CN114113887B - Fault positioning method and system for power distribution network - Google Patents
Fault positioning method and system for power distribution network Download PDFInfo
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- CN114113887B CN114113887B CN202111384728.5A CN202111384728A CN114113887B CN 114113887 B CN114113887 B CN 114113887B CN 202111384728 A CN202111384728 A CN 202111384728A CN 114113887 B CN114113887 B CN 114113887B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/08—Locating faults in cables, transmission lines, or networks
- G01R31/081—Locating faults in cables, transmission lines, or networks according to type of conductors
- G01R31/086—Locating faults in cables, transmission lines, or networks according to type of conductors in power transmission or distribution networks, i.e. with interconnected conductors
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- Y04—INFORMATION OR COMMUNICATION TECHNOLOGIES HAVING AN IMPACT ON OTHER TECHNOLOGY AREAS
- Y04S—SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS
- Y04S10/00—Systems supporting electrical power generation, transmission or distribution
- Y04S10/50—Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications
- Y04S10/52—Outage or fault management, e.g. fault detection or location
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Abstract
The invention relates to a power distribution network fault positioning method and a system, wherein the method and the system combine information reported by a user to preliminarily determine a fault area, then determine fault equipment and the position thereof from the fault area, reduce data processing amount and provide efficiency; and training two neural network models by different methods, fusing the two neural network models, and determining the fault equipment and the position thereof by the identification results of the three neural network models, so that the method is more accurate.
Description
Technical Field
The invention relates to the technical field of power distribution networks, in particular to a power distribution network fault positioning method and system.
Background
Along with the continuous expansion of the power distribution network scale, the continuous increase of power consumption demands leads to the increase of the probability of power grid faults, and the power consumption clients have higher and higher requirements on the power supply reliability. At present, after a power failure event occurs, the power consumption client is mainly relied on to dial 95598, but the method cannot accurately locate fault equipment, and power supply rush repair is affected.
Disclosure of Invention
The invention aims to provide a power distribution network fault positioning method and system, which can be used for realizing rapid and accurate positioning of power distribution network fault equipment.
The embodiment of the invention provides a power distribution network fault positioning method, which comprises the following steps:
step S10, obtaining power outage information reported by a user, extracting a user number from the power outage information, and determining a fault area in the power distribution network according to the user number;
step S20, an electrical parameter acquisition request is sent to each intelligent terminal in the fault area so as to acquire the current electrical parameters of each branch line in the fault area; each branch line of the power distribution network is provided with one intelligent terminal;
step S30, obtaining topology information of a power distribution network;
s40, inputting the electric parameters and the power distribution network topology information into a pre-trained first neural network model for recognition, and obtaining a first recognition result; the first identification result comprises equipment information of at least one fault equipment and position information of a branch line where the equipment information is located;
s50, inputting the electric parameters and the power distribution network topology information into a pre-trained second neural network model for recognition, and obtaining a second recognition result; the first neural network model and the second neural network model are respectively obtained by adopting different model structures and training modes, and the second recognition result comprises equipment information of at least one fault equipment and position information of a branch circuit where the equipment information is located;
step S60, inputting the electric parameters and the power distribution network topology information into a pre-trained third neural network model for recognition, and obtaining a third recognition result; the third neural network model is obtained by fusing the first neural network model and the second neural network model, and the third recognition result comprises equipment information of at least one fault equipment and position information of a branch circuit where the equipment information is located;
and step S70, comparing the first identification result, the second identification result and the third identification result, and determining the fault equipment and the position thereof according to the comparison result.
Preferably, the step S70 includes:
and if the first recognition result, the second recognition result and the third recognition result are different, executing the steps S20 to S70 again.
Preferably, the step S70 includes:
if the equipment information of the fault equipment and the position information of the branch line where the fault equipment is located in any two of the first identification result, the second identification result and the third identification result are the same, determining the fault equipment and the position where the fault equipment is located according to any one of the two identification results.
