CN105469210A - Main and distribution network automation model splicing error detection method - Google Patents

Abstract

The invention discloses a main and distribution network automation model splicing error detection method and belongs to the power grid dispatching field. The method comprises the following steps of reading a main network model, a distribution network model and measurement data of a plurality of history measurement sections respectively and splicing the main network model and the distribution network model into a power-grid physical model; converting the power-grid physical model into a mathematical model; carrying out a network topology analysis step and carrying out parameter identification step according to a transformer substation. By using the main and distribution network automation model splicing error detection method, an overall error decrease index and an active measurement balance degree are used to identify a splicing error of the main network model and the distribution network model so that a problem that the model splicing error is difficult to identify is effectively solved.

Description

A kind of main distribution automation model splicing error-detecting method

Technical field

The present invention relates to a kind of main distribution automation model splicing error-detecting method, belong to dispatching of power netwoks field.

Background technology

For a long time, by the system& mechanism of electrical network differentiated control, power network schedule automation and power distribution automation two cover system are not connected mutually, major network, distribution network model are respectively by power network schedule automation, power distribution automation independent maintenance, unitarity between data cannot effectively ensure, when causing main distribution network model to be spliced, mistake is difficult to effective identification.

In view of this, the present inventor studies this, and develop a kind of main distribution automation model splicing error-detecting method specially, this case produces thus.

Summary of the invention

The object of this invention is to provide a kind of main distribution automation model splicing error-detecting method, utilize global error decline index and the meritorious quality of balance that measures to identify major network model, distribution network model splicing mistake, effectively solve model splicing mistake and be difficult to identification problem.

To achieve these goals, solution of the present invention is:

A kind of main distribution automation model splicing error-detecting method, comprises the steps:

Step 1: the metric data reading major network model, distribution network model and multiple historical metrology section respectively, and major network model, distribution network model are spliced into electrical network physical model;

Step 2: convert electrical network physical model to mathematical model: connected node merger is formed topological node;

Step 3: carry out Network topology: centered by non-zero impedance element, forms topological island by topological node merger;

Step 4: carry out parameter identification by transformer station: carry out parameter identification one by one to all transformer stations in a topological island, with global error decline index for weighing foundation, adopt multiple measuring section to carry out combined parameters identification, obtain metric data and the parameter of mistake;

Step 5: the wrong metric data obtained according to step 4 and parameter, provides corresponding model splicing miscue.

As preferably, described main distribution automation model splicing error-detecting method can also with meritorious measurement quality of balance for foundation, carries out identification, obtain metric data and the parameter of mistake to every metric data of transformer station and parameter.

Main distribution automation model splicing error-detecting method of the present invention, utilizes global error decline index and the meritorious quality of balance that measures to identify major network model, distribution network model splicing mistake, effectively solves model splicing mistake and be difficult to identification problem.

Below in conjunction with specific embodiment, the present invention is described in further detail.

Embodiment

A kind of main distribution automation model splicing error-detecting method, comprises the steps:

Step 1: read major network model, distribution network model respectively, and major network model, distribution network model are spliced into electrical network physical model, reads in and the metric data of multiple historical metrology section, for subsequent calculations simultaneously.Major network model comprises 10kV, 35kV with Up Highway UHW and the physics such as main transformer, switch and connecting relation thereof; Distribution network model comprises 10kV and switch, can adopt CIM/E form during model read.

Step 2: convert electrical network physical model to mathematical model: when converting mathematical model to, first forms tie point the conductive equipment with annexation, if tie point is linked together by closed switch, then forms a topological point.

Described tie point (ConnectivityNode) refers to the virtual point that the terminal of transmission equipment can be linked together by zero impedance.End points (Terminal) is a physical connection point on transmission equipment, and it is the electric interfaces that conductive equipment is connected in electric system, and each end points belongs to a concrete conductive equipment.Conductive equipment can have the end points of varying number, and as switch has 2 end points, generator only has 1 end points, and the end points quantum count that bus contains is unrestricted.

Step 3: carry out Network topology: centered by non-zero impedance element, forms topological island by topological node merger; Centered by non-zero impedance element, topological node merger is formed topological island.Non-zero impedance element comprises transformer, circuit etc., and a topological island comprises the multiple topological nodes linked together by non-zero impedance element.

