A load frequency control method, device and equipment based on TS fuzzy control

Abstract

The application provides a load frequency control method, device and equipment based on TS fuzzy control, and relates to the technical field of power systems. The method comprises the following steps: obtaining a load frequency demand signal; wherein the load frequency demand signal comprises a frequency deviation generated by a disturbance at a load end and a system; determining a regional control error according to the load frequency demand signal; obtaining a plurality of control parameter sets corresponding to the regional control error according to a preset mapping relationship; and taking the plurality of control parameter sets as inputs of a preset TS optimization model. According to the application, the regional control error ACE is determined by obtaining the load frequency demand signal, the plurality of control parameter sets of the TS fuzzy controller are obtained according to the preset mapping relationship, the optimal solution of the control parameters of the TS fuzzy controller is obtained through the optimization model, so that the output power of the generator set in the power system can be accurately adjusted, the fluctuation of the power system is reduced, and the stability of the power system is improved.

Description

Technical Field

[0001] This invention relates to the field of power system technology, and in particular to a load frequency control method, apparatus and equipment based on TS fuzzy control. Background Technology

[0002] With the development of my country's power industry, the power system has become a complex and vast system composed of multiple regions. AGC (Automatic Generation Control) is an indispensable function in modern power grid energy management systems (EMS). It balances the grid's output power with user electricity consumption by adjusting the output changes of frequency-regulating units in the grid, while optimizing unit output to ensure units operate at their most economical output points. Load Frequency Control (LFC) is the core of AGC frequency regulation applications.

[0003] The basic task of load frequency control (LFC) is to regulate the power supply frequency of the power system to maintain it at a reference value (such as 50Hz) and to maintain the power exchange between different areas of the power grid at the planned value. It is an important measure to maintain the power and frequency stability of the power system.

[0004] However, with the large-scale integration of renewable energy sources such as wind and solar power into the grid, their randomness and volatility will have a significant impact on interconnected power systems. Frequency controllers previously designed for conventional interconnected power system equipment are gradually becoming unable to meet current frequency regulation requirements. Summary of the Invention

[0005] In view of this, the purpose of the present invention is to provide a load frequency control method based on TS fuzzy control to reduce power system fluctuations and improve power system stability.

[0006] In a first aspect, embodiments of the present invention provide a load frequency control method based on TS fuzzy control, the method being applied to a TS fuzzy controller, the method comprising:

[0007] Obtain the load frequency demand signal; wherein, the load frequency demand signal includes the frequency deviation generated by the load end disturbance and the system.

[0008] The area control error is determined based on the load frequency demand signal;

[0009] According to the preset mapping relationship, obtain multiple sets of control parameters corresponding to the regional control error;

[0010] The multiple sets of control parameters are used as input to a preset TS optimization model, and the optimal control parameters are solved by a preset algorithm.

[0011] The generator set output power is adjusted according to the optimized control parameters.

[0012] Optionally, the step of determining the area control error based on the load frequency demand signal includes:

[0013] Determine the area control error according to the following specified formula:

[0014] ACE i =ΔP i +β i Δf i ;

[0015] Among them, ACE i Let ΔP be the regional control error of the i-th control region. i β represents the regional control error of the i-th control region. i Let be the frequency deviation constant of the i-th region; Δf i Let be the frequency deviation of the i-th region.

[0016] Optionally, the step of using the plurality of control parameter sets as input to a preset TS optimization model and solving for the optimal control parameters using a preset algorithm includes:

[0017] The multiple sets of control parameters are used as input to a preset TS optimization model, and the optimal solution is obtained by using the whale algorithm.

[0018] Optionally, before the step of using the plurality of control parameter sets as input to a preset TS optimization model and solving for the optimal control parameters using a preset algorithm, the following steps are included:

[0019] Construct a load frequency control model that considers nonlinear loads;

[0020] The custom control area system internal state variable X i The load frequency control model is input for prediction to obtain the mathematical model of the i-th region;

[0021] The mathematical model is transformed into a TS optimization model based on the TS fuzzy rules.

