CN104462769A - Transformer substation current measurement loop fault detection method based on genetic algorithm - Google Patents

Abstract

The invention discloses a transformer substation current measurement loop fault detection method based on a genetic algorithm, which comprises the following steps: searching out a switch island in the transformer substation, carrying out voltage state estimation to obtain the most reliable phase voltage effective value of the switch island, finding out branches associated with each physical node of the electrical island, establishing the following equation for the physical node i, the following are established for the physical legThe equations are set forth in the form of,and solving the most reliable values of the three-phase active, three-phase reactive and single-phase current amplitudes, and judging possible faults of the current measuring loop. The method adopts the genetic algorithm to solve the most credible value of each measurement of the current measurement loop, can consider complex constraints, has low requirements on the completeness and observability of the measurement, and is convenient for engineering application.

Description

Based on transformer station's current measurement loop fault detection method of genetic algorithm

Technical field

The invention belongs to power system device fault diagnosis field, relate to particularly and utilize SCADA data, adopt genetic algorithm to solve current measurement loop respectively measures most confidence values, thus realize the fault detect to transformer station's current measurement loop.

Background technology

The fault in Timeliness coverage transformer station current measurement loop is conducive to the quality of data ensureing transformer station, thus guarantee that the running status of operations staff to transformer station provides correct judgement, the basic skills of discovery transformer station current measurement loop fault is whether discovery amount measuring error is bigger than normal, and this solves with regard to needing the true value of current measurement loop measurement amount and estimate.Traditional measurement amount state estimation solves by setting up quantity of state and measuring the Jacobian matrix of total error, and this Method Modeling is complicated, exploitation and debugging cycle long, be not easy to the constraint condition of consideration complexity.

Summary of the invention

The object of the invention is the deficiency existed to improve said method, a kind of transformer station's current measurement loop fault detection method based on genetic algorithm is provided.

Technical solution of the present invention is as described below:

Based on transformer station's current measurement loop fault detection method of genetic algorithm, it is characterized in that, comprise the following steps:

S1: hunt out the switch island in transformer station, described switch island by bus, isolating switch, disconnecting link and short leg form electrically on one group of equipment being connected, the impedance of each equipment is 0;

S2: carry out voltage status estimation, obtains the most credible phase voltage effective value on switch island, if system is in non-equilibrium state or voltage measurement has problem, then stops current measurement loop and detects;

S3: find out the branch road associated by each physical node of electrical island;

S4: for physical node i, sets up following equation according to node power law of conservation, measures incomplete, then do not consider respective physical node if exist,

$\underset{k=1}{\overset{N_i}{\mathrm{\Σ}}}{P}_{i\_k}=e_{\mathrm{pi}}\≈0---\left(1\right)$

$\underset{k=1}{\overset{N_i}{\mathrm{\Σ}}}{Q}_{i\_k}=e_{\mathrm{qi}}\≈0---\left(2\right)$

In formula, Ni is the number of branches that node i is connected, and k is the branch road sequence number be connected with node i, and Pi_k, Qi_k are the three-phase general power of the k branch road be connected with node i, total NODE physical node, i.e. i=1 ~ NODE;

S5: set up following equation for physical tributary, measures if exist incomplete, does not then consider respective branch,

${\left(\sqrt{3}{I}_{j}U\right)}^2-(P_j^2+Q_j^2)=e_{\mathrm{sj}}\≈0---\left(3\right)$

In formula, Ij is the line current amplitude of branch road j, Pj and Qj is that the three-phase of branch road j is gained merit and the idle measurement of three-phase;

S6: all branch currents making following objective function minimum with genetic algorithm for solving, meritorious, idle, obtains in each branch road measurement amount that three-phase is gained merit, three-phase is idle, the most confidence values of single-phase current amplitude,

$\min e=\underset{i=1}{\overset{\mathrm{NODE}}{\mathrm{\Σ}}}{e}_{\mathrm{pi}}^2+\underset{i=1}{\overset{\mathrm{NODE}}{\mathrm{\Σ}}}{e}_{\mathrm{qi}}^2+\underset{j=1}{\overset{\mathrm{BRANCH}}{\mathrm{\Σ}}}{e}_{\mathrm{sj}}^2---\left(4\right)$

Each constraint condition or departure are obtained by (1) ~ (3) formula;

S7: current measurement loop possible breakdown judges, calculate the deviation of the most confidence values of I, P, Q and measuring value, if certain branch road provides each phase current of abc tri-, then calculate the deviation of each phase current respectively, when deviation is greater than 10%, then provide the alarm that corresponding current measurement loop may exist fault.

