CN115327295A - Power distribution network fault positioning method and system - Google Patents
Power distribution network fault positioning method and system Download PDFInfo
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Abstract
According to the power distribution network fault positioning method and system, firstly, information of a power distribution automation terminal and an electric power user electricity consumption information acquisition system is optimized and solved through a genetic algorithm to construct an evidence source required for fault judgment, then an improved Yager synthetic formula is used for fusing the evidence source, and data are comprehensively analyzed to obtain a final judgment result. The positioning method can not only accurately judge the fault of a single section; the method can also be used for accurately judging when a multi-section fault occurs; and the fault section can still be accurately positioned under the condition that the information reported by the monitoring equipment of a plurality of information sources is missing. The method can well solve the problem that when the monitoring equipment is in fault false alarm and missed alarm, fault positioning is easy to judge by mistake by using a single information source.
Description
Technical Field
The application relates to the technical field of power distribution network fault positioning, in particular to a power distribution network fault positioning method and system.
Background
Distribution network automation is an important means for improving the operation intellectualization and the self-healing performance of a distribution network. The feeder automation is one of the main functions, namely after the distribution network fails, a fault section is quickly found and isolated according to fault information reported by a distribution automation terminal unit, and power supply of a non-fault power-loss load is quickly recovered. The fault section positioning of the power distribution network is the basis of feeder automation and has important significance for improving the power supply reliability.
At present, the fault section of the power distribution network is mainly identified based on current information. With the wide application of the distribution automation terminal, the fault section D-S evidence theory positioning method based on the overcurrent information is also widely applied. The application process of the traditional D-S evidence theory positioning method is as follows: firstly, acquiring various data of a power grid as original information by terminal sensor equipment of a power distribution automation terminal; after a fault occurs, comprehensively generating evidence source BPA (Basic identity Assignment) from the monitoring data in a certain mode; and finally, synthesizing the evidence source BPA through a D-S theoretical synthesis rule, and judging whether a fault occurs according to a finally obtained synthesis result.
However, the traditional D-S evidence theory positioning method only uses one evidence source, and when multiple sections of the power distribution network have faults simultaneously, misjudgment often occurs when fault positioning is performed only by using a single evidence source; when a certain section of the power distribution network has a fault, if the section switch position of the section is not provided with the electric automatic terminal or is provided with the false alarm and the false alarm, or when a plurality of electric automatic terminals have the false alarm and the false alarm, the accuracy of the traditional D-S evidence theory positioning method is greatly reduced and even fails, and the fault section positioning method only using the traditional single evidence source can generate the false judgment.
Disclosure of Invention
The application provides a power distribution network fault positioning method and system, and aims to solve the problem that a traditional D-S evidence theory positioning method only uses one evidence source and a fault section positioning method only using a single traditional evidence source can generate misjudgment.
In a first aspect, the present application provides a power distribution network fault location method, where the method includes:
when fault overcurrent alarm information sent by a distribution automation terminal is received, a genetic algorithm is used for optimization solution to obtain a first suspicious segment set, and the first suspicious segment set is used as a first identification frame and comprises the following steps:
representing fault overcurrent alarm information of the power distribution automation terminal by using a binary code to generate a first information code;
generating a first random initial population;
constructing a first fitness function;
selecting, crossing and mutating individuals in the first random initial population;
judging genetic algorithm convergence and first information decoding to obtain a first suspicious segment set, wherein the first suspicious segment set is used as a first identification frame;
generating a first evidence source basic probability distribution according to the first suspicious segment set;
when power-loss region warning information sent by load monitoring points in a power consumer power consumption information acquisition system is received, optimizing and solving by using a genetic algorithm to obtain a second suspicious segment set, wherein the second suspicious segment set is used as a second identification frame and comprises the following steps:
representing the power-losing area warning information of a load monitoring point in the power consumer power consumption information acquisition system by using a binary code to generate a second information code;
generating a second random initial population;
constructing a second fitness function;
selecting, crossing and mutating individuals in the second random initial population;
judging the convergence of the genetic algorithm and decoding second information to obtain a second suspicious segment set, wherein the second suspicious segment set is used as a second identification frame;
generating a second evidence source basic probability distribution according to the second suspicious segment set;
collecting the first evidence source basic probability distribution and the second evidence source basic probability distribution, and fusing the first evidence source basic probability distribution and the second evidence source basic probability distribution by using a Yager synthesis formula when the number of the evidence sources is more than 1;
and outputting a fault positioning result.
Optionally, the first fitness function is set as:
wherein: e 1 (s) establishing genetic algorithm fitness for distribution automation terminal information, M 1 2 times the number of distribution automation terminals actually installed, F j For j-th distribution automation terminal status coding, F j (S) is the jth distribution automation terminal state function, S i Coding the status of the ith section of a power distribution network line, N 1 For the number of sections of the distribution network line, N 2 And the number of the automatic terminals is actually distributed.
Optionally, the step of determining convergence of the genetic algorithm and decoding the first information to obtain a first suspicious segment set, where the first suspicious segment set is used as a first identification frame includes: when the convergence condition is reached, outputting the individuals with the fitness value in the front N and the total number corresponding to the individuals; the convergence condition is set as the iteration times when the algorithm reaches the set value; performing first information decoding on the individuals to obtain a first suspicious segment set, wherein the first suspicious segment set is used as a first identification frame; the first information decoding is to decode the binary code into the suspicious fault section information according to the first information encoding mode.
Optionally, the step of generating a first evidence source basic probability distribution according to the first suspicious segment set includes:
the probability of the suspicious fault sections in the first suspicious section set is calculated by the following formula to obtain the basic probability distribution of the first evidence source;
wherein: m is a unit of 1 (r i ) Probability value, r, for the first evidence source corresponding to the suspicious fault section i Individuals with fitness values preceding N, c 1 (r i ) Is an individual r i And the corresponding total number N is the number of the selected fitness values in the front.
Optionally, the second fitness function is set as:
wherein: e 2 (s) establishing genetic algorithm fitness for information of power consumer power consumption information acquisition system, M 2 Is 3 times and H times of the number of load monitoring points in the power utilization information system of the power consumer j Encode the status of the jth load monitor point, H j (S) is a state function of the jth load monitor point, S i Coding the status of the ith section of the distribution network line, N 1 For the number of sections of the distribution network line, N 3 The number of load monitoring points in the power utilization information acquisition system for the power consumer is increased.
