CN103872678B - A kind of load model identification method measured based on transformer station - Google Patents

Abstract

The invention discloses a kind of load model modeling method of electric power system, first Coherent Generator Group division is carried out to the transformer station in electrical network, build the load model of equivalent induction motor, set up identification criterion function, particle cluster algorithm is adopted to carry out optimizing to load parameter to be identified in load model by the output response of input voltage and real system, add up the load model of different website, obtain area power grid load model's databank.The present invention introduces initial active power Proportional coefficient K in identified parameters _pmwith specified initial load rate coefficient M _lf, the impact that the time variation eliminating load amplitude brings, makes load model more accurate; And be provided with speed Dynamic gene and the inertial factor of particle cluster algorithm, improve algorithmic statement precision.

Description

A kind of load model identification method measured based on transformer station

Technical field

The invention belongs to power system load model technical field, more specifically say, relate to a kind of load model identification method measured based on transformer station.

Background technology

The organic whole that electric power system is made up of generating, transmission of electricity, distribution and electric load four major part.Wherein, electric load is the general name of power consumption equipment in electric power networks, sometimes also comprises the power distribution network for connecting power equipment.Cannot store in a large number because electric power system has electric energy, the electrical network characteristic that relevance is extremely strong to each other, therefore when electrical network normal condition, electrical network links keeps dynamic equilibrium, and when arbitrary portion breaks down in electrical network, remainder likely can be subject to great impact, finally cause electrical network local disorders, even cause the concussion of whole electrical network, cause social safety and the great loss of economic development.Along with socioeconomic sustained and rapid development, electric load also becomes more and more heavy, this make the fail safe to electric power system, reliability requirement stricter.Current China electrical network is just towards the future development of " bulk power grid; superhigh pressure; large-sized unit; remote ", research dynamic load characteristic effectively can find the fragile position of electrical network, and adopt various in-advance measure in time, ensure the safety and reliability of electrical network, this improves the fail safe of electric power system, the key of reliability.

In recent years, all achieved many achievements in load modeling research field both at home and abroad, but a lot of model or relative coarseness or even differ very large with actual characteristic.Inaccurate load model and other component models of accurate electric power system seem very inharmonious, even limit the raising of grid simulation precision.In simulation calculation, use the load model of this relative coarseness, if simulation result is too conservative, the ability of the defeated generating of electrical network can be made fully can not to use and cause the wasting of resources; If simulation result too optimistic, design planning can be caused unreasonable, reduce the stabilizing power of operation of power networks, even cause the system splitting collapse consequence caused owing to controlling misoperation.In addition, Characteristics of Electric Load has profound influence to the everyway that grid simulation calculates: part throttle characteristics all can produce certain impact to the Load flow calculation of electric power system, transient stability, small-signal dynamic stability and voltage stabilization.Therefore the good influence on system operation of accuracy to electric power system of load model is great, and it is very necessary for carrying out deep research to electric load model.In model structure research, current primary study mechanism load model (mainly static load three rank induction machines in parallel), and difference equation model in non-mechanism formula model; In System Discrimination algorithm, the discrimination method of some advanced persons has also been applied in load modeling as artificial nerve network model have also been obtained a large amount of concern in the application in load modeling field.Although the research of forefathers has achieved certain achievement, along with the complexity of electrical network is more and more higher, the accuracy of load model has still had much room for improvement.

Summary of the invention

The object of the invention is to overcome the deficiencies in the prior art, a kind of load model identification method measured based on transformer station is provided, improves the accuracy of load parameter identification, improve the accuracy of load model.

For achieving the above object, the present invention is based on the load model identification method that transformer station measures, comprise the following steps:

S1: divide according to electric pressure the transformer station in electrical network, adopts induction motor static load in parallel to represent by the equivalent load model of each transformer station;

