CN102280877B - Method for identifying parameter of poor branch of power system through a plurality of measured sections - Google Patents

Abstract

The invention relates to a method for identifying the parameter of a poor branch of a power system based on a multi-measured section Lagrange multiplier method, and belongs to the technical fields of power system scheduling automation and power grid simulation. The method comprises the following steps of: reading in a plurality of measured sections from a historical database one by one; establishing a state estimation model of each section, solving, and calculating the regularization Lagrange multiplier of each section; and accumulating the regularization Lagrange multiplier vector of each section to obtain a suspicious degree index of a branch parameter, namely the regularization Lagrange multiplier vectors of the plurality of sections. The method is convenient to implement; meanwhile, the accuracy of the original poor parameter identification program can be greatly improved.

Description

A kind of volume is surveyed the bad branch road parameter identification method of electric power system of section

Technical field

The invention belongs to dispatching automation of electric power systems and grid simulation technical field, particularly a kind of bad branch road parameter identification method of electric power system of surveying the section method of Lagrange multipliers based on volume.

Background technology

What guarantee grid branch parameter (comprising resistance, reactance, direct-to-ground capacitance etc.) is accurately major issue in the electric power system modeling.Yet in the electric power system modeling process of reality, may there be the inaccurate wrong branch road parameter of some numerical value, these wrong branch road parameters can have a strong impact on precision and the credibility that power system analysis is calculated, and therefore just need the discrimination method of the bad branch road parameter of research electric power system.

The bad branch road parameter of electric power system generally acknowledges that more effective discrimination method is method of Lagrange multipliers at present.The main flow process of its method is to be 0 to join in the state estimation Optimized model as equality constraint with electric power system branch road parameter error, namely sets up following Power system state estimation Optimized model:

$\min J\left(x\right)=\frac{1}{2}{r}^T\mathrm{Wr}$

s.t.c(x，p _e)＝0

p _e＝0

Wherein x is system state variables, comprises amplitude and the phase angle of node voltage; R is the measurement residuals vector, r=z-h (x).Z and h are respectively the real-time measurement values and measure function.W is for measuring weight diagonal matrix, p _eBe electric power system branch road parameter error vector, c (x, p _eEquality constraint is injected in)=0 expression zero.Above state estimation Optimized model is found the solution, can obtain the state estimation result And corresponding

Calculate respectively afterwards the covariance matrix of residual error, the covariance matrix of the Lagrange multiplier vector sum Lagrange multiplier that the branch road parameter is corresponding.Wherein covariance matrix Cov (r) computing formula of residual error is:

$\mathrm{Cov}\left(r\right)=W^{-1}-H_x{\left(H_x^{T}W{H}_x\right)}^{-1}{H}_x^T$

H wherein _xFor measuring the Jacobian matrix to state variable, subscript T represents transposition.

The computing formula of Lagrange multiplier is:

$\mathrm{\λ}=H_p^T\mathrm{Wr}$

H wherein _pFor measuring the Jacobian matrix to the branch road parameter.

The computing formula of Lagrange multiplier covariance matrix Cov (λ) is:

$\mathrm{Cov}\left(\mathrm{\λ}\right)=H_P^T\mathrm{WCov}\left(r\right)W{H}_P$

Utilize afterwards above result of calculation to calculate regularization residual vector r _NRegularization Lagrange multiplier vector λ _N, computing formula is:

$r_{\mathrm{Ni}}=\frac{{\hat{r}}_i}{\sqrt{\mathrm{Cov}{\left(r\right)}_{\mathrm{ii}}}}$

${\mathrm{\λ}}_{\mathrm{Ni}}=\frac{{\mathrm{\λ}}_i}{\sqrt{\mathrm{Cov}{\left(\mathrm{\λ}\right)}_{\mathrm{ii}}}}$

And obtain r _NAnd λ _NIn maximum, if max (r _N, λ _N)＜c thinks not have bad remote measurement and bad grid branch parameter, calculates and finishes; Wherein c is the artificial threshold value of setting, and generally gets 3.0.Otherwise, if max is (r _N)＞max (λ _N), reject max (r _N) corresponding measurement, re-start above-mentioned calculating; If max is (r _N)＜max (λ _N), think max (λ _N) corresponding branch road parameter is wrong, utilizes branch road parameter corresponding to electric power system branch road method for parameter estimation correction, re-starts afterwards above-mentioned calculating.