Preferably, the step S70 includes:
if the position information of the branch line where the fault equipment is located in any two of the first identification result, the second identification result and the third identification result is the same, and the equipment information of the fault equipment is different, determining the position of the fault equipment according to any one of the two identification results, acquiring the historical electrical parameters and the current electrical parameters of the fault equipment of the two identification results, comparing the historical electrical parameters with the current electrical parameters, and determining the fault equipment according to the comparison result.
Preferably, the location information of the branch line where the fault device is located is a line number.
The embodiment of the invention also provides a power distribution network fault positioning system, which comprises:
the fault area determining unit is used for acquiring power outage information reported by a user, extracting a user number from the power outage information and determining a fault area in the power distribution network according to the user number;
the electrical parameter acquisition unit is used for sending an electrical parameter acquisition request to each intelligent terminal in the fault area so as to acquire the current electrical parameters of each branch line in the fault area; each branch line of the power distribution network is provided with one intelligent terminal;
the distribution network topology acquisition unit is used for acquiring the topology information of the distribution network;
the first identification unit is used for inputting the electric parameters and the power distribution network topology information into a pre-trained first neural network model for identification, and obtaining a first identification result; the first identification result comprises equipment information of at least one fault equipment and position information of a branch line where the equipment information is located;
the second identification unit is used for inputting the electric parameters and the power distribution network topology information into a pre-trained second neural network model for identification, and obtaining a second identification result; the first neural network model and the second neural network model are respectively obtained by adopting different model structures and training modes, and the second recognition result comprises equipment information of at least one fault equipment and position information of a branch circuit where the equipment information is located;
the third identification unit is used for inputting the electric parameters and the power distribution network topology information into a pre-trained third neural network model for identification, and obtaining a third identification result; the third neural network model is obtained by fusing the first neural network model and the second neural network model, and the third recognition result comprises equipment information of at least one fault equipment and position information of a branch circuit where the equipment information is located; and
the fault identification unit is used for comparing the first identification result, the second identification result and the third identification result, and determining fault equipment and the position thereof according to the comparison result.
Preferably, the fault recognition unit is specifically configured to re-execute steps S20 to S70 if the first recognition result, the second recognition result, and the third recognition result are all different.
Preferably, the fault identifying unit is specifically configured to determine, if the device information of the fault device and the location information of the branch line where the fault device is located in any two of the first identifying result, the second identifying result, and the third identifying result are the same, the fault device and the location where the fault device is located according to any one of the two identifying results.
Preferably, the fault identification unit is specifically configured to determine, if the location information of the branch line where the fault device of any two of the first identification result, the second identification result, and the third identification result is located is the same, and the device information of the fault device is different, determine, according to any one of the two identification results, the location of the fault device, and obtain a historical electrical parameter and a current electrical parameter of the fault device of the two identification results, compare the historical electrical parameter with the current electrical parameter, and determine, according to the comparison result, the fault device.
Preferably, the location information of the branch line where the fault device is located is a line number.
The embodiment of the invention has the following beneficial effects:
combining information reported by a user to preliminarily determine a fault area, and then determining fault equipment and the position thereof from the fault area, so that the data processing amount is reduced, and the efficiency is improved; and training two neural network models by different methods, fusing the two neural network models, and determining the fault equipment and the position thereof by the identification results of the three neural network models, so that the method is more accurate.
Additional features and advantages of embodiments of the invention will be set forth in the description which follows.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a fault location method for a power distribution network according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a local topology of a power distribution network according to an embodiment of the present invention.
Fig. 3 is a block diagram of a fault location system of a power distribution network according to an embodiment of the present invention.
Detailed Description
Various exemplary embodiments, features and aspects of the disclosure will be described in detail below with reference to the drawings. In addition, numerous specific details are set forth in the following examples in order to provide a better illustration of the invention. It will be understood by those skilled in the art that the present invention may be practiced without some of these specific details. In some instances, well known means have not been described in detail in order to not obscure the present invention.
