CN116520084A - Fault positioning method, device and storage medium for source-containing power distribution network - Google Patents
Fault positioning method, device and storage medium for source-containing power distribution network Download PDFInfo
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Abstract
The invention discloses a fault locating method, a fault locating device and a storage medium for an active distribution network, which belong to the technical field of fault locating of power systems and comprise the following steps: obtaining an evaluation function of the active power distribution network in a fault state according to the obtained fault current information of the active power distribution network; using an evaluation function as an adaptability function of an improved particle swarm optimization algorithm, using a candidate solution of a fault section of the active power distribution network as particles, and updating the speed and the position of the particles through the improved particle swarm optimization algorithm to perform fault positioning of the active power distribution network to obtain the fault section of the active power distribution network; the improved particle swarm optimization algorithm updates the speed and position of particles by adopting self-adaptive inertial weights; and constraining the output result of the improved particle swarm optimization algorithm through preset power distribution network stable operation constraint conditions. The method and the system can quickly and accurately obtain the fault section of the active distribution network, and realize high-precision fault positioning of the active distribution network.
Description
Technical Field
The invention relates to a fault positioning method, device and storage medium for an active distribution network, and belongs to the technical field of fault positioning of power systems.
Background
In recent years, along with the rapid development of national economy in China, the living standard of people is continuously improved, and the demand for electric energy is gradually increased. The distribution system is used as a junction between the transmission system and the power consumer, and plays a key role in the distribution of electric energy. In the carbon peak, carbon neutralization background, in order to accelerate energy transformation, the proportion of a distributed power supply (DG) in a power distribution network is obviously improved, so that the network structure is more and more complex, the network loss is increased, and the resource waste is caused.
The fault positioning of the power distribution network is an initial link in the power distribution network fault self-healing technology, and a quick and accurate positioning scheme is a premise for improving the safety and reliability of the power distribution network. The fault location of the power distribution network is divided into the location of fault points and the location of fault sections. The positioning of the fault point is mainly to measure the distance between the monitoring point and the fault point so as to determine the position of the fault, which is also called fault ranging, and the traditional methods include a traveling wave method, an impedance method and an S injection method; the fault section positioning is to position the fault section between the two sectionalizing switches, and the method has a great help effect on fault isolation after faults occur.
At present, algorithms for fault location of a power distribution network are mainly divided into two types, one type is a matrix algorithm based on intelligent feeder terminal equipment (FTU) of the power distribution network, and the matrix algorithm is also called a direct method and has the problems of low fault tolerance and the like; the other is artificial intelligence algorithms, such as particle swarm algorithms, genetic algorithms, harmony search algorithms, immune algorithms, simulated annealing algorithms, and the like. As more DG are connected to the power distribution network, the power distribution system is changed from unidirectional power flow to multidirectional power flow, the network structure is increasingly complex, and the direct method has a certain limitation and is not suitable for engineering practice, so that the artificial intelligence algorithm is gradually applied to the fault location of the power distribution network. However, the existing fault positioning methods of the power distribution network based on the artificial intelligence algorithm have the problems of high algorithm complexity, low operation speed, insufficient fault positioning accuracy and the like, so that how to provide the fault positioning method of the power distribution network with higher accuracy and faster operation is a problem to be solved by the technicians in the field.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a fault positioning method, a fault positioning device and a storage medium for a power distribution network with a source, wherein the fault positioning method, the fault positioning device and the storage medium are used for performing fault positioning on the power distribution network with the source by utilizing an improved particle swarm optimization algorithm, and accurate and reliable fault sections of the power distribution network are obtained.
In order to solve the technical problems, the invention adopts the following technical means:
in a first aspect, the present invention provides a fault locating method for an active power distribution network, including the following steps:
obtaining an evaluation function of the active power distribution network in a fault state according to the obtained fault current information of the active power distribution network;
using the evaluation function as an adaptability function of an improved particle swarm optimization algorithm, using a candidate solution of a fault section of the source-containing power distribution network as particles, and updating the speed and the position of the particles through the improved particle swarm optimization algorithm to perform fault positioning of the source-containing power distribution network to obtain the fault section of the source-containing power distribution network;
the improved particle swarm optimization algorithm updates the speed and the position of particles by adopting self-adaptive inertial weights;
and constraining the output result of the improved particle swarm optimization algorithm through preset power distribution network stable operation constraint conditions.
