Intelligent primary frequency control controller and control method for gas turbine

Abstract

This invention discloses an intelligent primary frequency regulation controller and control method for gas turbines, solving the problem that existing methods cannot account for fluctuations in grid frequency, thus preventing gas turbine models from immediately adjusting their load. The method includes establishing a gas turbine simulation model using basic parameters of the gas turbine and grid infrastructure; simulating the gas turbine simulation model and outputting frequency regulation fluctuation coefficients; discretizing and clustering these coefficients into N discrete frequency regulation scenarios; calculating the frequency regulation probability values ​​corresponding to the discrete frequency regulation scenarios based on a fuzzy clustering algorithm; and constructing and training a sub-Blu-ray bar frequency regulation model based on the frequency regulation fluctuation coefficients and frequency regulation probability values. This invention incorporates basic parameters of the gas turbine and grid infrastructure when constructing the gas turbine simulation model, thus considering grid frequency fluctuations during the execution of the gas turbine simulation model and ensuring the primary frequency regulation efficiency and success rate of the sub-Blu-ray bar frequency regulation model when the grid frequency fluctuates.

Description

Technical Field

[0001] This invention belongs to the field of gas turbine control technology, specifically relating to an intelligent primary frequency controller and control method for gas turbines. Background Technology

[0002] Gas turbines are widely used in ground-based power generation, grid peak shaving, and ship propulsion. The working cycle of a gas turbine includes three basic processes: air compression, combustion, and expansion. First, air is drawn in and compressed by the compressor, then enters the combustion chamber to mix with fuel and burn, producing high-temperature, high-pressure gas. Finally, this gas expands in the turbine, doing work and driving the turbine to rotate, thereby driving the generator to produce electricity. The gas turbine controller is the core component of the gas turbine control system, responsible for monitoring and adjusting the operating status of the gas turbine.

[0003] Chinese patent CN118068715A discloses a method and apparatus for constructing a gas turbine speed controller model. This method considers the cooling extraction volume during the modeling process, resulting in a gas turbine model that better matches the actual operating conditions of the unit and has higher reliability. The initial speed controller model constructed based on the LADRC structure has short settling time, low overshoot, and better disturbance rejection. The speed controller model is obtained by using the gas turbine model and a global search algorithm to tune the parameters of the initial speed controller model. However, existing methods cannot take into account the fluctuation of the grid frequency when constructing the gas turbine model, which makes it impossible for the gas turbine model to immediately adjust the gas turbine load and ensure the stability of the grid frequency. To address the above problems, we propose an intelligent primary frequency control controller and control method for gas turbines. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing an intelligent primary frequency control controller and control method for gas turbines. This solves the problem that existing methods cannot take into account fluctuations in the power grid frequency when constructing gas turbine models, thus preventing the gas turbine model from immediately adjusting the gas turbine load and ensuring the stability of the power grid frequency.

[0005] Existing methods for constructing gas turbine models cannot account for fluctuations in grid frequency, thus preventing the gas turbine model from immediately adjusting the gas turbine load to ensure grid frequency stability. To address this issue, we propose an intelligent primary frequency regulation controller and control method for gas turbines. In short, the control method first establishes a gas turbine simulation model using basic parameters of the gas turbine and grid infrastructure. Then, it simulates and executes the gas turbine simulation model, which outputs at least one set of frequency regulation fluctuation coefficients. Based on a fuzzy clustering algorithm, it calculates the frequency regulation probability value corresponding to discrete frequency regulation scenarios. Then, based on the frequency regulation fluctuation coefficients and frequency regulation probability values, it constructs and trains a sub-Bluerband frequency regulation model. Finally, the sub-Bluerband frequency regulation model uses a load suppression algorithm to allocate the primary frequency regulation output of the gas turbine. In this embodiment of the invention, when constructing the gas turbine simulation model, the basic parameters of the gas turbine and power grid facilities are combined. This allows the fluctuation of the power grid frequency to be considered when the gas turbine simulation model is executed, ensuring the frequency regulation efficiency and success rate of the split-bulb frequency regulation model when the power grid frequency fluctuates. This overcomes the problem that existing methods cannot take into account the fluctuation of the power grid frequency when constructing the gas turbine model, which makes it impossible for the gas turbine model to adjust the gas turbine load immediately and ensure the stability of the power grid frequency.

[0006] This invention is implemented as follows: a control method for an intelligent primary frequency controller of a gas turbine, the control method comprising:

[0007] By iterating through the basic parameters of the gas turbine and power grid facilities, a simulation model of the gas turbine is established using the basic parameters of the gas turbine and power grid facilities.

[0008] The gas turbine operating parameters and power grid fluctuation frequency are acquired in real time. Using the gas turbine operating parameters and power grid fluctuation frequency as input, the gas turbine simulation model is executed. The gas turbine simulation model outputs at least one set of frequency regulation fluctuation coefficients.

[0009] Load at least one set of frequency regulation fluctuation coefficients, obtain the day-ahead dispatch plan of the power grid, discretize and cluster the frequency regulation fluctuation coefficients into N discrete frequency regulation scenarios, and calculate the frequency regulation probability value corresponding to the discrete frequency regulation scenario based on the fuzzy clustering algorithm;

[0010] Obtain the frequency modulation fluctuation coefficient and the corresponding frequency modulation probability value. Based on the frequency modulation fluctuation coefficient and the frequency modulation probability value, construct and train the sub-Bluer bar frequency modulation model, and output the trained sub-Bluer bar frequency modulation model.

[0011] The sub-Bluerg bar frequency regulation model performs correlation analysis on the gas turbine operating parameters and grid fluctuation frequency. The sub-Bluerg bar frequency regulation model uses a load suppression algorithm to allocate the primary frequency regulation output of the gas turbine, and solves the gas turbine start-up and shutdown parameters and unit output parameters based on the primary frequency regulation output allocation value.

[0012] Preferably, the method for establishing a gas turbine simulation model using basic parameters of the gas turbine and power grid facilities specifically includes:

[0013] Obtain basic parameters of the gas turbine and power grid facilities, and use 3D visualization tools to construct the initial model of the controller;

[0014] The frequency regulation-related facilities are traversed through the basic parameters of gas turbines and power grid facilities. Based on the previous frequency regulation parameters of the frequency regulation-related facilities, the response weights of the frequency regulation-related facilities are simulated and divided.