Further, in described step S2, voltage status is estimated to comprise following steps:

S21: take away each voltage of a phase in Guam and measure, average;

S22: ask each a phase voltage to measure the deviation with mean value;

S23: remove each that be greater than 5% with mean deviation measure in the worst measurement, return S21, if do not have deviation to be greater than the point of 5%, then stop the calculating of the most credible a phase voltage amplitude Ua;

S24: calculate Ub and Uc with said method;

S25: the mean value U calculating Ua, Ub, Uc;

S26: when the deviation of Ua, Ub, Uc and U is all less than 5%, think that system is in equilibrium state, gets U for the most credible phase voltage, otherwise thinks that system is in non-equilibrium state or voltage measurement has problem, stops current measurement loop and detects.

Further, described step S6 comprises following steps:

S61: variable-definition, the phase current magnitude of getting each branch road measures I, three-phase gains merit P and the idle Q of three-phase as the parameter solved to be optimized, in order to shortcut calculation, no longer to this tittle classification process, represent the expectation value of this tittle with unified Xi, Xmi represents measured value, and i represents sequence number, ki represents the deviation of i-th amount expectation value and measured value, meets following formula:

X _i＝X _mi(1+k _i)

Wherein i=1 ~ N, wherein N is the number of all I, P, Q, and the span of Ki is [-10,10];

S62: initialization of population;

S63: the fitness evaluation of each individuality, each individuality asks for corresponding Xi and I, the value of P, Q according to ki, then utilizes (4) formula to calculate each individual distance optimal value, i.e. the departure of 0, and by each the individual order ascending by departure sequence;

S64: end condition judge: continuous 100 generation optimum individual departure no longer diminish, then stop optimizing;

S65: original seed group will be selected half, namely 15N individuality remains into the next generation, is specially,

A) 1/3 optimum individuality all retains,

B) 1/3 individuality of suboptimum retains 1/2 in a random way,

C) 1/3 the poorest individuality does not all retain;

S66: copy

A) individuality remained all is copied, thus recovers population at individual number to original number 30N,

B) mark 1 and only 1 optimum individual as the individuality not participating in intersection, variation;

S67: optional two individualities are intersection behaviour and do, be 8N time altogether, make 16N individual participation interlace operation, avoid selecting to be marked as the individuality not participating in cross and variation, the method of cross exchanged gene is: to the individuality selected, arbitrary selection two gene locations, then exchange the array content between these two gene locations;

S68: variation

A) optional individuality from an above-mentioned 30N individuality, avoids selecting to be marked as the individuality not participating in cross and variation,

B) to the gene location of the optional half of each individuality, respective counts group element is revised as ki=5 × random (-1,1),

C) step S63 is returned.

Further, in described step S61, phase current magnitude measures the mean value that I is abc phase current.

Further, in described step S62, in population, the initial value following methods of each individuality generates, and take ki as gene structure chromosome Yj, each chromosome Yj is 1 one-dimension array, and each ki is array element, and population number elects 30 × N as, i.e. j=1 ~ 30 × N, wherein

A) ki of 10 × N number of body adopts ki=0.1 × random (-1,1) to generate, and wherein random (-1,1) expression gets random number in (-1,1) scope,

B) ki of 10 × N number of body adopts ki=0.5 × random (-1,1) to generate,

C) ki of 5 × N number of body adopts ki=1 × random (-1,1) to generate,

D) ki of 5 × N number of body adopts ki=10 × random (-1,1) to generate,

E) ki of body one by one optional in c group is taken as 0, as 1 individuality.

Further, in described step S64, the departure of optimum individual is less than 0.001 stopping optimizing.

