Optimal configuration method of electric energy quality control device based on improved Hemis algorithm

Abstract

The invention relates to an optimal configuration method for an electric energy quality management device based on an improved He-Make algorithm, and the method comprises the steps: obtaining the operation data of a single-machine infinite bus system, including SVC device data, STATCOM device data, generator data, economic cost data and power grid operation data; constructing an SVC mathematical model based on the SVC device data, constructing an STATCOM mathematical model based on the STATCOM device data, and constructing a generator model based on the generator data; linearizing the SVC mathematical model, the STATCOM mathematical model and the generator model to obtain a state space model of the single-machine infinite bus system, and converting the state space model into a transfer function to calculate a gain matrix; defining constraint conditions by taking economic cost minimization as a target function, and constructing an optimal configuration model of the power quality control device; solving and obtaining an optimal configuration scheme by utilizing an improved He-Ma algorithm; the advantages of various electric energy quality treatment devices are exerted to the maximum extent, and the effect of the electric energy quality treatment device is improved.

Description

Technical Field

[0001] The present invention relates to the field of power quality management, and mainly to a method for optimizing configuration of a power quality management device based on an improved Hippo algorithm. Background Art

[0002] In modern power systems, with the widespread application of renewable energy and the diversification of power loads, power quality issues are becoming increasingly prominent. The decline in power quality not only affects the normal operation of user equipment, but may also lead to equipment failure, system instability and economic losses. In order to solve the power quality problem, flexible distribution system technology (DFACTS) devices are widely used in distribution networks. These devices can effectively suppress harmonics, improve power factor and voltage fluctuations, thereby improving power quality. However, with the increase in the number of DFACTS devices, negative interactions may occur between the devices, resulting in a decline in overall performance. Therefore, it is particularly important to optimize the configuration of power quality management devices. At present, the optimization configuration methods of power quality management devices mainly include traditional optimization algorithms and some heuristic-based algorithms. Although these methods have achieved certain results, they still have some shortcomings. When multiple power quality management devices are running at the same time, the interaction between the devices is often ignored, resulting in unsatisfactory optimization results. After considering the interaction between the devices, many traditional optimization algorithms are prone to fall into local optimal solutions and cannot find the global optimal solution, which may lead to unsatisfactory management effects.

[0003] For example, CN110690712A "Coordinated Optimization Control Method and System for Multiple Power Quality Management Devices" discloses "Coordinated Optimization Control Method and System for Multiple Power Quality Management Devices, establishing a linearized model for the distribution network system with multiple power quality management devices with different voltage levels and converting it into a transfer function, analyzing the interactive influencing factors of the system through the relative gain matrix analysis method, and then obtaining the relative gain matrix, and adjusting the system parameters according to the relative gain matrix results to reduce the interactive influence between the devices in the system; converting the coordination problem between the power quality management devices into a multi-objective optimization problem, and coordinating the controller parameters of the power quality management devices through the optimization algorithm to reduce the interactive influence between the power quality management devices and achieve power supply reliability", but the distribution network system with multiple power quality management devices established by the method has a relatively complicated establishment process and faces great challenges in linearization and subsequent calculations; in addition, the optimization algorithm selected by the method may require a long calculation time, which affects the response speed of the distribution network system and increases the operating cost of the distribution network system. Summary of the invention

[0004] In order to solve the above-mentioned problems existing in the prior art, the present application provides an optimization configuration method for power quality management devices based on an improved Hippo algorithm.

[0005] The technical solution of this application is as follows:

[0006] A method for optimizing configuration of power quality management devices based on an improved Hippo algorithm, the method comprising:

[0007] Acquire the operation data of a single-machine infinite system, the single-machine infinite system includes a power quality management device and a generator model, the power quality management device includes a static VAR compensation SVC device and a static synchronous compensator STATCOM device; the operation data includes SVC device data, STATCOM device data, generator data, economic cost data and power grid operation data; construct an SVC mathematical model based on the SVC device data, construct a STATCOM mathematical model based on the STATCOM device data, and construct a generator model based on the generator data;

[0008] Linearize the SVC mathematical model, the STATCOM mathematical model and the generator model to obtain the linearized equations, organize the linearized equations into the standard form of the state space model of a single-machine infinite system, and convert them into transfer functions; calculate the gain matrix based on the transfer function, and use the diagonal elements of the gain matrix as the interaction influence parameters between the power quality management devices;

[0009] Constructing an optimal configuration model for power quality management devices, wherein the optimal configuration model for power quality management devices is based on economic cost data, constructs a corresponding objective function with the goal of minimizing economic cost, and defines corresponding constraints based on power grid operation data, wherein the constraints include interaction constraints between power quality management devices and related configuration constraints of power quality management devices;

[0010] The improved Hippo algorithm that introduces chaotic mapping and adaptive weights is used to solve the optimal configuration model of the power quality management device to obtain an optimal configuration solution.

[0011] Preferably, the operation data includes SVC device data, STATCOM device data, generator data, economic cost data and power grid operation data, specifically:

[0012] The SVC device data includes equivalent admittance, reactive admittance in equivalent admittance, key parameters of the SVC device at the control node voltage, reactance of the capacitor, maximum compensation power, current range and SVC nameplate parameters;

[0013] The STATCOM device data includes a controllable voltage source, an amplitude of the controllable voltage source, modem parameters, an inverter voltage ratio, a modulation ratio, an initial phase angle of the controllable voltage source, an angular velocity, a conjugate of an output current, a conjugate d-axis component, a conjugate q-axis component, a conjugate of a voltage, a current on the DC side of the inverter, a capacitance value for smoothing the voltage, active power, reactive power, an adjustable output voltage range, and STATCOM nameplate parameters;

[0014] The generator data include output electric power, input mechanical power, output voltage, current flowing through, damping characteristics, phase difference between the rotor of the generator and the synchronous rotating field, angular velocity, initial angular velocity, moment of inertia, excitation electromotive force, instantaneous excitation electromotive force, rate of change of excitation current, target voltage, time constant related to excitation current, gain of excitation system, time constant of excitation system, reactance, maximum active output power, reactive power output capacity, efficiency of the generator under different loads and generator nameplate parameters;

[0015] The economic cost data includes the cost of the active power filter APF device, the cost of the STATCOM device, the cost of the SVC device, the device fixed cost coefficient, the device variable cost coefficient and the device rated capacity;

[0016] The grid operation data includes the total number of buses, harmonic order, highest harmonic order, bus voltage, upper and lower limits of bus voltage, bus current, conductance, susceptance, reference current, reference voltage, maximum current, bus reactive power, bus reactive power, power factor, rated capacity of the bus and operating capacity of the power quality management device.

