CN103345546A - Speed regulator parameter identification method combined with frequency locus and particle swarm optimization - Google Patents

Abstract

The invention relates to a speed regulator parameter identification method combined with a frequency locus and the particle swarm optimization. The speed regulator parameter identification method can be used for identifying parameters of a unit speed regulator. According to the speed regulator parameter identification method, an input and output model of a speed regulator system is set up, and identifiability of corresponding parameters is proved; an electromechanical transient simulating calculation module of the PSASP software is directly called through an interface, the speed regulator parameters are obtained, a simulation frequency curve after a power system is interfered is obtained, the speed regulator parameters are regulated based on the particle swarm optimization, and therefore the simulation frequency curve is close to an actual measurement frequency curve as much as possible; the optimized speed regulator parameters are obtained through repeated parameter optimization. According to the speed regulator parameter identification method, parameter identification of a speed regulating system can be finished only by utilizing a fault recording curve of a frequency and parameters of other components of the system, much troublesome on-spot tests can be avoided, and the relevant parameters of the speed regulator can be conveniently obtained by dispatch personnel in time.

Description

The speed regulator parameter identification method that frequency locus combines with particle cluster algorithm

Technical field

The present invention relates to the speed regulator parameter identification method that a kind of frequency locus combines with particle cluster algorithm, can be applicable to the machine unit speed regulating device parameter is carried out parameter identification.

Background technology

Governing system is as the important component part of generator control system, and the correctness of its parameter directly affects stability and the security of operation of power networks.See " modern power systems analysis " that Wang Xifan writes for details. Beijing: Science Press, 2007.Because the system operation mode conversion is frequent, the governing system of each unit often also needs frequently to adjust for the conversion of the method for operation of adaptive system.For the dispatching center, how to obtain the parameter of all set speed adjustment systems of system in time, be one of strong guarantee of system's safety in operation and stability.

About the parameter identification problem of governing system, research and engineering technical personnel have proposed many significant methods, roughly can be divided into two class methods at present.

First kind method is test obtains governing system in conjunction with identification technique parameter." steam turbine and the electrohydraulic governor system model important parameter method of testing thereof " of being write by Wen Libin, Guangxi electric power .2012,35 (1): 9-12,16, propose to adopt load dump test to obtain generator power measurement links parameter, by introducing the test instruction signal, reach the parameter that servo card PID signal obtains the PID link.

" research of turbine regulating system parameter identification method " of being write by Wu Haicheng, Cai Bing, Chen Shengli etc., Anhui electric power, 2010,27 (1): 21-25 has proposed the turbine regulating system parameters identification method according to differential evolution algorithm and in conjunction with the steam turbine load disturbance experiments.

" identification of turbine regulating system component parameters and the application thereof " of being write by Sun Ping, Yuan Baiying, China Power, 1995,11:7-12, proposition adopts least square method that the parameter of each link of governing system is carried out identification and obtained corresponding governing system parameter by the input and output curve of off-line testing acquisition steam turbine electro-hydraulic control system then.The advantage of these class methods is that the model and the parameter that obtain are more accurate, and shortcoming is just can carry out under the unit maintenance situation.

Second class methods are based on measured curve, carry out the identification of corresponding set speed adjustment systematic parameter.

" difficulty when extracting knowledge from actual measurement track and the prospect " of being write by xuwei, Xue Yusheng, Chen Shi etc., Automation of Electric Systems, 2009,33 (15): 1-7, propose to utilize system failure practical frequency fluctuation track to realize the conception of set speed adjustment systematic parameter identification, but do not proposed specific algorithm.

Write " factor analysis of power system frequency dynamic simulation result precision ", Heilungkiang electric power, 2012,34 (1): 50-52,61 by Li Yulong, Wang Hao, Ren Jihong etc.; And " the making merit-frequency process simulation track approach the model parameter research of actual measurement track " of being write by Liu Hongbo, Mu Gang, Xu Xingwei etc.Electric power network technique, 2006,30 (18): 20-24.The coefficient type that has the governing system of strong correlation relation with the frequency jitter track analyzed in these two pieces of articles, and obtain as drawing a conclusion: the speed regulator difference coefficient influences minimum point and the rise frequency that frequency descends, and governor dead time influences the minimum point that frequency descends.

