CN115693655A - Load frequency control method, device and equipment based on TS fuzzy control - Google Patents
Load frequency control method, device and equipment based on TS fuzzy control Download PDFInfo
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Abstract
The invention 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: acquiring a load frequency demand signal; the load frequency demand signal comprises the disturbance at the load end and the frequency deviation generated by the system; determining a region control error according to the load frequency demand signal; acquiring a plurality of control parameter sets corresponding to the regional control errors according to a preset mapping relation; and taking the plurality of control parameter sets as the input of a preset TS optimization model. According to the method and the device, the regional control error ACE is determined by acquiring the load frequency demand signal, a plurality of control parameter sets of the TS fuzzy controller are obtained according to the preset mapping relation, and 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
The invention relates to the technical field of power systems, in particular to a load frequency control method, device and equipment based on TS fuzzy control.
Background
With the development of the power industry in China, the current power system has become a huge system which is complex in structure and consists of a plurality of areas. The AGC is an indispensable function in a modern power grid Energy Management System (EMS), and adjusts output variation of a frequency modulation unit in a power grid to keep balance between power grid output power and user power consumption, and simultaneously optimizes unit output to enable the unit to work on an economical output point. And Load Frequency Control (LFC) is the core of AGC Frequency modulation applications.
The basic task of Load Frequency Control (LFC) is to regulate the power supply frequency of the power system to a reference value (e.g. 50 Hz) and to maintain the link exchange power between different areas of the grid to a planned value, which is an important measure for maintaining the power and frequency stability of the power system.
However, with the large-scale integration of renewable energy sources such as wind energy and light energy into the power grid, the randomness and the volatility of the renewable energy sources will cause great impact on the interconnected power system, so that the conventional frequency controller for the interconnected power system equipment cannot meet the existing frequency modulation requirement gradually.
Disclosure of Invention
In view of this, the present invention provides a load frequency control method based on TS fuzzy control to reduce power system fluctuation and improve power system stability.
In a first aspect, an embodiment of the present invention provides a load frequency control method based on TS fuzzy control, where the method is applied to a TS fuzzy controller, and the method includes:
acquiring a load frequency demand signal; the load frequency demand signal comprises the disturbance at the load end and the frequency deviation generated by the system;
determining a region control error according to the load frequency demand signal;
acquiring a plurality of control parameter sets corresponding to the regional control errors according to a preset mapping relation;
taking the plurality of control parameter sets as the input of a preset TS optimization model, and solving optimal control parameters through a preset algorithm;
and adjusting the output power of the generator set according to the optimized control parameters.
Optionally, the step of determining a zone control error according to the load frequency demand signal comprises:
determining a zone control error according to the following specified equation:
ACE i ＝ΔP i +β i Δf i ；
wherein, ACE i For zone control error of i-th control zone, Δ P i A zone control error for the ith control zone; beta is a i Is the frequency deviation constant of the ith area; Δ f i Is the frequency deviation of the i-th area.
Optionally, the step of solving the optimal control parameters by using the plurality of control parameter sets as the input of a preset TS optimization model through a preset algorithm includes:
and taking the plurality of control parameter sets as the input of a preset TS optimization model, and performing optimal solution solving through a whale algorithm to obtain the optimal control parameters.
Optionally, before the step of solving the optimal control parameters by using the preset algorithm with the multiple control parameter sets as the input of the preset TS optimization model, the method includes:
constructing a load frequency control model considering nonlinear load;
self-defining control area system internal state quantity X i Inputting the load frequency control model for prediction to obtain a mathematical model of the ith area;
and converting the mathematical model into a TS optimization model based on a TS fuzzy rule.
Optionally, the internal state quantity of the customized ith control area system is:
x i ＝[Δf i ΔP mi ΔP ri ΔP gi ΔP tiei ∫ACE i dt] T ；
wherein x is i For the ith control area system internal state quantity, Δ f i For i-th control region frequency deviation, Δ P mi Is the unit output variation, delta P ri For incremental changes in output thermal power of reheat trains, Δ P gi For the turbine valve variation, Δ P tiet ACEi is the zone control deviation of the ith control zone for the tie line power deviation.
