CN109932644A  Circuit breaker failure diagnostic method based on integrated intelligent algorithm  Google Patents
Circuit breaker failure diagnostic method based on integrated intelligent algorithm Download PDFInfo
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 CN109932644A CN109932644A CN201910153404.7A CN201910153404A CN109932644A CN 109932644 A CN109932644 A CN 109932644A CN 201910153404 A CN201910153404 A CN 201910153404A CN 109932644 A CN109932644 A CN 109932644A
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 XEEYBQQBJWHFJMUHFFFAOYSAN iron Chemical group data:image/svg+xml;base64,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 data:image/svg+xml;base64,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 [Fe] XEEYBQQBJWHFJMUHFFFAOYSAN 0.000 description 1
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Abstract
The present invention relates to a kind of circuit breaker failure diagnostic method based on integrated intelligent algorithm, including the following steps: monitoring breaker operation circuit coil current simultaneously extracts its current characteristic amount and time characteristic quantity, collects training sample and test sample；Standard state selection is carried out using invasive weed optimization algorithm；Data Discretization is carried out using the continuous variable discretization algorithm based on standard state probability assignments；Prior probability is calculated according to training sample；Calculate the posterior probability that test sample belongs to each fault type；Test sample is included into the maximum fault type of posterior probability, obtains fault diagnosis result.
Description
Technical field
The invention belongs to electrical equipment and electrical engineering technical fields.
Background technique
The safe and stable operation of electric system has the fast development of national economy and the normal operation of society great
Meaning.The main function of breaker is: the excision faulty line when electric system is broken down rapidly and accurately, to cut off event
Hinder electric current, prevents from causing the more serious consequences such as largearea powercuts, guarantee the safe and stable operation of electric system.Therefore, if
The normal work of breaker tripping or malfunction, electric system or in which a part will be destroyed, and cause few to user
Power transmission or power quality degenerate to unacceptable stage, or even cause the damage of personal injury and electrical equipment.Therefore, how
Fast and accurately carrying out fault diagnosis to breaker has important research significance.
Existing research shows that the failure cause of breaker is caused to have very much, wherein mechanical breakdown (including operating mechanism and control
Circuit processed) account for the 70%~80% of whole failures.The aging of breaker secondary circuit and operating mechanism in During Process of Longterm Operation
It can cause failure, such as operation brownout, combined floodgate incipient stage bite, operating mechanism bite etc..Since operation circuit belongs to
Secondary circuit, voltage class is lower and signal acquisition is relatively easy to, by monitoring that breaker operator circuit coil electric current can obtain
To the information of circuit breaker internal, therefore, research is based on the diagnosis of breaker operator circuit coil failure of the current to diagnosis breaker
Fault category guarantees that safe and stable operation of power system is of great significance.
Summary of the invention
The purpose of the present invention is be difficult to realize for accuracy rate is not high, algorithm is complicated present in artificial intelligence diagnosis' algorithm
The problems such as, by studying the feature of breaker operator circuit coil electric current, proposes one kind and broken by Bayes classifier
The method of road device fault diagnosis.Based on mentioned bayes classification method, the continuous change based on standard state probability assignments has been used
Discretization algorithm is measured, solves the problems, such as that Bayes classifier is only applicable to discretization variable, meanwhile, optimized using invasive weed
Algorithm carries out standard state selection, has obtained optimality criterion state Choice, has quick and precisely carried out circuit breaker failure to reach
The purpose of diagnosis.Technical solution is as follows:
A kind of circuit breaker failure diagnostic method based on integrated intelligent algorithm, including the following steps:
1) monitoring breaker operation circuit coil current and its current characteristic amount and time characteristic quantity are extracted, collects training sample
Sheet and test sample；
2) standard state selection is carried out using invasive weed optimization algorithm
2.1) initialization of population
Determine initialization population scale N_{0}With maximum population scale N_{max}, maximum number of iterations iter_{max}, required problem dimension
D can generate the upper limit seed of seed number_{max}With lower limit seed_{min}, nonlinear exponent n, standard deviation initial value σ_{init}And end value