[0022] Optionally, the internal state variables of the custom i-th control region system are:

[0023] x i =[Δf i ΔP mi ΔP ri ΔP gi ΔP tiei ∫ACE i dt] T ;

[0024] Where, x i Let Δf be the internal state variable of the system in the i-th control region. i Let ΔP be the frequency deviation of the i-th control region. mi ΔP is the change in unit output. ri For the change in the incremental output heat power of the reheat unit, ΔP gi ΔP represents the change in turbine valve position. tiet ACEi represents the power deviation of the tie line, and ACEi represents the regional control deviation of the i-th control region.

[0025] Optionally, the construction of the load frequency control model considering nonlinear loads includes:

[0026] The components are connected in sequence: governor dead zone model, governor model, prime mover model, generator load model, and load frequency control model.

[0027] Optionally, the set of control parameters includes proportional gain Kp, integral time Ki, and differential amplification factor Kd.

[0028] Optionally, the TS fuzzy rule is an If-Then fuzzy rule.

[0029] A second aspect of the present invention provides a load frequency control device based on TS fuzzy control, the device being applied to a TS fuzzy controller, the device comprising:

[0030] A signal acquisition module is used to acquire load frequency demand signals; wherein, the load frequency demand signals include frequency deviations caused by disturbances at the load end and those generated by the system.

[0031] An error determination module is used to determine the area control error based on the load frequency demand signal;

[0032] The parameter set acquisition module is used to acquire multiple control parameter sets corresponding to the regional control error according to a preset mapping relationship;

[0033] The solution module is used to take the multiple sets of control parameters as input to the preset TS optimization model and solve for the optimal control parameters through a preset algorithm.

[0034] The power regulation module is used to adjust the output power of the generator set according to the optimized control parameters.

[0035] A third aspect of this application provides a load frequency control device based on TS fuzzy control, the device including the above-described apparatus.

[0036] The embodiments of the present invention bring the following beneficial effects: The present invention provides a load frequency control method, apparatus, and device based on TS fuzzy control. The method includes: acquiring a load frequency demand signal; wherein, the load frequency demand signal includes frequency deviations caused by load-end disturbances and system-generated disturbances; determining a regional control error based on the load frequency demand signal; acquiring multiple sets of control parameters corresponding to the regional control error according to a preset mapping relationship; using the multiple sets of control parameters as input to a preset TS optimization model, and solving for the optimal control parameters using a preset algorithm; and adjusting the generator set output power according to the optimal control parameters.

[0037] This application determines the regional control error ACE by acquiring the load frequency demand signal, obtains multiple control parameter sets of the TS fuzzy controller according to the preset mapping relationship, and obtains the optimal solution of the control parameters of the TS fuzzy controller through the optimization model. In this way, the output power of the generator set in the power system can be accurately adjusted, the fluctuation of the power system can be reduced, and the stability of the power system can be improved.

[0038] Other features and advantages of the invention will be set forth in the description which follows, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention are realized and obtained in accordance with the structures particularly pointed out in the description, claims and drawings.

[0039] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0040] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0041] Figure 1 A flowchart of a load frequency control method for TS fuzzy control provided in an embodiment of the present invention;

[0042] Figure 2 A schematic diagram of a load frequency control principle for TS fuzzy control provided in an embodiment of the present invention;

[0043] Figure 3 A flowchart of another TS fuzzy control load frequency control method provided in an embodiment of the present invention;

[0044] Figure 4This is a schematic diagram of the composition structure of the load frequency control device for TS fuzzy control provided in an embodiment of the present invention.

[0045] The attached figures are labeled as follows:

[0046] 11-Signal acquisition module, 12-Error determination module, 13-Parameter set acquisition module, 14-Solution module, 15-Power adjustment module. Detailed Implementation

[0047] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] To facilitate understanding of this embodiment, the technical terms used in this application will be briefly introduced below.

[0049] The load frequency control structure of a generator set includes a speed governor, a prime mover, a generator load model, and a load frequency controller.

[0050] Speed ​​governor: When the system load changes, the speed governor changes the steam valve to change the input power of the prime mover.

