CN105932693A - State estimation method for reactive power compensation device applied to utility tunnel - Google Patents

Abstract

The present invention provides a kind of reactive compensation device state estimation method applied to pipe gallery, establishes the discrete time L-R fuzzy number of reactive power compensator; Design the Fuzzy Observer for state estimation; Off-line calculation is used for the Fuzzy Observer parameter matrix of state estimation; Carry out the state estimation of reactive power compensator online using Fuzzy Observer; When When, it is believed that state estimation effect reaches allowed band, by state estimation result It is sent to the control of reactive power compensating unit, is used for subsequent the control of reactive power compensating, ε is threshold value whether preset differentiation Fuzzy Observer puts into subsequent use here. The present invention can effectively solve the problem that the nonlinear terms processing technique problem in reactive power compensator physical system.

Description

A kind of reactive compensation device state estimation method being applied to pipe gallery

Technical field

The invention belongs to the Control System Design field of pipe gallery reactive power compensator, specific design one is applied to pipe gallery Reactive compensation device state estimation method.

Background technology

TCR type High Pressure Static Dynamic Reactive Compensation device is widely used in high pressure, ultrahigh voltage AC and DC transmission system and integrated pipe In the municipal administration such as corridor power distribution network, its Main Function improves power supply network service condition exactly, administers Electric Power Disserve, improves defeated, power distribution system The reliability of system, suppression system overvoltage, improve its dynamic characteristic, suppress harmonic wave, reduce pollution that electric energy causes by harmonic wave and Voltage flicker, stabilisation systems voltage, quickly follow the tracks of reactive-load compensation, improve power factor.

It is 6KV's～35KV that TCR type High Pressure Static Dynamic Reactive Compensation device reactive compensation control system is applicable to electric pressure Being automatically adjusted and monitoring of TCR type SVC device.System generally uses group screen mounting means, can complete the comprehensive prison to TCR Control.This system is used and is quickly regulated algorithm, it is achieved to idle quick compensation, can effectively suppress voltage pulsation, flickering, reduce Harmonic wave in power system, improves power factor.Thus play and improve the quality of power supply, improve the effect of production efficiency.

It is important to note that STATE FEEDBACK CONTROL is TCR type High Pressure Static Dynamic Reactive Compensation device obtains good moving Effective and commonly used a kind of control device of static properties.But, due to the physical restriction of real system, the state of control system Vector is the most immeasurablel, and the most disadvantageously the nonlinear characteristic of device itself also makes conventional sense method of estimation not Practical.Now, design high-precision reactive compensation device state estimation method, become TCR type high voltage static type dynamic reactive and mend Repay a technical barrier in device the control of reactive power compensating field.

Summary of the invention

The technical problem to be solved in the present invention is: provide a kind of reactive compensation device state estimation method being applied to pipe gallery, Can effectively solve the problem that the nonlinear terms treatment technology problem in reactive power compensator physical system.

The present invention solves that the technical scheme that above-mentioned technical problem is taked is: a kind of reactive power compensator being applied to pipe gallery Method for estimating state, it is characterised in that: it comprises the following steps:

S1, set up the discrete time L-R fuzzy number of reactive power compensator:

$\begin{array}{c}x(k+1)=\underset{i=1}{\overset{r}{\mathrm{\Σ}}}{h}_i\left(z\right(k\left)\right)\left(A_{i}x\right(k)+B_{i}u(k\left)\right)\\ y\left(k\right)=\underset{i=1}{\overset{r}{\mathrm{\Σ}}}{h}_i\left(z\right(k\left)\right)C_{i}x\left(k\right)\end{array},$

In formula, x (k) is the reactive power compensator state vector in k moment, and dimension is n₁×1；X (k+1) is the reactive-load compensation in k+1 moment Unit state vector, dimension is n₁×1；U (k) is the reactive power compensator input vector in k moment, and dimension is n₂×1；Y (k) is The reactive power compensator output vector in k moment, dimension is n₃×1；Z (k) is that the fuzzy former piece of the reactive power compensator in k moment becomes Amount, h_i(z (k)) is the k moment i-th fuzzy membership functions about z (k)；A_iFor n₁×n₁Dimension matrix, B_iFor n₁×n₂Dimension matrix, C_iFor n₃×n₁Dimension matrix, and A_i(i.e. pipe gallery annual burden with power coefficient), B_i(i.e. pipe gallery annual load or burden without work Coefficient), C_i(i.e. the pipe gallery power factor when electrical network peak) is known matrix, and r is number of fuzzy rules；

