CN104037776A - Reactive power grid capacity configuration method for random inertia factor particle swarm optimization algorithm - Google Patents

Abstract

The invention discloses a reactive power grid capacity configuration method for a random inertia factor particle swarm optimization algorithm. The reactive power grid capacity configuration method includes steps that I, acquiring system parameters of a WAMS system in real time and setting particle boundary conditions; II, initializing a swarm and determining an adaptive value of the particle; III, dividing iterative stages; IV, updating the speed and position of the particle; V, judging whether the iteration times arrives at the maximum iteration times of a global search stage; VI, judging whether the iteration times arrives at the maximum iteration times of a primary solution stabilization stage; VII, judging whether the iteration times arrives at the upper iteration limit; VIII, iterating till arriving at the maximum times, and outputting an online reactive capacity configuration method. Compared with a standard algorithm and an adaptive mutation algorithm, the reactive power grid capacity configuration method for the random inertia factor particle swarm optimization algorithm enables the optimization precision to be improved and realizes to improve the early global search capability and the late local search precision based on guaranteeing a convergence rate through combining with actual situations of reactive optimization, and the global optimal solution is ultimately obtained.

Description

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

Technical field

The present invention relates to a kind of method in electric network intelligent scheduling back-up system operation of power networks state estimation and early warning field, be specifically related to a kind of electric network reactive-load capacity collocation method of random inertial factor particle swarm optimization algorithm.

Background technology

Along with the fast development of society, economy and power industry, electrical network develops on a large scale gradually, at a distance, the form such as the interconnected and new forms of energy access ratio increasing of extra-high voltage alternating current-direct current, increased the uncertainty of operation of power networks.And receiving end electrical network is mainly centered by load area of concentration, is connected by periphery interconnection and the remote broad sense power supply of making a start, and then realizes the equilibrium of supply and demand of electric energy.Due to not mating of the energy and load center area distribution, and consider the factor restrictions such as environment, receiving-end system internal electric source underbraced, a large amount of electric energy need to carry out from a distant place long-distance transmissions, and the swift and violent increase of receiving-end system scale and its complexity are got over complicated.

Since the eighties in 20th century, in succession there is lasting on the low side, the voltage collapse event of a lot of voltage in multiple large-scale power systems in the world, cause huge economic loss and social influence, make voltage stabilization become gradually the focus that International Power educational circles pays close attention to, the receiving end Network Voltage Stability on-line monitoring under running environment is had higher requirement.At present, for the numerical algorithm comparative maturity of power system steady state voltage stability and dynamic electric voltage stability simulation, cause that reason that simulation result and real system are misfitted is mainly the inaccurate of component models and parameter in system.In addition, based on the analytical method of mathematical modeling and emulation, be subject to the restriction of the factors such as electric network model, parameter and numerical computations, be difficult to adapt to the requirement of the online real-time assessment of voltage stabilization at aspects such as application scale, speed and reliabilities.

Configuration that AC network is idle is the important real-time voltage control and management technology that improves systematic function.Generally, power transmission network reactive power compensation means can be divided into two large classes, i.e. power plant reactive power output regulates and substation capacitors voltage support.The transmission of the two combination on reactive power in power transmission network and the numerical value of network node voltage have appreciable impact, and its optimization is belonged to multiple target combination optimizing problem.

In the reactive capability optimization allocation based on loss minimization, attempt the while under a series of given conditions, the value of control variables is set to optimization.These control variables comprise input, the transformer voltage ratio of generator reactive, idle output of shunt capacitance/anti-device etc.Many documents have carried out Modeling Research to it in recent years, and adopt evolution algorithmic to solve it, such as genetic algorithm, ant group algorithm and TABU search method etc.

Particle swarm optimization algorithm is the one in intelligent algorithm.Particle cluster algorithm, due to advantages such as modeling are simple and easy, convergence is fast, is distributed optimizing problem solving field rationally at reactive power capacity and has been obtained sufficient development.

Because particle swarm optimization algorithm has fast convergence rate, easily realizes and need the advantages such as parameter is few, existing many documents, with regard to reactive capability optimization problem, have proposed improved PSO algorithm.But, in the time that PSO is applied to higher-dimension challenge, easily there is Premature Convergence and cause the problems such as local optimum, cause this algorithm can not ensure to converge to global optimum.The main cause that occurs this situation is that premature convergence speed is fast, to the later stage do not obtain operative constraint make algorithm depart from minimal point.

