CN112152221B - Load frequency control device and method suitable for information uncertainty system - Google Patents
Load frequency control device and method suitable for information uncertainty system Download PDFInfo
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/04—Circuit arrangements for ac mains or ac distribution networks for connecting networks of the same frequency but supplied from different sources
- H02J3/06—Controlling transfer of power between connected networks; Controlling sharing of load between connected networks
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J13/00—Circuit arrangements for providing remote indication of network conditions, e.g. an instantaneous record of the open or closed condition of each circuitbreaker in the network; Circuit arrangements for providing remote control of switching means in a power distribution network, e.g. switching in and out of current consumers by using a pulse code signal carried by the network
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/24—Arrangements for preventing or reducing oscillations of power in networks
- H02J3/241—The oscillation concerning frequency
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- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2203/00—Indexing scheme relating to details of circuit arrangements for AC mains or AC distribution networks
- H02J2203/20—Simulating, e g planning, reliability check, modelling or computer assisted design [CAD]
Abstract
The invention relates to a load frequency control device suitable for an information uncertainty system, which comprises: the system comprises an information acquisition module, an optimization decision module and a self-adaptive control module, wherein the information acquisition module is used for accessing a state signal of a communication system at the moment N into the device through sensor sampling measurement and integrally uploading the sampling signal to a proxy node; the optimization decision module receives the information output by the information acquisition module, carries out synchronous sampling conversion according to the probability measure of occurrence of a random event of the communication system, analyzes the information uncertainty degree of the system through the established model of the information entropy H, and inputs the analysis result into the self-adaptive control module; the self-adaptive control module inputs the data analysis result into the self-adaptive control module designed based on the fuzzy rule, and establishes a mathematical model of information entropy aiming at probability measure representation of each quantity affecting the uncertainty of the system information.
Description
Technical Field
The invention relates to a load frequency self-adaptive control device, in particular to frequency control of an information uncertainty system based on a self-adaptive fuzzy controller.
Background
The single power system analysis is based on network modeling and tide calculation, and is used for completing various works such as power transmission and distribution planning, transient steady state fault analysis, reliability calculation and the like. The load frequency control (Load frequency control, LFC) system realizes the mapping relation between the input deviation and the output control quantity of the controller through the power exchange stability of the control area interconnection line, and along with the development of ICT (Information and communications technology) technology in the power system, the uncertainty of information presents a serious challenge to the steady-state operation of the power system.
The new energy has volatility and randomness and cannot be stored. And thus cannot generate electric power while maintaining the primary energy relatively stable, as with conventional energy sources. In order to solve the requirements of minimum frequency deviation and strong robustness of a complex system, a control scheme based on an intelligent algorithm technology is introduced, and the defects of the traditional control mode in the aspects of system nonlinearity, uncertainty and the like are overcome. Generating a decision instruction by adopting a generalized predictive control algorithm to compensate delay, packet loss and disorder of an information system so as to inhibit low-frequency oscillation of the system; a frequency control planning method of random hierarchical distributed model predictive control is used for establishing a power predictive error model of each time scale, and the power predictive error model is cooperated with a traditional power supply to carry out frequency modulation; island load frequency control methods combined by fuzzy logic sets and improved algorithms are used to reduce microgrid frequency deviations for load disturbances.
The use of an open communication integrated network in the LFC system increases the influence of time lag and other information uncertainty problems on frequency stability. In order to solve the frequency robust control under high information uncertainty, an effective PI controller is built on the basis of a random stability criterion by adopting a random stabilization method in a single-area LFC system; presetting LFC time lag to realize optimization setting of fractional order PID controller parameters based on computation intelligence; the multi-area LFC system provides a Markov-based method by estimating time delay and data packet loss probability, and reduces frequency deviation under steady state and transient response. In the double-area system, as new energy sources such as wind energy and the like are accessed, the bidirectional flow of energy and information depends on the use of various intelligent primary and secondary devices, and the communication system has the characteristics of more complexity, flexibility and variability. The specific communication indexes of each service of the power system can be obtained, and the time lag, packet loss, error code and other information uncertainty characteristics of the communication system in a stable running state need to be maintained in a basic stable range. Therefore, considering modeling the uncertainty of each information as an information entropy model, the requirement on the information entropy can be used for measuring the importance degree of the system service in a unified way, and the higher the importance degree is, the smaller the allowed information entropy is.