Optionally, the constructing a load frequency control model considering the nonlinear load includes:
the speed regulator dead zone model, the speed regulator model, the prime mover model, the generator load model and the load frequency control model are sequentially connected.
Optionally, the control parameter set includes a proportional gain Kp, an integral time Ki, and a differential amplification Kd.
Optionally, the TS fuzzy rule is If-Then fuzzy rule.
The second aspect of the present invention provides a load frequency control device based on TS fuzzy control, the device is applied to a TS fuzzy controller, and the device comprises:
the signal acquisition module is used for acquiring a load frequency demand signal; the load frequency demand signal comprises the disturbance at the load end and the frequency deviation generated by the system;
the error determining module is used for determining a region control error according to the load frequency demand signal;
the parameter set acquisition module is used for acquiring a plurality of control parameter sets corresponding to the regional control errors according to a preset mapping relation;
the solving module is used for taking the plurality of control parameter sets as the input of a preset TS optimization model and solving the optimal control parameters through a preset algorithm;
and the power adjusting module is used for adjusting the output power of the generator set according to the optimized control parameter.
The third aspect of the present application provides a load frequency control device based on TS fuzzy control, which includes the above-mentioned apparatus.
The embodiment of the invention has the following beneficial effects: the invention provides a load frequency control method, a load frequency control device and load frequency control equipment based on TS fuzzy control. The method comprises the following steps: acquiring a load frequency demand signal; the load frequency demand signal comprises the disturbance at the load end and the frequency deviation generated by the system; determining a region control error according to the load frequency demand signal; acquiring a plurality of control parameter sets corresponding to the regional control errors according to a preset mapping relation; taking the plurality of control parameter sets as the input of a preset TS optimization model, and solving optimal control parameters through a preset algorithm; and adjusting the output power of the generator set according to the optimal control parameter.
This application confirms regional control error ACE through acquireing load frequency demand signal, obtains a plurality of control parameter sets of TS fuzzy controller according to predetermined mapping relation, finds TS fuzzy controller's control parameter optimal solution through the optimization model to can accurately adjust generating set's output among the electric power system, reduce electric power system's fluctuation, improve electric power system's stability.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
In order to make the aforementioned and other objects, features and advantages of the present invention comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a load frequency control method for TS fuzzy control according to an embodiment of the present invention;
fig. 2 is a schematic diagram of load frequency control of TS fuzzy control according to an embodiment of the present invention;
fig. 3 is a flowchart of another load frequency control method for TS fuzzy control according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of a load frequency control device for TS fuzzy control according to an embodiment of the present invention.
The reference numbers are as follows:
the device comprises a signal acquisition module, a 12-error determination module, a 13-parameter set acquisition module, a 14-solving module and a 15-power regulation module.
Detailed Description
To make the objects, 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, and it is apparent that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
For the convenience of understanding the present embodiment, the technical terms designed in the present application will be briefly described below.
The load frequency control structure of the generator set comprises a speed regulator, a prime motor, a generator load model and a load frequency controller.
A speed regulator: when the system load changes, the primary frequency modulation controller changes the input power of the prime motor through the inherent attribute change valve of the speed regulator.
Speed regulator blind spot: also called the insensitive area of the speed regulator, when the frequency deviation of the system is not larger than the insensitive area, the speed regulator does not act. The speed regulator of the power system unit is provided with a certain dead zone, and the aim is to reduce the frequent action of the speed regulator caused by the micro frequency deviation in the system, protect the speed regulator of the system and prolong the service life of the speed regulator.
A prime mover: refers to a device that converts primary energy into mechanical energy.
The generator-power system model mainly describes the relationship between the system power change and the frequency change when the system is unbalanced due to the change of the output power or the load power of the generator when the system works in a stable state.
The load frequency controller is a secondary frequency modulation controller which changes the position of an air valve of the speed regulator through a frequency modulator of the load frequency controller so as to change the input power of the prime motor when the system load changes, the load frequency controller is used as a secondary frequency modulation link of manual control, and the secondary frequency modulation is a feedback link of primary frequency modulation.