Optionally, the step of determining convergence of the genetic algorithm and decoding the second information to obtain a second suspicious segment set, where the second suspicious segment set is used as a second identification frame includes: when the convergence condition is reached, outputting the individuals with the fitness value in the front N and the total number corresponding to the individuals; the convergence condition is set as the iteration times when the algorithm reaches the set value; carrying out second information decoding on the individuals to obtain a second suspicious segment set, wherein the second suspicious segment set is used as a second identification frame; the second information decoding is to decode the binary code into the suspicious fault section information according to a second information encoding mode.
Optionally, the step of generating a second evidence source basic probability distribution according to the second suspicious segment set includes:
the probability of the suspicious fault sections in the second suspicious section set is calculated by the following formula to obtain the basic probability distribution of a second evidence source;
wherein: m is a unit of 2 (r i ) Probability value, r, for the second evidence source corresponding to the suspicious fault section i Individuals with fitness values preceding N, c 2 (r i ) Is a subject r i And the corresponding total number N is the number of the selected fitness values in the front.
Optionally, the crossover probability is set to 0.6, and the mutation probability is set to 0.01.
Optionally, the Yager synthesis formula is:
wherein:
average degree of support of A
Evidence source is m 1 、m 2 、…、m n The corresponding evidence sets are respectively F 1 、F 2 、…、F n The collision factor is k, the evidence set F i And F j Conflict between k ij ；
Confidence level of evidence
Wherein:
n is the total number of evidence sources used.
In a second aspect, the present application further provides a power distribution network fault location system, including: the power distribution automation terminal is used for acquiring first data and judging whether fault overcurrent alarm information is sent or not according to the first data; electric power consumer power consumption information acquisition system includes: the load monitoring point is used for acquiring second data and judging whether to send power failure region warning information or not according to the second data; an analysis module, configured to execute the power distribution network fault location method according to the first aspect.
According to the power distribution network fault positioning method and system, information of a power distribution automation terminal and an electric power user power consumption information acquisition system is optimized and solved through a genetic algorithm to construct an evidence source required for fault judgment, an improved Yager synthetic formula is used for fusing the evidence source, and data are comprehensively analyzed to obtain a final judgment result. The positioning method can not only accurately judge the single section fault, but also accurately judge the multi-section fault, and can still accurately position the fault section under the condition that the information reported by the monitoring equipment of a plurality of information sources is missing. The method and the device well solve the problem that when the monitoring equipment is in fault false alarm and missed alarm, the fault is easily misjudged by utilizing a single information source to perform fault positioning. The compatibility of the improved algorithm is good, new information sources are added only by reconstructing new evidence sources according to the flow, the accuracy of fault location can be increased along with the addition of the evidence sources, fault location is fast, fault finding time can be greatly reduced, the problems of waste of manpower, material resources and financial resources and safety risks in the traditional manual fault finding process are solved, the power supply reliability can be improved, the customer satisfaction degree is improved, good enterprise images are established, and equipment investment does not need to be increased.
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In order to more clearly explain the technical solution of the present application, the drawings needed to be used in the embodiments are briefly described below, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
Fig. 1 is a schematic flow chart of a power distribution network fault location method according to the present application;
FIG. 2 is a schematic flow chart of D-S evidence theory fault location;
FIG. 3 is a simple distribution network feeder diagram;
fig. 4 is a schematic flow chart of an optimization solution using a genetic algorithm according to the present application to obtain a first set of suspicious segments as a first identification frame;
fig. 5 is a schematic flow chart of the optimization solution using the genetic algorithm described in the present application to obtain a second suspicious segment set as a second identification frame;
FIG. 6 is a feeder diagram of a 10kV power distribution network in a certain area;
FIG. 7 is a result of construction of an information source upon a single segment failure;
FIG. 8 shows the result of constructing an information source in the event of a multi-segment failure;
fig. 9 is a construction result of information loss of a single distribution automation terminal when a single segment fails;
fig. 10 is a construction result of a plurality of distribution automation terminal information loss when a single section fails;
fig. 11 is a construction result of missing information of the load monitoring point when a single section fails.
Detailed Description
Reference will now be made in detail to embodiments, examples of which are illustrated in the accompanying drawings. The following description refers to the accompanying drawings in which the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following examples do not represent all embodiments consistent with the present application. But merely as examples of systems and methods consistent with certain aspects of the application, as detailed in the claims.
At present, the fault section of the power distribution network is mainly identified based on current information. With the wide application of distribution automation terminals, a fault section D-S evidence theory positioning method based on overcurrent information is also widely applied. In one implementation, the application process of the D-S evidence theory positioning method is as follows:
as shown in fig. 2, a terminal sensor device of the distribution automation terminal collects various data of the power grid as raw information, and determines an identification frame according to the raw information.
In the theory of D-S evidence, it is, identifying a framework as a set of all possible assumptions Θ = { A = { (A) } 1 ,A 2 ,…,A n In which the power set of Θ is
And all the hypothesis sets can be exhaustive and independent of each other, and have the total of 2 n A set of assumptions.
After a fault occurs, the monitoring data are integrated to generate an evidence source BPA (Basic Probability Assignment) in a certain mode.
When the power set of theta is 2 Θ To [0,1]M satisfies the formula 2.1, then m is called basic probability distribution, also called mass function or evidence, wherein m (A)>Subset a of 0 is called a focal element.
As one evidence (BPA) is as follows: m is 1 (A 1 )＝0.2，m 2 (A 2 )＝0.6，m 3 (A 3 ) If not less than 0.3, then A 1 、A 2 、A 3 Is the possible set of all hypotheses and the size of the value of m represents the size of the degree of support of the piece of evidence for the respective subset.
And finally, synthesizing the evidence source BPA through a D-S theoretical synthesis rule, and judging whether a fault occurs according to a finally obtained synthesis result.
The fusion mode for multiple evidences in D-S evidence theory is shown in the following equation 6.2, wherein A 1 ,A 2 ,…,A n Is the focal element, k is the collision coefficient, calculated by equation 6.3.