[0015] Obtain the response weights of the frequency modulation associated facilities, retrieve the frequency modulation output data of the frequency modulation associated facilities, and determine the frequency modulation fluctuation coefficient of each group of frequency modulation associated facilities;

[0016] The initial frequency modulation coefficient of the frequency modulation associated facilities is quantized, a frequency modulation coefficient merging threshold is set, and it is determined whether the initial frequency modulation coefficient is greater than the frequency modulation coefficient merging threshold.

[0017] If the initial frequency modulation coefficient is greater than the frequency modulation coefficient merging threshold, the current frequency modulation associated facilities are retained. If the initial frequency modulation coefficient is less than the frequency modulation coefficient merging threshold, the four associated frequency modulation associated facilities are merged, and the initial frequency modulation coefficients of the four associated frequency modulation associated facilities are summed to obtain the corrected frequency modulation coefficient.

[0018] By integrating the initial frequency regulation coefficient and the corrected frequency regulation coefficient, the frequency regulation fluctuation coefficient is obtained. The frequency regulation fluctuation coefficient is then visualized in the controller initial model to obtain the gas turbine simulation model.

[0019] Preferably, the method for simulating the execution of a gas turbine simulation model and outputting at least one set of frequency regulation fluctuation coefficients specifically includes:

[0020] Obtain gas turbine operating parameters and power grid fluctuation frequency;

[0021] The frequency regulation output data of frequency regulation associated facilities corresponding to the power grid fluctuation frequency are calculated based on the speed control algorithm.

[0022]

[0023] Among them, C i This represents the frequency modulation output data of frequency modulation associated facilities, x i ω represents the operating parameters of the gas turbine. c ω represents the frequency of power grid fluctuations. max ω min These represent the maximum and minimum frequencies of the power grid per unit time, respectively.

[0024] Obtain the response weights of the frequency modulation associated facilities, combine them with the frequency modulation output data of the frequency modulation associated facilities, and perform power spectrum analysis on the response weights and frequency modulation output data of the frequency modulation associated facilities based on the Wiener-Khinchin theorem to determine the frequency modulation fluctuation coefficient of each group of frequency modulation associated facilities.

[0025]

[0026] Among them, f c σ represents the frequency modulation fluctuation coefficient of frequency modulation associated facilities. c This represents the autocorrelation function in power spectrum analysis, where Δt is the frequency modulation unit time, and q... i Indicates the response weight of frequency modulation associated facilities, t c is the frequency regulation constraint coefficient, and L is the number of frequency regulation associated facilities.

[0027] Preferably, the method for calculating the frequency modulation probability value corresponding to a discrete frequency modulation scenario based on a fuzzy clustering algorithm specifically includes:

[0028] Obtain the day-ahead dispatch plan of the power grid, decompose the day-ahead dispatch plan of the power grid, and obtain the power generation component per unit time;

[0029] Obtain at least one set of frequency modulation fluctuation coefficients, and calculate the scene membership degree of N discrete frequency modulation scenes corresponding to the frequency modulation fluctuation coefficients based on the fuzzy clustering algorithm;

[0030] The scene membership degree is calculated using the following formula:

[0031]

[0032] Where, d s The scene membership degree is represented by N, where N is the number of discrete frequency modulation scenes, and Q is the number of scenes. c f is the amount of electricity generated per unit time. c Indicates the frequency modulation fluctuation coefficient;

[0033] Load the scene membership degree and frequency modulation fluctuation coefficient, introduce scene constraint quantities, and calculate the frequency modulation probability value corresponding to the discrete frequency modulation scene;

[0034]

[0035] Where, d g λ represents the frequency modulation probability value corresponding to the discrete frequency modulation scenario, and λ represents the scenario constraint constant.

[0036] Preferably, the method for constructing and training a split-bulb frequency modulation model based on the frequency modulation fluctuation coefficient and the frequency modulation probability value specifically includes:

[0037] An initial model for the split-bulb frequency modulation model is pre-constructed by combining the improved chicken flock algorithm and the related vector machine model;

[0038] A multi-objective genetic algorithm is used to define the kernel function of the initial model, and the initial model of the active disturbance rejection control strategy based on the multi-objective genetic algorithm and the initial frequency modulation strategy are obtained.

[0039] The frequency modulation fluctuation coefficient and frequency modulation probability value are randomly shuffled, and the frequency modulation fluctuation coefficient and frequency modulation probability value are used as the training set and the test set, respectively. The initial model is iteratively trained using the training set.

[0040] By using active correlation decision theory to remove uncorrelated points in the frequency modulation fluctuation coefficient and frequency modulation probability value, a sparse model is obtained.

[0041] The accuracy of the initial model is tested using a test set. If it meets the preset accuracy, the trained split-Blule bar frequency modulation model is output. If it does not meet the preset accuracy, the parameters of the initial model are optimized by maximizing the marginal likelihood function.

[0042] On the other hand, the present invention also provides an intelligent primary frequency control controller for gas turbines, wherein the intelligent primary frequency control controller for gas turbines specifically includes:

[0043] The simulation model building module iterates through the basic parameters of the gas turbine and power grid facilities, and uses the basic parameters of the gas turbine and power grid facilities to build a simulation model of the gas turbine.

[0044] The fluctuation coefficient calculation module is used to acquire the gas turbine operating parameters and the power grid fluctuation frequency in real time. Using the gas turbine operating parameters and the power grid fluctuation frequency as input, the gas turbine simulation model is simulated and executed. The gas turbine simulation model outputs at least one set of frequency regulation fluctuation coefficients.

[0045] The frequency regulation probability determination module loads at least one set of frequency regulation fluctuation coefficients, obtains the day-ahead dispatch plan of the power grid, discretizes and clusters the frequency regulation fluctuation coefficients into N discrete frequency regulation scenarios, and calculates the frequency regulation probability value corresponding to the discrete frequency regulation scenario based on the fuzzy clustering algorithm.

