CN114264912A - Multi-source power distribution network fault positioning method based on improved Jaya algorithm - Google Patents
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Abstract
The invention discloses a multisource power distribution network fault location method based on an improved Jaya algorithm in the field of power distribution network fault location, which comprises the following steps: s1: establishing a fault positioning model according to the characteristics that an active power distribution network contains various distributed power sources, wherein the fault positioning model comprises the following steps: setting a fault current information coding scheme; and establishing a switching function and forming a model evaluation function. S2: optimizing the Jaya algorithm, wherein the optimizing process comprises the following steps: optimizing the generation of a random number of variable position iteration based on a chaos theory; correcting variables of individual codes; s3: based on the model of S1, solution calculation is performed by the improved algorithm of S2. According to the method, the fault location problem of single and multiple faults under different distributed energy distribution conditions can be solved, fault location containing simple distortion information can be processed, the fault section location speed when the power distribution network fails is increased, and the reliability of the power system is improved.
Description
Technical Field
The invention belongs to the field of power distribution network fault location, and particularly relates to a multi-source power distribution network fault location method based on an improved Jaya algorithm.
Background
Along with the wide access of the multi-type distributed energy to the power distribution network, the power distribution network is changed from a traditional single-source radial network into a multi-source power distribution network containing the multi-type distributed energy. The difference of the operation characteristics of the heterogeneous distributed power supplies changes the original topological structure and the trend direction of the power distribution network to a great extent. Meanwhile, with the improvement of the reliability of power supply of a power grid in social life, when a power distribution network fails, a fault section needs to be determined and isolated more quickly and accurately.
Therefore, the power failure time is shorter, the fault processing is more efficient, a fault location technology based on Feeder Terminal Unit (FTU) is gradually developed, and the fault location of the power distribution network is mainly performed through a matrix algorithm or an artificial intelligence algorithm and an optimization algorithm thereof. However, most of the current research is limited to a single power supply network, and the fault of a power distribution network containing multiple types of distributed power supplies is not considered.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide a multisource power distribution network fault locating method based on an improved Jaya algorithm so as to realize quick and accurate fault locating and improve the power supply reliability and the self-healing capability of a power distribution system. In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a multisource power distribution network fault locating method based on an improved Jaya algorithm comprises the following steps:
calculating evaluation function values under the switching fault states of all positions of the multi-source power distribution network through a pre-constructed fault positioning model, optimizing the evaluation function values by adopting a Jaya algorithm based on a chaos theory to obtain an optimal solution, and determining fault points through the optimal solution;
wherein: the construction process of the fault location model comprises the steps of setting a fault current information coding scheme; establishing a switching function and an evaluation function; calculating a function value of each switch through a switch function, and calculating an evaluation function value in the fault state based on the evaluation function according to the function value of each switch;
the method for optimizing the evaluation function value by the Jaya algorithm based on the chaos theory comprises the following steps:
setting initialization parameters, wherein the initialization parameters comprise: population number, variable dimension and iteration times; wherein the variable dimension is the total number of switches;
generating individual values according to the set parameters, and representing the running states of switches at all places in the multi-source power distribution network;
calculating evaluation function values of different fault conditions of the multi-source power distribution network represented by the individuals to serve as individual fitness values;
comparing the fitness of different individuals in the population to obtain the optimal solution and the worst solution in the population;
correcting the value of a random number in the individual position iterative formula through a logistic chaotic mapping function;
obtaining a new generation of individual value through an individual position iterative formula;
calculating the fitness value of the new generation of individuals to obtain the optimal solution and the worst solution in the population;
obtaining a correction solution according to the optimal solution and the worst solution correction variable;
comparing the corrected solution with the original optimal solution, taking the corrected solution as the optimal solution if the corrected solution is smaller than the original optimal solution, and keeping the original optimal solution if the original optimal solution is smaller than the corrected solution;
when the convergence condition is met or the maximum iteration times are exceeded, the optimization process is ended, and the optimal solution, namely the fault positioning result, is output.
