CN104466932A - Short-circuit current limiting measure optimizing method and system - Google Patents

Abstract

The embodiment of the invention provides a short-circuit current limiting measure optimizing method and system. The method includes the steps that the network analysis and cost of various current limiting measures are determined; a nonlinear and unconstrained short circuit current limiting measure optimizing configuration model is set up according to the network analysis and cost of the various current limiting measures; an improved mode search method is used for solving the short circuit current limiting measure optimizing configuration model, and the optimal solution set enabling the sequence generated through iteration to be converged is obtained. In the computing process of the improved model search method, the step size is set again after iteration is conducted by the number, and the global optimization capability of the algorithm is improved.

Description

A kind of short circuit current current limiting measures optimization method and system

Technical field

The present invention relates to electric power project engineering field, particularly relate to a kind of short circuit current current limiting measures optimization method and system.

Background technology

Along with the continuous increase of network load, system short-circuit levels of current also increases thereupon, how configuration is optimized to the measure of limiting short-circuit current, become current problem demanding prompt solution, current limiting measures are distributed rationally and are referred to meeting under the conditions such as conventional short-circuit restriction of current, trend constraint, stability of a system constraint, optimize existing short-circuit current limiting measure to reach the object of economy optimum.In prior art, use traditional mode search method to ask the excellent configuration reasoning optimal solution of general current limiting measures, get locally optimal solution only, whole optimizing of algorithm are indifferent.

Summary of the invention

In view of this, the embodiment of the present invention provides a kind of short circuit current current limiting measures optimization method and system, uses traditional mode search method to solve optimal solution, get locally optimal solution only to solve in prior art, the problem that whole optimizing of algorithm are indifferent.

For achieving the above object, the embodiment of the present invention provides following technical scheme:

A kind of short circuit current current limiting measures optimization method, comprising:

Determine network analysis and the cost of each current limiting measures;

Non-linear unconfined short-circuit current limiting measure Optimal Allocation Model is set up according to the network analysis of each current limiting measures and cost;

Extended pattern search algorithms is used to solve described short-circuit current limiting measure Optimal Allocation Model, the optimum obtaining the sequence converges that iteration is produced optimizes disaggregation, wherein, described improved model search method is in computational process, and every iteration resets step-length after making a reservation for this number.

Wherein, described current limiting measures comprise: install current limiting reactor CLR additional, adopt fault current limiter FCL and adopt high-impedance transformer HIT.

Wherein, the network analysis and the cost that install CLR additional comprise:

When CLR installs additional on circuit, the impedance Δ z ' of parallel branch _ijwith the impedance Δ z of series arm _ijrelation:

$\mathrm{\Δ}{z}_{\mathrm{ij}}^{\′}=-\frac{z_{\mathrm{ij}}(z_{\mathrm{ij}}+\mathrm{\Δ}{z}_{\mathrm{ij}})}{\mathrm{\Δ}{z}_{\mathrm{ij}}};$

The self-admittance of node i and node j and transadmittance:

$\left\{\begin{array}{c}\mathrm{\Δ}{Y}_{\mathrm{ij}}=\mathrm{\Δ}{Y}_{\mathrm{ij}}=-\frac{u_{\mathrm{ij}}\mathrm{\Δ}{z}_{\mathrm{ij}}}{z_{\mathrm{ij}}(z_{\mathrm{ij}}+\mathrm{\Δ}{z}_{\mathrm{ij}})}\\ \mathrm{\Δ}{Y}_{\mathrm{ij}}=\mathrm{\Δ}{Y}_{\mathrm{ij}}=\frac{u_{\mathrm{ij}}\mathrm{\Δ}{z}_{\mathrm{ij}}}{z_{\mathrm{ij}}(z_{\mathrm{ij}}+\mathrm{\Δ}{z}_{\mathrm{ij}})}\end{array}\right.,---(0-1)$

Wherein, u _ijfor 0-1 control variables.U _ij=0 represents that circuit i-j does not install CLR; u _ij=1 represents that circuit i-j installation impedance is Δ z _ijcLR;

Cost of investment c:

c＝u _ij(A ₁+B ₁Δz _ij)。

Wherein, the network analysis of FCL and cost is adopted to comprise:

When FCL is superconductive current limiter SFCL, equiva lent impedance model:

Z _SFCL＝Z _m(1-exp(-t/T _sc))，

Wherein, Z _mfor the maximum impedance value of SFCL normal state, T _scfor the time constant that SFCL superconducting state changes to normal state;

When FCL is solid-state current limiter, current-limiting reactor value X _fCLwith the relation of the angle of flow of solid-state switch GTO:

$X_{\mathrm{SFCL}}=X_{L_2}+\frac{X\left(\mathrm{\σ}\right)X_C}{X\left(\mathrm{\σ}\right)+X_C},$

Wherein, X (σ) and induction reactance X _l2with the relation of the angle of flow σ of GTO:

$X\left(\mathrm{\σ}\right)=X_{L_1}\frac{\mathrm{\π}}{\mathrm{\σ}-\sin \mathrm{\σ}};$

When FCL is short-circuited fault:

$\left\{\begin{array}{c}{Z}_{\mathrm{FCL}}=0,t=0\\ Z_{\mathrm{FCL}}=Z_m,t=T_0\end{array}\right.,$

Wherein, T ₀for secondary transient state short circuit time constant;

Cost of investment c:

c＝u _ij(A ₂+B ₂Z _FCL)，

Wherein, A ₂, B ₂for constant.