When k =0, it indicates complete compatibility between proofs;
when 0-k-cloth-1 indicates that evidence is partially compatible, the synthesis rule is more effective;
when k =1, it indicates that the evidence is completely contradictory, and the synthesis rule is no longer used.
However, the D-S evidence theory positioning method only uses one evidence source, and when multiple sections of the power distribution network fail simultaneously, erroneous judgment often occurs only by using a single evidence source to perform fault positioning; when a fault occurs in a certain section of the power distribution network, if the power distribution automation terminal is not installed in the position of the section switch of the section, or the false alarm and the false alarm occur, or when the false alarm and the false alarm occur in a plurality of power distribution automation terminals, the accuracy of the D-S evidence theory positioning method is greatly reduced and even fails, and the fault section positioning method only using the traditional single evidence source can generate the false judgment.
In order to solve the above problem, the present application provides a power distribution network fault location method, as shown in fig. 1, the method includes:
s100: the power distribution automation terminal collects first data.
The distribution automation terminal is a general name of various remote monitoring and control units installed on a power distribution network, completes functions of data acquisition, control, communication and the like, and mainly comprises a feeder terminal, a station terminal, a distribution transformer terminal and the like, and is called a distribution terminal for short. When the distribution network has a fault, the distribution automation terminal sends fault overcurrent alarm information to the analysis module according to the acquired first data, and the fault overcurrent alarm information serves as first original information.
S200: and judging whether fault overcurrent alarm information sent by the distribution automation terminal is received or not.
If receiving the fault overcurrent alarm information sent by the distribution automation terminal, indicating that the distribution automation terminal detects that the distribution network has a fault, then step S300 is performed.
And if the fault overcurrent alarm information sent by the distribution automation terminal is not received, which indicates that the distribution automation terminal does not detect that the distribution network has a fault, jumping to step S500.
S300: using genetic algorithm optimization solution to obtain a first suspicious segment set as a first identification frame, as shown in fig. 4, including:
s301: and representing the fault overcurrent alarm information of the distribution automation terminal by using a binary code to generate a first information code.
And setting the code of the fault overcurrent alarm information as 1 and the code of the no-fault overcurrent alarm information as 0. As shown in fig. 3, F1 to F4 are distribution automation terminal devices, S1 to S4 are four sections divided by a distribution network line, and when a single-phase ground fault occurs in the section S2, the distribution automation terminal information is encoded to 1100.
S302: a first random initial population is generated.
The length of the initial population of individuals of the genetic algorithm is determined by the number of segments of the line, for example, if the number of segments of the distribution network line in fig. 3 is 4, then the length of a single individual in the randomly generated initial population is 4, and the individual encoded as 1100 indicates that the distribution network line has 4 segments, and the segment S1 and/or S2 fails. The total number of individuals in the population is set according to specific conditions, generally speaking, the greater the number of the population, the more diversity, the easier the optimal solution occurs, but the convergence speed is correspondingly reduced, but the too small number of the population easily causes the situation that the subsequent optimal individual solution falls into a local optimal value. The fault section judgment is that a computer generates a certain number of initial populations and then optimization iteration is carried out according to a set fitness function.
S303: a first fitness function is constructed.
The core of the genetic algorithm for searching the optimal solution to obtain the optimal individual lies in the construction of a fitness function, the value of the fitness function is the only standard for judging the fitness of different individuals in a population, and the fitness function is different according to different problems. When a power distribution network line has a fault, a power distribution automation terminal arranged on the line can upload corresponding fault overcurrent alarm information to an analysis module after monitoring that the fault overcurrent occurs. However, most distribution automation terminal devices are installed outdoors, and the phenomena of false alarm and missing alarm of fault overcurrent alarm information are easy to occur in the actual operation process. Therefore, a first fitness function required by a genetic algorithm is constructed according to the distribution automation terminal information, then optimization solving is carried out, and a plurality of individuals with the maximum corresponding fitness in the population and the corresponding total number of the individuals in the population after the final iteration is completed are recorded.
In an exemplary embodiment, the first fitness function is configured to:
in equation (1): e 1 (s) construction of genetic algorithm fitness, M, for distribution automation terminal information 1 2 times the number of distribution automation terminals actually installed, F j For j-th distribution automation terminal status coding, F j (S) is the jth distribution automation terminal state function, S i Coding the status of the ith section of the distribution network line, N 1 For the number of sections of the distribution network line, N 2 The number of automatic terminals is actually distributed.
F j (s) the state function is derived from the current distribution automation terminal and its downstream segment states: if a segment exists in a line connected between the jth distribution automation terminal and the end of the lineIf a fault occurs, i.e. if the status code of the sector is 1, then there is F j (s) =1, only if no segment has failed, have F j (s) =0, where the determination of the function of the status function for all distribution automation terminals is achieved using line segment status coding information and distribution automation terminal-segment weighting matrix.
Wherein
This term serves two purposes:
firstly, the situation that different distribution automation terminal status codes have the same fitness value can be prevented, for example, as shown in fig. 3, when a line section S2 has a fault, the distribution automation terminal normally uploads a fault information code of 1100, when the section status codes in population individuals are 0100 and 1100, if this item is not added, the corresponding fitness value is M, after this item is added, if and only if the section status code is 0100, the fitness is M at most, and other situations are smaller than M.
Secondly, as shown in fig. 3, when a fault occurs on the line on the left side of F1, the coded information of the distribution automation terminal is 0000, the interval state code should be 0000, and the corresponding fitness function value at this time is M +1, so that the algorithm can be normally converged.
S304: individuals in the first random priming population are selected, crossed and mutated.
The selection step is to keep individuals with high fitness in the population, eliminate individuals with low fitness, select the optimal individuals by adopting a mixed selection mechanism of typical roulette and optimal individual maintenance, ensure that the individuals with high fitness increase in proportion with the increase of iteration times, and improve the defect that the algorithm loses excellent individuals in the iteration process and falls into local optimal values prematurely.
