CN112395814A - Optimization method for parameter identification of load model of power system - Google Patents

Optimization method for parameter identification of load model of power system Download PDF

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CN112395814A
CN112395814A CN202011377492.8A CN202011377492A CN112395814A CN 112395814 A CN112395814 A CN 112395814A CN 202011377492 A CN202011377492 A CN 202011377492A CN 112395814 A CN112395814 A CN 112395814A
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郭成
谢浩
李文云
和鹏
向川
孟贤
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Electric Power Research Institute of Yunnan Power Grid Co Ltd
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Abstract

The method comprises the steps of firstly, building a power system model in BPA, and setting a three-phase short-circuit fault to obtain voltage sag data at a bus for load modeling; secondly, an optimization criterion that the weight factor of the objective function is large when the fault voltage drop is large, the weight factor of the objective function is stable and the weight factor of the objective function is small when the fault voltage drop is small is provided, and the problems that the load model outputs active curves and reactive curves are difficult to follow the actually measured active and reactive curves when the fault voltage drop is large in the identification process of the existing load modeling technology are solved. Finally, a novel optimization algorithm, namely a wolf algorithm, is introduced for load model parameter identification, and the problems of long time consumption, low precision, easy trapping in local optimal solution and the like in the identification process in the prior art are solved.

Description

Optimization method for parameter identification of load model of power system
Technical Field
The application relates to the technical field of power supply, in particular to an optimization method for parameter identification of a load model of a power system.
Background
The simulation calculation of the power system is not only a basic tool for dynamic analysis and safety control of the power system, but also a basic basis for guiding the operation of a power grid by a power production department, and the accuracy of a digital simulation result directly influences the correctness of system operation, planning and decision. As a core link of electric energy consumption, electric power load influences the design, analysis and control of an electric power system, and whether a load model is accurate or not has an important influence on the electric power system. So far, the methods for load modeling mainly include statistical synthesis and ensemble discrimination. The basic idea of the statistical synthesis method is to regard the load as the set of individual users, classify the electric appliances of the users, determine the average characteristics of various types of electric appliances, then count the proportion of the various types of electric appliances, and finally synthesize to obtain the overall load model. The basic idea of the overall measurement and identification method is to regard a load group as a whole, install a measurement and recording device at a load point, collect voltage, frequency, active and reactive data of a bus where a load is located on site, and then determine a load model structure and parameters according to a system identification theory.
After the load model is determined, the next task is to identify the parameters of the load model based on the measured data. The load model parameter identification method can be roughly divided into linear and nonlinear methods. Linear methods, including least squares estimation and kalman filtering, are generally effective for parametric linear models. The nonlinear parameter identification method is mostly based on optimization at present, and the main process is to find a group of optimal parameter vectors to minimize a preset error objective function, and the method mainly comprises a gradient method, a random search method and a simulated evolution method.
The most important thing is to measure and distinguish the method by the totality and need the measured data, the data source mainly includes PMU data, trouble record ripples data and electric energy quality monitoring data 3 categories. The fault data usually contains steady-state data of a certain number of cycles before and after disturbance, however, the load modeling usually focuses more on the load characteristics when the voltage drop in the fault curve is large, but the existing load modeling technology has the following problems: in the identification process, the objective function is not subjected to weighted processing, so that the active and reactive curves output by the load model are difficult to follow the actual active and reactive curves when the fault voltage drop is large.
Disclosure of Invention
In order to solve the above problems, the present application provides an optimization method for identifying parameters of a load model of an electrical power system, so as to solve the problem that an active curve and a reactive curve output by the load model are difficult to follow an actually measured active curve and reactive curve when a fault voltage drop is large because a target function is not subjected to weighted processing in an identification process. The objective function optimization criterion is provided, wherein the objective function optimization criterion is large in weight when the fault voltage drop is large and small in weight when the fault voltage drop is small, and therefore the fitting effect of the load model is improved.