Wherein, the network analysis of HIT and cost is adopted to comprise:

When HIT is built-in reactor HIT, the shunt transformer impedance Δ z ' of equivalence _ijwith the impedance Δ z of built-in reactor _ijpass be:

$\mathrm{\Δ}{z}_{\mathrm{ij}}^{\′}=-\frac{z_{\mathrm{ij}}(z_{\mathrm{ij}}+\mathrm{\Δ}{z}_{\mathrm{ij}})}{\mathrm{\Δ}{z}_{\mathrm{ij}}};$

When HIT is built-in reactor HIT, the variable quantity of admittance matrix is also with the relation of the no-load voltage ratio of transformer:

$\left\{\begin{array}{c}\mathrm{\Δ}{Y}_{\mathrm{ii}}=\frac{u_{\mathrm{ij}}}{\mathrm{\Δ}{z}_{\mathrm{ij}}^{\′}}=-\frac{u_{\mathrm{ij}}\mathrm{\Δ}{z}_{\mathrm{ij}}}{z_{\mathrm{ij}}(z_{\mathrm{ij}}+\mathrm{\Δ}{z}_{\mathrm{ij}})}\\ \mathrm{\Δ}{Y}_{\mathrm{jj}}=\frac{u_{\mathrm{ij}}}{k^2\mathrm{\Δ}{z}_{\mathrm{ij}}^{\′}}=-\frac{u_{\mathrm{ij}}\mathrm{\Δ}{z}_{\mathrm{ij}}}{k^2{z}_{\mathrm{ij}}(z_{\mathrm{ij}}+\mathrm{\Δ}{z}_{\mathrm{ij}})}\\ \mathrm{\Δ}{Y}_{\mathrm{ij}}=\mathrm{\Δ}{Y}_{\mathrm{ij}}=-\frac{u_{\mathrm{ij}}}{\mathrm{k\Δ}{z}_{\mathrm{ij}}^{\′}}=\frac{u_{\mathrm{ij}}\mathrm{\Δ}{z}_{\mathrm{ij}}}{k{z}_{\mathrm{ij}}(z_{\mathrm{ij}}+\mathrm{\Δ}{z}_{\mathrm{ij}})}\end{array}\right.,$

Wherein, u _ij=1 represents employing built-in reactor HIT; u _ij=0 represents employing common transformer;

The cost c of built-in reactor HIT:

c＝u _ij(A ₃+B ₄Δz _ij)，

Wherein, A ₃, B ₃for constant.

Wherein, described non-linear unconfined short-circuit current limiting measure Optimal Allocation Model is:

$\min F(u,w,x,\mathrm{\ρ})=f(u,w,x)+\mathrm{\ρ}\underset{i=1}{\overset{m}{\mathrm{\Σ}}}{g}_i^{+}(u,w,x)+\mathrm{\ρ}\underset{j=1}{\overset{m}{\mathrm{\Σ}}}\left|h_j(u,w,x)\right|$

Wherein, ρ is penalty factor, h _j(u, w, x) is equality constraint, g _i(u, w, x) is inequality constraints, $g_i^{+}(u,w,x)=\left\{\begin{array}{c}{g}_i(u,w,x),g_i(u,w,x)>0\\ 0,\mathrm{else}\end{array}\right.,$ M, n are respectively total number of two class constraints.

Wherein, use extended pattern search algorithms to solve described short-circuit current limiting measure Optimal Allocation Model, the optimum optimization disaggregation obtaining the sequence converges that iteration is produced comprises:

Steps A: i-th coordinate direction setting the state variable x that does things is e _i=[0..., 1 ... 0] ^t;

Step B: given initial point x ⁽¹⁾, initial step length δ, accelerated factor α, reduction factor β ∈ (0,1) and N number of coordinate direction e ₁, e ₂..., e _n, permissible error ξ >0, maximum probe number of times T _max, detect n at most _maxstep-length is reseted, maximum step-length δ after secondary _max, put y ⁽¹⁾=x ⁽¹⁾, k=1, i=1, n=1;

Step C: if F is (y ⁽ⁱ⁾+ δ _ei) <F (y ⁽ⁱ⁾), then make y ⁽ⁱ⁺¹⁾=y ⁽ⁱ⁾+ δ _ei, go to step E, otherwise enter step F;

Step D: if F is (y ⁽ⁱ⁾-δ _ei) <F (y ⁽ⁱ⁾), then make y ⁽ⁱ⁺¹⁾=y ⁽ⁱ⁾-δ _ei, otherwise make y ⁽ⁱ⁺¹⁾=y ⁽ⁱ⁾, continue;

Step e: if i<N, then put i=i+1, go to step C, otherwise enter step F;

Step F: if F is (y ^(N+1)) <F (X ^(K)), then continue, otherwise enter step H;

Step G: put basic point x ^(k+1)=y ^(N+1), make y ⁽ⁱ⁾=x ^(k+1)+ α ^{(x (k+1)-x (k))}, k=k+1, i=l, get back to step C;

Step H: if δ≤ξ or k=T _max, then an x is obtained ^(k); Otherwise, put δ=β δ, y ⁽ⁱ⁾=x ^(k), k=k+l, i=1;

Step I: if n=n _max, δ=rand (δ _max), n=1, gets back to step C, otherwise n=n+1, get back to step C;

Wherein, rand (δ _max) be 0 to δ _maxrandom number.