[0017] Preferably, a SVC mathematical model is constructed based on the SVC device data, which is expressed as:

[0018]

[0019] 0 <C(α)=2(α-sin2α) / π-1<1;

[0020] In the formula, represents the equivalent admittance of the SVC device; z represents an imaginary variable; B SVC represents the reactive admittance in the equivalent admittance of the SVC device; C(α) represents the key parameter of the SVC device in controlling the node voltage; X C Indicates the reactance of the capacitor;

[0021] The STATCOM mathematical model is constructed based on the STATCOM device data and is expressed as follows:

[0022] V0(t)=V 0m sin(ωt-ψ);

[0023]

[0024] c = mk;

[0025] Where V0(t) represents the controllable voltage source generated by the voltage source inverter of the STATCOM device at time t, V 0m represents the amplitude of the controllable voltage source; c represents the modem parameter; k represents the inverter voltage ratio; m represents the modulation ratio; ψ represents the initial phase angle of the controllable voltage source; ω represents the angular velocity of the generator rotor; represents the conjugate of the output current of the STATCOM device, I lod represents the conjugate d-axis component of the output current, I loq Represents the conjugate q-axis component of the output current; I represents the conjugate of the voltage of the STATCOM device; DC Indicates the current on the DC side of the inverter; C DC Indicates the capacitance value for smoothing voltage;

[0026] The generator model is constructed based on the generator data and is expressed as follows:

[0027]

[0028] P e =E' q I tq +(X q -X' d )I td I tq ;

[0029]

[0030] V td =X q I tq ;

[0031] V tq =E' q -X' d I td ;

[0032] E q =E' q +(X d -X' d )I td ;

[0033] Where P e Indicates the electrical power output by the generator; P m Represents the mechanical power input to the generator; V t Indicates the voltage at the generator output; Vtd Indicates the voltage output by the generator d-axis; V tq Indicates the voltage output by the generator q axis; I td Represents the current flowing through the generator d axis; I tq represents the current flowing through the generator q axis; D ω Indicates the damping characteristics of the generator; represents the phase difference between the generator rotor and the synchronous rotating field; ω represents the angular velocity of the generator rotor; ω o represents the initial angular velocity; M represents the moment of inertia of the generator; E q Represents the excitation electromotive force; E fd Represents the direct-axis electromotive force of the excitation current; E' q represents the excitation electromotive force; Represents the instantaneous excitation electromotive force of the generator; E q Indicates the rate of change of the excitation current; The dynamic equation that represents the change of excitation current; V l Indicates the target voltage of the generator; T d '0 represents the time constant related to the excitation current; K A Represents the gain of the excitation system; T A Represents the time constant of the generator excitation system; X q and X d represents the quadrature-axis reactance and direct-axis reactance of the generator in steady state; X' d It represents the direct-axis reactance of the generator under non-steady-state conditions.

[0034] Preferably, the linearized equations are organized into the standard form of the state space model of a single machine infinite system:

[0035]

[0036]

[0037] u=[ΔcΔψΔα] T ;

[0038] y=[ΔV l ΔV DC ΔV s ] T ;

[0039] Where x represents the state vector; represents the derivative of the state vector x; u represents the input vector; y represents the output vector; represents the change in phase difference between the generator rotor and the synchronous rotating field; Δω represents the change in angular velocity of the generator rotor; ΔE' q Indicates the change in the excitation electromotive force; ΔE fdIndicates the change in the direct-axis electromotive force of the excitation current; ΔV DC represents the capacitor voltage change of the STATCOM device; Δc represents the modem parameter change of the STATCOM device; Δψ represents the initial phase angle change of the controllable voltage source; Δα represents the phase angle change of the thyristor-controlled reactor of the SVC device; ΔV l Indicates the change in the generator target voltage; ΔV s represents the bus voltage change of the SVC device; A, B, C and D all represent coefficient matrices;

[0040] And converted into a transfer function, expressed in the formula:

[0041] G=u T y;

[0042] Calculate the gain matrix R based on the transfer function RGA , expressed as:

[0043]

[0044] In the formula, λ yq Represents the elements in the gain matrix; y represents the row index of the gain matrix; q represents the column index of the gain matrix; the diagonal elements represent the interaction between the power quality management devices.

[0045] Preferably, the objective function F i , expressed as:

[0046]

[0047] P an =a an +b an S an ;

[0048] P dn =a dn +b dn S dn ;

[0049] P sn =a sn +b sn S sn ;

[0050] Where, N represents the total number of buses in the distribution network; n represents the node; P an P represents the cost of installing an active power filter APF device at the bus node n; dn P represents the cost of installing a STATCOM device at the bus node n; snrepresents the cost of installing an SVC device at node n on the bus; a represents the fixed cost coefficient of the device; b represents the variable cost coefficient of the device; S represents the rated capacity of the device; μ an , μ dn and μ sn They respectively indicate whether the node n is equipped with an APF device, a STATCOM device, and an SVC device. If the node n is equipped with a corresponding power quality control device, then μ an , μ dn and μ sn Assign a value of 1, otherwise μ an , μ dn and μ sn Assign a value of 0.

[0051] Preferably, the interaction constraint between the power quality management devices is expressed as follows:

[0052] 1≤λ nn ≤λ nmax ;

[0053] In the formula, λ nn represents the diagonal elements in the gain matrix; λ nmax represents the maximum value of the diagonal elements in the gain matrix;

[0054] The configuration constraints of the power quality control device include power flow balance constraints, harmonic constraints, voltage upper and lower limit constraints, power factor constraints and power quality control device capacity constraints, among which:

[0055] The power flow balance constraint is expressed as follows:

[0056]

[0057] Where P Gn Indicates the active power of the distribution network generator; Q Gn Indicates the reactive power of the distribution network generator; P Ln Indicates the active power of the distribution network load; Q Ln Indicates the reactive power of the distribution network load; W an , W dn and W sn It represents the active power of APF, STACOM and SVC installed at the bus at node n; Q an , Q dn and Q sn represents the reactive power of the APF device, STACOM device and SVC device installed at the bus at node n; U n represents the bus voltage at node n; I n Indicates bus current; G nmrepresents the conductance between node n and node m; B nm represents the susceptance between node n and node m; U m represents the bus voltage at node m;

[0058] The harmonic constraint is expressed as:

[0059]

[0060] In the formula, h represents the harmonic order; H represents the highest harmonic order; I represents the system reference current; U represents the system reference voltage; λ Imax Indicates the maximum current allowed by the system; Umax Indicates the voltage harmonic distortion rate allowed by the system; U nmax Indicates the upper limit of bus voltage; U nmin Indicates the lower limit of bus voltage; λ THDI Indicates the harmonic current distortion rate; λ THDU Indicates the harmonic voltage distortion rate;

[0061] The voltage upper and lower limit constraints are expressed as follows:

[0062] U nmin ≤U n ≤U nmax ;

[0063] The power factor constraint is expressed as:

[0064]

[0065] In the formula, Q0 represents the reactive power of the bus; P0 represents the reactive power of the bus; β represents the lower limit of the power factor;

[0066] The capacity constraint of the power quality control device is expressed as follows:

[0067] S n ≤θS En ,

[0068] Where, θ represents the maximum capacity factor; S En Indicates the rated capacity of the bus at node n; S n Indicates the operating capacity of the power quality management device.