S2: equivalent induction motor adopts three order induction motor load model, α=[R _s, X _s, X _m, R _r, X _r, H, A, B, k _pz, k _pi, k _qz, k _qi, K _pm, M _lf] ^tindependence for load model parameter vector to be identified, β=[e _x0, e _y0, s ₀, x _s, K _l] ^tfor the identified parameters vector can tried to achieve by motor limit and α, wherein R _sfor stator resistance, X _sfor stator winding leakage reactance, X _mfor excitation reactance, R _rfor rotor resistance, X _rfor rotor leakage reactance, H be rotor inertia time constant, A, B be electromechanics torque characteristics parameter, k _pz, k _pi, k _qz, k _qifor static characteristic parameter, e _x0, e _y0for the transient voltage that motor is initial, s ₀for the revolutional slip of motor, x _sfor the synchronous reactance between stators and rotators, K _lfor load factor; K _pmrepresent initial active power proportionality coefficient, K _pm=P ₀'/P, P ₀' be the initial active power of motor, P by the active power that consumes in transient process of survey load point; M _lffor specified initial load rate coefficient, s _mBthe rated capacity of induction motor, U _bload reference voltage, U ₀it is load busbar voltage initial value in transient process;

S3: carry out load model parameters identification, concrete steps comprise:

S3.1: input voltage excitation U (k) in actual transformer substation system sampled measurements obtains N+1 actual output responds y (t)=[P (t), Q (t)] ^twherein t represents measurement sample sequence number, span is the active power value that load point that 0≤t≤N, P (t) is moment t consumes in transient process, the reactive power value that the load point that Q (t) is moment t consumes in transient process;

S3.2: reality is exported the response corresponding moment discrete-time series u (0), u (1) ..., u (N) } and input load model, the initial steady state condition of load model is: initial voltage U ₀=u (0), initial active-power P ₀=P (0), initial reactive power Q ₀=Q (0); N the initial value of the independent parameter vector α to be identified of setting, adopts particle cluster algorithm to following target function optimizing:

$\min J(\mathrm{\α},\mathrm{\β})=\min \underset{t=0}{\overset{N}{\mathrm{\Σ}}}{[y\left(t\right)-y_m\left(t\right)]}^T[y\left(t\right)-y_m\left(t\right)]$

Wherein, y _m(t)=[P _m(t), Q _m(t)] ^tfor the output response that load model during input u (t) obtains;

Speed Dynamic gene a (m) in particle cluster algorithm is determined according to following formula:

$a\left(m\right)=\frac{1}{F}\underset{i=1}{\overset{D}{\mathrm{\Σ}}}\sqrt{a_{\max }\left(m\right)+a_{\min }\left(m\right)}$

$a_{\max }\left(m\right)=\frac{\underset{j=1}{\overset{D}{\mathrm{\Σ}}}{(n_{\mathrm{ij}}^1-c_{\mathrm{ij}}^1)}^2}{\underset{j=1}{\overset{D}{\mathrm{\Σ}}}{\left(c_{\mathrm{ij}}^1\right)}^2}$

$a_{\min }\left(m\right)=\frac{\underset{j=1}{\overset{D}{\mathrm{\Σ}}}{(n_{\mathrm{ij}}^2-c_{\mathrm{ij}}^2)}^2}{\underset{j=1}{\overset{D}{\mathrm{\Σ}}}{\left(c_{\mathrm{ij}}^2\right)}^2}$

Wherein, F is particle number in population, and D is the dimension of independent parameter vector α to be identified, represent the maximal rate of the parameters of setting and fitting data corresponding to minimum speed respectively, represent the maximal rate of the parameters of setting and measurement data calculated value corresponding to minimum speed respectively.

S4: the load model of different website, as transformer station's mounting inferior duty value model of bus and load parameter, is added up, obtained area power grid load model's databank by the load model of each equivalent induction motor and load parameter identification result thereof.

The present invention is based on the load model identification method that transformer station measures, first motor cluster division is carried out to the transformer station in electrical network, build the load model of equivalent induction motor, set up identification criterion function, particle cluster algorithm is adopted to carry out optimizing to load parameter to be identified in load model by the output response of input voltage and real system, finally add up the load model of different website, obtain area power grid load model's databank.

The present invention has following beneficial effect:

(1) for the transformer station of the different electric pressure of area power grid, gather transformer station and go out to survey busbar voltage, current data, to the first time cluster of websites all in region, as the priori conditions of load classification;

(2) by introducing initial active power Proportional coefficient K _pmwith specified initial load rate coefficient M _lf, the impact that the time variation eliminating load amplitude brings;

(3) adopt the location updating algorithm of variable coefficient, reset speed Dynamic gene and the inertial factor of particle cluster algorithm, prevented the appearance of precocious phenomenon from avoiding falling into into local best points simultaneously, improve algorithmic statement precision.