Disclosure of Invention
The invention aims to solve the technical problem of providing a load frequency self-adaptive control device suitable for an information uncertainty system. As described in detail below.
The invention relates to a load frequency control device suitable for an information uncertainty system, which comprises: the system comprises an information acquisition module, an optimization decision module and a self-adaptive control module. The device structure is as follows.
And the information acquisition module is used for: and (3) adopting a sensor based on the shannon sampling theorem to sample and measure, accessing a state signal of the communication system at the moment N into the device, and integrating and uploading the sampling signal to the proxy node.
And an optimization decision module: synchronous sampling conversion is carried out on the information obtained by the information acquisition module, and the information uncertainty of the system is analyzed through the established model of the information entropy H according to the probability measure of occurrence of random events of the communication system; and inputting the analysis result into the control module.
And the self-adaptive control module is used for: the data analysis result enters an adaptive control module designed based on fuzzy rules, the probability measure expression of each quantity affecting the uncertainty of the system information is selected, the information entropy change error delta H and the error differential d delta H are adjusted in an online adaptive parameter, the output parameters of a controller are optimized, and then the regional control error ACE is judged according to the output parameters i The value of the system frequency deviation Δf; and storing and outputting the result as the i-area system state at the time of N+1 to an information acquisition module, and returning to the reset state signal and the control signal with time information mark management.
A load frequency self-adaptive control device suitable for an information uncertain system comprises the following steps:
1) Initializing, establishing mathematical models of all parts aiming at the communication process of the power system affected by randomness, wherein the basic power transmission is realized through a connecting line by taking a reheat turbine and a non-reheat turbine as an example as described in claim 2. And determining a communication networking mode of the star connection, and providing prior support for parameter selection of the communication uncertainty measure.
2) Setting an operation period N, time lag and error probability measure distribution parameters, and determining mathematical representations of various quantities affecting the uncertainty of the information: and describing the time lag distribution situation by adopting 3-parameter Weibull distribution, wherein the 3-parameter Weibull distribution presets a threshold value, and the independent variable distribution presents cliff-broken distribution phenomenon before and after the threshold value.
3) The error rate of communication is equivalent to the error rate of reliability theory, which is a probability event based on poisson distribution, namely
4) The traditional event triggering mechanism calculates the packet loss rate by judging the packet loss probability possibly occurring in the platform by the TCP stream, and establishes the system packet loss rate P l Based on the information flow model that fluctuates with time, it is closely related to system topology and network traffic.
5) On the premise that the time lag, the error code and the packet loss are independent of each other and no coupling relation exists, a mathematical model of the information entropy can be obtained as
Wherein P is sum And represents the sum of probability measures affecting the respective amounts of uncertainty of the system information.
6) Establishing an information entropy H model to analyze and calculate the information uncertainty degree of a multi-region load frequency control system through the probability measure of occurrence of random events
Wherein X represents each event; h (X) represents the sum of the entropy values of each event in the system, namely, the information entropy; n is the total number of events; a, a i Is one possible value of X.
7) And designing a self-adaptive fuzzy controller based on a fuzzy rule, converting the input quantity into the output quantity of the adjusting parameter, and optimizing the configuration of the controller through on-line self-adaptive parameter adjustment.
8) Judging an area control error ACE according to the output parameters of the controller i If the variation of the system frequency deviation Deltaf is far beyond the allowable fluctuation range, checking the communication networking mode, returning a control signal for fault processing, and if the variation is within the allowable fluctuation range, returning to the operation periodThe change condition ends.
2. A load frequency adaptive control device for an information uncertainty system as claimed in claim 1, wherein stage 1) comprises the steps of:
1) The reheat turbine, turbine governor and inertia links of the ith system are respectively expressed as:
wherein T is it Representing a turbine time constant; t (T) ig Representing a governor time constant; m is M i Representing the inertial constant of the unit; d (D) i Representing the load damping coefficient.