In combination with the first aspect, further, the method for obtaining the evaluation function of the active distribution network in the fault state comprises the following steps:
according to the positions of the sectionalizing switches in the active power distribution network, sectionalizing the active power distribution network to obtain a plurality of sections corresponding to the sectionalizing switches one by one;
encoding the segment switch and section;
obtaining an actual state value and an expected state value of each sectional switch in the active power distribution network under a fault state according to the codes and the fault current information of the active power distribution network;
and obtaining an evaluation function of the active power distribution network under the fault state according to the actual state value and the expected state value of each sectional switch.
With reference to the first aspect, further, the expression of the evaluation function is as follows:
wherein F (S) B ) Is an evaluation function of a power distribution network with a source, S B Is the state value of the power distribution network containing sources, I j For the actual state value of the jth sectionalizer in the active distribution network,the expected state value of the jth sectionalizer in the active power distribution network is represented by omega, and S B (j) Is the state value of a jth section feeder line in the active power distribution network, S B (j) =1 indicates the j-th section feeder fault, S B (j) The expression of =0 indicates that the j-th section feeder is normal, j=1, 2, …, N is the total number of sectionalizing switches in the active distribution network.
With reference to the first aspect, further, according to the fault current information of the active power distribution network, an actual state value of each segment switch is obtained, I j The expression of (2) is:
in combination with the first aspect, further, the method for performing fault location of the active distribution network by updating the speed and the position of the particles through an improved particle swarm optimization algorithm to obtain a fault section of the active distribution network includes:
initializing network parameters and particle swarm parameters, wherein the particle swarm parameters comprise the position and the speed of particles;
calculating the fitness value of each particle according to the evaluation function and the position of the particle;
obtaining an individual optimal position of each particle and a population optimal position of all particles according to the fitness value of all particles;
updating the self-adaptive inertia weight of each particle according to the fitness value of all the particles;
updating the speed and the position of each particle according to the self-adaptive weight;
calculating a new fitness value of each particle by using an evaluation function according to the updated particle position;
updating the individual optimal position of each particle and the population optimal position of all particles according to the new fitness value of all particles;
and when the iteration convergence condition is met, outputting the iteration convergence population optimal position to obtain the fault section of the source-containing power distribution network.
With reference to the first aspect, further, updating the adaptive inertial weight of each particle according to the fitness values of all particles includes:
obtaining the average fitness value of the population at the current moment according to the fitness values of all the particles
According to the fitness value f of the optimal position of the population at the current moment max Average fitness valueThe weight selection ratio of each particle is calculated, and the expression is as follows:
wherein k is i The weight selection ratio of the particles i is represented, i=1, 2, …, M being the total number of particles in the particle group;
updating the self-adaptive inertia weight of each particle according to the weight selection proportion and the fitness value of each particle, wherein the expression is as follows:
wherein P is i Representing the adaptive inertial weight of particle i, f i The fitness value of particle i is indicated.
In combination with the first aspect, further, in the improved particle swarm optimization algorithm, when a distance between a position of the particle i and an optimal position of the population at the current moment is smaller than a preset threshold, the position of the particle i is updated by adopting chaotic search, and i=1, 2, …, M and M are total number of particles in the particle swarm.
With reference to the first aspect, further, the stable operation condition of the power distribution network includes a node voltage constraint condition and a branch current constraint condition.
When the improved particle swarm optimization algorithm meets the iteration convergence condition, carrying out power flow calculation on the power distribution network containing the source according to the iteration convergence population optimal position to obtain a voltage value and a current value corresponding to the iteration convergence population optimal position; and judging whether the voltage value and the current value meet the stable operation condition of the power distribution network, if so, outputting a fault section of the power distribution network containing the source according to the optimal position of the iteratively converged population, and if not, re-utilizing an improved particle swarm optimization algorithm to perform particle swarm optimization.
In a second aspect, the present invention provides an active power distribution network fault location device, including:
the data acquisition module is used for acquiring fault current information of the power distribution network with the source;
the model construction module is used for obtaining an evaluation function of the source-containing power distribution network in a fault state according to the obtained fault current information of the source-containing power distribution network;
the fault positioning module is used for performing fault positioning on the active distribution network by using the evaluation function as an adaptability function of an improved particle swarm optimization algorithm and using a candidate solution of the active distribution network fault section as particles and updating the speed and the position of the particles through the improved particle swarm optimization algorithm to obtain the fault section of the active distribution network;
in the fault positioning module, the improved particle swarm optimization algorithm updates the speed and the position of particles by adopting self-adaptive inertial weights;
the fault positioning module is also used for restraining the output result of the improved particle swarm optimization algorithm through preset power distribution network stable operation constraint conditions.