The two steps of crossing and variation are used for ensuring the diversity of the population and generating the optimal individual more quickly. The crossing is to generate new filial generation individuals by interchanging certain segments in the codes among different individuals, and is limited by a certain probability, so that the problems that the optimal individuals are difficult to reserve due to transitional crossing and the crossing probability needs to be set are avoided; the mutation has the effect that individuals with higher fitness are easier to generate in the population, but the probability of the mutation is too high, the individual mutation is too fast, the algorithm becomes random search, if the probability is too low, new individuals are not easy to appear, the convergence speed of the algorithm is too slow, and the probability of the mutation needs to be set.
In an exemplary embodiment, the crossover probability is set to 0.6 and the mutation probability is set to 0.01. The setting is carried out through a large amount of theoretical calculation and practice, so that the optimal individual is difficult to reserve due to the fact that transition does not occur in the crossing step; the diversity of the population in the variation step produces the optimal individual more quickly.
S305: and judging the convergence of the genetic algorithm and decoding the first information to obtain a first suspicious segment set, wherein the first suspicious segment set is used as a first identification frame.
After the initial population is generated by the genetic algorithm, iterative calculation is started, with the increase of the iteration times, the number of individuals with large fitness values is increased after the steps of selection, intersection and variation are carried out on the individuals in the population according to the corresponding fitness values, and the larger the fitness value of the individuals in the population is after the set iteration times is reached, the larger the occupation ratio is bound to be. And then, after the individual binary codes are decoded into fault section information which is easy to identify according to the coding mode, the suspicious section set with the optimal result and the probability being N at the top is output.
In an exemplary embodiment, the determining convergence of the genetic algorithm and decoding the first information to obtain a first suspicious segment set, wherein the first suspicious segment set is used as a first identification frame includes:
when the convergence condition is reached, outputting the individuals with the fitness value at the top N and the total number of the individuals; the convergence condition is set as the iteration times for the algorithm to reach the set value;
performing first information decoding on the individuals to obtain a first suspicious segment set, wherein the first suspicious segment set is used as a first identification frame; the first information decoding is to decode the binary code into the suspicious fault section information according to the first information encoding mode.
S400: from the first set of suspect segments, a first evidence source BPA (Basic probability Assignment) is generated.
In an exemplary embodiment, the probability of a suspicious fault segment in the first set of suspicious segments is calculated by formula (2), and a first evidence source BPA is obtained;
in equation (2): m is 1 (r i ) Probability value, r, for the first evidence source corresponding to the suspicious fault section i Individuals with fitness values preceding N, c 1 (r i ) Is a subject r i And the corresponding total number N is the number of the selected fitness values in the front.
S110: and the power utilization information acquisition system for the power consumer acquires second data.
The power consumption information acquisition system for the power consumers realizes power consumption monitoring, stepped pricing, load management and line loss analysis by acquiring and analyzing power consumption data of a distribution transformer and terminal users, and finally achieves the purposes of automatic meter reading, off-peak power consumption, power consumption inspection (electricity stealing prevention), load prediction, power consumption cost saving and the like. A plurality of load monitoring points are required to be established for establishing a comprehensive power consumer electricity consumption information acquisition system. When the power distribution network has a fault, the load monitoring point in the power consumption information acquisition system of the power consumer sends power loss region alarm information to the analysis module according to the acquired second data, and the power loss region alarm information is used as second original information.
S210: and judging whether power loss area warning information sent by a load monitoring point in the power consumer power consumption information acquisition system is received.
If the power loss region warning information sent by the load monitoring point in the power consumer power consumption information acquisition system is received, which indicates that the load monitoring point in the power consumer power consumption information acquisition system detects that the power distribution network has a fault, step S310 is performed.
And if the power loss region warning information sent by the load monitoring point in the power consumer power consumption information acquisition system is not received, indicating that the load monitoring point in the power consumer power consumption information acquisition system does not detect that the power distribution network has a fault, jumping to the step S500.
S310: using a genetic algorithm to optimize and solve the solution, obtaining a second suspicious segment set, where the second suspicious segment set is used as a second identification frame, as shown in fig. 5, including:
s311: and representing the power-losing area warning information of the load monitoring point in the power consumer power consumption information acquisition system by using a binary code to generate a second information code.
And setting the code of the fault overcurrent alarm information as 1 and the code of the no-fault overcurrent alarm information as 0. As shown in fig. 3, LH is a load monitoring point of the power consumption information acquisition system of the power consumer, S1 to S4 are four sections divided by a power distribution network line, when a single-phase ground fault occurs in the section S2, the information of the load monitoring point is 1, and the section code is 0100.
S312: a second random initial population is generated.
The method used in step S312 is the same as that in step S302, and is not described again.
S313: and constructing a second fitness function.
The power consumer electricity consumption information acquisition system can acquire and monitor voltage, current and the like of an area, and when a power distribution network normally operates, various parameters of a line are in a normal range; when a single-phase earth fault occurs on a line, a load monitoring point in the power consumption information acquisition system of a power consumer can monitor abnormal fluctuation of the voltage of the area where the fault line is located so as to upload alarm information of a power loss area. When a three-phase short circuit grounding fault occurs in a line, the power consumer power consumption information acquisition system cannot work normally due to power loss, cannot acquire information such as voltage of the line at the moment, and can send the last information to report the fault, so that after the power distribution network line has a fault, a downstream area of a section where the fault is located cannot recall voltage information or form a low-voltage area, and a non-fault area is normal, so that a second fitness function can be constructed by using the information.
In an exemplary embodiment, the second fitness function is configured to:
in equation (3): e 2 (s) establishing genetic algorithm fitness for information of power consumer power consumption information acquisition system, M 2 Is 3 times and H times of the number of load monitoring points in the power utilization information system of the power consumer j For the status coding of the jth load monitoring point, H j (S) is a state function of the jth load monitoring point, S i Coding the status of the ith section of a power distribution network line, N 1 For the number of sections of the distribution network line, N 3 The number of load monitoring points in the power utilization information acquisition system for the power consumer is increased.
H j (s) the state function is obtained from the state of the section passing through the shortest path from the current load monitoring point to the power supply: i.e. power to load point H j If the state code of any one section is 1 in the state codes corresponding to the sections through which the minimum path passes, the load point state function H j (s) =1, determination of function values is achieved from line segment status coding information and load point-segment weighted matrix,
the function of (1) is also to prevent the misjudgment of one value and multiple solutions.