Further, the method for setting the fault current information coding scheme comprises the following steps:
when fault current information in the direction from a main power supply end of the system to a monitoring node is detected, marking the fault current information as 1; when the information from the distributed power supply end to the monitoring node direction is detected, recording the information as-1; when the fault flow information is not monitored, the fault flow information is recorded as 0, the positive direction is the direction from the main power supply of the system to the fault line, and the detected switch state information is as follows:
further, the switching function is:
in the formula: sigma||Represents a logical or operation; kj.up、Kj.downThe power supply coefficients of the upper and lower streams of the j-th switch are respectively, if the j-th switch is in a grid-connected operation state, the coefficient is 1, otherwise, the coefficient is 0; x is the number ofj2g.up、xj2g.downThe feeder line section state values which are respectively passed by the j switch to the upstream area power supply and the downstream area power supply; x is the number ofj.up(m)、 xj.down(n) are the state values of the feeder sections in the upstream and downstream areas of the switch No. j, when the feeder fails, the value is 1, otherwise, the value is 0; m, N are the total number of feeder sections in the upstream and downstream areas of the j-th switch.
Further, the merit function is:
in the formula: i isjThe state information value actually detected by the FTU at the switch No. j;
a switch function desired value for switch # j; p is the total number of switches; omega is a weight coefficient and has a value interval of [0, 1%]；SB(j) Is in a sector state; and Q is the number of feeder sections in the power distribution network.
Further, the Jaya algorithm based on chaos theory comprises: selecting a logistic chaotic mapping function:
Yu+1＝μYu(1-Yu) u＝1，2，3... (5)
in the formula: control parameter [ mu ] e (0, 4)]，YuE (0, 1). When 3.5699.<When mu is less than or equal to 4, the system is in a chaotic state. And when mu is 4, the system is in a completely chaotic state.
Further, the Jaya algorithm based on chaos theory further includes: individual code modification in the algorithm:
in the formula: < > represents rounding and rounding; xi is a correction threshold, and maxF/2 is taken here; x' and X represent the corrected solution and the original solution, respectively.
Compared with the prior art, the invention has the following beneficial effects: the method can solve the problem of fault location of single and multiple faults under different distributed energy distribution conditions, and can process fault location containing simple distortion information. By providing the Jaya algorithm to solve the power distribution network fault location model, fusing the chaos theory and the Jaya algorithm on the basis, and optimizing the setting of random numbers, the defect that the traditional intelligent algorithm is easy to converge on local optimization is overcome, and the fault location efficiency is improved.
Drawings
Fig. 1 is a schematic flow chart of a multi-source power distribution network fault location method based on an improved Jaya algorithm according to an embodiment of the present invention;
FIG. 2 is a flow chart of the Jaya algorithm employed by an embodiment of the present invention;
fig. 3 is a structural diagram of an improved power distribution network according to an embodiment of the present invention.
Detailed Description
The invention is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
As shown in fig. 1, a multi-source power distribution network fault location method based on an improved Jaya algorithm includes:
s1: establishing a fault positioning model according to the characteristics that an active power distribution network contains various distributed power sources, wherein the fault positioning model comprises the following steps: setting a fault current information coding scheme; and establishing a switching function and forming a model evaluation function.
S2: optimizing the Jaya algorithm, wherein the optimizing process comprises the following steps: optimizing variable position iteration based on a chaos theory; correcting variables of individual codes;
s3: based on the model of S1, solution calculation is performed by the improved algorithm of S2.