In a third aspect, the present invention proposes a computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method for locating faults in an active distribution network according to the first aspect.
The following advantages can be obtained by adopting the technical means:
the invention provides a method, a device and a storage medium for locating faults of a source-containing power distribution network, which can quickly and accurately obtain fault sections of the source-containing power distribution network through an improved particle swarm optimization algorithm, realize high-precision fault location of the source-containing power distribution network, provide favorable support for fault isolation and power restoration of the source-containing power distribution network, and improve the safety of a source-containing power distribution network system. According to the invention, the particle swarm optimization algorithm can be prevented from being trapped into local optimum through the self-adaptive inertia weight optimization algorithm, and the convergence speed and the convergence performance of the particle swarm optimization algorithm are improved. The invention also constrains the output result of the particle swarm optimization algorithm through the preset stable operation constraint condition of the power distribution network, ensures that the finally output fault section meets the basic operation requirement of the power distribution network, and further improves the accuracy of fault positioning.
Drawings
FIG. 1 is a flow chart of steps of a method for locating faults in an active power distribution network according to the present invention;
FIG. 2 is a schematic diagram of an optimization process of an improved particle swarm optimization algorithm according to an embodiment of the present invention;
FIG. 3 is a simplified schematic diagram of a conventional distribution network in accordance with an embodiment of the present invention;
fig. 4 is a simplified schematic diagram of a distribution network after DG tie-in an embodiment of the present invention;
fig. 5 is a schematic diagram of an IEEE33 node power distribution system in accordance with an embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings:
what needs to be described is: the particle swarm algorithm (PSO) is derived from research on prey behaviors of the flocks, and the core idea is to utilize cooperation and information sharing among each individual in the population to enable the whole population to move from disorder to progress of orderly evolution in a solution space of an optimization target, so that a global optimal value is finally obtained. The algorithm has simple mathematical principle, easy programming and high convergence speed, but is easy to sink into local optimum, and the phenomenon of 'early ripening' occurs. There are many improvements to particle swarm optimization algorithms, such as Binary Particle Swarm Optimization (BPSO), but the improved algorithms still have drawbacks. The invention further improves on the basis of BPSO, introduces the self-adaptive proportion selection strategy and the chaotic search into the algorithm, enhances the convergence performance of the algorithm, and simultaneously restricts the output result of the algorithm according to the characteristics of the power distribution network with the source to obtain an accurate and reliable fault positioning result.
Example 1:
introduction to the embodiment the present invention provides a fault location method for an active power distribution network, as shown in fig. 1, which specifically includes the following steps:
and step A, obtaining an evaluation function of the active distribution network in a fault state according to the obtained fault current information of the active distribution network.
With the sectionalizing switch as a node, the conventional distribution network can be simplified to fig. 3, where S 1 ,S 2 ,S 3 ,S 4 ,S 5 Is a sectional switch L 1 ,L 2 ,L 3 ,L 4 ,L 5 The switch corresponds to the section. The distribution network can be simplified to fig. 4 after DG is connected to the grid, wherein DG 1 、DG 2 Representing distributed power sources incorporated into a power distribution network, K 1 、K 2 Representing a switch controlling the switching of the distributed power supply.
According to the method, the system and the device, according to the condition that the power distribution network is in a radial shape and no island and ring network exist during normal operation, the power distribution network with the source is segmented according to the positions of the segmented switches in the power distribution network with the source, a plurality of segments corresponding to the segmented switches one by one are obtained, then the segmented switches and the segments are encoded, the DG grid connection effect is considered, and a power distribution network model with the source is established according to the logic relation between the segmented switches and the fault segments in the power distribution circuit, wherein the power distribution network model comprises a switch function and an evaluation function.
According to the invention, through fault current information acquired by intelligent feeder terminal equipment (FTU) of the power distribution network, the actual state value and expected state value of each sectional switch in the power distribution network with the source in the fault state can be obtained according to the fault current information, and the actual state value and the expected state value of each sectional switch are brought into a power distribution network model with the source, so that an evaluation function of the power distribution network with the source in the fault state can be obtained.