After introducing the technical terms related to the present application, the following briefly introduces the application scenarios and design ideas of the embodiments of the present application.
At present, with the rapid development of the power industry, the power system of today becomes a huge system composed of a plurality of areas. The active balance and the frequency stability of the interconnected large power grid are always important problems of safe operation of the system. In recent years, various intermittent and fluctuating distributed power sources are connected to a power grid in large quantity and have random load disturbance which does not exist at any time, so that the maintenance of the frequency stability of the interconnected power grid is more challenging. In particular, in a deregulated power market environment, each interconnected region contains various uncertainties and random disturbances, further increasing the complexity of frequency control.
The main means for solving the problem of power grid Frequency stability is to adopt Load Frequency Control (LFC) for adjusting the power supply Frequency of the power system to keep the power supply Frequency at a reference value and maintain the exchange power of the tie lines between different areas of the power grid as a planned value.
However, as various renewable energy sources (such as wind energy, light energy, and the like) are incorporated into the power grid on a large scale, the randomness and the volatility of the renewable resources cause great impact on the interconnected power system, and the conventional load frequency controller in the related art gradually cannot meet the frequency modulation requirement of the existing interconnected power system.
Based on the above, the application provides a load frequency control method of TS fuzzy control, so as to reduce the impact of renewable energy on the interconnected power system, and improve the stability of the interconnected power system. The load frequency control method of the TS fuzzy control is applied to a TS fuzzy controller, and the TS fuzzy controller comprises a memory for storing a computer program and a processor for executing the computer program in the memory.
Example 1
With reference to fig. 1, an embodiment of the present application provides a load frequency control method for TS fuzzy control, where the method is applied to a TS fuzzy controller, and the method includes the following steps:
s110, the processor acquires a load frequency demand signal; the load frequency demand signal comprises the disturbance at the load end and the frequency deviation generated by the system.
And S120, determining the area control error by the processor according to the load frequency demand signal.
And S130, the processor acquires a plurality of control parameter sets corresponding to the regional control errors according to the preset mapping relation.
And S140, the processor takes the plurality of control parameter sets as the input of a preset TS optimization model, and solves the optimal control parameters through a preset algorithm.
And S150, the processor adjusts the output power of the generator set according to the optimized control parameters.
In the embodiment of the application, the area control error ACE is determined by obtaining the load frequency demand signal, a plurality of control parameter sets of the TS fuzzy controller are obtained according to the preset mapping relation, and the optimal solution of the control parameters of the TS fuzzy controller is obtained through the optimization model, so that the output power of a 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.
As shown in fig. 2, in step S110 of this embodiment, the load frequency demand signal obtained by the processor includes the disturbance occurring at the load end and the frequency deviation Δ f generated by the system. In fig. 2, the frequency deviation Δ f of the power system is generated after the power generation of the generator set and the disturbance Δ P at the load end.
In step S120 of this embodiment, the processor determines the area control error ACE according to the load frequency demand signals Δ P and Δ f. The area control error, i.e. the conventional Area Control Error (ACE), only considers the frequency variation and the exchange power variation, and the area control error ACE is used for measuring the power generation and load standard in a certain area. The value of the area control error ACE is usually used to judge the control effect. In this step the area control error ACE is calculated in order to determine the control parameters of the TS controller from the area control error ACE.
Optionally, the step S120 of determining the zone control error according to the load frequency demand signal includes:
determining a zone control error according to the following specified equation: ACE i ＝ΔP i +β i Δf i ；
Wherein, ACE i Zone control error, Δ P, for the ith control zone i Controlling the error for the ith control region; beta is a beta i Frequency deviation for i-th regionA constant; Δ f i Is the frequency deviation of the i-th area.
And controlling the error ACE by area i And the previous area control error ACE i-1 Calculating the frequency deviation change delta ACE i 。
In step S130 of this embodiment, the processor obtains a plurality of control parameter sets corresponding to the area control errors according to a preset mapping relationship. Since there are a plurality of control areas, the area control error ACE of each area i And corresponding to the control parameters of the TS controller according to the preset mapping relation in the TS controller.