Referring to fig. 1, an embodiment of the present invention provides a fault location method for a power distribution network, including the following steps:
step S10, obtaining power outage information reported by a user, extracting a user number from the power outage information, and determining a fault area in the power distribution network according to the user number;
specifically, the user can report the power failure information through dialing 95598, and after obtaining the user code, the user can determine the area where the user is located according to the user code. The power distribution network can be divided into a plurality of user areas, and branches where users in each user area are located are associated, so that after power failure information is reported by a certain user, only the user area corresponding to the user can be determined first, and the user area is determined to be a fault area;
step S20, an electrical parameter acquisition request is sent to each intelligent terminal in the fault area so as to acquire the current electrical parameters of each branch line in the fault area; each branch line of the power distribution network is provided with one intelligent terminal;
wherein, each intelligent terminal is arranged on each branch line in the fault area, as shown in fig. 2, the electrical parameters can include current, voltage, active and reactive parameters, etc.;
step S30, obtaining topology information of a power distribution network;
s40, inputting the electric parameters and the power distribution network topology information into a pre-trained first neural network model for recognition, and obtaining a first recognition result; the first identification result comprises equipment information of at least one fault equipment and position information of a branch line where the equipment information is located;
s50, inputting the electric parameters and the power distribution network topology information into a pre-trained second neural network model for recognition, and obtaining a second recognition result; the first neural network model and the second neural network model are respectively obtained by adopting different model structures and training modes, and the second recognition result comprises equipment information of at least one fault equipment and position information of a branch circuit where the equipment information is located;
step S60, inputting the electric parameters and the power distribution network topology information into a pre-trained third neural network model for recognition, and obtaining a third recognition result; the third neural network model is obtained by fusing the first neural network model and the second neural network model, and the third recognition result comprises equipment information of at least one fault equipment and position information of a branch circuit where the equipment information is located;
and step S70, comparing the first identification result, the second identification result and the third identification result, and determining the fault equipment and the position thereof according to the comparison result.
Further, the step S70 includes:
if the equipment information of the fault equipment and the position information of the branch line where the fault equipment is located in any two of the first identification result, the second identification result and the third identification result are the same, determining the fault equipment and the position where the fault equipment is located according to any one of the two identification results;
for example, the first recognition result includes a fault device 1 (location 1) and a fault device 2 (location 2), the second recognition result includes a fault device 3 (location 3) and a fault device 4 (location 4), and the third recognition result includes a fault device 5 (location 5), a fault device 6 (location 6), and a fault device 7 (location 7); if the fault device 1 is the same as the fault device 3, the position 1 is the same as the position 3, the fault device 2 is the same as the fault device 4, and the position 2 is the same as the position 4, the fault device and the position thereof are determined according to the first identification result or the second identification result. Since the third neural network model is obtained by fusing the first neural network model and the second neural network model, in the actual recognition process, if the first recognition result and the second recognition result are the same, the third recognition result is generally the same as the first recognition result and the second recognition result.
If the position information of the branch line where the fault equipment is located in any two of the first identification result, the second identification result and the third identification result is the same, and the equipment information of the fault equipment is different, determining the position of the fault equipment according to any one of the two identification results, acquiring the historical electrical parameters and the current electrical parameters of the fault equipment of the two identification results, comparing the historical electrical parameters with the current electrical parameters, and determining the fault equipment according to the comparison result;
for example, the first recognition result includes a faulty device 1 (location 1) and a faulty device 2 (location 2), the second recognition result includes a faulty device 3 (location 3) and a faulty device 4 (location 4), and the third recognition result includes a faulty device 5 (location 5) and a faulty device 6 (location 6); if the fault equipment 1 is the same as the fault equipment 3 and the fault equipment 5, and the position 1 is the same as the position 3 and the position 5, determining that the fault equipment 1 on the branch where the position 1 is located has a fault; if the position 2, the position 4 and the position 6 are the same, but the fault equipment 2, the fault equipment 4 and the fault equipment 6 are different, determining that faults exist on the branches where the position 1, the position 3 and the position 5 are located, and not determining that the fault exists in particular, then acquiring historical electrical parameters of the fault equipment 2, the fault equipment 4 and the fault equipment 6, and comparing the current electrical parameters with the historical electrical parameters to determine the equipment most likely to have the fault; preferably, the historical electrical parameter can be data under the same current condition, such as similar or same time, similar or same weather, and the like, so as to improve the reliability of the comparison result.