[0046] The frequency modulation model construction module is used to obtain the frequency modulation fluctuation coefficient and the corresponding frequency modulation probability value, construct and train the sub-Bluer bar frequency modulation model based on the frequency modulation fluctuation coefficient and the frequency modulation probability value, and output the trained sub-Bluer bar frequency modulation model.

[0047] The frequency regulation allocation module performs correlation analysis on the gas turbine operating parameters and grid fluctuation frequency based on the sub-Bluer rod frequency regulation model. The sub-Bluer rod frequency regulation model uses a load suppression algorithm to allocate the primary frequency regulation output of the gas turbine. Based on the primary frequency regulation output allocation value, the gas turbine start-up and shutdown parameters and unit output parameters are solved.

[0048] Preferably, the simulation model construction module specifically includes:

[0049] The initial model building unit is used to obtain basic parameters of the gas turbine and power grid facilities, and to build the initial model of the controller using 3D visualization tools;

[0050] The simulation partitioning unit is used to traverse the frequency regulation-related facilities in the basic parameters of gas turbine and power grid facilities, and combine the past frequency regulation parameters of the frequency regulation-related facilities to simulate and partition the response weights of the frequency regulation-related facilities;

[0051] The initial coefficient determination unit is used to obtain the response weight of the frequency modulation associated facilities, retrieve the frequency modulation output data of the frequency modulation associated facilities, and determine the frequency modulation fluctuation coefficient of each group of frequency modulation associated facilities.

[0052] Preferably, the simulation model construction module further includes:

[0053] The quantization judgment unit is used to quantize the initial frequency modulation coefficient of the frequency modulation associated facilities, set a frequency modulation coefficient merging threshold, and determine whether the initial frequency modulation coefficient is greater than the frequency modulation coefficient merging threshold. If the initial frequency modulation coefficient is greater than the frequency modulation coefficient merging threshold, the current frequency modulation associated facilities are retained. If the initial frequency modulation coefficient is less than the frequency modulation coefficient merging threshold, the four associated frequency modulation associated facilities are merged, and the initial frequency modulation coefficients of the four associated frequency modulation associated facilities are accumulated to obtain the corrected frequency modulation coefficient.

[0054] The coefficient integration unit is used to integrate the initial frequency regulation coefficient and the corrected frequency regulation coefficient to obtain the frequency regulation fluctuation coefficient. The frequency regulation fluctuation coefficient is then visualized and presented to the controller initial model to obtain the gas turbine simulation model.

[0055] Compared with the prior art, the embodiments of this application have the following main advantages:

[0056] In this embodiment of the invention, when constructing the gas turbine simulation model, the basic parameters of the gas turbine and power grid facilities are combined. This allows the fluctuation of the power grid frequency to be considered when the gas turbine simulation model is executed, ensuring the frequency regulation efficiency and success rate of the split-bulb frequency regulation model when the power grid frequency fluctuates. This overcomes the problem that existing methods cannot take into account the fluctuation of the power grid frequency when constructing the gas turbine model, which makes it impossible for the gas turbine model to adjust the gas turbine load immediately and ensure the stability of the power grid frequency.

[0057] In this embodiment of the invention, when establishing a gas turbine simulation model, the initial frequency regulation coefficients of the frequency regulation associated facilities are quantified, and a frequency regulation coefficient merging threshold is set, thereby merging frequency regulation associated facilities with small weights, simplifying the gas turbine simulation model, improving the simulation analysis efficiency of the gas turbine simulation model, and significantly reducing the load on the gas turbine simulation model, thus providing support for the rapid response of the distributed bar frequency regulation model to grid frequency fluctuations.

[0058] In this embodiment of the invention, the probability of the corresponding discrete frequency modulation scenario is measured by the frequency modulation probability value, thereby ensuring that the sub-Bluer rod frequency modulation model accurately identifies the frequency modulation fluctuation coefficient and the frequency modulation probability value, thus ensuring the successful allocation of the primary frequency modulation output of the gas turbine and also ensuring the success rate of the primary frequency modulation of the gas turbine.

[0059] In this embodiment of the invention, a sub-Bluerg bar frequency regulation model is provided. The sub-Bluerg bar frequency regulation model introduces an improved chicken flock algorithm, a correlation vector machine model, and a multi-objective genetic algorithm. This enables the generation of an initial primary frequency regulation strategy based on an active disturbance rejection control strategy by using the frequency regulation fluctuation coefficient and the frequency regulation probability value. By optimizing the control logic and parameter tuning, the intelligent primary frequency regulation controller can reduce the frequency regulation dead zone and improve the unit's response speed to the primary frequency regulation command. Attached Figure Description

[0060] Figure 1 This is a schematic diagram illustrating the implementation process of the intelligent primary frequency control controller for gas turbines provided by the present invention;

[0061] Figure 2 This is a schematic diagram illustrating the implementation process of the method for establishing a gas turbine simulation model using basic parameters of gas turbine and power grid facilities provided by the present invention.

[0062] Figure 3 This is a schematic diagram illustrating the implementation process of the gas turbine simulation model provided by the present invention, which outputs at least one set of frequency modulation fluctuation coefficients.

[0063] Figure 4 This is a schematic diagram illustrating the implementation process of the method for calculating the frequency modulation probability value corresponding to a discrete frequency modulation scenario based on the fuzzy clustering algorithm provided by the present invention.

[0064] Figure 5 This is a schematic diagram illustrating the implementation process of constructing and training a split-blob bar frequency modulation model based on the frequency modulation fluctuation coefficient and frequency modulation probability value provided by the present invention.

[0065] Figure 6 This is a schematic diagram of the structure of the intelligent primary frequency control controller for gas turbines provided by the present invention. Detailed Implementation

[0066] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used herein in the specification of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "comprising" and "having," and any variations thereof, in the specification, claims, and foregoing drawings of this application are intended to cover non-exclusive inclusion. The terms "first," "second," etc., in the specification, claims, or foregoing drawings of this application are used to distinguish different objects, not to describe a particular order.