By " based on the complicated electric power system frequency dynamic process analysis procedure analysis of track " that Xu Xingwei writes, Beijing, North China Electric Power University, 2010.Proposed so-called system frequency and stablized evaluation index, comprised that time, frequency rate of return of angle, rise frequency etc. fall, frequently fall in initial frequency; But the index that this article is carried its essence is the evaluation index of the fluctuation size of system frequency track, is not the frequency stabilization index.

Summary of the invention

Governing system parameter identification at there not being concrete effective ways realization based on the practical frequency track in the present prior art the invention provides the speed regulator parameter identification method that a kind of frequency locus combines with particle cluster algorithm.The present invention provides new method for the problem that traffic department in time obtains the speed regulator parameter, the present invention has simultaneously designed the interface that can directly call PSASP simulation calculation module when obtaining simulation curve, realize that for calling PSASP transient state calculating provides new approaches, expects that this invention plays a significant role in the middle of addressing these problems.

The technical solution adopted for the present invention to solve the technical problems is:

The speed regulator parameter identification method that frequency locus combines with particle cluster algorithm is: set up the input of speed regulator system, and proved the identifiability of relevant parameter; Directly call the electromechanical transient simulation computing module of PSASP software by interface, and obtain the speed regulator parameter, comprise obtaining the stimulation frequency curve of electric system after disturbed, and then based on particle swarm optimization algorithm the speed regulator parameter is regulated and to make stimulation frequency curve and practical frequency curve approaching as far as possible; Parameter optimization by repeatedly obtains optimum speed regulator parameter.

The described input of setting up speed regulator system when setting up the input of speed regulator system, requires the electrohydraudic servomechanism of speed regulator, the steam turbine model, and generator system, excitation system and network etc. are known system.

Described interface, by the ODBC interface, Putcollect and Getcollect statement, realizations such as SetCursorPos function and mouse_event function are read in from file, read the speed regulator parameter and directly call PSASP software transient stability computing module.

Described when obtaining the speed regulator parameter, to analyze simultaneously at many measured curves, the function of structure is based on that many measured curves and PSASP simulation curve obtain.

Described concrete calculation process, be initial value and the constant interval of at first given iterations and convergence and speed regulator parameter to be identified, then parameter is read into the concrete module of PSASP and carries out transient stability calculating, the stimulation frequency data are read into corresponding document, and calculate and obtain exemplary frequency deviation values, judge that whether the iteration convergence criterion satisfies, and utilize particle cluster algorithm to be optimized.

Described basic identification principle is at first to obtain actual output Export with model

, obtaining actual output Export with model The back calculate to obtain deviation delta z, the deviation delta z that obtains is adopted optimize algorithm and regulate, and reaches the parameter of the corresponding speed regulator electro-hydraulic control system that hour also just obtains as deviate Δ z.

The present invention proposes the new method of the speed regulator parameter identification that a kind of frequency locus combines with particle cluster algorithm.At first set up the input of speed regulator system, and proved the identifiability of relevant parameter; The electromechanical transient simulation computing module that directly calls PSASP software then by design interface obtains the stimulation frequency curve of electric system after disturbed, and then based on particle swarm optimization algorithm the speed regulator parameter is regulated and to make stimulation frequency curve and practical frequency curve approaching as far as possible; Parameter optimization by repeatedly obtains optimum speed regulator parameter, thereby has realized the identification based on the genset governing system parameter of practical frequency curve.

The invention has the beneficial effects as follows: the parameter that only need utilize the other parts of the failure wave-recording curve of frequency and system, can finish the parameter identification of the governing system of system, can avoid very loaded down with trivial details on-the-spot test, make things convenient for the dispatcher in time to obtain the speed regulator correlation parameter.

The present invention is further detailed explanation below in conjunction with accompanying drawing.

Description of drawings

Fig. 1 is total system transport function block diagram of the present invention;

Fig. 2 is module 1 transport function;

Fig. 3 is module 2 transport functions;

Fig. 4 is module 3 transport functions;

Fig. 5 is module 4 transport functions;

Fig. 6 is module 5 transport functions;

Fig. 7 is module 6 transport functions;

Fig. 8 is schematic diagram of the present invention;

Fig. 9 is calculation flow chart of the present invention;

Figure 10 is 20090731 failure-frequencies track/stimulation frequency track;

Figure 11 is 20090906 failure-frequencies track/stimulation frequency track;

Figure 12 is 200909062 failure-frequencies track/stimulation frequency track;

Figure 13 is 20091107 failure-frequencies track/stimulation frequency track;

Figure 14 is 20091111 failure-frequencies track/stimulation frequency track.

