in the load segment model parameter library to obtain the simulated response curve. The simulated response curve is then compared with the actual response curve, and the root mean square error between the simulated and actual response curves is calculated to verify and correct the identified parameters in the load segment model parameter library, resulting in the corrected identified parameter set. This process includes the following steps:

[0031] The identification parameter set corresponding to the current load segment is called from the load segment model parameter library, and the identification parameter set is substituted into the joint frequency modulation model to perform frequency response simulation and obtain the simulation response curve.

[0032] Obtain the actual response curve corresponding to the current load segment, compare the actual response curve with the simulated response curve, and calculate the root mean square error of the two.

[0033] The root mean square error is compared with a preset error threshold. When the root mean square error is less than the preset error threshold, the identification parameter set is determined to be valid and is saved to the load segment model parameter library.

[0034] When the root mean square error exceeds the preset error threshold, the parameter identification is re-executed based on the root mean square error to obtain the corrected identification parameter set, and the parameter library of the load segment model is updated using the corrected identification parameter set.

[0035] In one embodiment, when the root mean square error meets the preset requirements, the joint frequency modulation model of the corresponding load segment and the corrected identification parameter set are called according to the current operating load. A frequency step signal is input to perform frequency modulation response simulation, and the joint output response curve is obtained by the following steps:

[0036] Obtain the current operating load, and match the joint frequency regulation model and identification parameter set of the corresponding load segment from the load segment model parameter library based on the current operating load;

[0037] Input a frequency step signal into the joint frequency modulation model and configure the model parameters of the joint frequency modulation model using the identification parameter set;

[0038] Based on the frequency step signal and the identified parameter set, joint frequency modulation simulation was performed to obtain the output response curves of thermal power units and electrode hot steam boilers.

[0039] The combined output power change is calculated based on the output response curves of the thermal power unit and the electrode thermal steam boiler, and the combined output response curve is obtained.

[0040] In one embodiment, the response time, steady-state adjustment error, and steady-state fluctuation amplitude are extracted from the joint output response curve, and the frequency modulation performance score is calculated based on the response time, steady-state adjustment error, and steady-state fluctuation amplitude according to preset evaluation rules. The output frequency modulation performance evaluation result includes the following steps:

[0041] Feature parameters are extracted from the joint output response curve to obtain the response time, steady-state regulation error, and steady-state fluctuation amplitude.

[0042] Response speed score is calculated based on response time;

[0043] Calculate the adjustment accuracy score based on the steady-state adjustment error;

[0044] The stability score is calculated based on the steady-state fluctuation amplitude.

[0045] The frequency modulation performance score is obtained by weighting the response speed score, adjustment accuracy score and stability score according to the preset evaluation rules.

[0046] The corresponding frequency modulation performance evaluation result is output based on the frequency modulation performance score.

[0047] According to a second aspect of the present invention, a frequency regulation performance evaluation system for unit-coupled electrode hot steam boilers is provided.

[0048] In one embodiment, a frequency regulation performance evaluation system for a unit-coupled electrode hot steam boiler includes:

[0049] The data acquisition module is used to acquire operational data, frequency data, and coal quality data, and generate a valid raw dataset.

[0050] The model building module is used to establish frequency response models for thermal power units, frequency response models for electrode thermal steam boilers, and joint frequency regulation models.

[0051] The parameter identification module is used to identify the parameters of the model based on the valid original dataset and obtain the identified parameter set.

[0052] The parameter library construction module is used to construct a parameter library for the load segment model based on the load segment division results.

[0053] The model verification module is used to perform simulation verification using the load segment model parameter library and output model error results;

[0054] The performance evaluation module is used to call the verified joint frequency modulation model to perform frequency modulation response simulation and output the frequency modulation performance evaluation results based on the response results.

[0055] In one embodiment, the data acquisition module includes a PMU measurement submodule and a data aggregation submodule;

[0056] The PMU measurement submodule is used to collect generator terminal voltage and current data, high-voltage transformer low-voltage side voltage and current data, and electrode hot steam boiler side voltage and current data.

[0057] The data aggregation submodule is used to upload the collected data to the scheduling VAMS system and generate a valid raw dataset.

[0058] In one embodiment, the model building module includes a thermal power unit modeling submodule, an electrode thermal steam boiler modeling submodule, and a joint frequency regulation modeling submodule.

[0059] The thermal power unit modeling submodule is used to establish the frequency response model of the thermal power unit.

[0060] The electrode-heated steam boiler modeling submodule is used to establish the frequency response model of the electrode-heated steam boiler.

[0061] The joint frequency modulation modeling submodule is used to build a joint frequency modulation model.

[0062] In one embodiment, the parameter identification module includes a thermal power unit parameter identification submodule, an electrode thermal steam boiler parameter identification submodule, and a particle swarm optimization submodule.

[0063] The particle swarm optimization submodule is used to iteratively optimize the model parameters based on the error between the model output response curve and the actual response curve, so as to obtain the identification parameter set.

[0064] In one embodiment, the performance evaluation module is used to extract the response time, steady-state adjustment error, and steady-state fluctuation amplitude from the joint output response curve, calculate the frequency modulation performance score according to the preset evaluation rules, and output the frequency modulation performance evaluation result.

[0065] According to a third aspect of the present invention, a computer device is provided.

[0066] In some embodiments, the computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program to implement the steps of the method described above.

[0067] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0068] 1. A variable decoupling object parameter identification method based on a lockout strategy is adopted. When identifying thermal power units or electrode thermal steam boilers separately, the frequency regulation function of another device is locked, eliminating coupling interference and improving the accuracy and reliability of model parameter identification.

[0069] 2. Introduce a coal quality influence coefficient into the frequency response model of thermal power units to dynamically adjust the boiler power gain and time constant according to the calorific value, volatile matter, ash content and blending ratio of the coal, thereby improving the universality and accuracy of frequency regulation performance evaluation under complex coal quality conditions.

[0070] 3. Construct a load segment model parameter library covering the full load conditions. The model parameters corresponding to each load segment can be quickly called online without repeating on-site disturbance tests, ensuring the real-time performance evaluation and adaptability to operating conditions.

[0071] 4. A quantitative scoring system based on three dimensions—response speed, adjustment accuracy, and stability—was designed, providing accurate and intuitive data support for scheduling personnel and control strategy optimization, and reducing the subjectivity of manual interpretation.