The evaluation function represents the difference value between the fault current information and the expected value of the switch, and the essence of the fault positioning of the power distribution network is the minimum value optimizing process of the evaluation function by using an intelligent algorithm. Because of network complexity caused by DG grid connection, the problems of information distortion, information deletion and the like in the conventional evaluation process can occur, and therefore, the invention constructs a novel evaluation function, and the expression of the evaluation function is as follows:
wherein F (S) B ) Is an evaluation function of a power distribution network with a source, S B Is the state value of the power distribution network containing sources, I j For the actual state value of the jth sectionalizer in the active distribution network,for the expected state value of the jth sectionalizer in the active distribution network, ω is an evaluation weight, ω takes a value within (0, 1), typically 0.5, SB(j) is the state value of a jth section feeder line in the active power distribution network, S B (j) =1 indicates the j-th section feeder fault, S B (j) The expression of =0 indicates that the j-th section feeder is normal, j=1, 2, …, N is the total number of sectionalizing switches in the active distribution network.
In the traditional single-power distribution network, when a fault occurs at a certain point, the coding of fault current is only divided into two states, namely, a sectional switch is only in 1 and 0, and because grid connection of DGs can influence the flow direction of the power distribution network after the fault, the traditional fault current coding is not applicable.
I when the fault current direction at the j-th sectionalizer is positive j Taking a '1'; when the fault current direction at the j-th sectionalizer is negative direction I j Taking a '1'; when the jth point is dividedI when no fault current is detected at the section switch j Taking a "0", it can be expressed specifically as:
taking fig. 4 as an example, if section L 3 Fault, sectional switch S 3 The fault current of (1) flows from the system power supply G to the load and is taken as positive direction; sectionalizer switch S 4 Is from distributed power source DG 1 The flow direction load is the opposite direction, taking "-1".
The expected states of the switches are changed according to the actual condition change of the power distribution network, and the invention can obtain the expected states of each switch according to the switches and the sections after the sectional coding, by taking fig. 4 as an example,the expression of (2) is as follows:
the traditional switching function is commonly used for fault location of a single power supply network, and is not applicable any more along with the condition that a large number of DGs are accessed to a power distribution network at present, so that the novel switching function considering DG switching is constructed.
The switching function of the present invention is as follows:
wherein I is j (s) is a novel switching function; pi is logical OR operation; i ju (s)、I jd (s) is the upper and lower end switch function value of the j-th sectionalizer; m is M 1 、N 2 Respectively representing the number of power supplies and the number of feeder intervals at the upper end; n (N) 1 、M 2 Respectively representing the number of power supplies and the number of feeder intervals at the lower end; s is S j,Gu 、K u Respectively representing the state from the upper power supply to the node and the access coefficient of the power supply in the interval, if the power supply is detected, K u And is denoted as "1", and conversely as "0".
With the continuous development of DGs, after a large number of DGs are connected to a power grid, the randomness of power grid load prediction can be enlarged to a certain extent, the normal operation of the power distribution network is affected to a certain extent, and if the DGs cannot be reasonably integrated, the stability of the power distribution system can be reduced. Fig. 5 is a standard IEEE33 node power distribution system, in which various DG are added, and the grid-tie parameters of DG are shown in table 1.
TABLE 1
In fault section positioning of a distribution network containing DGs, DGs are generally treated as PQ nodes, the randomness of the output is not considered, the capacity and the output size of the DGs are given, grid connection of the DGs has supporting effect on the network, the network loss can be reduced, and the operation of the whole distribution system is optimized.
And B, performing fault positioning on the source-containing power distribution network through an improved particle swarm optimization algorithm to obtain a fault section of the source-containing power distribution network. Specifically, an evaluation function is used as an adaptability function of an improved particle swarm optimization algorithm, a candidate solution of a fault section of the power distribution network with a source is used as particles, the speed and the position of the particles are updated through the improved particle swarm optimization algorithm, the position of the particles with the smallest adaptability is searched, and then the fault section of the power distribution network with the source is obtained.
As shown in fig. 2, the specific operation of step B is as follows:
step B01, initializing network parameters and particle swarm parameters, wherein the network parameters comprise maximum iteration times and learning factors C 1 、C 2 Particle velocity limit V max Etc., particle swarm parameters include position, velocity, and grain of the particleSubgroup population size M, etc.