And if the first recognition result, the second recognition result and the third recognition result are different, executing the steps S20 to S70 again.
Further, the position information of the branch line where the fault equipment is located is a line number.
Referring to fig. 2, the intelligent terminal is configured to be disposed on each branch line of the power distribution network, and is configured to collect electrical parameters, such as current, voltage, active and reactive parameters, of each branch line, and send the electrical parameters to the server, where the server is used as an execution body for executing the method in this embodiment; the server is configured to train the neural network model with training data, which may include electrical parameters of the branch lines, locations of faulty equipment, topology information of the distribution network. The server is also used for inputting the electric parameters and topology information acquired during faults into the trained neural network model, and identifying the positions of fault equipment in the power distribution network. The intelligent terminal can be arranged on each branch line to collect the electrical parameters of the branch. Three intelligent terminals on the right are located on the next-stage branch lines 11, 12, 13, and one intelligent terminal on the left is located on the previous-stage branch line 1. In theory, the electrical parameters of three branch lines collected by the three intelligent terminals on the right side correspond to the electrical parameters of one branch line collected by the one intelligent terminal on the left side. For example, the sum of the current/power of the left three branch lines is equal to the current/power of the right branch line.
According to the embodiment of the invention, the fault area is preliminarily determined by combining the information reported by the user, and then the fault equipment and the position thereof are determined from the fault area, so that the data processing amount is reduced, and the efficiency is improved; and training two neural network models by different methods, fusing the two neural network models, and determining the fault equipment and the position thereof by the identification results of the three neural network models, so that the method is more accurate.
For example, the training process of the first neural network model includes the steps of:
a1, extracting characteristics of each training data in a training set;
acquiring a training set, wherein the training set comprises electric parameters (voltage, current, active and reactive parameters and the like) of a plurality of branch lines, the position of power distribution network fault equipment and topology information of the power distribution network; after the training set is obtained, extracting information such as numerical value, position, topology and the like of data from the training set as characteristics;
a2, dividing the features of each training data in the training set into Q feature subsets;
wherein Q is greater than 1, and the number of features contained in each feature subset is not less than 1;
for example, the features of each training data in the training set may be randomly divided into Q feature subsets according to a cluster division manner;
clustering is divided into: setting the number of clustering centers as Q, and clustering the original data features into Q parts by using a clustering algorithm; the random division is as follows: q feature subsets are created, features are selected randomly for each feature subset in sequence, and the scale of each feature subset is equivalent;
step A3, training a first neural sub-network based on the Q feature subsets to obtain Q first neural sub-networks;
firstly, calculating the hidden layer node number of a first neural sub-network corresponding to the feature subset
To round operator, { FQ i Is feature subset FQ i The number of features included, N being the number of features of the original dataset;
then, determining target training subsets corresponding to the feature subsets according to the feature subsets;
let T be j Extracting the training set and the feature T for the j-th feature in the i-th feature subset j The corresponding data columns form the j-th column of the i-th target training subset corresponding to the i-th feature subset, and the steps are repeated, namely the data columns corresponding to the features in the i-th feature subset in the training set are extracted to form the i-th target training subset corresponding to the i-th feature subset; the final target training subset obtained is mxn i ' dimension, wherein M is the number of data corresponding to a feature in the training set, N i ' is the number of features contained in the ith feature subset;
then, training the first neural sub-network corresponding to each feature subset based on the number of hidden layer nodes and the target training subset;
in K i The method comprises the steps of taking each piece of data in an ith target training subset as input data and output data of the ith first neural sub-network (namely, the input data and the output data of the first neural sub-network are the same), calculating parameters of the ith first neural sub-network by adopting a two-stage method, wherein the parameters of the ith first neural sub-network comprise the center and the width of the first neural sub-network hidden layer node, the weight of the hidden layer node and the output layer node and the like; optionally, the training processes of training the corresponding first neural sub-networks based on each feature subset and the target training subset corresponding to each feature subset may be performed in parallel, that is, the training of the Q first neural sub-networks may be performed simultaneously, so as to achieve the purposes of shortening the time and accelerating the training speed;
step A4, the Q first neural sub-networks are spliced into a first neural network model in parallel; arranging the Q trained first neural sub-networks obtained in the steps, and splicing the Q trained first neural sub-networks in parallel to form a wider neural network;
step A5, training the first neural network model based on preset classification of each training data;
inputting each training data in the training set into an input layer of a first neural network model, and calculating an output matrix of nodes of an hidden layer of the first neural network model; and calculating weights from hidden layers to output layers in the first neural network model based on the output matrix according to preset classifications of all training data in the training set.
