CN116191476B - Intelligent power grid primary frequency modulation system - Google Patents
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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
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- H—ELECTRICITY
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- 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
- H02J3/007—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources
- H02J3/0075—Arrangements for selectively connecting the load or loads to one or several among a plurality of power lines or power sources for providing alternative feeding paths between load and source according to economic or energy efficiency considerations, e.g. economic dispatch
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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
- H02J3/12—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load
- H02J3/14—Circuit arrangements for ac mains or ac distribution networks for adjusting voltage in ac networks by changing a characteristic of the network load by switching loads on to, or off from, network, e.g. progressively balanced loading
- H02J3/144—Demand-response operation of the power transmission or distribution network
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- H—ELECTRICITY
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- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
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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
- H02J2310/00—The network for supplying or distributing electric power characterised by its spatial reach or by the load
- H02J2310/50—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads
- H02J2310/56—The network for supplying or distributing electric power characterised by its spatial reach or by the load for selectively controlling the operation of the loads characterised by the condition upon which the selective controlling is based
- H02J2310/58—The condition being electrical
- H02J2310/60—Limiting power consumption in the network or in one section of the network, e.g. load shedding or peak shaving
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Abstract
The patent relates to an intelligent electric wire netting primary frequency modulation system, and this system includes collector, controller, executor and predictor. The collector is used for collecting historical data and real-time data of the power system, the controller responds to the system frequency change of the power system based on the collected historical data and real-time data, the frequency modulation control quantity is calculated, and the executor comprises a hardware controller, a monitor and a mode regulator and is used for controlling the operation of the power system, monitoring the operation state of the power system and switching the power system between a frequency modulation mode and a standby mode according to the monitoring result or the prediction result. The predictor predicts the probability of power system anomalies within a future set time frame based on historical data and real-time data of the power system. The system can improve the frequency modulation precision and the load response capability of the power system, reduce the running cost and risk of the power system, and has higher practicability and economic benefit.
Description
Technical Field
The invention belongs to the technical field of power grid control, and particularly relates to an intelligent power grid primary frequency modulation system.
Background
With the rapid development of society and the increasing population, the demand for energy is also increasing. In order to meet such demands, the construction of intelligent power grids is actively advanced in all countries around the world to improve the operation efficiency and reliability of the power system. However, in practical applications, there are still problems in the operation of the grid primary frequency modulation system, which limit the further development of the intelligent grid. Therefore, we need to conduct intensive research on these problems to realize intelligent control and optimization of the grid primary frequency modulation system.
Currently, some progress has been made in the research of the grid primary frequency modulation system. For example, US8285487B2 describes a grid primary frequency modulation system in an electrical power system, which includes a control system and a regulation system. The control system adjusts the frequency of the power system according to the real-time state and the demand of the power system. However, this method has problems such as inability to achieve a rapid and accurate frequency response, and susceptibility to overshoot and instability.
In order to solve these problems, chinese patent document CN106661739a proposes an improvement of the primary frequency modulation system of the power grid. The scheme utilizes deep learning and neural network technology to intelligently control and optimize the primary frequency modulation system of the power grid. Specifically, the scheme establishes a prediction model of the primary frequency modulation system of the power grid through learning and analyzing historical data, and performs intelligent control and optimization according to a prediction result. The scheme can realize quick and accurate frequency response, and can avoid problems of over-regulation, instability and the like. However, this approach still has problems, such as high demands on the history data, large amounts of computing resources and time, and is not well suited for real-time applications.
In summary, the primary frequency modulation system of the power grid has some problems in practical application, such as slower frequency response speed, easy occurrence of excessive regulation and instability, and the like. Currently, solutions have been proposed, such as methods based on control systems and regulation systems, and methods using deep learning and neural network techniques. However, these methods still have some problems, and further research and improvement are needed to achieve intelligent control and optimization of the grid primary frequency modulation system.
Disclosure of Invention
The invention mainly aims to provide an intelligent power grid primary frequency modulation system and aims to solve the problems in the prior art. Specifically, the system adopts a novel intelligent control and optimization method, can realize quick and accurate frequency response, and can avoid problems of excessive regulation, instability and the like. The system may also adaptively adjust parameters to accommodate real-time conditions and demands of the power system. Compared with the prior art, the system has higher reliability and flexibility, and can meet the application requirements in different scenes.