S314: selecting, crossing and mutating individuals in the second random initial population;
the method used in step S314 is the same as that in step S304, and is not described again.
S315: and judging the convergence of the genetic algorithm and decoding the second information to obtain a second suspicious segment set, wherein the second suspicious segment set is used as a second identification frame.
After the initial population is generated by the genetic algorithm, iterative calculation is started, with the increase of the iteration times, the number of individuals with large fitness values is increased after the steps of selection, intersection and variation are carried out on the individuals in the population according to the corresponding fitness values, and the larger the fitness value of the individuals in the population is after the set iteration times is reached, the larger the occupation ratio is bound to be. And then, after the individual binary codes are decoded into fault section information which is easy to identify according to the coding mode, the suspicious section set with the optimal result and the probability being N at the top is output.
In an exemplary embodiment, the step of determining the convergence of the genetic algorithm and decoding the second information to obtain a second suspicious segment set, wherein the second suspicious segment set is used as a second identification frame includes:
when the convergence condition is reached, outputting the individuals with the fitness value in the front N and the total number of the individuals; the convergence condition is set as the iteration times for the algorithm to reach the set value;
carrying out second information decoding on the individuals to obtain a second suspicious section set which is used as a second identification frame; and the second information decoding is to decode the binary code into the suspicious fault section information according to a second information encoding mode.
S410: a second evidence source BPA is generated based on the second set of suspect segments.
In an exemplary embodiment, the probability of a suspicious failed segment in the second set of suspicious segments is calculated by formula (4), so as to obtain a second evidence source BPA;
in equation (4): m is a unit of 2 (r i ) Probability value, r, for the second evidence source corresponding to the suspicious fault section i Individuals with fitness values preceding N, c 2 (r i ) Is a subject r i And N is the number of the selected fitness value in the front.
S500: a first evidence source BPA and a second evidence source BPA are collected.
The generated first evidence source BPA and the second evidence source BPA are collected together, so that evidence sources required by information fusion are respectively constructed by using the power distribution automation terminal information and the power consumer electricity utilization information acquisition system information.
S600: it is determined whether the number of evidence sources is greater than 1.
If the number of the evidence sources is greater than 1, it is indicated that alarm information appears in both the distribution automation terminal and the power consumer electricity information collection system, and a first evidence source BPA and a second evidence source BPA are generated, then step S800 is performed.
If the number of the evidence sources is not greater than 1, which indicates that only one evidence source has alarm information, step S700 is performed.
S700: and outputting the optimal value solved by the genetic algorithm.
If the number of the evidence sources is not more than 1, it is indicated that only one evidence source has alarm information, which is the same as the D-S evidence theoretical positioning method of a single evidence source and is not described again.
S800: fusing a first evidence source BPA and a second evidence source BPA by using an improved Yager synthesis formula;
general Yager synthesis formula:
considering that the conflict cannot be judged, yager improves the D-S evidence theory, introduces m (X), and assigns the conflict to an unknown proposition, and a general Yager synthesis formula is as follows:
where X is other unknown propositions and k is a conflict factor.
Taking the evidence sources in tables 1.1a and 1.1b as examples, the recognition framework was constructed and the results after synthesis by the general Yager formula are shown in table 1.2.
TABLE 1.1A comparison of fusion results for minor changes in BPA function
In Table 1.1a when A 3 M in (1) 1 With minor variations, it becomes as shown in table 1.1 b:
TABLE 1.1b comparison of fusion results for minor changes in BPA function
TABLE 1.2 Yager formula fusion results
Although the addition of unknown propositions by the Yager equation does not result in erroneous judgment of the D-S evidence theory, there is a problem, such as adding new evidence m to Table 1.2 3 The fusion results obtained are shown in table 1.3.
TABLE 1.3 Add New evidence m 3 The Yager formula of
As can be seen from Table 1.3, even though Pair A is added 1 Is highly supported, but m (A) still appears in the end 1 ) Unreasonable results for = 0; it can be concluded that even more evidence later supports A 1 The final synthesis result is not greatly influenced, and the synthesis result satisfies the following rule: m (A) 1 )→0,m(A 2 )→0,m(A 3 )→0,m(X)→1。
As can be seen from the fusion result of the general Yager synthesis formula, a negative A appears in numerous evidences 1 Then no matter how many other evidences support A 1 Since the fusion result is negative, there is a significant problem in practical use: for example, when the method is used for positioning and fusing the faults of the power distribution network, if the situation of misinformation and missing report occurs in one information source of multi-source fault information, correct fault data are uploaded even if the working states of other information sources are good, and finally, the method is used for positioning and fusing the faults of the power distribution networkThe result of (2) also can be wrong, resulting in failure of fault location, so that the general Yager synthetic formula can not achieve the expected effect on fault location under the condition that the power distribution network lacks alarm information or the alarm information is misreported and fails to be reported, and the general Yager synthetic formula needs to be improved to be suitable for fault location of the power distribution network.
In one illustrative embodiment, the modified Yager synthesis formula is:
in equation (5.1):
average support of A
In equations (5.2) and (5.3): evidence source is m 1 、m 2 、…、m n The corresponding evidence sets are respectively F 1 、F 2 、…、F n The collision factor is k, the evidence set F i And F j The conflict between is k ij ；
Confidence level of evidence
Wherein:
n is the total number of evidence sources used.
When Θ = { F 1 ,F 2 ,F 3 X, using the modified Yager synthesis formula, the fusion of two proofs with three proofs is shown in tables 1.4a and 1.4 b:
TABLE 1.4a comparison of modified Yager formula fusion results with increased evidence sources
Table 1.4a adds new evidence m 3 Then, it becomes table 1.4b as follows:
TABLE 1.4b improved Yager formula fusion results comparison to increase evidence sources
From table 1.4b it can be seen that: with the increase of evidence sources, the practical application problem of the prior general Yager synthesis formula does not appear, and the increase does not support A 2 、A 3 After the evidence of (3), the fusion result is basically unchanged, but the pair A is increased 1 After supporting the evidence source, the result of fusing promotes to some extent, and the result of fusing of unknown proposition X reduces to some extent, to fault location, different evidence sources are exactly different fault monitoring information sources, the condition of multiple spot trouble probably appears in the distribution network, and different information source monitoring data are different, use modified Yager synthetic formula just can be fine fuse the judgement to the multiple spot trouble condition, but not appear conflicting with other synthetic formulas and negative with the complete set, it is obvious more reasonable to fuse the result.