Embodiment

The present invention is the speed regulator parameter identification method that a kind of frequency locus combines with particle cluster algorithm.Mainly finished the governing system modeling analysis; Parameter identification algorithm based on the speed regulator electro-hydraulic control system of optimisation technique is realized; The work of three aspects such as operation realization relevant with the PSASP software transfer.

The speed regulator parameter identification method that frequency locus of the present invention combines with particle cluster algorithm is: set up the input of speed regulator system, and proved the identifiability of relevant parameter; Directly call the electromechanical transient simulation computing module of PSASP software by the interface of design, and obtain the speed regulator parameter, comprise obtaining the stimulation frequency curve of electric system after disturbed, and then based on particle swarm optimization algorithm the speed regulator parameter is regulated and to make stimulation frequency curve and practical frequency curve approaching as far as possible; Parameter optimization by repeatedly obtains optimum speed regulator parameter.

This speed regulator parameter identification method requires the electrohydraudic servomechanism of speed regulator when setting up the input of speed regulator system, the steam turbine model, and generator system, excitation system and network etc. are known system.

This speed regulator parameter identification method is when design interface, by the ODBC interface, Putcollect and Getcollect statement, realizations such as SetCursorPos function and mouse_event function are read in from file, read the speed regulator parameter and directly call PSASP software transient stability computing module.

This speed regulator parameter identification method is analyzed simultaneously at many measured curves when obtaining the speed regulator parameter, and the function of structure is based on that many measured curves and PSASP simulation curve obtain.

The concrete operations step is as follows:

1, governing system modeling.The speed regulator electro-hydraulic control system is carried out detailed modeling, the electrohydraudic servomechanism of speed regulator, the steam turbine model, and generator system, excitation system and network etc. are known system.The transport function block diagram of the total system that comprises excitation, generator and network under the effect of speed regulator electro-hydraulic control system, as shown in Figure 1.In Fig. 1, G ₁(s) represent the comprehensive transport function of the inner remainders of four type speed regulators such as electrohydraudic servomechanism, steam turbine model; G ₂(s) represent the corresponding comprehensive transport function of mechanical output-frequency translation process included generator system, excitation system and network.Y (s) represents system frequency excursion, the input of U (s) expression system, and in this system, U (s)=Y (s).

Because the inner remainder of speed regulator such as electrohydraudic servomechanism, steam turbine model in whole process, it is known and constant that all parameters of generator system, excitation system and network are, so G ₁(s), G ₂(s) be changeless.Only need set up the input-output function relation of corresponding each link to the transport function of each link in the frame of broken lines among Fig. 1, and then set up the funtcional relationship between the input and output of whole speed regulator electro-hydraulic control system.

Respectively each module in Fig. 1 frame of broken lines is carried out modeling respectively below, and then obtain its whole mathematical model.

For module shown in Figure 21 transport function, wherein U (s) represents the system frequency excursion Δ f of input.x ₁Represent parameter T to be identified ₁Y ₁(s) representative output; U, y ₁Be respectively input and output time-domain function form (as follows).Then the corresponding differential equation of this transport function is:

$u={\mathrm{<figure-callout\; id="1"\; label="y"\; filenames="130614143242.png"\; state="\{\{state\}\}">y</figure-callout>}}_1+\frac{{\mathrm{<figure-callout\; id="1"\; label="dy"\; filenames="130614143242.png"\; state="\{\{state\}\}">dy</figure-callout>}}_1}{\mathrm{<figure-callout\; id="1"\; label="dt\; T"\; filenames="130614143242.png"\; state="\{\{state\}\}">dt</figure-callout>}}{T}_{}{}_1$

Abbreviation gets:

y ₁＝f(T ₁,u)＝f ₁(x ₁,u) (1)；

For module 2 transport functions as shown in Figure 3, Y ₁(s) representative input, Y ₂(s) representative output, x ₂Represent parameter ε to be identified, K; Transport function is turned to algebraic equation to be got:

Make f ₂₁(y ₁, x ₂)=K (y ₁+ ε/2), f ₂₂(y ₁, x ₂)=K (y ₁-ε/2),

f ₂₃(y ₁,x ₂)=0。

Then $y_2=\left\{\begin{array}{cc}K(y_1+\mathrm{\&epsiv;}/2)& y_1<-\mathrm{\&epsiv;}/2\\ K(y_1-\mathrm{\&epsiv;}/2)& y_1>\mathrm{\&epsiv;}/2\\ 0& -\mathrm{\&epsiv;}/2\&le;y_1\&le;\mathrm{\&epsiv;}/2\end{array}\right.=\left\{\begin{array}{cc}{f}_{21}(y_1,x_2)& y_1<{-x}_2/2\\ f_{22}(y_1,x_2)& y_1>x_2/2\\ f_{23}(y_1,x_2)& -x_2/2\&le;y_1\&le;x_2/2\end{array}\right.---\left(2\right)$ ；

For module 3 transport functions as shown in Figure 4, Y ₃(s) representative input, Y ₄(s) representative output, x ₃Represent parameter K to be identified _IAs seen from Figure 1, input Y ₃(s)=P _REF-Y ₂(s)-P _E, P wherein _REFConstant load, P are given in representative _ERepresent feedback load, therefore, the transport function corresponding equation represented with Fig. 1-9 is:

Make f ₄₁(x ₃, y ₃)=P _Up, f ₄₂(x ₃, y ₃)=P _Down,

f ₄₃(x ₃,y ₃)＝∫K _Iy ₃ dt＝∫x ₃ y ₃ dt

Then $y_4=f_4(x_3,y_3)$

$=\left\{\begin{array}{cc}{P}_{\mathrm{up}}& f_{43}(x_3,y_3)>P_{\mathrm{up}}\\ P_{\mathrm{down}}& f_{43}(x_3,y_3)<P_{\mathrm{down}}\\ f_{43}(x_3,y_3)& P_{\mathrm{down}}\&le;f_{43}(x_3,y_3)\&le;P_{\mathrm{up}}\end{array}\right.=\left\{\begin{array}{cc}{f}_{41}(x_3,y_3)& f_{43}(x_3,y_3)>P_{\mathrm{up}}\\ f_{42}(x_3,y_3)& f_{43}(x_3,y_3)<P_{\mathrm{down}}\\ f_{43}(x_3,y_3)& P_{\mathrm{down}}\&le;f_{43}(x_3,y_3)\&le;P_{\mathrm{up}}\end{array}\right.---\left(3\right)$ ；

For module 4 transport functions as shown in Figure 5, Y ₄(s) representative input, Y ₅(s) be output, x ₄Represent parameter K to be identified _P, K _DThe equation corresponding with Fig. 5 is:

${\mathrm{<figure-callout\; id="5"\; label="y"\; filenames="130614143317.png,130614143328.png"\; state="\{\{state\}\}">y</figure-callout>}}_5=y_4+K_P{y}_3+K_D\frac{{\mathrm{dy}}_3}{\mathrm{dt}}{f}_5(y_3,y_4,x_4)---\left(4\right);$

For module 5 transport functions as shown in Figure 6, Y ₅(s) representative input; x ₅Represent parameter to be identified, comprise P _Max, P _MinY ₆(s) representative output.The equation corresponding with Fig. 6 is:

Make f ₆₁(x ₅, y ₅)=P _Max, f ₆₂(x ₅, y ₅)=P _Min,

f ₆₃(x ₅,y ₅)＝ky ₅

Then $y_6=f_6(x_5,y_5)$

$=\left\{\begin{array}{cc}{P}_{\max }& f_{63}({\mathrm{<figure-callout\; id="5"\; label="x"\; filenames="130614143317.png,130614143328.png"\; state="\{\{state\}\}">x</figure-callout>}}_5,y_5)>P_{\max }\\ P_{\min }& f_{63}({\mathrm{<figure-callout\; id="5"\; label="x"\; filenames="130614143317.png,130614143328.png"\; state="\{\{state\}\}">x</figure-callout>}}_5,y_5)<P_{\min }\\ f_{63}({\mathrm{<figure-callout\; id="5"\; label="x"\; filenames="130614143317.png,130614143328.png"\; state="\{\{state\}\}">x</figure-callout>}}_5,y_5)& P_{\min }\&le;f_{63}({\mathrm{<figure-callout\; id="5"\; label="x"\; filenames="130614143317.png,130614143328.png"\; state="\{\{state\}\}">x</figure-callout>}}_5,y_5)\&le;P_{\max }\end{array}\right.=\left\{\begin{array}{cc}{f}_{61}({\mathrm{<figure-callout\; id="5"\; label="x"\; filenames="130614143317.png,130614143328.png"\; state="\{\{state\}\}">x</figure-callout>}}_5,y_5)& f_{63}({\mathrm{<figure-callout\; id="5"\; label="x"\; filenames="130614143317.png,130614143328.png"\; state="\{\{state\}\}">x</figure-callout>}}_5,y_5)>P_{\max }\\ f_{62}({\mathrm{<figure-callout\; id="5"\; label="x"\; filenames="130614143317.png,130614143328.png"\; state="\{\{state\}\}">x</figure-callout>}}_5,y_5)& f_{63}({\mathrm{<figure-callout\; id="5"\; label="x"\; filenames="130614143317.png,130614143328.png"\; state="\{\{state\}\}">x</figure-callout>}}_5,y_5)<P_{\min }\\ f_{63}({\mathrm{<figure-callout\; id="5"\; label="x"\; filenames="130614143317.png,130614143328.png"\; state="\{\{state\}\}">x</figure-callout>}}_5,y_5)& P_{\min }\&le;f_{63}({\mathrm{<figure-callout\; id="5"\; label="x"\; filenames="130614143317.png,130614143328.png"\; state="\{\{state\}\}">x</figure-callout>}}_5,y_5)\&le;P_{\max }\end{array}\right.---\left(5\right)$ ；

For module 6 transport functions as shown in Figure 7, Y ₆(s), Y ₇(s) representative input; As shown in Figure 1, Y ₇=K ₂(P _REF-Y ₂), P _CVRepresentative output, expression is by the pitch instruction of electro-hydraulic control system output.The equation corresponding with Fig. 7 is:

P _CV＝y ₆+y ₇＝f ₇(y ₆,y ₇) (6)；

U substitution formula (1) is obtained y ₁, with y ₁Substitution formula (2) obtains y ₂, by y ₂Express y ₃And y ₇, and with y ₃Substitution formula (3) obtains y ₄, with y ₃And y ₄Substitution formula (4) obtains y ₅, with y ₅Substitution formula (5) obtains y ₆, with y ₆And y ₇Substitution formula (6) obtains the interior represented input and output function expression of dotted line block diagram among Fig. 1:

P _CV＝f(u,x) (7)；

Wherein the u representative is the system frequency excursion Δ f of input, and x represents the parameter to be identified of each link in the electro-hydraulic control system, comprises T ₁, K, ε, K _I, K _D, K _P, K ₂, P _Up, P _Down, P _Max, P _MinP _CVRepresent output mechanical power.Can prove that the parameter x in the formula (7) is cognizable.

2, based on the parameter identification algorithm of the speed regulator electro-hydraulic control system of optimisation technique.

If the actual value of parameter to be identified among Fig. 1 vector

Expression, actual input variable is with vectorial

Expression, push away output function express formula formula (7) and G ₁(s), G ₂(s) corresponding time domain expression formula ff (P _CV) multiply each other, obtain the whole time domain expression formula of Fig. 1; If the actual output of Fig. 1 is with vectorial

Expression, then the mathematic(al) representation between the input and output of Fig. 1 is:

$\stackrel{\&RightArrow;}{z}=f(\stackrel{\&RightArrow;}{x},\stackrel{\&RightArrow;}{u})\&CenterDot;\mathrm{ff}\left(f(\stackrel{\&RightArrow;}{x},\stackrel{\&RightArrow;}{u})\right)---\left(8\right);$

If the variable estimated value to be identified of speed regulator electro-hydraulic control system is designated as

Emulation is input as

The time corresponding model be output as

According to identification principle of the present invention shown in Figure 8, at first obtain actual output