2) The model expression of the i-th control region is:
wherein A is i Is a state matrix of the system, B i For the input matrix of the system, F i Is a disturbance matrix; x is x i U is a state variable i To control the quantity omega i Is the disturbance quantity of the system.
3. A load frequency adaptive control device for an information uncertainty system as claimed in claim 1, wherein stage 6) comprises the steps of:
the input quantity is information entropy change error delta H and error differential ddelta H, the theory domain [ -6,6]The method comprises the steps of carrying out a first treatment on the surface of the Output is a proportion of K p Integral K i Differential K d The controller parameter adjustment amount of (a) the domain [ -6,6]The method comprises the steps of carrying out a first treatment on the surface of the And selecting a control rule of the 7 fuzzy set, and a triangle membership function. The setting algorithm takes the proportional parameter as an example
K p (k)＝K p0 +ΔK p (k)
And k is sampling time, parameters are adaptively adjusted according to a control rule, and controller parameters which are more in line with system changes are output.
Advantageous effects
The invention relates to a load frequency self-adaptive control device suitable for an information uncertainty system, which has the following characteristics:
the existing research generally regards time lag, packet loss and other factors as known information determination quantity, and is not applicable to the frequency control problem of a complex information uncertainty system. Therefore, the method for controlling the load frequency of the double-area system based on the information entropy is provided, the self-adaptive fuzzy control of the load frequency is realized on the basis of a system information uncertainty model, and the double-area system is used as an application calculation analysis to prove the general applicability of the proposal. And integrating time lag, packet loss and error code of the information system to establish a mathematical model of information entropy, completing modeling of system information uncertainty, and establishing a system load frequency model by quantifying the information uncertainty to realize frequency control considering the information uncertainty. The information entropy deviation delta H is used as the input of the self-adaptive fuzzy controller, so that the singleness of the traditional fuzzy controller is obviously improved, the controller design under the consideration of information uncertainty is effectively met, random disturbance and impact disturbance are simulated at the same time, and the robustness of systems with different information uncertainties is effectively verified.
Drawings
The accompanying drawings, which are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention. In the drawings:
FIG. 1 is a diagram of a load frequency adaptive control device for an information uncertainty system
FIG. 2 is a flow chart of a system load frequency control routine
FIG. 3 is a schematic view of a non-reheat steam turbine configuration;
FIG. 4 is a schematic view of a reheat steam turbine;
FIG. 5 is an integral parameter fuzzy control rule;
FIG. 6 is a P of a two-zone system s A change condition;
FIG. 7 shows the frequency deviation Δf of a two-zone system zone i;
Detailed Description
The following describes in further detail the embodiments of the present invention with reference to the drawings and examples. The following examples are illustrative of the invention and are not intended to limit the scope of the invention.
Aiming at the influence of high information uncertainty on the physical state of a system in a complex multidimensional system environment, the core is a load frequency self-adaptive control strategy of the information uncertainty system, which is realized in an optimization decision and self-adaptive control unit. Firstly, an information entropy model of an information system is established, and the information uncertainty of the power system is quantified. And then calculating the entropy value according to the state of the system, thereby judging the uncertainty degree of the system. And finally, realizing the load frequency control of the system through the self-adaptive fuzzy controller.
The invention relates to a load frequency self-adaptive control device suitable for an information uncertainty system, and a device diagram is shown in fig. 1. The device comprises an information acquisition module, an optimization decision module and a self-adaptive control module. The information acquisition module integrates an Ethernet MAC layer based on communication dynamic protocol analysis and shannon sampling theorem by taking the initial state of the system as input and the microcontroller as a kernel, so that the interface circuit design is simplified; the optimization decision module comprises a system priori database module, a data management module and a topology analysis module, so as to obtain a system information uncertainty measure calculated according to the acquired information; the self-adaptive control module comprises a communication interface which is in bidirectional communication with the MCU unit, a sensor unit, a power supply unit, a display unit and a command execution unit.
And the information acquisition module is used for: and (3) adopting a sensor based on the shannon sampling theorem to sample and measure, accessing a state signal of the communication system at the moment N into the device, and integrating and uploading the sampling signal to the proxy node.