[0072] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description

[0073] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.

[0074] Figure 1 This is a flowchart illustrating a method for evaluating the frequency regulation performance of a unit-coupled electrode hot steam boiler according to an exemplary embodiment.

[0075] Figure 2 This is a schematic diagram of a unit-coupled electrode hot steam boiler frequency regulation performance evaluation system according to an exemplary embodiment;

[0076] Figure 3 This is a flowchart illustrating the model parameter identification process according to an exemplary embodiment;

[0077] Figure 4 This is a frequency response model diagram of a coupled system illustrated according to an exemplary embodiment;

[0078] Figure 5 This is a diagram illustrating a coupled system of a thermal power plant and an electrode heat steam boiler, according to an exemplary embodiment.

[0079] Figure 6 This is a schematic diagram of the structure of a computer device according to an exemplary embodiment. Detailed Implementation

[0080] The following description and accompanying drawings fully illustrate specific embodiments described herein to enable those skilled in the art to practice them. Some portions and features of certain embodiments may be included in or replace portions and features of other embodiments. The scope of the embodiments herein includes the entire scope of the claims and all available equivalents thereof. The various embodiments described herein are presented in a progressive manner, with each embodiment focusing on its differences from other embodiments; similar or identical parts between embodiments can be referred to interchangeably.

[0081] The modules in the apparatus or system of this application can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0082] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0083] Figure 1 and Figure 3 An embodiment of the unit-coupled electrode hot steam boiler frequency regulation performance evaluation method of the present invention is shown.

[0084] In this optional embodiment, the method for evaluating the frequency regulation performance of a unit-coupled electrode hot steam boiler is as follows:

[0085] Step S1: Perform perturbation screening and preprocessing on the acquired operating data of thermal power units and electrode hot steam boilers to generate valid raw datasets;

[0086] In this optional embodiment, the acquisition of operating data from thermal power units and electrode hot steam boilers is subjected to perturbation filtering and preprocessing to generate a valid raw dataset, including the following steps:

[0087] Step S11: Acquire real-time acquisition system frequency data from the PMU measurement device;

[0088] Step S12: Calculate the absolute value of the system frequency deviation, and identify data with an absolute value of frequency deviation greater than the preset disturbance threshold as large disturbance event data and remove them;

[0089] Step S13: Retain data whose absolute value of frequency deviation is less than or equal to the disturbance threshold as small disturbance event data; perform outlier removal, time axis alignment, and data format unification processing on the small disturbance event data to generate a valid original dataset.

[0090] Specifically, raw data such as generator terminal frequency and grid bus frequency are collected in real time through PMU measurement devices. Combined with the actual situation of the power grid frequency regulation project, a frequency fluctuation amplitude judgment threshold of 0.05Hz is set to determine the system frequency deviation. Events with a frequency greater than 0.05 Hz are defined as large disturbances. Events with a Hz frequency of 0.05 or less are defined as small disturbances. Large disturbances are mostly caused by grid faults, large load switching, etc., and are low-probability events. Their data will cause the identification parameters to deviate from the normal operating characteristics of the equipment, so they need to be eliminated.

[0091] The frequency fluctuation amplitude (the absolute value of the difference between the current frequency and the rated frequency) is calculated every moment and compared with the 0.05Hz threshold. If the amplitude exceeds the threshold at a certain moment or in a continuous period, the period is marked as a period of large disturbance event, and the start and end times of the event are recorded for subsequent data filtering.

[0092] Operational data from all periods of large disturbances are filtered out, retaining only valid data from periods of small disturbances. This includes thermal power unit output, electrode steam boiler power, frequency fluctuations, terminal voltage and current, plant bus voltage and current, and coal quality data. Simultaneously, the filtered data undergoes preprocessing to remove outliers such as data jumps and signal loss. Data from different sources is time-axis aligned (using unified sampling frequency and timestamps), and the format is standardized to a uniform data format, generating a valid raw dataset for parameter identification. This ensures the validity and consistency of the identified data. The generated valid raw dataset for parameter identification serves as the foundational input dataset for subsequent model parameter identification and validation.

[0093] Step S2: Based on the effective original dataset, establish the frequency response model of thermal power unit, the frequency response model of electrode thermal steam boiler, and the joint frequency regulation model, and generate the parameter identification process for the frequency response model of thermal power unit and the frequency response model of electrode thermal steam boiler.

[0094] In this optional embodiment, when establishing the joint frequency modulation model, the joint power formula is as follows:

[0095] ;

[0096] in, This represents the combined power change when the response frequency is adjusted. This represents the change in power output of thermal power units. The power change represents the change in power of the electrode-heated steam boiler; the combined power change is used as the output of the combined frequency regulation model for subsequent parameter identification and frequency regulation performance evaluation.

[0097] Specifically, this equation is the core equation of coupled modeling, directly reflecting the synergistic relationship between the electrode thermal steam boiler and the thermal power unit. The change in the power of the electrode thermal steam boiler is inversely related to the change in the net output power of the unit. This is also the core principle that the electrode thermal steam boiler can assist the thermal power unit in frequency regulation (when the frequency drops, the electrode thermal steam boiler reduces power consumption, the equivalent unit net output power increases, and the frequency drop is suppressed; when the frequency rises, the electrode thermal steam boiler increases power consumption, the equivalent unit net output power decreases, and the frequency rise is suppressed).

[0098] In this optional embodiment, the process of establishing a frequency response model for thermal power units, a frequency response model for electrode thermal steam boilers, and a joint frequency regulation model based on valid original datasets, and generating parameter identification procedures for the frequency response models of thermal power units and electrode thermal steam boilers, includes the following steps:

[0099] Step S21: Lock the primary frequency regulation function of the thermal power unit to maintain the constant output operation of the thermal power unit; use the effective original dataset to identify the parameters of the frequency response model of the electrode hot steam boiler and obtain the controller time constant and water level regulation time constant.

[0100] It should be noted that during the parameter identification process of the electrode thermal steam boiler model, data such as the power adjustment command, actual power change curve, and plant bus voltage and current of the electrode thermal steam boiler are extracted from the effective original dataset and used as input data for the particle swarm optimization algorithm to identify the electrode thermal steam boiler model parameters.