Wherein, the described network analysis according to each current limiting measures and cost are set up non-linear unconfined short-circuit current limiting measure Optimal Allocation Model and are comprised:

Determine that short-circuit current limiting measure distributes function rationally:

$f(u,w,x)=\min [k_1\underset{s=1}{\overset{N_s}{\mathrm{\&Sigma;}}}{c}_s(u_s,w_s)/P_s+k_2{c}_e(u,w,x)],$

Wherein, N _sfor the sum of applied current limiting measures, c _efor other target functions, c _ecomprise operating cost or reliability, k ₁, k ₂for weight coefficient;

Determine the constraints of measure decision variable:

$\underset{\&OverBar;}{w_s}\&le;w_s\&le;\stackrel{\&OverBar;}{w_s},$

u _s∈{0,1}，

Wherein, with for the bound of current-limiting impedance,

When decision variable gets different numerical value time, the constraints of measure decision variable:

f(u,w,x)＝0，

l(u,w,x)＝0，

$\underset{\&OverBar;}{x}\&le;x\&le;\stackrel{\&OverBar;}{x},$

Wherein, f (u, w, x)=0 is Load flow calculation equation; L (u, w, x)=0 is calculation of short-circuit current equation; for systematic function constraint, comprising: the constraint of system short-circuit restriction of current, voltage, trend constraint and the constraint of N-1 trend;

Function is distributed rationally to short-circuit current limiting measure and determines that the constraints of measure decision variable uses non-linear penalty function method, mixed integer programming problem is converted into unconstrained non-linear programming problem, obtains non-linear unconfined short-circuit current limiting measure Optimal Allocation Model

Wherein, also comprise:

By constraints u _s{ 0,1} is converted into ∈: u _s(1-u _s)=0;

By constraints $\underset{\&OverBar;}{w_s}\&le;w_s\&le;\stackrel{\&OverBar;}{w_s}$ Be converted to: $(w_s-\underset{\&OverBar;}{w_s})(w_s-\underset{\&OverBar;}{w_s}-1)...(w_s-\stackrel{\&OverBar;}{w_s})=0;$

By w _sbe expressed as: $w_s=a_0{2}^0+a_1{2}^1+...+a_p{2}^p+\underset{\&OverBar;}{w_s}.$

A kind of short circuit current current limiting measures optimization system, comprising: determination module, model checking module and solve module; Wherein,

Described determination module, for determining network analysis and the cost of each current limiting measures;

Described model building module, for setting up non-linear unconfined short-circuit current limiting measure Optimal Allocation Model according to the network analysis of each current limiting measures and cost;

Describedly solve module, for using extended pattern search algorithms to solve described short-circuit current limiting measure Optimal Allocation Model, the optimum obtaining the sequence converges that iteration is produced optimizes disaggregation, wherein, described improved model search method is in computational process, and every iteration resets step-length after making a reservation for this number.

Based on technique scheme, the short circuit current current limiting measures optimization method that the embodiment of the present invention provides and system, after the network analysis determining each current limiting measures and cost, non-linear unconfined short-circuit current limiting measure Optimal Allocation Model is set up according to the network analysis of each current limiting measures and cost, extended pattern search algorithms is used to solve described short-circuit current limiting measure Optimal Allocation Model afterwards, the optimum obtaining the sequence converges that iteration is produced optimizes disaggregation, wherein, described improved model search method is in computational process, every iteration resets step-length after making a reservation for this number, in conjunction with current limiting measures cost calculation, design a kind of non-linear unconfined short-circuit current limiting measure Optimal Allocation Model based on pattern search method, when solving concrete scheme, by changing the initial step length of pattern search method computing each time, namely improved model search method technology optimal solution is used, avoid traditional mode search method can only obtain the deficiency of locally optimal solution, thus strengthen the global optimizing ability of algorithm.

Accompanying drawing explanation

In order to be illustrated more clearly in the embodiment of the present invention or technical scheme of the prior art, be briefly described to the accompanying drawing used required in embodiment or description of the prior art below, apparently, accompanying drawing in the following describes is only embodiments of the invention, for those of ordinary skill in the art, under the prerequisite not paying creative work, other accompanying drawing can also be obtained according to the accompanying drawing provided.

The flow chart of the short circuit current current limiting measures optimization method that Fig. 1 provides for the embodiment of the present invention;

The system block diagram of the short circuit current current limiting measures optimization system that Fig. 2 provides for the embodiment of the present invention.

Embodiment

Below in conjunction with the accompanying drawing in the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.

The flow chart of the short circuit current current limiting measures optimization method that Fig. 1 provides for the embodiment of the present invention, in conjunction with current limiting measures cost calculation, design a kind of non-linear unconfined short-circuit current limiting measure Optimal Allocation Model based on pattern search method, when solving concrete scheme, by changing the initial step length of pattern search method computing each time, namely improved model search method technology optimal solution is used, avoid traditional mode search method can only obtain the deficiency of locally optimal solution, thus strengthen the global optimizing ability of algorithm; With reference to Fig. 1, this short circuit current current limiting measures optimization method can comprise:

Step S100: network analysis and the cost of determining each current limiting measures;

Step S110: set up non-linear unconfined short-circuit current limiting measure Optimal Allocation Model according to the network analysis of each current limiting measures and cost;

Step S120: use extended pattern search algorithms to solve described short-circuit current limiting measure Optimal Allocation Model, the optimum obtaining the sequence converges that iteration is produced optimizes disaggregation, wherein, described improved model search method is in computational process, and every iteration resets step-length after making a reservation for this number.

Based on technique scheme, the short circuit current current limiting measures optimization method that the embodiment of the present invention provides and system, after the network analysis determining each current limiting measures and cost, non-linear unconfined short-circuit current limiting measure Optimal Allocation Model is set up according to the network analysis of each current limiting measures and cost, extended pattern search algorithms is used to solve described short-circuit current limiting measure Optimal Allocation Model afterwards, the optimum obtaining the sequence converges that iteration is produced optimizes disaggregation, wherein, described improved model search method is in computational process, every iteration resets step-length after making a reservation for this number, in conjunction with current limiting measures cost calculation, design a kind of non-linear unconfined short-circuit current limiting measure Optimal Allocation Model based on pattern search method, when solving concrete scheme, by changing the initial step length of pattern search method computing each time, namely improved model search method technology optimal solution is used, avoid traditional mode search method can only obtain the deficiency of locally optimal solution, thus strengthen the global optimizing ability of algorithm.

Optionally, current limiting measures can comprise: install current limiting reactor CLR additional, adopt fault current limiter FCL and adopt high-impedance transformer HIT.