The preset mapping relation is obtained by adopting a TS fuzzy rule through fuzzy reasoning. Is a fuzzy parameter set. Wherein, the TS fuzzy rule adopts If-Then rule:
R l :If(ace i ＝M 1l )and(Δace i ＝M 2l ) Then; wherein Ri is the fuzzy rule of the i-th area, M 1i M 2i Is a system parameter.
In step S140, the processor uses a plurality of control parameter sets as inputs of the preset TS optimization model, and solves the optimal control parameters through a preset algorithm. And transmitting the control parameters of the TS fuzzy controller of the optimal solution to the TS fuzzy controller after the optimal solution is obtained by optimizing the control parameters.
In step S150 of this embodiment, the TS fuzzy controller adjusts the output power of the generator set according to the received optimal solution control parameter, so as to compensate for the system deviation, reduce the system fluctuation, and improve the stability of the power system.
Optionally, in step S140 of this embodiment, the step of the processor using a plurality of control parameter sets as inputs of the preset TS optimization model and solving the optimal control parameters through a preset algorithm includes:
and the processor takes the plurality of control parameter sets as the input of a preset TS optimization model, and performs optimal solution solving through a whale algorithm to obtain the optimal control parameters.
In the related art, genetic algorithms, particle swarm optimization algorithms, differential evolution algorithms, bacterial foraging algorithms and the like are mostly adopted. The intelligent optimization algorithms have the advantages of strong adaptability and low dependence on system models and parameters. In the embodiment, whale algorithm is adopted to solve the optimal solution.
In the TS fuzzy control system, parameters having an influence on the control performance are set of a quantization factor and a scale factor in the TS fuzzy controller in addition to the fuzzy control rule summarized according to the operation experience.
The manner of determining the quantization factor and the scale is usually also manual experience, so that it is difficult to obtain a set of parameters with higher quality. And parameter optimization is carried out through a particle swarm algorithm, a group of proper scale factors and parameter factor values can be conveniently obtained, and the fuzzy controller has a more excellent control result.
For a power grid frequency modulation system, the frequency deviation of a power generation end should reach a stable state as soon as possible, and meanwhile, the output of a controller should be as small as possible, so that the following quadratic performance indexes are adopted in the optimization process:
in the formula, Q and R are the weighting matrix of the state vector and the weighting matrix of the control vector, respectively, and the optimization goal is to minimize the performance index, so the fitness function may be:
through an intelligent optimization algorithm, the scale factor and the quantization factor of the fuzzy control can be optimized and buried, so that parameters which can enable the output performance to be better are obtained, and the optimal control of the system frequency is realized.
And initializing solving dimensions, population quantity and maximum iteration times in the whale algorithm, and randomly generating whale population positions according to the solving range. And setting the parameters in each TS fuzzy rule as the position components of the whales. Calculating the fitness value of each whale in the initial state through an objective function and sequencingDetermining a suitable whale position as an initial optimal solution of the algorithm, wherein the position X of the optimal whale * And its corresponding global optimum adaptation value F * 。
Entering a main algorithm loop, wherein the implementation process of the whale algorithm mainly comprises three stages: surrounding prey, foam net attack, searching prey.
Wherein, surrounding the prey: d = | CX * (t)-X(t)|；X(t+1)＝X * (t) -AD; in the formula: t denotes the current number of iterations, X * (t) is expressed as the position vector of the optimal whale in the t iteration, and X (t) is expressed as the position vector of the ith whale in the t iteration. A is a convergence factor, C is a sway factor, and the expressions of A and C are: a =2ar 1 -a；
C＝2r 2 ；
Wherein r is 1 And r 2 Is a random number in (0, 1), and a decreases linearly from 2 to 0 as the number of iterations increases.
Wherein, foaming net attack: x (t + 1) = X * (t)+D P e bl cos(2πl)；
In the formula D P ＝|X * (t) -X (t) | represents the distance between whale and prey, b is the logarithmic spiral shape constant, and l is a random number in (-1, 1).