[0101] Step S22: Lock the frequency regulation function of the electrode hot steam boiler to keep the electrode hot steam boiler in constant power operation; use the effective original dataset to identify the parameters of the frequency response model of the thermal power unit and obtain the governor gain, governor time constant, reheater time constant, boiler gain and boiler time constant.

[0102] It should be noted that during the identification of thermal power unit model parameters, the unit frequency response signal, output change data, terminal voltage and current data, and coal quality data are extracted from the effective original dataset and used as input data for thermal power unit model parameter identification.

[0103] In this optional embodiment, parameter identification is achieved using a particle swarm optimization algorithm. The mean square error between the model output power response curve and the actual power response curve is used as the objective function. The minimum value of the objective function is searched iteratively through particle velocity update and particle position update. The parameter set corresponding to the minimum objective function value is determined as the identification parameter set.

[0104] Specifically, since there is coupling interference when thermal power units and electrode thermal steam boilers are operated together, directly identifying the parameters of the two types of equipment at the same time will lead to a decrease in accuracy. Therefore, a single variable control lockout method is adopted to achieve independent identification of each object, clarify the core parameters to be identified for the two types of equipment, and use a specific algorithm to complete the identification.

[0105] A primary frequency regulation interlock command is sent to the thermal power unit control system to lock the primary frequency regulation operation logic of the control system, ensuring that the output of the thermal power unit remains constant. Specifically, after sending the primary frequency regulation interlock command to the thermal power unit DEH control system, the primary frequency regulation function of the unit is put into the off state, the governor valve position setpoint is fixed, and the output of the differential regulation link is locked, so that the governor no longer adjusts the turbine inlet valve opening according to the system frequency deviation; at the same time, the unit load command is kept constant, and the changes in the unit's active power are monitored. When the fluctuation of the unit's active power is less than ±0.5% of the rated power, the unit is determined to be in a constant output operation state, thereby eliminating the influence of thermal power unit regulation on the parameter identification of electrode hot steam boiler.

[0106] Start the response data acquisition program of the electrode thermal steam boiler control system to record in real time the power adjustment command, actual output change curve, and 6kV plant bus voltage and current response data of the electrode thermal steam boiler under small disturbance events.

[0107] Based on the frequency response model of the electrode thermal steam boiler, the particle swarm optimization algorithm is used for parameter identification. The particle swarm optimization algorithm uses the controller time constant in the frequency response model of the electrode thermal steam boiler. and water level regulation time constant The mean square error between the power response curve of the electrode thermal steam boiler calculated by the model and the actual measured power response curve is used as the objective function, serving as the parameter to be identified. First, multiple particles are randomly generated to form a parameter population, with each particle corresponding to a set of parameters to be identified. , Then, based on the frequency perturbation input, the model output power curve is calculated, and the objective function value is calculated; by iteratively updating the particle velocity and position, the parameter values ​​are continuously corrected until the objective function converges or reaches the preset number of iterations, ultimately obtaining the value that minimizes the mean square error. and Parameter value.

[0108] The core regulation logic of the electrode thermal steam boiler is from frequency deviation to water level regulation command to electrode immersion area change to power change. The modeling focuses on reflecting the dynamic characteristics of water level regulation, while unifying the form of the droop coefficient with that of thermal power units to ensure consistency with the coordinated frequency regulation logic.

[0109] The frequency response process of an electrode-heated steam boiler is divided into two core components: the frequency controller component and the water level regulation dynamic component.

[0110] The function of the frequency modulation controller is to adjust the frequency based on the system frequency deviation. It generates water level regulation commands (controlling the opening of the inlet valve), and its dynamic characteristics are determined by the controller time constant. The decision (reflecting the delay in instruction generation and execution) is determined by the transfer function:

[0111] ;

[0112] in, For the Laplace operator.

[0113] Dynamic aspects of water level regulation:

[0114] The power regulation of an electrode-heated steam boiler depends on the change in the electrode immersion area, which is determined by the water level inside the boiler—the higher the water level, the larger the electrode immersion area and the greater the heating power; the lower the water level, the smaller the electrode immersion area and the lower the heating power. Water level changes have inertia (delays in the water intake and evaporation processes), which is addressed by the water level regulation time constant. Characterized by the transfer function:

[0115] ;

[0116] in, For the Laplace operator.

[0117] The frequency response model of the electrode-heated steam boiler is as follows:

[0118] ;

[0119] This is the Laplace operator. To ensure coordination between the primary frequency regulation characteristics of the electrode thermal steam boiler and the thermal power unit, a droop coefficient for the electrode thermal steam boiler is introduced. ,make = , This is the droop coefficient for thermal power units.

[0120] A power regulation interlock command is sent to the electrode thermal steam boiler control system to lock the power regulation logic of the electrode thermal steam boiler, keeping the output of the electrode thermal steam boiler constant. After sending the power regulation interlock command to the electrode thermal steam boiler control system, the frequency response control loop of the electrode thermal steam boiler is locked, fixing the power setpoint of the electrode thermal steam boiler and keeping the inlet valve opening unchanged, so that the electrode thermal steam boiler no longer responds to changes in system frequency. The power of the electrode thermal steam boiler is monitored in real time. When its fluctuation range is less than ±0.5% of the rated power, it is determined that the electrode thermal steam boiler is in a constant power operation state, thereby eliminating the interference of electrode thermal steam boiler regulation on the parameter identification of thermal power unit.

[0121] The primary frequency response acquisition program of the thermal power unit control system is initiated to record in real time the unit's speed / frequency response signals, output regulation commands, terminal voltage and current changes, and main steam pressure changes under small disturbance events. The thermal power unit employs a governor-turbine-boiler combined model, using an identification algorithm consistent with the electrode-heated steam boiler model. The governor gain G and governor time constant in the thermal power unit's frequency response model are also considered. Reheater time constant Boiler gain and boiler time constant The parameters to be identified are G, G, and G. The particle swarm optimization algorithm uses the mean square error between the actual power response curve and the model output power response curve as the objective function. It optimizes the parameters G through steps such as initializing the particle swarm, calculating fitness values, updating particle velocities and positions, and iterative optimization. , , and Joint optimization was performed to obtain model parameters that most accurately characterize the dynamic characteristics of thermal power units under current operating conditions.