Current limiting reactor (Current Limit Reactor, CLR) can be installed on the line or bus section place, and both are slightly different.First a line CLR is studied.The isopleth map that circuit installs CLR additional can be simulated with a substitutional connection in parallel.

Optionally, when CLR installs additional on circuit, the impedance Δ z ' of parallel branch _ijwith the impedance Δ z of series arm _ijrelation can be:

$\mathrm{\&Delta;}{z}_{\mathrm{ij}}^{\&prime;}=-\frac{z_{\mathrm{ij}}(z_{\mathrm{ij}}+\mathrm{\&Delta;}{z}_{\mathrm{ij}})}{\mathrm{\&Delta;}{z}_{\mathrm{ij}}};$

Optionally, circuit installs CLR additional and does not change system node sum, only need revise self-admittance and the transadmittance of node i and node j:

$\left\{\begin{array}{c}\mathrm{\&Delta;}{Y}_{\mathrm{ij}}=\mathrm{\&Delta;}{Y}_{\mathrm{ij}}=-\frac{u_{\mathrm{ij}}\mathrm{\&Delta;}{z}_{\mathrm{ij}}}{z_{\mathrm{ij}}(z_{\mathrm{ij}}+\mathrm{\&Delta;}{z}_{\mathrm{ij}})}\\ \mathrm{\&Delta;}{Y}_{\mathrm{ij}}=\mathrm{\&Delta;}{Y}_{\mathrm{ij}}=\frac{u_{\mathrm{ij}}\mathrm{\&Delta;}{z}_{\mathrm{ij}}}{z_{\mathrm{ij}}(z_{\mathrm{ij}}+\mathrm{\&Delta;}{z}_{\mathrm{ij}})}\end{array}\right.,$

Wherein, u _ijfor 0-1 control variables.U _ij=0 represents that circuit i-j does not install CLR; u _ij=1 represents that circuit i-j installation impedance is Δ z _ijcLR;

Optionally, the expenses such as the cost of CLR and installation are relevant with its resistance size, and resistance is larger, and correlative charges is higher, and the cost of investment installing CLR additional can calculate with following formula simply:

c＝u _ij(A ₁+B ₁Δz _ij)，

Wherein, A ₁, B ₁for cost coefficient.

The current limliting principle of current fault current limiter (FCL) is a lot, only introduces common superconductive current limiter (SFCL) and a kind of solid-state current limiter with series compensation as space is limited here.SFCL presents Low ESR when electrical network normally runs, and during grid collapses, can present high impedance again.

Optionally, when FCL is superconductive current limiter SFCL, equiva lent impedance model can simply be expressed as follows:

Z _SFCL＝Z _m(1-exp(-t/T _sc))，

Wherein, Z _mfor the maximum impedance value of SFCL normal state, T _scfor the time constant that SFCL superconducting state changes to normal state;

When FCL is solid-state current limiter, current-limiting reactor value X _fCLwith the relation of the angle of flow of solid-state switch GTO:

$X_{\mathrm{SFCL}}=X_{L_2}+\frac{X\left(\mathrm{\&sigma;}\right)X_C}{X\left(\mathrm{\&sigma;}\right)+X_C},$

Wherein, X (σ) and induction reactance X _l2with the relation of the angle of flow σ of GTO:

$X\left(\mathrm{\&sigma;}\right)=X_{L_1}\frac{\mathrm{\&pi;}}{\mathrm{\&sigma;}-\sin \mathrm{\&sigma;}};$

When angle of flow σ reduces gradually from 180, degree of flow restriction increases; When the angle of flow reaches σ ₀time, inductance L ₁with electric capacity C, parallel resonance occurs, now degree of flow restriction is maximum, and electric current is minimum.

In a word, regardless of the FCL of which kind, when being short-circuited fault, high impedance status can being converted to from low impedance state very soon, playing the effect of restriction system short circuit current.

Optionally, when FCL is short-circuited fault:

$\left\{\begin{array}{c}{Z}_{\mathrm{FCL}}=0,t=0\\ Z_{\mathrm{FCL}}=Z_m,t=T_0\end{array}\right.,$

Wherein, T ₀for secondary transient state short circuit time constant;

Optionally, cost of investment c can think that the cost of investment of fault current limiter is directly proportional with the size of its current-limiting impedance here simply:

c＝u _ij(A ₂+B ₂Z _FCL)，

Wherein, A ₂, B ₂for constant.

The implementation of high-impedance transformer (HIT) has two kinds usually: 1) high pressure or middle pressure winding are split into two parts, increases the leakage reactance of winding; 2) adopt built-in reactor, increase winding leakage reactance at low pressure winding ends by lead-in wire series reactor.The cost of investment of scheme 2 is lower, and loss is less, run more economical, steady, reliable, and impedance deviation easily ensures, easily meets the condition etc. of enlarging station parallel running.

Optionally, when HIT is built-in reactor HIT, the shunt transformer impedance Δ z ' of equivalence _ijwith the impedance Δ z of built-in reactor _ijrelation can be:

$\mathrm{\&Delta;}{z}_{\mathrm{ij}}^{\&prime;}=-\frac{z_{\mathrm{ij}}(z_{\mathrm{ij}}+\mathrm{\&Delta;}{z}_{\mathrm{ij}})}{\mathrm{\&Delta;}{z}_{\mathrm{ij}}};$

Optionally, when HIT is built-in reactor HIT, the variable quantity of admittance matrix with the relation of the no-load voltage ratio of transformer can be also:

$\left\{\begin{array}{c}\mathrm{\&Delta;}{Y}_{\mathrm{ii}}=\frac{u_{\mathrm{ij}}}{\mathrm{\&Delta;}{z}_{\mathrm{ij}}^{\&prime;}}=-\frac{u_{\mathrm{ij}}\mathrm{\&Delta;}{z}_{\mathrm{ij}}}{z_{\mathrm{ij}}(z_{\mathrm{ij}}+\mathrm{\&Delta;}{z}_{\mathrm{ij}})}\\ \mathrm{\&Delta;}{Y}_{\mathrm{jj}}=\frac{u_{\mathrm{ij}}}{k^2\mathrm{\&Delta;}{z}_{\mathrm{ij}}^{\&prime;}}=-\frac{u_{\mathrm{ij}}\mathrm{\&Delta;}{z}_{\mathrm{ij}}}{k^2{z}_{\mathrm{ij}}(z_{\mathrm{ij}}+\mathrm{\&Delta;}{z}_{\mathrm{ij}})}\\ \mathrm{\&Delta;}{Y}_{\mathrm{ij}}=\mathrm{\&Delta;}{Y}_{\mathrm{ij}}=-\frac{u_{\mathrm{ij}}}{\mathrm{k\&Delta;}{z}_{\mathrm{ij}}^{\&prime;}}=\frac{u_{\mathrm{ij}}\mathrm{\&Delta;}{z}_{\mathrm{ij}}}{k{z}_{\mathrm{ij}}(z_{\mathrm{ij}}+\mathrm{\&Delta;}{z}_{\mathrm{ij}})}\end{array}\right.,$

Wherein, u _ij=1 represents employing built-in reactor HIT; u _ij=0 represents employing common transformer;

Optionally, the cost c of built-in reactor HIT can be:

c＝u _ij(A ₃+B ₄Δz _ij)，

Wherein, A ₃, B ₃for constant.

Optionally, the non-linear unconfined short-circuit current limiting measure Optimal Allocation Model of foundation can be:

$\min F(u,w,x,\mathrm{\&rho;})=f(u,w,x)+\mathrm{\&rho;}\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{g}_i^{+}(u,w,x)+\mathrm{\&rho;}\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}\left|h_j(u,w,x)\right|$

Wherein, ρ is penalty factor, h _j(u, w, x) is equality constraint, g _i(u, w, x) is inequality constraints, $g_i^{+}(u,w,x)=\left\{\begin{array}{c}{g}_i(u,w,x),g_i(u,w,x)>0\\ 0,\mathrm{else}\end{array}\right.,$ M, n are respectively total number of two class constraints.

Optionally, use extended pattern search algorithms to solve described short-circuit current limiting measure Optimal Allocation Model, the optimum optimization disaggregation obtaining the sequence converges that iteration is produced can comprise:

Steps A: i-th coordinate direction setting the state variable x that does things is e _i=[0..., 1 ... 0] ^t;

Step B: given initial point x ⁽¹⁾, initial step length δ, accelerated factor α, reduction factor β ∈ (0,1) and N number of coordinate direction e ₁, e ₂..., e _n, permissible error ξ >0, maximum probe number of times T _max, detect n at most _maxstep-length is reseted, maximum step-length δ after secondary _max, put y ⁽¹⁾=x ⁽¹⁾, k=1, i=1, n=1;

Step C: if F is (y ⁽ⁱ⁾+ δ _ei) <F (y ⁽ⁱ⁾), then make y ⁽ⁱ⁺¹⁾=y ⁽ⁱ⁾+ δ _ei, go to step E, otherwise enter step F;

Step D: if F is (y ⁽ⁱ⁾-δ _ei) <F (y ⁽ⁱ⁾), then make y ⁽ⁱ⁺¹⁾=y ⁽ⁱ⁾-δ _ei, otherwise make y ⁽ⁱ⁺¹⁾=y ⁽ⁱ⁾, continue;

Step e: if i<N, then put i=i+1, go to step C, otherwise enter step F;

Step F: if F is (y ^(N+1)) <F (X ^(K)), then continue, otherwise enter step H;

Step G: put basic point x ^(k+1)=y ^(N+1), make y ⁽ⁱ⁾=x ^(k+1)+ α ^{(x (k+1)-x (k))}, k=k+1, i=l, get back to step C;

Step H: if δ≤ξ or k=T _max, then an x is obtained ^(k); Otherwise, put δ=β δ, y ⁽ⁱ⁾=x ^(k), k=k+l, i=1;

Step I: if n=n _max, δ=rand (δ _max), n=1, gets back to step C, otherwise n=n+1, get back to step C;

Wherein, rand (δ _max) be 0 to δ _maxrandom number.

Pattern search method is very strong to the search capability of local location, and this is also that it is easily absorbed in the main cause of local optimum solution.The pattern search method every iteration in computational process improved resets step-length nmax time, and algorithm can be jumped out from local solution.

Optionally, can by determining that short-circuit current limiting measure distributes function rationally:

$f(u,w,x)=\min [k_1\underset{s=1}{\overset{N_s}{\mathrm{\&Sigma;}}}{c}_s(u_s,w_s)/P_s+k_2{c}_e(u,w,x)],$

Wherein, N _sfor the sum of applied current limiting measures, c _efor other target functions, c _ecomprise operating cost or reliability, k ₁, k ₂for weight coefficient;

Determine the constraints of measure decision variable:

$\underset{\&OverBar;}{w_s}\&le;w_s\&le;\stackrel{\&OverBar;}{w_s},$

u _s∈{0,1}，

Wherein, with for the bound of current-limiting impedance,

When decision variable gets different numerical value time, the constraints of measure decision variable:

f(u,w,x)＝0，

l(u,w,x)＝0，

$\underset{\&OverBar;}{x}\&le;x\&le;\stackrel{\&OverBar;}{x},$

Wherein, f (u, w, x)=0 is Load flow calculation equation; L (u, w, x)=0 is calculation of short-circuit current equation; for systematic function constraint, comprising: the constraint of system short-circuit restriction of current, voltage, trend constraint and the constraint of N-1 trend;

Function is distributed rationally to short-circuit current limiting measure and determines that the constraints of measure decision variable uses non-linear penalty function method, mixed integer programming problem is converted into unconstrained non-linear programming problem, obtains non-linear unconfined short-circuit current limiting measure Optimal Allocation Model.