In order to solve the problems, the technical scheme of the invention is realized as follows:
An intelligent power grid primary frequency modulation system, the system comprising: the system comprises a collector, a controller, an executor and a predictor; the collector is configured to collect historical data and real-time data of the power system; the controller is configured to respond to the change of the system frequency of the power system based on the collected historical data and real-time data, and calculate the frequency modulation control quantity; the actuator comprises: a hardware controller, a monitor, and a mode adjuster; the hardware controller is configured to control the operation of hardware in the power system according to the calculated frequency modulation control quantity; the monitor is configured to monitor the running state of the power system according to the collected historical data and the real-time data so as to judge whether the power system has faults or anomalies and obtain monitoring results; the mode regulator is configured to switch the power system between a frequency modulation mode and a standby mode according to a monitoring result of the monitor or a prediction result of the predictor; when the electric power system is in the frequency modulation mode, the controller responds to the change of the system frequency of the electric power system, the frequency modulation control quantity is calculated, and the hardware controller in the actuator controls the frequency of the electric power system to fluctuate within a set range according to the calculated frequency modulation control quantity; when the power system is in a standby mode, the controller can keep the generator capacity with the set capacity value as the standby capacity so as to cope with sudden load change of the power system, and when the power system has load fluctuation, the standby capacity is output through the hardware controller to maintain the stability of the power system; the predictor is configured to predict an abnormality probability of the power system within a set time range in the future according to the historical data and the real-time data of the power system, and if the predicted abnormality probability exceeds a set abnormality probability threshold, generate a prediction result, and send the prediction result to the mode regulator.
Further, the historical data of the power system includes: historical frequency data and historical load data; the real-time data includes: real-time frequency data and real-time load data.
Further, the controller, based on the collected historical data and real-time data, responds to the change of the system frequency of the power system, and the method for calculating the frequency modulation control quantity comprises the following steps: the frequency modulation control amount is calculated using the following formula:
u(t)＝Kp*e(t)+Ki*∫e(t)dt+Kd*de(t)/dt+Kf*ln(f(t)/f0)+
Kc*(y(t)-y(t-1))^T*(R(t)-R(t-1))；
wherein e (t) is the frequency deviation between the real-time frequency data and the standard frequency data of the power system, kp, ki and Kd are the proportional coefficient, integral coefficient and differential coefficient respectively, de (t)/dt is the change rate of the frequency deviation, kf is the proportional coefficient of a logarithmic function, f (t) is the real-time frequency data of the power system, f0 is the standard frequency of the power system, y (t) is the real-time load data of the power system, R (t) is the matrix in the generalized predictive control, and Kc is the proportional coefficient in the generalized predictive control.
Further, the controller can adaptively adjust parameters in the frequency modulation control quantity calculation formula so as to adapt to the real-time state and the requirement of the power system; the algorithm formula of the self-adaptive method is as follows:
Kp,Ki,Kd,Kf,Kc＝PSO(fitness)；
wherein PSO is a particle swarm optimization algorithm function, fitness is an fitness function, and Kp, ki, kd, kf and Kc are proportional coefficients, integral coefficients, differential coefficients, proportional coefficients of logarithmic functions and proportional coefficients in generalized predictive control, respectively.
Further, the fitness function fitness has the expression:
fitness＝1/(1+J)；
wherein J is the performance index of the controller and is the square sum of control errors.
Further, the hardware controller includes: a generator speed controller, a load controller, and a capacitor controller; the generator rotating speed controller adjusts the rotating speed of the generator according to the frequency modulation control quantity obtained by the controller; the load controller adjusts the load according to the instruction of the controller; the capacitor controller controls the charge and discharge states of the capacitor according to instructions of the controller.
Further, the monitor constructs a matrix X by using collected historical data and real-time data of the power system, and then decomposes the matrix X into products of two low-rank matrices U and V ζ through a matrix decomposition algorithm, namely:
X≈UV^T；
representing the historical data and the real-time data as a linear combination of a set of basic factors; wherein: matrix X represents the matrix of the historical data and the real-time data of the power system, and matrix U and matrix V-T are the low-rank approximate matrix of matrix X; t represents the transpose operation, V T is the transpose of the matrix V; and comparing the element values in the matrix U and the matrix V-T with preset expected values to judge whether faults or anomalies occur, and if so, generating a monitoring result, wherein the value of the monitoring result is 1.
Further, the mode regulator, according to the collected historical data and real-time data, switches the power system between the frequency modulation mode and the standby mode, the method comprises the following steps: the automatic switching between the frequency modulation mode and the standby mode is carried out by adopting a method based on matrix decomposition and fuzzy logic, and the formula is as follows:
IF(P*O*|X(t)|<|μ1|)THEN(Mode＝R)ELSEIF(P*O*|X(t)|>
|μ2|)THEN(Mode＝F)ELSE(Mode＝μ3*Mode(t)+(1-μ3)*Mode(t-1))；
wherein X (t) represents a matrix of historical load data and historical frequency data of the power system, and is a low-rank approximate matrix obtained based on a matrix decomposition algorithm; mu 1 is a parameter matrix in the fuzzy logic and is used for controlling a switching threshold value of the standby mode, and when the load and the frequency of the power system deviate from standard values, the standby mode is switched; μ2 is a parameter matrix in the fuzzy logic, and is used for controlling a switching threshold value of the frequency modulation mode, and switching to the frequency modulation mode when the load and the frequency of the power system deviate from standard values; mu 3: is a parameter in the fuzzy logic for controlling the smooth switching of modes, smoothly switching to a new mode by linearly combining the modes at the previous time and the current time; mode (t) represents the current power system Mode, its value is 0 or 1,0 represents the standby Mode, and 1 represents the frequency modulation Mode; mode (t-1): a power system mode representing a previous time; r represents a standby mode, and F is a frequency modulation mode; mode represents a Mode of the power system; p is the monitoring result; o is the anomaly probability.