Further, in described step S5, current amplitude Ij is the mean value of abc three-phase current amplitude.

The present invention compared with prior art, its beneficial effect is, the present invention need not solve Jacobian matrix, modeling is simple, only the amount that can meet dependent equation requirement is optimized, therefore requires low to the ornamental measured and complete property, complicated constraint condition can be considered, exploitation debugging cycle is short, and is easy to expansion and the maintenance of algorithm in the future.Because the malfunction monitoring real-time of equipment amount measuring circuit is much larger than level second, therefore can not have an impact to the practicality of method owing to adopting the computation period that causes of genetic algorithm to expand to second level.

Accompanying drawing explanation

Fig. 1 is FB(flow block) of the present invention.

Embodiment

In order to make object of the present invention, technical scheme and advantage clearly understand, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.

The invention provides a kind of transformer station's current measurement loop fault detection method based on genetic algorithm.Fig. 1 is for realizing method flow diagram of the present invention.

Proposed by the invention based on RTU/SCADA data, method that transformer station's current measurement loop fault detects can be applicable to dispatching center, the equipment state monitoring module of patrolling in fibrillar center or transformer station to adopt genetic algorithm to realize.Raw data directly can pick up from RTU, also can obtain from the real-time database of SCADA or history library or forward.Because RTU/SCADA measurement amount is generally upgrade when measuring change and exceeding threshold value, this patent requires to obtain metric data simultaneously, but does not require that metric data refreshes simultaneously.For adopted method for estimating state do not need each branch road or node voltage correlative to measure complete, the modal equation used in method and visual power solving equation are only set up when there being Correlated Case with ARMA Measurement, if Correlated Case with ARMA Measurement is incomplete, then can not set up.Therefore this method is tied little in the measurement environment of reality.

In order to monitor the situation in each current measurement loop within the scope of transformer station in real time, EMS application server or set up independent equipment monitor server in real time with about 1 minute for the cycle, primary fault detection is carried out to the current measurement loop of monitored transformer station, when finding that amount measured value and most confidence values difference are larger, operations staff is reminded to carry out finer diagnosis to corresponding current measurement loop.Also further combined with statistics, the deep diagnosis of emphasis can be carried out to the measurement loop of alarm of frequently breaking down.

Concrete steps are as follows:

Step 1: hunt out the switch island in transformer station.

In transformer station switch island by bus, isolating switch, disconnecting link and short leg form electric on one group of equipment being connected, the impedance of each equipment can think 0.

Step 2: carry out voltage status estimation, obtains the most credible phase voltage effective value on switch island.

1) take away each voltage of a phase in Guam to measure, average,

2) each a phase voltage is asked to measure the deviation with mean value,

3) remove each that be greater than 5% with mean deviation measure in the worst measurement, return 1).If do not have deviation to be greater than the point of 5%, then stop the calculating of the most credible a phase voltage amplitude Ua,

4) Ub and Uc is calculated with same method,

5) the mean value U of Ua, Ub, Uc is calculated,

6) when the deviation of Ua, Ub, Uc and U is all less than 5%, think that system is in equilibrium state, get U for the most credible phase voltage.Otherwise think that system is in non-equilibrium state or voltage measurement has problem, stop current measurement loop and detect.

Step 3: find out the branch road associated by each physical node of electrical island.

Step 4: for physical node i, sets up following equation according to node power law of conservation, measures incomplete, then do not consider respective physical node if exist.

$\underset{k=1}{\overset{N_i}{\mathrm{\Σ}}}{P}_{i\_k}=e_{\mathrm{pi}}\≈0---\left(1\right)$

$\underset{k=1}{\overset{N_i}{\mathrm{\Σ}}}{Q}_{i\_k}=e_{\mathrm{qi}}\≈0---\left(2\right)$

In formula, Ni is the number of branches that node i is connected, and k is the branch road sequence number be connected with node i, and Pi_k, Qi_k are the three-phase general power of the k branch road be connected with node i, total NODE physical node, i.e. i=1 ~ NODE.

Step 5: set up following equation for physical tributary, measures if exist incomplete, does not then consider respective branch.