[0067] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0068] Existing methods for constructing gas turbine models cannot account for fluctuations in grid frequency, thus preventing the gas turbine model from immediately adjusting the gas turbine load to ensure grid frequency stability. To address this issue, we propose an intelligent primary frequency regulation controller and control method for gas turbines. In short, the control method first establishes a gas turbine simulation model using basic parameters of the gas turbine and grid infrastructure. Then, it simulates and executes the gas turbine simulation model, which outputs at least one set of frequency regulation fluctuation coefficients. Based on a fuzzy clustering algorithm, it calculates the frequency regulation probability value corresponding to discrete frequency regulation scenarios. Then, based on the frequency regulation fluctuation coefficients and frequency regulation probability values, it constructs and trains a sub-Bluerband frequency regulation model. Finally, the sub-Bluerband frequency regulation model uses a load suppression algorithm to allocate the primary frequency regulation output of the gas turbine. In this embodiment of the invention, when constructing the gas turbine simulation model, the basic parameters of the gas turbine and power grid facilities are combined. This allows the fluctuation of the power grid frequency to be considered when the gas turbine simulation model is executed, ensuring the frequency regulation efficiency and success rate of the split-bulb frequency regulation model when the power grid frequency fluctuates. This overcomes the problem that existing methods cannot take into account the fluctuation of the power grid frequency when constructing the gas turbine model, which makes it impossible for the gas turbine model to adjust the gas turbine load immediately and ensure the stability of the power grid frequency.

[0069] This invention provides a control method for an intelligent primary frequency controller for gas turbines. Figure 1 The diagram illustrates the implementation flow of the intelligent primary frequency control controller for gas turbines. The intelligent primary frequency control controller for gas turbines specifically includes:

[0070] Step S10: Iterate through the basic parameters of the gas turbine and power grid facilities, and establish a gas turbine simulation model using the basic parameters of the gas turbine and power grid facilities;

[0071] It should be noted that the basic parameters of the gas turbine and power grid facilities include, but are not limited to, the coordinate parameters of the frequency regulation associated facilities in the gas turbine, the rated operating parameters of the power grid facilities, and the coordinate parameters of the power grid facilities.

[0072] Step S20: Real-time acquisition of gas turbine operating parameters and power grid fluctuation frequency; using gas turbine operating parameters and power grid fluctuation frequency as input, simulate execution of gas turbine simulation model; gas turbine simulation model outputs at least one set of frequency regulation fluctuation coefficients.

[0073] It should be noted that the operating parameters of the gas turbine and the frequency of power grid fluctuations include, but are not limited to, the power, thermal efficiency, exhaust temperature, speed and air flow of the gas turbine during operation.

[0074] Step S30: Load at least one set of frequency regulation fluctuation coefficients, obtain the day-ahead dispatch plan of the power grid, discretize and cluster the frequency regulation fluctuation coefficients into N discrete frequency regulation scenarios, and calculate the frequency regulation probability value corresponding to the discrete frequency regulation scenario based on the fuzzy clustering algorithm.

[0075] Step S40: Obtain the frequency modulation fluctuation coefficient and the corresponding frequency modulation probability value. Based on the frequency modulation fluctuation coefficient and the frequency modulation probability value, construct and train the sub-Bluer bar frequency modulation model, and output the trained sub-Bluer bar frequency modulation model.

[0076] Step S50: The sub-Bluer rod frequency regulation model performs correlation analysis on the gas turbine operating parameters and grid fluctuation frequency. The sub-Bluer rod frequency regulation model uses a load suppression algorithm to allocate the primary frequency regulation output of the gas turbine. Based on the primary frequency regulation output allocation value, the gas turbine start-up and shutdown parameters and unit output parameters are solved.

[0077] In this embodiment of the invention, when constructing the gas turbine simulation model, the basic parameters of the gas turbine and power grid facilities are combined. This allows the fluctuation of the power grid frequency to be considered when the gas turbine simulation model is executed, ensuring the frequency regulation efficiency and success rate of the split-bulb frequency regulation model when the power grid frequency fluctuates. This overcomes the problem that existing methods cannot take into account the fluctuation of the power grid frequency when constructing the gas turbine model, which makes it impossible for the gas turbine model to adjust the gas turbine load immediately and ensure the stability of the power grid frequency.

[0078] This invention provides a method for establishing a gas turbine simulation model using basic parameters of the gas turbine and power grid facilities. Figure 2This diagram illustrates the implementation flow of the method for establishing a gas turbine simulation model using basic parameters of the gas turbine and power grid facilities. The method specifically includes:

[0079] Step S101: Obtain basic parameters of the gas turbine and power grid facilities, and construct the initial model of the controller using a 3D visualization tool;

[0080] In this embodiment, the 3D visualization tools include, but are not limited to, Unity3D, UnrealEngines, Three.js, OpenGL, etc., while the initial model of the controller can be a 3D network graph, a 3D flow field graph, or a virtual reality augmented model.

[0081] Step S102: Traverse the frequency regulation-related facilities in the basic parameters of gas turbine and power grid facilities, and combine the previous frequency regulation parameters of the frequency regulation-related facilities to simulate and divide the response weights of the frequency regulation-related facilities.

[0082] It should be noted that, based on the previous frequency modulation parameters of frequency-related facilities, frequency modulation related facilities can be divided into first-level response facilities, second-level response facilities, third-level response facilities, fourth-level response facilities, and fifth-level response facilities. The weighting of the response of frequency modulation related facilities can be done by linear analysis or expert consultation.

[0083] Step S103: Obtain the response weight of the frequency modulation associated facilities, retrieve the frequency modulation output data of the frequency modulation associated facilities, and determine the frequency modulation fluctuation coefficient of each group of frequency modulation associated facilities.

[0084] Step S104: Quantize the initial frequency modulation coefficients of the frequency modulation associated facilities and set a frequency modulation coefficient merging threshold. In this embodiment, the frequency modulation coefficient merging threshold can be set to 0.3-0.35.

[0085] Step S105: Determine whether the initial frequency modulation coefficient is greater than the frequency modulation coefficient merging threshold;

[0086] Step S106: If the initial frequency modulation coefficient is greater than the frequency modulation coefficient merging threshold, retain the current frequency modulation associated facilities;

[0087] Step S107: If the initial frequency modulation coefficient is less than the frequency modulation coefficient merging threshold, merge the four associated frequency modulation associated facilities, and sum the initial frequency modulation coefficients of the four associated frequency modulation associated facilities to obtain the corrected frequency modulation coefficient.