S2, design the Fuzzy Observer for state estimation:

$\begin{array}{c}\hat{x}(k+1)=\underset{i=1}{\overset{r}{\mathrm{\Σ}}}{h}_i\left(z\right(k\left)\right)\left(A_i\hat{x}\right(k)+B_{i}u(k\left)\right)\\ +{\left(\underset{i=1}{\overset{r}{\mathrm{\Σ}}}\underset{j=1}{\overset{r}{\mathrm{\Σ}}}{h}_i\right(z\left(k-1\right)\left)h_i\right(z\left(k\right)\left)G_{ij}\right)}^{-1}\left(\underset{i=1}{\overset{r}{\mathrm{\Σ}}}\underset{j=1}{\overset{r}{\mathrm{\Σ}}}{h}_i\right(z\left(k-1\right)\left)h_i\right(z\left(k\right)\left)K_{ij}\right)\left(y\right(k)-\hat{y}(k\left)\right)\end{array}$

$\hat{y}\left(k\right)=\underset{i=1}{\overset{r}{\mathrm{\Σ}}}{h}_i\left(z\right(k\left)\right)C_i\hat{x}\left(k\right)$

In formula,For the reactive power compensator state estimation vector in k moment, dimension is n₁×1；Nothing for the k+1 moment Reactive power compensation installations state estimation vector, dimension is n₁×1；For the reactive power compensator output estimation vector in k moment, dimension For n₃×1；G_ij、K_ijFor Fuzzy Observer parameter matrix to be solved, G_ijFor n₁×n₁Dimension matrix, K_ijFor n₁×n₃Dimension matrix；

S3, calculated off line are for the Fuzzy Observer parameter matrix of state estimation, and computational methods are to solve the linear moment of following form Battle array inequality:

$\left[\begin{array}{cc}{P}_{li}& *\\ G_{li}{A}_i-K_{li}{C}_i& G_{li}+G_{li}^T-P_{is}\end{array}\right]>0,1\≤i\≤r,1\≤l\≤r,1\≤s\≤r;$

$\left[\begin{array}{cc}{P}_{li}& *\\ G_{lj}{A}_i-K_{lj}{C}_i& G_{lj}+G_{lj}^T-P_{is}\end{array}\right]+\left[\begin{array}{cc}{P}_{lj}& *\\ G_{li}{A}_j-K_{li}{C}_j& G_{li}+G_{li}^T-P_{js}\end{array}\right]>0,1\&le;i<j\&le;r,1\&le;l\&le;r,1\&le;s\&le;r;$

In formula, P_liBe l article of fuzzy rule and i-th article of fuzzy rule influence each other under the fuzzy Li Yapu of reactive power compensator design Promise husband's matrix, and it is n₁×n₁Dimension matrix；P_isBe i-th article of fuzzy rule and the s article fuzzy rule influence each other under idle The fuzzy Liapownoff's matrix of compensation device design, for n₁×n₁Dimension matrix；P_ljIt is that the l article fuzzy rule obscures with j-th strip Rule influence each other under reactive power compensator design fuzzy Liapownoff's matrix, for n₁×n₁Dimension matrix；P_jsFor j-th strip Fuzzy rule and the s article fuzzy rule influence each other under reactive power compensator design fuzzy Liapownoff's matrix, for n₁× n₁Dimension matrix；G_liBe l article of fuzzy rule and i-th article of fuzzy rule influence each other under the rear portion of reactive power compensator design increase Benefit matrix, for n₁×n₁Dimension matrix；G_ljIt it is the reactive-load compensation dress under the l article fuzzy rule influences each other with j-th strip fuzzy rule Install the rear portion gain matrix of meter, for n₁×n₁Dimension matrix；K_liIt is l article of fuzzy rule and i-th article of fuzzy rule influences each other Under the fuzzy Liapownoff's matrix of reactive power compensator design, and it is n₁×n₁Dimension matrix；, K_ljIt is the l article fuzzy rule The fuzzy Liapownoff's matrix of the reactive power compensator design under influencing each other with j-th strip fuzzy rule, and it is n₁×n₁Dimension Matrix, C_jFor reactive power compensator state estimation vector, dimension is n₁×1；

