CN104037776B - The electric network reactive-load capacity collocation method of random inertial factor particle swarm optimization algorithm - Google Patents

Abstract

The invention provides a method for configuring reactive power capacity of a power grid based on a random inertial factor particle swarm optimization algorithm. The method comprises the following steps: 1. obtaining system parameters of the WAMS system in real time, and setting the boundary conditions of the particles; 2. initializing the population, determining the The fitness value of the particle; III, dividing the iteration stage; IV, updating the speed and position of the particle; V, judging whether the number of iterations reaches the maximum iteration number of the global search stage; VI, judging whether the number of iterations reaches the maximum iteration number of the primary solution stabilization stage number of times; VII, judging whether the number of iterations reaches the upper limit of iterations; VIII, downgrading to the maximum number of times, and outputting an online reactive capacity configuration scheme. Compared with the standard algorithm and the self-adaptive mutation algorithm, the method of the present invention improves the precision of optimization, while ensuring the convergence speed, combined with the actual situation of reactive power optimization, it realizes the improvement of the global search ability in the early stage and the local search precision later , and finally get the global optimal solution.

Description

Power Grid Reactive Capacity Allocation Method Based on Random Inertia Factor Particle Swarm Optimization Algorithm

technical field

The invention relates to a method in the field of power grid operation state evaluation and early warning of a power grid intelligent dispatching support system, in particular to a power grid reactive capacity configuration method based on a random inertia factor particle swarm optimization algorithm.

Background technique

With the rapid development of society, economy and power industry, the power grid has gradually developed into a large-scale, long-distance, UHV AC-DC interconnection and an increased proportion of new energy access, which increases the uncertainty of power grid operation. The receiving-end power grid is mainly centered on the load-concentrated area, and is connected to the long-distance generalized originating power supply through the surrounding tie-lines, so as to achieve the balance between supply and demand of electric energy. Due to the mismatch between the distribution of energy and the load center area, as well as the consideration of factors such as the environment, the internal power supply support of the receiving end system is insufficient, and a large amount of electric energy needs to be transmitted from a long distance. The scale of the receiving end system is rapidly increasing and its complexity is becoming more and more complicated. .

Since the 1980s, many large-scale power systems in the world have successively experienced multiple incidents of continuous low voltage and voltage collapse, causing huge economic losses and social impacts, making voltage stability gradually become the focus of international electrotechnical circles. The on-line monitoring of the voltage stability of the receiving end power grid has put forward higher requirements. At present, the numerical algorithms used for static voltage stability and dynamic voltage stability simulation of power system are relatively mature. The reason why the simulation results do not match the real system is mainly due to the inaccuracy of the component models and parameters in the system. In addition, analysis methods based on mathematical modeling and simulation are restricted by grid models, parameters, and numerical calculations, making it difficult to meet the requirements of online real-time evaluation of voltage stability in terms of application scale, speed, and reliability.

AC grid reactive power allocation is an important real-time voltage control management technology to improve system performance. In general, the reactive power compensation methods of the transmission network can be divided into two categories, namely, the reactive power output regulation of the power plant and the capacitor voltage support of the substation. The combination of the two has a significant impact on the transmission of reactive power in the transmission network and the value of the network node voltage, and its optimization is a multi-objective combination optimization problem.

In the optimal configuration problem of reactive power capacity based on network loss minimization, it is attempted to optimally set the value of control variables under a series of given conditions at the same time. These control variables include generator reactive power input, transformer ratio, reactive power output of shunt capacitor/reactor, etc. In recent years, many literatures have carried out modeling research on it, and used evolutionary algorithms to solve it, such as genetic algorithm, ant colony algorithm and tabu search method.

Particle swarm optimization algorithm is a kind of intelligent algorithm. Due to the advantages of simple modeling and fast convergence, the particle swarm optimization algorithm has been fully developed in the field of reactive power capacity optimization configuration optimization problems.

Since the particle swarm optimization algorithm has the advantages of fast convergence speed, easy implementation and less parameters required, many literatures have proposed an improved PSO algorithm for reactive power capacity optimization. However, when PSO is applied to high-dimensional complex problems, it is prone to problems such as premature convergence and local optimum, which leads to the fact that the algorithm cannot guarantee to converge to the global optimum. The main reason for this situation is that the convergence speed is fast in the early stage, and in the later stage, no effective constraints are obtained to make the algorithm break away from the minimum point.