Summary of the invention

In order to overcome the defect of above-mentioned prior art, the invention provides a kind of electric network reactive-load capacity collocation method of random inertial factor particle swarm optimization algorithm.

In order to realize foregoing invention object, the present invention takes following technical scheme:

An electric network reactive-load capacity collocation method for random inertial factor particle swarm optimization algorithm, its improvements are: said method comprising the steps of:

The system parameters of I, Real-time Obtaining WAMS system, the boundary condition of setting particle;

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

III, division iteration phase;

IV, the speed of upgrading described particle and position;

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

VI, judge whether iterations arrives elementary solution stabilization sub stage maximum iteration time;

VII, judge whether iterations arrives the iteration upper limit;

VIII, fall generation to maximum times, export online reactive capability allocation plan.

Further, the described system parameters of described step I comprises PMU measured value and the described EMS data of described network system;

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

Described EMS data comprise busbar voltage, busbar voltage, active power and reactive power;

The border of described particle is the estimated result of the state situation based on described network system and real-time each point voltage upper lower limit value of current scheduling operating provisions, the solution space scope of the corresponding algorithm of setting;

Further, described Step II comprises the following steps:

S201, the nodal information that obtains distribution network system and branch road information, arrange the population size of the number of control variables and the span of each control variables and initial population;

Described initial population is carried out initialization and initial parameter is set, obtain primary group;

Initialization obtains described initial population and refers to initial velocity and the initial position of the particle in described initial population being selected at random particle in particle span, and described initial parameter comprises maximum iteration time and adapts to threshold value;

S202, to choose electric power transmission network active power loss be target function, the Mathematical Modeling as shown in the formula determining idle work optimization:

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

In formula, k=(i, j), i ∈ N _b, N _bfor all bus nodes set, j ∈ N _i, N _ifor the node set being associated with bus nodes i; for electric power transmission network active power loss; g _kfor the admittance of branch road k; v _i, v _jthe voltage magnitude of bus nodes i and j respectively; θ _ijfor the differential seat angle of load bus i and j;

S202, as shown in the formula definite equality constraint:

$P_{\mathrm{gi}}-P_{\mathrm{di}}-v_i\underset{j\∈N_i}{\mathrm{\Σ}}{v}_j(g_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\θ}}_{\mathrm{ij}})=0$

$Q_{\mathrm{gi}}-Q_{\mathrm{di}}-v_i\underset{j\∈N_i}{\mathrm{\Σ}}{v}_j(g_{\mathrm{ij}}\sin {\mathrm{\θ}}_{\mathrm{ij}}+B_{\mathrm{ij}}\cos {\mathrm{\θ}}_{\mathrm{ij}})=0$

In formula, P _githe generator active power of node i injects; P _difor the load active power of node i; g _ij, B _ijthe electricity being respectively between node i, j is led and susceptance; Q _githe generator reactive power that is node i injects; Q _difor the reactive load power of node i; v _i, v _jthe voltage magnitude of bus nodes i and j respectively; θ _ijfor the differential seat angle of load bus i and j.

Further, described Step II I comprises the following steps:

S301, primary iteration number of times are 1, according to iterations, iteration are divided into global search stage, elementary solution stabilization sub stage and high precision solution stabilization sub stage; As shown in the formula (1), inertial factor w is regulated:

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

In formula, w _maxbe positioned at primary iteration, w _minbe positioned at the iteration end in period, iter is current iteration number, iter _maxfor maximum iteration time,

S302, as shown in the formula (2), (3)) (4) determine respectively inertial factor w and accelerated factor c of different iteration phase ₁, c ₂:

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

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

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

In formula, k _mfor global search stage maximum iteration time, k _nfor elementary solution stabilization sub stage maximum iteration time, k _mAXfor high precision solution stabilization sub stage maximum iteration time.