Whale will shrink the surrounding net while swimming in a spiral shape to the prey, generally choose to shrink the surrounding net and swim to the prey in a spiral shape, each with a 50% probability, expressed mathematically;
searching prey: the algorithm sets that when the absolute value of the convergence factor A is less than 1, whales attack prey, and when the absolute value of A is more than 1, individuals of current whales may not approach the current optimal whale position, but a random whale position is selected to update the positions of other whales. This enhances the exploration ability of the algorithm to enable a global search, although it may be off target. The specific mathematical model is as follows:
D＝|CX rand -X(t)|；
X(t+1)＝X rand -AD；
in the formula, X rand Is a random whale position vector.
When P is less than 0.5, the whale adopts a surrounding predation mode, and the whale position is updated according to the absolute value of A.
In conclusion, the updating method of the whale position in the whale algorithm is determined by the absolute values of P and A, when P is less than 0.5, the whale adopts a surrounding predation mode, and the whale position is updated according to the absolute value of A; when P is more than or equal to 0.5, the whale adopts spiral position updating.
After the position of each whale is updated, the fitness calculation is carried out on the position of each whale, and the fitness calculation value and the global optimal fitness value F are used * And (5) comparing to obtain the optimal individual and position. And adding 1 to the iteration times, judging whether the maximum iteration times are reached, stopping iteration and outputting the current global optimal individual and position if the maximum iteration times are reached, and returning to enter the algorithm main loop if the maximum iteration times are not reached.
Example 2
Referring to fig. 3, an embodiment of the present application provides another load frequency control method based on TS fuzzy control, and the method is applied to a TS fuzzy controller. In this embodiment, the method includes the steps of:
s210, the processor acquires a load frequency demand signal; the load frequency demand signal comprises the disturbance at the load end and the frequency deviation generated by the system.
And S220, determining the area control error by the processor according to the load frequency demand signal.
And S230, the processor acquires a plurality of control parameter sets corresponding to the area control errors according to a preset mapping relation.
S240, the processor constructs a load frequency control model considering the nonlinear load.
S250, the processor customizes the internal state quantity X of the control area system i And inputting a load frequency control model for prediction to obtain a mathematical model of the ith area.
And S260, converting the mathematical model into a TS optimization model by the processor based on the TS fuzzy rule.
And S270, the processor takes the plurality of control parameter sets as input of a preset TS optimization model, and the optimal control parameters are solved through a preset algorithm.
And S280, the processor adjusts the output power of the generator set according to the optimized control parameters.
In the present embodiment, a load frequency control model considering the nonlinear load is constructed in step S240. In an actual power grid system, a plurality of nonlinear links exist, and a plurality of influences exist on the stable operation of a power grid. The dead zone of the speed regulator is also called the insensitive zone of the speed regulator, and when the frequency deviation of the system is not larger than the dead zone, the speed regulator does not act. The speed regulator of the power system unit is provided with a certain dead zone, and the purpose is to reduce frequent actions of the speed regulator caused by micro frequency deviation in the system, protect the speed regulator of the system and prolong the service life of the speed regulator. However, the dead zone of the speed regulator is intentionally adjusted to avoid primary frequency modulation responsibility and the like, and the dead zone setting of the speed regulator in the current power system is generally required to be less than 1r/min, namely 0.0017Hz. Governor dead band has a nonlinear problem of hysteresis. In the embodiment, the dead zone factor of the speed regulator is used as a parameter when the load frequency control model is constructed, so that the output power of the generator set can be more accurately adjusted according to the fluctuation in the power system, and the stability of the power system is improved.
In step S240 of the present embodiment, as shown in fig. 2, the load frequency control model in which the nonlinear load is considered includes: and the load frequency control model considering the nonlinear load is obtained by sequentially connecting a speed regulator dead zone model, a prime motor model, a generator load model and a load frequency control model of the generator set.
Wherein, the expression of the speed regulator model is as follows:
in the formula, T g Is the governor time constant.
The expression of the prime mover model is:
in the formula, T t The formula is a time constant of the steam turbine; t is a unit of r Is the reheat time constant; k is r Is the reheat coefficient.