Further, the predictor predicts the abnormality probability of the power system within a future set time range according to the historical data and the real-time data of the power system, and if the predicted abnormality probability exceeds a set abnormality probability threshold, the method for obtaining the prediction result comprises: predicting an anomaly probability of the power system within a set time range in the future using the following formula:
P(t)＝1/(1+exp(-z(t)))；
wherein z (t) = (x (t) - μ) ×σ -1;
x (t) is a matrix of historical load data and historical frequency data of the power system, and is a low-rank approximate matrix obtained based on a matrix decomposition algorithm; μ is the mean vector, σ is the square root of the covariance matrix, and P (t) is the anomaly probability.
Further, the value range of the anomaly probability threshold value is as follows: 30% -40%.
The intelligent power grid primary frequency modulation system has the following beneficial effects:
first, the system can improve the frequency modulation precision and load response capability of the power system. The traditional power system frequency modulation generally adopts mechanical frequency modulation, has the problems of low regulation precision, low response speed and the like, and is difficult to meet the requirements of a modern power system on regulation precision and response speed. The intelligent power grid primary frequency modulation system adopts a modern power system frequency modulation technology, and predicts the abnormal probability of the power system in a future set time range by collecting historical data and real-time data, so as to realize more accurate frequency modulation control and improve the frequency modulation precision of the power system. Meanwhile, a hardware controller in an actuator of the system can quickly respond to the change of the system frequency of the power system according to the calculated frequency modulation control quantity, and the frequency of the power system is controlled to fluctuate within a set range, so that the load response capability of the power system is improved.
Second, the system can reduce the operating cost and risk of the power system. The operating costs and risks of the power system are mainly due to fluctuations in the power load and to malfunctions of the power system. The intelligent power grid primary frequency modulation system provided by the patent timely discovers the abnormal condition of the power system by predicting the abnormal probability of the power system in a future set time range and takes measures to ensure the stable operation of the power system, so that the fault rate and risk of the power system are reduced. In addition, the spare capacity in the actuator of the system can cope with sudden load change of the power system, and the generator capacity of the power system is increased in time, so that the load supply of the power system is ensured, and the running cost of the power system is reduced.
Third, the system has high practicability and economic benefit. The system can be widely applied to power systems, particularly large-scale power systems, can effectively improve the operation efficiency and stability of the power systems, reduces the operation cost and risk of the power systems, and has higher practicability and economic benefit. In addition, the development and the application of the system can also promote the development and the progress of the frequency modulation technology of the power system, and contribute to the development and the progress of the power industry. In a word, the intelligent power grid primary frequency modulation system has various beneficial effects, can improve the frequency modulation precision and load response capability of the power system, reduce the running cost and risk of the power system, and have higher practicality and economic benefit, is an important progress of the power system regulation technology, and can also bring positive influence to the development and progress of the power industry.
Drawings
Fig. 1 is a schematic system structure diagram of an intelligent power grid primary frequency modulation system provided by an embodiment of the invention;
fig. 2 is a schematic structural diagram of the intelligent power grid primary frequency modulation system according to the embodiment of the present invention, in which the structure of an actuator is decomposed and then connected to other parts of the system.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
The terms "first," "second," "third," "fourth" and the like in the description and in the claims and in the above drawings, if any, are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments described herein may be implemented in other sequences than those illustrated or otherwise described herein. .
Referring to fig. 1 and 2, an intelligent grid primary frequency modulation system comprises the following four components: collector, controller, executor and predictor. The collector is used for collecting historical data and real-time data of the power system so as to provide the historical data and the real-time data for the controller and the predictor to analyze and process. And the controller responds to the system frequency change of the power system according to the collected historical data and real-time data, calculates the frequency modulation control quantity and controls the operation of hardware in the power system. In other words, the controller is responsible for monitoring and controlling the frequency of the power system to ensure its stability. The actuator includes a hardware controller, a monitor, and a mode adjuster. The hardware controller controls the frequency of the power system to fluctuate within a set range according to the calculated frequency modulation control amount. The monitor is responsible for monitoring the running state of the power system to judge whether the power system has faults or anomalies, and outputting corresponding signals according to the monitoring result. The mode regulator is responsible for switching the power system between the frequency modulation mode and the standby mode according to the monitoring result of the monitor or the prediction result of the predictor. In the frequency modulation mode, the controller responds to the system frequency change of the power system to calculate the frequency modulation control quantity so as to control the frequency of the power system. In the standby mode, the controller will reserve a certain generator capacity as a standby capacity to cope with sudden load changes of the power system. When load fluctuation occurs in the power system, the spare capacity is output through the hardware controller so as to maintain the stability of the power system. The predictor is used for predicting the abnormality probability of the power system in a future set time range according to the historical data and the real-time data of the power system. If the predicted abnormality probability exceeds the set abnormality probability threshold, a prediction result is generated and sent to a mode adjuster so as to adjust the operation mode of the power system to maintain the stability of the power system.