[0122] The frequency response process of thermal power units is divided into three core stages: the governor stage, the reheater stage, and the coal quality influence stage.

[0123] The governor's function is to adjust the opening of the turbine inlet valves according to the system frequency deviation, thereby changing the unit's output power. Its dynamic characteristics are determined by the governor's time constant. The transfer function is determined to be:

[0124] ;

[0125] For the Laplace operator.

[0126] In a reheat unit, steam, after performing work in the high-pressure cylinder, needs to enter the reheater for further heating before entering the intermediate and low-pressure cylinders to continue performing work. The reheating process involves a thermal delay, determined by the reheater's time constant. and the power share of high-pressure cylinder (Generally taken as 0.3) to characterize, the transfer function is:

[0127] ;

[0128] For the Laplace operator.

[0129] The function of a boiler is to convert the chemical energy of fuel (blended with different types of coal) into the thermal energy of steam, which in turn drives a steam turbine to do work. Its dynamic characteristics are directly affected by the quality of the blended coal. The better the quality of the coal, the faster the combustion speed, the higher the heat exchange efficiency, the faster the boiler response, and the greater the power gain. Conversely, the worse the quality of the coal, the greater the boiler inertia and the smaller the power gain.

[0130] Introducing coal quality influence coefficient (Comprehensive characterization of the calorific value, volatile matter, ash content, and blending ratio of blended coals), the calorific value, volatile matter, ash content, and blending ratio are derived from the unit's coal input analysis data. The boiler power gain is then considered. and boiler time constant All are defined as The function, i.e. = ), = When coal quality deteriorates, combustion lag increases. Increase), heat conversion efficiency decreases ( When the coal quality improves, the combustion lag decreases, resulting in slower frequency response and reduced regulation capacity of the unit. Reduced), improved heat conversion efficiency ( (Increase), the unit's frequency regulation response becomes faster and its regulation capability is enhanced. Therefore, the boiler transfer function is:

[0131] ;

[0132] For the Laplace operator.

[0133] ;

[0134] The frequency response model of thermal power units is as follows:

[0135] ;

[0136] For the Laplace operator, The change in frequency This is the droop coefficient for thermal power units (generally 0.05).

[0137] Based on the regulation characteristics of thermal power units and electrode thermal steam boilers, a frequency response model of the coupled system is established, such as... Figure 4 As shown, the transfer function of the frequency response model of the coupled system is:

[0138] ;

[0139] Step S3: Divide the effective raw dataset into load segments according to the load level of the thermal power unit, and perform the parameter identification process based on the data corresponding to each load segment to obtain the identification parameter set corresponding to each load segment; construct a load segment model parameter library based on the identification parameter set.

[0140] It should be noted that during the load segment identification process, the effective original dataset is classified according to the unit load interval to form the identification data subset corresponding to each load segment, which is used to build the load segment model parameter library.

[0141] In this optional embodiment, the effective raw dataset is divided into load segments according to the load level of the thermal power unit, and a parameter identification process is performed based on the data corresponding to each load segment to obtain the identification parameter set corresponding to each load segment; the construction of a load segment model parameter library based on the identification parameter set includes the following steps:

[0142] Step S31: Divide the load into multiple load zones according to the proportion of the thermal power unit load to the rated load;

[0143] Specifically, under different load conditions, the boiler heat storage, turbine efficiency, and governor characteristics of thermal power units will all change, resulting in significant differences in frequency regulation performance. Therefore, it is necessary to conduct segmented identification according to load segments, construct a parameter library for each load segment model, and divide the unit's operating load into four independent identification intervals based on the rated output and minimum technical output standards of the thermal power unit, combined with actual engineering operating experience: 50%~100% rated load interval (high load segment, stable unit operation, optimal frequency regulation performance), 40%~50% rated load interval (medium-high load segment, smooth transition of unit characteristics), 30%~40% rated load interval (medium-low load segment, decreased boiler heat storage capacity), and minimum technical output to 30% rated load interval (low load segment, weaker unit frequency regulation performance). The boundary values ​​of each load interval are retained to two decimal places to ensure the accuracy of load segment division and avoid abrupt parameter changes near the load boundaries.

[0144] Step S32: Perform the parameter identification process for each load interval to obtain the corresponding identification parameter set;

[0145] Specifically, for each defined load range, the power output of the thermal power unit is adjusted through the thermal power unit control system to ensure stable operation of the unit within that load range (load fluctuations controlled within ±1% of the rated load). The disturbance condition screening process in step S1 is repeated to obtain valid data on small disturbances within that load range. Then, the thermal power unit is locked down according to the process in step S21 to identify all core model parameters of the electrode thermal steam boiler under that load range. The unit is kept running stably in that load range, and the electrode thermal steam boiler is locked down according to the process in step S22 to identify all core model parameters of the thermal power unit under that load range. After completion, the load range of that load range, all identified parameter values ​​of the electrode thermal steam boiler and the thermal power unit, and their corresponding units are recorded in detail to ensure data traceability.

[0146] Step S33: Link and store the load interval information, equipment type information, parameter name, parameter value and unit to build a load segment model parameter library.

[0147] Specifically, all model parameters of thermal power units and electrode steam boilers identified in the four load ranges are classified, organized, and archived in the format of load range number-equipment type-parameter name-parameter value-unit; a joint frequency regulation model parameter library is built using a relational database, and load range tables, equipment parameter tables, and parameter traceability tables are designed to enable rapid retrieval, calling, and online updating of parameters in different load ranges; this parameter library can be directly used for performance evaluation of the joint frequency regulation system, providing accurate model support for practical engineering applications.

[0148] Step S4: Perform joint frequency modulation simulation on the identified parameter set in the load segment model parameter library to obtain the simulation response curve, compare the simulation response curve with the actual response curve, calculate the root mean square error between the simulation response curve and the actual response curve, so as to verify and correct the identified parameters in the load segment model parameter library and obtain the corrected identified parameter set.

[0149] In this optional embodiment, joint frequency modulation simulation is performed on the identified parameter set in the load segment model parameter library to obtain the simulation response curve. The simulation response curve is then compared with the actual response curve, and the root mean square error between the simulation response curve and the actual response curve is calculated to verify and correct the identified parameters in the load segment model parameter library, thereby obtaining the corrected identified parameter set. This includes the following steps:

[0150] Step S41: Call the identification parameter set corresponding to the current load segment from the load segment model parameter library, substitute the identification parameter set into the joint frequency modulation model, perform frequency response simulation, and obtain the simulation response curve;

[0151] Step S42: Obtain the actual response curve corresponding to the current load segment, compare the actual response curve with the simulated response curve, and calculate the root mean square error of the two.