Further, in described step IV, upgrade respectively the speed v of described particle as shown in the formula (5), (6) _i ^k+1with position x _i ^k+1:

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

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

In formula, v _i ^k+1be i particle k+1 for time velocity; W is the inertial factor of particle; v _i ^kbe i particle k for time velocity; c ₁, c ₂for accelerator coefficient; r ₁, r ₂scope be the random numeral producing between [0,1]; for the optimum position based on historical i the particle of population iteration, g _bestifor G population particle global optimum position; x _i ^k+1be k+1 for time the position of i particle; x _i ^kbe k for time the position of i particle; χ is penalty factor;

Judge whether described particle control variables exceedes the boundary condition of described particle, if exceed value again.

Further, in described step V, if judge, iterations does not exceed global search stage maximum iteration time, revises inertial factor w and accelerated factor c ₁, c ₂, iteration k+1 word iterative value;

In described step VI, if judge, iterations does not exceed elementary solution stabilization sub stage maximum iteration time, revises inertial factor w and accelerated factor c ₁, c ₂, iteration k+1 word iterative value;

In described step VII, if judge, iterations does not exceed the iteration upper limit, revises inertial factor w and accelerated factor c ₁, c ₂, iteration k+1 word iterative value.

Further, described step VIII comprises:

If the current particle state of particle is better than the individual extreme value of history in iterative process, with the more individual extreme value of new historical of this state if have the current state particle of particle to be better than the historical extreme value of neighborhood in iterative process in neighborhood particle, upgrade the historical optimum g of neighborhood with this state _best;

The node voltage of measuring according to described WAMS provides line voltage level, determines reactive capability allocation plan.

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

1, method of the present invention makes full use of basic electric network model, parameter and the operation section information of EMS system, in conjunction with the high accuracy high-density acquisition data of WAMS system, has realized online quiescent voltage enabling capabilities assessment.

2, method of the present invention is that search volume is divided into some subspaces, carries out the optimizing of POS algorithm in each subspace; By analyzing on the basis of the inertial factor mechanism of action, in each sub regions, design one according to the inertial factor computational methods of population diversity and the adjusting of evolutionary generation self adaptation, by transformation search step-length, improve the local search ability of algorithm.

3, compare with self adaptation mutation algorithm with canonical algorithm, method of the present invention has improved the precision of optimizing, in ensureing convergence rate, in conjunction with the actual conditions of idle work optimization, realize ability of searching optimum in early stage, the raising of its Local Search precision afterwards, finally obtained globally optimal solution.

4, the dynamic self-adapting when system parameters of algorithm of the present invention based on WAMS system can realize the variation of system service conditions.

Brief description of the drawings

Fig. 1 is the electric network reactive-load capacity collocation method flow chart of random inertial factor particle swarm optimization algorithm provided by the invention.

Embodiment

Below in conjunction with accompanying drawing, the invention will be further described.

Taking the active loss (being network loss) of the electric power system that will be optimized as fitness function, to find the minimum network loss of system as object; Adopt the trend that solves of random inertial factor particle swarm optimization algorithm, total system network loss is asked in each branch road network loss stack.The globally optimal solution of trying to achieve is the minimum network loss of system, and corresponding optimal particle is the control variables parameters such as generator voltage, load tap changer gear, Shunt Capacitor Unit switching group number.

As shown in Figure 1, Fig. 1 is the electric network reactive-load capacity collocation method flow chart of random inertial factor particle swarm optimization algorithm provided by the invention; The method comprises the following steps:

The system parameters of step 1, Real-time Obtaining WAMS system, the boundary condition of setting particle;

Step 2, initialization population, determine the adaptive value of described particle;

Step 3, division iteration phase;

Step 4, the speed of upgrading described particle and position;

Step 5, judge whether iterations arrives global search stage maximum iteration time;

Step 6, judge whether iterations arrives elementary solution stabilization sub stage maximum iteration time;

Step 7, judge whether iterations arrives the iteration upper limit;

Step 8, fall generation to maximum times, export online reactive capability allocation plan.

Step 1, the system parameters of Real-time Obtaining WAMS system, the boundary condition of setting particle.