${\left(\sqrt{3}{I}_{j}U\right)}^2-(P_j^2+Q_j^2)=e_{\mathrm{sj}}\≈0---\left(3\right)$

In formula, Ij is the line current amplitude of branch road j, Pj and Qj is that the three-phase of branch road j is gained merit and the idle measurement of three-phase.If branch road j gives abc three-phase current amplitude, then get the mean value of three electric currents.

Step 6: all branch currents making following objective function minimum with genetic algorithm for solving, meritorious, idle, obtains the most confidence values of each branch road measurement amount (three-phase is meritorious, three-phase is idle, single-phase current amplitude).

$\min e=\underset{i=1}{\overset{\mathrm{NODE}}{\mathrm{\Σ}}}{e}_{\mathrm{pi}}^2+\underset{i=1}{\overset{\mathrm{NODE}}{\mathrm{\Σ}}}{e}_{\mathrm{qi}}^2+\underset{j=1}{\overset{\mathrm{BRANCH}}{\mathrm{\Σ}}}{e}_{\mathrm{sj}}^2---\left(4\right)$

Constraint condition or departure define square journey (1) ~ (3).The concrete steps of genetic algorithm are as follows:

1) variable-definition: the phase current magnitude of getting each branch road measures I (if provide abc phase current, then get the mean value of abc phase current), three-phase gains merit P and the idle Q of three-phase as the parameter solved to be optimized, in order to shortcut calculation, no longer to this tittle classification process, represent the expectation value of this tittle with unified Xi, Xmi represents measured value, and i represents sequence number, ki represents the deviation of i-th amount expectation value and measured value, meets following formula:

X _i＝X _mi(1+k _i)

I=1 ~ N, wherein N is the number of all I, P, Q.The span of Ki is [-10,10].

2) initialization of population: take ki as gene structure chromosome Yj, each chromosome Yj is 1 one-dimension array, and each ki is array element.Population number elects 30 × N as, i.e. j=1 ~ 30 × N.In population, the initial value following methods of each individuality generates:

The ki of 10 × N number of body adopts ki=0.1 × random (-1,1) to generate, and wherein random (-1,1) expression gets random number in (-1,1) scope,

The ki of 10 × N number of body adopts ki=0.5 × random (-1,1) to generate,

The ki of 5 × N number of body adopts ki=1 × random (-1,1) to generate,

The ki of 5 × N number of body adopts ki=10 × random (-1,1) to generate,

The ki of body one by one optional in c group is taken as 0, as 1 individuality.

3) fitness evaluation of each individuality: each individuality asks for corresponding Xi and I, the value of P, Q according to ki, then (4) formula is utilized to calculate the departure of the distance optimal value (namely 0) of each individuality, and by each the individual order ascending by departure sequence.

4) end condition judge: continuous 100 generation optimum individual departure no longer diminish, then stop optimizing.Or the departure of optimum individual is less than 0.001 stopping optimizing.

5) select:

A) 1/3 optimum individuality all retains;

B) 1/3 individuality of suboptimum retains 1/2 in a random way;

C) 1/3 the poorest individuality does not all retain.

Such original seed group will select half, and namely 15N individuality remains into the next generation.

6) copy:

The individuality remained all is copied, thus recovers population at individual number to original number 30N,

Mark 1 and only 1 optimum individual as the individuality not participating in intersection, variation.

7) intersect:

A) optional two individualities do intersect behaviour do, be 8N time altogether, make 16N individuality participation interlace operation.Avoid selecting to be marked as the individuality not participating in cross and variation,

B) method of cross exchanged gene is: to the individuality selected, arbitrary selection two gene locations, then exchange the array content between these two gene locations.

8) make a variation:

A) optional individuality from an above-mentioned 30N individuality, avoids selecting to be marked as the individuality not participating in cross and variation,

B) to the gene location of the optional half of each individuality, respective counts group element is revised as ki=5 × random (-1,1),

C) step 3 is returned).