[0051] Governor dead zone: Also known as the insensitive zone of the governor, the governor will not operate when the frequency deviation of the system is not greater than this zone. Power system generator governors are all designed with a certain dead zone to reduce frequent governor operation caused by minute frequency deviations in the system, thus protecting the governor and extending its service life.

[0052] Prime mover: refers to a device that converts primary energy into mechanical energy.

[0053] Generator load model: A generator is a device that converts mechanical energy into electrical energy. The generator-power system model mainly describes the relationship between system power change and frequency change when the generator output power or load power changes, causing system imbalance, when the system is operating in a steady state.

[0054] Load frequency controller: When the system load changes, the load frequency controller changes the position of the governor valve through the frequency modulator, thereby changing the input power of the prime mover. The load frequency controller is a secondary frequency regulation link controlled manually, and the secondary frequency regulation is the feedback link of the primary frequency regulation.

[0055] After introducing the technical terms used in this application, the application scenarios and design concepts of the embodiments of this application will be briefly described below.

[0056] Currently, with the rapid development of the power industry, the power system has become a vast system composed of multiple regions. Active power balance and frequency stability of the interconnected power grid have always been critical issues for the safe operation of the system. In recent years, the large-scale integration of various intermittent and fluctuating distributed power sources into the grid, along with the ever-present random load disturbances, has made maintaining frequency stability in the interconnected power grid even more challenging. Especially in a deregulated electricity market environment, each interconnected region contains various uncertainties and random disturbances, further increasing the complexity of frequency control.

[0057] The main means of solving the problem of power grid frequency stability is to use load frequency control (LFC) to regulate the power supply frequency of the power system, keep it at the reference value and maintain the power exchange between different areas of the power grid at the planned value.

[0058] However, with the large-scale integration of various renewable energy sources (such as wind and solar power) into the power grid, the randomness and volatility of renewable resources have had a significant impact on interconnected power systems. Conventional load frequency controllers in related technologies are gradually unable to meet the frequency regulation requirements of existing interconnected power systems.

[0059] Based on this, this application provides a load frequency control method for TS fuzzy control to reduce the impact of renewable energy on interconnected power systems and improve the stability of interconnected power systems. The load frequency control method for TS fuzzy control is applied to a TS fuzzy controller, which includes a memory for storing computer programs and a processor for executing the computer programs in the memory.

[0060] Example 1

[0061] Combination Figure 1 As shown in the figure, this application provides a load frequency control method for TS fuzzy control. The method is applied to a TS fuzzy controller and includes the following steps:

[0062] S110, the processor acquires the load frequency demand signal; wherein, the load frequency demand signal includes the frequency deviation caused by the load end disturbance and the system.

[0063] S120, the processor determines the area control error based on the load frequency demand signal.

[0064] S130, the processor obtains multiple sets of control parameters corresponding to the area control error according to the preset mapping relationship.

[0065] S140, the processor takes multiple sets of control parameters as input to the preset TS optimization model and solves for the optimal control parameters through a preset algorithm.

[0066] The S150 processor adjusts the generator set's output power based on optimized control parameters.

[0067] In this embodiment, the regional control error ACE is determined by acquiring the load frequency demand signal, and multiple control parameter sets of the TS fuzzy controller are obtained according to the preset mapping relationship. The optimal solution of the control parameters of the TS fuzzy controller is obtained by optimizing the model, thereby accurately adjusting the output power of the generator set in the power system, reducing the fluctuation of the power system, and improving the stability of the power system.

[0068] Combination Figure 2 As shown, in step S110 of this embodiment, the load frequency demand signal acquired by the processor includes disturbances occurring at the load end and frequency deviation Δf generated by the system. Figure 2 The frequency deviation Δf that the power system will generate after the generator sets produce power and the disturbance ΔP occurs at the load end.

[0069] In step S120 of this embodiment, the processor determines the area control error ACE based on the load frequency demand signals ΔP and Δf. The area control error, also known as the conventional area control error (ACE), only considers frequency and switching power variations. The area control error ACE is used to measure the generation and load standards within a specific area. The value of the area control error ACE is typically used to judge the effectiveness of the control. The area control error ACE is calculated in this step to determine the control parameters of the TS controller based on the area control error ACE.