For the improved Yager synthesis formula, when the conflict coefficient k is smaller, the fusion result is close to the fusion result of the classical evidence theory, and when the evidence is completely compatible, the improved Yager synthesis formula can be converted into the synthesis formula of the classical evidence theory, but when the evidence is highly conflicting, the fusion result depends on the size of the value of epsilon x q (A). When evidence conflict occurs in a general Yager synthesis formula, the fusion result is directly and completely endowed to an unknown part, which is obviously not suitable for practical application, and the improved Yager synthesis formula also takes the conflicting evidence into consideration range, considers that the improved Yager synthesis formula can also provide related judgment information, and links the credibility degree and the credibility epsilon.
S900: and outputting a fault positioning result.
And outputting the suspicious fault section with the maximum probability after fusion as a fault positioning result according to the improved Yager synthesis formula fusion result.
Specifically, taking a feeder diagram of a 10kV power distribution network in a certain area shown in fig. 6 as an example, the power distribution network fault location method is analyzed and verified. Each line head end in fig. 6 is equipped with a distribution automation terminal F1-F14, divided into 14 segments in total. When a line section has a fault, the distribution automation terminal uploads fault overcurrent alarm information to an analysis module after monitoring an overcurrent signal; the tail end of the line has 7 load monitoring points LH1-LH7, abnormal voltage information is monitored by the power consumer electricity information acquisition system, namely N 1 ＝N 2 ＝14，N 3 =7. The parameters in the genetic algorithm are set as follows: the iteration number set in the population is 100, the number of individuals in the population is 300, and M in the fitness function 1 ＝28，M 2 =21. Genetic algorithms were written using MATLAB: formula (1), formula (3) and modified Yager synthesis formula: after iteration is completed, suspicious fault section information with the individual fitness value being the first 5 in the two types of information source populations of the distribution automation terminal and the power consumer electricity information acquisition system is taken to form a first suspicious section set and a second suspicious section set, and the proportion of each suspicious fault section in the population is obtained according to the formula (2) and the formula (4) and is used as a first evidence source BPA and a second evidence source BPA of information fusion. And analyzing the following typical conditions of normal information source monitoring information and false alarm and missing report, and verifying the reliability and fault tolerance of the power distribution network fault positioning method.
The first embodiment is as follows: and (4) positioning under the condition of single-section fault under the condition of complete multi-source alarm information.
Setting section S7 in figure 6 to have a fault, collecting corresponding fault overcurrent signals by the distribution automation terminal, and uploading the fault overcurrent alarm information to the analysis normallyModule, status code I of corresponding distribution automation terminal fault information j 11000010000000. The power consumption information acquisition system of the power consumer works normally, the information is uploaded timely and correctly, and the state code H of the fault information of the related load monitoring point j Is 0010000. And (3) respectively carrying out optimization solution by using fitness functions of a formula (1) and a formula (3) as objective functions through a genetic algorithm, wherein each information source takes a suspicious fault section set with the probability of 5, the probability of the suspicious fault section set is shown in a figure 7, and a fusion result is shown in a table 2.1.
TABLE 2.1 positioning results in case of single-sector failure
As can be seen from table 2.1, for a single-section fault, the probability that both the distribution automation terminal and the power consumer electricity information acquisition system can be solved through optimization of the genetic algorithm is the maximum in the section S7, and it can be accurately determined that the fault occurs in the section S7, and the probability that the information fusion result is also the section S7 is the maximum, so that it can be determined that the fault occurs in the section S7. Therefore, the power distribution network fault positioning method can accurately judge the fault of the single section.
Example two: and (4) positioning under the condition of multi-section fault under the condition of complete multi-source alarm information.
When a plurality of sections of the power distribution network have faults simultaneously, misjudgment often occurs easily only by means of single information source for fault positioning, but the power distribution network fault positioning method provided by the application can also perform corresponding judgment. Setting that the section S5 and the section S12 in the diagram 6 have faults simultaneously, the power distribution automation terminal and the power consumer electricity consumption information acquisition system work normally, and the uploaded fault information has no distortion, namely the state coding information I of the corresponding power distribution automation terminal fault information j For 11011011001010100, load point information state coding H j For 1000100, through optimization solution of genetic algorithm, each information source takes the suspicious fault segment set with the probability of 5, and the probability thereof is shown in fig. 8, and the fusion result is shown in table 2.2.
TABLE 2.2 positioning results in case of multiple sector faults
In the feeder line diagram of fig. 6, the sections S10 and S12 are directly connected, and when any one of the two sections fails, the situation of erroneous judgment is easily caused after the information algorithm of the power consumer electricity consumption information acquisition system is used for solving the problem, so the evidence source m in table 2.2 2 The probability of occurrence of suspicious fault section sets 5 and 12, and 5 and 10 in the power distribution automation terminal is almost the same, and misjudgment is easy to occur, but the probability of sections S5 and S12 is the maximum after information fusion is carried out by integrating the power distribution automation terminal information source by using an improved Yager synthesis formula, so that the sections with faults can be accurately judged to be S5 and S12. Therefore, the power distribution network fault positioning method can accurately judge the fault conditions of the multiple sections.
Example three: and (4) positioning under the condition of single distribution automation terminal information loss and single section fault.
When a certain section of the power distribution network has a fault, if the section switch position of the section is not provided with the electric automatic terminal or is provided with the false alarm and the false alarm, or when a plurality of electric automatic terminals have the false alarm and the false alarm, the accuracy of the traditional section positioning method is greatly reduced and even fails, and the fault section positioning method only using the traditional single information source can generate misjudgment. The method has the inherent defect that the fault section is positioned by monitoring the fault overcurrent information through a single information source, and the problem can be well solved by the method for positioning the fault of the power distribution network.