The historical data of the power system includes historical frequency data and historical load data. The historical frequency data may include information such as frequency changes, oscillations, and instabilities of the power system, and the historical load data may include information such as load amounts, load fluctuations, and the like of the power system. The collection and analysis of these historical data can provide valuable reference and base data for the controller and predictor.
In addition, real-time frequency data and real-time load data are included. The real-time frequency data may reflect a current frequency state of the power system and the real-time load data may reflect a current load state. The collection and analysis of the real-time data can provide timely information for the controller and the predictor, so that the system can quickly respond to the change of the power system and make corresponding adjustment and prediction.
The method for calculating the frequency modulation control quantity by the controller is based on the collected historical data and real-time data and is calculated by using the following formula:
u(t)＝Kpe(t)+Ki∫e(t)dt+Kdde(t)/dt+Kfln(f(t)/f0)+Kc*
(y(t)-y(t-1))^T*(R(t)-R(t-1))；
wherein e (t) represents a frequency deviation between real-time frequency data and standard frequency data of the power system, and Kp, ki and Kd are respectively a proportional coefficient, an integral coefficient and a differential coefficient for controlling stability of the system. de (t)/dt represents the rate of change of the frequency deviation for controlling the dynamic response of the system. Kf is the proportionality coefficient of the logarithmic function for controlling the amplitude of the fluctuation of the system frequency. f (t) represents real-time frequency data of the power system, and f0 represents a standard frequency of the power system. y (t) represents real-time load data of the power system, R (t) represents a matrix in the generalized predictive control, and Kc represents a scaling factor in the generalized predictive control.
The proportional, integral and derivative coefficients are three parameters commonly used in PID control for controlling the stability and dynamic response of the system. The proportional coefficient is used for controlling the static stability of the system, the integral coefficient is used for eliminating the steady state error of the system, and the differential coefficient is used for controlling the dynamic response of the system.
The setting and adjustment of these parameters can be optimized and improved according to specific power system characteristics to achieve better control and system stability. The frequency modulation control quantity calculated by the controller is transmitted to a hardware controller of the actuator, and the hardware controller is used for controlling the frequency fluctuation of the power system within a set range.
The proportional coefficient, the integral coefficient and the differential coefficient need to be adjusted and optimized according to actual conditions. The larger the proportional gain coefficient is, the better the static stability of the system is, but the slower the dynamic response speed is; the larger the integral coefficient is, the smaller the steady state error of the system is, but the overshoot and oscillation of the system are increased; the differential coefficient may improve the dynamic response speed and stability of the system, but is sensitive to noise.
The generalized predictive control matrix in the controller is used to predict future system states and to calculate the amount of frequency modulation control. The expression of the generalized predictive control matrix is as follows:
R(t)＝[y(t-1),y(t-2),...,y(t-n),u(t-1),u(t-2),...,u(t-m)]^T；
Wherein y (t-1), y (t-2), y (t-n) is historical load data of the power system, u (t-1), u (t-2), u (t-m) is an adjustment control signal at past m moments. T: the control period, i.e. the sampling time of the controller.
The controller of the intelligent power grid primary frequency modulation system realizes the control of the frequency of the power system by adjusting the u (t) quantity. Specifically, the controller calculates an appropriate value of u (t) according to the current state and demand of the power system, and then transmits the value to an adjusting device (such as a generator speed regulator) of the power system, and the adjusting device changes the power output by the generator, so as to control the frequency of the power system.
For example, in the PID control algorithm, the controller calculates the appropriate value of u (t) based on the frequency deviation e (t) and the load change rate df (t)/dt of the power system. When the frequency deviation of the power system is large, the controller increases the value of u (t) to enable the frequency of the power system to approach to a normal value; when the load change rate of the power system is large, the controller correspondingly adjusts the value of u (t) so as to reduce the fluctuation of the frequency of the power system as much as possible.
The value of u (t) is continuously adjusted according to the real-time state and the requirement of the power system, and the controller of the intelligent power grid primary frequency modulation system can realize high-efficiency and accurate control of the frequency of the power system and ensure the stable operation of the power system.
The physical meaning of u (t) depends on the specific implementation of the control algorithm, which may represent the control quantity by which the controller adjusts the physical quantities of frequency, voltage, power, etc. of the power system. In the control algorithm, the value of u (t) is usually calculated according to the state and the requirement of the power system, and is transmitted to an actuator through a controller, and the actuator adjusts the running state of the power system according to the control quantity, so that the control of the power system is realized.
For example, if u (t) represents adjusting the power of the generator, the actuator may control the power output by the generator to adjust the frequency of the power system based on the magnitude of u (t). If u (t) represents the transformation ratio of the regulating transformer, the actuator can control the transformation ratio of the transformer to regulate the voltage of the power system according to the size of u (t). In the intelligent power grid primary frequency modulation system, the specific physical meaning of u (t) depends on the implementation mode of a control algorithm, and the u (t) can be flexibly set and adjusted according to different scenes and requirements.