In the invention, 0 and 1 are adopted to represent the status words of each section in the power distribution network, 0 represents no fault, 1 represents fault, all sections are encoded to form N-dimensional particles, taking fig. 4 as an example, if the section L 2 When a fault occurs, the final fault section result obtained by adopting the algorithm is as follows: [0 10 0 0 0 0]。
Step B02, obtaining S corresponding to the current particle according to the initialized particle position B (j) And then brings into formula (1), the initial fitness value of each particle is calculated by the evaluation function. And according to the initial fitness values of all the particles, obtaining initial individual optimal positions of each particle and initial population optimal positions of all the particles, specifically, comparing fitness values of each particle, wherein the smaller the fitness value is, the better the particle positions corresponding to the fitness values are.
And step B03, updating the adaptive inertia weight of each particle according to the fitness value of all the particles.
In the invention, the position of the particle is related to the speed of the particle, the position of each particle can only be 0 or 1, the larger the speed of the particle is, the larger the probability of the position of the particle is, and the speed of the particle is related to the inertia weight.
The expression of the adaptive inertial weights is as follows:
wherein P is i Representing the adaptive inertial weight of particle i, f i Indicating the fitness value, k, of particle i i The weight selection ratio of the particles i is represented, i=1, 2, …, M being the total number of particles in the particle group.
In the self-adaptive inertial weight, the smaller the weight selection proportion is, the smaller the selected pressure is, and particles with poor adaptability have the chance of survival, so that the searching range is increased; conversely, the larger the weight selection ratio, the larger the selected pressure, and the particles with poor adaptability will be eliminated, so that the search range will be reduced.
Weight selection ratio k i The expression of (2) is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,mean fitness value, f, representing the population at the current time max And the fitness value of the optimal position of the population at the current moment is represented.
If the algorithm is too focused on global searching, the searching range is possibly too large, and the searching efficiency is reduced; if the algorithm is too focused on local searching, it may fall into a locally optimal solution, resulting in the algorithm failing to find a better solution. In order to better balance the relation between the global search and the local search, the invention adopts the P value (adaptive inertia weight) and the k value (weight selection proportion) for carrying out algorithm improvement. In different stages of algorithm iteration, the k value can be dynamically adjusted, the P value is further adjusted, and finally the algorithm searching range is adjusted. In the first stage of iteration, as the population is randomly generated, the difference between individuals is larger, and the gap between the average fitness of the population and the optimal fitness is larger, the k value calculated according to the formula (6) is small, the search range can be enlarged, and the early ripening phenomenon of the particle swarm is avoided; in the second stage of iteration, the k value is adaptively adjusted, so that the speed of the population to reach global optimum is increased; in the third stage of iteration, the average fitness value of the population approaches the optimal fitness, the k value approaches infinity, and the pressure of the particle selection becomes infinity, thereby facilitating the algorithm to converge on a globally optimal solution. The self-adaptive inertia weight can simultaneously improve the convergence speed and convergence precision of the algorithm, thereby improving the accuracy of fault location of the active power distribution network.
And step B04, updating the speed and the position of each particle according to the self-adaptive weight.
The updated formula for particle velocity is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,representing the speed of particle i in the d-th dimension at the t+1st iteration,/o>Representing the speed of particle i in the d-th dimension at the t-th iteration, r 1 、r 2 Represents [0,1 ]]Random number in->Representing the optimal position of particle i in the d-th dimension at the t-th iteration,/th>Represents the optimal position of the d-th dimension of the whole particle swarm at the t-th iteration,/for the t-th iteration>The position of particle i in the d-th dimension at the t-th iteration, d=1, 2.
The position of each particleTaking 0 or 1, and updating the particle position according to the updated particle speed, wherein the expression is as follows:
wherein, the liquid crystal display device comprises a liquid crystal display device,indicating that particle i was at the t+1st iterationThe d-th dimension of the position, r, represents 0,1]Random number in, sigmoid (x) =1/1+e -x 。
In the step B05, in the later stage of the improved particle swarm optimization algorithm, the convergence speed and convergence precision often cannot meet the expected requirements, and because the chaotic variable has initial value sensitivity and ergodic property, the invention combines a logics mapping formula in chaotic optimization with the improved particle swarm optimization algorithm to perform chaotic search on an example with poor adaptation degree in the later stage of iteration, thereby expanding the search precision of particles and accelerating the convergence speed.