Export with model

Obtaining actual output

Export with model

The back calculate to obtain deviation, the deviation delta z that obtains is adopted optimize algorithm and regulate, and reaches the parameter of the corresponding speed regulator electro-hydraulic control system that hour also just obtains as deviate Δ z.Then the parameter identification problem of speed regulator electro-hydraulic control system is to ask and makes the output of analogue system (being model) and real system export the most near the optimal parameter problem under the situation, and available mathematic(al) representation is expressed as:

$\underset{\hat{x}}{\mathrm{Min}}{(\hat{z}-\stackrel{\&RightArrow;}{z})}^2=\underset{\hat{x}}{\mathrm{Min}}{(f(\hat{x},\hat{u})\&CenterDot;\mathrm{ff}\left(f(\hat{x},\hat{u})\right)-\stackrel{\&RightArrow;}{z})}^2---\left(9\right);$

The present invention is practical frequency track and the PSASP software systems of parameter identification process in conjunction with real system, has proposed the parameter identification new method of speed regulator electro-hydraulic control system.Basic ideas are: for all systems except speed regulator, comprise excitation system, generator system, network is known s bar frequency record ripple curve all, one group of speed regulator electro-hydraulic control system of primary election initial parameter, utilize PSASP software to obtain to organize corresponding to this s bar frequency emulation track of speed regulator electro-hydraulic control system parameter, and calculate s bar frequency emulation track respectively with respect to the deviation of actual measurement track, choose 1 pair of curve of emulation and corresponding practical frequency curve alignment deviation value maximum, according to optimizing algorithm existing speed regulator electro-hydraulic control system parameter is revised, and obtained new speed regulator electro-hydraulic control system parameter; And carry out a new round as stated above and calculate and revise, so loop iteration meets the demands up to stopping to calculate criterion.Its concrete calculation flow chart as shown in Figure 9, initial value and the constant interval of at first given iterations and convergence and speed regulator parameter to be identified, then parameter is read into the concrete module of PSASP and carries out transient stability calculating, the stimulation frequency data are read into corresponding document, and calculate and obtain exemplary frequency deviation values, judge that whether the iteration convergence criterion satisfies, and utilize particle cluster algorithm to be optimized.Details are as follows:

(1) initialization: k=0; Given iterations N, convergence criterion ε; The initial value of given speed regulator parameter to be identified and constant interval thereof.

(2) based on the estimated value of parameter to be identified

(k represents to carry out the k subparameter and estimates), parameter is read into the concrete module of PSASP and carries out transient stability calculating, acquisition is organized the s bar frequency emulation track (corresponding to the s kind fault of system) of speed regulator parameter corresponding to this, and utilizes formula (10) to calculate s bar frequency emulation track with respect to the deviation of actual measurement track.

$g_i\left({\hat{x}}_k\right)\underset{t=0}{\overset{m}{\mathrm{\&Sigma;}}}{[{\hat{z}}_i({\hat{x}}_k,\hat{u},t)-{\stackrel{\&RightArrow;}{z}}_i(\stackrel{\&RightArrow;}{x},\stackrel{\&RightArrow;}{u},t)]}^2---\left(10\right);$

Wherein, i represents i bar frequency locus, and m represents that with the hits after the frequency curve discretize t represents t sampled point of frequency locus,

The k time estimated value representing parameter to be identified,

The true value of expression identified parameters, Expression utilizes after the i bar frequency emulation track discretize of the k time estimates of parameters to be identified acquisition the value corresponding to t sampled point,

Represent after the i bar frequency actual measurement track discretize value corresponding to t sampled point,

The quadratic sum of the i bar frequency emulation track that expression obtains with the k time estimated value of parameter to be identified and the deviation of actual measurement track.

(3) it is right to choose the curve of emulation and practical frequency curve alignment deviation maximum, that is:

$g\left({\hat{x}}_k\right)=\underset{i}{\max }\left[g_i\left({\hat{x}}_k\right)\right]---\left(11\right);$

If

The simulation curve of explain deviations maximum and the deviate of measured curve meet the demands, and have obtained the best estimate of parameter to be identified, and iteration finishes; Otherwise adopt the particle group optimizing technology, right based on the emulation of choosing and actual frequency curve, for current estimated value to be measured

Revise, obtain its new value ${\hat{x}}_{k+1}$ 。

(4) k=k+1 turns to step (1).