Contents of the invention

In order to overcome the above-mentioned defects in the prior art, the present invention provides a method for configuring reactive power capacity of a power grid based on a random inertia factor particle swarm optimization algorithm.

In order to realize the above-mentioned purpose of the invention, the present invention takes the following technical solutions:

A method for configuring reactive power capacity of a power grid based on a stochastic inertial factor particle swarm optimization algorithm, the improvement of which is that the method includes the following steps:

I. Obtain the system parameters of the WAMS system in real time, and set the boundary conditions of the particles;

II. Initialize the population, establish a reactive power optimization model of the power grid, and determine the fitness value of the particles;

III. Divide iteration stages;

IV. Updating the velocity and position of the particles;

V, judge whether the number of iterations reaches the maximum number of iterations in the global search stage;

VI. Determine whether the number of iterations has reached the maximum number of iterations in the primary solution stabilization stage;

VII, judging whether the number of iterations reaches the upper limit of iterations;

VIII. When downgrading to the maximum number of times, output the online reactive capacity configuration scheme.

Further, the system parameters in the step 1 include the PMU measurement value and the EMS data of the power grid system;

The PMU measured value includes bus current, bus voltage, active power and reactive power;

The EMS data includes bus voltage, bus voltage flow, active power and reactive power;

The boundary of the particle is the solution space range of the corresponding algorithm set based on the estimated result of the state situation of the power grid system and the real-time upper and lower voltage limits of each point specified by the current scheduling operation;

Further, said step II includes the following steps:

S201. Obtain node information and branch information of the distribution network system, set the number of control variables, the value range of each control variable, and the group size of the initial population;

Initializing the initial population and setting initial parameters to obtain an initial particle population;

Initializing and obtaining the initial population refers to randomly selecting the initial velocity and initial position of the particles in the initial population within the particle value range, and the initial parameters include a maximum number of iterations and an adaptation threshold;

S202. Select the active power loss of the transmission network as the objective function, and determine the mathematical model of reactive power optimization as follows:

In the formula, k=(i,j), i∈N _B , N _B is the set of all bus nodes, j∈N _i , N _i is the set of nodes associated with bus node i; is the active power loss of the transmission network; g _k is the admittance of branch k; v _i , v _j are the voltage amplitudes of bus nodes i and j respectively; θ _ij is the angle difference between load bus i and j;

S202. Determine the equality constraints as follows:

In the formula, P _gi is the generator active power injection of node i; P _di is the load active power of node i; g _ij and B _ij are the conductance and susceptance between nodes i and j respectively; Q _gi is the power generation of node i machine reactive power injection; Q _di is the load reactive power of node i; v _i , v _j are the voltage amplitudes of bus nodes i and j respectively; θ _ij is the angle difference between load bus i and j.

Further, said step III includes the following steps:

S301, the initial number of iterations is 1, and according to the number of iterations, the iteration is divided into a global search stage, a primary solution stabilization stage, and a high-precision solution stabilization stage; the inertia factor w is adjusted by the following formula (1):

In the formula, w _max is located at the initial iteration, w _min is located at the end of the iteration period, iter is the current iteration number, and iter _max is the maximum iteration number,

S302. Determine the inertia factor w and acceleration factors c ₁ and c ₂ at different iteration stages according to the following formulas (2), (3), and) (4):

In the formula, k _M is the maximum number of iterations in the global search stage, k _N is the maximum number of iterations in the stage of primary solution stabilization, and k _MAX is the maximum number of iterations in the stage of high-precision solution stabilization.

Further, in the step IV, the velocity v _i ^k+1 and the position x _i ^{k +1} of the particle are updated respectively as follows:

In the formula, v _i ^k+1 is the velocity vector of the i-th particle at generation k+1; w is the inertia factor of the particle; v _i ^k is the velocity vector of the i-th particle at generation k; c _{1 ,} c ₂ is the acceleration coefficient; the range of r ₁ and r ₂ is a randomly generated number between [0,1]; is the best position of the i-th particle based on the particle swarm iteration history, g _besti is the global optimal position of the G population particle; x _i ^k+1 is the position of the i-th particle at the k+1th generation; x _i ^k is the position of the i-th particle in the k-th generation; χ is the penalty factor;

It is judged whether the particle control variable exceeds the boundary condition of the particle, and if so, the value is reset.