When the distance d between the position of the particle i and the optimal position of the population at the current moment i When the position of the particle i is smaller than a preset threshold value, the particle i is worse, and the position of the particle i is updated by adopting chaotic search; when the distance d between the position of the particle i and the optimal position of the population at the current moment i And if the position of the particle i is not smaller than the preset threshold value, the position of the particle i is kept unchanged. In the embodiment of the invention, the threshold value is 10 -2 。
d i ＝(X i -X gbest ) 2 (9)
Wherein X is i Indicating the position of particle i, X gbest Representing the optimal position of the population.
According to the invention, the positions of particles are mapped into a chaotic space, the positions of the particles are updated through a logics formula, and then the positions of the particles are mapped out in a reverse way, so that the positions of the particles in chaotic search are obtained.
In the prior art, the application of initializing the particle swarm by utilizing the chaotic search exists, but the chaotic search algorithm needs to adjust a plurality of parameters, such as parameters of a chaotic system, a search range and the like, the chaotic search is used for initializing the particle swarm, the complexity and the debugging difficulty of the algorithm are easy to increase, once the chaotic search parameter is adjusted wrongly, the global search can be influenced, the search precision is low, the random value of the chaotic search is used as an initial value, the particle swarm needs a long time to reach a stable state, and the convergence speed of the algorithm is influenced. According to the invention, chaotic search is carried out on a small part of particles with poor fitness at the later stage of iteration, under the condition that the distance between the current particle and the optimal solution is smaller than a fixed value, the probability that the particle can obtain the optimal solution is larger, in this case, if the speed and the position of the particle are updated by continuously using formulas (7) and (8), the particle can be trapped into local optimal solution and deviate from the optimal solution, the chaotic search is adopted to expand the searching precision of the particle, on one hand, the chaotic search quantity is not large, so that an algorithm can be converged more quickly, and on the other hand, even if the parameter of the chaotic search is wrong, the global search is not influenced, and higher searching precision can be realized.
And B06, calculating a new fitness value of each particle by using an evaluation function according to the updated particle positions in the steps B04 and B05.
And B07, updating the individual optimal position of each particle and the population optimal position of all particles according to the new fitness value of all particles.
Step B08, judging whether iteration convergence conditions are met, and if so, outputting the optimal position of the iteration convergence population; if not, returning to the step B03 to continuously update the particles until the iteration convergence condition is met. In the embodiment of the invention, the iteration convergence condition is that the maximum iteration number is reached.
And B09, before outputting the final fault section of the source-containing power distribution network, constraining the optimal position of the iterative convergent population in the step B08 through preset power distribution network stable operation constraint conditions.
When the improved particle swarm optimization algorithm meets the iteration convergence condition, carrying out power flow calculation on the power distribution network containing the source according to the iteration convergence population optimal position to obtain a voltage value and a current value corresponding to the iteration convergence population optimal position; and judging whether the voltage value and the current value meet the stable operation condition of the power distribution network, if so, outputting a fault section of the power distribution network containing the source according to the optimal position of the iteratively converged population, and if not, re-utilizing an improved particle swarm optimization algorithm to perform particle swarm optimization.
In the invention, the stable operation condition of the power distribution network comprises a node voltage constraint condition and a branch current constraint condition.
(1) The node voltage constraint conditions are:
U jmin ≤U j ≤U jmax (10)
wherein U is j Node j voltage value, U representing load flow calculation jmin And U jmax Is the minimum and maximum value of the voltage at node j, which represents the j-th segment switch node in the active distribution network.
If the voltage of a certain node cannot meet the upper and lower voltage limit requirements, the voltage needs to be corrected until the voltage meets the voltage constraint condition, and the common method comprises the following steps: the active power and the reactive power of the node are adjusted, the output power of a generator is increased or reduced, the transformer transformation ratio is adjusted, and the like; the adjustment method can be adjusted according to the specific conditions everywhere.
U j '＝ω 1 P j +ω 2 Q j +ω 3 P G +ω 4 k T (11)
Wherein U is j ' represents the voltage at the modified node j, P j 、Q j Representing the active and reactive power at node j, respectively; p (P) G The active power is output by the generator; k (k) T For transformer transformation ratio, weight ω 1 、ω 2 、ω 3 、ω 4 Can be adjusted according to actual requirements.