There are two important deficiencies in above method flow, and at first, present method of Lagrange multipliers all only utilizes the measurement information of discontinuity surface when single, i.e. single measuring section; In single measuring section, very limited with the measurement number of branch road parameter strong correlation, these limited measurements can't embody the expectation of branch road parameter on statistical significance-be the actual value of parameter well.Secondly, above method flow carries out the identification of bad remote measurement and bad branch road parameter simultaneously, need to repeatedly carry out finding the solution of state estimation model, and amount of calculation is very large.Therefore the state estimation result that needs research how to calculate based on the state estimation software of present business picks out wrong branch road parameter in electrical network easily with a plurality of measuring sections.

Summary of the invention

The objective of the invention is for overcoming the weak point of prior art, a kind of bad branch road parameter identification method of electric power system of surveying the section method of Lagrange multipliers based on volume is proposed, this method can obtain more credible more rational identification result, and has the ability of very strong practical.

A kind of bad branch road parameter identification method of electric power system of surveying the section method of Lagrange multipliers based on volume that the present invention proposes is characterized in that the method comprises: read in a plurality of measuring sections from historical data base one by one; Set up the state estimation model of each section and find the solution, calculating the regularization Lagrange multiplier of each section; And the regularization Lagrange multiplier of each section vector is added up, obtain the suspicious level index of this branch road parameter of regularization Lagrange multiplier vector of multibreak.

The method specifically comprises the following steps:

(1) volume of each the branch road parameter in the initialization electric power system is surveyed section regularization Lagrange multiplier

, with the suspicious level index of regularization Lagrange multiplier vector as this branch road parameter, initialization measuring section sequence number m=1, and set the measuring section sum m that participates in parameter identification _∑, common desirable m _∑=96;

(2) read in m measuring section from the historical database server of electric power system, and set up electric power system the least square estimation model;

$\min J_m\left(x_m\right)=\frac{1}{2}{r}^T\mathrm{Wr}$

s.t c _m(x _m)＝0

Wherein, subscript m represents the measuring section sequence number, and r is the measurement residuals vector, and subscript T represents transposition, r=z _m-h _m(x _m); z _mM the real-time measurement values vector in measuring section, h _m(x _m) be m the measurement equation vector in measuring section, x _mBe the electric network state variable of m measuring section, this variable comprises voltage magnitude and the phase angle of all nodes; W is for measuring weight diagonal matrix, c _m(x _m)=the 0th do not articulate zero injection equality constraint of load and the node of generator;

(3) the least square estimation model of step (2) is found the solution, obtain the electric network state variable x of m measuring section _mEstimated value

(4) calculate the Lagrange multiplier vector λ of m measuring section _m, λ _mTo calculate the suspicious level index of branch road parameter A necessary intermediate variable, its computing formula is:

${\mathrm{\λ}}_m=H_P^{T}W\hat{r}$

H wherein _PFor measuring the Jacobian matrix to the branch road parameter;

For

The time residual vector;

(5) calculate Lagrange multiplier vector λ _mCorresponding covariance matrix Cov (λ), computing formula is:

$\mathrm{Cov}\left(\mathrm{\λ}\right)=H_P^T\mathrm{WCov}\left(r\right)W{H}_P$

Wherein Cov (r) is the covariance matrix of measurement residuals, and its computing formula is:

$\mathrm{Cov}\left(r\right)=W^{-1}-H_x{\left(H_x^{T}W{H}_x\right)}^{-1}{H}_x^T$

H wherein _xFor measuring the Jacobian matrix to state variable, subscript T represents transposition.

(6) with the Lagrange multiplier regularization, obtain the regularization Lagrange multiplier vector of m section

, k the regularization Lagrange multiplier that the branch road parameter is corresponding Computing formula be:

${\mathrm{\λ}}_{m,k}^N=\frac{{\mathrm{\λ}}_{m,k}}{\sqrt{{\mathrm{Cov}\left(\mathrm{\λ}\right)}_{\mathrm{kk}}}}$

λ wherein _{M, k}Be k the Lagrange multiplier that the branch road parameter is corresponding of m section, Cov (λ) _kkK diagonal element for Lagrange multiplier covariance matrix Cov (λ);

(7) the regularization Lagrange multiplier of m measuring section is added to the regularization Lagrange multiplier of multibreak

On, namely

(8) if m=m _∑, carry out step (9); If m＜m _∑, make m=m+1, return to step (2)

(9) for i branch road parameter, if

Think that corresponding branch road parameter is suspicious branch road parameter.Wherein ε is the artificial suspicious branch road parameter threshold value of setting, common desirable 3.0.