σ_{final}, initial ranging space minimum value x_{min}With maximum value x_{max}；
2.2) fitness function
According to the extracted current characteristic amount of step 1) and time characteristic quantity, definition variance is fitness function:
In formula, e (x) indicates standard state belonging when dividing according to the standard state currently chosen, and x indicates data root
Numerical value after being divided according to the standard state currently chosen, θ are a multidimensional data, respectively indicate each characteristic quantity and are characterized
Attribute standard state choosing method；
2.3) it breeds
Weeds generated seed amount in reproductive process is related with the fitness value of weeds, and fitness value is better, numerous
The seed grown is more, calculates seed number m according to following equation:
In formula, m indicates seed number；F indicates the fitness value of current weeds；f_{min}And f_{max}It respectively indicates miscellaneous in current population
The minimum fitness value and maximum adaptation angle value of grass
2.4) space diffusion profile
The seed that weeds generate is distributed in parent weeds by σ according to being mean value with 0 in the way of the normal distribution of standard deviation
Around, weeds of new generation are grown into, the standard deviation of every generation calculates according to the following formula:
In formula, iter indicates current iteration number；δ_{cur}Indicate current standard deviation；N indicates the nonlinear reconciliation factor, usually
It is set as 3；
2.5) competitive exclusion rule
When the sum of weeds and the number of offspring reach preset maximum population scale N_{max}When, algorithm will execute competitive row
Reprimand rule is ranked up the fitness value of all individuals in the population of weeds and offspring's composition by size, retains fitness value
High individual eliminates remaining individual；
2.6) termination condition
Maximum number of iterations iter_{max}Or the fitness function value of adjacent generations does not terminate iteration in variation twice, most
Standard state choosing method θ is obtained eventually；
3) continuous variable discretization algorithm of the application based on standard state probability assignments carries out Data Discretization；
3.1) standard state is reasonably selected using invasive weed optimization algorithm, data is formed to the discretization with discrimination
As a result；
3.2) after selecting optimality criterion state, according to selected standard state, the standard state probability of the attribute is calculated
Allocation matrix P_{x}；
4) prior probability is calculated according to training sample.
5) posterior probability that test sample belongs to each fault type is calculated.
6) test sample is included into the maximum fault type of posterior probability, obtains fault diagnosis result.
Detailed description of the invention
Fig. 1 invasive weed optimization algorithm flow chart
Fig. 2 Bayes's classification flow chart
Fig. 3 fault diagnosis result figure
Fig. 4 divideshut brake coil current signature waveform figure
Specific embodiment
Bayesian algorithm is a kind of probabilistic approach using statistics as Fundamentals of Mathematics, and the failure of smallscale data is examined
Disconnected performance is good, is capable of handling more classification tasks
Naive Bayes Classifier is earliest bayes classification method, and core concept is that selection highest posterior probability is made
For the index for determining classification.This method is assumed to be independent from each other between each conditional attribute, i.e. any one conditional attribute value
Probability do not influenced completely by other conditions attribute.
Equipped with n breaker monitoring state information, X is used_{i}Indicate (status information is 8 characteristic values), X_{i}Value be x_{i},
Wherein 1≤i≤8 and i ∈ Z indicate that (fault type of division is divided into six kinds to fault category variable, respectively normally, operates with C
Brownout, close a floodgate the incipient stage have bite, operating mechanism have bite, iron core idle stroke excessive and auxiliary switch movement contact not
It is good), the value of C is c_{i}, c_{i}∈{1,2,3,4,5,6}.T represents training sample, T={ x_{1},x_{2},…,x_{n},c_{i}Indicate training sample
This.At this point, c_{i}Conditional probability can rewrite are as follows:
Therefore, according to Bayesian formula, posterior probability it is writeable are as follows:
Test sample is calculated for the posterior probability of whole fault types according to formula (4), and highest posterior probability is selected to make
For the index for determining classification.
In order to reduce loss of data in discretization process, discretization probability matrix is calculated using formula (6).
Troubleshooting step is as follows:
1) monitoring breaker operation circuit coil current and three of them current characteristic amount and five temporal characteristics amounts are extracted, such as
Shown in Fig. 4, training sample is collected, format is as shown in table 1.