The expression of the generator load model is:
in the formula, K p Is the power system gain; t is p Is a power system time constant; delta P a The sum of all power supplies in the system; delta P d Is the sum of fluctuating loads within the system.
In step S250 of the present application, the internal state quantity X of the control area system is customized i Comprises the following steps:
x i ＝[Δf i ΔP mi ΔP ri ΔP gi ΔP tiei ∫ACE i dt] T (ii) a In the formula,. DELTA.f i Is the ith area frequency deviation; delta P mi The output variation of the unit is obtained; delta P ri The output thermal power increment of the reheating unit is changed; delta P gi Is the variable quantity of the valve of the steam turbine; delta P tiet Is the tie line power deviation; ACE i The error is controlled for the zone of the ith control zone.
Self-defining control area system internal state quantity X i And substituting into the LFC model which is constructed in the step S240 and takes the nonlinear load into consideration to obtain a mathematical model, and then converting into a TS optimization model based on a TS fuzzy rule. The mapping relation between the control parameters of the ACE and the TS controller is preset in the optimization model.
Optionally, the control parameter set comprises proportional gain Kp, integral time Ki and differential amplification factor Kd, the multiple control parameter sets are brought into the TS optimization model, and an optimal solution is obtained according to a whale algorithm, so that an optimal solution of the control parameters is obtained.
Optionally, the TS fuzzy rule is If-Then fuzzy rule.
Specifically, the expression form of the If-Then fuzzy rule is as follows:
R l :IfΔX i1 isM 1l ,andΔX i2 isM 2l ,ΔX i3 isM 3l ,Then
in the formula, M ij Is a fuzzy set; a. The i ，B i Is a coefficient matrix corresponding to the ith system; Δ X ij Is a front-part variable.
Referring to fig. 4, a second aspect of the present application provides a load frequency control device based on TS fuzzy control, which is 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 solving module 14 and a power adjustment module 15.
The signal obtaining module 11 is configured to obtain a load frequency demand signal; the load frequency demand signal comprises the disturbance at the load end and the frequency deviation generated by the system.
The error determination module 12 is configured to determine a zone control error according to the load frequency demand signal.
The parameter set obtaining module 13 is configured to obtain a plurality of control parameter sets corresponding to the area control errors according to a preset mapping relationship.
The solving module 14 is configured to use the plurality of control parameter sets as inputs of a preset TS optimization model, and solve the optimal control parameters through a preset algorithm.
The power adjusting module 15 is configured to adjust the output power of the generator set according to the optimized control parameter.
The load frequency control device based on the TS fuzzy control, provided by the embodiment of the application, is used for optimizing a load frequency control model considering the nonlinear load into a TS optimization model through the TS fuzzy control and applying the TS optimization model to a TS fuzzy controller. In practical application, a load frequency demand signal is obtained, a regional control error is determined, and an optimal solution of control parameters is sought after a plurality of control parameter sets are obtained through a preset mapping relation, so that the output power of the generator set is accurately adjusted. And further, the stability of the power system is improved, and system fluctuation is reduced.
A third aspect of the embodiments of the present application provides a load frequency control device based on TS fuzzy control, including the apparatus as described above.
It should be noted that, in the present application, the terms "first", "second", and the like are used for distinguishing similar objects, and are not necessarily used for describing a specific order or sequence. It is to be understood that such terms are interchangeable under appropriate circumstances such that the embodiments described herein are capable of operation in other sequences than described or illustrated herein.