[0088] Step S108: Integrate the initial frequency regulation coefficient and the corrected frequency regulation coefficient to obtain the frequency regulation fluctuation coefficient. Visualize the frequency regulation fluctuation coefficient to the controller initial model to obtain the gas turbine simulation model.

[0089] In this embodiment of the invention, when establishing a gas turbine simulation model, the initial frequency regulation coefficients of the frequency regulation associated facilities are quantified, and a frequency regulation coefficient merging threshold is set, thereby merging frequency regulation associated facilities with small weights, simplifying the gas turbine simulation model, improving the simulation analysis efficiency of the gas turbine simulation model, and significantly reducing the load on the gas turbine simulation model, thus providing support for the rapid response of the distributed bar frequency regulation model to grid frequency fluctuations.

[0090] This invention provides a method for simulating the execution of a gas turbine simulation model, wherein the gas turbine simulation model outputs at least one set of frequency modulation fluctuation coefficients. Figure 3 This diagram illustrates the implementation flow of the method for simulating the execution of a gas turbine simulation model and outputting at least one set of frequency regulation fluctuation coefficients. The method specifically includes:

[0091] Step S201: Obtain gas turbine operating parameters and power grid fluctuation frequency;

[0092] Step S202: Calculate the frequency regulation output data of frequency regulation associated facilities corresponding to the power grid fluctuation frequency based on the speed control algorithm;

[0093]

[0094] Among them, C i This represents the frequency modulation output data of frequency modulation associated facilities, x i ω represents the operating parameters of the gas turbine. c ω represents the frequency of power grid fluctuations. max ω min These represent the maximum and minimum frequencies of the power grid per unit time, respectively.

[0095] Step S203: Obtain the response weights of the frequency modulation associated facilities, and combine them with the frequency modulation output data of the frequency modulation associated facilities. Based on the Wiener-Khinchin theorem, perform power spectrum analysis on the response weights and output data of the frequency modulation associated facilities to determine the frequency modulation fluctuation coefficient of each group of frequency modulation associated facilities.

[0096]

[0097] Among them, f c σ represents the frequency modulation fluctuation coefficient of frequency modulation associated facilities. c This represents the autocorrelation function in power spectrum analysis, where Δt is the frequency modulation unit time, and q... i Indicates the response weight of frequency modulation associated facilities, t c L is the frequency modulation constraint coefficient, and L is the number of frequency modulation associated facilities. In this embodiment, the number of frequency modulation associated facilities is 1-50.

[0098] In this embodiment of the invention, the frequency regulation fluctuation coefficient of the frequency regulation associated facility in response to grid frequency fluctuations is an important indicator for measuring its regulation capability. The frequency regulation fluctuation coefficient is affected by various factors, including the design of the gas turbine and the performance of the control system, the frequency characteristics of the grid, and external environmental conditions. Therefore, when calculating the frequency regulation fluctuation coefficient, these factors need to be comprehensively considered to ensure that the frequency regulation fluctuation coefficient meets the requirements of the grid. This embodiment of the invention introduces a speed control algorithm and the Wiener-Khinchin theorem to ensure the accuracy of the frequency regulation fluctuation coefficient.

[0099] This invention provides a method for calculating the frequency modulation probability value corresponding to discrete frequency modulation scenarios based on a fuzzy clustering algorithm. Figure 4 The diagram illustrates the implementation flow of the method for calculating the frequency modulation probability value corresponding to a discrete frequency modulation scenario based on the fuzzy clustering algorithm. The method specifically includes:

[0100] Step S301: Obtain the day-ahead dispatch plan of the power grid, decompose the day-ahead dispatch plan of the power grid, and obtain the power generation component per unit time.

[0101] Step S302: Obtain at least one set of frequency modulation fluctuation coefficients, and calculate the scene membership degree of N discrete frequency modulation scenes corresponding to the frequency modulation fluctuation coefficients based on the fuzzy clustering algorithm;

[0102] The scene membership degree is calculated using the following formula:

[0103]

[0104] Where, d s The scene membership degree is represented by N, where N is the number of discrete frequency modulation scenes, and Q is the number of scenes. c f is the amount of electricity generated per unit time. c Indicates the frequency modulation fluctuation coefficient;

[0105] Step S303: Load the scene membership degree and frequency modulation fluctuation coefficient, introduce scene constraint quantity, and calculate the frequency modulation probability value corresponding to the discrete frequency modulation scene;

[0106]

[0107] Where, d g λ represents the frequency modulation probability value corresponding to the discrete frequency modulation scenario, and λ represents the scenario constraint constant. In this embodiment, the scenario constraint constant is set to an integer between 3 and 7.

[0108] In this embodiment of the invention, the probability of the corresponding discrete frequency modulation scenario is measured by the frequency modulation probability value, thereby ensuring that the sub-Bluer rod frequency modulation model accurately identifies the frequency modulation fluctuation coefficient and the frequency modulation probability value, thus ensuring the successful allocation of the primary frequency modulation output of the gas turbine and also ensuring the success rate of the primary frequency modulation of the gas turbine.

[0109] This invention provides a method for constructing and training a split-bulb frequency modulation model based on the frequency modulation fluctuation coefficient and the frequency modulation probability value. Figure 5 This diagram illustrates the implementation process of constructing and training a sub-Bluerband frequency modulation model based on the frequency modulation fluctuation coefficient and frequency modulation probability value. The method for constructing and training a sub-Bluerband frequency modulation model based on the frequency modulation fluctuation coefficient and frequency modulation probability value specifically includes:

[0110] Step S401: Combine the improved chicken flock algorithm and the related vector machine model to preconstruct the initial model of the split-blob bar frequency modulation model;

[0111] Step S402: Use a multi-objective genetic algorithm to define the kernel function of the initial model, and obtain the initial model of the active disturbance rejection control strategy based on the multi-objective genetic algorithm and the initial frequency modulation strategy.