S4, use Fuzzy Observer carry out the state estimation of reactive power compensator online: the Fuzzy Observer using S2 to provide is carried outWithIn line computation；

S5, whenTime, it is believed that state estimation effect reaches allowed band, by state estimation resultSend extremely The control of reactive power compensating unit, for follow-up the control of reactive power compensating, ε is that default differentiation Fuzzy Observer puts into follow-up making here By whether threshold value.

By such scheme, described ε is the arithmetic number less than 0.1.

The invention have the benefit that the discrete time L-R fuzzy number utilizing fuzzy modeling technique to set up reactive power compensator, Efficiently solve the nonlinear terms treatment technology problem in reactive power compensator physical system；Propose a kind of to be different from conventional use In the Fuzzy Observer framework of state estimation, the physical characteristic of reactive power compensator can be taken into full account, and give with linearly The Fuzzy Observer parameter matrix solving condition that MATRIX INEQUALITIES form solves；It is able to ensure that high-precision reactive power compensator state Estimate, obtain TCR type High Pressure Static Dynamic Reactive Compensation device in time and obtain real-time status amount.

Accompanying drawing explanation

Fig. 1 is the method flow diagram of one embodiment of the invention.

Fig. 2 is first group of state estimation curve of error of one embodiment of the invention.

Fig. 3 is second group of state estimation curve of error of one embodiment of the invention.

Detailed description of the invention

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

A kind of reactive compensation device state estimation method being applied to pipe gallery, as it is shown in figure 1, it comprises the following steps:

S1, set up the discrete time L-R fuzzy number of reactive power compensator:

$\begin{array}{c}x(k+1)=\underset{i=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_i\left(z\right(k\left)\right)\left(A_{i}x\right(k)+B_{i}u(k\left)\right)\\ y\left(k\right)=\underset{i=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_i\left(z\right(k\left)\right)C_{i}x\left(k\right)\end{array},$

In formula, x (k) is the reactive power compensator state vector in k moment, and dimension is n₁×1；X (k+1) is the reactive-load compensation in k+1 moment Unit state vector, dimension is n₁×1；U (k) is the reactive power compensator input vector in k moment, and dimension is n₂×1；Y (k) is The reactive power compensator output vector in k moment, dimension is n₃×1；Z (k) is that the fuzzy former piece of the reactive power compensator in k moment becomes Amount, h_i(z (k)) is the k moment i-th fuzzy membership functions about z (k)；A_iFor n₁×n₁Dimension matrix, B_iFor n₁×n₂Dimension matrix, C_iFor n₃×n₁Dimension matrix, and A_i、B_i、C_iBeing known matrix, r is number of fuzzy rules；

S2, design the Fuzzy Observer for state estimation:

$\begin{array}{c}\hat{x}(k+1)=\underset{i=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_i\left(z\right(k\left)\right)\left(A_i\hat{x}\right(k)+B_{i}u(k\left)\right)\\ +{\left(\underset{i=1}{\overset{r}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_i\right(z\left(k-1\right)\left)h_i\right(z\left(k\right)\left)G_{ij}\right)}^{-1}\left(\underset{i=1}{\overset{r}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_i\right(z\left(k-1\right)\left)h_i\right(z\left(k\right)\left)K_{ij}\right)\left(y\right(k)-\hat{y}(k\left)\right)\end{array}$

$\hat{y}\left(k\right)=\underset{i=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_i\left(z\right(k\left)\right)C_i\hat{x}\left(k\right)$

In formula,For the reactive power compensator state estimation vector in k moment, dimension is n₁×1；Nothing for the k+1 moment Reactive power compensation installations state estimation vector, dimension is n₁×1；For the reactive power compensator output estimation vector in k moment, dimension For n₃×1；G_ij、K_ijFor Fuzzy Observer parameter matrix to be solved, G_ijFor n₁×n₁Dimension matrix, K_ijFor n₁×n₃Dimension matrix；

It is important to note that the Fuzzy Observer that S2 proposes is different from conventional conventional Fuzzy Observer framework, it is examined first Consider the system status information having arrived the k-1 moment.