[0069] Preferably, the improved Hippo algorithm that introduces chaotic mapping and adaptive weights is used to solve the optimal configuration model of the power quality management device to obtain an optimal configuration scheme. The improved Hippo algorithm includes an initialization phase, a search phase, and a development phase, specifically:

[0070] The initialization stage specifically introduces the values ​​of the candidate set of configuration schemes and the candidate set of configuration schemes to initialize the chaotic map, which can be expressed as:

[0071] X(i,:)=X min (i)+r1(X max (i)-X min (i)), i = 1, 2, ..., Num;

[0072]

[0073] Where X(i,:) represents the value of the i-th configuration candidate; Num represents the number of candidate solutions in the configuration candidate set; r1 represents a random number in the interval [0, 1]; X min (i) represents the lower limit of the value of the i-th configuration candidate; X max (i) represents the upper limit of the value of the i-th configuration candidate; P represents a random number in the interval [0, 0.5]; r 1_i represents the value of r1 at the i-th iteration;

[0074] The search phase includes a position update phase and a predator defense phase; the position update phase specifically calculates the objective function value corresponding to each candidate solution according to the initial configuration solution candidate set, selects the candidate solution whose objective function value is within a preset range as the dominant candidate solution; introduces adaptive weights to update the value of the dominant candidate solution, iterates the position update phase; selects the dominant candidate solution with the smallest objective function value as the optimal candidate solution;

[0075] The value of the dominant candidate solution is expressed as follows:

[0076] X_P1(i,:)=w(t)×X(i,:)+r2×(D hippo -R1X(i,:));

[0077]

[0078] Where X_P1(i,:) represents the value of the dominant candidate solution; D hippo represents the value of the optimal candidate solution; r2 is a random number in the interval [0, 1]; R1 represents a random number in the interval [1, 2]; w(t w ) represents the weight factor; Max_iter represents the maximum number of iterations, t w Indicates the time range;

[0079] Update the value of the non-dominant candidate solution, expressed as:

[0080]

[0081] Where X_P2(i,:) represents the value of the non-dominant candidate solution; v1 represents a random vector; MG i represents a candidate solution randomly selected from the candidate set, and calculates the average value of the objective function value corresponding to the candidate solution; T represents the selection probability; R2 represents a random number in the interval [1, 2];

[0082] The defense against predators stage is specifically when the current candidate solution is threatened by the current optimal candidate solution or other better candidate solutions in the search space, which can be expressed by the formula:

[0083]

[0084]

[0085] Where X_P3(i,:) represents the value of other better candidate solutions; RL represents a random vector with Levy distribution; Predator j Indicates the value of the current best candidate solution; represents the distance from the i-th current candidate solution to the optimal candidate solution; b represents a random number in the interval [2, 4]; k represents a random number in the interval [1, 1.5]; d represents a random number in the interval [2, 3]; g represents a random number in the interval [2, 4]; r3 represents a random number in the interval [2, 4]; represents the strategy adopted by the current candidate solution to deal with the threat, which includes changing the search direction, adjusting the search step size, and increasing the search intensity; F i Represents the objective function value of the current candidate solution;

[0086] The development phase specifically uses the reverse learning strategy to update the value of the candidate solution, which is expressed as:

[0087]

[0088] In the formula, Represents the value of the candidate solution for the reverse learning strategy update; a g and b g They represent the upper and lower limits of the g-th dimension in the feasible domain of the search respectively; γ represents the dynamic adaptive value related to the number of iterations; x g represents X_P1(i,:) and X_P2(i,:), i.e., the value of the dominant candidate solution and the value of the non-dominant candidate solution; g represents the index value of the g-th dimension;

[0089] Until the maximum number of iterations is reached or the objective function value converges, the optimal configuration solution is obtained, which is expressed as:

[0090]

[0091] Where X_P4(i,:) represents the value of the optimized configuration solution; r4 represents a random number in the interval [0, 1]; r5 represents a random number that conforms to the normal distribution; and L represents the current number of iterations.

[0092] The power quality management device optimization configuration system based on the improved Hippo algorithm includes a data acquisition module, a mathematical model construction module, a state space model construction module, a gain matrix calculation module, an optimization configuration model construction module, an optimization solution module and an output module, wherein:

[0093] The data acquisition module is used to acquire the operating data of the single-machine infinite system and perform data cleaning. The single-machine infinite system includes a power quality management device and a generator model. The power quality management device includes a static VAR compensation SVC device and a static synchronous compensator STATCOM device; the operating data includes SVC device data, STATCOM device data, generator data, economic cost data and power grid operation data; the cleaned SVC device data, STATCOM device data and generator data are transmitted to the mathematical model construction module;

[0094] The mathematical model building module builds an SVC mathematical model based on the SVC device data, builds a STATCOM mathematical model based on the STATCOM device data, and builds a generator model based on the generator data; and transmits the SVC mathematical model, the STATCOM mathematical model and the generator model to the state space model building module;

[0095] The state space model building module linearizes the SVC mathematical model, the STATCOM mathematical model and the generator model to obtain linearized equations, organizes the linearized equations into a standard form of a state space model of a single machine infinite system, converts them into transfer functions, and transmits the transfer functions to the gain matrix calculation module;

[0096] The gain matrix calculation module is used to calculate the gain matrix based on the transfer function, use the diagonal elements of the gain matrix as the interaction influence parameters between the power quality management devices, and transmit the interaction influence parameters to the optimization configuration model construction module; the optimization configuration model construction module constructs the power quality management device optimization configuration model according to the economic cost data, the interaction influence parameters and the operation data of the cleaned single-machine infinite system. The power quality management device optimization configuration model is based on the economic cost data, and the corresponding objective function is constructed with the goal of minimizing the economic cost. The corresponding constraint conditions are defined based on the power grid operation data, and the constraint conditions include the interaction influence constraints between the power quality management devices and the relevant configuration constraints of the power quality management devices;

[0097] The optimization solution module uses an improved Hippo algorithm that introduces chaotic mapping and adaptive weights to solve the optimization configuration model of the power quality management device to obtain an optimization configuration solution, which includes optimal configuration parameters and optimal economic costs;

[0098] The output module is used to display the optimized configuration solution.

[0099] An electronic device comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the method for optimizing configuration of a power quality management device based on an improved Hippo algorithm as described in any one of the embodiments is implemented.

[0100] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the method for optimizing the configuration of a power quality management device based on an improved Hippo algorithm as described in any one of the embodiments.