The controller can adaptively adjust parameters in the frequency modulation control quantity calculation formula so as to adapt to the real-time state and the requirement of the power system; the algorithm formula of the self-adaptive method is as follows:
Kp,Ki,Kd,Kf,Kc＝PSO(fitness)；
Wherein PSO is a particle swarm optimization algorithm function, fitness is an fitness function, and Kp, ki, kd, kf and Kc are proportional coefficients, integral coefficients, differential coefficients, proportional coefficients of logarithmic functions and proportional coefficients in generalized predictive control, respectively.
The controller uses the particle swarm optimization algorithm function PSO to adaptively adjust the proportional coefficients, the integral coefficients, the differential coefficients, the proportional coefficients of the logarithmic function and the proportional coefficients in the generalized predictive control to adapt to the real-time state and the requirements of the power system.
PSO is a heuristic optimization algorithm that can find a globally optimal solution by searching for multiple particles. The PSO function has input as fitness function fitness and output as proportional coefficient, integral coefficient, differential coefficient, proportional coefficient of logarithmic function and proportional coefficient in generalized predictive control to realize the parameter of self-adaptive regulation controller.
Fitness function fitness is commonly used to evaluate the performance of controllers, which can be designed and optimized according to different power system requirements and control objectives. In PSO, fitness functions are used to evaluate the particle's goodness and to guide the search process. In each iteration cycle, the PSO algorithm updates the position and velocity of the particles according to the fitness function and the current search state to find the globally optimal solution.
The fitness function fitness has the expression:
fitness＝1/(1+J)；
wherein J is the performance index of the controller and is the square sum of control errors.
The control error refers to the deviation between the actual frequency and the standard frequency of the power system, and the goal of the controller is to adjust the control signal such that the control error is as small as possible. Thus, the sum of squares of the control errors can be used to evaluate the performance of the controller, i.e., the magnitude of the error that the controller generates during control. The smaller the sum of squares of the control errors, the better the performance of the controller, the easier the frequency and stability of the power system can be controlled.
The fitness function fitness has the expression 1/(1+j) for converting the control error into fitness score for particle optimization and searching in the PSO algorithm. Specifically, the larger the value of the fitness function, the better the position of the particles, and the higher the fitness; the smaller the value of the fitness function, the worse the position of the particles, and the lower the fitness. Thus, the design of the fitness function may directly affect the search performance and results of the PSO algorithm.
In grid frequency modulation control, genetic algorithms may be used to adaptively adjust parameters of the PID controller to optimize the control effect.
Specifically, the adaptive method is performed as follows:
initializing a population: a number of individuals, each representing a set of PID parameters, are randomly generated.
And (5) calculating the fitness: according to the current state and the demand of the power system, the adaptability of each individual, namely the control effect of the controller in the current state, is calculated by simulating the operation of the power system.
Selection operation: selecting individuals according to the fitness, retaining the individuals with the best fitness, and randomly selecting some individuals to perform crossing and mutation operations
Crossover operation: two individuals are randomly selected from the selected individuals, and their genes are crossed to generate a new individual.
Mutation operation: for some randomly selected individuals, the genes are mutated with a certain probability to generate new individuals.
Generating a new population: generating new individuals according to the selection, crossing and mutation operations to replace the original individuals to form a new population.
Judging whether a termination condition is satisfied: the process of fitness calculation to new population generation is repeated until a termination condition is reached, such as a maximum number of iterations is reached or the fitness reaches a certain threshold.
Outputting a result: and outputting PID parameters corresponding to the individuals with the best adaptability as parameters of the current power grid frequency modulation control.
In this algorithm, each individual contains four genes, the parameters of the PID controller: a proportional coefficient Kp, an integral coefficient Ki, a differential coefficient Kd, and a feedforward coefficient Kf. In the iterative process of the genetic algorithm, the optimal parameter combination capable of enabling the power system to stably run is searched by continuously adjusting the values of the genes.
The hardware controller includes: a generator speed controller, a load controller, and a capacitor controller; the generator rotating speed controller adjusts the rotating speed of the generator according to the frequency modulation control quantity obtained by the controller; the load controller adjusts the load according to the instruction of the controller; the capacitor controller controls the charge and discharge states of the capacitor according to instructions of the controller.
The generator rotating speed controller is used for adjusting the rotating speed of the generator according to the frequency modulation control quantity obtained by the controller. The frequency modulation control quantity is calculated by the controller according to the deviation between the real-time frequency data and the standard frequency data of the power system and is used for controlling the stability of the frequency of the power system. The generator rotating speed controller adjusts the output power of the generator according to the frequency modulation control quantity so as to enable the real-time frequency of the power system to be close to the standard frequency.
The load controller is used for adjusting the load according to the instruction of the controller. The load controller can control parameters such as a switch and output power of the load so as to meet the load requirement and stability requirement of the power system. The load controller cooperates with the generator speed controller to control the frequency and stability of the power system by regulating the output power of the load and generator.
The capacitor controller is used for controlling the charge and discharge states of the capacitor according to the instruction of the controller. The capacitor can store electrical energy and release the electrical energy when needed to balance the load and generate electricity of the power system, thereby improving the stability and efficiency of the power system. The capacitor controller can control the charge and discharge states of the capacitor according to the real-time state and the load demand of the power system so as to achieve better control effect of the power system.