And an optimization decision module: and carrying out synchronous sampling conversion on the information obtained by the information acquisition module, and analyzing the information uncertainty of the system through the established model of the information entropy H according to the occurrence probability measure of the random event of the communication system. And inputting the analysis result into the control module.
And the self-adaptive control module is used for: the data analysis result enters an adaptive control module designed based on fuzzy rules, and the information entropy change error delta H and the error differential ddelta H are selected in the probability measure representation of each quantity affecting the uncertainty of the system informationLine self-adaptive parameter adjustment, optimizing controller output parameters, and judging regional control error ACE according to the output parameters i The value of the system frequency deviation deltaf. And storing and outputting the result as the i-area system state at the time of N+1 to an information acquisition module, and returning a reset state signal and a control signal with time information mark management.
The main program of the system software of the load frequency adaptive control device suitable for the information uncertainty system is shown in fig. 2, and comprises the following parts:
1) Uploading a program: program writing micro control unit;
2) Initializing a system: initializing clock data, serial I/O interface, A/D conversion module, topology analysis module, MCU unit, and display unit zero correction.
3) Setting a sampling period T;
4) Setting a running break point: and the monitoring fluctuation result of the read data after T periods is displayed by the display module, and the control result is output.
A load frequency self-adaptive control device suitable for an information uncertain system comprises the following steps:
step one: initializing, establishing mathematical models of all parts aiming at the communication process of the power system affected by randomness, taking a reheat turbine and a non-reheat turbine as examples, and realizing basic power transmission through a connecting line. And determining a communication networking mode of the star connection, and providing prior support for parameter selection of the communication uncertainty measure. For a double-zone system consisting of LFC, each zone contains traditional power generation equipment, a non-reheat turbine and a reheat turbine are respectively used in the two zones, the two-way flow of power is realized between the two zones through a connecting line, and the structural schematic diagrams of the reheat/non-reheat turbines are respectively shown in figures 3 and 4.
(1) The reheat turbine, turbine governor and inertia are respectively expressed as
Wherein T is it Representing a turbine time constant; t (T) ig Representing a governor time constant; m is M i Representing the inertial constant of the unit; d (D) i Representing the load damping coefficient.
(2) The model expression of the i-th control region is:
wherein A is i Is a state matrix of the system, B i For the input matrix of the system, F i Is a disturbance matrix; x is x i U is a state variable i To control the quantity omega i Is the disturbance quantity of the system.
Step two: setting running period N, time lag and error probability measure distribution parameters, and determining mathematical representations of various quantities affecting the uncertainty of the information. Establishing an information entropy H model to analyze and calculate the information uncertainty degree of a multi-region load frequency control system through the probability measure of occurrence of random events
Wherein X represents each event; h (X) represents the sum of the entropy values of each event in the system, namely, the information entropy; n is the total number of events; a, a i Is one possible value of X.
Describing the time lag distribution situation by adopting 3-parameter Weibull distribution, presetting a threshold value existing in the 3-parameter Weibull distribution, and enabling independent variable distribution to present cliff-broken distribution phenomenon before and after the threshold value, wherein the phenomenon can be approximately regarded as time lag tau>τ max When the data packet transmission time lag is too large, the packet loss is equivalent. Communication ofError rate equivalent to reliability theory in terms of error numerology is a probability event based on poisson distribution, namely
The traditional event triggering mechanism calculates the packet loss rate by judging the packet loss probability possibly occurring in the platform by the TCP stream, and establishes the system packet loss rate P l Based on the information flow model that fluctuates with time, it is closely related to system topology and network traffic. On the premise that the time lag, the error code and the packet loss are independent of each other and no coupling relation exists, a mathematical model of the information entropy can be obtained as
Wherein P is sum And represents the sum of probability measures affecting the respective amounts of uncertainty of the system information.