Further, in the step V, if it is judged that the number of iterations does not exceed the maximum number of iterations in the global search stage, the inertia factor w and the acceleration factors c ₁ and c ₂ are corrected, and the iteration value of the k+1th word is iterated;

In the step VI, if it is judged that the number of iterations does not exceed the maximum number of iterations in the primary solution stabilization stage, the inertia factor w and the acceleration factors c ₁ and c ₂ are corrected, and the iteration value of the k+1 word is iterated;

In the step VII, if it is judged that the number of iterations does not exceed the upper limit of iterations, the inertia factor w and the acceleration factors c ₁ and c ₂ are corrected, and the iteration value of the k+1th word is iterated.

Further, said step VIII includes:

If the current particle state of the particle is better than the historical individual extremum in the iterative process, update the historical individual extremum with this state If there are particles in the neighborhood whose current state particles are better than the neighborhood history extremum in the iterative process, update the neighborhood history optimal g _best with this state;

The grid voltage level is given according to the node voltage measured by the WAMS, and the reactive capacity configuration scheme is determined.

Compared with prior art, the beneficial effect of the present invention is:

1. The method of the present invention makes full use of the basic power grid model, parameters and operating section information of the EMS system, combined with the high-precision and high-density collection data of the WAMS system, and realizes the online static voltage support capability evaluation.

2. The method of the present invention is to divide the search space into several subspaces, and carry out POS algorithm optimization in each subspace; on the basis of analyzing the mechanism of action of inertial factors, in each subarea, a system is designed according to population diversity and evolution Algebraic self-adaptive adjustment inertia factor calculation method improves the local search ability of the algorithm by changing the search step size.

3. Compared with the standard algorithm and the self-adaptive mutation algorithm, the method of the present invention improves the precision of optimization. While ensuring the convergence speed, combined with the actual situation of reactive power optimization, it realizes the global search ability in the early stage and the local search accuracy in the later stage. Finally, the global optimal solution is obtained.

4. The method and algorithm of the present invention can realize dynamic self-adaptation when system operating conditions change based on the system parameters of the WAMS system.

Description of drawings

Fig. 1 is a flowchart of a method for configuring reactive power capacity of a power grid based on a random inertial factor particle swarm optimization algorithm provided by the present invention.

detailed description

The present invention will be further described below in conjunction with accompanying drawing.

Taking the active power loss (i.e. network loss) of the power system to be optimized as the fitness function, the purpose is to find the minimum network loss of the system; the random inertia factor particle swarm optimization algorithm is used to solve the power flow, and the network loss of each branch is superimposed to obtain the total System network loss. The obtained global optimal solution is the minimum network loss of the system, and the corresponding optimal particles are control variable parameters such as generator voltage, transformer tap position, and number of shunt capacitor bank switching groups.

As shown in Fig. 1, Fig. 1 is the flow chart of the grid reactive capacity configuration method of the stochastic inertial factor particle swarm optimization algorithm provided by the present invention; the method comprises the following steps:

Step 1. Obtain the system parameters of the WAMS system in real time, and set the boundary conditions of the particles;

Step 2, initialize the population, and determine the fitness value of the particles;

Step 3: Divide iteration phases;

Step 4, updating the velocity and position of the particles;

Step 5, judging whether the number of iterations reaches the maximum number of iterations in the global search phase;

Step 6, judging whether the number of iterations reaches the maximum number of iterations in the stable stage of the primary solution;

Step 7, judging whether the number of iterations reaches the upper limit of iterations;

Step 8: Downgrade to the maximum number of times, and output the online reactive capacity configuration scheme.

Step 1: Obtain the system parameters of the WAMS system in real time, and set the boundary conditions of the particles.

