CN109765458A - A kind of temporary drop source localization method based on glowworm swarm algorithm - Google Patents
A kind of temporary drop source localization method based on glowworm swarm algorithm Download PDFInfo
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Abstract
The present invention relates to a kind of temporary drop source localization method based on glowworm swarm algorithm.First, for any temporary drop source system to be identified, several temporarily drop source positions are generated at random, calculate the positive sequence voltage variable quantity of all nodes when each temporary drop source occurs and are standardized, corresponding mode to be identified is formed, forms objective function further according to pattern match degree to be identified；Secondly, finding the temporary drop source position for enabling objective function bigger, and make initial temporarily drop source position to it across further；It repeats to find to make the maximum point of objective function across close step until finding, the position in source is temporarily dropped in mode as to be identified.The present invention solves the problems, such as that traditional temporarily drop source positioning mode is influenced to be accurately positioned by fault type and transition resistance, at the same also than can carry out pinpoint intelligent algorithm principle it is simple, using facilitating.
Description
Technical field
The present invention relates to a kind of temporary drop source localization method based on glowworm swarm algorithm.
Background technique
Make load special due to nonlinear-load substantial increase, distributed generation resource are grid-connected etc. using power electronic technique
Property become complicated, while end user device is also higher than in the past to the susceptibility of power quality, therefore power quality is important
Property is increasingly promoted.And voltage dip is the power quality problem of occurrence frequency height, weight losses.Voltage dip, which is accurately positioned, to be facilitated
It carries out power quality diagnosis and formulates mitigation strategy, play an important role to power quality is improved.
Voltage sag source method can be divided into two kinds at present: range positioning mode and method be accurately positioned.Range positioning mode mainly has
Power of disturbance and energy method and its improved method, system trajectory Slope Method, real part current method, disturbance watt current method, equivalent resistance
Anti- real part method, distance relay positioning mode, instantaneous sequence current method etc..Accurate positionin method is mainly estimated using multicriterion, probability
Meter, genetic algorithm etc. carry out voltage sag source positioning.
Range positioning mode is mostly immediately arrived at according to mechanism, and it is that synthesis has used mechanism model sum number that rule, which is accurately positioned, mostly
Method.The former provides basis for the latter, however because mechanism simply can only often judge that failure occurs in the upstream of monitoring point
Or downstream, the specific location in temporarily drop source cannot be accurately located.The latter preferably determines temporary drop source to merge mathematical method
Accurate location, may selectively weaken some part of mechanism model, for example to have ignored transition resistance temporary to voltage
The influence of drop source positioning, causes position error.
Summary of the invention
The purpose of the present invention is to provide a kind of temporary drop source localization method based on glowworm swarm algorithm solves traditional temporarily drop
Source positioning mode is influenced the problem of cannot being accurately positioned by fault type and transition resistance, while also than that can carry out pinpoint intelligence
Can algorithm principle it is simple, using convenient.
To achieve the above object, the technical scheme is that a kind of temporary drop source localization method based on glowworm swarm algorithm,
Firstly, for any temporary drop source system to be identified, several temporarily drop source positions are generated at random, are calculated each temporary drop source and are occurred
The positive sequence voltage variable quantity of Shi Suoyou node is simultaneously standardized, and corresponding mode to be identified is formed, further according to mode to be identified
Matching degree forms objective function；Secondly, find the temporary drop source position for enabling objective function bigger, and make initial temporarily drop source position to its
Across further；It repeats to find to make the maximum point of objective function across close step until finding, temporarily drop source in mode as to be identified
Position.