[0152] Step S43: Compare the root mean square error with the preset error threshold. When the root mean square error is less than the preset error threshold, determine that the identification parameter set is valid and save it to the load segment model parameter library.

[0153] Step S44: When the root mean square error exceeds the preset error threshold, the parameter identification is re-executed based on the root mean square error to obtain the corrected identification parameter set, and the parameter library of the load segment model is updated using the corrected identification parameter set.

[0154] It should be noted that during the parameter identification verification and correction process, typical small disturbance event data are selected from the valid original dataset as benchmark data for comparing and verifying the simulation results with the actual response curves, thereby verifying the validity of the identified parameters. Typical small disturbance events (frequency fluctuation amplitude between 0.02 and 0.05 Hz, which are representative) within each load segment are selected, and the identified parameters of that load segment are substituted into the joint frequency regulation simulation model of the thermal power unit-electrode thermal steam boiler to simulate the frequency response process of the unit and the electrode thermal steam boiler, thus obtaining the simulation response curve. The simulation response curve is compared with the actual response curve measured by the PMU, and the root mean square error (RMSE) of the two is calculated. When the RMSE is less than the preset engineering allowable threshold (5%), the identified parameters of that load segment are deemed valid. When the error value exceeds the threshold, it indicates that there is a deviation in the identified parameters, and the process returns to step S2 to re-execute the parameter identification process until suitable model parameters are obtained.

[0155] Step S5: When the root mean square error meets the preset requirements, call the joint frequency modulation model of the corresponding load segment and the corrected identification parameter set according to the current operating load, input the frequency step signal to perform frequency modulation response simulation, and obtain the joint output response curve.

[0156] In this optional embodiment, when the root mean square error meets the preset requirements, the joint frequency modulation model of the corresponding load segment and the corrected identification parameter set are called according to the current operating load. The frequency step signal is input to perform frequency modulation response simulation, and the joint output response curve is obtained by the following steps:

[0157] Step S51: Obtain the current operating load, and match the joint frequency regulation model and identification parameter set of the corresponding load segment from the load segment model parameter library according to the current operating load;

[0158] Step S52: Input a frequency step signal into the joint frequency modulation model and configure the model parameters of the joint frequency modulation model using the identification parameter set;

[0159] Step S53: Perform joint frequency modulation simulation based on the frequency step signal and the identified parameter set to obtain the output response curve of the thermal power unit and the output response curve of the electrode hot steam boiler.

[0160] Step S54: Calculate the combined output power change based on the output response curve of the thermal power unit and the output response curve of the electrode thermal steam boiler, and obtain the combined output response curve.

[0161] Specifically, based on actual power grid frequency regulation scenarios, a standardized frequency step signal is set to simulate sudden frequency disturbances in the power grid, ensuring the uniformity and comparability of the tests. The settings are as follows:

[0162] 1) Step signal amplitude: Select the frequency fluctuation range of common small disturbances in the power grid, and set the step amplitude to ±0.03Hz and ±0.05Hz (corresponding to the small disturbance threshold boundary and typical small disturbance amplitude mentioned above), covering the main frequency fluctuation scenarios under small disturbance conditions;

[0163] 2) Step signal duration: Set the step signal duration to 60~120s to ensure that the model has sufficient response time to reach steady state, while avoiding invalid data redundancy caused by excessive test duration;

[0164] 3) Model test execution: For the joint frequency regulation model of each load segment, input the frequency step signals of the above two amplitudes respectively, keep the model parameters consistent with the parameter library of the load segment, collect the dynamic response curves of the thermal power unit output, electrode hot steam boiler output and joint total output of the model output in real time, record the key index data (such as response time, regulation accuracy, etc.) during the response process, and complete the model step test.

[0165] The testing process can be started at any time without the need to re-identify parameters. It only requires calling the model and parameters of the corresponding load segment to achieve rapid evaluation of the system's frequency regulation performance.

[0166] Step S6: Extract the response time, steady-state adjustment error, and steady-state fluctuation amplitude from the joint output response curve, and calculate the frequency modulation performance score based on the response time, steady-state adjustment error, and steady-state fluctuation amplitude according to the preset evaluation rules, and output the frequency modulation performance evaluation result.

[0167] In this optional embodiment, the response time, steady-state adjustment error, and steady-state fluctuation amplitude are extracted from the joint output response curve, and the frequency modulation performance score is calculated based on the response time, steady-state adjustment error, and steady-state fluctuation amplitude according to preset evaluation rules. The output frequency modulation performance evaluation result includes the following steps:

[0168] Step S61: Extract characteristic parameters from the joint output response curve to obtain the response time, steady-state adjustment error, and steady-state fluctuation amplitude;

[0169] Step S62: Calculate the response speed score based on the response time;

[0170] Step S63: Calculate the adjustment accuracy score based on the steady-state adjustment error;

[0171] Step S64: Calculate the stability score based on the steady-state fluctuation amplitude;

[0172] Step S65: Calculate the response speed score, adjustment accuracy score, and stability score by weighting them according to the preset evaluation rules to obtain the frequency modulation performance score.

[0173] Step S66: Output the corresponding frequency modulation performance evaluation result based on the frequency modulation performance score.

[0174] Specifically, a 100-point quantitative evaluation system is designed. Focusing on the core performance indicators of the model's step response, evaluation dimensions are defined and scoring standards are set to achieve an objective and accurate assessment of the simulation output results. The evaluation system covers four core evaluation dimensions, with the following weights and scoring standards: a higher total score indicates better frequency modulation performance of the simulated system.

[0175] 1) Response Speed ​​Evaluation (Weight 30 points): The core evaluation metric is the timeliness of the model's output power response to a frequency step signal. Response time is defined as the time from the input of the frequency step signal to the point where the model's output power begins to show an effective change (change ≥ 0.1 MW). Specific scoring criteria are as follows:

[0176] ① Response time ≤ 1s: 25~30 minutes; ② 1s < response time ≤ 2s: 18~24 minutes; ③ 2s < response time ≤ 3s: 10~17 minutes; ④ Response time > 3s: 0~9 minutes; If the response time > 5s, this item is scored as 0 (considered as slow response, unable to meet frequency modulation requirements).