[0070] Optionally, step S120, which involves determining the area control error based on the load frequency demand signal, includes:

[0071] Determine the area control error according to the following specified formula: ACE i =ΔP i +β i Δf i ;

[0072] Among them, ACE i Let ΔP be the regional control error of the i-th control region. i β represents the regional control error of the i-th control region. i Let be the frequency deviation constant of the i-th region; Δf i Let be the frequency deviation of the i-th region.

[0073] And through regional control error ACE i And the previous area control error ACE i-1Calculate the frequency deviation change ΔACE i .

[0074] In step S130 of this embodiment, the processor obtains multiple sets of control parameters corresponding to the regional control error according to a preset mapping relationship. Since there are multiple control regions, the regional control error ACE of each region... i The control parameters of the TS controller are based on the preset mapping relationship within the TS controller.

[0075] The preset mapping relationship is obtained through fuzzy inference using TS fuzzy rules. It is a fuzzy parameter set. The TS fuzzy rules employ the If-Then rule:

[0076] R l If(ace i =M 1l )and(Δace i =M 2l ),Then; where Ri is the fuzzy rule for the i-th region, M 1i M 2i These are system parameters.

[0077] In step S140 of this embodiment, the processor uses multiple sets of control parameters as input to a preset TS optimization model and solves for the optimal control parameters using a preset algorithm. After optimizing the control parameters to obtain the optimal solution, the optimal solution's control parameters are transmitted to the TS fuzzy controller.

[0078] In step S150 of this embodiment, the TS fuzzy controller adjusts the generator set output power according to the received optimal solution control parameters to compensate for system deviations, reduce system fluctuations, and improve the stability of the power system.

[0079] Optionally, in step S140 of this embodiment, the step of the processor using multiple sets of control parameters as input to a preset TS optimization model and solving for the optimal control parameters using a preset algorithm includes:

[0080] The processor takes multiple sets of control parameters as input to a preset TS optimization model and uses the whale algorithm to find the optimal solution to obtain the optimal control parameters.

[0081] In related technologies, genetic algorithms, particle swarm optimization algorithms, differential evolution algorithms, and bacterial foraging algorithms are commonly used. These intelligent optimization algorithms have the advantages of strong adaptability and low dependence on system models and parameters. In this embodiment, the whale algorithm is used to find the optimal solution.

[0082] In the TS fuzzy control system, in addition to the fuzzy control rules summarized from operational experience, the parameters that affect control performance also include the settings of the quantization factor and the proportional factor in the TS fuzzy controller.

[0083] Determining quantization factors and scaling factors typically relies on manual experience, making it difficult to obtain a high-quality set of parameters using this method. However, particle swarm optimization can easily yield a suitable set of scaling and scaling factor values, resulting in superior control performance from the fuzzy controller.

[0084] For power grid frequency regulation systems, the frequency deviation at the generation end should reach a stable state as quickly as possible, while the controller output should be as small as possible. Therefore, the following quadratic performance indicators are adopted in the optimization process:

[0085]

[0086] In the formula, Q and R are the weighted matrices of the state vector and the control vector, respectively. The optimization objective is to minimize the performance index, so the fitness function can be taken as:

[0087]

[0088] Through intelligent optimization algorithms, the scaling factor and quantization factor of fuzzy control can be optimized to obtain parameters that improve output performance and achieve optimal control of the system frequency.

[0089] Initialize the solution dimension, population size, and maximum number of iterations in the whale algorithm, and randomly generate whale population positions based on the solution range. Let the parameters in each TS fuzzy rule be the positional components of the whales. Calculate the fitness value of each whale in the initial state using the objective function and sort them to determine a suitable whale position as the initial optimal solution of the algorithm. The optimal whale position X is... * and its corresponding global optimal fitness value F * .

[0090] Entering the main loop of the algorithm, the implementation process of the whale algorithm mainly includes three stages: surrounding the prey, bubble web attack, and searching for the prey.