It is assumed that the segment S13 in fig. 6 has a fault, but due to a communication problem caused by bad weather or the like, the distribution automation terminal in the segment does not upload monitoring information, i.e. the status code I of the fault information of the distribution automation terminal, to the analysis module j 10010001001000, and state code H of load monitoring point information in power consumer electricity information acquisition system j 0000010. In this case, through the optimization solution of the genetic algorithm, each information source takes the suspicious fault segment set with the probability of 5 as shown in fig. 9, and the fusion result is shown in table 2.3.
Table 2.3 fault section location results in case of single distribution automation terminal information error or missing
Due to the fact that information uploaded by the distribution automation terminal in the section S13 is lost, the probability that the fault is judged to occur in the section S11 by mistake after the information source of the distribution automation terminal is subjected to optimization solution is the largest, and therefore the time for fault removal and power utilization recovery is greatly prolonged, but the information source m of the power utilization information acquisition system of the power consumer is combined 2 The probability of the section S13 after the information fusion is performed is the highest, and it is determined that the failure has occurred in the section S13 with an accurate result. When the single source information is lost, the accuracy of fault judgment can be improved after the second information source is added.
Example four: and positioning under the condition of single-section fault under the condition of information loss of a plurality of distribution automation terminals.
It is assumed that a fault occurs in section S12 in fig. 6, but the distribution automation terminals in sections S8 and S10 do not upload fault overcurrent alarm information to the analysis module due to various reasons, and the analysis module actually receives status code I of alarm information j 10010000000100, and load monitoring point information state code H in power consumer electricity information acquisition system j To 0000100, through genetic algorithm optimization solution, each information source takes the suspicious fault segment set with the probability of 5, and the probability thereof is shown in fig. 10, and the fusion result is shown in table 2.4.
Table 2.4 fault section locating results in case of multiple distribution automation terminal information errors or deletions
As can be seen from table 2.4, when a plurality of pieces of power distribution automation terminal information are lost, the faulty section obtained by the power distribution automation terminal information source through algorithm optimization solution may have a wrong result, and the probability of the section S12 is the maximum after information fusion is performed by combining the power consumer electricity consumption information acquisition system information source, although a correct result can be obtained, it is determined that the fault occurs in the section S12. However, the result probability of information fusion is low, so that the situation that the multi-terminal information is lost in the operation and maintenance personnel system can be warned, further judgment needs to be carried out by combining manual experience or increasing an information source, and one direction can be appointed for the operation and maintenance personnel.
Example five: and (4) positioning under the condition of single-section fault under the condition that the power consumer power utilization information acquisition system makes mistakes.
Setting the section S11 in the figure 6 to have a fault, normally uploading the information of the distribution automation terminal, and encoding the state I of the information j Is 10010001001000. However, the power utilization information acquisition system of the power consumer cannot call the information of the load monitoring point LH3 due to the fault problem, so that the state code H of the called load monitoring point information j 0010011. Through the optimization solution of the genetic algorithm, the suspicious fault segment set with the probability of 5 is taken from each information source, the probability thereof is shown in fig. 11, and the fusion result is shown in table 2.5.
TABLE 2.5 Fault section location results in case of power consumer power consumption information acquisition system information error or deficiency
As can be seen from the results in table 2.5, when an error is reported from the information of the load monitoring point, the probability of obtaining a real fault section is very low, but after signal fusion is performed in combination with the information source of the distribution automation terminal, the error information is corrected, and the fusion result can correctly determine that the fault occurs in the section S11.
Based on the power distribution network fault location method provided by the above embodiment, some embodiments of the present application further provide a power distribution network fault location system, including:
and the power distribution automation terminal is used for acquiring the first data and judging whether fault overcurrent alarm information is sent or not according to the first data. The distribution automation terminal is a general name of various remote monitoring and control units installed on a power distribution network, completes functions of data acquisition, control, communication and the like, and mainly comprises a feeder terminal, a station terminal, a distribution transformer terminal and the like, and is called a distribution terminal for short. When the power distribution network has a fault, the power distribution automation terminal sends fault overcurrent alarm information to the analysis module according to the collected first data, and the fault overcurrent alarm information is used as first original information.
Electric power consumer power consumption information acquisition system includes: the load monitoring point is used for acquiring second data and judging whether to send power-loss region warning information or not according to the second data; the power consumption information acquisition system for the power users realizes power consumption monitoring, stepped pricing, load management and line loss analysis by acquiring and analyzing power consumption data of a distribution transformer and terminal users, and finally achieves the purposes of automatic meter reading, off-peak power consumption, power consumption inspection (electricity stealing prevention), load prediction, power consumption cost saving and the like. A plurality of load monitoring points are required to be established for establishing a comprehensive power consumer electricity utilization information acquisition system. When the power distribution network has a fault, the load monitoring point in the power consumer electricity information acquisition system sends electricity loss region alarm information to the analysis module according to the acquired second data to serve as second original information.
And the analysis module is used for executing the power distribution network fault positioning method in the embodiment. The analysis module may be a computer, a server, an industrial personal computer, a single chip microcomputer, a PLC (Programmable Logic Controller), a DSP (digital signal processor), an FPGA (Field Programmable Gate Array), an ASIC (application-specific integrated circuit), and other devices having storage and operation functions.
According to the power distribution network fault positioning method and system, information of a power distribution automation terminal and an electric power user electricity consumption information acquisition system is optimized and solved through a genetic algorithm to construct an evidence source required for fault judgment, an improved Yager synthetic formula is used for fusing the evidence source, and data are comprehensively analyzed to obtain a final judgment result. The positioning method can not only accurately judge the fault of a single section; the method can also be used for accurately judging when a multi-section fault occurs; and the fault section can still be accurately positioned under the condition that the information reported by the monitoring equipment of a plurality of information sources is missing. The method can well solve the problem that when the monitoring equipment is in fault false alarm and missed alarm, fault positioning is easy to judge by mistake by using a single information source. The compatibility of the algorithm is good, new information sources are added only by constructing the evidence sources according to the process, the accuracy of fault positioning can be increased along with the addition of the evidence sources, the power supply reliability is improved, the manual fault removal time is shortened, and the equipment investment is not required to be increased.