[0177] 2) Evaluation of regulation accuracy (weight 30 points): The core evaluation is the degree of deviation between the model output power and the theoretical optimal regulation power. The steady-state regulation error is the core indicator. The steady-state regulation error is defined as the percentage of the absolute value of the difference between the actual output power and the theoretical optimal regulation power after the model response reaches steady state. The actual output power is collected in real time through the PMU measurement device.

[0178] The theoretical optimal regulating power refers to the steady-state target power change calculated based on the joint frequency regulation model of thermal power units and electrode hot steam boilers under a given frequency deviation condition.

[0179] According to the principle of primary frequency modulation and droop adjustment, when the system frequency deviation is... At that time, the theoretical optimal regulation power calculation formula for the combined system is:

[0180] ;

[0181] in, For system frequency deviation, This is the droop coefficient of the combined frequency regulation system of thermal power unit and electrode hot steam boiler.

[0182] Specific scoring criteria:

[0183] ① Steady-state adjustment error ≤ 2%: 25~30 points; ② 2% < steady-state adjustment error ≤ 5%: 18~24 points; ③ 5% < steady-state adjustment error ≤ 8%: 10~17 points; ④ Steady-state adjustment error > 8%: 0~9 points; If the steady-state adjustment error > 10%, this item will be scored as 0 points (considered as insufficient adjustment accuracy, unable to meet frequency modulation requirements).

[0184] 3) Steady-state stability evaluation (weight 40 points): This is the core evaluation of the model's operational stability after the response reaches steady state. The steady-state fluctuation amplitude is the core indicator, defined as the maximum fluctuation value of the model's output power (the difference between the maximum and minimum values) during the steady-state phase (the last 20 seconds). Specific scoring criteria are as follows:

[0185] ① Steady-state fluctuation amplitude ≤ 0.2MW: 16~20 points; ② 0.2MW < steady-state fluctuation amplitude ≤ 0.5MW: 10~15 points; ③ 0.5MW < steady-state fluctuation amplitude ≤ 1.0MW: 4~9 points; ④ Steady-state fluctuation amplitude > 1.0MW: 0~3 points; If there is continuous fluctuation (no obvious steady state), this item will be scored as 0 points.

[0186] For each load segment and each frequency step amplitude model test results, three core indicators are extracted: response time, steady-state adjustment error, and steady-state fluctuation amplitude. Based on the above scoring criteria, the scores for each dimension are calculated and the total score is obtained. The evaluation level is divided into four levels according to the total score, which are used to intuitively judge the system frequency regulation performance: excellent (90~100 points), good (80~89 points), qualified (60~79 points), and unqualified (<60 points).

[0187] Figure 2 An embodiment of the unit-coupled electrode hot steam boiler frequency regulation performance evaluation system of the present invention is shown.

[0188] In this optional embodiment, the unit-coupled electrode hot steam boiler frequency regulation performance evaluation system includes:

[0189] The data acquisition module 201 is used to acquire operational data, frequency data, and coal quality data, and generate a valid raw dataset;

[0190] Model building module 202 is used to establish frequency response models for thermal power units, frequency response models for electrode thermal steam boilers, and joint frequency regulation models.

[0191] The parameter identification module 203 is used to identify the parameters of the model based on the valid original dataset and obtain the identified parameter set.

[0192] The parameter library construction module 204 is used to construct a parameter library for the sub-load segment model based on the load segment division results.

[0193] Model verification module 205 is used to perform simulation verification using the load segment model parameter library and output model error results;

[0194] The performance evaluation module 206 is used to call the verified joint frequency modulation model to perform frequency modulation response simulation and output frequency modulation performance evaluation results based on the response results.

[0195] In this optional embodiment, the data acquisition module includes a PMU measurement submodule and a data aggregation submodule;

[0196] The PMU measurement submodule is used to collect generator terminal voltage and current data, high-voltage transformer low-voltage side voltage and current data, and electrode hot steam boiler side voltage and current data.

[0197] The data aggregation submodule is used to upload the collected data to the scheduling VAMS system and generate a valid raw dataset.

[0198] In this optional embodiment, the model building module includes a thermal power unit modeling submodule, an electrode thermal steam boiler modeling submodule, and a joint frequency regulation modeling submodule;

[0199] The thermal power unit modeling submodule is used to establish the frequency response model of the thermal power unit.

[0200] The electrode-heated steam boiler modeling submodule is used to establish the frequency response model of the electrode-heated steam boiler.

[0201] The joint frequency modulation modeling submodule is used to build a joint frequency modulation model.

[0202] In this optional embodiment, the parameter identification module includes a thermal power unit parameter identification submodule, an electrode thermal steam boiler parameter identification submodule, and a particle swarm optimization submodule.

[0203] The particle swarm optimization submodule is used to iteratively optimize the model parameters based on the error between the model output response curve and the actual response curve, so as to obtain the identification parameter set.

[0204] In this optional embodiment, the performance evaluation module is used to extract the response time, steady-state adjustment error and steady-state fluctuation amplitude based on the joint output response curve, calculate the frequency modulation performance score according to the preset evaluation rules, and output the frequency modulation performance evaluation result.

[0205] Specifically, the data acquisition module is deployed at the generator end, the low-voltage side of the high-voltage transformer, and the plant power bus via the PMU measurement submodule to measure voltage, current, and frequency changes in real time; and the measurement data is aggregated and sent to the scheduling VAMS system via the data aggregation submodule to generate a valid raw dataset.

[0206] When performing the identification, the parameter identification module communicates with the DEH control system of the thermal power unit and locks the unit output by sending a frequency modulation interlock command. Then, the particle swarm optimization submodule and the electrode thermal steam boiler parameter identification submodule complete the decoupled parameter identification.

[0207] After calculating the frequency regulation performance score and level, the performance evaluation module uploads the frequency regulation performance evaluation results to the dispatch VAMS system in real time, so as to provide accurate models and data basis for dispatch optimization and adjustment of the coordinated control strategy of the electrode thermal steam boiler control system.