Uncertainty of descriptive information system is
P＝(ε 1 P τ )·(ε 2 P l )·(ε 3 P ber )
Wherein P is τ Representing probability distribution conditions of system time lags; p (P) l Representing the packet loss rate of the system; p (P) ber Representing the error rate of the system; epsilon 1 、ε 2 、ε 3 The time lag distribution probability, the packet loss rate and the weight coefficient of the error rate are respectively. Then there is
Step three: self-adaptive fuzzy controller based on fuzzy rule
The input quantity is converted into the output quantity of the adjusting parameter, the controller is optimally configured through on-line self-adaptive parameter adjustment, the input quantity is information entropy change error delta H and error differential ddelta H, and the theory is [ -6,6]The method comprises the steps of carrying out a first treatment on the surface of the Output is a proportion of K p Integral K i Differential K d The controller parameter adjustment amount of (a) the domain [ -6,6]The method comprises the steps of carrying out a first treatment on the surface of the And selecting a control rule of the 7 fuzzy set, and a triangle membership function. The setting algorithm is
K p (k)＝K p0 +ΔK p (k)
K i (k)＝K i0 +ΔK i (k)
K d (k)＝K d0 +ΔK d (k)
And k is sampling time, parameters are adaptively adjusted according to a control rule, and controller parameters which are more in line with system changes are output. Wherein the proportional, integral and differential parameter fuzzy rules are shown in figure 5. The control rules for the 7 fuzzy sets are shown in the following table
Step four: judging influence of system uncertainty change on information entropy
The information entropy value of the system can be calculated according to the expression of the information entropy in the second step, and the influence of different weights on the information entropy is shown in the following table
When the sum of all weights is equal, the larger the weight of a single uncertain item is, the smaller the information entropy H is, namely when the ratio of one item in the uncertain information amount of the system is too high, only the item mainly affects the system, and the influence of other uncertain amounts is gradually reduced because the weight is reduced, so that the total uncertainty of the system is correspondingly reduced; when the weights are close, the information entropy H is larger, and because a plurality of uncertainty amounts influence the system simultaneously in the running process of the system, the total uncertainty of the system is increased. This result also corresponds to the definition of the entropy of the information in step two, i.e. the entropy is the largest when the probability of occurrence of all random events is the same.
Step five: system load frequency control by self-adaptive fuzzy controller
The uncertainty system is characterized by higher information entropy H, and an accurate system model cannot be established. Therefore, the adaptive control method is adopted, the mathematical model of the uncertain system and the external environment thereof is uncertain, the adaptive control research of the linear uncertain system is equivalent to the uncertain nonlinear system model by using the estimated value through the equivalent principle, and the estimated value is continuously approximate to the true value under continuous excitation, so as to realize the adaptive control on the system, thereby firstly ensuring the stability of the uncertain system.
The flow chart of load frequency control of the system by using the self-adaptive fuzzy controller is shown in fig. 6, the effectiveness of the proposed method is verified, and a two-area LFC system is taken as an example, n i The area 3,i builds the original simulation parameters of the simulation model to the actual system on the Simulink simulation platform, and the simulation time t=100 s is shown in the following table.
According to the output parameters of the controller, in order to verify a load frequency self-adaptive control device suitable for an information uncertainty system in the invention, the frequency deviation of the system under the time domain of the controller without considering information entropy is compared, and the abscissa of fig. 7 is simulation time, and the ordinate is frequency deviation change. The method is obviously better than the control effect of the PI controller, and improves the robustness and response speed of the system.
Claims (3)
1. A load frequency control device suitable for an information uncertainty system is composed of a device body; the device is characterized by comprising an information acquisition module, an optimization decision module and a self-adaptive control module;
the information acquisition module integrates an Ethernet MAC layer by taking the initial state of a system as input and a microcontroller as a kernel based on communication dynamic protocol analysis and shannon sampling theorem, so that the interface circuit design is simplified;
the optimization decision module comprises a system priori database module, a data management module and a topology analysis module, so as to obtain a system information uncertainty measure calculated according to the acquired information;
the self-adaptive control module comprises a communication interface which is in bidirectional communication with the MCU unit, a sensor unit, a power supply unit, a display unit and a command execution unit; wherein:
the information acquisition module is used for accessing a state signal of the communication system at the moment N into the device through sensor sampling measurement, and integrating the sampling signal and uploading the sampling signal to the optimization decision module; the optimization decision module receives the information output by the information acquisition module and performs synchronous sampling conversion according to the probability measure of occurrence of the random event of the communication system;
analyzing the information uncertainty of the system through the established model of the information entropy H, and inputting an analysis result into the self-adaptive control module; the self-adaptive control module inputs the data analysis result into the self-adaptive control module designed based on fuzzy rule, selects the information entropy change error delta H and the error differential d delta H to adjust the self-adaptive parameters on line according to the probability measure expression of each quantity affecting the uncertainty of the system information, optimizes the output parameters of the controller, and then judges the regional control error ACE according to the output parameters i The value of the system frequency deviation Δf; and storing and outputting the result as the i-area system state at the time of N+1 to an information acquisition module, and returning a reset state signal and a control signal with time information mark management.