Optionally, obtaining in non-linear unconfined short-circuit current limiting measure Optimal Allocation Model process,

Can by constraints u _s{ 0,1} is converted into ∈: u _s(1-u _s)=0; Equality constraint is converted into by 0-1 Integer constrained characteristic.

Can by common Integer constrained characteristic $\underset{\&OverBar;}{w_s}\&le;w_s\&le;\stackrel{\&OverBar;}{w_s}$ Be converted to: $(w_s-\underset{\&OverBar;}{w_s})(w_s-\underset{\&OverBar;}{w_s}-1)...(w_s-\stackrel{\&OverBar;}{w_s})=0;$

Work as w _swhen bound gap is larger, the number of times of above formula equation will be too high, unfavorable to solving, therefore, and can by w _sbe expressed as: $w_s=a_0{2}^0+a_1{2}^1+...+a_p{2}^p+\underset{\&OverBar;}{w_s}.$

The short circuit current current limiting measures optimization method that the embodiment of the present invention provides, in conjunction with current limiting measures cost calculation, design a kind of non-linear unconfined short-circuit current limiting measure Optimal Allocation Model based on pattern search method, when solving concrete scheme, by changing the initial step length of pattern search method computing each time, namely use improved model search method technology optimal solution, avoid traditional mode search method can only obtain the deficiency of locally optimal solution, thus strengthen the global optimizing ability of algorithm.

Be introduced the short circuit current current limiting measures optimization system that the embodiment of the present invention provides below, short circuit current current limiting measures optimization system described below can mutual corresponding reference with above-described short circuit current current limiting measures optimization method.

The system block diagram of the short circuit current current limiting measures optimization system that Fig. 2 provides for the embodiment of the present invention, with reference to Fig. 2, this short circuit current current limiting measures optimization system can comprise: determination module 100, model checking module 200 and solve module 300; Wherein,

Determination module 100, for determining network analysis and the cost of each current limiting measures;

Model building module 200, for setting up non-linear unconfined short-circuit current limiting measure Optimal Allocation Model according to the network analysis of each current limiting measures and cost;

Solve module 300, for using extended pattern search algorithms to solve described short-circuit current limiting measure Optimal Allocation Model, the optimum obtaining the sequence converges that iteration is produced optimizes disaggregation, wherein, described improved model search method is in computational process, and every iteration resets step-length after making a reservation for this number.

The short circuit current current limiting measures optimization system that the embodiment of the present invention provides, in conjunction with current limiting measures cost calculation, design a kind of non-linear unconfined short-circuit current limiting measure Optimal Allocation Model based on pattern search method, when solving concrete scheme, by changing the initial step length of pattern search method computing each time, namely use improved model search method technology optimal solution, avoid traditional mode search method can only obtain the deficiency of locally optimal solution, thus strengthen the global optimizing ability of algorithm.

In this specification, each embodiment adopts the mode of going forward one by one to describe, and what each embodiment stressed is the difference with other embodiments, between each embodiment identical similar portion mutually see.For device disclosed in embodiment, because it corresponds to the method disclosed in Example, so description is fairly simple, relevant part illustrates see method part.

Professional can also recognize further, in conjunction with unit and the algorithm steps of each example of embodiment disclosed herein description, can realize with electronic hardware, computer software or the combination of the two, in order to the interchangeability of hardware and software is clearly described, generally describe composition and the step of each example in the above description according to function.These functions perform with hardware or software mode actually, depend on application-specific and the design constraint of technical scheme.Professional and technical personnel can use distinct methods to realize described function to each specifically should being used for, but this realization should not thought and exceeds scope of the present invention.

The software module that the method described in conjunction with embodiment disclosed herein or the step of algorithm can directly use hardware, processor to perform, or the combination of the two is implemented.Software module can be placed in the storage medium of other form any known in random asccess memory (RAM), internal memory, read-only memory (ROM), electrically programmable ROM, electrically erasable ROM, register, hard disk, moveable magnetic disc, CD-ROM or technical field.

To the above-mentioned explanation of the disclosed embodiments, professional and technical personnel in the field are realized or uses the present invention.To be apparent for those skilled in the art to the multiple amendment of these embodiments, General Principle as defined herein can without departing from the spirit or scope of the present invention, realize in other embodiments.Therefore, the present invention can not be restricted to these embodiments shown in this article, but will meet the widest scope consistent with principle disclosed herein and features of novelty.

Claims (10)

1. a short circuit current current limiting measures optimization method, is characterized in that, comprising:

Determine network analysis and the cost of each current limiting measures;

Non-linear unconfined short-circuit current limiting measure Optimal Allocation Model is set up according to the network analysis of each current limiting measures and cost;

Extended pattern search algorithms is used to solve described short-circuit current limiting measure Optimal Allocation Model, the optimum obtaining the sequence converges that iteration is produced optimizes disaggregation, wherein, described improved model search method is in computational process, and every iteration resets step-length after making a reservation for this number.

2. short circuit current current limiting measures optimization method according to claim 1, it is characterized in that, described current limiting measures comprise: install current limiting reactor CLR additional, adopt fault current limiter FCL and adopt high-impedance transformer HIT.