[0091] Among them, surrounding the prey: D=|CX * (t)-X(t)|;X(t+1)=X * (t)-AD; where: t represents the current iteration number, X * Let X(t) represent the position vector of the optimal whale in the t-th iteration, and let X(t) represent the position vector of the i-th whale in the t-th iteration. A is the convergence factor, and C is the swing factor. The expressions for A and C are: A = 2ar1 - a;

[0092] C = 2r²;

[0093]

[0094] Where r1 and r2 are random numbers in (0, 1), and a decreases linearly from 2 to 0 as the number of iterations increases.

[0095] Among them, the foaming net attack: X(t+1)=X * (t)+D P e bl cos(2πl);

[0096] In the formula D P =|X * (t)-X(t)| represents the distance between the whale and its prey, b is the logarithmic spiral shape constant, and l is a random number in (-1,1).

[0097] Whales will tighten their nets as they swim toward their prey in a spiral pattern. They typically choose to tighten their nets or swim toward their prey in a spiral pattern with a 50% probability each. This can be expressed mathematically as follows:

[0098]

[0099] Searching for prey: The algorithm is set so that when the absolute value of the convergence factor A is less than 1, the whale attacks the prey. When the absolute value of A is greater than 1, the individual whale may not move closer to the current optimal whale position, but instead chooses a random whale position to update the positions of other whales. Although this may deviate from the target prey, it enhances the algorithm's reconnaissance capabilities, enabling it to perform a global search. The specific mathematical model is as follows:

[0100] D = |CX rand -X(t)|;

[0101] X(t+1)=X rand -AD;

[0102] In the formula, X rand Let be a random whale position vector.

[0103] When P < 0.5, the whale adopts an encirclement hunting method, and the whale's position is updated according to the absolute value of A.

[0104]

[0105] In summary, the method for updating the position of each whale in the whale algorithm is determined by the absolute values ​​of P and A. When P < 0.5, the whale adopts an encirclement hunting method, and the whale position is updated according to the absolute value of A. When P ≥ 0.5, the whale adopts a spiral position update method.

[0106] After updating the position of each whale, the fitness of each whale position is calculated, and the calculated fitness value is compared with the global optimal fitness value F. * The algorithm compares the results to find the optimal individual and its position. The iteration count is incremented by 1, and it is checked whether the maximum number of iterations has been reached. If it has, the iteration stops, and the current global optimal individual and its position are output; otherwise, the algorithm returns to its main loop.

[0107] Example 2

[0108] Combination Figure 3 As shown in the illustration, this application provides another load frequency control method based on TS fuzzy control, which is applied to a TS fuzzy controller. In this embodiment, the method includes the following steps:

[0109] S210, the processor acquires the load frequency demand signal; wherein, the load frequency demand signal includes the frequency deviation caused by the load end and the frequency deviation generated by the system.

[0110] S220, the processor determines the area control error based on the load frequency demand signal.

[0111] S230: The processor obtains a set of multiple control parameters corresponding to the area control error according to a preset mapping relationship.

[0112] The S240 processor constructs a load frequency control model that takes into account nonlinear loads.

[0113] S250, the processor will customize the internal state variable X of the control area system. i The load frequency control model is used for prediction to obtain the mathematical model of the i-th region.

[0114] The S260 processor transforms the mathematical model into a TS optimized model based on TS fuzzy rules.

[0115] S270: The processor takes multiple sets of control parameters as input to a preset TS optimization model and solves for the optimal control parameters using a preset algorithm.

[0116] The S280 processor adjusts the generator set's output power based on optimized control parameters.