3, the realization of the operation relevant with the PSASP software transfer.

For the simulation calculation of real system, be an important step in the algorithm of the present invention, the present invention directly calls the computing module of Power System Analysis synthesizer (PSASP) by design interface, and has realized simulation calculation easily.

PSASP software provides User Defined modeling (UD) and user program interface (UPI) environment, yet PSASP does not provide the interface of computing module and output data, can't realize its computing module directly call and to the processing of result of calculation.The present invention has designed PSASP software computing module and user program interface interchange method, has realized calling and handling of its computing module and result of calculation.In the present invention, in the speed regulator parameter identification algorithm data relevant with PSASP software to read, repeatedly carry out automatically the specific implementation method of 3 aspect problems such as calling of computing module and result of calculation automatically respectively as follows:

(1) from file, reads in the speed regulator parameter.

PSASP provides the parameter of reading in all control system from interactive interface, does not read in corresponding parameter function but provide from file.Because the parameter of speed regulator is to be stored in the file after each the optimization, therefore for the carrying out repeatedly of simulation calculation, must realizes being stored in speed regulator parameter in the file and read in operation in the computing module of PSASP.

Because the speed regulator parameter of PSASP software systems is stored in the gov.dbf form of the commpar.dbc database under the lib file, namely its computing module can read the relevant parameter that is stored in the gov.dbf form automatically.Therefore, the present invention adopts ODBC as interface, the realization file links with the gov.dbf form, and by the Putcollect statement speed regulator parameter in the corresponding document is read in the gov.dbf form of commpar.dbc database, thereby realized that the speed regulator parameter directly reads in the PSASP calculation procedure from file.Corresponding logical code is:

(2) PSASP transient stability computing module repeatedly calls automatically;

For the emulation to many class frequency record ripple curves approaches, need carry out calling repeatedly of PSASP computing module.And the PSASP system only provides once and to calculate, and the manual operation of the start button that the triggering of calculating must be by interactive interface just can carry out, and carrying out automatically of calculating is not provided.Method of the present invention is by batch processing module hstbatcal.exe, and SetCursorPos function and mouse_event function realize that the repeated multiple times of PSASP computing module calls automatically.Specifically be to open the wpsasp.exe program interface by window function findwindow, then by SetCursorPos function and mouse_event function call batch processing module hstbatcal.exe, obtain start button position and click in the PSASP system interaction interface, thereby be implemented in repeatedly calling automatically for computing module.Corresponding logical code is as follows:

While (" not clicking the transient stability computed push-buttom ")

Sleep();

WM-close();

End。

(3) the resulting frequency locus curve data of simulation calculation reads in the corresponding document;

After PSASP calculates and finishes, its result deposits in the Output table of database stresult.dbc under the result file automatically, the present invention is by the ODBC interface, and the Getcollect statement is read the resulting frequency locus curve data of simulation calculation in the Output table and deposit in the corresponding txt file.Because this process is similar with the process of from file the speed regulator parameter being read in the corresponding gov.dbf form, its corresponding logical code just repeats no more at this.

The present invention carries out application experiment in the governing system parameter identification of electrical network northeastward with said method, to verify the validity of this method.The northeast following unit of electrical network 100MW is to adopt the mechanical governing system, and existing field measurement parameter, but 100MW and above unit, electric hydraulic formula governing system all is installed, the northeast electrical network has been carried out the speed regulator model parameter actual measurement work of typical unit, and wherein: the #2 of Suizhong factory machine adopts PSASP3 type speed regulator model and actual measurement parameter; The #5 of Bai Shan factory machine adopts PSASP7 type speed regulator model and actual measurement parameter; The #1 of Yi Min factory machine, #4 machine adopt PSASP4 type speed regulator model and actual measurement parameter, and do not have measured data for the governing system of other unit.

The governing system of other unit of surveying unit place power plant is directly applied mechanically this factory surveyed parameter, to the PSASP4 type speed regulator model of the equal northeast of the governing system dispatching of power netwoks of actual measurement suggestion not more than the unit capacity 100MW, it is the aforementioned model of this paper, and according to unit capacity the parameter of corresponding governing system is divided into 3 classes: unit capacity arrives 300MW at 100MW, and its speed regulator parameter is the 1st class; Unit capacity arrives 500MW at 300MW, and its speed regulator parameter is the 2nd class; Unit capacity is more than 500MW, and its speed regulator parameter is the 3rd class.Utilize the method for introducing among the present invention to pick out the parameter of above three groups of speed regulators respectively, and compare with the practical frequency curve, to prove its validity.