Step 7: current measurement loop possible breakdown judges.Calculate the deviation (if certain branch road provides each phase current of abc tri-, then calculating the deviation of each phase current respectively) of the most confidence values of I, P, Q and measuring value, when deviation is greater than 10%, then provide the alarm that corresponding current measurement loop may exist fault.

Step 2 ensures, and now voltage circuit is no problem.

The present invention is based on genetic algorithm and carry out measurement amount state estimation, need not solve Jacobian matrix, modeling is simple, require low to the ornamental of measurement amount and complete property, can consider complicated constraint condition, exploitation debugging cycle is short, and is easy to expansion and the maintenance of algorithm in the future.Because the malfunction monitoring real-time of equipment amount measuring circuit is much larger than level second, therefore can not have an impact to the practicality of method owing to adopting the computation period that causes of genetic algorithm to expand to second level.

Finally should be noted that: above embodiment is only for above embodiment, only in order to technical scheme of the present invention to be described but not to be limited, although with reference to above-mentioned embodiment to invention has been detailed description, those of ordinary skill in the field are to be understood that: still can modify to the specific embodiment of the present invention or equivalent replacement, and not departing from any amendment of spirit and scope of the invention or equivalent replacement, it all should be encompassed in the middle of right of the present invention.

Claims (7)

1., based on transformer station's current measurement loop fault detection method of genetic algorithm, it is characterized in that, comprise the following steps:

S1: hunt out the switch island in transformer station, described switch island by bus, isolating switch, disconnecting link and short leg form electrically on one group of equipment being connected, the impedance of each equipment is 0;

S2: carry out voltage status estimation, obtains the most credible phase voltage effective value on switch island, if system is in non-equilibrium state or voltage measurement has problem, then stops current measurement loop and detects;

S3: find out the branch road associated by each physical node of electrical island;

S4: for physical node i, sets up following equation according to node power law of conservation, measures incomplete, then do not consider respective physical node if exist,

$\underset{k=1}{\overset{N_i}{\mathrm{\Σ}}}{P}_{i\_k}=e_{\mathrm{pi}}\≈0---\left(1\right)$

$\underset{k=1}{\overset{N_i}{\mathrm{\Σ}}}{Q}_{i\_k}=e_{\mathrm{qi}}\≈0---\left(2\right)$

In formula, Ni is the number of branches that node i is connected, and k is the branch road sequence number be connected with node i, and Pi_k, Qi_k are the three-phase general power of the k branch road be connected with node i, total NODE physical node, i.e. i=1 ~ NODE;

S5: set up following equation for physical tributary, measures if exist incomplete, does not then consider respective branch,

${\left(\sqrt{3}{I}_{j}U\right)}^2-(P_j^2+Q_j^2)=e_{\mathrm{sj}}\≈0---\left(3\right)$

In formula, Ij is the line current amplitude of branch road j, Pj and Qj is that the three-phase of branch road j is gained merit and the idle measurement of three-phase;

S6: all branch currents making following objective function minimum with genetic algorithm for solving, meritorious, idle, obtains in each branch road measurement amount that three-phase is gained merit, three-phase is idle, the most confidence values of single-phase current amplitude,

$\min e=\underset{i=1}{\overset{\mathrm{NODE}}{\mathrm{\Σ}}}{e}_{\mathrm{pi}}^2+\underset{i=1}{\overset{\mathrm{NODE}}{\mathrm{\Σ}}}{e}_{\mathrm{qi}}^2+\underset{j=1}{\overset{\mathrm{BRANCH}}{\mathrm{\Σ}}}{e}_{\mathrm{sj}}^2---\left(4\right)$

Each constraint condition or departure are obtained by (1) ~ (3) formula;

S7: current measurement loop possible breakdown judges, calculate the deviation of the most confidence values of I, P, Q and measuring value, if certain branch road provides each phase current of abc tri-, then calculate the deviation of each phase current respectively, when deviation is greater than 10%, then provide the alarm that corresponding current measurement loop may exist fault.