(2) Branch current constraint conditions:
I jmin ≤I j ≤I jmax (12)
wherein I is j Current representing branch j of power flow calculation, wherein branch j represents branch in jth section in active power distribution network, I jmin And I jmax Is the minimum maximum current limit allowed to flow on branch j.
If the current of a certain branch is out of limit, the current needs to be corrected until the current meets the current constraint condition, and the common method comprises the following steps: active power and reactive power are adjusted, transformer transformation ratio is adjusted, wire sectional area is adjusted, line length is adjusted, and the like; the specific method can be adjusted according to actual conditions.
I j '＝ω 1 P j +ω 2 Q j +ω 3 L S +ω 4 k T +ω 5 L j (13)
Wherein I is j ' denotes the corrected current, P j 、Q j Representing the active and reactive power, L, respectively, at branch j S Is the cross-sectional area of the wire, k T For transformer transformation ratio, L j For the line length weight, ω, of branch j 1 、ω 2 、ω 3 、ω 4 、ω 5 Can be adjusted according to actual requirements.
The two correction methods need to comprehensively consider the actual requirements of different grades of power grids, obey various standards of stable operation of the power grids, and ensure safe and stable operation of the power distribution network.
Compared with the existing algorithms, the method has the advantages of high convergence speed and good convergence performance, and better fault positioning effect is realized.
Example 2:
based on the method for locating the fault of the active distribution network introduced in the embodiment 1, the embodiment introduces a device for locating the fault of the active distribution network for realizing the method, which mainly comprises a data acquisition module, a model construction module and a fault locating module.
The data acquisition module acquires fault current information of the active power distribution network through intelligent feeder terminal equipment of the power distribution network, wherein the fault current information comprises current information flowing through each node (sectional switch) in the power distribution network.
The model building module is mainly used for obtaining an evaluation function of the active distribution network in a fault state according to the obtained fault current information of the active distribution network, and the specific operation of the model building module is consistent with the step A in the embodiment 1.
The fault positioning module is mainly used for updating the speed and the position of particles through an improved particle swarm optimization algorithm, and performing fault positioning on the power distribution network with the source to obtain a fault section of the power distribution network with the source. In the improved particle swarm optimization algorithm, the evaluation function is used as an adaptability function of the improved particle swarm optimization algorithm, and a candidate solution of a fault section of the source-containing power distribution network is used as particles; the speed and position of the particles are updated using adaptive inertial weights. The fault positioning module is also used for restraining the output result of the improved particle swarm optimization algorithm through preset power distribution network stable operation constraint conditions.
The specific operation of the fault location module is consistent with step B in example 1.
Example 3:
this embodiment describes a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the active distribution network fault location method described in embodiment 1.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The embodiments of the present invention have been described above with reference to the accompanying drawings, but the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the claims, which are all within the protection of the present invention.
Claims (10)
1. The fault positioning method for the source-containing power distribution network is characterized by comprising the following steps of:
obtaining an evaluation function of the active power distribution network in a fault state according to the obtained fault current information of the active power distribution network;
using the evaluation function as an adaptability function of an improved particle swarm optimization algorithm, using a candidate solution of a fault section of the source-containing power distribution network as particles, and updating the speed and the position of the particles through the improved particle swarm optimization algorithm to perform fault positioning of the source-containing power distribution network to obtain the fault section of the source-containing power distribution network;
the improved particle swarm optimization algorithm updates the speed and the position of particles by adopting self-adaptive inertial weights;
and constraining the output result of the improved particle swarm optimization algorithm through preset power distribution network stable operation constraint conditions.
2. The method for locating faults in an active power distribution network according to claim 1, wherein the method for obtaining an evaluation function of the active power distribution network in a fault state is as follows:
according to the positions of the sectionalizing switches in the active power distribution network, sectionalizing the active power distribution network to obtain a plurality of sections corresponding to the sectionalizing switches one by one;
encoding the segment switch and section;
obtaining an actual state value and an expected state value of each sectional switch in the active power distribution network under a fault state according to the codes and the fault current information of the active power distribution network;
and obtaining an evaluation function of the active power distribution network under the fault state according to the actual state value and the expected state value of each sectional switch.