[0112] Step S403: Randomly shuffle the frequency modulation fluctuation coefficient and frequency modulation probability value, and use the frequency modulation fluctuation coefficient and frequency modulation probability value as the training set and test set, and use the training set to iteratively train the initial model;

[0113] Step S404: Use active correlation decision theory to remove uncorrelated points in the frequency modulation fluctuation coefficient and frequency modulation probability value to obtain a sparse model;

[0114] Step S405: Use a test set to test the accuracy of the initial model;

[0115] Step S406: If the preset accuracy is met, output the trained split-Blule bar frequency modulation model.

[0116] If the preset accuracy is not met, the initial model parameters are optimized by maximizing the marginal likelihood function, and the process returns to step S403 to continue iterative training of the initial model.

[0117] In this embodiment of the invention, a sub-Bluerg bar frequency regulation model is provided. The sub-Bluerg bar frequency regulation model introduces an improved chicken flock algorithm, a correlation vector machine model, and a multi-objective genetic algorithm. This enables the generation of an initial primary frequency regulation strategy based on an active disturbance rejection control strategy by using the frequency regulation fluctuation coefficient and the frequency regulation probability value. By optimizing the control logic and parameter tuning, the intelligent primary frequency regulation controller can reduce the frequency regulation dead zone and improve the unit's response speed to the primary frequency regulation command.

[0118] This invention also provides an intelligent primary frequency control controller for gas turbines. Figure 6A schematic diagram of the structure of the intelligent primary frequency control controller for the gas turbine is shown. The intelligent primary frequency control controller for the gas turbine specifically includes:

[0119] The simulation model construction module 100 iterates through the basic parameters of the gas turbine and power grid facilities, and uses the basic parameters of the gas turbine and power grid facilities to build a simulation model of the gas turbine.

[0120] The fluctuation coefficient calculation module 200 is used to acquire the gas turbine operating parameters and the power grid fluctuation frequency in real time. Using the gas turbine operating parameters and the power grid fluctuation frequency as input, it simulates the execution of the gas turbine simulation model. The gas turbine simulation model outputs at least one set of frequency regulation fluctuation coefficients.

[0121] The frequency regulation probability determination module 300 loads at least one set of frequency regulation fluctuation coefficients, obtains the day-ahead dispatch plan of the power grid, discretizes and clusters the frequency regulation fluctuation coefficients into N discrete frequency regulation scenarios, and calculates the frequency regulation probability value corresponding to the discrete frequency regulation scenario based on the fuzzy clustering algorithm.

[0122] The frequency modulation model construction module 400 is used to obtain the frequency modulation fluctuation coefficient and the frequency modulation probability value corresponding to the frequency modulation fluctuation coefficient, construct and train the sub-Bluer bar frequency modulation model based on the frequency modulation fluctuation coefficient and the frequency modulation probability value, and output the trained sub-Bluer bar frequency modulation model.

[0123] The frequency regulation allocation module 500 performs correlation analysis on the gas turbine operating parameters and power grid fluctuation frequency based on the sub-Bluer rod frequency regulation model. The sub-Bluer rod frequency regulation model uses a load suppression algorithm to allocate the primary frequency regulation output of the gas turbine. Based on the primary frequency regulation output allocation value, the gas turbine start-up and shutdown parameters and unit output parameters are solved.

[0124] In this embodiment, the simulation model construction module 100 specifically includes:

[0125] Initial model building unit 110 is used to obtain basic parameters of gas turbine and power grid facilities, and to build the initial model of controller using a 3D visualization tool.

[0126] The simulation partitioning unit 120 is used to traverse the frequency regulation-related facilities in the basic parameters of gas turbine and power grid facilities, and simulate the partitioning of the response weights of frequency regulation-related facilities in combination with the previous frequency regulation parameters of the frequency regulation-related facilities.

[0127] The initial coefficient determination unit 130 is used to obtain the response weight of the frequency modulation associated facilities, retrieve the frequency modulation output data of the frequency modulation associated facilities, and determine the initial frequency modulation coefficient of each group of frequency modulation associated facilities.

[0128] The quantization judgment unit 140 is used to quantize the initial frequency modulation coefficient of the frequency modulation associated facilities, set a frequency modulation coefficient merging threshold, and determine whether the initial frequency modulation coefficient is greater than the frequency modulation coefficient merging threshold. If the initial frequency modulation coefficient is greater than the frequency modulation coefficient merging threshold, the current frequency modulation associated facilities are retained. If the initial frequency modulation coefficient is less than the frequency modulation coefficient merging threshold, the four associated frequency modulation associated facilities are merged, and the initial frequency modulation coefficients of the four associated frequency modulation associated facilities are accumulated to obtain the corrected frequency modulation coefficient.

[0129] The coefficient integration unit 150 is used to integrate the initial frequency regulation coefficient and the corrected frequency regulation coefficient to obtain the frequency regulation fluctuation coefficient. The frequency regulation fluctuation coefficient is then visualized and presented to the controller initial model to obtain the gas turbine simulation model.

[0130] On the other hand, embodiments of the present invention also provide a computer device, the computer device including a memory and a processor, the memory storing a computer program, which, when executed by the processor, implements the method of any of the above embodiments.

[0131] Furthermore, in another aspect, the present invention provides a computer-readable storage medium storing computer program instructions that can be executed by a processor. When executed, the computer program instructions implement the method of any of the above embodiments.

[0132] Memory, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules, such as the program instructions / modules corresponding to the gas turbine intelligent primary frequency controller control method in the embodiments of this application. Memory may include a program storage area and a data storage area, wherein the program storage area may store the operating system and application programs required for at least one function; the data storage area may store data created by using the gas turbine intelligent primary frequency controller control method, etc. Furthermore, memory may include high-speed random access memory and may also include non-volatile memory, such as at least one disk storage device, flash memory device, or other non-volatile solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the local module via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0133] Finally, it should be noted that the computer-readable storage medium (e.g., memory) described herein can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. By way of example, and not limitation, non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM), which can act as external cache memory. By way of example, and not limitation, RAM can be obtained in various forms, such as synchronous RAM (DRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), and direct Rambus RAM (DRRAM). The storage devices disclosed herein are intended to include, but are not limited to, these and other suitable types of memory.