S3, calculated off line are for the Fuzzy Observer parameter matrix of state estimation, and computational methods are to solve the linear moment of following form Battle array inequality:

$\left[\begin{array}{cc}{P}_{li}& *\\ G_{li}{A}_i-K_{li}{C}_i& G_{li}+G_{li}^T-P_{is}\end{array}\right]>0,1\&le;i\&le;r,1\&le;l\&le;r,1\&le;s\&le;r;$ $\left[\begin{array}{cc}{P}_{li}& *\\ G_{lj}{A}_i-K_{lj}{C}_i& G_{lj}+G_{lj}^T-P_{is}\end{array}\right]+\left[\begin{array}{cc}{P}_{lj}& *\\ G_{li}{A}_j-K_{li}{C}_j& G_{li}+G_{li}^T-P_{js}\end{array}\right]>0,1\&le;i<j\&le;r,1\&le;l\&le;r,1\&le;s\&le;r;$

In formula, P_liBe l article of fuzzy rule and i-th article of fuzzy rule influence each other under the fuzzy Li Yapu of reactive power compensator design Promise husband's matrix, and it is n₁×n₁Dimension matrix；P_isBe i-th article of fuzzy rule and the s article fuzzy rule influence each other under idle The fuzzy Liapownoff's matrix of compensation device design, for n₁×n₁Dimension matrix；P_ljIt is that the l article fuzzy rule obscures with j-th strip Rule influence each other under reactive power compensator design fuzzy Liapownoff's matrix, for n₁×n₁Dimension matrix；P_jsFor j-th strip Fuzzy rule and the s article fuzzy rule influence each other under reactive power compensator design fuzzy Liapownoff's matrix, for n₁× n₁Dimension matrix；G_liBe l article of fuzzy rule and i-th article of fuzzy rule influence each other under the rear portion of reactive power compensator design increase Benefit matrix, for n₁×n₁Dimension matrix；G_ljIt it is the reactive-load compensation dress under the l article fuzzy rule influences each other with j-th strip fuzzy rule Install the rear portion gain matrix of meter, for n₁×n₁Dimension matrix；K_liIt is l article of fuzzy rule and i-th article of fuzzy rule influences each other Under the fuzzy Liapownoff's matrix of reactive power compensator design, and it is n₁×n₁Dimension matrix；, K_ljIt is the l article fuzzy rule The fuzzy Liapownoff's matrix of the reactive power compensator design under influencing each other with j-th strip fuzzy rule, and it is n₁×n₁Dimension Matrix, C_jFor reactive power compensator state estimation vector, dimension is n₁×1；

S4, use Fuzzy Observer carry out the state estimation of reactive power compensator online: the Fuzzy Observer using S2 to provide is carried outWithIn line computation；

S5, whenTime, it is believed that state estimation effect reaches allowed band, by state estimation resultSend extremely The control of reactive power compensating unit, for follow-up the control of reactive power compensating, ε is that default differentiation Fuzzy Observer puts into follow-up making here By whether threshold value, the usual value of ε is the arithmetic number less than 0.1.It has the advantage that first, it is to avoid overcompensation；The Two, it is to avoid the overtension when underloading, cause some device damage；3rd, meet the electricity in the case of various operating loads Pressure deviation requirement.