[0208] like Figure 5 As shown, in this embodiment, the DEH control system of the thermal power unit, the electrode thermal steam boiler control system, the dispatching VAMS system, the PMU measuring device, the high-voltage substation, and the plant auxiliary power bus together constitute a mutually coordinated physical hardware support. The DEH control system receives speed or same-source frequency signals for primary frequency regulation control of the thermal power unit, and its output controls the generator output. The electrode thermal steam boiler control system receives the unit's primary frequency regulation parameters uploaded by the DEH system, realizing coordinated frequency regulation control between the electrode thermal steam boiler and the thermal power unit. The PMU measuring device collects the generator terminal voltage and current, the low-voltage side of the high-voltage substation (6kV plant auxiliary bus), and the electrode thermal steam boiler side voltage and current, respectively, to realize real-time measurement of the combined output of the unit and the electrode thermal steam boiler, and uploads the measurement data to the dispatching VAMS system. The electrode thermal steam boiler is connected to the 6kV plant auxiliary bus via the high-voltage substation, and its thermal power is adjusted by the electrode thermal steam boiler control system to cooperate with the thermal power unit to quickly respond to the grid frequency regulation requirements and improve the unit's frequency regulation performance.

[0209] At the level of physical power transmission and distribution, part of the active power output by the thermal power unit is used to meet the load demand of the power grid, and the other part is introduced into the plant auxiliary power bus after being stepped down by the high-voltage transformer, and supplied to the electrode thermal steam boiler in the form of plant auxiliary power. When the system frequency deviates, the thermal power unit adjusts the output power through the speed governor and the boiler system, and the electrode thermal steam boiler changes the power consumption by adjusting the water level. The two work together to suppress frequency fluctuations and achieve frequency regulation.

[0210] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 6As shown, the computer device includes a processor, memory, and a network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores static and dynamic information data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in the above method embodiments.

[0211] Those skilled in the art will understand that Figure 6 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0212] In addition, the present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above method embodiments.

[0213] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the methods described above. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0214] This invention is not limited to the structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this invention is limited only by the appended claims.

Claims

1. A method for evaluating the frequency regulation performance of a unit-coupled electrode hot steam boiler, characterized in that, Includes the following steps: The acquired operating data of thermal power units and electrode hot steam boilers are subjected to perturbation screening and preprocessing to generate valid raw datasets; Based on the effective original dataset, establish a frequency response model for thermal power units, a frequency response model for electrode thermal steam boilers, and a joint frequency regulation model, and generate a parameter identification process for the frequency response models for thermal power units and electrode thermal steam boilers. The effective raw dataset is divided into load segments according to the load level of the thermal power unit, and the parameter identification process is executed based on the data corresponding to each load segment to obtain the identification parameter set corresponding to each load segment. A parameter library for load segment models is constructed based on the identified parameter set; Joint frequency modulation simulation is performed on the identification parameter set in the load segment model parameter library to obtain the simulation response curve. The simulation response curve is then compared with the actual response curve, and the root mean square error between the simulation response curve and the actual response curve is calculated to verify and correct the identification parameters in the load segment model parameter library, thus obtaining the corrected identification parameter set. When the root mean square error meets the preset requirements, the joint frequency modulation model of the corresponding load segment and the corrected identification parameter set are called according to the current operating load, and the frequency step signal is input to perform frequency modulation response simulation to obtain the joint output response curve. The response time, steady-state adjustment error, and steady-state fluctuation amplitude are extracted from the joint output response curve. The frequency modulation performance score is calculated based on the response time, steady-state adjustment error, and steady-state fluctuation amplitude according to the preset evaluation rules, and the frequency modulation performance evaluation result is output.

2. The method for evaluating the frequency regulation performance of a unit-coupled electrode hot steam boiler according to claim 1, characterized in that, The operating data of the thermal power unit and the electrode hot steam boiler include: generator terminal frequency, power grid bus frequency, thermal power unit output, electrode hot steam boiler power, frequency fluctuation value, generator terminal voltage and current, plant bus voltage and current, and coal quality data, which are collected in real time by the PMU measuring device.

3. The method for evaluating the frequency regulation performance of a unit-coupled electrode hot steam boiler according to claim 1, characterized in that, The process of perturbation filtering and preprocessing the acquired operating data of thermal power units and electrode hot steam boilers to generate a valid raw dataset includes the following steps: Acquire real-time frequency data from the PMU measurement device; Calculate the absolute value of the system frequency deviation, and identify data with an absolute value of frequency deviation greater than a preset disturbance threshold as large disturbance event data and remove them; Data with an absolute frequency deviation less than or equal to the perturbation threshold are retained as small perturbation event data; outlier removal, time axis alignment, and data format unification are performed on the small perturbation event data to generate the effective original dataset.

4. The method for evaluating the frequency regulation performance of a unit-coupled electrode hot steam boiler according to claim 1, characterized in that, When establishing the joint frequency modulation model, the joint power formula is as follows: ； in, This represents the combined power change when the response frequency is adjusted. This represents the change in power output of thermal power units. The power change represents the change in power of the electrode-heated steam boiler; the combined power change is used as the output of the combined frequency regulation model for subsequent parameter identification and frequency regulation performance evaluation.

5. The method for evaluating the frequency regulation performance of a unit-coupled electrode hot steam boiler according to claim 1, characterized in that, The process of establishing a frequency response model for thermal power units, a frequency response model for electrode thermal steam boilers, and a joint frequency regulation model based on the effective original dataset, and generating parameter identification for the frequency response models of thermal power units and electrode thermal steam boilers, includes the following steps: The primary frequency regulation function of the thermal power unit is locked to maintain the thermal power unit in a constant output operation state; using the effective original dataset, the parameters of the frequency response model of the electrode hot steam boiler are identified to obtain the controller time constant and the water level regulation time constant. The power frequency regulation function of the electrode hot steam boiler is locked to keep the electrode hot steam boiler in a constant power operation state; using the effective original dataset, the parameters of the frequency response model of the thermal power unit are identified to obtain the governor gain, governor time constant, reheater time constant, boiler gain, and boiler time constant.

6. The method for evaluating the frequency regulation performance of a unit-coupled electrode hot steam boiler according to claim 5, characterized in that, The parameter identification is achieved using a particle swarm optimization algorithm. The mean square error between the model output power response curve and the actual power response curve is used as the objective function. The minimum value of the objective function is searched iteratively through particle velocity update and particle position update. The parameter set corresponding to the minimum objective function value is determined as the identification parameter set.