For example, the training process of the second neural network model includes the steps of:
step B1, obtaining a training set;
the training set comprises electric parameters (voltage, current, active and reactive parameters and the like) of a plurality of branch lines, the position of power distribution network fault equipment and the topology information of the power distribution network;
step B2, training the second neural network model by adopting the training set to acquire a plurality of weights and thresholds of the second neural network model meeting preset requirements;
step B3, adopting an algorithm to determine the optimal weight and the optimal threshold of the second neural network model from the plurality of weight and the threshold;
the specific process is as follows:
1) Randomly generating a plurality of individuals to generate a parent particle swarm; each individual corresponds to a set of weights and thresholds of the neural network model; calculating the fitness of each particle in the parent particle swarm;
2) Selecting a larger value in the fitness of target particles in a parent particle swarm and the fitness of the individual optimal positions of the target particles as an individual extremum of the target particles, wherein the target particles are any one particle in the parent particle swarm;
3) Selecting the maximum value of the fitness of each particle in the parent particle swarm and the fitness of the population optimal position of the parent particle swarm as the local extremum of the parent particle swarm;
4) Updating the position and the speed of the particles according to the individual extremum and the local extremum, generating a child particle swarm, taking the child particle swarm as a parent particle swarm of the next iteration, and updating a learning factor and an inertia factor;
5) If the current iteration number reaches the preset maximum iteration number, outputting an optimal weight and an optimal threshold;
step B4, taking the optimal weight and the optimal threshold value as the weight initial value and the threshold initial value of the second neural network model, and training the second neural network model again to obtain a trained second neural network model;
the process of training the neural network model in the step B2 and the step B4 is the same, and the weight initial value and the threshold initial value are different.
For example, after obtaining the trained first neural network model and the trained second neural network model, fusing the first neural network model and the second neural network model to obtain a third neural network model;
assume that the input channel of the first neural network model is X 1 ,X 2 ,...,X c The input channel of the second neural network model is Y 1 ,Y 2 ,...,Y c After the first neural network model and the second neural network model are fused, the output channel is
K is a fusion coefficient;
the fused third neural network model comprises an input layer, a nonlinear transformation layer, a linear fusion layer and an output layer; the input layer comprises two first branches with the same network structure, and each first branch comprises a convolution layer and a correction linear unit; the nonlinear transformation layer comprises two second branches which have the same network structure and are respectively connected with the corresponding first branches, each second branch comprises 5 layers of networks, and each layer of network comprises a convolution layer, a batch normalization function and a ReLU activation function; the linear fusion layer fuses the results of the two second branches of the nonlinear transformation layer to obtain an output result; the output layer comprises a global average pooling layer, a random discarding neuron connection layer and a full connection layer, and the output result of the linear fusion layer is output to the global average pooling layer.