[0117] In this embodiment, step S240 constructs a load frequency control model considering nonlinear loads. Many nonlinear elements exist in actual power grid systems, significantly impacting grid stability. The governor dead zone, also known as the governor's insensitivity zone, prevents the governor from operating when a frequency deviation no greater than its limit occurs. Power system generator governors are designed with a certain dead zone to reduce frequent governor operation caused by minute frequency deviations, protecting the governors and extending their lifespan. However, there have been instances of deliberately increasing the governor dead zone to evade primary frequency regulation responsibilities. Currently, the governor dead zone in power systems is generally required to be less than 1 r / min, or 0.0017 Hz. The governor dead zone exhibits nonlinearity with hysteresis. In this embodiment, the governor dead zone factor is used as a parameter when constructing the load frequency control model, allowing for more accurate adjustment of generator output power based on power system fluctuations, thus improving power system stability.

[0118] In step S240 of this embodiment, combined with Figure 2 As shown, the load frequency control model considering nonlinear loads includes: the governor dead zone model of the generator set, the prime mover model, the generator load model, and the load frequency control model connected in sequence, thus obtaining the load frequency control model considering nonlinear loads.

[0119] The expression for the governor model is as follows: In the formula, T g This is the time constant of the speed controller.

[0120] The expression for the prime mover model is: In the formula, T t The formula represents the turbine time constant; T r K is the reheat time constant. r This is the reheat coefficient.

[0121] The expression for the generator load model is: In the formula, K p For power system gain; T p ΔP is the time constant of the power system. a ΔP is the sum of the outputs of all power sources within the system. d This is the sum of fluctuating loads within the system.

[0122] In step S250 of this application, the internal state variable X of the custom control region system is defined. i for:

[0123] x i =[Δf i ΔP mi ΔP ri ΔP gi ΔPtiei ∫ACE i dt] T In the formula, Δf i ΔP represents the frequency deviation in the i-th region. mi ΔP represents the change in unit output. ri The change in the output heat power of the reheat unit; ΔP gi ΔP represents the change in turbine valve position. tiet For tie-line power deviation; ACE i Let be the regional control error of the i-th control region.

[0124] The custom control area system internal state variable X i Substituting the values ​​into the LFC model considering nonlinear load constructed in step S240, a mathematical model is obtained, which is then transformed into a TS optimization model based on TS fuzzy rules. In this optimization model, a mapping relationship is preset between the ACE and the control parameters of the TS controller.

[0125] Optionally, the control parameter set includes proportional gain Kp, integral time Ki, and differential amplification factor Kd. By substituting multiple control parameter sets into the TS optimization model and obtaining the optimal solution according to the whale algorithm, the optimal solution of the control parameters is obtained.

[0126] Optionally, the TS fuzzy rule is an If-Then fuzzy rule.

[0127] Specifically, the If-Then fuzzy rule is represented as follows:

[0128] R l IfΔX i1 isM 1l ,andΔX i2 isM 2l ,ΔX i3 isM 3l Then

[0129]

[0130] In the formula, M ij A is a fuzzy set; i B i It is the coefficient matrix corresponding to the i-th system; ΔX ij It is the antecedent variable.

[0131] Combination Figure 4 As shown, the second aspect of this application provides a load frequency control device based on TS fuzzy control, applied to a TS fuzzy controller. The device includes: a signal acquisition module 11, an error determination module 12, a parameter set acquisition module 13, a solution module 14, and a power adjustment module 15.

[0132] The signal acquisition module 11 is used to acquire the load frequency demand signal; wherein, the load frequency demand signal includes the frequency deviation caused by the load end disturbance and the system.

[0133] The error determination module 12 is used to determine the area control error based on the load frequency demand signal.

[0134] The parameter set acquisition module 13 is used to acquire multiple control parameter sets corresponding to the regional control error according to a preset mapping relationship.

[0135] The solver module 14 is used to take multiple sets of control parameters as input to a preset TS optimization model and solve for the optimal control parameters using a preset algorithm.

[0136] The power regulation module 15 is used to adjust the output power of the generator set according to the optimal control parameters.

[0137] The load frequency control device based on TS fuzzy control provided in this application establishes a load frequency control model considering nonlinear loads, optimizes it into a TS optimized model using TS fuzzy control, and applies it to a TS fuzzy controller. In practical applications, by acquiring the load frequency demand signal, determining the regional control error, and obtaining multiple control parameter sets through preset mapping relationships, the optimal solution for the control parameters is sought to accurately adjust the generator set output power. This improves the stability of the power system and reduces system fluctuations.