Step S1, establishing a fault positioning model according to the characteristics that the active power distribution network contains various distributed power sources, specifically:
s11: setting a fault current information coding scheme;
the FTU can monitor fault overcurrent information on the switch node, and when fault current information in the direction from a main power supply end of the system to the monitoring node is detected, the fault current information is marked as 1; when the information from the distributed power supply end to the monitoring node direction is detected, recording the information as-1; and when the fault flow information is not monitored, marking as 0. Now, assume that the positive direction is the direction from the main power source of the system to the faulty line, and therefore the detected switch state information is as follows:
s12: establishing a switching function;
the method comprises the following steps of firstly dividing a power distribution network into an upstream area and a downstream area according to the position of a switch, wherein a line from the switch to a system power supply is the upstream area, and a line from the switch to a distributed power supply is the downstream area. Aiming at a power distribution network containing multiple types of distributed power supplies, the operation state of each power supply in the power distribution network is represented by adding a distributed power supply coefficient into a traditional switching function, as shown in a formula (8)
In the formula: sigma||Represents a logical or operation; kj.up、Kj.downThe power supply coefficients of the upper and lower streams of the j-th switch are respectively, if the j-th switch is in a grid-connected operation state, the coefficient is 1, otherwise, the coefficient is 0; x is the number ofj2g.up、xj2g.downThe feeder line section state values which are respectively passed by the j switch to the upstream area power supply and the downstream area power supply; x is the number ofj.up(m)、 xj.down(n) are the state values of the feeder sections in the upstream and downstream areas of the switch No. j, when the feeder fails, the value is 1, otherwise, the value is 0; m, N are the total number of feeder sections in the upstream and downstream areas of the j-th switch.
S13: constructing a model evaluation function;
according to the defined network switching function, improvement is carried out on the basis of the traditional evaluation function, and the fault current direction is allowed to be inconsistent when the upstream and downstream sections of the grid-connected running distributed power supply have faults. An evaluation function of the active power distribution network fault section positioning model is constructed, and the formula is shown as (9)
In the formula: i isjThe state information value actually detected by the FTU at the switch No. j;
a switch function desired value for switch # j; p is the total number of switches; omega is a weight coefficient and has a value interval of [0, 1%]Taking 0.5 in the text; sB(j) Is in a sector state; and Q is the number of feeder sections in the power distribution network.
Step S2, performing optimization processing on the Jaya algorithm, including:
s21: the Jaya algorithm is a novel heuristic optimization algorithm based on population. The method can be used for solving the problems of constrained and unconstrained optimization, and the core idea of the method is that an individual position updating strategy requires that the change of a candidate solution is close to an optimal solution and the worst solution is avoided. Unlike most heuristic algorithms, the algorithm is not affected by any specific parameters, and therefore, the user only needs to set simple parameters.
The solving process of the traditional Jaya algorithm is as follows:
for the objective function F (x), xi＝,(x,1x2,…,xiD) Is the position of the ith candidate solution, and D is the variable dimension.
In the formula: xj,k,iIs the value of the jth variable of the kth candidate in the ith iteration (j ═ 1,2, …, D); xj,best,iAnd Xj,worst,iRespectively representing the optimal solution and the worst solution of the population of the current iteration variable j; r is1,j,iAnd r2,j,iIndicates that the j variable is [0, 1] in the ith iteration]Two random numbers with intervals varying; wherein r is1,j,i(Xj,best,i-|Xj,k,iI) represents the tendency of the solution to approach the optimal solution, r2,j,i(Xj,worst,i-|Xj,k,i|) represents the trend of the solution away from the worst solution.
Accepting a new value of the decision variable if the new solution scheme provides a better function value. All the function values accepted at the end of an iteration are retained and these values will become the input for the next iteration.
S22: optimizing a Jaya algorithm based on a chaos theory;
for the Jaya algorithm, in the iteration process, when the random value r is larger, the candidate solution is accelerated to approach the optimal solution speed, otherwise, the candidate solution is slowed down. In order to improve the optimizing capability of the algorithm, a method for determining a position updating formula random value of a Jaya algorithm based on a chaos theory is provided. The chaotic motion is used for representing the irregular motion of particles in space, and all states can be traversed in a certain range according to the 'rule' of the chaotic motion without repetition. Therefore, the random value obtained according to the chaotic mapping has randomness and ergodicity, the influence of random value selection on the algorithm optimizing capability can be reduced, and the method has the advantage of strong global searching capability.
Selecting a logistic chaotic mapping function:
Yu+1＝μYu(1-Yu) u＝1，2，3... (11)
in the formula: control parameter [ mu ] e (0, 4)]，YuE (0, 1). When 3.5699.<When mu is less than or equal to 4, the system is in a chaotic state. And when mu is 4, the system is in a completely chaotic state.