Accompanying drawing explanation

Fig. 1 is a kind of embodiment flow chart that the present invention is based on the load model identification method that transformer station measures;

Fig. 2 is the flow chart of particle cluster algorithm in the present invention.

Embodiment

Below in conjunction with accompanying drawing, the specific embodiment of the present invention is described, so that those skilled in the art understands the present invention better.Requiring particular attention is that, in the following description, when perhaps the detailed description of known function and design can desalinate main contents of the present invention, these are described in and will be left in the basket here.

Embodiment

Fig. 1 is a kind of embodiment flow chart that the present invention is based on the load model identification method that transformer station measures.As shown in Figure 1, the load model identification method that the present invention is based on transformer station's measurement comprises the following steps:

S101: divide motor cluster, equivalence is induction motor:

Power System Interconnection is integrated, it is analyzed and calculates the support needing whole network data, and the operation of electric power system, management and scheduling are again layering and zonings, Real-time Obtaining whole network data is extremely difficult and loaded down with trivial details, in order to build the electrical network dynamic equivalent model of clear layer, the present invention adopts the hierarchical coordinative mode between electrical network to calculate.Therefore first motor cluster division is carried out to the transformer station in electrical network, adopted by each motor cluster equivalent induction motor and static load to represent.Concrete grammar is: transformer station is carried out cluster according to electric pressure, for the transformer station of same electric pressure according to the ratio reclassification shared in load of motor.The clustering method of current maturation has a lot, such as K mean algorithm, FCM Algorithms, hard mean algorithm etc.

S102: build load model:

The present invention considers that network load direct screening is connected on low-voltage bus bar side, and adopt three order induction motor load model, rotor condition equation and the stator output equation of induction motor are as follows:

$\left\{\begin{array}{c}\frac{{{\mathrm{dE}}_d}^{\′}}{\mathrm{dt}}=-\frac{1}{T_0^{\′}}[{E_d}^{\′}+(X-X^{\′})I_q]+(\mathrm{\ω}-{\mathrm{\ω}}_r){E_d}^{\′}\\ \frac{{{\mathrm{dE}}_q}^{\′}}{\mathrm{dt}}=-\frac{1}{T_0^{\′}}[{E_q}^{\′}+(X-X^{\′})I_d]+(\mathrm{\ω}-{\mathrm{\ω}}_r){E_q}^{\′}\\ \frac{{\mathrm{d\ω}}_r}{\mathrm{dt}}=\frac{1}{2H}[({E_d}^{\′}{I}_d+{E_q}^{\′}{I}_q)-T_L({{\mathrm{A\ω}}_r}^2+{\mathrm{B\ω}}_r+C))]\end{array}\right.---\left(1\right)$

$\left\{\begin{array}{c}{I}_d=\frac{1}{{R_S}^2+{X_S}^{\′2}}[R_S(U_d-{E_d}^{\′})+X^{\′}(U_q-{E_q}^{\′})]\\ I_q=\frac{1}{{R_S}^2+{X_S}^{\′2}}[R_S(U_q-{E_q}^{\′})-X^{\′}(U_d-{E_d}^{\′})]\end{array}\right.---\left(2\right)$

Wherein E _d' represent rotor d axle electromotive force, E _q' representing rotor q axle electromotive force, ω represents the synchronous speed of stator field, ω _rrepresent the rotating speed of rotor, T ₀'=(X _r+ X _m)/R _r, A, B, C are electromechanics torque characteristics parameter, A+B+C=1.H is rotor inertia time constant, X and X ' is respectively induction motor steady-state reactance and transient state reactance, X=X _s+ X _m, X'=X _s+ (X _mx _r)/(X _m+ X _r).I _d, I _qbe respectively the cross, straight axle component flowing through induction motor electric current.X _sfor stator winding leakage reactance, X _mfor excitation reactance, X _rfor rotor leakage reactance, R _rfor rotor resistance, R _sfor stator resistance, U _d, U _qbe respectively the cross, straight axle component value of the voltage on load bus.