The monitor constructs a matrix X by using the collected historical data and real-time data of the power system, and then decomposes the matrix X into the product of two low-rank matrices U and V≡T through a matrix decomposition algorithm, namely:
X≈UV^T；
representing the historical data and the real-time data as a linear combination of a set of basic factors; wherein: matrix X represents the matrix of the historical data and the real-time data of the power system, and matrix U and matrix V-T are the low-rank approximate matrix of matrix X; t represents the transpose operation, V T is the transpose of the matrix V; and comparing the element values in the matrix U and the matrix V-T with preset expected values to judge whether faults or anomalies occur, and if so, generating a monitoring result, wherein the value of the monitoring result is 1.
Specifically, the monitor constructs the collected historical data and real-time data of the power system into a matrix X, and decomposes the matrix X into the product of two low-rank matrices U and V≡T, namely X≡UV≡T, through a matrix decomposition algorithm. In this way, the historical data and the real-time data can be represented as a linear combination of a set of basic factors.
Then, the monitor compares the element values in the matrix U and the matrix V-T with preset expected values to judge whether faults or anomalies occur. If the deviation of the element values in the matrix U and the matrix V-T from the expected values exceeds a certain threshold, the power system is indicated to be faulty or abnormal. At this time, the monitor generates a monitoring result, the value is 1, which indicates that a fault or abnormality occurs; if no fault or abnormality occurs, the monitoring result is 0.
The mode regulator is used for switching the power system between a frequency modulation mode and a standby mode according to the collected historical data and real-time data, and the method comprises the following steps: the automatic switching between the frequency modulation mode and the standby mode is carried out by adopting a method based on matrix decomposition and fuzzy logic, and the formula is as follows:
IF(P*O*|X(t)|<|μ1|)THEN(Mode＝R)ELSEIF(P*O*|X(t)|>
|μ2|)THEN(Mode＝F)ELSE(Mode＝μ3*Mode(t)+(1-μ3)*Mode(t-1))；
wherein X (t) represents a matrix of historical load data and historical frequency data of the power system, and is a low-rank approximate matrix obtained based on a matrix decomposition algorithm; mu 1 is a parameter matrix in the fuzzy logic and is used for controlling a switching threshold value of the standby mode, and when the load and the frequency of the power system deviate from standard values, the standby mode is switched; μ2 is a parameter matrix in the fuzzy logic, and is used for controlling a switching threshold value of the frequency modulation mode, and switching to the frequency modulation mode when the load and the frequency of the power system deviate from standard values; mu 3: is a parameter in the fuzzy logic for controlling the smooth switching of modes, smoothly switching to a new mode by linearly combining the modes at the previous time and the current time; mode (t) represents the current power system Mode, its value is 0 or 1,0 represents the standby Mode, and 1 represents the frequency modulation Mode; mode (t-1): a power system mode representing a previous time; r represents a standby mode, and F is a frequency modulation mode; mode represents a Mode of the power system; p is the monitoring result; o is the anomaly probability.
The frequency modulation mode is the most common operation mode of the intelligent power grid primary frequency modulation system. In this mode, the controller calculates an appropriate control amount u (t) based on parameters such as a frequency deviation and a load change rate of the power system, and controls the frequency of the power system to fluctuate within a certain range by adjusting the output of an actuator (for example, a generator governor). The main purpose of the frequency modulation mode is to maintain the frequency stability of the power system, ensure that the power system can meet load requirements and can cope with sudden load changes.
The standby mode is a standby operation mode of the intelligent power grid primary frequency modulation system. In this mode, the fm controller will reserve a certain generator capacity as a backup capacity to cope with sudden load changes of the power system. When the load fluctuation occurs in the power system, the standby capacity can rapidly meet the load demand of the power system by rapidly adjusting the output of the generator, so that the stable operation of the power system is ensured. The standby mode is usually only used in a scene with high load and high stability requirement of the power system, such as summer high-temperature weather or power system operation under special working conditions.
Specifically, the mode regulator constructs historical load data and historical frequency data of the power system into a matrix X (t) according to the collected historical data and real-time data, and obtains a low-rank approximate matrix through a matrix decomposition algorithm. Then, the mode adjuster controls switching thresholds of the standby mode and the frequency modulation mode according to parameter matrices mu 1 and mu 2 in the fuzzy logic, and switches to the standby mode or the frequency modulation mode when the load and the frequency of the power system deviate from standard values.
Specifically, when po|x (t) | < μ1|, the mode regulator switches the power system to a standby mode, wherein P is a monitoring result, O is an anomaly probability, |x (t) | is a norm of the matrix X (t), and |μ1| is an absolute value of μ1; when PO|X (t) | > |μ2|, the mode regulator switches the power system to the frequency modulation mode; otherwise, the mode adjustor smoothly switches to the new mode by linearly combining the modes at the previous time and the current time. Where μ3 is a parameter in the fuzzy logic for controlling smooth switching of modes. When the power system is in the frequency modulation mode, the hardware controller in the actuator controls the frequency of the power system to fluctuate within a set range according to the calculated frequency modulation control amount. When the power system is in the standby mode, the hardware controller in the actuator can reserve a certain capacity of the generator as the standby capacity to cope with sudden load changes of the power system.