In order to achieve the purpose, the application is realized by the following technical scheme:
a method of optimizing identification of load model parameters of an electrical power system, the method comprising:
acquiring measured data, wherein the measured data comprises bus voltage, bus active power, bus reactive power, motor load active power, motor load reactive power, static load active power and static load reactive power;
according to a gray wolf optimizing algorithm, taking a parameter to be optimized of a load model of the power system as a gray wolf in a population, taking a preset position as a population position, calculating and obtaining an objective function value of each gray wolf in the population according to the population position, the measured data and a preset weight objective function, sequencing the objective function values of the gray wolfs in the population to obtain positions of alpha gray wolf, beta gray wolf and delta gray wolf, judging whether the objective function value of the alpha gray wolf is smaller than a preset optimizing target value or not, and if so, determining an identification parameter value of the load model of the power system according to the objective function value;
if not, determining the current iteration times, and calculating to obtain a convergence factor, an attack coefficient vector and a cooperative coefficient vector according to the current iteration times;
calculating the distances between the alpha grey wolf, the beta grey wolf and the delta grey wolf and the cang wolf according to the positions of the alpha grey wolf, the beta grey wolf and the delta grey wolf and the synergistic coefficient vector;
determining the position values of all the gray wolves in the population according to the convergence factor, the attack coefficient vector, the positions of the alpha gray wolves, the beta gray wolves and the delta gray wolves and the distances between the alpha gray wolves, the beta gray wolves and the delta gray wolves and the cang wolves;
and updating the positions of all the gray wolves in the population according to the position values, taking the positions of all the gray wolves in the updated population as the population positions, increasing the current iteration times once, repeatedly executing the steps, calculating an objective function value of each gray wolve in the population according to the population positions, the measured data and a preset weight objective function until the iteration times are greater than a preset maximum iteration time, and determining the identification parameter value of the power system load model according to the positions of the alpha gray wolves.
Optionally, the method for determining the preset position includes: initializing the population position according to the wolf optimizing algorithm, a preset search lower limit, a preset search upper limit, a population scale, a maximum iteration number and the dimension of the parameter to be optimized, and determining the preset position.
Optionally, the formula of the weight objective function is as follows:
Figure BDA0002807513230000021
wherein J (k) is an objective function value at the time k, P (k) is active power flowing out of the bus at the time k, Q (k) is reactive power flowing out of the bus at the time k, and P (k) isM(k) Active power absorbed for the motor load at time k, QM(k) Reactive power absorbed for the motor load at time k, PS(k) Active power absorbed for the static load at time k, QS(k) Reactive power absorbed by the static load at time k, U (k) is the bus voltage at time k, U0Is the bus voltage at the initial moment.
Optionally, the formula for calculating the distance between the α grey wolf, the β grey wolf, the δ grey wolf and the cockwolf is as follows:
Figure BDA0002807513230000031
wherein D represents the dimension of the search space, t represents the current iteration number, X (t) represents the position vector of the t-th iteration wolf, DαRepresents the distance vector between alpha gray wolf and pale wolf, DβRepresents the distance vector between the beta grey wolf and the pale wolf, DδRepresents the distance vector between delta gray wolf and pale wolf, Xα(t) represents the position vector of the alpha grayish wolf at the t-th iteration, Xβ(t) represents the position vector of the beta grayish wolf at the t-th iteration, Xδ(t) represents the position vector of the delta gray wolf at the t-th iteration, C1、C2And C3Is a coefficient of synergy.
Optionally, the formula for determining the position values of all the gray wolves in the population according to the convergence factor, the attack coefficient vector, the positions of the α gray wolves, the β gray wolves and the δ gray wolves, and the distances between the α gray wolves, the β gray wolves and the δ gray wolves and the cang wolves is as follows:
Figure BDA0002807513230000032
wherein, X1Indicates that alpha gray wolf directs the cang wolf betterNew position value, X2Represents the position value, X, of the beta grayish wolf guiding the updating of the sirius3Represents the delta grey wolf guiding the position value of the grey wolf updating, X (t +1) represents the final updating position value of the grey wolf, DαRepresents the distance vector between alpha gray wolf and pale wolf, DβRepresents the distance vector between the beta grey wolf and the pale wolf, DδRepresents the distance vector between delta gray wolf and pale wolf, XαDenotes the position vector of alpha grayish wolf and XβDenotes the position vector of the beta grayish wolf, XδRepresents the position vector of delta gray wolf, A1、A2And A3Is the attack coefficient.
