CN113391164A - Intelligent identification method and device for single-phase earth fault of power distribution network - Google Patents
Intelligent identification method and device for single-phase earth fault of power distribution network Download PDFInfo
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Abstract
The invention relates to a method and a device for intelligently identifying single-phase earth faults of a power distribution network, wherein the method comprises the following steps: when the value of the real-time zero-sequence voltage of the transformer substation in the power distribution network, which is greater than the effective value of the normal phase voltage, exceeds a threshold value; inputting the collected zero sequence voltage amplitude value of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the real-time zero sequence voltage mutation moment of the transformer substation into a pre-trained fault recognition model, and judging whether the power distribution network has a single-phase earth fault; the fault identification model is obtained by training by taking a zero-sequence voltage amplitude value of each historical moment of the transformer substation and an effective value of zero-sequence current of the initial end of each feeder line in a cycle after a zero-sequence voltage mutation moment as input data and taking an operation condition corresponding to whether a single-phase earth fault occurs as output data, and by integrating multi-fault characterization, abnormal states such as unbalanced operation of a power grid and the like are effectively distinguished, and the accurate identification rate of the single-phase earth fault is improved.
Description
Technical Field
The invention relates to the field of power systems and automation thereof, in particular to a method and a device for intelligently identifying single-phase earth faults of a power distribution network.
Background
The power distribution network mostly adopts a low-current grounding mode, and a single-phase grounding fault is one of the faults with the highest occurrence frequency. Because the neutral point is not directly grounded, the impedance value of a fault path formed by a single-phase ground fault is large, the characterization of the caused fault is not obvious, and simultaneously, due to the problems of noise interference, the influence of the operating condition (such as asymmetry) of the power distribution network and the like, the existing single-phase ground fault identification method of the small-current grounding system has defects in the aspects of reliability and universality although being widely researched for a long time.
The single-phase earth fault recognition in the prior art is mainly carried out according to zero sequence voltage. If the zero sequence voltage exceeds a certain specific threshold value, the single-phase earth fault in the power distribution network is judged, and in the high-resistance earth fault, the zero sequence voltage can change but is not obvious; in addition, the power distribution network is close to users, the phenomenon of unbalanced load sometimes occurs, and zero sequence voltage can be caused. In summary, the setting of the zero sequence voltage threshold is very important, and it is required that various single-phase earth faults can be accurately identified and cannot be confused with the three-phase unbalanced operation condition. In practical application, the threshold is difficult to set, and a uniform calculation method is difficult to find according to different regional power grid configurations and obvious differences, so that the accuracy of the existing single-phase earth fault identification technology is low.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an intelligent identification method of a single-phase earth fault, which comprises the following steps:
when the value of the real-time zero-sequence voltage of the transformer substation in the power distribution network, which is greater than the effective value of the normal phase voltage, exceeds a threshold value;
inputting the collected zero sequence voltage amplitude value of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the real-time zero sequence voltage mutation moment of the transformer substation into a pre-trained fault recognition model, and judging whether the power distribution network has a single-phase earth fault;
the fault identification model is obtained by training by taking the zero sequence voltage amplitude of each historical moment of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the zero sequence voltage mutation moment as input data and taking the running condition corresponding to whether the single-phase earth fault occurs as output data.
Preferably, the training of the fault recognition model includes:
determining a fault identification model structure based on the number of feeder lines of a transformer substation in a single-phase earth fault identification area of a power distribution network;
constructing a sample set by using the zero sequence voltage amplitude of each historical moment of a transformer substation in a single-phase earth fault identification area of a power distribution network, the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the zero sequence voltage mutation moment and the running condition of whether a single-phase earth fault occurs at the same moment, wherein the historical moments comprise: historical fault time, historical normal operation time and historical unbalanced operation time;
dividing the sample set into a training set and a testing set according to a preset proportion;
training the fault recognition model structure by adopting a machine learning method based on a training set;
and verifying by using the trained fault recognition model structure based on the test set to determine a fault recognition model.
Further, the preset proportion is as follows:
training data accounts for 70% to 98%, and test data accounts for 30% to 2%.
Further, the proportion of the training data is 90%, and the proportion of the testing data is 10%.
Further, the machine learning method includes: BP neural network learning algorithm.
Further, the fault identification model structure comprises the following steps of determining the number of input layer nodes and the number of hidden layer nodes:
taking the number of feeder lines plus one dimension corresponding to the zero sequence voltage amplitude as the dimension number of input data of the BP neural network, and setting the number of nodes of an input layer of the BP neural network according to the dimension number of the input data;
and calculating the hidden layer node number of the BP neural network according to the input layer node number.