[0134] In summary, this invention provides an intelligent primary frequency control controller and control method for gas turbines. In the embodiments of this invention, when constructing the gas turbine simulation model, the basic parameters of the gas turbine and power grid facilities are combined, thereby taking into account the fluctuation of the power grid frequency when the gas turbine simulation model is executed. This ensures the primary frequency control efficiency and success rate of the distributed blue bar frequency regulation model when the power grid frequency fluctuates. This overcomes the problem that existing methods cannot take into account the fluctuation of the power grid frequency when constructing the gas turbine model, which makes it impossible for the gas turbine model to immediately adjust the load of the gas turbine and ensure the stability of the power grid frequency.

[0135] It should be noted that, for the sake of simplicity, the foregoing embodiments are all described as a series of actions. However, those skilled in the art should understand that the present invention is not limited to the described order of actions, as some steps may be performed in other orders or simultaneously according to the present invention. Furthermore, those skilled in the art should also understand that the embodiments described in the specification are preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0136] It should be understood that the disclosed apparatus can be implemented in other ways, given the several embodiments provided in this application. For example, the apparatus embodiments described above are merely illustrative; the division of units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or communication connections shown or discussed may be through some interfaces; the indirect coupling or communication connections between devices or units may be telecommunications or other forms.

[0137] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit the scope of protection of the invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on these embodiments, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art can still combine, add, delete, or otherwise adjust the features of the various embodiments of the present invention according to the circumstances without conflict or creative effort, thereby obtaining different technical solutions that do not fundamentally depart from the concept of the present invention. These technical solutions also fall within the scope of protection of the present invention.

Claims

1. A control method for an intelligent primary frequency control controller of a gas turbine, characterized in that, The control method of the intelligent primary frequency controller for the gas turbine includes: By iterating through the basic parameters of the gas turbine and power grid facilities, a simulation model of the gas turbine is established using the basic parameters of the gas turbine and power grid facilities. The gas turbine operating parameters and power grid fluctuation frequency are acquired in real time. Using the gas turbine operating parameters and power grid fluctuation frequency as input, the gas turbine simulation model is executed. The gas turbine simulation model outputs at least one set of frequency regulation fluctuation coefficients. Load at least one set of frequency regulation fluctuation coefficients, obtain the day-ahead dispatch plan of the power grid, discretize and cluster the frequency regulation fluctuation coefficients into N discrete frequency regulation scenarios, and calculate the frequency regulation probability value corresponding to the discrete frequency regulation scenario based on the fuzzy clustering algorithm; Obtain the frequency modulation fluctuation coefficient and the corresponding frequency modulation probability value. Based on the frequency modulation fluctuation coefficient and the frequency modulation probability value, construct and train the sub-Bluer bar frequency modulation model, and output the trained sub-Bluer bar frequency modulation model. The sub-Bluerg bar frequency regulation model performs correlation analysis on the gas turbine operating parameters and grid fluctuation frequency. The sub-Bluerg bar frequency regulation model uses a load suppression algorithm to allocate the primary frequency regulation output of the gas turbine, and solves the gas turbine start-up and shutdown parameters and unit output parameters based on the primary frequency regulation output allocation value.

2. The control method for a gas turbine intelligent primary frequency controller according to claim 1, characterized in that: The method for establishing a gas turbine simulation model using basic parameters of the gas turbine and power grid facilities specifically includes: Obtain basic parameters of the gas turbine and power grid facilities, and use 3D visualization tools to build an initial model of the controller; The frequency regulation-related facilities are traversed through the basic parameters of gas turbines and power grid facilities. Based on the previous frequency regulation parameters of the frequency regulation-related facilities, the response weights of the frequency regulation-related facilities are simulated and divided. Obtain the response weights of the frequency modulation associated facilities, retrieve the frequency modulation output data of the frequency modulation associated facilities, and determine the initial frequency modulation coefficients of each group of frequency modulation associated facilities; The initial frequency modulation coefficient of the frequency modulation associated facilities is quantized, a frequency modulation coefficient merging threshold is set, and it is determined whether the initial frequency modulation coefficient is greater than the frequency modulation coefficient merging threshold. If the initial frequency modulation coefficient is greater than the frequency modulation coefficient merging threshold, the current frequency modulation associated facilities are retained. If the initial frequency modulation coefficient is less than the frequency modulation coefficient merging threshold, the four associated frequency modulation associated facilities are merged, and the initial frequency modulation coefficients of the four associated frequency modulation associated facilities are summed to obtain the corrected frequency modulation coefficient. By integrating the initial frequency regulation coefficient and the corrected frequency regulation coefficient, the frequency regulation fluctuation coefficient is obtained. The frequency regulation fluctuation coefficient is then visualized in the controller initial model to obtain the gas turbine simulation model.

3. The control method for a gas turbine intelligent primary frequency control controller according to claim 1, characterized in that: The method for simulating the execution of a gas turbine simulation model, wherein the gas turbine simulation model outputs at least one set of frequency modulation fluctuation coefficients, specifically includes: Obtain gas turbine operating parameters and power grid fluctuation frequency; The frequency regulation output data of frequency regulation associated facilities corresponding to the power grid fluctuation frequency are calculated based on the speed control algorithm. Among them, C i This represents the frequency modulation output data of frequency modulation associated facilities, x i ω represents the operating parameters of the gas turbine. c ω represents the frequency of power grid fluctuations. max ω min These represent the maximum and minimum frequencies of the power grid per unit time, respectively. Obtain the response weights of the frequency modulation associated facilities, combine them with the frequency modulation output data of the frequency modulation associated facilities, and perform power spectrum analysis on the response weights and frequency modulation output data of the frequency modulation associated facilities based on the Wiener-Khinchin theorem to determine the frequency modulation fluctuation coefficient of each group of frequency modulation associated facilities. Among them, f c σ represents the frequency modulation fluctuation coefficient of frequency modulation associated facilities. c This represents the autocorrelation function in power spectrum analysis, where Δt is the frequency modulation unit time, and q... i Indicates the response weight of frequency modulation associated facilities, t c is the frequency regulation constraint coefficient, and L is the number of frequency regulation associated facilities.