Step 4: carry out parameter identification by transformer station: comprise multiple transformer station in a topological island, need to carry out parameter identification one by one to all transformer stations in multiple topological island, with global error decline index for weighing foundation, multiple measuring section is adopted to carry out conjunctive model splicing misidentification, obtain the model splicing parameter of mistake, the concrete identification process of the present embodiment is as follows:

Using the parameter total error of each outlet switch, bus as the index weighing the suspicious degree of model splicing mistake.In the present embodiment, each can get when detecting suspicious parameter maximum weighted measurement residuals square as threshold value, when Correlated Case with ARMA Measurement global error is less than this threshold value, think outlet switch, bus parameter or measure credible.When outlet switch, bus Correlated Case with ARMA Measurement global error are greater than this threshold value, just think this switch or bus model parameter suspicious, be classified to suspicious parameter/measurement collection.In the next step, only identification is carried out to the parameter that suspicious parameter/measurement is concentrated.The efficiency that suspicious parameter detecting greatly can improve parameter error identification while not missing real model splicing mistake is carried out according to Correlated Case with ARMA Measurement global error.

By decomposing the measurement model of Power system state estimation, traditional weighted least square comprising parameter error can be converted into optimization problem.Then define the foundation of global error decline index as parameter of measurement or whether mistake, for single operation section, exist when being greater than the global error slippage of 9, can judge that having switch to splice mistake exists.

Multiple measuring section and global error decline index are combined, if obviously there is switch splicing mistake, then switch splicing has an impact to the global error of all measuring sections, thus makes corresponding global error decline index become large.

The principle of this step is explained as follows:

Consider following measurement model

z＝h(x,p _e)+ε(1)

Wherein, z represents measurement vector; H (x, p _e) be measurement equation; X is state vector, comprises node voltage amplitude and phase place; p _efor electrical network parameter error vector; ε is error in measurement vector.

Measurement vector error is divided into two parts, namely

ε＝v _e+r(2)

Wherein v _esuspicious error in measurement vector, r is measurement residuals vector.

(2) are substituted into (1) can obtain

r＝z-h(x,p _e)-v _e(3)

By network parameter vector description be:

p _t＝p+p _e(4)

Wherein, p and p _tbe respectively supposition and real network parameter vector; p _eit is parameter error vector.

The weighted least square problem that then there is parameter error and measurement bad data can be described as following optimization problem:

Minimize:L(x,p _e,v _e)＝r ^TWr(5)

Wherein, W is weight matrix, is generally taken as diagonal matrix, and its inverse matrix is for measuring covariance matrix.

Do not consider the conventional weight least-squares estimation hypothesis of bad data identification

p _e＝0(6)

v _e＝0(7)

Therefore following optimization problem can be described as:

Minimize:L(x,0,0)＝r′ ^TWr′(8)

Wherein, r '=z-h (x, 0) is measurement residuals vector.

Suppose that problem (8) converges on and separate x ⁰, at this Xie Chu, Taylor series expansion is carried out to (3), and reservation causes linear term, then have:

r＝z-h(x ⁰+Δx,p _e)-v _e

＝z-h(x ⁰,0)-H _xΔx-H _pp _e-v _e+h.o.t

≈r ⁰-H _xΔx-H _pp _e-v _e(9)

Wherein,

$H_x=\frac{\∂h(x,p_e)}{\∂x}{|}_{x=x^0,p_e=0}---\left(10\right)$

$H_p=\frac{\∂h(x,p_e)}{\∂p_e}{|}_{x=x^0,p_e=0}---\left(11\right)$

r ⁰＝z-h(x ⁰,0)(12)

For convenience of description, define

$s=\left(\begin{array}{c}{p}_e\\ v_e\end{array}\right)---\left(13\right)$

H _s＝(H _p,I)(14)

J(Δx,s)＝L(x ⁰+Δx,p _e,v _e)(15)

Obviously, s represents parameter and Measurement Biases vector.

Then (9) can be rewritten as

r＝r ⁰-H _xΔx-H _ss(16)

(16) substitution (5) can be obtained Linear least square estimation problem as follows:

Minimize:J(Δx,s)＝(r ⁰-H _xΔx-H _ss) ^TW(r ⁰-H _xΔx-H _ss)(17)

The optimum solution of problem (17) is:

$s={\left(H_s^T{A}^T{\mathrm{WAH}}_s\right)}^{-1}{H}_s^T{A}^T{\mathrm{WAr}}^0={\left(H_s^T{\mathrm{WAH}}_s\right)}^{-1}{H}_s^T{\mathrm{WAr}}^0---\left(18\right)$

$\mathrm{\Δ}x={\left(H_x^T{\mathrm{WH}}_x\right)}^{-1}{H}_x^{T}W(r^0-H_{s}s)---\left(19\right)$

Wherein,

$A=I-H_x{\left(H_x^T{\mathrm{WH}}_x\right)}^{-1}{H}_x^{T}W---\left(20\right)$

Can derive:

$\begin{array}{c}{A}^{T}WA=\[W-{\mathrm{WH}}_x{\left(H_x^T{\mathrm{WH}}_x\right)}^{-1}{H}_x^{T}W\]\[I-H_x{\left(H_x^T{\mathrm{WH}}_x\right)}^{-1}{H}_x^{T}W\]\\ =W-2{\mathrm{WH}}_x{\left(H_x^T{\mathrm{WH}}_x\right)}^{-1}{H}_x^{T}W+{\mathrm{WH}}_x{\left(H_x^T{\mathrm{WH}}_x\right)}^{-1}{H}_x^T{\mathrm{WH}}_x{\left(H_x^T{\mathrm{WH}}_x\right)}^{-1}{H}_x^{T}W\\ =W-{\mathrm{WH}}_x{\left(H_x^T{\mathrm{WH}}_x\right)}^{-1}{H}_x^{T}W\\ =WA\end{array}--\left(21\right)$

Can be obtained by (19) and (20):

$r^0-H_x\mathrm{\Δ}x-H_{s}s=r^0-H_{s}s-H_x{\left(H_x^T{\mathrm{WH}}_x\right)}^{-1}{H}_x^{T}W(r^0-H_{s}s)=A(r^0-H_{s}s)---\left(22\right)$

Due to x ⁰be that convergence state is estimated to separate, therefore have

${\mathrm{Ar}}^0=r^0-H_x{\left(H_x^T{\mathrm{WH}}_x\right)}^{-1}{H}_x^T{\mathrm{Wr}}^0=r^0---\left(23\right)$

The objective function that be can be derived from problem (17) by (22) and (23) is as follows:

$\begin{array}{c}J(\mathrm{\Δ}x,s)={(r^0-H_{s}s)}^T{A}^{T}WA(r^0-H_{s}s)\\ =r^{0T}{A}^T{\mathrm{WAr}}^0-2r^{0T}{A}^T{\mathrm{WAH}}_{s}s+s^T{H_s}^T{A}^T{\mathrm{WAH}}_{s}s\\ =r^{0T}{A}^T{\mathrm{WAr}}^0-r^{0T}{A}^T{\mathrm{WAH}}_s{\left(H_s^T{A}^T{\mathrm{WAH}}_s\right)}^{-1}{H}_s^T{A}^T{\mathrm{WAr}}^0\\ =r^{0T}{\mathrm{WAr}}^0-r^{0T}{\mathrm{WAH}}_s{\left(H_s^T{\mathrm{WAH}}_s\right)}^{-1}{H}_s^T{\mathrm{WAr}}^0\\ =r^{0T}{\mathrm{Wr}}^0-r^{0T}{\mathrm{WH}}_s{\left(H_s^T{\mathrm{WAH}}_s\right)}^{-1}{H}_s^T{\mathrm{Wr}}^0\end{array}---\left(24\right)$

Note B ^kfor kth walks bad data and the wrong parameter collection of identification, can global error be obtained by (17) as follows:

J(Δx ^k,s ^k)＝(r ⁰-H _xΔx ^k-H _ss ^k) ^TW(r ⁰-H _xΔx ^k-H _ss ^k)(25)

Now hypothesis is intended detecting suspicious measurement or parameter j, definition set

B ^k,j＝B ^k+{j}(26)

Then corresponding global error is:

J(Δx ^k,j,s ^k,j)＝(r ⁰-H _xΔx ^k,j-H _ss ^k,j) ^TW(r ⁰-H _xΔx ^k,j-H _ss ^k,j)(27)

Define global error decline index to weigh the suspicious degree of suspicious measurement or parameter j, namely

ΔJ ^k,j＝J(Δx ^k,s ^k)-J(Δx ^k,j,s ^k,j)(28)

As k=0, according to (25), (27) and (28) can obtain

${\mathrm{\ΔJ}}^{0,j}=J(0,0)-J({\mathrm{\Δx}}^{0,j},s^{0,j})=r^{0T}{\mathrm{WH}}_s{\left(H_s^T{\mathrm{WAH}}_s\right)}^{-1}{H}_s^T{\mathrm{Wr}}^0---\left(29\right)$

Obviously, if j corresponds to measure (parameter), then (28) equal corresponding regularization residual error (regularization La Ge Lang day multiplier) square, generally speaking, the regularization residual error or the regularization La Ge Lang day multiplier that are greater than 3 can as the foundations judging bad data or parameter error, and the global error slippage being therefore greater than 9 can as the foundation judging bad data or parameter error.

If once only carry out identification to a bad data or parameter error, then global error decline index method and regularization La Ge Lang day multiplier method equivalence.But the global error decline index method proposed here can be used to carry out identification to multiple bad data and parameter error, and can be further used for correcting wrong parameter simultaneously.