Advantage of the present invention is:

1, the method for Lagrange multipliers parameter identification of single section has stronger theoretical foundation and practical experience, and the method for Lagrange multipliers of multibreak also has the ability of very strong practical as an improvement of the method for Lagrange multipliers of single section.

2, the method for Lagrange multipliers of multibreak can overcome the problem of the method for Lagrange multipliers measurement redundancy deficiency of single section, can obtain than the more credible more rational identification result of the method for Lagrange multipliers parameter identification method of single section.

3, the direct state-based estimated result of the branch road parameter identification method of this patent carries out, and has avoided repeat mode to estimate the amount of calculation problem of bringing, and has very high computational efficiency.

Embodiment

A kind of bad branch road parameter identification method of electric power system of surveying the section method of Lagrange multipliers based on volume that the present invention proposes is described in detail as follows in conjunction with the embodiments:

A kind of bad branch road parameter identification method of electric power system of surveying the section method of Lagrange multipliers based on volume that the present invention proposes is characterized in that, the method comprises the following steps:

(1) different from traditional Lagrange multiplier branch road parameter identification method of introducing in background technology, the method for this patent adopts a plurality of measuring sections to carry out the identification of bad branch road parameter.At first the volume of each the branch road parameter in the initialization electric power system is surveyed section regularization Lagrange multiplier

, with the suspicious level index of regularization Lagrange multiplier vector as this branch road parameter, initialization measuring section sequence number m=1, and set the measuring section sum m that participates in parameter identification _∑, common desirable m _∑=96;

(2) read in m measuring section from the historical database server of electric power system, and set up electric power system the least square estimation model;

$\min J_m\left(x_m\right)=\frac{1}{2}{r}^T\mathrm{Wr}$

s.t c _m(x _m)＝0

Wherein, subscript m represents the measuring section sequence number, and r is the measurement residuals vector, and subscript T represents transposition, r=z _m-h _m(x _m); z _mM the real-time measurement values vector in measuring section, h _m(x _m) be m the measurement equation vector in measuring section, x _mBe the electric network state variable of m measuring section, this variable comprises voltage magnitude and the phase angle of all nodes; W is for measuring weight diagonal matrix, c _m(x _m)=the 0th do not articulate zero injection equality constraint of load and the node of generator;

(3) the least square estimation model of step (2) is found the solution, obtain the electric network state variable x of m measuring section _mEstimated value

(4) calculate the Lagrange multiplier vector λ of m measuring section _m, λ _mTo calculate the suspicious level index of branch road parameter

A necessary intermediate variable, its computing formula is:

${\mathrm{\λ}}_m=H_P^{T}W\hat{r}$

H wherein _PFor measuring the Jacobian matrix to the branch road parameter;

For

The time residual vector;

Can find out, traditional Lagrange multiplier branch road parameter identification method of introducing in this paper method and background technology is different, it is not the identification of carrying out simultaneously bad remote measurement and bad branch road parameter, but first carry out bad remote measurement identification, the bad branch road parameter of identification more afterwards, the state estimation of so just having avoided repeating is calculated, and has improved computational efficiency.

(5) calculate Lagrange multiplier vector λ _mCorresponding covariance matrix Cov (λ), computing formula is:

$\mathrm{Cov}\left(\mathrm{\λ}\right)=H_P^T\mathrm{WCov}\left(r\right)W{H}_P$

Wherein Cov (r) is the covariance matrix of measurement residuals, and its computing formula is:

$\mathrm{Cov}\left(r\right)=W^{-1}-H_x{\left(H_x^{T}W{H}_x\right)}^{-1}{H}_x^T$

H wherein _xFor measuring the Jacobian matrix to state variable, subscript T represents transposition.