[0101] Compared with the prior art, the present invention has the following beneficial effects:

[0102] 1) The present invention provides an optimization configuration method for power quality management devices based on an improved Hippo algorithm. By integrating the SVC device, the STATCOM device and the generator model, a single-machine infinite system model is constructed, which enhances the comprehensive consideration of the interaction between the components, improves the accuracy of quantifying the interaction effect, and ensures the integrity of the data;

[0103] 2) The present invention provides an optimized configuration method for power quality management devices based on an improved Hippo algorithm, which uses a gain matrix to quantify the interaction between power quality management devices, and uses the diagonal elements of the gain matrix as the interaction between the power quality management devices, thereby improving the efficiency and pertinence of the configuration process of the power quality management devices and ensuring the feasibility and effectiveness of the optimized configuration scheme;

[0104] 3) The present invention provides an optimal configuration method for power quality management devices based on an improved Hippo algorithm. By constructing an optimal configuration model for power quality management devices with the goal of minimizing economic costs, and defining constraints including relevant configuration constraints and interactive influence constraints, the Hippo algorithm is used to solve the optimal configuration scheme, which reduces the economic cost of the device, enhances the relevant configuration constraints and interactive influence constraints of the power quality management device, and ensures the feasibility and effectiveness of the optimal configuration scheme. BRIEF DESCRIPTION OF THE DRAWINGS

[0105] Figure 1 is a method flow chart of an embodiment of the present invention;

[0106] Figure 2 Schematic diagram of a single-machine infinite system according to an embodiment of the present invention. DETAILED DESCRIPTION

[0107] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0108] The present invention provides the following technical solution: an optimization configuration method for a power quality management device based on an improved Hippo algorithm.

[0109] Example 1

[0110] This embodiment provides a method for optimizing configuration of a power quality management device based on an improved Hippo algorithm, and the specific steps include:

[0111] S1. Please refer to Figure 2 , obtaining operation data of a single-machine infinite system, the single-machine infinite system including a power quality management device and a generator model, the power quality management device including a static VAR compensation SVC device and a static synchronous compensator STATCOM device; the operation data including SVC device data, STATCOM device data, generator data, economic cost data and power grid operation data;

[0112] S11, the SVC device data includes equivalent admittance, reactive admittance in equivalent admittance, key parameters of the SVC device at the control node voltage, reactance of the capacitor, maximum compensation power, current range and SVC nameplate parameters;

[0113] S12, the STATCOM device data includes a controllable voltage source, an amplitude of the controllable voltage source, modem parameters, an inverter voltage ratio, a modulation ratio, an initial phase angle of the controllable voltage source, an angular velocity, a conjugate of an output current, a conjugate d-axis component, a conjugate q-axis component, a conjugate of a voltage, a current on the DC side of the inverter, a capacitance value of a smoothed voltage, active power, reactive power, an adjustable output voltage range, and STATCOM nameplate parameters;

[0114] S13, the generator data includes output electric power, input mechanical power, output voltage, current flowing through, damping characteristics, phase difference between the rotor of the generator and the synchronous rotating field, angular velocity, initial angular velocity, moment of inertia, excitation electromotive force, instantaneous excitation electromotive force, rate of change of excitation current, target voltage, time constant related to excitation current, gain of excitation system, time constant of excitation system, reactance, maximum active output power, reactive power output capacity, efficiency of the generator under different loads and generator nameplate parameters;

[0115] S14, the economic cost data includes the cost of the active power filter APF device, the cost of the STATCOM device, the cost of the SVC device, the device fixed cost coefficient, the device variable cost coefficient and the device rated capacity;

[0116] S15. The grid operation data includes the total number of buses, harmonic order, highest harmonic order, bus voltage, upper and lower limits of bus voltage, bus current, conductance, susceptance, reference current, reference voltage, maximum current, bus reactive power, bus reactive power, power factor, bus rated capacity and operating capacity of power quality control device;

[0117] S2. constructing an SVC mathematical model based on the SVC device data, constructing a STATCOM mathematical model based on the STATCOM device data, and constructing a generator model based on the generator data;

[0118] S21. Construct an SVC mathematical model based on the SVC device data, expressed as follows:

[0119]

[0120] 0 <C(α)=2(α-sin2α) / π-1<1;

[0121] In the formula, represents the equivalent admittance of the SVC device; z represents an imaginary variable; B SVC represents the reactive admittance in the equivalent admittance of the SVC device; C(α) represents the key parameter of the SVC device in controlling the node voltage; X C Indicates the reactance of the capacitor;

[0122] S22. Construct a STATCOM mathematical model based on the STATCOM device data, which is expressed as follows:

[0123] V0(t)=V 0m sin(ωt-ψ);

[0124]

[0125] c = mk;

[0126] Where V0(t) represents the controllable voltage source generated by the voltage source inverter of the STATCOM device at time t, V 0m represents the amplitude of the controllable voltage source; c represents the modem parameter; k represents the inverter voltage ratio; m represents the modulation ratio; ψ represents the initial phase angle of the controllable voltage source; ω represents the angular velocity of the generator rotor; represents the conjugate of the output current of the STATCOM device, I lod represents the conjugate d-axis component of the output current, Iloq Represents the conjugate q-axis component of the output current; I represents the conjugate of the voltage of the STATCOM device; DC Indicates the current on the DC side of the inverter; C DC Indicates the capacitance value for smoothing voltage;

[0127] S23. A generator model is constructed based on the generator data, which is expressed as follows:

[0128]

[0129] P e =E' q I tq +(X q -X' d )I td I tq ;

[0130]

[0131] V td =X q I tq ;

[0132] V tq =E' q -X' d I td ;

[0133] E q =E' q +(X d -X' d )I td ;

[0134] Where P e Indicates the electrical power output by the generator; P m Represents the mechanical power input to the generator; V t Indicates the voltage at the generator output; V td Indicates the voltage output by the generator d-axis; V tq Indicates the voltage output by the generator q axis; I td Represents the current flowing through the generator d axis; I tq represents the current flowing through the generator q axis; D ω Indicates the damping characteristics of the generator; represents the phase difference between the generator rotor and the synchronous rotating field; ω represents the angular velocity of the generator rotor; ω o represents the initial angular velocity; M represents the moment of inertia of the generator; E q Represents the excitation electromotive force; E fdRepresents the direct-axis electromotive force of the excitation current; E' q represents the excitation electromotive force; Represents the instantaneous excitation electromotive force of the generator; E q Indicates the rate of change of the excitation current; The dynamic equation that represents the change of excitation current; V l Indicates the target voltage of the generator; T′ d0 Represents the time constant related to the excitation current; K A Represents the gain of the excitation system; T A Represents the time constant of the generator excitation system; X q and X d represents the quadrature-axis reactance and direct-axis reactance of the generator in steady state; X' d represents the direct-axis reactance of the generator under non-steady-state conditions;

[0135] S3. Arrange the linearized equation into the standard form of the state space model of a single-machine infinite system. The specific method of linearization is to set the generator target voltage V l , capacitor voltage V of STATCOM device DC and the bus voltage V of the SVC device s Taylor expansion is performed on the initial phase angle variation ψ of the controllable voltage source, the modem parameter c of the STATCOM device, and the phase angle variation α of the thyristor-controlled reactor of the SVC device, retaining the first-order terms and constant terms and ignoring the higher-order terms; the state space model of the single-machine infinite power system is expressed as follows:

[0136]

[0137]

[0138] u=[Δc Δψ Δα] T ;

[0139] y=[ΔV l ΔV DC ΔV s ] T ;