7. The method for evaluating the frequency regulation performance of a unit-coupled electrode hot steam boiler according to claim 1, characterized in that, The effective raw dataset is divided into load segments according to the load level of the thermal power unit, and the parameter identification process is executed based on the data corresponding to each load segment to obtain the identification parameter set corresponding to each load segment. The steps involved in constructing a load segment model parameter library based on the identified parameter set are as follows: The load range is divided according to the proportion of the thermal power unit load to the rated load; For each load interval, a parameter identification process is executed to obtain the corresponding set of identification parameters; The load range information, equipment type information, parameter name, parameter value, and unit are associated and stored to build a load segment model parameter library.

8. The method for evaluating the frequency regulation performance of a unit-coupled electrode hot steam boiler according to claim 1, characterized in that, The steps involved in performing joint frequency modulation simulation on the identified parameter set in the load segment model parameter library to obtain the simulation response curve, comparing the simulation response curve with the actual response curve, calculating the root mean square error between the simulation response curve and the actual response curve, and verifying and correcting the identified parameters in the load segment model parameter library to obtain the corrected identified parameter set include the following steps: The identification parameter set corresponding to the current load segment is called from the load segment model parameter library, and the identification parameter set is substituted into the joint frequency modulation model to perform frequency response simulation and obtain the simulation response curve. Obtain the actual response curve corresponding to the current load segment, compare the actual response curve with the simulated response curve, and calculate the root mean square error of the two. The root mean square error is compared with a preset error threshold. When the root mean square error is less than the preset error threshold, the identification parameter set is determined to be valid and is retained in the load segment model parameter library. When the root mean square error exceeds a preset error threshold, the parameter identification is re-executed based on the root mean square error to obtain a corrected identification parameter set, and the load segment model parameter library is updated using the corrected identification parameter set.

9. The method for evaluating the frequency regulation performance of a unit-coupled electrode hot steam boiler according to claim 1, characterized in that, When the root mean square error meets the preset requirements, the joint frequency modulation model of the corresponding load segment and the corrected identification parameter set are called according to the current operating load. A frequency step signal is input to perform frequency modulation response simulation, and the joint output response curve is obtained by the following steps: Obtain the current operating load, and match the joint frequency regulation model and identification parameter set of the corresponding load segment from the load segment model parameter library based on the current operating load; A frequency step signal is input to the joint frequency modulation model, and the model parameters of the joint frequency modulation model are configured using the identification parameter set; Based on the frequency step signal and the identification parameter set, a joint frequency modulation simulation is performed to obtain the output response curve of the thermal power unit and the output response curve of the electrode hot steam boiler. The combined output power change is calculated based on the output response curve of the thermal power unit and the output response curve of the electrode thermal steam boiler, and the combined output response curve is obtained.

10. The method for evaluating the frequency regulation performance of a unit-coupled electrode hot steam boiler according to claim 1, characterized in that, The steps of extracting response time, steady-state adjustment error, and steady-state fluctuation amplitude from the joint output response curve, calculating frequency modulation performance scores for the response time, steady-state adjustment error, and steady-state fluctuation amplitude according to preset evaluation rules, and outputting frequency modulation performance evaluation results include the following steps: Feature parameters are extracted from the joint output response curve to obtain the response time, steady-state adjustment error, and steady-state fluctuation amplitude. Calculate the response speed score based on the response time; Calculate the adjustment accuracy score based on the steady-state adjustment error; Calculate the stability score based on the steady-state fluctuation amplitude; The frequency modulation performance score is obtained by weighting the response speed score, adjustment accuracy score and stability score according to the preset evaluation rules. The corresponding frequency modulation performance evaluation result is output based on the frequency modulation performance score.

11. A frequency regulation performance evaluation system for a unit-coupled electrode hot steam boiler, characterized in that, include: The data acquisition module is used to acquire operational data, frequency data, and coal quality data, and generate a valid raw dataset. The model building module is used to establish frequency response models for thermal power units, frequency response models for electrode thermal steam boilers, and joint frequency regulation models. The parameter identification module is used to identify the parameters of the model based on the effective original dataset to obtain the identified parameter set; The parameter library construction module is used to construct a parameter library for the load segment model based on the load segment division results. The model verification module is used to perform simulation verification using the load segment model parameter library and output model error results; The performance evaluation module is used to call the verified joint frequency modulation model to perform frequency modulation response simulation and output the frequency modulation performance evaluation results based on the response results.

12. The frequency regulation performance evaluation system for unit-coupled electrode hot steam boilers according to claim 11, characterized in that, The data acquisition module includes a PMU measurement submodule and a data aggregation submodule; The PMU measurement submodule is used to collect generator terminal voltage and current data, high-voltage transformer low-voltage and current data, and electrode thermal steam boiler side voltage and current data; the data aggregation submodule is used to upload the collected data to the scheduling VAMS system and generate the effective raw dataset.

13. The frequency regulation performance evaluation system for unit-coupled electrode hot steam boilers according to claim 11, characterized in that, The model building module includes a thermal power unit modeling submodule, an electrode thermal steam boiler modeling submodule, and a joint frequency regulation modeling submodule; The thermal power unit modeling submodule is used to establish a frequency response model for thermal power units. The electrode hot steam boiler modeling submodule is used to establish the frequency response model of the electrode hot steam boiler. The joint frequency modulation modeling submodule is used to establish a joint frequency modulation model.

14. The frequency regulation performance evaluation system for unit-coupled electrode hot steam boilers according to claim 11, characterized in that, The parameter identification module includes a thermal power unit parameter identification submodule, an electrode thermal steam boiler parameter identification submodule, and a particle swarm optimization submodule. The particle swarm optimization submodule is used to iteratively optimize the model parameters based on the error between the model output response curve and the actual response curve to obtain the identification parameter set.

15. The frequency regulation performance evaluation system for unit-coupled electrode hot steam boilers according to claim 11, characterized in that, The performance evaluation module is used to extract response time, steady-state adjustment error and steady-state fluctuation amplitude from the joint output response curve, calculate the frequency modulation performance score according to the preset evaluation rules, and output the frequency modulation performance evaluation result.

16. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 10.

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