Referring to fig. 3, another embodiment of the present invention provides a fault location system for a power distribution network, where the system of the present embodiment corresponds to the method of the foregoing embodiment, and the system of the present embodiment includes:
the fault area determining unit 1 is used for acquiring power outage information reported by a user, extracting a user number from the power outage information, and determining a fault area in the power distribution network according to the user number;
an electrical parameter obtaining unit 2, configured to send an electrical parameter obtaining request to each intelligent terminal in the fault area, so as to obtain current electrical parameters of each branch line in the fault area; each branch line of the power distribution network is provided with one intelligent terminal;
the distribution network topology acquisition unit 3 is used for acquiring the topology information of the distribution network;
the first recognition unit 4 is used for inputting the electric parameters and the power distribution network topology information into a pre-trained first neural network model for recognition, and obtaining a first recognition result; the first identification result comprises equipment information of at least one fault equipment and position information of a branch line where the equipment information is located;
the second recognition unit 5 is used for inputting the electric parameters and the power distribution network topology information into a pre-trained second neural network model for recognition, and obtaining a second recognition result; the first neural network model and the second neural network model are respectively obtained by adopting different model structures and training modes, and the second recognition result comprises equipment information of at least one fault equipment and position information of a branch circuit where the equipment information is located;
the third recognition unit 6 is used for inputting the electric parameters and the power distribution network topology information into a pre-trained third neural network model for recognition, and obtaining a third recognition result; the third neural network model is obtained by fusing the first neural network model and the second neural network model, and the third recognition result comprises equipment information of at least one fault equipment and position information of a branch circuit where the equipment information is located; and
and the fault identification unit 7 is used for comparing the first identification result, the second identification result and the third identification result, and determining the fault equipment and the position thereof according to the comparison result.
Further, the fault identifying unit 7 is specifically configured to re-execute steps S20 to S70 if the first identifying result, the second identifying result and the third identifying result are all different.
Further, the fault identifying unit 7 is specifically configured to determine, if the device information of the fault device and the location information of the branch line where the fault device is located in any two of the first identifying result, the second identifying result and the third identifying result are the same, the fault device and the location where the fault device is located according to any one of the two identifying results.
Further, the fault identifying unit 7 is specifically configured to determine, if the location information of the branch line where the fault device is located is the same for any two of the first identifying result, the second identifying result, and the third identifying result, and the device information of the fault device is different, determine the location of the fault device according to any one of the two identifying results, obtain a historical electrical parameter and a current electrical parameter of the fault device of the two identifying results, compare the historical electrical parameter with the current electrical parameter, and determine the fault device according to the comparison result.
Further, the position information of the branch line where the fault equipment is located is a line number.
The system of the present embodiment corresponds to the method of the foregoing embodiment, and therefore, a portion of the system of the present embodiment that is not described in detail may be obtained by referring to the method of the foregoing embodiment, which is not described herein again.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvements in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. The fault positioning method for the power distribution network is characterized by comprising the following steps of:
s10, acquiring power outage information reported by a user, extracting a user number from the power outage information, and determining a fault area in the power distribution network according to the user number;
step S20, an electrical parameter acquisition request is sent to each intelligent terminal in the fault area so as to acquire the current electrical parameters of each branch line in the fault area; each branch line of the power distribution network is provided with one intelligent terminal;
step S30, obtaining topology information of a power distribution network;
s40, inputting the electric parameters and the power distribution network topology information into a pre-trained first neural network model for recognition, and obtaining a first recognition result; the first identification result comprises equipment information of at least one fault equipment and position information of a branch line where the equipment information is located;
s50, inputting the electric parameters and the power distribution network topology information into a pre-trained second neural network model for recognition, and obtaining a second recognition result; the first neural network model and the second neural network model are respectively obtained by adopting different model structures and training modes, and the second recognition result comprises equipment information of at least one fault equipment and position information of a branch circuit where the equipment information is located;
step S60, inputting the electric parameters and the power distribution network topology information into a pre-trained third neural network model for recognition, and obtaining a third recognition result; the third neural network model is obtained by fusing the first neural network model and the second neural network model, and the third recognition result comprises equipment information of at least one fault equipment and position information of a branch circuit where the equipment information is located;
and step S70, comparing the first identification result, the second identification result and the third identification result, and determining the fault equipment and the position thereof according to the comparison result.