[0138] A third aspect of this application provides a load frequency control device based on TS fuzzy control, including the apparatus described above.

[0139] It should be noted that the terms "first," "second," etc., used in this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such terms can be used interchangeably where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein.

[0140] The computer program product provided in the embodiments of the present invention includes a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods described in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0141] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and apparatus described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0142] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0143] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0144] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0145] Finally, it should be noted that the above embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A load frequency control method based on TS fuzzy control, characterized in that, The method is applied to a TS fuzzy controller, and the method includes: Obtain the load frequency demand signal; wherein, the load frequency demand signal includes the frequency deviation generated by the load end disturbance and the system. The area control error is determined based on the load frequency demand signal; According to the preset mapping relationship, obtain multiple sets of control parameters corresponding to the regional control error; Construct sequentially connected governor dead zone model, governor model, prime mover model, generator load model, and load frequency control model; The custom control area system internal state variable X i The load frequency control model is input for prediction to obtain the mathematical model of the i-th region; wherein, the internal state variables of the custom i-th control region system are: ； Where, x i Let Δf be the internal state variable of the system in the i-th control region. i Let ΔP be the frequency deviation of the i-th control region. mi ΔP is the change in unit output. ri For the change in the incremental output heat power of the reheat unit, ΔP gi ΔP represents the change in turbine valve position. tiet The tie-line power deviation is denoted as ACEi, and the region control deviation of the i-th control region is denoted as ACEi. The mathematical model is transformed into a TS optimization model based on the TS fuzzy rules. The multiple sets of control parameters are used as input to a preset TS optimization model, and the optimal control parameters are solved using the whale algorithm. The position update of the whale algorithm includes three stages: surrounding prey, bubble web attack, and searching for prey. The surrounding prey stage uses a convergence factor A and a swing factor C, where A decreases linearly from 2 to 0 with the number of iterations. The bubble web attack stage uses spiral updates, containing a logarithmic spiral constant b and a random number l. Surrounding prey is used when probability P < 0.5, and spiral updates are used when P ≥ 0.

5. When |A| ≥ 1, a whale position is randomly selected for updating. The whale algorithm stops iterating after reaching the maximum number of iterations and outputs the optimal solution. The optimization objective of the whale algorithm is to minimize the quadratic performance index. The generator set output power is adjusted according to the optimal control parameters.

2. The load frequency control method based on TS fuzzy control according to claim 1, characterized in that, The step of determining the area control error based on the load frequency demand signal includes: Determine the area control error according to the following specified formula: ACE i =ΔP i +b i Δf i ; Among them, ACE i Let ΔP be the regional control error of the i-th control region. i β represents the regional control error of the i-th control region. i Let be the frequency deviation constant of the i-th region; Δf i Let be the frequency deviation of the i-th region.

3. The load frequency control method based on TS fuzzy control according to claim 1, characterized in that, The set of control parameters includes proportional gain Kp, integral time Ki, and differential amplification factor Kd.

4. The load frequency control method based on TS fuzzy control according to claim 1, characterized in that, The TS fuzzy rule is the If-Then fuzzy rule.

5. A load frequency control device based on TS fuzzy control, characterized in that, The apparatus is applied to a TS fuzzy controller for performing the method as described in any one of claims 1-4; the apparatus comprises: A signal acquisition module is used to acquire load frequency demand signals; wherein, the load frequency demand signals include frequency deviations caused by disturbances at the load end and those generated by the system. An error determination module is used to determine the area control error based on the load frequency demand signal; The parameter set acquisition module is used to acquire multiple control parameter sets corresponding to the regional control error according to a preset mapping relationship; The solution module is used to take the multiple sets of control parameters as input to the preset TS optimization model and solve for the optimal control parameters through a preset algorithm. The power regulation module is used to adjust the output power of the generator set according to the optimal control parameters.

6. A load frequency control device based on TS fuzzy control, characterized in that, The device includes the apparatus as described in claim 5.

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