1 sample data of table
Table 1Sampledata
2) standard state selection is carried out using invasive weed optimization algorithm.
2.1) initialization of population
Determine initialization population scale N_{0}With maximum population scale N_{max}, maximum number of iterations iter_{max}, required problem dimension
D can generate the upper limit seed of seed number_{max}With lower limit seed_{min}, nonlinear exponent n, standard deviation initial value σ_{init}And end value
σ_{final}, initial ranging space minimum value x_{min}With maximum value x_{max}。
2.2) it breeds
Weeds generated seed amount in reproductive process is related with the fitness value of weeds, and fitness value is better, numerous
The seed grown is more.Therefore, the seed number of different weeds generations is not identical, can calculate seed number with formula (9).
2.3) space diffusion profile
The seed that weeds generate is distributed in parent weeds by σ according to being mean value with 0 in the way of the normal distribution of standard deviation
Around, grow into weeds of new generation.The standard deviation of every generation is calculated with formula (10).
2.4) competitive exclusion rule
When the sum of weeds and the number of offspring reach preset maximum population scale N_{max}When, algorithm will execute competitive row
Reprimand rule is ranked up the fitness value of all individuals in the population of weeds and offspring's composition by size, retains fitness value
High individual (for optimizing maximum value) eliminates remaining individual.Weeds are first made to breed and capture rapidly the field of adaptation,
Then the stronger weeds of competitiveness under opposite stable environment are remained and continue searching space.
3) continuous variable discretization algorithm of the application based on standard state probability assignments carries out Data Discretization.
3.1) standard state e is reasonably selected using invasive weed optimization algorithm_{x}=[e_{x1},e_{x2},…,e_{xn}], data are formed
Discretization results with discrimination.
3.2) after selecting optimality criterion state, according to selected standard state, the standard state probability of the attribute is calculated
Allocation matrix P_{x}, P_{x}For the matrix of m × n rank, P_{x}(m, n) indicates the x of mth of sample_{i}Belong to nth of standard state e_{xn}Probability.
Due to a data only can in probability assignments to two continuous standard state, P_{x}The calculation formula of middle element is formula
(11).
4) prior probability is calculated according to following formula
P (y=e_{yj}, z=e_{zl}...) and=P_{y}(k,j)·P_{z}(k,l)…
5) posterior probability that test sample belongs to each fault type is calculated according to the following formula.
In formula, P_{x}Indicate that fault category standard state probability assignments matrix (is assigned to the probability in each standard state
It can be 1 or 0)；P_{y}And P_{z}Indicate the standard state probability assignments matrix of each conditional attribute.
6) test sample is included into the maximum fault type of posterior probability.
Conventional method is the Bayes's classification that discretization is carried out based on interval division, and method of the invention is invasive weed
Optimization and Bayes's integrated intelligent algorithm.In order to analyze the validity that this method diagnoses circuit breaker failure, with temporal characteristics amount
Parameter is inputted as sample with 8 parameters of current characteristic amount, randomly selects 20 groups of data as training sample, then is taken 10 groups remaining
Data are as test sample, respectively using the bayes method of conventional discrete and invasive weed of the present invention optimization and shellfish
This hybrid algorithm of leaf carries out fault diagnosis to breaker, and fault diagnosis result is as shown in table 2, theoretical value, the method in table
Value, the numerical value expression breaker mechanical state of conventional method value.
The result shows that the use of the accuracy rate that conventional method carries out fault diagnosis being 50%, and the method is used to carry out
The predicted value of fault diagnosis and theoretical value coincidence factor are higher, and the accuracy rate of fault diagnosis reaches 100%, significantly larger than conventional side
Method.
Although simultaneously this method failure diagnosis time be 2.29ms compared to conventional method fault diagnosis speed under
Drop, but still there is greater advantage compared to the diagnosis speed (66ms) of neural network algorithm.