Described system parameters comprises PMU measured value and the described EMS data of described network system;

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

Described EMS data comprise busbar voltage, busbar voltage, active power and reactive power;

The border of described particle is the estimated result of the state situation based on described network system and real-time each point voltage upper lower limit value of current scheduling operating provisions, the solution space scope of the corresponding algorithm of setting;

In embodiment mono-, obtain in real time system parameters from WAMS system and carry out capacity configuration, comprise 500kV system PMU measured value (for example 500kV bus current of goal systems, electric current, meritorious and reactive power) and EMS data (for example 220kV busbar voltage of 220kV system, electric current, meritorious and reactive power) form the state estimation result of current system, and the state estimation result of network system based on above formation and real-time each point voltage upper lower limit value of current scheduling operating provisions (this voltage upper lower limit value is manually set, for example, drafted at present the voltage bound curve of the variation of countershaft at any time of some nodes by higher level traffic department), set the solution space scope in corresponding algorithm, , the boundary condition of particle.

Described state estimation result refers to the in the situation that of given SCADA data and PMU data, by algorithm for estimating, and voltage magnitude, the phase angle of a certain moment each node of electrical network calculating, and then draw active power and the reactive power on each road.

Because the quantity of particle masses particle is inversely proportional to the computational speed of asking for, be directly proportional to computational accuracy, therefore set population quantity according to the current situation degree of risk of operation of power networks.

Population boundary condition is subject to electrical network actual motion regulation, as 1 day 24 hours not in the same time, a season have a special magnitude of voltage bandwidth curve for electrical voltage point certain any voltage float area is had to regulation.Algorithm final search to optimal solution must be the solution within corresponding moment bandwidth curve.

Population quantity is to carry out dynamic setting along with the variation of electrical network time, for example, and during 9 o'clock of the morning being morning peak, network load fluctuation is larger, now pursuit speed is greater than pursuit precision, and therefore the setting of population quantity is less, to seek out fast capacity configuration solution.And to 5:00 AM, system loading is generally lower at 12 in evening, now load variations is little, and required precision is larger, and the population population quantity therefore setting will be relatively high.The method that at present set point can employing experience be set specific to quantity.

Step 2, initialization population, determine the adaptive value of described particle.Comprise the following steps:

S201, the nodal information that obtains distribution network system and branch road information, arrange the population size of the number of control variables and the span of each control variables and initial population;

Described initial population is carried out initialization and initial parameter is set, obtain primary group;

Described initialization refers to initial velocity and the initial position of the particle in described initial population being selected at random particle in particle span, and described initial parameter comprises maximum iteration time and adapts to threshold value;

S202, as shown in the formula determining the target function of idle work optimization:

$\min \underset{k\&Element;N_E}{\mathrm{\&Sigma;}}{P}_{\mathrm{kloss}}=\underset{k\&Element;N_E}{\mathrm{\&Sigma;}}{g}_k({v_i}^2+{v_j}^2-2v_i{v}_j\cos {\mathrm{\&theta;}}_{\mathrm{ij}})$

In formula, k=(i, j), i ∈ N _b, N _bfor all bus nodes set, j ∈ N _i, N _ifor the node set being associated with bus nodes i; for electric power transmission network active power loss; g _kfor the admittance of branch road k; v _i, v _jthe voltage magnitude of bus nodes i and j respectively; θ _ijfor the differential seat angle of load bus i and j;

Described electric power transmission network active power loss obtains in real time system parameters according to WAMS system and determines, in the present embodiment, parameter comprises the 500kV system PMU measured value (for example 500kV bus current, electric current, meritorious and reactive power) of goal systems and the EMS data (for example 220kV busbar voltage, electric current, meritorious and reactive power) of 220kV system, determines active power loss according to above-mentioned measured value.

S202, as shown in the formula definite equality constraint:

Active power balance constraint $P_{\mathrm{gi}}-P_{\mathrm{di}}-v_i\underset{j\&Element;N_i}{\mathrm{\&Sigma;}}{v}_j(g_{\mathrm{ij}}\cos {\mathrm{\&theta;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\sin {\mathrm{\&theta;}}_{\mathrm{ij}})=0;$

It is reactive power equilibrium constraint $Q_{\mathrm{gi}}-Q_{\mathrm{di}}-v_i\underset{j\&Element;N_i}{\mathrm{\&Sigma;}}{v}_j(g_{\mathrm{ij}}\sin {\mathrm{\&theta;}}_{\mathrm{ij}}+B_{\mathrm{ij}}\cos {\mathrm{\&theta;}}_{\mathrm{ij}})=0$

In formula, P _githe generator active power of node i injects; P _difor the load active power of node i; g _ij, B _ijthe electricity being respectively between node i, j is led and susceptance; Q _githe generator reactive power that is node i injects; Q _difor the reactive load power of node i; v _i, v _jthe voltage magnitude of bus nodes i and j respectively; θ _ijfor the differential seat angle of load bus i and j.