2. the transformer station's current measurement loop fault detection method based on genetic algorithm according to claim 1, is characterized in that, in described step S2, voltage status is estimated to comprise following steps:

S21: take away each voltage of a phase in Guam and measure, average;

S22: ask each a phase voltage to measure the deviation with mean value;

S23: remove each that be greater than 5% with mean deviation measure in the worst measurement, return S21, if do not have deviation to be greater than the point of 5%, then stop the calculating of the most credible a phase voltage amplitude Ua;

S24: calculate Ub and Uc with said method;

S25: the mean value U calculating Ua, Ub, Uc;

S26: when the deviation of Ua, Ub, Uc and U is all less than 5%, think that system is in equilibrium state, gets U for the most credible phase voltage, otherwise thinks that system is in non-equilibrium state or voltage measurement has problem, stops current measurement loop and detects.

3. the transformer station's current measurement loop fault detection method based on genetic algorithm according to claim 1, it is characterized in that, described step S6 comprises following steps:

S61: variable-definition, the phase current magnitude of getting each branch road measures I, three-phase gains merit P and the idle Q of three-phase as the parameter solved to be optimized, in order to shortcut calculation, no longer to this tittle classification process, represent the expectation value of this tittle with unified Xi, Xmi represents measured value, and i represents sequence number, ki represents the deviation of i-th amount expectation value and measured value, meets following formula:

X _i＝X _mi(1+k _i)

Wherein i=1 ~ N, wherein N is the number of all I, P, Q, and the span of Ki is [-10,10];

S62: initialization of population;

S63: the fitness evaluation of each individuality, each individuality asks for corresponding Xi and I, the value of P, Q according to ki, then utilizes (4) formula to calculate each individual distance optimal value, i.e. the departure of 0, and by each the individual order ascending by departure sequence;

S64: end condition judge: continuous 100 generation optimum individual departure no longer diminish, then stop optimizing;

S65: original seed group will be selected half, namely 15N individuality remains into the next generation, is specially,

A) 1/3 optimum individuality all retains,

B) 1/3 individuality of suboptimum retains 1/2 in a random way,

C) 1/3 the poorest individuality does not all retain;

S66: copy

A) individuality remained all is copied, thus recovers population at individual number to original number 30N,

B) mark 1 and only 1 optimum individual as the individuality not participating in intersection, variation;

S67: optional two individualities are intersection behaviour and do, be 8N time altogether, make 16N individual participation interlace operation, avoid selecting to be marked as the individuality not participating in cross and variation, the method of cross exchanged gene is: to the individuality selected, arbitrary selection two gene locations, then exchange the array content between these two gene locations;

S68: variation

A) optional individuality from an above-mentioned 30N individuality, avoids selecting to be marked as the individuality not participating in cross and variation,

B) to the gene location of the optional half of each individuality, respective counts group element is revised as ki=5 × random (-1,1),

C) step S63 is returned.

4. the transformer station's current measurement loop fault detection method based on genetic algorithm according to claim 3, is characterized in that, in described step S61, phase current magnitude measures the mean value that I is abc phase current.

5. the transformer station's current measurement loop fault detection method based on genetic algorithm according to claim 3, it is characterized in that, in described step S62, in population, the initial value following methods of each individuality generates, and take ki as gene structure chromosome Yj, each chromosome Yj is 1 one-dimension array, each ki is array element, and population number elects 30 × N as, i.e. j=1 ~ 30 × N, wherein

A) ki of 10 × N number of body adopts ki=0.1 × random (-1,1) to generate, and wherein random (-1,1) expression gets random number in (-1,1) scope,

B) ki of 10 × N number of body adopts ki=0.5 × random (-1,1) to generate,

C) ki of 5 × N number of body adopts ki=1 × random (-1,1) to generate,

D) ki of 5 × N number of body adopts ki=10 × random (-1,1) to generate,

E) ki of body one by one optional in c group is taken as 0, as 1 individuality.

6. the transformer station's current measurement loop fault detection method based on genetic algorithm according to claim 3, is characterized in that, in described step S64, the departure of optimum individual is less than 0.001 stopping optimizing.

7. the transformer station's current measurement loop fault detection method based on genetic algorithm according to claim 1, it is characterized in that, in described step S5, current amplitude Ij is the mean value of abc three-phase current amplitude.