In three order induction motor load model, static part is:

$\left\{\begin{array}{c}\frac{P}{P_0}=k_{\mathrm{Pz}}\left(\frac{U}{U_0}\right)+k_{\mathrm{Pi}}\left(\frac{U}{U_0}\right)+k_{\mathrm{Pp}}\\ \frac{Q}{Q_0}=k_{\mathrm{Qz}}\left(\frac{U}{U_0}\right)+k_{\mathrm{Qi}}\left(\frac{U}{U_0}\right)+k_{\mathrm{Qp}}\end{array}\right.---\left(3\right)$

Wherein, k _pz, k _pi, k _ppfor static characteristic parameter, k _pz+ k _pi+ k _pp=1; k _qz, k _qi, k _qpfor static characteristic parameter, k _qz+ k _qi+ k _qp=1; P ₀, Q ₀the active power and reactive power value that surveyed load point consumes when transient process starts and in transient process is respectively with P, Q; U, U ₀be respectively magnitude of voltage and the initial value of load bus in transient process.

In order to adapt to the time variation of load, realize the self adaptation of load capacity, the present invention introduces two parameter K _pmand M _lf, wherein, K _pmrepresent initial active power proportionality coefficient, M _lffor specified initial load rate coefficient, formula is:

K _pm＝P ₀′/P(4)

$M_{\mathrm{lf}}=\left(\frac{{P_0}^{\′}}{S_{\mathrm{MB}}}\right)/\left(\frac{U_0}{U_B}\right)---\left(5\right)$

Wherein, P ₀' be the initial active power of motor, S _mBthe rated capacity of induction motor, U _bit is load reference voltage.

By introducing parameter K _pmand M _lfthe impact that the time variation eliminating load amplitude completely brings can be ensured, that is: if load constituent proportions constant, no matter how payload changes, and can carry out all loads of matching by one group of parameter, greatly simplify load modeling process, add the practicality of model.

Induction motor model is nonlinearized Mathematical Modeling, is a very difficult problem in power system analysis to the identification analysis of nonlinear model.The parameter identification method of non linear system is mostly that its main process is the parameter vector of searching one group of optimum at present based on optimization method, and make predetermined error target function value reach minimum, error target function is the function of the parameter needing identification.Analytic relationship due to this function is difficult to represent, solution space is often also quite complicated, there is multiple limit, and in numerous load model parameters discrimination methods, particle cluster algorithm has good robustness and convergence rate faster.But also exist as poor in ability of searching optimum, the flying speed iterating to particle during the later stage is too small, easily sinks into local extremum, the not high defect of convergence precision.Therefore the present invention is by improving particle cluster algorithm, promotes convergence precision.

S103: load parameter identification:

The identification criterion function of load model is as shown in the formula shown in (4):

$\min J(\mathrm{\α},\mathrm{\β})=\min \underset{t=0}{\overset{N}{\mathrm{\Σ}}}{[y\left(t\right)-y_m\left(t\right)]}^T[y\left(t\right)-y_m\left(t\right)]---\left(4\right)$

Wherein t=0 is sampling start time, and N is the moment that sampling terminates, and amounts to sampling N+1 time.。α=[R _s, X _s, X _m, R _r, X _r, H, A, B, K _pm, M _lf, k _pz, k _pi, k _qz, k _qi] ^tfor the parameter vector to be identified of load model, β=[e _x0, e _y0, s ₀, x _s, K _l] ^tfor the identified parameters vector can tried to achieve by motor limit and α, wherein e _x0, e _y0for the transient voltage that motor is initial, s ₀for the revolutional slip of motor, x _sfor the synchronous reactance between stators and rotators, K _lfor load factor.Y (t)=[P (t), Q (t)] ^tand y _m(t)=[P _m(t), Q _m(t)] ^tfor the output response that output response and the identification model of the actual measurement of moment t obtain.

Concrete identification process is:

S3.1: input voltage excitation U (k) in actual transformer substation system sampled measurements obtains N+1 actual output responds y (t)=[P (t), Q (t)] ^twherein t represents measurement sample sequence number, span is the active power value that load point that 0≤t≤N, P (t) is moment t consumes in transient process, the reactive power value that the load point that Q (t) is moment t consumes in transient process.