3. short circuit current current limiting measures optimization method according to claim 2, is characterized in that, the network analysis and the cost that install CLR additional comprise:

When CLR installs additional on circuit, the impedance Δ z ' of parallel branch _ijwith the impedance Δ z of series arm _ijrelation:

${\mathrm{\&Delta;z}}_{\mathrm{ij}}^{\&prime;}=\frac{z_{\mathrm{ij}}(z_{\mathrm{ij}}+{\mathrm{\&Delta;z}}_{\mathrm{ij}})}{{\mathrm{\&Delta;z}}_{\mathrm{ij}}};$

The self-admittance of node i and node j and transadmittance:

$\left\{\begin{array}{c}{\mathrm{\&Delta;Y}}_{\mathrm{ii}}=\mathrm{\&Delta;}{Y}_{\mathrm{jj}}=-\frac{u_{\mathrm{ij}}{\mathrm{\&Delta;z}}_{\mathrm{ij}}}{z_{\mathrm{ij}}(z_{\mathrm{ij}}+{\mathrm{\&Delta;z}}_{\mathrm{ij}})}\\ {\mathrm{\&Delta;Y}}_{\mathrm{ij}}=\mathrm{\&Delta;}{Y}_{\mathrm{ji}}=\frac{u_{\mathrm{ij}}{\mathrm{\&Delta;z}}_{\mathrm{ij}}}{z_{\mathrm{ij}}(z_{\mathrm{ij}}+{\mathrm{\&Delta;z}}_{\mathrm{ij}})}\end{array}\right.,$

Wherein, u _ijfor 0-1 control variables.U _ij=0 represents that circuit i-j does not install CLR; u _ij=1 represents that circuit i-j installation impedance is Δ z _ijcLR;

Cost of investment c:

c＝u _ij(A ₁+B ₁Δz _ij)，

Wherein, A ₁, B ₁for cost coefficient.

4. short circuit current current limiting measures optimization method according to claim 2, is characterized in that, adopts the network analysis of FCL and cost to comprise:

When FCL is superconductive current limiter SFCL, equiva lent impedance model:

Z _SFCL＝Z _m(1-exp(-t/T _sc))，

Wherein, Z _mfor the maximum impedance value of SFCL normal state, T _scfor the time constant that SFCL superconducting state changes to normal state;

When FCL is solid-state current limiter, current-limiting reactor value X _fCLwith the relation of the angle of flow of solid-state switch GTO:

$X_{\mathrm{SFCL}}=X_{L_2}+\frac{X\left(\mathrm{\&sigma;}\right)X_C}{X\left(\mathrm{\&sigma;}\right)+X_C},$

Wherein, X (σ) and induction reactance X _l2with the relation of the angle of flow σ of GTO:

$X\left(\mathrm{\&sigma;}\right)=X_{L_1}=\frac{\mathrm{\&pi;}}{\mathrm{\&sigma;}-\sin \mathrm{\&sigma;}};$

When FCL is short-circuited fault:

$\left\{\begin{array}{c}{Z}_{\mathrm{FCL}}=0,t=0\\ Z_{\mathrm{FCL}}=Z_m,t=T_0\end{array}\right.,$

Wherein, T ₀for secondary transient state short circuit time constant;

Cost of investment c:

c＝u _ij(A ₂+B ₂Z _FCL)，

Wherein, A ₂, B ₂for constant.

5. short circuit current current limiting measures optimization method according to claim 2, is characterized in that, adopts the network analysis of HIT and cost to comprise:

When HIT is built-in reactor HIT, the shunt transformer impedance Δ z ' of equivalence _ijwith the impedance Δ z of built-in reactor _ijpass be:

${\mathrm{\&Delta;z}}_{\mathrm{ij}}^{\&prime;}=-\frac{z_{\mathrm{ij}}(z_{\mathrm{ij}}+{\mathrm{\&Delta;z}}_{\mathrm{ij}})}{{\mathrm{\&Delta;z}}_{\mathrm{ij}}};$

When HIT is built-in reactor HIT, the variable quantity of admittance matrix is also with the relation of the no-load voltage ratio of transformer:

$\left\{\begin{array}{c}{\mathrm{\&Delta;Y}}_{\mathrm{ii}}=\frac{u_{\mathrm{ij}}}{{\mathrm{\&Delta;z}}_{\mathrm{ij}}^{\&prime;}}=-\frac{u_{\mathrm{ij}}{\mathrm{\&Delta;z}}_{\mathrm{ij}}}{z_{\mathrm{ij}}(z_{\mathrm{ij}}+{\mathrm{\&Delta;z}}_{\mathrm{ij}})}\\ {\mathrm{\&Delta;Y}}_{\mathrm{jj}}=\frac{u_{\mathrm{ij}}}{k^2{\mathrm{\&Delta;z}}_{\mathrm{ij}}^{\&prime;}}=-\frac{u_{\mathrm{ij}}{\mathrm{\&Delta;z}}_{\mathrm{ij}}}{k^2{z}_{\mathrm{ij}}(z_{\mathrm{ij}}+{\mathrm{\&Delta;z}}_{\mathrm{ij}})}\\ {\mathrm{\&Delta;Y}}_{\mathrm{ij}}=\mathrm{\&Delta;}{Y}_{\mathrm{ji}}=-\frac{u_{\mathrm{ij}}}{k{\mathrm{\&Delta;z}}_{\mathrm{ij}}^{\&prime;}}=\frac{u_{\mathrm{ij}}{\mathrm{\&Delta;z}}_{\mathrm{ij}}}{k{z}_{\mathrm{ij}}(z_{\mathrm{ij}}+{\mathrm{\&Delta;z}}_{\mathrm{ij}})}\end{array}\right.,$

Wherein, u _ij=1 represents employing built-in reactor HIT; u _ij=0 represents employing common transformer;

The cost c of built-in reactor HIT:

c＝u _ij(A ₃+B ₄Δz _ij)，

Wherein, A ₃, B ₃for constant.