2. The method for controlling the load frequency of an information uncertainty system according to claim 1, comprising the steps of:
the hardware module of the load frequency control device in claim 1 is initialized, mathematical models of all parts are established aiming at the communication process of the power system affected by randomness, and basic power transmission is realized through a connecting line; determining a communication networking mode of star connection, and providing prior support for parameter selection of communication uncertainty measure;
setting an operation period T, time lag and error probability measure distribution parameters, and determining mathematical representations of various quantities affecting the uncertainty of the information; establishing an information entropy H model to analyze and calculate the information uncertainty degree of a multi-region load frequency control system through the probability measure of occurrence of random events
Wherein X represents each event; h (X) represents the sum of the entropy values of each event in the system, namely, the information entropy; n is the total number of events; a, a i Is one possible value of X;
describing the distribution condition of time lags by adopting 3-parameter Weibull distribution, presetting a threshold value of the 3-parameter Weibull distribution, and enabling independent variable distribution to present cliff-broken distribution before and after the threshold value; the error rate of communication is equivalent to the error rate of reliability theory, which is a probability event based on poisson distribution, namely
The traditional event triggering mechanism calculates the packet loss rate by judging the packet loss probability possibly occurring in the platform of the data stream based on the TCP transmission mode, and establishes the system packet loss rate P l Based on the information flow model fluctuating with time, the information flow model is closely related to the system topology and the network flow; on the premise that the time lag, the error code and the packet loss are independent of each other and no coupling relation exists, a mathematical model of the information entropy can be obtained as
Wherein P is sum A sum of probability measures representing respective amounts affecting the uncertainty of the system information;
the self-adaptive fuzzy controller based on the fuzzy rule is designed, the input quantity is converted into the output quantity of the adjusting parameter, and the controller configuration is optimized through the on-line self-adaptive parameter adjustment;
judging the influence of the uncertainty change of the system on the information entropy;
realizing system load frequency control through the self-adaptive fuzzy controller, namely judging the area control error ACE according to the output parameters of the controller i And if the variation condition of the system frequency deviation delta f is far beyond the allowable fluctuation range, checking the communication networking mode, returning a control signal aiming at fault processing, and if the variation condition is within the allowable fluctuation range, returning to the variation condition in the operation period, and ending.
3. The method for controlling the load frequency of the information uncertainty system according to the apparatus of claim 2, wherein the adaptive fuzzy controller optimizes:
the input quantity is information entropy change error delta H and error differential ddelta H, the theory domain [ -6,6]The method comprises the steps of carrying out a first treatment on the surface of the Output is a proportion of K p Integral K i Differential K d The controller parameter adjustment amount of (a) the domain [ -6,6]；
Selecting a control rule of the fuzzy set, and a triangle membership function; the setting algorithm takes the proportional parameter as an example
K p (k)＝K p0 +ΔK p (k)
And k is sampling time, parameters are adaptively adjusted according to a control rule, and controller parameters which are more in line with system changes are output.
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| CN105958512A (en) * | 2016-03-18 | 2016-09-21 | 上海电力学院 | Load-frequency control method of multi-domain time-lag electric power system with high wind energy permeability |
| CN106451495A (en) * | 2016-10-21 | 2017-02-22 | 上海电力学院 | Multi-domain electric power system load frequency control method with wind storage |
| CN108306340A (en) * | 2018-02-05 | 2018-07-20 | 河北工业大学 | Interconnected electric power system LOAD FREQUENCY Planar clouds control method containing new energy |
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