S3.2: reality is exported the response corresponding moment discrete-time series u (0), u (1) ..., u (N) } and input load model, the initial steady state condition of load model is: initial voltage U ₀=u (0), initial active-power P ₀=P (0), initial reactive power Q ₀=Q (0); The initial value of the independent parameter vector α to be identified of setting, adopts particle cluster algorithm to following target function optimizing:

$\min J(\mathrm{\α},\mathrm{\β})=\min \underset{t=0}{\overset{N}{\mathrm{\Σ}}}{[y\left(t\right)-y_m\left(t\right)]}^T[y\left(t\right)-y_m\left(t\right)]---\left(5\right)$

Wherein, y _m(t)=[P _m(t), Q _m(t)] ^tfor the output response that load model during input u (t) obtains.

Visible, according to independent parameter vector α to be identified and initial steady state condition, identified parameters vector β can be calculated, thus substitute into target function optimizing.

Particle cluster algorithm is the optimal solution by determining function with the particle searched in plain space, and all particle objects are all made up of the speed an of adaptive value determined by majorized function and their headings of decision.The operation behavior of particle cluster algorithm is:

$\left\{\begin{array}{c}{v}_{\mathrm{id}}^{k+1}={\mathrm{wv}}_{\mathrm{id}}^k+\mathrm{Random}(0,c_1)(p_{\mathrm{id}}^k-x_{\mathrm{id}}^k)+\mathrm{Random}(0,c_2)(p_{\mathrm{gd}}^k-x_{\mathrm{id}}^x)\\ x_{\mathrm{id}}^{k+1}=x_{\mathrm{id}}^k+a\left(m\right)v_{\mathrm{id}}^{k+1}\\ v_{d\min }\≤v_{\mathrm{id}}^k\≤v_{d\max }\\ x_{d\max }\≤x_{\mathrm{id}}^k\≤x_{\mathrm{dmx}}\end{array}\right.---\left(6\right)$

In note population, particle number is F, and each particle i.e. dimension of independent parameter vector α to be identified is D, and the position of i-th particle can be expressed as the vector x of a D dimension _i=(x _i1, x _i2..., x _id..., x _iD), i=1,2 ..., F, d=1,2 ..., D.By x _isubstitute into target function and just can calculate adaptive value, just can weigh x by adaptive value _iquality.The flying speed of particle is also a D dimensional vector, is designated as v _i=(v _i1, v _i2..., v _id..., v _iD).The optimal location that i-th particle searches up to now is p _i=(p _i1, p _i2..., p _id..., p _iD), the optimal location that whole population searches up to now is p _g=(q _g1, q _g2..., q _gd..., q _gD).W is inertial factor, c ₁, c ₂be Studying factors, Random () is random function, and subscript k is the number of times of current iteration.A (m) is speed Dynamic gene, usually gets 1.But redefined the position that a (m) carrys out convergent-divergent particle of future generation in the present invention, adopt annular to follow the example of, speed interval number is D-1, m=1,2 ..., D-1.The size of a (m) is determined by many curvilinear finite differences fitting process.Concrete grammar is as follows:

If the measurement data of a jth parameter is c in i-th particle _ij, fitting data is n _ij, then the target function of many curvilinear finite differences fitting process is:

$a\left(m\right)=\frac{1}{F}\underset{i=1}{\overset{F}{\mathrm{\Σ}}}\left(\frac{\underset{j=1}{\overset{D}{\mathrm{\Σ}}}{(n_{\mathrm{ij}}-c_{\mathrm{ij}})}^2}{\underset{j=1}{\overset{D}{\mathrm{\Σ}}}{c_{\mathrm{ij}}}^2}\right)---\left(7\right)$

When speed Dynamic gene is finally determined, require that " flight " speed of particulate can not depart from actual maximal rate and minimum speed value, therefore, need to consider both when determining target function, fitting data that therefore can be corresponding in the maximal rate and minimum speed arranging parameters is respectively measurement data calculated value is respectively prerequisite under, the speed Dynamic gene of particle maximal rate and minimum speed can be set to respectively:

$a_{\max }\left(m\right)=\frac{\underset{j=1}{\overset{D}{\mathrm{\Σ}}}{(n_{\mathrm{ij}}^1-c_{\mathrm{ij}}^1)}^2}{\underset{j=1}{\overset{D}{\mathrm{\Σ}}}{\left(c_{\mathrm{ij}}^1\right)}^2}---\left(8\right)$