Further, the calculation formula for calculating the number of nodes of the input layer of the BP neural network by using the number of feeder lines is as follows:
n＝k+1
wherein n is the number of nodes of the input layer, and k is the number of feeder lines.
Further, the calculation formula for calculating the number of hidden nodes of the BP neural network by using the number of input layer nodes is as follows:
wherein, I is the number of hidden nodes, n is the number of input nodes, and a is a constant.
Further, inputting the collected zero sequence voltage amplitude of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the real-time zero sequence voltage mutation moment of the transformer substation into a pre-trained fault recognition model, wherein the method comprises the following steps:
and respectively inputting the collected zero sequence voltage amplitude of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the real-time zero sequence voltage mutation moment of the transformer substation into corresponding nodes of an input layer of a pre-trained BP neural network.
Based on the same invention concept, the invention also provides a device for identifying the single-phase earth fault of the power distribution network, which comprises the following components:
the identification module is used for calling the judgment module when the numerical value of the real-time zero-sequence voltage of the transformer substation in the power distribution network, which is greater than the effective value of the normal phase voltage, exceeds a threshold value;
the judging module is used for inputting the collected zero sequence voltage amplitude of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the real-time zero sequence voltage mutation moment of the transformer substation into a pre-trained fault recognition model and judging whether the power distribution network has a single-phase ground fault;
the fault identification model is obtained by training by taking the zero sequence voltage amplitude of each historical moment of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the zero sequence voltage mutation moment as input data and taking the running condition corresponding to whether the single-phase earth fault occurs as output data.
Compared with the closest prior art, the invention has the following beneficial effects:
the invention provides a method and a device for identifying single-phase earth faults of a power distribution network, which comprise the following steps: when the value of the real-time zero-sequence voltage of the transformer substation in the power distribution network, which is greater than the effective value of the normal phase voltage, exceeds a threshold value; inputting the collected zero sequence voltage amplitude value of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the real-time zero sequence voltage mutation moment of the transformer substation into a pre-trained fault recognition model, and judging whether the power distribution network has a single-phase earth fault; the fault identification model is obtained by taking a zero-sequence voltage amplitude value of each historical moment of the transformer substation and an effective value of zero-sequence current at the starting end of each feeder line in a cycle after a zero-sequence voltage mutation moment as input data and taking an operation condition corresponding to whether a single-phase earth fault occurs as output data for training.
Drawings
FIG. 1 is a flow chart of an intelligent identification method for single-phase earth faults of a power distribution network, which is provided by the invention;
FIG. 2 is a schematic diagram of the connection mode of switches and feeders of a substation in a power distribution network system according to the present invention;
FIG. 3 is an input-output relationship diagram of a single-phase earth fault identification model of the power distribution network provided by the invention;
FIG. 4 is a diagram of a BP neural network architecture according to the present invention;
FIG. 5 is a flowchart of the overall method for intelligently identifying a single-phase earth fault of a power distribution network according to the present invention;
fig. 6 is a flow chart of the intelligent identification device for the single-phase earth fault of the power distribution network provided by the invention.
Detailed Description
The following describes embodiments of the present invention in further detail with reference to the accompanying drawings.
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Example 1:
the intelligent identification method for the single-phase earth fault of the power distribution network, provided by the invention, is shown in figure 1 and comprises the following steps:
step 1: when the value of the real-time zero-sequence voltage of the transformer substation in the power distribution network, which is greater than the effective value of the normal phase voltage, exceeds a threshold value;
step 2: and inputting the collected zero sequence voltage amplitude value of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the real-time zero sequence voltage mutation moment of the transformer substation into a pre-trained fault recognition model, and judging whether the power distribution network has a single-phase earth fault.
The connection mode of each switch and feeder of the transformer substation in the power distribution network system for single-phase ground fault identification in the method is shown in fig. 2.
Before the intelligent identification method for the single-phase earth fault of the power distribution network is executed, a trained fault identification model needs to be established, and the method comprises the following steps:
determining model input and output:
the input comprises the amplitude of zero-sequence voltage acquired at a transformer substation and the effective value of zero-sequence current at the initial end of each feeder line within a cycle (20ms) after the zero-sequence voltage mutation moment, and whether the target area has single-phase earth fault or not is output according to the effective value.
In this embodiment, the input-output relationship of the power distribution network single-phase ground fault identification model is shown in fig. 3.