S23: correcting individual codes in the algorithm;
the numerical value interval of the information uploaded by the distribution network FTU is [ -1,1] according to the current code. In the optimization process, the variable optimization results in the individual gradually tend to integer values in the interval. In order to accelerate the iterative convergence speed, the individual codes are optimized by setting a threshold, and when the individual fitness value is smaller than the threshold, the variable correction can be carried out according to the formula (12), so that the candidate solution is accelerated to approach to the optimal solution.
In the formula: < > represents rounding and rounding; xi is a correction threshold, and maxF/2 is taken here; x' and X represent the corrected solution and the original solution, respectively.
And step S3, based on the model of S1, carrying out solving calculation through an improved algorithm of S2.
(1) Setting basic parameters of the improved Jaya algorithm provided by S2, wherein the basic parameters specifically comprise variable dimensions, variable upper and lower limits and iteration times; setting parameters of a Logitics function, namely control parameters;
(2) initializing a candidate particle set, namely a running state matrix of the line, for representing the line fault condition of the network, and calculating the fitness value of the candidate particles based on the model of S1;
firstly, simple load flow calculation is carried out on a fault network to obtain the power flow condition of each branch of the network;
determining information coding of the line and the running state of the line according to the power flowing condition of the line in the first step based on a fault current information coding scheme;
calculating function values of all switches based on the switching function provided by S12 and the line information codes obtained in the step two;
and fourthly, determining an evaluation function value in the fault state, namely the fitness value of the particle, according to the function value of each switch of the network based on the evaluation function calculation method provided by S13.
(3) And when the optimization algorithm provided by the S2 meets the convergence condition or exceeds the maximum iteration number, ending the optimization process and outputting the optimal solution, namely a fault positioning result.
Examples
To verify the feasibility of the proposed method, the analysis was performed here based on a DG-containing IEEE33 node distribution network, the network being shown in fig. 3. The distributed power supply switch is used for controlling the switching of the distributed power supplies, wherein S is a power supply node of a main network power supply, a black dot represents a switch for installing an FTU, and DG 1-DG 3 are distributed power supplies, so that the distribution network structure under different distributed energy distribution is reflected. The parameter settings for improving the Jaya algorithm include: the population number is 50 and the maximum number of iterations is 100.
The simulation example comprises single and multiple faults of the distribution network when the FTU uploading information is undistorted and multiple fault positioning tests when the FTU uploading information is distorted. The number and distribution position difference of the distributed power supplies are simulated by changing the power supply coefficient so as to show the adaptability of the model under different network distribution structures.
TABLE 1 Fault location test results
From the test results shown in table 1, it can be found that the determination result output by the fault location model is consistent with the preset fault condition. Even if affected by the external environment, when information uploaded by FTUs at part of switches is distorted, the model can still complete accurate identification of the fault interval. Meanwhile, the positioning of the distortion information of the FTU is realized, and the fault tolerance of a fault positioning model is reflected.
Besides, the accuracy of the algorithm is analyzed and compared, and the solving speed of the algorithm is also very important. Different algorithms are respectively adopted to solve and calculate the fault positioning model aiming at the same fault condition in the calculation power distribution network, each algorithm is repeatedly tested for 100 times, the mean value of indexes is obtained to be compared and verified, so that a universality conclusion is obtained, and the test result is shown in table 2.
TABLE 2 Algorithm comparison results
As shown in table 2, conventional genetic algorithm and particle swarm algorithm are used as the target of the comparative analysis. According to the calculation results, the Jaya algorithm and the improved Jaya algorithm are superior to the traditional algorithm in the calculation speed of solving the fault location model, and the higher accuracy is kept. The improved Jaya algorithm can further reduce the calculation time of model solution and improve the fault location efficiency of the power distribution network.
In summary, in the fault location of the distribution network with multiple DGs, no matter single fault or double faults, the proposed improved Jaya algorithm can realize accurate location of the fault, and has a faster solving speed and better optimizing performance than the traditional algorithm.
The above description is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, several modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be regarded as the protection scope of the present invention.