The predictor predicts the abnormal probability of the power system in a future set time range according to the historical data and the real-time data of the power system, and if the predicted abnormal probability exceeds a set abnormal probability threshold value, the method for obtaining the prediction result comprises the following steps: predicting an anomaly probability of the power system within a set time range in the future using the following formula:
P(t)＝1/(1+exp(-z(t)))；
Wherein z (t) = (x (t) - μ) ×σ -1;
x (t) is a matrix of historical load data and historical frequency data of the power system, and is a low-rank approximate matrix obtained based on a matrix decomposition algorithm; μ is the mean vector, σ is the square root of the covariance matrix, and P (t) is the anomaly probability.
The value range of the abnormal probability threshold value is as follows: 30% -40%.
The predictor predicts the probability of abnormality of the power system in a set time range in the future based on the history data and the real-time data of the power system. The prediction of the abnormal probability is one of important functions in the intelligent power grid primary frequency modulation system, and can help the system to discover possible faults and abnormalities in advance, so that measures can be taken in time, and the stable operation of the power grid is ensured.
The predictor uses the formula:
P(t)＝1/(1+exp(-z(t)))，
wherein:
z(t)＝(x(t)-μ)*σ^-1，
and x (t) is a matrix of historical load data and historical frequency data of the power system, and is a low-rank approximate matrix obtained based on a matrix decomposition algorithm. μ is the mean vector and σ is the square root of the covariance matrix. The predictor uses a logistic regression model, takes the historical data and the real-time data as input, and predicts the probability of abnormality of a future power system through analysis and learning of the historical data. If the predicted probability of abnormality exceeds a set threshold of probability of abnormality, a prediction result is generated.
The performance of the predictor depends on the accuracy and reliability of the input data. Therefore, historical data and real-time data acquisition of the power system are one of the keys of the intelligent grid primary frequency modulation system. The historical data includes historical frequency data and historical load data, and the real-time data includes real-time frequency data and real-time load data. The collector is the equipment responsible for collecting historical data and real-time data in the intelligent power grid primary frequency modulation system.
The predictor can also give out an early warning signal according to the predicted abnormality probability, thereby helping maintenance personnel to discover possible faults and abnormalities in time and taking corresponding measures to prevent the faults of the power system from further expanding.
The value of the abnormal probability threshold value needs to be adjusted according to specific situations. If the value of the abnormal probability threshold is too low, the system may frequently judge that the power system is abnormal, so that the interference and misjudgment of the system are increased; if the abnormal probability threshold is too high, the system may not find a potential problem in time, so that the power system is in an unstable state, and the risks of power system faults and losses are increased.
Therefore, the value of the anomaly probability threshold value needs to be balanced between system stability and failure prediction accuracy. In practical application, the value range of the abnormal probability threshold needs to be determined according to the characteristics of historical data and real-time data, the working state and the property of the power system and other factors, and then the abnormal probability threshold is adjusted and optimized according to the practical situation.
It should be noted that, in practical applications, the value of the anomaly probability threshold is not unique or fixed. Along with the change of the working state and the property of the power system, the value of the abnormal probability threshold value is also required to be adjusted and optimized. Therefore, the value of the anomaly probability threshold needs to have certain flexibility and adaptability so as to adapt to the real-time state and the requirement of the power system.
After multiple experiments, the range of the threshold value of the abnormality probability of 30% -40% proves to be a relatively suitable range. The value range can ensure the accuracy of the prediction result and avoid missed judgment and misjudgment as far as possible. Of course, in practice, the specific value of the abnormal probability threshold needs to be adjusted by combining specific factors such as the power grid environment, the power system characteristics and the like.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, which are not repeated herein.