According to the technical scheme, the method for optimizing the parameter identification of the load model of the power system comprises the steps of firstly building the power system model in BPA, setting three-phase short-circuit faults to obtain voltage sag data at a bus for load modeling; secondly, an optimization criterion that the weight factor of the objective function is large when the fault voltage drop is large, the weight factor of the objective function is stable and the weight factor of the objective function is small when the fault voltage drop is small is provided, and the problems that the load model outputs active curves and reactive curves are difficult to follow the actually measured active and reactive curves when the fault voltage drop is large in the identification process of the existing load modeling technology are solved. Finally, a novel optimization algorithm, namely a wolf algorithm, is introduced for load model parameter identification, and the problems of long time consumption, low precision, easy trapping in local optimal solution and the like in the identification process in the prior art are solved.
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In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious to those skilled in the art that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic flow chart illustrating an optimization method for identifying parameters of a load model of an electrical power system according to an embodiment of the present disclosure;
FIG. 2 is a schematic view of a node 9 of a 3-machine in the embodiment of the present application;
FIG. 3 is a fault voltage diagram of a second bus in an embodiment of the present application;
FIG. 4 is a fault voltage phase angle diagram for a second bus bar in an embodiment of the present application;
FIG. 5 is a graph of active power of a second bus in an embodiment of the present application;
FIG. 6 is a reactive power diagram of a second bus in an embodiment of the present application;
FIG. 7 is a power fit graph of a second bus in an embodiment of the present application;
FIG. 8 is a reactive fit plot of a second bus in an embodiment of the present application;
fig. 9 is a fitness curve diagram of an optimization method for identifying parameters of a load model of an electrical power system according to an embodiment of the present disclosure.
Detailed Description
In order to make the technical means, the creation features, the achievement purposes and the effects of the application easy to understand, the application is further described in the following with the specific embodiments.
Fig. 1 is a schematic flow chart of an optimization method for identifying parameters of a load model of an electrical power system in an embodiment of the present application, and referring to fig. 1, the optimization method for identifying parameters of a load model of an electrical power system includes:
s1, obtaining measured data, wherein the measured data comprises bus voltage U, bus active power P, bus reactive power Q and motor load active power PMMotor load reactive power QMStatic load active power PSAnd static load reactive power QS；
Fig. 2 is a schematic diagram of a 3-machine 9 node in an embodiment of the present application, and referring to fig. 2, a 3-machine 9 node model is built in a PSD-BPA, and includes a first bus 1, a second bus 2, a third bus 3, a fourth bus 4, a fifth bus 5, a sixth bus 6, a first generator 7, a second generator 8, and a third generator 9. Loads are set at the second bus bar 2, the fourth bus bar 4, the fifth bus bar 5, and the sixth bus bar 6 as an induction motor parallel ZIP model.
The motor parameters are: stator resistance Rs0.02, stator reactance Xs0.18, exciting reactance Xm3.499 rotor resistance Rr0.02, rotor reactance Xr0.12 square rotor drag torque system a 0.85, 0 square rotor drag torque system B, and time constant of inertia TjMotor proportionality coefficient k 2.0pm0.5, initial motor load factor KL0.0116. The ZIP parameters are: constant impedance active load percent apConstant current active load percentage b ═ 1p0, constant power active load percentage c p0; constant impedance reactive load percent aqConstant current reactive load percentage b ═ 1qConstant power reactive load percent cq＝0。
FIG. 3 is a fault voltage plot for a second bus in an embodiment of the present application, FIG. 4 is a fault voltage phase angle plot for a second bus in an embodiment of the present application, FIG. 5 is an active power plot for a second bus in an embodiment of the present application, and FIG. 6 is a reactive power plot for a second bus in an embodiment of the present application; referring to fig. 3 to 6, the disturbance is set as that 50% of lines of the fourth bus 4 and the second bus 2 are in a three-phase short-circuit fault at the 5 th cycle, the 10 th cycle of the fourth bus 4 and the second bus 2 are respectively disconnected from the fault phase, and the bus voltage U, the phase angle θ, the active power P and the reactive power Q at the second bus 2 are recorded.