2 fault diagnosis result of table
Table 5The result offault diagnosis
The accuracy of fault diagnosis, training are carried out to breaker in order to analyze this method in the case where different sample sizes
Sample size takes 10,12,14,16,18,20 respectively, and corresponding test sample quantity all takes 10 groups, uses the mixing intelligence
Energy algorithm and conventional method carry out fault diagnosis to breaker, and every group of repetition is diagnosed 30 times and be averaged, fault diagnosis
Accuracy rate is as shown in Figure 3.
It can be seen from figure 3 that obviously high using invasive weed optimization and Bayes's integrated intelligent algorithm, fault diagnosis accuracy rate
In the fault diagnosis accuracy rate of conventional method.Because conventional method will cause data information and largely lose, and of the present invention
Invasive weed optimization and Bayes's integrated intelligent algorithm by apply invasive weed optimization algorithm selection standard state, will be discrete
As a result it is divided into two parts, a part is discrete standard state, and a part is continuous probability, is believed to reduce in discretization process
Breath is lost, to improve the accuracy rate of fault diagnosis.
Simultaneously it can also be seen that being carried out using invasive weed of the present invention optimization and Bayes's integrated intelligent algorithm
When fault diagnosis, fault diagnosis accuracy rate will not with the reduction of sample number sharp fall, therefore, for sample
This data carry out fault diagnosis, still there is good diagnosis effect.
Claims (1)
1. a kind of circuit breaker failure diagnostic method based on integrated intelligent algorithm, including the following steps:
1) monitoring breaker operation circuit coil current and its current characteristic amount and time characteristic quantity are extracted, collect training sample and
Test sample；
2) standard state selection is carried out using invasive weed optimization algorithm
2.1) initialization of population
Determine initialization population scale N_{0}With maximum population scale N_{max}, maximum number of iterations iter_{max}, required problem dimension D, energy
Generate the upper limit seed of seed number_{max}With lower limit seed_{min}, nonlinear exponent n, standard deviation initial value σ_{init}With end value σ_{final},
Initial ranging space minimum value x_{min}With maximum value x_{max}；
2.2) fitness function
According to the extracted current characteristic amount of step 1) and time characteristic quantity, definition variance is fitness function:
In formula, e (x) indicates standard state belonging when dividing according to the standard state currently chosen, and x indicates data according to working as
The standard state of preceding selection divided after numerical value, θ be a multidimensional data, respectively indicate the category that each characteristic quantity is characterized
The standard state choosing method of property；
2.3) it breeds
Weeds generated seed amount in reproductive process is related with the fitness value of weeds, and fitness value is better, breeding
Seed is more, calculates seed number m. according to following equation
In formula, m indicates seed number；F indicates the fitness value of current weeds；f_{min}And f_{max}Respectively indicate weeds in current population
Minimum fitness value and maximum adaptation angle value
2.4) space diffusion profile
The seed that weeds generate is distributed in the week of parent weeds by σ according to being mean value with 0 in the way of the normal distribution of standard deviation
It encloses, grows into weeds of new generation, the standard deviation of every generation calculates according to the following formula:
In formula, iter indicates current iteration number；δ_{cur}Indicate current standard deviation；N indicates the nonlinear reconciliation factor, is usually arranged
It is 3；
2.5) competitive exclusion rule
When the sum of weeds and the number of offspring reach preset maximum population scale N_{max}When, algorithm will execute competitive exclusion rule
Then, the fitness value of all individuals is ranked up by size in the population formed to weeds and offspring, and it is high to retain fitness value
Individual eliminates remaining individual；
2.6) termination condition
Maximum number of iterations iter_{max}Or the fitness function value of adjacent generations does not terminate iteration in variation twice, it is final to obtain
To standard state choosing method θ；
3) continuous variable discretization algorithm of the application based on standard state probability assignments carries out Data Discretization；
3.1) standard state is reasonably selected using invasive weed optimization algorithm, data is formed to the discretization knot with discrimination
Fruit；
3.2) after selecting optimality criterion state, according to selected standard state, the standard state probability assignments of the attribute are calculated
Matrix P_{x}；
4) prior probability is calculated according to training sample；
5) posterior probability that test sample belongs to each fault type is calculated；
6) test sample is included into the maximum fault type of posterior probability, obtains fault diagnosis result.
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