6. short circuit current current limiting measures optimization method according to claim 2, is characterized in that, described non-linear unconfined short-circuit current limiting measure Optimal Allocation Model is:

$\min F(u,w,x,\mathrm{\&rho;})=f(u,w,x)+\mathrm{\&rho;}\underset{i=1}{\overset{m}{\mathrm{\&Sigma;}}}{g}_i^{+}(u,w,x)+\mathrm{\&rho;}\underset{j=1}{\overset{m}{\mathrm{\&Sigma;}}}\left|h_j(u,w,x)\right|$

Wherein, ρ is penalty factor, h _j(u, w, x) is equality constraint, g _i(u, w, x) is inequality constraints, $g_i^{+}(u,w,x)=\left\{\begin{array}{cc}{g}_i(u,w,x),& g_i(u,w,x)>0\\ 0,& \mathrm{else}\end{array}\right.,$ M, n are respectively total number of two class constraints.

7. short circuit current current limiting measures optimization method according to claim 2, is characterized in that, uses extended pattern search algorithms to solve described short-circuit current limiting measure Optimal Allocation Model, and the optimum optimization disaggregation obtaining the sequence converges that iteration is produced comprises:

Steps A: i-th coordinate direction setting the state variable x that does things is e _i=[0..., 1 ... 0] ^t;

Step B: given initial point x ⁽¹⁾, initial step length δ, accelerated factor α, reduction factor β ∈ (0,1) and N number of coordinate direction e ₁, e ₂..., e _n, permissible error ξ >0, maximum probe number of times T _max, detect n at most _maxstep-length is reseted, maximum step-length δ after secondary _max, put y ⁽¹⁾=x ⁽¹⁾, k=1, i=1, n=1;

Step C: if F is (y ⁽ⁱ⁾+ δ _ei) <F (y ⁽ⁱ⁾), then make y ⁽ⁱ⁺¹⁾=y ⁽ⁱ⁾+ δ _ei, go to step E, otherwise enter step F;

Step D: if F is (y ⁽ⁱ⁾-δ _ei) <F (y ⁽ⁱ⁾), then make y ⁽ⁱ⁺¹⁾=y ⁽ⁱ⁾-δ _ei, otherwise make y ⁽ⁱ⁺¹⁾=y ⁽ⁱ⁾, continue;

Step e: if i<N, then put i=i+1, go to step C, otherwise enter step F;

Step F: if F is (y ^(N+1)) <F (X ^(K)), then continue, otherwise enter step H;

Step G: put basic point x ^(k+1)=y ^(N+1), make y ⁽ⁱ⁾=x ^(k+1)+ α ^{(x (k+1)-x (k))}, k=k+1, i=l, get back to step C;

Step H: if δ≤ξ or k=T _max, then an x is obtained ^(k); Otherwise, put δ=β δ, y ⁽ⁱ⁾=x ^(k), k=k+l, i=1;

Step I: if n=n _max, δ=rand (δ _max), n=1, gets back to step C, otherwise n=n+1, get back to step C;

Wherein, rand (δ _max) be 0 to δ _maxrandom number.

8. short circuit current current limiting measures optimization method according to claim 1, is characterized in that, the described network analysis according to each current limiting measures and cost are set up non-linear unconfined short-circuit current limiting measure Optimal Allocation Model and comprised:

Determine that short-circuit current limiting measure distributes function rationally:

$f(u,w,x)=\min [k_1\underset{s=1}{\overset{N_s}{\mathrm{\&Sigma;}}}{c}_s(u_s,w_s)/P_s+k_2{c}_e(u,w,x)],$

Wherein, N _sfor the sum of applied current limiting measures, c _efor other target functions, c _ecomprise operating cost or reliability, k ₁, k ₂for weight coefficient;

Determine the constraints of measure decision variable:

$\underset{\&OverBar;}{w_s}\&le;w_s\&le;\stackrel{\&OverBar;}{w_s},$

u _s∈{0,1}，

Wherein, with w _s for the bound of current-limiting impedance,

When decision variable gets different numerical value time, the constraints of measure decision variable:

f(u,w,x)＝0，

l(u,w,x)＝0，

$\underset{\&OverBar;}{x}\&le;x\&le;\stackrel{\&OverBar;}{x},$

Wherein, f (u, w, x)=0 is Load flow calculation equation; L (u, w, x)=0 is calculation of short-circuit current equation; for systematic function constraint, comprising: the constraint of system short-circuit restriction of current, voltage, trend constraint and the constraint of N-1 trend;

Function is distributed rationally to short-circuit current limiting measure and determines that the constraints of measure decision variable uses non-linear penalty function method, mixed integer programming problem is converted into unconstrained non-linear programming problem, obtains non-linear unconfined short-circuit current limiting measure Optimal Allocation Model.

9. short circuit current current limiting measures optimization method according to claim 8, is characterized in that, also comprise:

By constraints u _s{ 0,1} is converted into ∈: u _s(1-u _s)=0;

By constraints $\underset{\&OverBar;}{w_s}\&le;w_s\&le;\stackrel{\&OverBar;}{w_s}$ Be converted to: $(w_s-\underset{\&OverBar;}{w_s})(w_s-\underset{\&OverBar;}{w_s}-1)...(w_s-\stackrel{\&OverBar;}{w_s})=0;$

By w _sbe expressed as: $w_s=a_0{2}^0+a_1{2}^1+...+a_p{2}^p+\underset{\&OverBar;}{w_s}.$

10. a short circuit current current limiting measures optimization system, is characterized in that, comprising: determination module, model checking module and solve module; Wherein,

Described determination module, for determining network analysis and the cost of each current limiting measures;

Described model building module, for setting up non-linear unconfined short-circuit current limiting measure Optimal Allocation Model according to the network analysis of each current limiting measures and cost;

Describedly solve module, for using extended pattern search algorithms to solve described short-circuit current limiting measure Optimal Allocation Model, the optimum obtaining the sequence converges that iteration is produced optimizes disaggregation, wherein, described improved model search method is in computational process, and every iteration resets step-length after making a reservation for this number.