The system parameters include PMU measurements of the power grid system and the EMS data;

The PMU measured value includes bus current, bus voltage, active power and reactive power;

The EMS data includes bus voltage, bus current, active power and reactive power;

The boundary of the particle is the solution space range of the corresponding algorithm set based on the estimated result of the state situation of the power grid system and the real-time upper and lower voltage limits of each point specified by the current scheduling operation;

In Embodiment 1, system parameters are obtained in real time from the WAMS system for capacity configuration, including the target system's 500kV system PMU measurement values (such as 500kV bus current, bus voltage, active and reactive power) and 220kV system EMS data (such as 220kV bus Voltage, bus current, active power and reactive power) form the state estimation result of the current system, and based on the state estimation result of the power grid system formed above and the real-time upper and lower voltage limits of each point specified in the current dispatching operation (the upper and lower limits of the voltage are artificial Setting, for example, currently the upper-level dispatching department draws up the upper and lower limit curves of the voltage of a certain node with the time axis), and sets the solution space range in the corresponding algorithm, that is, the boundary conditions of the particles.

The state estimation result refers to the voltage amplitude and phase angle of each node of the power grid at a certain moment calculated by the estimation algorithm in the case of given SCADA data and PMU data, and then the active power and reactive power of each road are obtained. work power.

Since the number of particle swarm particles is inversely proportional to the calculated calculation speed and directly proportional to the calculation accuracy, the number of particle swarms is set according to the current risk level of the power grid operation.

Particle swarm boundary conditions are regulated by the actual operation of the power grid. For example, there are special voltage value bandwidth curves for voltage points at different times of 24 hours a day and a season, and there are regulations for the voltage floating area of a certain point. The optimal solution searched by the algorithm at the end must be the solution within the bandwidth curve at the corresponding time.

The number of particle swarms is dynamically set as the grid time changes. For example, during the morning peak period at 9 o'clock in the morning, the grid load fluctuates greatly. At this time, the pursuit of speed is greater than the pursuit of accuracy, so the number of particle swarms is set less. In order to quickly find out the capacity allocation solution. From 12:00 pm to 5:00 am, the system load is generally low. At this time, the load does not change much, and the accuracy requirement is greater, so the set particle swarm population number will be relatively high. At present, the set value is specific to the quantity, and the method of empirical setting can be used.

Step 2: Initialize the population and determine the fitness value of the particles. Include the following steps:

S201. Obtain node information and branch information of the distribution network system, set the number of control variables, the value range of each control variable, and the group size of the initial population;

Initializing the initial population and setting initial parameters to obtain an initial particle population;

The initialization refers to the initial velocity and the initial position of the particles in the initial population randomly selected within the value range of the particles, and the initial parameters include the maximum number of iterations and the adaptation threshold;

S202. Determine the objective function of reactive power optimization as follows:

In the formula, k=(i,j), i∈N _B , N _B is the set of all bus nodes, j∈N _i , N _i is the set of nodes associated with bus node i; is the active power loss of the transmission network; g _k is the admittance of branch k; v _i , v _j are the voltage amplitudes of bus nodes i and j respectively; θ _ij is the angle difference between load bus i and j;

The active power loss of the transmission network is determined according to the system parameters obtained in real time by the WAMS system. In this embodiment, the parameters include the PMU measurement values of the 500kV system of the target system (such as 500kV bus voltage, current, active and reactive power) and the EMS data of the 220kV system (eg 220kV bus voltage, bus current, active and reactive power), and determine the active power loss based on the above measured values.

S202. Determine the equality constraints as follows:

Active Power Balance Constraints

i.e. reactive power balance constraint

In the formula, P _gi is the generator active power injection of node i; P _di is the load active power of node i; g _ij and B _ij are the conductance and susceptance between nodes i and j respectively; Q _gi is the power generation of node i machine reactive power injection; Q _di is the load reactive power of node i; v _i , v _j are the voltage amplitudes of bus nodes i and j respectively; θ _ij is the angle difference between load bus i and j.

Step 3: Divide iteration phases. Include the following steps:

The initial number of iterations is 1, and according to the number of iterations, the iterations are divided into the global search stage, the primary solution stabilization stage, and the high-precision solution stabilization stage; the following formula (1) adjusts the inertia factor w:

In the formula, w _max is located at the initial iteration, w _min is located at the end of the iteration period, iter is the current iteration number, and iter _max is the maximum iteration number,

Determine the inertia factor w and acceleration factors c ₁ and c ₂ at different iteration stages as follows (2), (3) and (4):

In the formula, k _M is the maximum number of iterations in the global search stage, k _N is the maximum number of iterations in the stage of primary solution stabilization, and k _MAX is the maximum number of iterations in the stage of high-precision solution stabilization.

Step 4, update the velocity and position of the particle.