4. The control method for a gas turbine intelligent primary frequency controller according to claim 3, characterized in that: The method for calculating the frequency modulation probability value corresponding to a discrete frequency modulation scenario based on the fuzzy clustering algorithm specifically includes: Obtain the day-ahead dispatch plan of the power grid, decompose the day-ahead dispatch plan of the power grid, and obtain the power generation component per unit time; Obtain at least one set of frequency modulation fluctuation coefficients, and calculate the scene membership degree of N discrete frequency modulation scenes corresponding to the frequency modulation fluctuation coefficients based on the fuzzy clustering algorithm; The scene membership degree is calculated using the following formula: Where, d s The scene membership degree is represented by N, where N is the number of discrete frequency modulation scenes, and Q is the number of scenes. c f is the amount of electricity generated per unit time. c Indicates the frequency modulation fluctuation coefficient; Load the scene membership degree and frequency modulation fluctuation coefficient, introduce scene constraint quantity, and calculate the frequency modulation probability value corresponding to the discrete frequency modulation scene; Where, d g λ represents the frequency modulation probability value corresponding to the discrete frequency modulation scenario, and λ represents the scenario constraint constant.

5. The control method for a gas turbine intelligent primary frequency controller according to claim 4, characterized in that: The method for constructing and training a split-bulb frequency modulation model based on the frequency modulation fluctuation coefficient and the frequency modulation probability value specifically includes: An initial model for the split-bulb frequency modulation model is pre-constructed by combining the improved chicken flock algorithm and the related vector machine model; A multi-objective genetic algorithm is used to define the kernel function of the initial model, and the initial model of the active disturbance rejection control strategy based on the multi-objective genetic algorithm and the initial frequency modulation strategy are obtained. The frequency modulation fluctuation coefficient and frequency modulation probability value are randomly shuffled, and the frequency modulation fluctuation coefficient and frequency modulation probability value are used as the training set and test set, respectively. The initial model is iteratively trained using the training set.

6. The control method for a gas turbine intelligent primary frequency controller according to claim 5, characterized in that: The method for constructing and training a split-bulb frequency modulation model based on the frequency modulation fluctuation coefficient and the frequency modulation probability value further includes: By using active correlation decision theory to remove uncorrelated points in the frequency modulation fluctuation coefficient and frequency modulation probability value, a sparse model is obtained. The accuracy of the initial model is tested using a test set. If it meets the preset accuracy, the trained split-Blule bar frequency modulation model is output. If it does not meet the preset accuracy, the parameters of the initial model are optimized by maximizing the marginal likelihood function.

7. A gas turbine intelligent primary frequency control controller, used to implement the gas turbine intelligent primary frequency control controller control method according to any one of claims 1 to 6, characterized in that: The intelligent primary frequency control controller for the gas turbine specifically includes: The simulation model building module iterates through the basic parameters of the gas turbine and power grid facilities, and uses the basic parameters of the gas turbine and power grid facilities to build a simulation model of the gas turbine. The fluctuation coefficient calculation module is used to acquire gas turbine operating parameters and power grid fluctuation frequency in real time. Using the gas turbine operating parameters and power grid fluctuation frequency as input, it simulates the execution of the gas turbine simulation model. The gas turbine simulation model outputs at least one set of frequency regulation fluctuation coefficients. The frequency regulation probability determination module loads at least one set of frequency regulation fluctuation coefficients, obtains the day-ahead dispatch plan of the power grid, discretizes and clusters the frequency regulation fluctuation coefficients into N discrete frequency regulation scenarios, and calculates the frequency regulation probability value corresponding to the discrete frequency regulation scenario based on the fuzzy clustering algorithm. The frequency modulation model construction module is used to obtain the frequency modulation fluctuation coefficient and the corresponding frequency modulation probability value. Based on the frequency modulation fluctuation coefficient and the frequency modulation probability value, it constructs and trains the sub-Bluer bar frequency modulation model and outputs the trained sub-Bluer bar frequency modulation model. The frequency regulation allocation module performs correlation analysis on the gas turbine operating parameters and grid fluctuation frequency based on the sub-Bluer rod frequency regulation model. The sub-Bluer rod frequency regulation model uses a load suppression algorithm to allocate the primary frequency regulation output of the gas turbine. Based on the primary frequency regulation output allocation value, the gas turbine start-up and shutdown parameters and unit output parameters are solved.

8. The intelligent primary frequency control controller for gas turbines according to claim 7, characterized in that: The simulation model construction module specifically includes: The initial model building unit is used to obtain basic parameters of the gas turbine and power grid facilities, and to build the initial model of the controller using 3D visualization tools; The simulation partitioning unit is used to traverse the frequency regulation-related facilities in the basic parameters of gas turbine and power grid facilities, and combine the past frequency regulation parameters of the frequency regulation-related facilities to simulate and partition the response weights of the frequency regulation-related facilities; The initial coefficient determination unit is used to obtain the response weight of the frequency modulation associated facilities, retrieve the frequency modulation output data of the frequency modulation associated facilities, and determine the initial frequency modulation coefficient of each group of frequency modulation associated facilities.

9. The intelligent primary frequency control controller for gas turbines according to claim 8, characterized in that: The simulation model construction module further includes: The quantization judgment unit is used to quantize the initial frequency modulation coefficient of the frequency modulation associated facilities, set a frequency modulation coefficient merging threshold, and determine whether the initial frequency modulation coefficient is greater than the frequency modulation coefficient merging threshold. If the initial frequency modulation coefficient is greater than the frequency modulation coefficient merging threshold, the current frequency modulation associated facilities are retained. If the initial frequency modulation coefficient is less than the frequency modulation coefficient merging threshold, the four associated frequency modulation associated facilities are merged, and the initial frequency modulation coefficients of the four associated frequency modulation associated facilities are accumulated to obtain the corrected frequency modulation coefficient. The coefficient integration unit is used to integrate the initial frequency regulation coefficient and the corrected frequency regulation coefficient to obtain the frequency regulation fluctuation coefficient. The frequency regulation fluctuation coefficient is then visualized and presented to the controller initial model to obtain the gas turbine simulation model.

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