Further, in order to improve the identification precision of the load model parameters, a novel optimization algorithm, namely a wolf algorithm, is introduced. The algorithm has good performance in the aspects of convergence speed, optimizing precision, self-adaptive global search and the like.
S21, according to a gray wolf optimizing algorithm, taking parameters to be optimized of a load model of the power system as gray wolfs in a population, and taking a preset position as a population position;
in some embodiments, the method of determining the preset position comprises: initializing population Positions according to a wolf optimizing algorithm, a preset search lower limit LB, a preset search upper limit UB, a population scale Searchgages _ no, a maximum iteration number Max _ iteration and a parameter dimension dim to be optimized, and determining a preset position.
S22, calculating an objective function value of each gray wolf in the population according to the population position, the measured data and a preset weight objective function, and sequencing the objective function values of the gray wolfs in the population to obtain positions of alpha gray wolfs, beta gray wolfs and delta gray wolfs;
s23, judging whether the objective function value of the alpha grayish wolf is smaller than a preset optimization target value or not, and if so, determining the identification parameter value of the power system load model according to the objective function value;
the Alpha grayish objective function value Alpha _ score is compared with a preset optimization target value J _ best; assuming that the optimization target value J _ best is 0.1, if the alpha grey wolf objective function value is less than 0.1, obtaining the value of the current identification parameter to be optimized, determining the value of the identification parameter of the load model of the power system, and completing the optimization process of the identification parameter, otherwise, performing the next iteration by using the grey wolf optimization criterion.
Solving an objective function value of each wolf according to the population position, the measured data and a preset weight objective function; in particular, the active power P of the motor partMAnd reactive power Q of the motor partMThe calculation formulas of (A) and (B) are respectively as follows:
Figure BDA0002807513230000051
Figure BDA0002807513230000052
wherein, UxIs x-axis voltage, UyIs the y-axis voltage; i isxIs x-axis current, IyIs the y-axis current. U shapexAnd UyKnown as Ux＝Ucosθ，Uy＝Usinθ。
In addition, the first and second substrates are,
Figure BDA0002807513230000053
wherein, P0For the initial active power of the bus, U0Is the initial voltage of the bus, SBSIs the system reference capacity, SBMThe motor capacity itself. Therefore, only need to obtain IxAnd IyP can be calculatedMAnd QM。IxAnd IyThe motor 3-order differential equation can be obtained by iteratively solving a 4-order Rungestota algorithm, and the calculation formula is as follows:
Figure BDA0002807513230000061
to obtain E'x、E′y. Wherein, E'xBeing motor x-axis transient potential, E'yFor the motor y-axis transient potential, ω is the motor speed, ωBFor synchronizing rotational speeds, omegaB＝100π，TjIs the rotor inertia time constant, T0Is the initial mechanical torque, T'd0For the rotor loop time constant, A, B and C are the mechanical torque coefficients. Substituting the following formula:
Figure BDA0002807513230000062
to obtain IxAnd Iy. Wherein X ═ Xs+Xm，X′＝Xs+XmXr/(Xm+Xr)，Td0′＝(Xm+Xr)/(ωB*Rr)。
The calculation formulas of the active power and the reactive power of the ZIP load are respectively as follows:
Figure BDA0002807513230000063
wherein, Ps0For static load initial active power, Qs0For static load initial reactive power, U0And U is the initial voltage of the bus and the actual measured voltage of the bus.
Fig. 7 is an active fitting graph of a second bus in the embodiment of the present application, fig. 8 is a reactive fitting graph of the second bus in the embodiment of the present application, and fig. 9 is a fitness curve graph of an optimization method for load model identification parameters of an electrical power system in the embodiment of the present application. Referring to fig. 7 to 8, in order to improve the capability of the load model to output the active and reactive curves to fit the measured active and reactive curves at the moment when the fault voltage drop is large, the present application provides an objective function optimization criterion with a large weight when the fault voltage drop is large and a small weight when the steady state and the fault voltage drop are small. Specifically, the formula of the weight objective function is as follows:
Figure BDA0002807513230000071
that is, the weight of the objective function at the moment is determined according to the voltage drop at the fault moment, which is specifically as follows:
when (U)0-U(k))/U0If the bus voltage is less than 0.1, the bus voltage is small in fluctuation, and the weight of the objective function at the moment is 0.1; when (U)0-U(k))/U0The bus voltage is larger than or equal to 0.1, the bus voltage is larger in fluctuation, and the weight of the objective function at the moment is 0.9.