[0140] Where x represents the state vector; represents the derivative of the state vector x; u represents the input vector; y represents the output vector; represents the change in phase difference between the generator rotor and the synchronous rotating field; Δω represents the change in angular velocity of the generator rotor; ΔE' q Indicates the change in the excitation electromotive force; ΔE fd Indicates the change in the direct-axis electromotive force of the excitation current; ΔV DCrepresents the capacitor voltage change of the STATCOM device; Δc represents the modem parameter change of the STATCOM device; Δψ represents the initial phase angle change of the controllable voltage source; Δα represents the phase angle change of the thyristor-controlled reactor of the SVC device; ΔV l Indicates the change in the generator target voltage; ΔV s represents the bus voltage change of the SVC device; A, B, C and D all represent coefficient matrices;

[0141] And converted into a transfer function, expressed in the formula:

[0142] G(s)=u T y;

[0143] S4, calculating the gain matrix R based on the transfer function RGA , expressed as:

[0144]

[0145] In the formula, λ yq represents the elements in the gain matrix; y represents the row index of the gain matrix; q represents the column index of the gain matrix; the diagonal elements represent the interaction between the power quality management devices;

[0146] S5. Construct an optimal configuration model for power quality control devices. The optimal configuration model for power quality control devices is based on economic cost data and constructs a corresponding objective function with the goal of minimizing economic cost. The objective function F i , expressed as:

[0147]

[0148] P an =a an +b an S an ;

[0149] P dn =a dn +b dn S dn ;

[0150] P sn =a sn +b sn S sn ;

[0151] Where, N represents the total number of buses in the distribution network; n represents the node; P an P represents the cost of installing an active power filter APF device at the bus node n; dnP represents the cost of installing a STATCOM device at the bus node n; sn represents the cost of installing an SVC device at node n on the bus; a represents the fixed cost coefficient of the device; b represents the variable cost coefficient of the device; S represents the rated capacity of the device; μ an , μ dn and μ sn They respectively indicate whether the node n is equipped with an APF device, a STATCOM device, and an SVC device. If the node n is equipped with a corresponding power quality control device, then μ an , μ dn and μ sn Assign a value of 1, otherwise μ an , μ dn and μ sn Assign a value of 0;

[0152] S6. Define corresponding constraints based on the grid operation data, wherein the constraints include interaction constraints between power quality management devices and related configuration constraints of the power quality management devices;

[0153] S61. The interaction constraints between the power quality management devices are expressed as follows:

[0154] 1≤λ nn ≤λ nmax ;

[0155] In the formula, λ nn represents the diagonal elements in the gain matrix; λ nmax represents the maximum value of the diagonal elements in the gain matrix;

[0156] S62. The relevant configuration constraints of the power quality control device include power flow balance constraints, harmonic constraints, voltage upper and lower limit constraints, power factor constraints and power quality control device capacity constraints;

[0157] S621, the power flow balance constraint is expressed as:

[0158]

[0159] Where P Gn Indicates the active power of the distribution network generator; Q Gn Indicates the reactive power of the distribution network generator; P Ln Indicates the active power of the distribution network load; Q Ln Indicates the reactive power of the distribution network load; W an , W dn and W sn It represents the active power of APF, STACOM and SVC installed at the bus at node n; Q an , Qdn and Q sn represents the reactive power of the APF device, STACOM device and SVC device installed at the bus at node n; U n Represents the bus voltage at node n; I n Indicates bus current; G nm represents the conductance between node n and node m; B nm represents the susceptance between node n and node m; U m represents the bus voltage at node m;

[0160] S622, the harmonic constraint is expressed as:

[0161]

[0162] In the formula, h represents the harmonic order; H represents the highest harmonic order; I represents the system reference current; U represents the system reference voltage; λ Imax Indicates the maximum current allowed by the system; Umax Indicates the voltage harmonic distortion rate allowed by the system; U nmax Indicates the upper limit of bus voltage; U nmin Indicates the lower limit of bus voltage; λ THDI Indicates the harmonic current distortion rate; λ THDU Indicates the harmonic voltage distortion rate;

[0163] S623, the voltage upper and lower limit constraints are expressed as follows:

[0164] U nmin ≤U n ≤U nmax ;

[0165] S624, the power factor constraint is expressed as:

[0166]

[0167] In the formula, Q0 represents the reactive power of the bus; P0 represents the reactive power of the bus; β represents the lower limit of the power factor;

[0168] S625. The capacity constraint of the power quality control device is expressed as:

[0169] S n ≤θS En ,

[0170] Where, θ represents the maximum capacity factor; S En Indicates the rated capacity of the bus at node n; S n Indicates the operating capacity of the power quality management device;

[0171] S7. Solving the optimization configuration model of the power quality management device by using an improved Hippo algorithm that introduces chaotic mapping and adaptive weights to obtain an optimization configuration scheme, wherein the improved Hippo algorithm includes an initialization phase, a search phase, and a development phase;

[0172] S71, the initialization stage specifically introduces the values ​​of the configuration scheme candidate set and the configuration scheme candidate set to initialize the chaotic map, which is expressed as:

[0173] X(i,:)=X min (i)+r1(X max (i)-X min (i)), i = 1, 2, ..., Num;

[0174]

[0175] Where X(i,:) represents the value of the i-th configuration candidate; Num represents the number of candidate solutions in the configuration candidate set; r1 represents a random number in the interval [0, 1]; X min (i) represents the lower limit of the value of the i-th configuration candidate; X max (i) represents the upper limit of the value of the i-th configuration candidate; P represents a random number in the interval [0, 0.5]; r 1_i represents the value of r1 at the i-th iteration;

[0176] S72, the search phase includes a position update phase and a predator defense phase;

[0177] S721, the position update phase specifically includes calculating the objective function value corresponding to each candidate solution according to the initial configuration solution candidate set, selecting the candidate solution whose objective function value is within a preset range as the dominant candidate solution; introducing an adaptive weight to update the value of the dominant candidate solution, iterating the position update phase; selecting the dominant candidate solution with the smallest objective function value as the optimal candidate solution;

[0178] The value of the dominant candidate solution is expressed as follows:

[0179] X_P1(i,:)=w(t)×X(i,:)+r2×(D hippo -R1X(i,:));

[0180]

[0181] Where X_P1(i,:) represents the value of the dominant candidate solution; D hippo represents the value of the optimal candidate solution; r2 is a random number in the interval [0, 1]; R1 represents a random number in the interval [1, 2]; w(t w) represents the weight factor; Max_iter represents the maximum number of iterations, t w Indicates the time range;

[0182] Update the value of the non-dominant candidate solution, expressed as:

[0183]

[0184] Where X_P2(i,:) represents the value of the non-dominant candidate solution; v1 represents a random vector; MG i represents a candidate solution randomly selected from the candidate set, and calculates the average value of the objective function value corresponding to the candidate solution; T represents the selection probability; R2 represents a random number in the interval [1, 2];