The northeast electrical network had the failure condition of frequency record ripple geometric locus and known its corresponding network structure and unit all control system parameters except governing system to list in the table 1 in 2009.

Because the northeast electrical network has detailed recorder data to preceding four groups of faults, every the 0.02s once sampling, so according to corresponding each systematic parameter of preceding four groups of faults in the table 1 and frequency record ripple geometric locus, adopt the parameter of the 3 class set speed adjustment systems that method proposed by the invention do not survey the northeast electrical network to carry out repeatedly identification, select one group of wherein best result as last identification, the 3 class speed regulator parameter values that obtain are as shown in table 2.Carrying out the system frequency curve that emulation was obtained and the comparison diagram that frequency is recorded the ripple track with five groups of corresponding other parameters of system of fault in back in the 3 class speed regulator parameters that picked out and the table 1 all lists among Figure 10 to Figure 14.

As can be seen, the speed regulator parameter of utilizing institute of the present invention identification can obtain the frequency curve very approaching with frequency record ripple geometric locus in conjunction with the parameter of corresponding failure and system's other parts from Figure 10 to Figure 14.The corresponding validity that has also proved institute of the present invention extracting method, thus very loaded down with trivial details on-the-spot test avoided, only need utilize the parameter of the other parts of the failure wave-recording curve of frequency and system, can finish the parameter identification of the governing system of system.

Table 1 northeast electrical network failure condition in 2009.

Table 2 northeast electrical network speed regulator system 3 class identified parameters.

Claims (6)

1. the speed regulator parameter identification method that combines with particle cluster algorithm of frequency locus is characterized in that: set up the input of speed regulator system, and proved the identifiability of relevant parameter; Directly call the electromechanical transient simulation computing module of PSASP software by interface, and obtain the speed regulator parameter, comprise obtaining the stimulation frequency curve of electric system after disturbed, and then based on particle swarm optimization algorithm the speed regulator parameter is regulated and to make stimulation frequency curve and practical frequency curve approaching as far as possible; Parameter optimization by repeatedly obtains optimum speed regulator parameter.

2. the speed regulator parameter identification method that combines with particle cluster algorithm of frequency locus according to claim 1, it is characterized in that: the described input of setting up speed regulator system, when setting up the input of speed regulator system, require the electrohydraudic servomechanism of speed regulator, the steam turbine model, and generator system, excitation system and network etc. are known system.

3. the speed regulator parameter identification method that combines with particle cluster algorithm of frequency locus according to claim 1, it is characterized in that: described interface, by the ODBC interface, Putcollect and Getcollect statement, realizations such as SetCursorPos function and mouse_event function are read in from file, read the speed regulator parameter and directly call PSASP software transient stability computing module.

4. the speed regulator parameter identification method that combines with particle cluster algorithm of frequency locus according to claim 1, it is characterized in that: described when obtaining the speed regulator parameter, analyze simultaneously at many measured curves, the function of structure is based on that many measured curves and PSASP simulation curve obtain.

5. the speed regulator parameter identification method that combines with particle cluster algorithm of frequency locus according to claim 1, it is characterized in that: described concrete calculation process, be initial value and the constant interval of at first given iterations and convergence and speed regulator parameter to be identified, then parameter is read into the concrete module of PSASP and carries out transient stability calculating, the stimulation frequency data are read into corresponding document, and calculate and obtain exemplary frequency deviation values, judge that whether the iteration convergence criterion satisfies, and utilize particle cluster algorithm to be optimized.

6. the speed regulator parameter identification method that combines with particle cluster algorithm of frequency locus according to claim 1, it is characterized in that: described basic identification principle is at first to obtain actual output

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, obtaining actual output

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The back calculate to obtain deviation delta z, the deviation delta z that obtains is adopted optimize algorithm and regulate, and reaches the parameter of the corresponding speed regulator electro-hydraulic control system that hour also just obtains as deviate Δ z.

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