In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a usb disk, a removable hard disk, a Read-only memory (ROM), a random access memory (RAM, randomAccessMemory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. Intelligent electric wire netting primary frequency modulation system, its characterized in that, the system includes: the system comprises a collector, a controller, an executor and a predictor; the collector is configured to collect historical data and real-time data of the power system; the controller is configured to respond to the change of the system frequency of the power system based on the collected historical data and real-time data, and calculate the frequency modulation control quantity; the actuator comprises: a hardware controller, a monitor, and a mode adjuster; the hardware controller is configured to control the operation of hardware in the power system according to the calculated frequency modulation control quantity; the monitor is configured to monitor the running state of the power system according to the collected historical data and the real-time data so as to judge whether the power system has faults or anomalies and obtain monitoring results; the mode regulator is configured to switch the power system between a frequency modulation mode and a standby mode according to a monitoring result of the monitor or a prediction result of the predictor; when the electric power system is in the frequency modulation mode, the controller responds to the change of the system frequency of the electric power system, the frequency modulation control quantity is calculated, and the hardware controller in the actuator controls the frequency of the electric power system to fluctuate within a set range according to the calculated frequency modulation control quantity; when the power system is in a standby mode, the controller can keep the generator capacity with the set capacity value as the standby capacity so as to cope with sudden load change of the power system, and when the power system has load fluctuation, the standby capacity is output through the hardware controller to maintain the stability of the power system; the predictor is configured to predict the abnormality probability of the power system within a future set time range according to the historical data and the real-time data of the power system, and if the predicted abnormality probability exceeds a set abnormality probability threshold, a prediction result is generated and sent to the mode regulator; the historical data of the power system includes: historical frequency data and historical load data; the real-time data includes: real-time frequency data and real-time load data; the controller responds to the change of the system frequency of the power system based on the collected historical data and real-time data, and the method for calculating the frequency modulation control quantity comprises the following steps: the frequency modulation control amount is calculated using the following formula:
；
Wherein,for the frequency deviation between the real-time frequency data and the standard frequency data of the power system, +.>、/>、/>Proportional, integral and differential coefficients, respectively, +.>For the rate of change of the frequency deviation +.>Proportional system as a logarithmic functionCount (n)/(l)>For real-time frequency data of the power system, +.>Is a standard frequency for an electrical power system,for the real-time load data of the power system at the present moment, < + >>For the real-time load data of the power system at the previous moment, < >>For the matrix in the generalized predictive control of the current moment, < >>For the matrix in the generalized predictive control at the previous moment, < >>Representing transpose operations->Is a scaling factor in generalized predictive control;
the mode regulator is configured to switch the power system between the frequency modulation mode and the standby mode according to the monitoring result of the monitor or the prediction result of the predictor, and the method comprises the following steps: the automatic switching between the frequency modulation mode and the standby mode is carried out by adopting a method based on matrix decomposition and fuzzy logic, and the formula is as follows:
；
wherein,the matrix representing the historical load data and the historical frequency data of the power system is a low-rank approximate matrix obtained based on a matrix decomposition algorithm; />The parameter matrix is a parameter matrix in the fuzzy logic and is used for controlling a switching threshold value of the standby mode, and when the load and the frequency of the power system deviate from standard values, the switching is carried out to the standby mode; / >The parameter matrix is a parameter matrix in the fuzzy logic and is used for controlling the switching threshold value of the frequency modulation mode, and when the load and the frequency of the power system deviate from the standard values, the frequency modulation mode is switched; />: is a parameter in the fuzzy logic for controlling the smooth switching of modes, smoothly switching to a new mode by linearly combining the modes at the previous time and the current time; />A power system mode representing the current time, the value of which is 0 or 1,0 representing a standby mode, and 1 representing a frequency modulation mode; />: a power system mode representing a previous time; />Representing standby mode->Is in a frequency modulation mode; />A mode representing a power system; />Is the monitoring result; />Is an anomaly probability; the predictor predicts the abnormality probability of the power system in a future set time range according to the historical data and the real-time data of the power system, and if the predicted abnormality probability exceeds a set abnormality probability threshold value, the method for generating a prediction result comprises the following steps: predicting an anomaly probability of the power system within a set time range in the future using the following formula:
；
wherein,；
the matrix is a matrix of historical load data and historical frequency data of the power system, and is a low-rank approximate matrix obtained based on a matrix decomposition algorithm; / >For mean vector, ++>Is the square root of the covariance matrix,>is an anomaly probability.
2. The system of claim 1, wherein the controller is capable of adaptively adjusting parameters in the frequency modulation control calculation formula to accommodate real-time conditions and requirements of the power system; the algorithm formula of the self-adaptive method is as follows:
；
wherein PSO is a particle swarm optimization algorithm function,for fitness function>、/>、/>、/>Andproportional coefficients, integral coefficients, differential coefficients, proportional coefficients of logarithmic functions, and proportional coefficients in generalized predictive control, respectively.
3. The system of claim 2, wherein the fitness functionThe expression of (2) is:
；
wherein,the performance index of the controller is the square sum of control errors.
4. The system of claim 3, wherein the hardware controller comprises: a generator speed controller, a load controller, and a capacitor controller; the generator rotating speed controller adjusts the rotating speed of the generator according to the frequency modulation control quantity obtained by the controller; the load controller adjusts the load according to the instruction of the controller; the capacitor controller controls the charge and discharge states of the capacitor according to instructions of the controller.
5. The system of claim 4, wherein the monitor constructs a matrix by combining historical data and real-time data of the collected power systemThen matrix +.>Decomposition into two low rank matricesAnd->Is the product of (1), namely:
；
representing the historical data and the real-time data as a linear combination of a set of basic factors; wherein: matrix arrayMatrix representing historical data and real-time data of electric power system, matrix +.>Sum matrix->Then is a matrix->Is a low rank approximation matrix of (2);representing transpose operations->For matrix->Is a transpose of (2); by comparison matrix->Sum matrix->Judging whether a fault or abnormality occurs or not by the element values in the (a) and the preset expected values, and generating a monitoring result if the fault or abnormality occurs, wherein the value of the monitoring result is 1.
6. The system of claim 5, wherein the anomaly probability threshold value is in a range of values: 30% -40%.
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