2. The method according to claim 1, wherein said step S70 comprises:
and if the first recognition result, the second recognition result and the third recognition result are different, executing the steps S20 to S70 again.
3. The method according to claim 2, wherein said step S70 comprises:
if the equipment information of the fault equipment and the position information of the branch line where the fault equipment is located in any two of the first identification result, the second identification result and the third identification result are the same, determining the fault equipment and the position where the fault equipment is located according to any one of the two identification results.
4. A method according to claim 3, wherein said step S70 comprises:
if the position information of the branch line where the fault equipment is located in any two of the first identification result, the second identification result and the third identification result is the same, and the equipment information of the fault equipment is different, determining the position of the fault equipment according to any one of the two identification results, acquiring the historical electrical parameters and the current electrical parameters of the fault equipment of the two identification results, comparing the historical electrical parameters with the current electrical parameters, and determining the fault equipment according to the comparison result.
5. The method according to any one of claims 1-4, wherein the location information of the branch line where the faulty equipment is located is a line number.
6. A power distribution network fault location system, comprising:
the fault area determining unit is used for acquiring power outage information reported by a user, extracting a user number from the power outage information and determining a fault area in the power distribution network according to the user number;
the electrical parameter acquisition unit is used for sending an electrical parameter acquisition request to each intelligent terminal in the fault area so as to acquire the current electrical parameters of each branch line in the fault area; each branch line of the power distribution network is provided with one intelligent terminal;
the distribution network topology acquisition unit is used for acquiring the topology information of the distribution network;
the first identification unit is used for inputting the electric parameters and the power distribution network topology information into a pre-trained first neural network model for identification, and obtaining a first identification result; the first identification result comprises equipment information of at least one fault equipment and position information of a branch line where the equipment information is located;
the second identification unit is used for inputting the electric parameters and the power distribution network topology information into a pre-trained second neural network model for identification, and obtaining a second identification result; the first neural network model and the second neural network model are respectively obtained by adopting different model structures and training modes, and the second recognition result comprises equipment information of at least one fault equipment and position information of a branch circuit where the equipment information is located;
the third identification unit is used for inputting the electric parameters and the power distribution network topology information into a pre-trained third neural network model for identification, and obtaining a third identification result; the third neural network model is obtained by fusing the first neural network model and the second neural network model, and the third recognition result comprises equipment information of at least one fault equipment and position information of a branch circuit where the equipment information is located; and
the fault identification unit is used for comparing the first identification result, the second identification result and the third identification result, and determining fault equipment and the position thereof according to the comparison result.
7. The system according to claim 6, wherein the fault recognition unit is specifically configured to re-execute steps S20 to S70 if the first recognition result, the second recognition result, and the third recognition result are all different.
8. The system according to claim 7, wherein the fault identification unit is specifically configured to determine the fault device and the location thereof according to any one of the first identification result, the second identification result, and the third identification result if the device information of the fault device and the location information of the branch line thereof of any two of the first identification result, the second identification result, and the third identification result are the same.
9. The system according to claim 8, wherein the fault identification unit is specifically configured to determine, if the location information of the branch line where the fault device is located is the same for any two of the first identification result, the second identification result, and the third identification result, and the device information of the fault device is different, determine the location of the fault device according to any one of the two identification results, obtain a historical electrical parameter and a current electrical parameter of the fault device for the two identification results, compare the historical electrical parameter with the current electrical parameter, and determine the fault device according to the comparison result.
10. The system according to any one of claims 6-9, wherein the location information of the branch line on which the faulty device is located is a line number.
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