[0185] S722, the predator defense stage is specifically that the current candidate solution is threatened by the current optimal candidate solution or other better candidate solutions in the search space, which is expressed by the formula:

[0186]

[0187]

[0188] Where X_P3(i,:) represents the value of other better candidate solutions; RL represents a random vector with Levy distribution; Predator j Indicates the value of the current best candidate solution; represents the distance from the i-th current candidate solution to the optimal candidate solution; b represents a random number in the interval [2, 4]; k represents a random number in the interval [1, 1.5]; d represents a random number in the interval [2, 3]; g represents a random number in the interval [2, 4]; r3 represents a random number in the interval [2, 4]; represents the strategy adopted by the current candidate solution to deal with the threat, which includes changing the search direction, adjusting the search step size, and increasing the search intensity; F i Represents the objective function value of the current candidate solution;

[0189] S73, the development stage specifically uses the reverse learning strategy to update the value of the candidate solution, which is expressed as:

[0190]

[0191] In the formula, Represents the value of the candidate solution for the reverse learning strategy update; a g and b g They represent the upper and lower limits of the g-th dimension in the feasible domain of the search respectively; γ represents the dynamic adaptive value related to the number of iterations; x grepresents X_P1(i,:) and X_P2(i,:), i.e., the value of the dominant candidate solution and the value of the non-dominant candidate solution; g represents the index value of the g-th dimension;

[0192] Until the maximum number of iterations is reached or the objective function value converges, the optimal configuration solution is obtained, which is expressed as:

[0193]

[0194] Where X_P4(i,:) represents the value of the optimized configuration solution; r4 represents a random number in the interval [0, 1]; r5 represents a random number that conforms to the normal distribution; and L represents the current number of iterations.

[0195] Example 2

[0196] This embodiment provides an optimization configuration system for power quality management devices based on an improved Hippo algorithm, the system comprising a data acquisition module, a mathematical model construction module, a state space model construction module, a gain matrix calculation module, an optimization configuration model construction module, an optimization solution module and an output module, wherein:

[0197] The data acquisition module is used to acquire the operating data of the single-machine infinite system and perform data cleaning. The single-machine infinite system includes a power quality management device and a generator model. The power quality management device includes a static VAR compensation SVC device and a static synchronous compensator STATCOM device; the operating data includes SVC device data, STATCOM device data, generator data, economic cost data and power grid operation data; the cleaned SVC device data, STATCOM device data and generator data are transmitted to the mathematical model construction module;

[0198] The mathematical model building module builds an SVC mathematical model based on the SVC device data, builds a STATCOM mathematical model based on the STATCOM device data, and builds a generator model based on the generator data; and transmits the SVC mathematical model, the STATCOM mathematical model and the generator model to the state space model building module;

[0199] The state space model building module linearizes the SVC mathematical model, the STATCOM mathematical model and the generator model to obtain linearized equations, organizes the linearized equations into a standard form of a state space model of a single machine infinite system, converts them into transfer functions, and transmits the transfer functions to the gain matrix calculation module;

[0200] The gain matrix calculation module is used to calculate the gain matrix based on the transfer function, use the diagonal elements of the gain matrix as the interaction influence parameters between the power quality management devices, and transmit the interaction influence parameters to the optimization configuration model construction module; the optimization configuration model construction module constructs the power quality management device optimization configuration model according to the economic cost data, the interaction influence parameters and the operation data of the cleaned single-machine infinite system. The power quality management device optimization configuration model is based on the economic cost data, and the corresponding objective function is constructed with the goal of minimizing the economic cost. The corresponding constraint conditions are defined based on the power grid operation data, and the constraint conditions include the interaction influence constraints between the power quality management devices and the relevant configuration constraints of the power quality management devices;

[0201] The optimization solution module uses an improved Hippo algorithm that introduces chaotic mapping and adaptive weights to solve the optimization configuration model of the power quality management device to obtain an optimization configuration solution, which includes optimal configuration parameters and optimal economic costs;

[0202] The output module is used to display the optimized configuration solution.

[0203] Example 3

[0204] This embodiment provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the program, the method for optimizing the configuration of a power quality management device based on the improved Hippo algorithm as described in any one of Embodiment 1 is implemented.

[0205] Example 4

[0206] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, it implements the method for optimizing the configuration of a power quality management device based on the improved Hippo algorithm as described in any one of Embodiments 1.

[0207] It is worth noting that the system, electronic device and computer-readable storage medium described in the present invention are based on the same principles as the method described in Example 1, and will not be described in detail here.

[0208] The above descriptions are merely embodiments of the present invention and are not intended to limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. The method for optimizing the configuration of power quality management devices based on the improved Hippo algorithm is characterized in that: The method comprises: Acquire the operation data of a single-machine infinite system, the single-machine infinite system includes a power quality management device and a generator model, the power quality management device includes a static VAR compensation SVC device and a static synchronous compensator STATCOM device; the operation data includes SVC device data, STATCOM device data, generator data, economic cost data and power grid operation data; construct an SVC mathematical model based on the SVC device data, construct a STATCOM mathematical model based on the STATCOM device data, and construct a generator model based on the generator data; Linearize the SVC mathematical model, the STATCOM mathematical model and the generator model to obtain the linearized equations, organize the linearized equations into the standard form of the state space model of a single-machine infinite system, and convert them into transfer functions; calculate the gain matrix based on the transfer function, and use the diagonal elements of the gain matrix as the interaction influence parameters between the power quality management devices; Constructing an optimal configuration model for power quality management devices, wherein the optimal configuration model for power quality management devices is based on economic cost data, constructs a corresponding objective function with the goal of minimizing economic cost, and defines corresponding constraints based on power grid operation data, wherein the constraints include interaction constraints between power quality management devices and related configuration constraints of power quality management devices; The improved Hippo algorithm that introduces chaotic mapping and adaptive weights is used to solve the optimal configuration model of the power quality management device to obtain an optimal configuration solution.

2. The method for optimizing configuration of power quality management devices based on the improved Hippo algorithm according to claim 1 is characterized in that: The operation data includes SVC device data, STATCOM device data, generator data, economic cost data and power grid operation data, specifically: The SVC device data includes equivalent admittance, reactive admittance in equivalent admittance, key parameters of the SVC device at the control node voltage, reactance of the capacitor, maximum compensation power, current range and SVC nameplate parameters; The STATCOM device data includes a controllable voltage source, an amplitude of the controllable voltage source, modem parameters, an inverter voltage ratio, a modulation ratio, an initial phase angle of the controllable voltage source, an angular velocity, a conjugate of an output current, a conjugate d-axis component, a conjugate q-axis component, a conjugate of a voltage, a current on the DC side of the inverter, a capacitance value for smoothing the voltage, active power, reactive power, an adjustable output voltage range, and STATCOM nameplate parameters; The generator data include output electric power, input mechanical power, output voltage, current flowing through, damping characteristics, phase difference between the rotor of the generator and the synchronous rotating field, angular velocity, initial angular velocity, moment of inertia, excitation electromotive force, instantaneous excitation electromotive force, rate of change of excitation current, target voltage, time constant related to excitation current, gain of excitation system, time constant of excitation system, reactance, maximum active output power, reactive power output capacity, efficiency of the generator under different loads and generator nameplate parameters; The economic cost data includes the cost of the active power filter APF device, the cost of the STATCOM device, the cost of the SVC device, the device fixed cost coefficient, the device variable cost coefficient and the device rated capacity; The grid operation data includes the total number of buses, harmonic order, highest harmonic order, bus voltage, upper and lower limits of bus voltage, bus current, conductance, susceptance, reference current, reference voltage, maximum current, bus reactive power, bus reactive power, power factor, rated capacity of the bus and operating capacity of the power quality management device.