$a_{\min }\left(m\right)=\frac{\underset{j=1}{\overset{D}{\mathrm{\Σ}}}{(n_{\mathrm{ij}}^2-c_{\mathrm{ij}}^2)}^2}{\underset{j=1}{\overset{D}{\mathrm{\Σ}}}{\left(c_{\mathrm{ij}}^2\right)}^2}---\left(8\right)$

On this basis, the final goal function of speed Dynamic gene a (m) can be defined as:

$a\left(m\right)=\frac{1}{F}\underset{i=1}{\overset{D}{\mathrm{\Σ}}}\sqrt{a_{\max }\left(m\right)+a_{\min }\left(m\right)}---\left(10\right)$

Due to the change curve more complicated of speed Dynamic gene, if expect, utility function approaches to obtain proper data, needs to improve number of times, increases undetermined coefficient thus increases amount of calculation.Consider that collection point is discrete point, difference equation does not require first derivative, and amount of calculation is less, and the scope of application is wider.

Inertial factor w can have larger impact to the performance of particle cluster algorithm in formula (6): larger w can the convergence rate of boosting algorithm, and less w can the precision of boosting algorithm.The present invention proposes a kind of method that w in an iterative process according to circumstances carries out adaptive correction, along with moving ahead of calculating, exponentially reduce w gradually, its formula is:

$w\left(k\right)=[2/(1+e^{\∂k/k_{\max }})]w^0---\left(11\right)$

Wherein represent normal number, k _maxfor the greatest iteration number of particle cluster algorithm; w ⁰for the upper limit of w (k); K is the number of times of current iteration.

Direction due to particle flight is the same with former algorithm is all sensing two " extreme value ", but particle position reduces adaptively by the impact of elastic wave velocity Dynamic gene or amplifies in innovatory algorithm used in the present invention, when having avoided iteration end, whole colony is all drawn close around same extreme point, therefore prevents the appearance of precocious phenomenon to avoid falling into into local best points simultaneously.Self adaptation inertial factor can also be adopted, avoid population in an iterative process to occur violent " homoplasy ", promote the convergence precision of particle cluster algorithm.

Fig. 2 is the flow chart of particle cluster algorithm in the present invention:

S201: within the scope of solution space, setting primary group.

S202: the adaptive value calculating each particle according to target function;

S203: judge whether adaptive value reaches optimum requirement, if reached, enter step S208; If do not reached, enter step S204;

S204: to each particle, if the adaptive value of current location is better than individual history optimal value, then replaces individual history adaptive optimal control value and the current situation of optimal location particle; To each particle, if the adaptive value of current location is better than the history optimal value of colony, then colony's history adaptive optimal control value and the current situation of optimal location particle are replaced;

S205: judge whether iterations reaches maximum iteration time, if reached, enter step S208; If do not reached, enter step S206:

S206: computational speed Dynamic gene a (m) and inertial factor w;

S207: the more position of new particle, returns step S202;

S208: export history optimum particle position, the particle position that namely history adaptive optimal control value is corresponding, algorithm terminates.

S104: the load of different qualities carries out respective load model parameters identification work according to above-mentioned steps, the load model of each equivalent induction motor and load parameter identification result thereof are as transformer station's mounting inferior duty value model of bus and load parameter, the load model of different website is added up, obtains area power grid load model's databank.

Although be described the illustrative embodiment of the present invention above; so that those skilled in the art understand the present invention; but should be clear; the invention is not restricted to the scope of embodiment; to those skilled in the art; as long as various change to limit and in the spirit and scope of the present invention determined, these changes are apparent, and all innovation and creation utilizing the present invention to conceive are all at the row of protection in appended claim.