The computer program product provided in the embodiment of the present invention includes a computer-readable storage medium storing a program code, where instructions included in the program code may be used to execute the method described in the foregoing method embodiment, and specific implementation may refer to the method embodiment, which is not described herein again.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the system and the apparatus described above may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In addition, in the description of the embodiments of the present invention, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meaning of the above terms in the present invention can be understood in specific cases for those skilled in the art.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will understand that the following embodiments are merely illustrative of the present invention, and not restrictive, and the scope of the present invention is not limited thereto: any person skilled in the art can modify or easily conceive the technical solutions described in the foregoing embodiments or equivalent substitutes for some technical features within the technical scope of the present disclosure; such modifications, changes or substitutions do not depart from the spirit and scope of the embodiments of the present invention, and they should be construed as being included therein. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A load frequency control method based on TS fuzzy control is characterized in that the method is applied to a TS fuzzy controller, and the method comprises the following steps:
acquiring a load frequency demand signal; the load frequency demand signal comprises the disturbance at the load end and the frequency deviation generated by the system;
determining a region control error according to the load frequency demand signal;
acquiring a plurality of control parameter sets corresponding to the regional control errors according to a preset mapping relation;
taking the plurality of control parameter sets as the input of a preset TS optimization model, and solving optimal control parameters through a preset algorithm;
and adjusting the output power of the generator set according to the optimal control parameter.
2. The TS fuzzy control based load frequency control method of claim 1 wherein said step of determining a zone control error from said load frequency demand signal comprises:
determining a zone control error according to the following specified equation:
ACE i ＝ΔP i +β i Δf i ；
wherein, ACE i Zone control error, Δ P, for the ith control zone i A zone control error for the ith control zone; beta is a i Is the frequency deviation constant of the ith area; Δ f i Is the frequency deviation of the i-th region.
3. The method for controlling load frequency based on TS fuzzy control according to claim 1, wherein said step of solving optimal control parameters by a preset algorithm using said plurality of control parameter sets as input of a preset TS optimization model comprises:
and taking the plurality of control parameter sets as the input of a preset TS optimization model, and performing optimal solution solving through a whale algorithm to obtain the optimal control parameters.
4. The method for controlling load frequency based on TS fuzzy control according to claim 1, wherein said step of solving optimal control parameters by a preset algorithm using said plurality of control parameter sets as inputs of a preset TS optimization model comprises:
constructing a load frequency control model considering nonlinear load;
self-defining control area system internal state quantity X i Inputting the load frequency control model for prediction to obtain a mathematical model of the ith area;
and converting the mathematical model into a TS optimization model based on a TS fuzzy rule.
5. The TS fuzzy control-based load frequency control method according to claim 4, wherein the customized ith control area system internal state quantity is:
x i ＝[Δf i ΔP mi ΔP ri ΔP gi ΔP tiei ∫ACE i dt] T ；
wherein x is i For the ith control area system internal state quantity, Δ f i For i-th control region frequency deviation, Δ P mi Is the unit output variation, delta P ri For incremental change in reheat unit output thermal power, Δ P gi For turbine valve variations, Δ P tiet ACEi is the zone control deviation of the ith control zone for the tie line power deviation.
6. The load frequency control method based on TS fuzzy control according to claim 4, wherein said constructing a load frequency control model considering nonlinear load comprises:
the speed regulator dead zone model, the speed regulator model, the prime mover model, the generator load model and the load frequency control model are sequentially connected.
7. The load frequency control method based on the TS fuzzy control of claim 1, wherein the control parameter set comprises a proportional gain Kp, an integral time Ki, and a differential amplification Kd.
8. The TS fuzzy control based load frequency control method of claim 4, wherein the TS fuzzy rule is If-Then fuzzy rule.
9. A load frequency control device based on TS fuzzy control is characterized in that the device is applied to a TS fuzzy controller, and the device comprises:
the signal acquisition module is used for acquiring a load frequency demand signal; the load frequency demand signal comprises the frequency deviation generated by the disturbance of a load end and a system;
the error determining module is used for determining a region control error according to the load frequency demand signal;
the parameter set acquisition module is used for acquiring a plurality of control parameter sets corresponding to the regional control errors according to a preset mapping relation;
the solving module is used for taking the plurality of control parameter sets as the input of a preset TS optimization model and solving the optimal control parameters through a preset algorithm;
and the power adjusting module is used for adjusting the output power of the generator set according to the optimal control parameter.
10. A load frequency control device based on TS fuzzy control, characterized in that the device comprises the apparatus according to claim 9.
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| CN117039941B (en) * | 2023-10-09 | 2024-01-26 | 长江三峡集团实业发展(北京)有限公司 | Optimization method and device for automatic power generation control, computer equipment and storage medium |
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