As a example by being connected to a TCR type High Pressure Static Dynamic Reactive Compensation device on certain pipe gallery power distribution network, its discrete time mould Paste state equation is:

$x(k+1)=\underset{i=1}{\overset{2}{\mathrm{\&Sigma;}}}{h}_i\left(z\right(k\left)\right)\left(A_{i}x\right(k)+B_{i}u(k\left)\right)$ $y\left(k\right)=\underset{i=1}{\overset{2}{\mathrm{\&Sigma;}}}{h}_i\left(z\right(k\left)\right)C_{i}x\left(k\right)$

Wherein,C₁=[1 1], C₂=[1 1]. A₁、A₂For pipe gallery annual burden with power coefficient, value is between 0.5 and 1；B₁、B₂For pipe gallery annual without Workload coefficient, value is between 0.5 and 1, C₁、C₂For the pipe gallery power factor when electrical network peak, general value It is 1；

According to the method for the invention, the most feasible Fuzzy Observer parameter matrix can be obtained:

$G_{11}=\left[\begin{array}{cc}0.0169& -0.0073\\ -0.0056& 0.0038\end{array}\right],G_{12}=\left[\begin{array}{cc}0.0171& -0.0001\\ -0.0034& 0.0010\end{array}\right],$

$G_{21}=\left[\begin{array}{cc}0.0168& -0.0074\\ -0.0049& 0.0030\end{array}\right],G_{22}=\left[\begin{array}{cc}0.0165& -0.0003\\ -0.0042& 0.0008\end{array}\right],$

$K_{11}=\left[\begin{array}{c}0.0035\\ -0.005\end{array}\right],K_{12}=\left[\begin{array}{c}0.0018\\ 0.0016\end{array}\right],K_{21}=\left[\begin{array}{c}0.0034\\ -0.0006\end{array}\right],K_{22}=\left[\begin{array}{c}0.0007\\ 0.0011\end{array}\right].$

In order to check the effectiveness of reactive compensation device state estimation method that the present invention proposes, embodiment tests one group of data. In this group test data, system mode initial value is chosen to be: x₁(0)=1.0 and x₁(0)=-1.0；And Fuzzy Observer state is initial Value is chosen for:WithWithout loss of generality, two groups between system state amount and estimator are defined inclined Difference signal is:WithFig. 2 is first group of state of one embodiment of the invention Estimation difference e₁K the curve of (), Fig. 3 is second group of state estimation error e of one embodiment of the invention₂The curve of (k).From Fig. 2 With Fig. 3 it can be seen that systematic error can rapidly level off to zero, say, that the inventive method has high-precision shape State estimates performance, and this also show the section of the reactive compensation device state estimation method being applied to pipe gallery that the present invention provides The property learned and practicality.

Above example is merely to illustrate design philosophy and the feature of the present invention, its object is to make those skilled in the art's energy Solution present disclosure much of that is also implemented according to this, and protection scope of the present invention is not limited to above-described embodiment.So, all according to this Equivalent variations that bright disclosed principle, mentality of designing are made or modification, all within protection scope of the present invention.

Claims (2)

1. the reactive compensation device state estimation method being applied to pipe gallery, it is characterised in that: it comprises the following steps:

S1, set up the discrete time L-R fuzzy number of reactive power compensator:

$\begin{array}{c}x(k+1)=\underset{i=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_i\left(z\right(k\left)\right)\left(A_{i}x\right(k)+B_{i}u(k\left)\right)\\ y\left(k\right)=\underset{i=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_i\left(z\right(k\left)\right)C_{i}x\left(k\right)\end{array},$

In formula, x (k) is the reactive power compensator state vector in k moment, and dimension is n₁×1；X (k+1) is the reactive-load compensation in k+1 moment Unit state vector, dimension is n₁×1；U (k) is the reactive power compensator input vector in k moment, and dimension is n₂×1；Y (k) is The reactive power compensator output vector in k moment, dimension is n₃×1；Z (k) is that the fuzzy former piece of the reactive power compensator in k moment becomes Amount, h_i(z (k)) is the k moment i-th fuzzy membership functions about z (k)；A_iFor n₁×n₁Dimension matrix, B_iFor n₁×n₂Dimension matrix, C_iFor n₃×n₁Dimension matrix, and A_i、B_i、C_iBeing known matrix, r is number of fuzzy rules；

S2, design the Fuzzy Observer for state estimation:

$\begin{array}{c}\hat{x}(k+1)=\underset{i=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_i\left(z\right(k\left)\right)\left(A_i\hat{x}\right(k)+B_{i}u(k\left)\right)\\ +{\left(\underset{i=1}{\overset{r}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_i\right(z\left(k-1\right)\left)h_i\right(z\left(k\right)\left)G_{ij}\right)}^{-1}\left(\underset{i=1}{\overset{r}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_i\right(z\left(k-1\right)\left)h_i\right(z\left(k\right)\left)K_{ij}\right)\left(y\right(k)-\hat{y}(k\left)\right)\end{array}$

$\hat{y}\left(k\right)=\underset{i=1}{\overset{r}{\mathrm{\&Sigma;}}}{h}_i\left(z\right(k\left)\right)C_i\hat{x}\left(k\right)$

In formula,For the reactive power compensator state estimation vector in k moment, dimension is n₁×1；Nothing for the k+1 moment Reactive power compensation installations state estimation vector, dimension is n₁×1；For the reactive power compensator output estimation vector in k moment, dimension For n₃×1；G_ij、K_ijFor Fuzzy Observer parameter matrix to be solved, G_ijFor n₁×n₁Dimension matrix, K_ijFor n₁×n₃Dimension matrix；

S3, calculated off line are for the Fuzzy Observer parameter matrix of state estimation, and computational methods are to solve the linear moment of following form Battle array inequality:

$\left[\begin{array}{cc}{P}_{li}& *\\ G_{li}{A}_i-K_{li}{C}_i& G_{li}+G_{li}^T-P_{is}\end{array}\right]>0,1\&le;i\&le;r,1\&le;l\&le;r,1\&le;s\&le;r;$

$\left[\begin{array}{cc}{P}_{li}& *\\ G_{lj}{A}_i-K_{lj}{C}_i& G_{lj}+G_{lj}^T-P_{is}\end{array}\right]+\left[\begin{array}{cc}{P}_{lj}& *\\ G_{li}{A}_j-K_{li}{C}_j& G_{li}+G_{li}^T-P_{js}\end{array}\right]>0,1\&le;i<j\&le;r,1\&le;l\&le;r,1\&le;s\&le;r;$

In formula, P_liBe l article of fuzzy rule and i-th article of fuzzy rule influence each other under the fuzzy Li Yapu of reactive power compensator design Promise husband's matrix, and it is n₁×n₁Dimension matrix；P_isBe i-th article of fuzzy rule and the s article fuzzy rule influence each other under idle The fuzzy Liapownoff's matrix of compensation device design, for n₁×n₁Dimension matrix；P_ljIt is that the l article fuzzy rule obscures with j-th strip Rule influence each other under reactive power compensator design fuzzy Liapownoff's matrix, for n₁×n₁Dimension matrix；P_jsFor j-th strip Fuzzy rule and the s article fuzzy rule influence each other under reactive power compensator design fuzzy Liapownoff's matrix, for n₁× n₁Dimension matrix；G_liBe l article of fuzzy rule and i-th article of fuzzy rule influence each other under the rear portion of reactive power compensator design increase Benefit matrix, for n₁×n₁Dimension matrix；G_ljIt it is the reactive-load compensation dress under the l article fuzzy rule influences each other with j-th strip fuzzy rule Install the rear portion gain matrix of meter, for n₁×n₁Dimension matrix；K_liIt is l article of fuzzy rule and i-th article of fuzzy rule influences each other Under the fuzzy Liapownoff's matrix of reactive power compensator design, and it is n₁×n₁Dimension matrix；, K_ljIt is the l article fuzzy rule The fuzzy Liapownoff's matrix of the reactive power compensator design under influencing each other with j-th strip fuzzy rule, and it is n₁×n₁Dimension Matrix, C_jFor reactive power compensator state estimation vector, dimension is n₁×1；

S4, use Fuzzy Observer carry out the state estimation of reactive power compensator online: the Fuzzy Observer using S2 to provide is carried outWithIn line computation；

S5, whenTime, it is believed that state estimation effect reaches allowed band, by state estimation resultSend extremely The control of reactive power compensating unit, for follow-up the control of reactive power compensating, ε is that default differentiation Fuzzy Observer puts into follow-up making here By whether threshold value.

A kind of reactive compensation device state estimation method being applied to pipe gallery the most according to claim 1, its feature exists In: described ε is the arithmetic number less than 0.1.