3. The method for locating faults in an active power distribution network of claim 2 in which the expression of the evaluation function is as follows:
wherein F (S) B ) Is an evaluation function of a power distribution network with a source, S B Is the state value of the power distribution network containing sources, I j For the actual state value of the jth sectionalizer in the active distribution network,the expected state value of the jth sectionalizer in the active power distribution network is represented by omega, and S B (j) Is the state value of a jth section feeder line in the active power distribution network, S B (j) =1 indicates the j-th section feeder fault, S B (j) The expression of =0 indicates that the j-th section feeder is normal, j=1, 2, …, N is the total number of sectionalizing switches in the active distribution network.
4. The method for locating faults in an active power distribution network of claim 2, whereinObtaining the actual state value of each sectional switch according to the fault current information of the source-containing power distribution network, I j The expression of (2) is:
5. the method for locating faults in an active power distribution network according to claim 1, wherein the steps of updating the speed and position of particles by an improved particle swarm optimization algorithm, locating faults in the active power distribution network, and obtaining fault sections of the active power distribution network include:
initializing network parameters and particle swarm parameters, wherein the particle swarm parameters comprise the position and the speed of particles;
calculating the fitness value of each particle according to the evaluation function and the position of the particle;
obtaining an individual optimal position of each particle and a population optimal position of all particles according to the fitness value of all particles;
updating the self-adaptive inertia weight of each particle according to the fitness value of all the particles;
updating the speed and the position of each particle according to the self-adaptive weight;
calculating a new fitness value of each particle by using an evaluation function according to the updated particle position;
updating the individual optimal position of each particle and the population optimal position of all particles according to the new fitness value of all particles;
and when the iteration convergence condition is met, outputting the iteration convergence population optimal position to obtain the fault section of the source-containing power distribution network.
6. The method for fault localization of an active power distribution network of claim 5 wherein updating the adaptive inertial weight of each particle based on fitness values of all particles comprises:
obtaining the average fitness of the population at the current moment according to the fitness values of all the particlesValue of
According to the fitness value f of the optimal position of the population at the current moment max Average fitness valueThe weight selection ratio of each particle is calculated, and the expression is as follows:
wherein k is i The weight selection ratio of the particles i is represented, i=1, 2, …, M being the total number of particles in the particle group;
updating the self-adaptive inertia weight of each particle according to the weight selection proportion and the fitness value of each particle, wherein the expression is as follows:
wherein P is i Representing the adaptive inertial weight of particle i, f i The fitness value of particle i is indicated.
7. The method for locating faults in an active power distribution network according to claim 5, wherein in the improved particle swarm optimization algorithm, when the distance between the position of a particle i and the optimal position of the population at the current moment is smaller than a preset threshold, the position of the particle i is updated by adopting chaotic search, and i=1, 2, …, M and M are the total number of particles in the particle swarm.
8. The method for locating faults in an active power distribution network of claim 1 in which said power distribution network steady operation conditions include node voltage constraints and branch current constraints;
when the improved particle swarm optimization algorithm meets the iteration convergence condition, carrying out power flow calculation on the power distribution network containing the source according to the iteration convergence population optimal position to obtain a voltage value and a current value corresponding to the iteration convergence population optimal position; and judging whether the voltage value and the current value meet the stable operation condition of the power distribution network, if so, outputting a fault section of the power distribution network containing the source according to the optimal position of the iteratively converged population, and if not, re-utilizing an improved particle swarm optimization algorithm to perform particle swarm optimization.
9. The utility model provides a contain source distribution network fault location device which characterized in that includes:
the data acquisition module is used for acquiring fault current information of the power distribution network with the source;
the model construction module is used for obtaining an evaluation function of the source-containing power distribution network in a fault state according to the obtained fault current information of the source-containing power distribution network;
the fault positioning module is used for performing fault positioning on the active distribution network by using the evaluation function as an adaptability function of an improved particle swarm optimization algorithm and using a candidate solution of the active distribution network fault section as particles and updating the speed and the position of the particles through the improved particle swarm optimization algorithm to obtain the fault section of the active distribution network;
in the fault positioning module, the improved particle swarm optimization algorithm updates the speed and the position of particles by adopting self-adaptive inertial weights;
the fault positioning module is also used for restraining the output result of the improved particle swarm optimization algorithm through preset power distribution network stable operation constraint conditions.
10. A computer readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the method for fault localization of an active distribution network according to any one of claims 1-8.
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| CN117388643B (en) * | 2023-12-11 | 2024-03-08 | 国网湖北省电力有限公司经济技术研究院 | Method, system, equipment and storage medium for positioning fault section of active power distribution network |
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