In an embodiment of the present invention, the specific implementation steps are as follows for this method:
Step S1, it is positioned according to the application source Zan Jiang and establishes objective function maxfun:
Firstly, calculating the positive sequence transfer impedance between monitoring point M and fault point FSuch as formula (1):
Wherein,Positive sequence mutual impedance between monitoring point M and node C,Between monitoring point M and node D just
Sequence mutual impedance, λ are normalized cumulant of the fault point F to route first node；
Secondly, constructing the feature mode of fault point according to positive sequence nodal impedance matrix；The positive sequence voltage of monitoring point M after failureAs shown in formula (2):
Wherein,For the positive sequence voltage of the preceding monitoring point M of failure,Positive sequence between monitoring point M and fault point F passes
Impedance is passed,It is the short circuit current positive-sequence component of fault point F；
Then the positive sequence voltage variable quantity of monitoring point M is constructed
The positive sequence voltage variable quantity of all monitoring points is acquired by formula (3), and non-monitored node is obtained by Load flow calculation
Positive sequence voltage variable quantity, then the positive sequence voltage variable quantity of all nodes is standardized as the following formula:
It is only related with fault distance and positive sequence transfer impedance after the standardization of formula (4) positive sequence voltage variable quantity；
The positive sequence voltage variable quantity sequence definition that positive sequence voltage variable quantity after all standardization is constituted is fault point F's
Feature mode H (i, λ):
H (i, λ)=[V1′,V2′,…Vn′,…VN′]T (5)
Wherein, i is circuit number, and λ is normalized cumulant of the fault point F to route first node, Vn' for the after standardization
N node positive sequence voltage variable quantity, and 1≤n≤N, n, N are positive integer；
Node positive sequence voltage variation detection values when unknown failure occurs are standardized, the sequence of composition is fixed
Justice is mode H to be identified*:
Wherein,Change detection values, and 1≤n≤N, n, N for the positive sequence voltage of n-th of node after standardization
For positive integer；
By H, H*The sum of inverse of difference of corresponding node positive sequence voltage variable quantity is used as objective function maxfun under mode:
Step S2, deployment phase:
The position that K failure of stochastic assumption may occur, this K position are equivalent to K monomer firefly with random side
Formula is distributed in search space:
The position of every firefly with address=(Xaddress, Yaddress) expression, meaning such as formula (8):
WhereinFor the circuit number after normalization, λ is normalized cumulant of the fault point F to route first node, and 0
< i*≤ 1,0≤λ≤1, then -0.5≤Xaddress≤0.5, -0.5≤Yaddress≤0.5；
Generate the initial position of K monomer firefly at random as the following formula first:
Wherein, xrand, yrand are random number, and 0≤xrand≤1,0≤xrand≤1, address0=
(Xaddress0, Yaddress0) it is firefly initial position sequence；
Then assign monomer firefly identical initial fluorescence element value iuc0And initial radius of dynamic decision domain rd 0=
0.5；
Step S3, the brightness more new stage:
The fluorescence prime sequences iuc that fluorescein possessed by the t times iteration of K monomer firefly is constituted is updated according to the following formulat:
iuct(k)=(1- ρ) × iuct-1(k)+γ×maxfunt(k) (10)
Wherein, ρ is fluorescein volatilization factor, and it is all fixed value, and 0≤ρ≤1,0≤γ that γ, which is fitness withdrawal ratio,
≤1；maxfuntIt (k) is the mode H that k-th of failure occurs and is formedk tWith H under failure emergence pattern to be identified*It is corresponding under mode
The sum of the inverse of difference of node positive sequence voltage variable quantity；
Step S4, mobile phase:
It is higher than firstly, kth monomer firefly in the radius of dynamic decision domain of its t times iteration, finds fluorescein value
The firefly group J of itself:
Wherein, dkj=| | addresst(j)-addresst(k) | |, meaning is the jth light of firefly in kth firefly and J
The linear distance of worm spatially；rd t(k) be the t times iteration when kth firefly radius of dynamic decision domain, andrsIndicate the perception radius, fixed value 0.5；Kj tLight of firefly borer population when for the t times iteration in firefly group J
Amount；
Secondly, calculating firefly k mobile Probability p of jth firefly into Jkj t:
And according to pkj tDetermine that firefly k a certain firefly (being denoted as j) into J is mobile；
Then, firefly k is mobile to firefly j:
Wherein, s indicates moving step length, is fixed value；
Step S5, radius update of dynamic decision domain:
The radius of dynamic decision domain of kth firefly is updated according to formula (14):
Wherein, KtIt is the threshold value for the firefly number for including in neighborhood collection, β is neighborhood change rate；
Step S6, iteration:
Return step S3, until the number of iterations t reaches preset value tmax；
Step S7, temporarily drop source is determined:
In tmaxThe fluorescence prime sequences iuc obtained after secondary iterationtmaxMiddle maximizing, corresponding mode HmaxIt is exactly
With the highest mode of mode H* matching degree to be identified, what is stored in the position address of this firefly is temporary drop source normalization
Circuit number afterwards and the normalized cumulant to route first node.