Claims (6)
1. A multisource power distribution network fault locating method based on an improved Jaya algorithm is characterized by comprising the following steps:
calculating evaluation function values under the switching fault states of all positions of the multi-source power distribution network through a pre-constructed fault positioning model, optimizing the evaluation function values by adopting a Jaya algorithm based on a chaos theory to obtain an optimal solution, and determining fault points through the optimal solution;
wherein: the construction process of the fault location model comprises the steps of setting a fault current information coding scheme; establishing a switching function and an evaluation function; calculating a function value of each switch through a switch function, and calculating an evaluation function value in the fault state based on the evaluation function according to the function value of each switch;
the method for optimizing the evaluation function value by the Jaya algorithm based on the chaos theory comprises the following steps:
setting initialization parameters, wherein the initialization parameters comprise: population number, variable dimension and iteration times; wherein the variable dimension is the total number of switches;
generating individual values according to the set parameters, and representing the running states of switches at all places in the multi-source power distribution network;
calculating evaluation function values of different fault conditions of the multi-source power distribution network represented by the individuals to serve as individual fitness values;
comparing the fitness of different individuals in the population to obtain the optimal solution and the worst solution in the population;
correcting the value of a random number in the individual position iterative formula through a logistic chaotic mapping function;
obtaining a new generation of individual value through an individual position iterative formula;
calculating the fitness value of the new generation of individuals to obtain the optimal solution and the worst solution in the population;
obtaining a correction solution according to the optimal solution and the worst solution correction variable;
comparing the corrected solution with the original optimal solution, taking the corrected solution as the optimal solution if the corrected solution is smaller than the original optimal solution, and keeping the original optimal solution if the original optimal solution is smaller than the corrected solution;
and repeating the iteration process, finishing the optimization process when the convergence condition is met or the maximum iteration times are exceeded, and outputting an optimal solution, namely a fault positioning result.
2. The multisource power distribution network fault location method based on the improved Jaya algorithm of claim 1, wherein the method for setting the fault current information coding scheme is as follows:
when fault current information in the direction from a main power supply end of the system to a monitoring node is detected, marking the fault current information as 1; when the information from the distributed power supply end to the monitoring node direction is detected, recording the information as-1; when the fault flow information is not monitored, the fault flow information is recorded as 0, the positive direction is the direction from the main power supply of the system to the fault line, and the detected switch state information is as follows:
3. the multisource power distribution network fault location method based on the improved Jaya algorithm of claim 2, wherein the switching function is:
in the formula:
represents a logical or operation; kj.up、Kj.downThe power supply coefficients of the upper and lower streams of the j-th switch are respectively, if the j-th switch is in a grid-connected operation state, the coefficient is 1, otherwise, the coefficient is 0; x is the number ofj2g.up、xj2g.downThe feeder line section state values which are respectively passed by the j switch to the upstream area power supply and the downstream area power supply; x is the number ofj.up(m)、xj.down(n) are the state values of the feeder sections in the upstream and downstream areas of the switch No. j, when the feeder fails, the value is 1, otherwise, the value is 0; m, N are the total number of feeder sections in the upstream and downstream areas of the j-th switch.
4. The multisource power distribution network fault location method based on the improved Jaya algorithm of claim 3, wherein the evaluation function is:
in the formula: i isjThe state information value actually detected by the FTU at the switch No. j;
a switch function desired value for switch # j; p is the total number of switches; omega is a weight coefficient and has a value interval of [0, 1%]；SB(j) Is in a sector state; and Q is the number of feeder sections in the power distribution network.
5. The multisource power distribution network fault location method based on the improved Jaya algorithm of claim 4, wherein the Jaya algorithm based on the chaos theory comprises: selecting a logistic chaotic mapping function:
Yu+1＝μYu(1-Yu) u＝1，2，3... (5)
in the formula: control parameter [ mu ] e (0, 4)]，YuE (0, 1). When 3.5699. < mu.ltoreq.4, the system is in a chaotic state. And when mu is 4, the system is in a completely chaotic state.
6. The multisource power distribution network fault location method based on the improved Jaya algorithm of claim 5, wherein the Jaya algorithm based on the chaos theory further comprises: individual code modification in the algorithm:
in the formula: < > represents rounding and rounding; xi is a correction threshold, and maxF/2 is taken here; x' and X represent the corrected solution and the original solution, respectively.
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