Wherein J (k) is an objective function value at the time k, P (k) is active power flowing out of the bus at the time k, Q (k) is reactive power flowing out of the bus at the time k, and P (k) isM(k) Active power absorbed for the motor load at time k, QM(k) Reactive power absorbed for the motor load at time k, PS(k) Active power absorbed for the static load at time k, QS(k) Reactive power absorbed by the static load at time k, U (k) is the bus voltage at time k, U0Is the bus voltage at the initial moment.
According to the objective function value of each gray wolf in the population, if the objective function value of the current gray wolf is smaller than the objective function value of Alpha gray wolf, the current gray wolf is marked as Alpha gray wolf, and the objective function value Alpha _ score of Alpha gray wolf and the position X of Alpha gray wolf are updatedα(ii) a If the objective function value of the current gray wolf is larger than the objective function value of alpha gray wolf and smaller than the objective function value of Beta gray wolf, the current gray wolf is marked as Beta gray wolf, and the objective function value Beta _ score of the Beta gray wolf and the position X of the Beta gray wolf are updatedβ(ii) a If the objective function value of the current gray wolf is greater than the objective function value of the beta gray wolf and less than the objective function value of the delta gray wolf, the current gray wolf is marked as the delta gray wolf, and the objective function value Delt of the delta gray wolf is updatedPosition X of a _ score, delta gray wolfδ。
S3, if not, determining the current iteration times, and calculating to obtain a convergence factor a and an attack coefficient vector according to the current iteration times
Figure BDA0002807513230000072
And a co-operative coefficient vector
Figure BDA0002807513230000073
Updating convergence factor a to 2-2i/Max _ iteration, attacking coefficient vector
Figure BDA0002807513230000074
Vector of co-ordinated coefficients
Figure BDA0002807513230000075
r1And r2Is the interval [0, 1]The random vector of (2).
S4, according to the positions of the alpha gray wolf, the beta gray wolf and the delta gray wolf and the vector of the cooperative coefficient
Figure BDA0002807513230000076
Calculating to obtain the distances between the alpha grey wolf, the beta grey wolf and the delta grey wolf and the cockscomb;
after the wolf pack has surrounded the prey, the hunting action starts, and the formula for calculating the distances between α, β and δ wolfs and the cockscomb is as follows:
Figure BDA0002807513230000077
wherein d represents a search space dimension; t represents the current iteration number, X (t) represents the position vector of the Tth iteration wolf, DαRepresents the distance vector between alpha gray wolf and pale wolf, DβRepresents the distance vector between the beta grey wolf and the pale wolf, DδRepresents the distance vector between delta gray wolf and pale wolf, Xα(t) represents the position vector of the alpha grayish wolf at the t-th iteration, Xβ(t) indicates the bit of the gray wolf at the t-th iterationLocation vector, Xδ(t) represents the position vector of the delta gray wolf at the t-th iteration, C1、C2、C3Is a coefficient of synergy.
S5, according to the convergence factor a and the attack coefficient vector
Figure BDA0002807513230000081
The positions of the alpha, beta and delta gray wolves and the distances between the alpha, beta and delta gray wolves and the cockscomb determine the position values of all gray wolves in the population.
And S6, updating the positions of all grey wolves in the population according to the position values, taking the positions of all grey wolves in the updated population as the population positions, increasing the current iteration times once, repeatedly executing the steps, calculating the objective function value of each grey wolve in the population according to the population positions, the measured data and a preset weight objective function until the iteration times are greater than a preset maximum iteration time, and determining the identification parameter value of the load model of the power system according to the positions of the alpha grey wolves.