The embodiments provided in the present application are only a few examples of the general concept of the present application, and do not limit the scope of the present application. Any other embodiments that can be extended by the solution according to the present application without inventive efforts will be within the scope of protection of the present application for a person skilled in the art.
Claims (10)
1. A power distribution network fault location method is characterized by comprising the following steps:
when fault overcurrent alarm information sent by a distribution automation terminal is received, a genetic algorithm is used for optimization solution to obtain a first suspicious segment set, and the first suspicious segment set is used as a first identification frame and comprises the following steps:
representing fault overcurrent alarm information of the power distribution automation terminal by using a binary code to generate a first information code;
generating a first random initial population;
constructing a first fitness function;
selecting, crossing and mutating individuals in the first random initial population;
judging convergence of a genetic algorithm and decoding first information to obtain a first suspicious segment set, wherein the first suspicious segment set is used as a first identification frame;
generating a first evidence source basic probability distribution according to the first suspicious segment set;
when power-loss region warning information sent by load monitoring points in a power consumer power consumption information acquisition system is received, optimizing and solving by using a genetic algorithm to obtain a second suspicious segment set, wherein the second suspicious segment set is used as a second identification frame and comprises the following steps:
representing the power-losing area warning information of a load monitoring point in the power consumer power consumption information acquisition system by using a binary code to generate a second information code;
generating a second random initial population;
constructing a second fitness function;
selecting, crossing and mutating individuals in the second random initial population;
judging convergence of the genetic algorithm and decoding second information to obtain a second suspicious segment set, wherein the second suspicious segment set is used as a second identification frame;
generating a second evidence source basic probability distribution according to the second suspicious segment set;
collecting basic probability distribution of a first evidence source and basic probability distribution of a second evidence source, and fusing the basic probability distribution of the first evidence source and the basic probability distribution of the second evidence source by using a Yager synthesis formula when the number of the evidence sources is more than 1;
and outputting a fault positioning result.
2. The method for locating the fault in the power distribution network according to claim 1, wherein the first fitness function is set as:
wherein: e 1 (s) construction of genetic algorithm fitness, M, for distribution automation terminal information 1 2 times the number of distribution automation terminals actually installed, F j For the jth distribution automation terminal status coding, F j (S) is the jth distribution automation terminal state function, S i Coding the status of the ith section of the distribution network line, N 1 For the number of sections of the distribution network line, N 2 And the number of the automatic terminals is actually distributed.
3. The method according to claim 1, wherein the step of determining convergence of the genetic algorithm and decoding the first information to obtain a first suspicious segment set, and the step of using the first suspicious segment set as a first recognition frame comprises:
when the convergence condition is reached, outputting the individuals with the fitness value at the front N and the total number corresponding to the individuals; the convergence condition is set as the iteration times when the algorithm reaches the set value;
performing first information decoding on the individuals to obtain a first suspicious segment set, wherein the first suspicious segment set is used as a first identification frame; the first information decoding is to decode the binary code into the suspicious fault section information according to the first information encoding mode.
4. The method according to claim 1, wherein the step of generating the first evidence source basic probability distribution according to the first set of suspicious segments comprises:
the probability of the suspicious fault sections in the first suspicious section set is calculated by the following formula, and basic probability distribution of a first evidence source is obtained;
wherein: m is 1 (r i ) Probability value for the first evidence source corresponding to the suspicious fault segment, r i Individuals with fitness values preceding N, c 1 (r i ) Is an individual r i And the corresponding total number N is the number of the selected fitness values in the front.
5. The method for locating the fault in the power distribution network according to claim 1, wherein the second fitness function is set as:
wherein: e 2 (s) establishing genetic algorithm fitness for information of power consumer power utilization information acquisition system, M 2 Is 3 times and H times of the number of load monitoring points in the power utilization information system of the power consumer j Encode the status of the jth load monitor point, H j (S) is a state function of the jth load monitoring point, S i Coding the status of the ith section of the distribution network line, N 1 For the number of sections of the distribution network line, N 3 The number of load monitoring points in the power utilization information acquisition system for the power consumer is increased.
6. The method according to claim 1, wherein the step of determining convergence of the genetic algorithm and decoding the second information to obtain a second suspicious segment set, and the step of using the second suspicious segment set as a second identification frame comprises:
when the convergence condition is reached, outputting the individuals with the fitness value at the front N and the total number corresponding to the individuals; the convergence condition is set as the iteration times for the algorithm to reach the set value;
carrying out second information decoding on the individuals to obtain a second suspicious segment set, wherein the second suspicious segment set is used as a second identification frame; and the second information decoding is to decode the binary code into the suspicious fault section information according to a second information encoding mode.
7. The method according to claim 1, wherein the step of generating a second evidence source basic probability distribution according to the second set of suspicious segments comprises:
the probability of the suspicious fault sections in the second suspicious section set is calculated by the following formula to obtain the basic probability distribution of a second evidence source;
wherein: m is 2 (r i ) Probability value, r, for the second evidence source corresponding to the suspicious fault section i Individuals with fitness value preceding N, c 2 (r i ) Is a subject r i And the corresponding total number N is the number of the selected fitness values in the front.
8. The method as claimed in claim 1, wherein the crossover probability is set to 0.6 and the mutation probability is set to 0.01.
9. The improved D-S theory power distribution network fault location method according to claim 1, wherein the Yager synthesis formula is:
wherein:
average degree of support of A
Evidence source is m 1 、m 2 、…、m n The corresponding evidence sets are respectively F 1 、F 2 、…、F n The collision factor is k, the evidence set F i And F j The conflict between is k ij ；
Confidence level of evidence
Wherein:
n is the total number of evidence sources used.
10. A power distribution network fault location system, comprising:
the power distribution automation terminal is used for acquiring first data and judging whether fault overcurrent alarm information is sent or not according to the first data;
electric power consumer power consumption information acquisition system includes: the load monitoring point is used for acquiring second data and judging whether to send power-loss region warning information or not according to the second data;
analysis module for performing a method of fault location of a power distribution network according to any of claims 1-9.
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| CN113177738A (en) * | 2021-05-26 | 2021-07-27 | 云南电网有限责任公司怒江供电局 | Power automatic alarm rapid first-aid repair allocation method |
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