Compared to the prior art, the invention has the following advantages: the present invention will be after the standardization of positive sequence voltage variable quantity
Obtained expression formula is only related with positive sequence transfer impedance and fault distance, and the objective function constructed accordingly is only about abort situation
The function of (including faulty line number and fault distance), it is all uncorrelated to voltage, fault current and transition resistance before failure,
Therefore for any fault type, whether consider transition resistance the case where is all applicable in.Traditional positioning mode part can only carry out range
Positioning, and the method that can be accurately positioned usually is influenced by fault type, so the mentioned method positioning of the present invention is more accurate.Separately
Outside, it is existing can equally carry out pinpoint intelligent algorithm mostly be by fault type, transition resistance, system operation mode, event
Correlated characteristics amount one fuzzy neural networks of unified composition such as barrier point position, peer-to-peer system impedance are determined further according to machine learning method
The data of abort situation, processing are huge, calculate complexity, it is more difficult to be put to engineering practice；And it is calculated based on firefly optimization
The positioning mode of method combines closely electrical quantity and algorithm parameter, and firefly position corresponds to abort situation, and objective function corresponds to
Pattern match degree, fluorescein concentration rely on objective function generation, and principle is simple, using convenient.
Detailed description of the invention
Fig. 1 is that the present invention is based on the temporary drop source localization method flow charts of glowworm swarm algorithm.
Specific embodiment
With reference to the accompanying drawing, technical solution of the present invention is specifically described.
As shown in Figure 1, the present invention provides a kind of temporary drop source localization method based on glowworm swarm algorithm, specially a kind of base
In the voltage sag source localization method of the positive sequence voltage Optimum Matching of firefly optimization algorithm, this method is by positive sequence voltage variable quantity
The expression formula obtained after standardization only it is related with positive sequence transfer impedance and fault distance, the objective function constructed accordingly be only about
The function of abort situation (including faulty line number and fault distance), with voltage, fault current and transition resistance before failure
It is all uncorrelated, therefore be all applicable in the case where for any fault type, whether consider transition resistance；In addition, this method is based on firefly
The positioning mode of fireworm optimization algorithm combines closely electrical quantity and algorithm parameter, and firefly position corresponds to abort situation, target
Function corresponds to pattern match degree, and fluorescein concentration relies on objective function generation, and principle is simple, using convenient.
Specific a kind of temporary drop source localization method based on glowworm swarm algorithm of the invention: firstly, for any temporary drop source
System to be identified generates several temporarily drop source positions at random, calculates the positive sequence electricity of all nodes when each temporary drop source occurs
Pressure variable quantity is simultaneously standardized, and corresponding mode to be identified is formed, and forms objective function further according to pattern match degree to be identified；
Secondly, finding the temporary drop source position for enabling objective function bigger, and make initial temporarily drop source position to it across further；Repeat find across
Close step makes the maximum point of objective function until finding, the position in source is temporarily dropped in mode as to be identified；This method is specifically real
It is existing that steps are as follows:
Step 1 positions according to the application source Zan Jiang and establishes objective function maxfun:
Firstly, calculating the positive sequence transfer impedance between monitoring point M and fault point FSuch as formula (1):
Wherein,Positive sequence mutual impedance between monitoring point M and node C,Between monitoring point M and node D just
Sequence mutual impedance, λ are normalized cumulant of the fault point F to route first node；
Secondly, constructing the feature mode of fault point according to positive sequence nodal impedance matrix；The positive sequence voltage of monitoring point M after failureAs shown in formula (2):
Wherein,For the positive sequence voltage of the preceding monitoring point M of failure,Positive sequence between monitoring point M and fault point F passes
Impedance is passed,It is the short circuit current positive-sequence component of fault point F；
Then the positive sequence voltage variable quantity of monitoring point M is constructed
The positive sequence voltage variable quantity of all monitoring points is acquired by formula (3), and non-monitored node is obtained by Load flow calculation
Positive sequence voltage variable quantity, then the positive sequence voltage variable quantity of all nodes is standardized as the following formula:
It is only related with fault distance and positive sequence transfer impedance after the standardization of formula (4) positive sequence voltage variable quantity；
The positive sequence voltage variable quantity sequence definition that positive sequence voltage variable quantity after all standardization is constituted is fault point F's
Feature mode H (i, λ):
H (i, λ)=[V1′,V2′,…Vn′,…VN′]T (5)
Wherein, i is circuit number, and λ is normalized cumulant of the fault point F to route first node, Vn' for the after standardization
N node positive sequence voltage variable quantity, and 1≤n≤N, n, N are positive integer；
Node positive sequence voltage variation detection values when unknown failure occurs are standardized, the sequence of composition is fixed
Justice is mode H to be identified*:
Wherein,Change detection values, and 1≤n≤N, n, N for the positive sequence voltage of n-th of node after standardization
For positive integer；
By H, H*The sum of inverse of difference of corresponding node positive sequence voltage variable quantity is used as objective function maxfun under mode:
Step 2, deployment phase:
The position that K failure of stochastic assumption may occur, this K position are equivalent to K monomer firefly with random side
Formula is distributed in search space:
The position of every firefly with address=(Xaddress, Yaddress) expression, meaning such as formula (8):
WhereinFor the circuit number after normalization, λ is normalized cumulant of the fault point F to route first node, and 0
< i*≤1,0≤λ≤1, then -0.5≤Xaddress≤0.5, -0.5≤Yaddress≤0.5；
Generate the initial position of K monomer firefly at random as the following formula first:
Wherein, xrand, yrand are random number, and 0≤xrand≤1,0≤xrand≤1, address0=
(Xaddress0, Yaddress0) it is firefly initial position sequence；
Then assign monomer firefly identical initial fluorescence element value iuc0And initial radius of dynamic decision domain rd 0=
0.5。
Step 3, the brightness more new stage:
The fluorescence prime sequences iuc that fluorescein possessed by the t times iteration of K monomer firefly is constituted is updated according to the following formulat:
iuct(k)=(1- ρ) × iuct-1(k)+γ×maxfunt(k) (10)
Wherein, ρ is fluorescein volatilization factor, and it is all fixed value, and 0≤ρ≤1,0≤γ that γ, which is fitness withdrawal ratio,
≤1；maxfuntIt (k) is the mode H that k-th of failure occurs and is formedk tIt is corresponding under H* mode under failure emergence pattern to be identified
The sum of the inverse of difference of node positive sequence voltage variable quantity.
Step 4, mobile phase:
It is higher than firstly, kth monomer firefly in the radius of dynamic decision domain of its t times iteration, finds fluorescein value
The firefly group J of itself:
Wherein, dkj=| | addresst(j)-addresst(k) | |, meaning is the jth light of firefly in kth firefly and J
The linear distance of worm spatially；rd t(k) be the t times iteration when kth firefly radius of dynamic decision domain, andrsIndicate the perception radius, fixed value 0.5；Kj tLight of firefly borer population when for the t times iteration in firefly group J
Amount；
Secondly, calculating firefly k mobile Probability p of jth firefly into Jkj t:
And according to pkj tDetermine that firefly k a certain firefly (being denoted as j) into J is mobile；
Then, firefly k is mobile to firefly j:
Wherein, s indicates moving step length, is fixed value.
Step 5, radius update of dynamic decision domain:
The radius of dynamic decision domain of kth firefly is updated according to formula (14):
Wherein, KtIt is the threshold value for the firefly number for including in neighborhood collection, β is neighborhood change rate.
Step 6, iteration:
Return step 3, until the number of iterations t reaches preset value tmax。
Step 7 determines temporarily drop source:
In tmaxThe fluorescence prime sequences iuc obtained after secondary iterationtmaxMiddle maximizing, corresponding mode HmaxIt is exactly
With mode H to be identified*The highest mode of matching degree, what is stored in the position address of this firefly is temporary drop source normalization
Circuit number afterwards and the normalized cumulant to route first node.
The above are preferred embodiments of the present invention, all any changes made according to the technical solution of the present invention, and generated function is made
When with range without departing from technical solution of the present invention, all belong to the scope of protection of the present invention.
Claims (2)
1. a kind of temporary drop source localization method based on glowworm swarm algorithm, which is characterized in that be identified firstly, for any temporary drop source
System, generate several temporarily drop source positions at random, calculate the positive sequence voltage variation of all nodes when each temporary drop source occurs
It measures and is standardized, form corresponding mode to be identified, form objective function further according to pattern match degree to be identified；Secondly,
The temporary drop source position for enabling objective function bigger is found, and makes initial temporarily drop source position to it across further；It repeats to find across close
Step makes the maximum point of objective function until finding, the position in source is temporarily dropped in mode as to be identified.