Step 3, division iteration phase.Comprise the following steps:

Primary iteration number of times is 1, according to iterations, iteration is divided into global search stage, elementary solution stabilization sub stage and high precision solution stabilization sub stage; As shown in the formula (1), inertial factor w is regulated:

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

In formula, w _maxbe positioned at primary iteration, w _minbe positioned at the iteration end in period, iter is current iteration number, iter _maxfor maximum iteration time,

As shown in the formula (2), (3)) (4) determine respectively inertial factor w and accelerated factor c of different iteration phase ₁, c ₂:

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

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

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

In formula, k _mfor global search stage maximum iteration time, k _nfor elementary solution stabilization sub stage maximum iteration time, k _mAXfor high precision solution stabilization sub stage maximum iteration time.

Step 4, upgrades speed and the position of described particle.

Speed and the position of upgrading described particle comprise the following steps:

As shown in the formula (5), (6) speed v to described particle respectively _i ^k+1with position x _i ^k+1do following renewal:

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

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

In formula, v _i ^k+1be i particle k+1 for time velocity; W is the inertial factor of particle; v _i ^kbe i particle k for time velocity; c ₁, c ₂for normal number, scope is [0,2.5]; r ₁, r ₂scope be the random numeral producing between [0,1]; for the optimum position based on historical i the particle of population iteration; g _bestifor G population particle global optimum position; x _i ^k+1be k+1 for time the position of i particle; x _i ^kbe k for time the position of i particle; χ is penalty factor, is used for guaranteeing convergence;

Judge whether described particle control variables exceedes the described particle boundary condition of a kind of setting of step, if exceed value again.

In step 5, if judge, iterations does not exceed global search stage maximum iteration time, revises inertial factor w and accelerated factor c ₁, c ₂, iteration k+1 word iterative value;

In step 6, if judge, iterations does not exceed elementary solution stabilization sub stage maximum iteration time, revises inertial factor w and accelerated factor c ₁, c ₂, iteration k+1 word iterative value;

In step 7, if judge, iterations does not exceed the iteration upper limit, revises inertial factor w and accelerated factor c ₁, c ₂, iteration k+1 word iterative value.

Step 8, fall generation to maximum times, export online reactive capability allocation plan.

If the current particle state of particle is better than the individual extreme value of history in iterative process, with the more individual extreme value of new historical of this state if have the current state particle of particle to be better than the historical extreme value of neighborhood in iterative process in neighborhood particle, upgrade the historical optimum g of neighborhood with this state _best;

The node voltage of measuring according to described WAMS provides line voltage level, determines reactive capability allocation plan.

Finally should be noted that: above embodiment is only in order to illustrate that technical scheme of the present invention is not intended to limit, although the present invention is had been described in detail with reference to above-described embodiment, those of ordinary skill in the field are to be understood that: still can modify or be equal to replacement the specific embodiment of the present invention, and do not depart from any amendment of spirit and scope of the invention or be equal to replacement, it all should be encompassed in the middle of claim scope of the present invention.

Claims (7)

1. an electric network reactive-load capacity collocation method for random inertial factor particle swarm optimization algorithm, is characterized in that: said method comprising the steps of:

The system parameters of I, Real-time Obtaining WAMS system, the boundary condition of setting particle;

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

III, division iteration phase;

IV, the speed of upgrading described particle and position;

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

VI, judge whether iterations arrives elementary solution stabilization sub stage maximum iteration time;

VII, judge whether iterations arrives the iteration upper limit;

VIII, fall generation to maximum times, export online reactive capability allocation plan.