3. The method for optimizing configuration of power quality management devices based on the improved Hippo algorithm according to claim 2 is characterized in that: The SVC mathematical model is constructed based on the SVC device data and is expressed as follows: 0 <C(α)=2(α-sin2α)π-1<1; In the formula, represents the equivalent admittance of the SVC device; z represents an imaginary variable; B SVC represents the reactive admittance in the equivalent admittance of the SVC device; C(α) represents the key parameter of the SVC device in controlling the node voltage; X C Indicates the reactance of the capacitor; The STATCOM mathematical model is constructed based on the STATCOM device data and is expressed as follows: V0(t)=V 0m sin(ωt-ψ); c = mk; Where V0(t) represents the controllable voltage source generated by the voltage source inverter of the STATCOM device at time t, V 0m represents the amplitude of the controllable voltage source; c represents the modem parameter; k represents the inverter voltage ratio; m represents the modulation ratio; ψ represents the initial phase angle of the controllable voltage source; ω represents the angular velocity of the generator rotor; represents the conjugate of the output current of the STATCOM device, I lod represents the conjugate d-axis component of the output current, I loq Represents the conjugate q-axis component of the output current; I represents the conjugate of the voltage of the STATCOM device; DC Indicates the current on the DC side of the inverter; C DC Indicates the capacitance value for smoothing voltage; The generator model is constructed based on the generator data and is expressed as follows: P e =E' q I tq +(X q -X' d )I td I tq ; V td =X q I tq ; V tq =E' q -X' d I td ; AND q =It's q +(X d -X' d )THE td ; Where P e Indicates the electrical power output by the generator; P m Represents the mechanical power input to the generator; V t Indicates the voltage at the generator output; V td Indicates the voltage output by the generator d-axis; V tq Indicates the voltage output by the generator q axis; I td Represents the current flowing through the generator d axis; I tq represents the current flowing through the generator q axis; D ω Indicates the damping characteristics of the generator; represents the phase difference between the generator rotor and the synchronous rotating field; ω represents the angular velocity of the generator rotor; ω o represents the initial angular velocity; M represents the moment of inertia of the generator; E q Represents the excitation electromotive force; E fd Represents the direct-axis electromotive force of the excitation current; E' q represents the excitation electromotive force; Represents the instantaneous excitation electromotive force of the generator; E q Indicates the rate of change of the excitation current; The dynamic equation that represents the change of excitation current; V l Indicates the target voltage of the generator; T d '0 represents the time constant related to the excitation current; K A Represents the gain of the excitation system; T A Represents the time constant of the generator excitation system; X q and X d represents the quadrature-axis reactance and direct-axis reactance of the generator in steady state; X' d It represents the direct-axis reactance of the generator under non-steady-state conditions.

4. The method for optimizing configuration of power quality management devices based on the improved Hippo algorithm according to claim 3 is characterized in that: The linearized equations are organized into the standard form of the state space model of a single-machine infinite system: u=[ΔcΔψΔa] T ; y=[ΔV l ΔV DC ΔV s ] T ; Where x represents the state vector; represents the derivative of the state vector x; u represents the input vector; y represents the output vector; represents the change in phase difference between the generator rotor and the synchronous rotating field; Δω represents the change in angular velocity of the generator rotor; ΔE' q Indicates the change in the excitation electromotive force; ΔE fd Indicates the change in the direct-axis electromotive force of the excitation current; ΔV DC Indicates the capacitor voltage change of the STATCOM device; Δc represents the change in modem parameters of the STATCOM device; Δψ represents the change in the initial phase angle of the controllable voltage source; Δα represents the phase angle change of the thyristor-controlled reactor of the SVC device; ΔV l Indicates the change in the generator target voltage; ΔV s represents the bus voltage change of the SVC device; A, B, C and D all represent coefficient matrices; And converted into a transfer function, expressed in the formula: G(s)=u T y; Calculate the gain matrix R based on the transfer function RGA , expressed as: In the formula, λ yq Represents the elements in the gain matrix; y represents the row index of the gain matrix; q represents the column index of the gain matrix; the diagonal elements represent the interaction between the power quality management devices.

5. The method for optimizing configuration of power quality management devices based on the improved Hippo algorithm according to claim 4 is characterized in that: Objective function F i , expressed as: P an =a an +b an S an ; P dn =a dn +b dn S dn ; P sn =a sn +b sn S sn ; Where, N represents the total number of buses in the distribution network; n represents the node; P an P represents the cost of installing an active power filter APF device at the bus node n; dn P represents the cost of installing a STATCOM device at the bus node n; sn represents the cost of installing an SVC device at node n on the bus; a represents the fixed cost coefficient of the device; b represents the variable cost coefficient of the device; S represents the rated capacity of the device; μ an , μ dn and μ sn They respectively indicate whether the node n is equipped with an APF device, a STATCOM device, and an SVC device. If the node n is equipped with a corresponding power quality control device, then μ an , μ dn and μ sn Assign a value of 1, otherwise μ an , μ dn and μ sn Assign a value of 0.

6. The method for optimizing configuration of power quality management devices based on the improved Hippo algorithm according to claim 5 is characterized in that: The interaction constraints between power quality management devices are expressed as follows: 1≤λ nn ≤λ nmax ; In the formula, λ nn represents the diagonal elements in the gain matrix; λ nmax represents the maximum value of the diagonal elements in the gain matrix; The configuration constraints of the power quality control device include power flow balance constraints, harmonic constraints, voltage upper and lower limit constraints, power factor constraints and power quality control device capacity constraints, among which: The power flow balance constraint is expressed as follows: Where P Gn Indicates the active power of the distribution network generator; Q Gn Indicates the reactive power of the distribution network generator; P Ln Indicates the active power of the distribution network load; Q Ln Indicates the reactive power of the distribution network load; W an , W dn and W sn It represents the active power of APF, STACOM and SVC installed at the bus at node n; Q an , Q dn and Q sn represents the reactive power of the APF device, STACOM device and SVC device installed at the bus at node n; U n represents the bus voltage at node n; I n Indicates bus current; G nm represents the conductance between node n and node m; B nm represents the susceptance between node n and node m; U m represents the bus voltage at node m; The harmonic constraint is expressed as: In the formula, h represents the harmonic order; H represents the highest harmonic order; I represents the single-machine infinite system reference current; U represents the system reference voltage; λ Imax Indicates the maximum current allowed by the system; Umax Indicates the voltage harmonic distortion rate allowed by the system; U nmax Indicates the upper limit of bus voltage; U nmin Indicates the lower limit of bus voltage; λ THDI Indicates the harmonic current distortion rate; λ THDU Indicates the harmonic voltage distortion rate; The voltage upper and lower limit constraints are expressed as follows: IN nmin ≤U n ≤U nmax ; The power factor constraint is expressed as: In the formula, Q0 represents the reactive power of the bus; P0 represents the reactive power of the bus; β represents the lower limit of the power factor; The capacity constraint of the power quality control device is expressed as follows: Where, θ represents the maximum capacity factor; S En Indicates the rated capacity of the bus at node n; S n Indicates the operating capacity of the power quality management device.