2. a kind of temporary drop source localization method based on glowworm swarm algorithm according to claim 1, which is characterized in that this method
The specific implementation steps are as follows:
Step S1, it is positioned according to the application source Zan Jiang and establishes objective function maxfun:
Firstly, calculating the positive sequence transfer impedance between monitoring point M and fault point FSuch as formula (1):
Wherein,Positive sequence mutual impedance between monitoring point M and node C,Positive sequence between monitoring point M and node D is mutual
Impedance, λ are normalized cumulant of the fault point F to route first node；
Secondly, constructing the feature mode of fault point according to positive sequence nodal impedance matrix；The positive sequence voltage of monitoring point M after failure
As shown in formula (2):
Wherein,For the positive sequence voltage of the preceding monitoring point M of failure,Positive sequence between monitoring point M and fault point F transmits resistance
It is anti-,It is the short circuit current positive-sequence component of fault point F；
Then the positive sequence voltage variable quantity of monitoring point M is constructed
The positive sequence voltage variable quantity of all monitoring points is acquired by formula (3), and the positive sequence of non-monitored node is obtained by Load flow calculation
Voltage variety, then the positive sequence voltage variable quantity of all nodes is standardized as the following formula:
It is only related with fault distance and positive sequence transfer impedance after the standardization of formula (4) positive sequence voltage variable quantity；
The positive sequence voltage variable quantity sequence definition that positive sequence voltage variable quantity after all standardization is constituted is the feature of fault point F
Mode H (i, λ):
H (i, λ)=[V1′,V2′,…Vn′,…VN′]T (5)
Wherein, i is circuit number, and λ is normalized cumulant of the fault point F to route first node, Vn' for standardization after n-th of section
Point positive sequence voltage variable quantity, and 1≤n≤N, n, N are positive integer；
Node positive sequence voltage variation detection values when unknown failure occurs are standardized, the sequence definition of composition is
Mode H to be identified*:
Wherein,Change detection values for the positive sequence voltage of n-th of node after standardization, and 1≤n≤N, n, N are positive
Integer；
By H, H*The sum of inverse of difference of corresponding node positive sequence voltage variable quantity is used as objective function maxfun under mode:
Step S2, deployment phase:
The position that K failure of stochastic assumption may occur, this K position are equivalent to K monomer firefly and divide in a random basis
Cloth is in search space:
The position of every firefly with address=(Xaddress, Yaddress) expression, meaning such as formula (8):
WhereinFor the circuit number after normalization, λ is normalized cumulant of the fault point F to route first node, and 0 < i*
≤ 1,0≤λ≤1, then -0.5≤Xaddress≤0.5, -0.5≤Yaddress≤0.5；
Generate the initial position of K monomer firefly at random as the following formula first:
Wherein, xrand, yrand are random number, and 0≤xrand≤1,0≤xrand≤1, address0=(Xaddress0,
Yaddress0) it is firefly initial position sequence；
Then assign monomer firefly identical initial fluorescence element value iuc0And initial radius of dynamic decision domain rd 0=0.5；
Step S3, the brightness more new stage:
The fluorescence prime sequences iuc that fluorescein possessed by the t times iteration of K monomer firefly is constituted is updated according to the following formulat:
iuct(k)=(1- ρ) × iuct-1(k)+γ×maxfunt(k) (10)
Wherein, ρ is fluorescein volatilization factor, and it is all fixed value, and 0≤ρ≤1 that γ, which is fitness withdrawal ratio, 0≤γ≤1；
maxfuntIt (k) is the mode H that k-th of failure occurs and is formedk tWith corresponding node under H* mode under failure emergence pattern to be identified
The sum of the inverse of difference of positive sequence voltage variable quantity；
Step S4, mobile phase:
Firstly, kth monomer firefly, which in the radius of dynamic decision domain of its t times iteration, finds fluorescein value, is higher than itself
Firefly group J:
Wherein, dkj=| | addresst(j)-addresst(k) | |, meaning is jth firefly in kth firefly and J in sky
Between on linear distance；rd t(k) be the t times iteration when kth firefly radius of dynamic decision domain, andrs
Indicate the perception radius, fixed value 0.5；Kj tFirefly quantity when for the t times iteration in firefly group J；
Secondly, calculating firefly k mobile Probability p of jth firefly into Jkj t:
And according to pkj tDetermine that firefly k a certain firefly (being denoted as j) into J is mobile；
Then, firefly k is mobile to firefly j:
Wherein, s indicates moving step length, is fixed value；
Step S5, radius update of dynamic decision domain:
The radius of dynamic decision domain of kth firefly is updated according to formula (14):
Wherein, KtIt is the threshold value for the firefly number for including in neighborhood collection, β is neighborhood change rate；
Step S6, iteration:
Return step S3, until the number of iterations t reaches preset value tmax；
Step S7, temporarily drop source is determined:
In tmaxThe fluorescence prime sequences iuc obtained after secondary iterationtmaxMiddle maximizing, corresponding mode HmaxBe exactly with to
Identify mode H*The highest mode of matching degree, after that stores in the position address of this firefly is temporary drop source normalization
Circuit number and normalized cumulant to route first node.
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