The updating of the positions of the gray wolves in the population is guided by the alpha, beta and delta gray wolves, and the formula for updating the positions of all the gray wolves in the population according to the positions of the alpha, beta and delta gray wolves and the distances between the alpha, beta and delta gray wolves and the cany wolfs is as follows:
Figure BDA0002807513230000082
wherein, X1Represents the position value, X, of the alpha Grey wolf guiding pall update2Represents the position value, X, of the beta grayish wolf guiding the updating of the sirius3Represents the delta grey wolf guiding the position value of the grey wolf updating, X (t +1) represents the final updating position value of the grey wolf, DαRepresents the distance vector between alpha gray wolf and pale wolf, DβRepresents the distance vector between the beta grey wolf and the pale wolf, DδRepresents the distance vector between delta gray wolf and pale wolf, XαDenotes the position vector of alpha gray wolf, XβDenotes the position vector of the beta grayish wolf, XδPosition vector representing delta gray wolf，A1、A2、A3Is the attack coefficient.
Comparing the current iteration time t with a preset maximum iteration time Max _ iteration; checking whether the current iteration time t is greater than the maximum iteration time Max _ iteration, if the current iteration time t is greater than the maximum iteration time, finishing the iteration, and outputting the optimal solution, namely the position X of the alpha grayish wolfαAnd determining the value of the identification parameter of the power system load model.
Table 1 shows the load model parameter identification results provided in the embodiments of the present application, and refer to table 1.
TABLE 1
Figure BDA0002807513230000083
Figure BDA0002807513230000091
According to the technical scheme, the method for optimizing the parameter identification of the load model of the power system comprises the steps of firstly building the power system model in BPA, setting three-phase short-circuit faults to obtain voltage sag data at a bus for load modeling; secondly, an optimization criterion that the weight factor of the objective function is large when the fault voltage drop is large, the weight factor of the objective function is stable and the weight factor of the objective function is small when the fault voltage drop is small is provided, and the problems that the load model outputs active curves and reactive curves are difficult to follow the actually measured active and reactive curves when the fault voltage drop is large in the identification process of the existing load modeling technology are solved. Finally, a novel optimization algorithm, namely a wolf algorithm, is introduced for load model parameter identification, and the problems of long time consumption, low precision, easy trapping in local optimal solution and the like in the identification process in the prior art are solved.
While there have been shown and described what are at present considered the fundamental principles and essential features of the application, and advantages thereof, it will be apparent to those skilled in the art that the application is not limited to the details of the foregoing exemplary embodiments, but is capable of other specific forms without departing from the spirit or essential characteristics thereof. The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the application being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein.
Furthermore, it should be understood that although the present description refers to embodiments, not every embodiment may contain only a single embodiment, and such description is for clarity only, and those skilled in the art should integrate the description, and the embodiments may be combined as appropriate to form other embodiments understood by those skilled in the art.
The embodiments provided in the present application are only a few examples of the general concept of the present application, and do not limit the scope of the present application. Any other embodiments extended according to the scheme of the present application without inventive efforts will be within the scope of protection of the present application for a person skilled in the art.

Claims (5)

1. An optimization method for identifying parameters of a load model of a power system, the method comprising:
acquiring measured data, wherein the measured data comprises bus voltage, bus active power, bus reactive power, motor load active power, motor load reactive power, static load active power and static load reactive power;
according to a gray wolf optimizing algorithm, taking a parameter to be optimized of a load model of the power system as a gray wolf in a population, taking a preset position as a population position, calculating and obtaining an objective function value of each gray wolf in the population according to the population position, the measured data and a preset weight objective function, sequencing the objective function values of the gray wolfs in the population to obtain positions of alpha gray wolf, beta gray wolf and delta gray wolf, judging whether the objective function value of the alpha gray wolf is smaller than a preset optimizing target value or not, and if so, determining an identification parameter value of the load model of the power system according to the objective function value;
if not, determining the current iteration times, and calculating to obtain a convergence factor, an attack coefficient vector and a cooperative coefficient vector according to the current iteration times;
calculating the distances between the alpha grey wolf, the beta grey wolf and the delta grey wolf and the cang wolf according to the positions of the alpha grey wolf, the beta grey wolf and the delta grey wolf and the synergistic coefficient vector;
determining the position values of all the gray wolves in the population according to the convergence factor, the attack coefficient vector, the positions of the alpha gray wolves, the beta gray wolves and the delta gray wolves and the distances between the alpha gray wolves, the beta gray wolves and the delta gray wolves and the cang wolves;
and updating the positions of all the gray wolves in the population according to the position values, taking the positions of all the gray wolves in the updated population as the population positions, increasing the current iteration times once, repeatedly executing the steps, calculating an objective function value of each gray wolve in the population according to the population positions, the measured data and a preset weight objective function until the iteration times are greater than a preset maximum iteration time, and determining the identification parameter value of the power system load model according to the positions of the alpha gray wolves.