2. the method for claim 1, is characterized in that: the described system parameters of described step I comprises PMU measured value and the described EMS data of described network system;

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

Described EMS data comprise busbar voltage, busbar voltage, active power and reactive power;

The border of described particle is state estimation result based on described network system and real-time each point voltage upper lower limit value of current scheduling operating provisions, the solution space scope of the corresponding algorithm of setting.

3. the method for claim 1, is characterized in that: described Step II comprises the following steps:

S201, the nodal information that obtains distribution network system and branch road information, arrange the population size of the number of control variables and the span of each control variables and initial population;

Described initial population is carried out initialization and initial parameter is set, obtain primary group;

Initialization obtains described initial population and refers to initial velocity and the initial position of the particle in described initial population being selected at random particle in particle span, and described initial parameter comprises maximum iteration time and adapts to threshold value;

S202, to choose electric power transmission network active power loss be target function, the Mathematical Modeling as shown in the formula determining idle work optimization:

In formula, k=(i, j), i ∈ N _b, N _bfor all bus nodes set, j ∈ N _i, N _ifor the node set being associated with bus nodes i; for electric power transmission network active power loss; g _kfor the admittance of branch road k; v _i, v _jthe voltage magnitude of bus nodes i and j respectively; θ _ijfor the differential seat angle of load bus i and j;

S203, as shown in the formula definite equality constraint:

with

In formula, P _githe generator active power of node i injects; P _difor the load active power of node i; g _ij, B _ijthe electricity being respectively between node i, j is led and susceptance; Q _githe generator reactive power that is node i injects; Q _difor the reactive load power of node i; v _i, v _jthe voltage magnitude of bus nodes i and j respectively; θ _ijfor the differential seat angle of load bus i and j.

4. the method for claim 1, is characterized in that: described Step II I comprises the following steps:

S301, primary iteration number of times are 1, according to iterations, iteration are divided into global search stage, elementary solution stabilization sub stage and high precision solution stabilization sub stage; As shown in the formula (1), inertial factor w is regulated:

In formula, w _maxbe positioned at primary iteration, w _minbe positioned at the iteration end in period, iter is current iteration number, iter _maxfor maximum iteration time,

S302, as shown in the formula (2), (3)) (4) determine respectively inertial factor w and accelerated factor c of different iteration phase ₁, c ₂:

In formula, k _mfor global search stage maximum iteration time, k _nfor elementary solution stabilization sub stage maximum iteration time, k _mAXfor high precision solution stabilization sub stage maximum iteration time.

5. the method for claim 1, is characterized in that: in described step IV, upgrade respectively the speed v of described particle as shown in the formula (5), (6) _i ^k+1with position x _i ^k+1:

In formula, v _i ^k+1be i particle k+1 for time velocity; W is the inertial factor of particle; v _i ^kbe i particle k for time velocity; c ₁, c ₂for accelerator coefficient; r ₁, r ₂scope be the random numeral producing between [0,1]; for the optimum position based on historical i the particle of population iteration, g _bestifor G population particle global optimum position; x _i ^k+1be k+1 for time the position of i particle; x _i ^kbe k for time the position of i particle; χ is penalty factor;

Judge whether described particle control variables exceedes the boundary condition of described particle, if exceed value again.

6. the method for claim 1, is characterized in that: in described step V, if judge, iterations does not exceed global search stage maximum iteration time, revises inertial factor w and accelerated factor c ₁, c ₂, iteration k+1 word iterative value;

In described step VI, if judge, iterations does not exceed elementary solution stabilization sub stage maximum iteration time, revises inertial factor w and accelerated factor c ₁, c ₂, iteration k+1 word iterative value;

In described step VII, if judge, iterations does not exceed the iteration upper limit, revises inertial factor w and accelerated factor c ₁, c ₂, iteration k+1 word iterative value.

7. the method for claim 1, is characterized in that: described step VIII comprises:

If the current particle state of particle is better than the individual extreme value of history in iterative process, with the more individual extreme value of new historical of this state if have the current state particle of particle to be better than the historical extreme value of neighborhood in iterative process in neighborhood particle, upgrade the historical optimum g of neighborhood with this state _best;

The node voltage of measuring according to described WAMS provides line voltage level, determines reactive capability allocation plan.