Updating the velocity and position of the particles includes the following steps:

The following equations (5) and (6) respectively update the velocity v _i ^k+1 and position x _i ^k+1 of the particle as follows:

In the formula, v _i ^k+1 is the velocity vector of the i-th particle at generation k+1; w is the inertia factor of the particle; v _i ^k is the velocity vector of the i-th particle at generation k; c _{1 ,} c ₂ is a normal number with a range of [0,2.5]; the range of r ₁ and r ₂ is a randomly generated number between [0,1]; is the best position of the i-th particle based on the particle swarm iteration history; g _besti is the global optimal position of the G population particle; x _i ^k+1 is the position of the i-th particle at the k+1th generation; x _i ^k is the position of the i-th particle at the kth generation; χ is a penalty factor to ensure convergence;

Judging whether the particle control variable exceeds the particle boundary condition set in step 1, and re-accepting the value if exceeding.

In step 5, if it is judged that the number of iterations does not exceed the maximum number of iterations in the global search stage, the inertia factor w and acceleration factors c ₁ and c ₂ are corrected, and the iteration value of the k+1th word is iterated;

In step six, if it is judged that the number of iterations does not exceed the maximum number of iterations in the primary solution stabilization stage, the inertia factor w and the acceleration factors c ₁ and c ₂ are corrected, and the iteration value of the k+1 word is iterated;

In step 7, if it is judged that the number of iterations does not exceed the upper limit of iterations, the inertia factor w and the acceleration factors c ₁ and c ₂ are corrected, and the iteration value of the k+1th word is iterated.

Step 8: Downgrade to the maximum number of times, and output the online reactive capacity configuration scheme.

If the current particle state of the particle is better than the historical individual extremum in the iterative process, update the historical individual extremum with this state If there are particles in the neighborhood whose current state particles are better than the neighborhood history extremum in the iterative process, update the neighborhood history optimal g _best with this state;

The grid voltage level is given according to the node voltage measured by the WAMS, and the reactive capacity configuration scheme is determined.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the present invention can still be Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the scope of the claims of the present invention.

Claims (6)

1. the electric network reactive-load capacity collocation method of a random inertial factor particle swarm optimization algorithm, it is characterised in that: described side Method comprises the following steps:

The systematic parameter of I, in real time acquisition WAMS system, sets the boundary condition of particle；

II, initialization population, set up the idle work optimization model of electrical network, determine the adaptive value of described particle；

III, division iteration phase；

IV, the speed updating described particle and position；

V, judge whether iterations arrives global search stage maximum iteration time；

VI, judge whether iterations arrives and primary solve stabilization sub stage maximum iteration time；

VII, judge whether iterations arrives high precision solution stabilization sub stage maximum iteration time；

VIII, iterate to each iteration phase maximum iteration time, export online reactive capability allocation plan；

In described step V, if judging, iterations not less than global search stage maximum iteration time, then revises inertial factor w With accelerated factor c₁、c₂,+1 iterative value of iteration kth；

In described step VI, if judge iterations not less than primary solution stabilization sub stage maximum iteration time, then revise inertia because of Sub-w and accelerated factor c₁、c₂,+1 iterative value of iteration kth；

In described step VII, if judging, iterations not less than high precision solution stabilization sub stage maximum iteration time, then revises inertia Factor w and accelerated factor c₁、c₂,+1 iterative value of iteration kth.

2. the method for claim 1, it is characterised in that: the described systematic parameter of described step I includes network system PMU measured value and EMS data；

Described PMU measured value includes bus current, busbar voltage, active power and reactive power；

Described EMS data include busbar voltage, bus current, active power and reactive power；

The boundary condition of described particle is state estimation result based on described network system and the reality of current scheduling operating provisions Time each point voltage upper lower limit value, the solution space scope of the corresponding algorithm of setting；

3. the method for claim 1, it is characterised in that: described step II comprises the following steps:

S201, obtain the nodal information of distribution network system and branch road information, the number of control variable and each control variable are set Span and the population size of initial population；

Described initial population is initialized and initial parameter is set, it is thus achieved that primary group；

Initialize the described primary group of acquisition to refer in particle span, the particle in described initial population be selected at random Selecting initial velocity and the initial position of particle, described initial parameter includes maximum iteration time and adaptive value；