Claims (4)

1., based on the load model identification method that transformer station measures, it is characterized in that, comprise the following steps:

S1: divide according to electric pressure the transformer station in electrical network, adopts induction motor static load in parallel to represent by the equivalent load model of each transformer station;

S2: equivalent induction motor adopts three order induction motor load model, α=[R _s, X _s, X _m, R _r, X _r, H, A, B, k _pz, k _pi, k _qz, k _qi, K _pm, M _lf] ^tindependence for load model parameter vector to be identified, β=[e _x0, e _y0, s ₀, x _s, K _l] ^tfor the identified parameters vector can tried to achieve by motor limit and α, wherein R _sfor stator resistance, X _sfor stator winding leakage reactance, X _mfor excitation reactance, R _rfor rotor resistance, X _rfor rotor leakage reactance, H be rotor inertia time constant, A, B be electromechanics torque characteristics parameter, k _pz, k _pi, k _qz, k _qifor static characteristic parameter, e _x0, e _y0for the transient voltage that motor is initial, s ₀for the revolutional slip of motor, x _sfor the synchronous reactance between stators and rotators, K _lfor load factor; K _pmrepresent initial active power proportionality coefficient, K _pm=P ₀'/P, P ₀' be the initial active power of motor, P by the active power that consumes in transient process of survey load point; M _lffor specified initial load rate coefficient, s _mBthe rated capacity of induction motor, U _bload reference voltage, U ₀it is load busbar voltage initial value in transient process;

S3: carry out load model parameters identification, concrete steps comprise:

S3.1: input voltage excitation U (k) in actual transformer substation system sampled measurements obtains N+1 actual output responds y (t)=[P (t), Q (t)] ^twherein t represents measurement sample sequence number, span is the active power value that load point that 0≤t≤N, P (t) is moment t consumes in transient process, the reactive power value that the load point that Q (t) is moment t consumes in transient process;

S3.2: reality is exported the response corresponding moment discrete-time series u (0), u (1) ..., u (N) input load model, the initial steady state condition of load model is: initial voltage U ₀=u (0), initial active-power P ₀=P (0), initial reactive power Q ₀=Q (0); N the initial value of the independent parameter vector α to be identified of setting, adopts particle cluster algorithm to following target function optimizing:

$\min J(\mathrm{\α},\mathrm{\β})=min\underset{t=0}{\overset{N}{\Σ}}{\[y\left(t\right)-y_m\left(t\right)\]}^T\[y\left(t\right)-y_m\left(t\right)\]$

Wherein, y _m(t)=[P _m(t), Q _m(t)] ^tfor the output response that load model during input u (t) obtains;

Speed Dynamic gene a (m) in particle cluster algorithm is determined according to following formula:

$a\left(m\right)=\frac{1}{F}\underset{i=1}{\overset{D}{\Σ}}\sqrt{a_{max}\left(m\right)+a_{min}\left(m\right)}$

$a_{max}\left(m\right)=\frac{\underset{j=1}{\overset{D}{\Σ}}{(n_{ij}^1-c_{ij}^1)}^2}{\underset{j=1}{\overset{D}{\Σ}}{\left(c_{ij}^1\right)}^2}$

$a_{min}\left(m\right)=\frac{\underset{j=1}{\overset{D}{\Σ}}{(n_{ij}^2-c_{ij}^2)}^2}{\underset{j=1}{\overset{D}{\Σ}}{\left(c_{ij}^2\right)}^2}$

Wherein, F is particle number in population, and D is the dimension of independent parameter vector α to be identified, represent the maximal rate of the parameters of setting and fitting data corresponding to minimum speed respectively, represent the maximal rate of the parameters of setting and measurement data calculated value corresponding to minimum speed respectively;

S4: the load model of different website, as transformer station's mounting inferior duty value model of bus and load parameter, is added up, obtained area power grid load model's databank by the load model of each equivalent induction motor and load parameter identification result thereof.

2. load model identification method according to claim 1, it is characterized in that, the concrete grammar that in described step S1, Coherent Generator Group divides is: transformer station is carried out cluster according to electric pressure, for the transformer station of same electric pressure according to the ratio reclassification shared in load of motor.

3. load model identification method according to claim 1, is characterized in that, in described step S3.2, the inertial factor w in particle cluster algorithm determines according to following formula:

$w\left(k\right)=\[2/(1+e^{\∂k/k_{max}})\]w^0$

Wherein represent normal number, k _maxfor the greatest iteration number of particle cluster algorithm; w ⁰for the upper limit of w (k); K is the number of times of current iteration.

4. load model identification method according to claim 1, is characterized in that, the dynamic equivalent in described step S4 adopts people having the same aspiration and interest equivalence method.