7. The method for optimizing configuration of power quality management devices based on the improved Hippo algorithm according to claim 6 is characterized in that: The improved Hippo algorithm that introduces chaotic mapping and adaptive weights is used to solve the optimal configuration model of the power quality management device to obtain an optimal configuration scheme. The improved Hippo algorithm includes an initialization phase, a search phase, and a development phase, specifically: The initialization stage specifically introduces the values ​​of the candidate set of configuration schemes and the candidate set of configuration schemes to initialize the chaotic map, which can be expressed as: X(i,:)=X min (i)+r1(X max (i)-X min (i)),i=1,2,...,Num; Where X(i,:) represents the value of the i-th configuration candidate; Num represents the number of candidate solutions in the configuration candidate set; r1 represents a random number in the interval [0, 1]; X min (i) represents the lower limit of the value of the i-th configuration candidate; X max (i) represents the upper limit of the value of the i-th configuration candidate; P represents a random number in the interval [0, 0.5]; r 1_i represents the value of r1 at the i-th iteration; The search phase includes a position update phase and a predator defense phase; the position update phase specifically calculates the objective function value corresponding to each candidate solution according to the initial configuration solution candidate set, selects the candidate solution whose objective function value is within a preset range as the dominant candidate solution; introduces adaptive weights to update the value of the dominant candidate solution, iterates the position update phase; selects the dominant candidate solution with the smallest objective function value as the optimal candidate solution; The value of the dominant candidate solution is expressed as follows: X_P1(i,:)=w(t)×X(i,:)+r2×(D hippo -R1X(i,:)); Where X_P1(i,:) represents the value of the dominant candidate solution; D hippo represents the value of the optimal candidate solution; r2 is a random number in the interval [0, 1]; R1 represents a random number in the interval [1, 2]; w(t w ) represents the weight factor; Max_iter indicates the maximum number of iterations, t w Indicates the time range; Update the value of the non-dominant candidate solution, expressed as: Where X_P2(i,:) represents the value of the non-dominant candidate solution; v1 represents a random vector; MG i represents a candidate solution randomly selected from the candidate set, and calculates the average value of the objective function value corresponding to the candidate solution; T represents the selection probability; R2 represents a random number in the interval [1, 2]; The defense against predators stage is specifically when the current candidate solution is threatened by the current optimal candidate solution or other better candidate solutions in the search space, which can be expressed by the formula: Where X_P3(i,:) represents the value of other better candidate solutions; RL represents a random vector with Levy distribution; Predator j Indicates the value of the current best candidate solution; represents the distance from the i-th current candidate solution to the optimal candidate solution; b represents a random number in the interval [2, 4]; k represents a random number in the interval [1, 1.5]; d represents a random number in the interval [2, 3]; g represents a random number in the interval [2, 4]; r3 represents a random number in the interval [2, 4]; represents the strategy adopted by the current candidate solution to deal with the threat, which includes changing the search direction, adjusting the search step size, and increasing the search intensity; F i Represents the objective function value of the current candidate solution; The development phase specifically uses the reverse learning strategy to update the value of the candidate solution, which is expressed as: In the formula, Represents the value of the candidate solution for the reverse learning strategy update; a g and b g They represent the upper and lower limits of the g-th dimension in the feasible domain of the search respectively; γ represents the dynamic adaptive value related to the number of iterations; x g represents X_P1(i,:) and X_P2(i,:), i.e., the value of the dominant candidate solution and the value of the non-dominant candidate solution; g represents the index value of the g-th dimension; Until the maximum number of iterations is reached or the objective function value converges, the optimal configuration solution is obtained, which is expressed as: Where X_P4(i,:) represents the value of the optimized configuration solution; r4 represents a random number in the interval [0, 1]; r5 represents a random number that conforms to the normal distribution; and L represents the current number of iterations.

8. The power quality management device optimization configuration system based on the improved Hippo algorithm is characterized by: The system includes a data acquisition module, a mathematical model construction module, a state space model construction module, a gain matrix calculation module, an optimization configuration model construction module, an optimization solution module and an output module, wherein: The data acquisition module is used to acquire the operating data of the single-machine infinite system and perform data cleaning. The single-machine infinite system includes a power quality management device and a generator model. The power quality management device includes a static VAR compensation SVC device and a static synchronous compensator STATCOM device; the operating data includes SVC device data, STATCOM device data, generator data, economic cost data and power grid operation data; the cleaned SVC device data, STATCOM device data and generator data are transmitted to the mathematical model construction module; The mathematical model building module builds an SVC mathematical model based on the SVC device data, builds a STATCOM mathematical model based on the STATCOM device data, and builds a generator model based on the generator data; and transmits the SVC mathematical model, the STATCOM mathematical model and the generator model to the state space model building module; The state space model building module linearizes the SVC mathematical model, the STATCOM mathematical model and the generator model to obtain linearized equations, organizes the linearized equations into a standard form of a state space model of a single machine infinite system, converts them into transfer functions, and transmits the transfer functions to the gain matrix calculation module; The gain matrix calculation module is used to calculate the gain matrix based on the transfer function, use the diagonal elements of the gain matrix as the interaction influence parameters between the power quality management devices, and transmit the interaction influence parameters to the optimization configuration model construction module; the optimization configuration model construction module constructs the power quality management device optimization configuration model according to the economic cost data, the interaction influence parameters and the operation data of the cleaned single-machine infinite system. The power quality management device optimization configuration model is based on the economic cost data, and the corresponding objective function is constructed with the goal of minimizing the economic cost. The corresponding constraint conditions are defined based on the power grid operation data, and the constraint conditions include the interaction influence constraints between the power quality management devices and the relevant configuration constraints of the power quality management devices; The optimization solution module uses an improved Hippo algorithm that introduces chaotic mapping and adaptive weights to solve the optimization configuration model of the power quality management device to obtain an optimization configuration solution, which includes optimal configuration parameters and optimal economic costs; The output module is used to display the optimized configuration solution.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, it implements the method for optimizing the configuration of a power quality management device based on the improved Hippo algorithm as described in any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, it implements the method for optimizing configuration of a power quality management device based on the improved Hippo algorithm as described in any one of claims 1 to 7.

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