(6) with the Lagrange multiplier regularization, obtain the regularization Lagrange multiplier vector of m section

, k the regularization Lagrange multiplier that the branch road parameter is corresponding

Computing formula be:

${\mathrm{\λ}}_{m,k}^N=\frac{{\mathrm{\λ}}_{m,k}}{\sqrt{{\mathrm{Cov}\left(\mathrm{\λ}\right)}_{\mathrm{kk}}}}$

λ wherein _{M, k}Be k the Lagrange multiplier that the branch road parameter is corresponding of m section, Cov (λ) _kkK diagonal element for Lagrange multiplier covariance matrix Cov (λ);

(7) the regularization Lagrange multiplier of m measuring section is added to the regularization Lagrange multiplier of multibreak

On, namely

(8) if m=m _∑, carry out step (9); If m＜m _∑, make m=m+1, return to step (2)

(9) for i branch road parameter, if

Think that corresponding branch road parameter is suspicious branch road parameter.Wherein ε is the artificial suspicious branch road parameter threshold value of setting, common desirable 3.0.

Claims (1)

1. the bad branch road parameter identification method of electric power system of surveying the section method of Lagrange multipliers based on volume, is characterized in that, the method comprises: read in a plurality of measuring sections from historical data base one by one; Set up the state estimation model of each section and find the solution, calculating the regularization Lagrange multiplier of each section; And the regularization Lagrange multiplier of each section vector is added up, obtain the suspicious level index of this branch road parameter of regularization Lagrange multiplier vector of multibreak;

The method specifically comprises the following steps:

(1) volume of each the branch road parameter in the initialization electric power system is surveyed section regularization Lagrange multiplier

With the suspicious level index of regularization Lagrange multiplier vector as this branch road parameter, initialization measuring section sequence number m=1, and set the measuring section sum m that participates in parameter identification _∑, common desirable m _∑=96;

(2) read in m measuring section from the historical database server of electric power system, and set up electric power system the least square estimation model;

$\min J_m\left(x_m\right)=\frac{1}{2}{r}^T\mathrm{Wr}$

s.t c _m(x _m)＝0

Wherein, subscript m represents the measuring section sequence number, and r is the measurement residuals vector, and subscript T represents transposition, r=z _m-h _m(x _m); z _mM the real-time measurement values vector in measuring section, h _m(x _m) be m the measurement equation vector in measuring section, x _mBe the electric network state variable of m measuring section, this variable comprises voltage magnitude and the phase angle of all nodes; W is for measuring weight diagonal matrix, c _m(x _m)=the 0th do not articulate zero injection equality constraint of load and the node of generator;

(3) the least square estimation model of step (2) is found the solution, obtain the electric network state variable x of m measuring section _mEstimated value

(4) calculate the Lagrange multiplier vector λ of m measuring section _m, λ _mTo calculate the suspicious level index of branch road parameter

A necessary intermediate variable, its computing formula is:

${\mathrm{\λ}}_m=H_P^{T}W\hat{r}$

H wherein _PFor measuring the Jacobian matrix to the branch road parameter;

The time residual vector;

(5) calculate Lagrange multiplier vector λ _mCorresponding covariance matrix Cov (λ), computing formula is:

$\mathrm{Cov}\left(\mathrm{\λ}\right)=H_P^T\mathrm{WCov}\left(r\right)W{H}_P$

Wherein Cov (r) is the covariance matrix of measurement residuals, and its computing formula is:

$\mathrm{Cov}\left(r\right)=W^{-1}-H_x{\left(H_x^T{\mathrm{WH}}_x\right)}^{-1}{H}_x^T$

H wherein _xFor measuring the Jacobian matrix to state variable, subscript T represents transposition;

(6) with the Lagrange multiplier regularization, obtain the regularization Lagrange multiplier vector of m section K the regularization Lagrange multiplier that the branch road parameter is corresponding

Computing formula be:

${\mathrm{\λ}}_{m,k}^N=\frac{{\mathrm{\λ}}_{m,k}}{\sqrt{\mathrm{Cov}{\left(\mathrm{\λ}\right)}_{\mathrm{kk}}}}$

λ wherein _m,kBe k the Lagrange multiplier that the branch road parameter is corresponding of m section, Cov (λ) _kkK diagonal element for Lagrange multiplier covariance matrix Cov (λ);

(7) the regularization Lagrange multiplier of m measuring section is added to the regularization Lagrange multiplier of multibreak

On, namely ${\mathrm{\λ}}_{\mathrm{\Σ}}^N={\mathrm{\λ}}_{\mathrm{\Σ}}^N+{\mathrm{\λ}}_m^N;$

(8) if m=m _∑, carry out step (9); If m＜m _∑, make m=m+1, return to step (2)

(9) for i branch road parameter, if

Think that corresponding branch road parameter is suspicious branch road parameter; Wherein ε is the artificial suspicious branch road parameter threshold value of setting, common desirable 3.0.