When given N number of measuring section, then the measurement residuals vector of each measuring section can be described as:

$\begin{array}{cc}{r}^i=z^i-h^i(x^i,p_e)-v_e^i& \∀i\∈\{1,2...N\}\end{array}---\left(30\right)$

Wherein,

I measuring section is numbered;

N measuring section number;

Z ⁱthe measurement vector of section i;

H ⁱ(x ⁱ, p _e) corresponding to the measurement equation of measuring section i;

X ⁱthe state vector of measuring section i, comprises voltage magnitude and the phase place of each node;

the bad data vector of measuring section i;

R ⁱthe residual vector of measuring section i.

Definition vector:

$r=\left[\begin{array}{c}{r}^1\\ r^2\\ \·\\ \·\\ \·\\ r^N\end{array}\right],z=\left[\begin{array}{c}{z}^1\\ z^2\\ \·\\ \·\\ \·\\ z^N\end{array}\right],x=\left[\begin{array}{c}{x}^1\\ x^2\\ \·\\ \·\\ \·\\ x^N\end{array}\right],v_e=\left[\begin{array}{c}{v}_e^1\\ v_e^2\\ \·\\ \·\\ \·\\ v_e^N\end{array}\right],h(x,p_e)=\left[\begin{array}{c}{h}^1(x^1,p_e)\\ h^2(x^2,p_e)\\ \·\\ \·\\ \·\\ h^N(x^N,p_e)\end{array}\right]---\left(31\right)$

Multibreak the combined parameters then based on (30) and (31) is similar to bad data cooperative identification method to the parameter based on single section to bad data cooperative identification method.

Obviously, if there is model splicing mistake, then parameter error has an impact to the global error of all measuring sections, thus makes corresponding global error decline index become large.For bad data, its global error decline index then has nothing to do with section number.Therefore, adopt multiple measuring section to carry out combined parameters identification and be conducive to correct identified parameters mistake.

Step 5: the wrong metric data obtained according to step 4 and parameter, provides corresponding model splicing miscue.Namely according to topological analysis result, find the conductive equipment corresponding to corresponding tie point, and provide abnormal prompt.

When described in the present embodiment, main distribution automation model splicing error-detecting method carries out Substation parameters identification, except adopt described in step 4 using global error decline index as bad data recognition according to except, can also to measure degree of unbalancedness for foundation, identification is carried out to every metric data of transformer station and parameter, obtains metric data and the parameter of mistake.Be specially: computational scheme, double winding change, three winding change, bus section, busbar section, load carry out measurement quality of balance and calculate respectively, to the meritorious amount of unbalance larger with original Measurement Biases, carry out rule-based filtering.

Described rule mainly comprises following:

1, there is the meritorious degree of unbalancedness that measures in the bus section, busbar section of same transformer station, circuit simultaneously.

2, same transformer station section bus line summation of gaining merit adds that network loss (getting minimum value) is greater than that this section of bus is meritorious to be measured, and under under another section of bus, active power loss (maximal value) adds this bus, all circuit active power losses are less than this section of Down Highway and gain merit and measure.

Main distribution automation model splicing error-detecting method described in the present embodiment, utilizes global error decline index and the meritorious quality of balance that measures to identify major network model, distribution network model splicing mistake, effectively solves model splicing mistake and be difficult to identification problem.

Above-described embodiment non-limiting product form of the present invention and style, any person of an ordinary skill in the technical field, to its suitable change done or modification, all should be considered as not departing from patent category of the present invention.

Claims (2)

1. a main distribution automation model splicing error-detecting method, is characterized in that comprising the steps:

Step 1: the metric data reading major network model, distribution network model and multiple historical metrology section respectively, and major network model, distribution network model are spliced into electrical network physical model;

Step 2: convert electrical network physical model to mathematical model: connected node merger is formed topological node;

Step 3: carry out Network topology: centered by non-zero impedance element, forms topological island by topological node merger;

Step 4: carry out parameter identification by transformer station: carry out parameter identification one by one to all transformer stations in a topological island, with global error decline index for weighing foundation, adopt multiple measuring section to carry out combined parameters identification, obtain metric data and the parameter of mistake;

Step 5: the wrong metric data obtained according to step 4 and parameter, provides corresponding model splicing miscue.

2. a kind of main distribution automation model splicing error-detecting method as claimed in claim 1, it is characterized in that: described main distribution automation model splicing error-detecting method can also with meritorious measurement quality of balance for foundation, identification is carried out to every metric data of transformer station and parameter, obtains metric data and the parameter of mistake.