2. The method of claim 1, wherein the step of determining the predetermined location comprises: initializing the population position according to the wolf optimizing algorithm, a preset search lower limit, a preset search upper limit, a population scale, a maximum iteration number and the dimension of the parameter to be optimized, and determining the preset position.
3. The method of claim 1, wherein the weight objective function is formulated as follows:
Figure FDA0002807513220000011
wherein J (k) is the target at time kFunction value, P (k) is the active power flowing out of the bus at time k, Q (k) is the reactive power flowing out of the bus at time k, PM(k) Active power absorbed for the motor load at time k, QM(k) Reactive power absorbed for the motor load at time k, PS(k) Active power absorbed for the static load at time k, QS(k) Reactive power absorbed by the static load at time k, U (k) is the bus voltage at time k, U0Is the bus voltage at the initial moment.
4. The method of claim 1, wherein the calculation of the distances between the sirius α, the sirius β and the sirius δ and the sirius are as follows:
Figure FDA0002807513220000021
wherein D represents the dimension of the search space, t represents the current iteration number, X (t) represents the position vector of the t-th iteration wolf, DαRepresents the distance vector between alpha gray wolf and pale wolf, DβRepresents the distance vector between the beta grey wolf and the pale wolf, DδRepresents the distance vector between delta gray wolf and pale wolf, Xα(t) represents the position vector of the alpha grayish wolf at the t-th iteration, Xβ(t) represents the position vector of the beta grayish wolf at the t-th iteration, Xδ(t) represents the position vector of the delta gray wolf at the t-th iteration, C1、C2And C3Is a coefficient of synergy.
5. The method of claim 1, wherein the formula for determining the position values of all gray wolves in the population according to the convergence factor, the attack coefficient vector, the positions of α gray wolves, β gray wolves and δ gray wolves and the distances between the α gray wolves, β gray wolves and δ gray wolves and the cang wolves is as follows:
Figure FDA0002807513220000022
wherein, X1Represents the position value, X, of the alpha Grey wolf guiding pall update2Represents the position value, X, of the beta grayish wolf guiding the updating of the sirius3Represents the delta grey wolf guiding the position value of the grey wolf updating, X (t +1) represents the final updating position value of the grey wolf, DαRepresents the distance vector between alpha gray wolf and pale wolf, DβRepresents the distance vector between the beta grey wolf and the pale wolf, DδRepresents the distance vector between delta gray wolf and pale wolf, XαDenotes the position vector of alpha grayish wolf and XβDenotes the position vector of the beta grayish wolf, XδRepresents the position vector of delta gray wolf, A1、A2And A3Is the attack coefficient.
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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113469432A (en) * 2021-06-29 2021-10-01 海南电网有限责任公司三亚供电局 Distribution network transfer intelligent analysis auxiliary method
CN115102235A (en) * 2022-04-29 2022-09-23 华南理工大学 Household photovoltaic optimization management method and system based on alternating current voltage regulator
CN116667543A (en) * 2023-06-14 2023-08-29 东北电力大学 Wireless power supply system for power transmission line and splicing sleeve inspection robot

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113469432A (en) * 2021-06-29 2021-10-01 海南电网有限责任公司三亚供电局 Distribution network transfer intelligent analysis auxiliary method
CN115102235A (en) * 2022-04-29 2022-09-23 华南理工大学 Household photovoltaic optimization management method and system based on alternating current voltage regulator
CN115102235B (en) * 2022-04-29 2024-04-16 华南理工大学 Household photovoltaic optimal management method and system based on alternating current voltage regulator
CN116667543A (en) * 2023-06-14 2023-08-29 东北电力大学 Wireless power supply system for power transmission line and splicing sleeve inspection robot

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