S202, to choose power transmission network active power loss be object function, as following formula determines the mathematical model of idle work optimization:

$\min \underset{k\∈N_E}{\Σ}{P}_{kloss}=\underset{k\∈N_E}{\Σ}{g}_k({v_i}^2+{v_j}^2-2v_i{v}_j{\mathrm{cos\θ}}_{ij})$

In formula, and k=(i, j), i ∈ N_B, N_BFor all bus nodes set, j ∈ N_i, N_iFor the node being associated with bus nodes i Set；For power transmission network active power loss；g_kAdmittance for branch road k；v_i,v_jIt is respectively bus nodes i and the electricity of j Pressure amplitude value；θ_ijDifferential seat angle for load bus i and j；

S203, determine equality constraint such as following formula:

With

In formula, P_giThe generator active power of node i injects；P_diLoad active power for node i；g_ij、B_ijIt is respectively node Conductance between i, j and susceptance；Q_giIt it is the generator reactive power injection of node i；Q_diReactive load power for node i；v_i, v_jBus nodes i and the voltage magnitude of j respectively；θ_ijDifferential seat angle for load bus i and j.

4. the method for claim 1, it is characterised in that: described step III comprises the following steps:

S301, primary iteration number of times are 1, according to iterations iteration is divided into the global search stage, primary solve the stabilization sub stage and The high precision solution stabilization sub stage；As inertial factor w is adjusted by following formula (1):

$w=w_{max}-\frac{w_{max}-w_{\min }}{{\mathrm{iter}}_{max}}\×iter---\left(1\right)$

In formula, w_maxIt is positioned at primary iteration, w_minBeing positioned at the end in iteration period, iter is current iteration number, iter_maxFor maximum Iterations,

S302, determine inertial factor w and accelerated factor c of different iteration phase respectively such as following formula (2), (3), (4)₁、c₂:

$c_1=\left\{\begin{array}{c}2.1,1\&le;iter<k_M\\ 1.05,k_M\&le;iter<k_N\\ 0.525,k_N\&le;iter<k_{max}\end{array}\right.---\left(2\right)$

$c_2=\left\{\begin{array}{c}2.0,1\&le;iter<k_M\\ 1,k_M\&le;iter<k_N\\ 0.5,k_N\&le;iter<k_{max}\end{array}\right.---\left(3\right)$

$w=\left\{\begin{array}{c}2.1,1\&le;iter<k_M\\ 1.05,k_M\&le;iter<k_N\\ 0.525,k_N\&le;iter<K_{MAX}\end{array}\right.---\left(4\right)$

In formula, k_MFor global search stage maximum iteration time, k_NFor primary solution stabilization sub stage maximum iteration time, k_MAXFor high-precision Degree solves stabilization sub stage maximum iteration time.

5. the method for claim 1, it is characterised in that: in described step IV, as described in following formula (5), (6) update respectively The speed v of particle_i ^k+1With position x_i ^k+1:

$v_i^{k+1}=w\&times;v_i^k+c_1\&times;r_1\&times;(P_{besti}-x_i^k)+c_2\&times;r_2\&times;(g_{besti}-x_i^k)---\left(5\right)$

$x_i^{k+1}=x_i^k+\mathrm{\&chi;}\&times;v_i^{k+1}---\left(6\right)$

In formula, v_i ^k+1For the i-th particle velocity when+1 generation of kth；W is the inertial factor of particle；v_i ^kFor i-th grain Son kth for time velocity；c₁,c₂For accelerated factor；r₁,r₂In the range of the numeral randomly generated between [0,1]；For optimum position based on population iteration historical i-th particle, g_bestiFor G population particle global optimum position； x_i ^k+1Position for i-th particle when+1 generation of kth；x_i ^kFor kth for time the position of i-th particle；X for punishment because of Son；

Judge whether particle control variable exceedes the boundary condition of described particle, if exceeding, value again.

6. method as claimed in claim 5, it is characterised in that: described step VIII includes:

If the history individuality extreme value that the current particle state of particle is better than in iterative process, then with this state more new historical individuality pole Value p_best；If neighborhood particle having the neighborhood history extreme value that the current particle state of particle is better than in iterative process, then with this shape State updates neighborhood history optimal value g_best；

Provide voltage level of power grid according to the node voltage that described WAMS measures, determine reactive capability allocation plan.