Sorting historical data:
and (3) sorting historical fault records, normal operation records and unbalanced operation records of the area, and sequentially obtaining zero sequence voltage amplitude values acquired at the transformer substation corresponding to each event record, effective values of zero sequence currents at the initial ends of all the feeder lines within one cycle (20ms) after the zero sequence voltage mutation moment, and corresponding fault conditions, namely whether single-phase earth faults occur or not.
And taking the actual historical data record of the region as data for establishing a model by a machine learning method.
Through also being used for training the fault recognition model with unbalanced operation record, can effectively distinguish abnormal state such as the unbalanced operation of electric wire netting when discerning single-phase earth fault, promote single-phase earth fault's accurate recognition rate greatly.
Training and building a model:
and (3) determining a model structure: the number n of the nodes of the input layer of the established BP neural network (namely the number of the feeder lines plus 1), the number of the nodes of the output layer is 1, and the number of the nodes of the hidden layer is set to be n
Wherein a is a constant of 1 to 10The specific value is obtained through multiple training and test experiments; in the embodiment of the method, the node number of the output layer is 1, so
)。
In this embodiment, the BP neural network structure is shown in fig. 4.
Training and building a model: and dividing the sorted historical data into two parts, wherein 90% of the historical data are used as training data, and 10% of the historical data are used as test data. Each training sample in the training data comprises a zero sequence voltage amplitude value acquired at a transformer substation corresponding to each event record and an effective value of zero sequence current at the initial end of each feeder line in a cycle after the zero sequence voltage mutation moment, and the data are used as input data required by model training. The event records the corresponding fault condition, namely whether the single-phase earth fault occurs, and the fault condition is used as output data required by network training. Based on the BP neural network theory, model training and testing are continuously carried out, building of the BP neural network model is finally completed, and a correlation model of electric quantity characteristics and fault properties, namely a power distribution network single-phase earth fault recognition model, is established.
And (3) executing the intelligent identification method for the single-phase earth fault of the power distribution network according to the steps 1 and 2 after the trained fault identification model is based.
The step 1 specifically comprises the following steps:
1-1: monitoring zero sequence voltage information at a transformer substation in real time, entering step 1-2 when the numerical value of the zero sequence voltage effective value exceeds a threshold value, and otherwise, continuing to monitor data in real time; in this embodiment, the threshold may be set to 110% of the effective value of the normal phase voltage.
1-2: and collecting the abnormal operation data of the power grid.
Wherein, the abnormal data of power grid operation includes:
and the effective value of zero sequence current at the initial end of each feeder line in a cycle after the zero sequence voltage mutation moment is acquired by the transformer substation.
The step 2 specifically comprises the following steps:
2-1: inputting the collected zero sequence voltage amplitude value of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the real-time zero sequence voltage mutation moment of the transformer substation into a pre-trained fault recognition model, and judging whether the power distribution network has a single-phase earth fault; in this embodiment, one cycle after the zero sequence voltage mutation time is 20 ms.
2-2: and (6) ending.
In this embodiment, the overall flow of the intelligent identification method for the single-phase ground fault of the power distribution network is shown in fig. 5.
Example 2:
based on the same inventive concept, the invention also provides a device for identifying the single-phase earth fault of the power distribution network, as shown in fig. 6, comprising:
the identification module is used for calling the judgment module when the numerical value of the real-time zero-sequence voltage of the transformer substation in the power distribution network, which is greater than the effective value of the normal phase voltage, exceeds a threshold value;
and the judging module is used for inputting the collected zero sequence voltage amplitude of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the real-time zero sequence voltage mutation moment of the transformer substation into a pre-trained fault recognition model and judging whether the power distribution network has a single-phase earth fault.
In an embodiment of the present invention, the training of the fault recognition model includes:
determining a fault identification model structure based on the number of feeder lines of a transformer substation in a single-phase earth fault identification area of a power distribution network;
constructing a sample set by using the zero sequence voltage amplitude of each historical moment of a transformer substation in a single-phase earth fault identification area of a power distribution network, the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the zero sequence voltage mutation moment and the running condition of whether a single-phase earth fault occurs at the same moment, wherein the historical moments comprise: historical fault time, historical normal operation time and historical unbalanced operation time;
dividing the sample set into a training set and a testing set according to a preset proportion;
training the fault recognition model structure by adopting a machine learning method based on a training set;
and verifying by using the trained fault recognition model structure based on the test set to determine a fault recognition model.
In an optimal embodiment provided by the present invention, the preset ratio is:
training data accounts for 70% to 98%, and test data accounts for 30% to 2%.
Wherein the training data accounts for 90%, and the test data accounts for 10%.
Wherein the machine learning method comprises: BP neural network learning algorithm.
The fault identification model structure comprises the following steps of determining the number of input layer nodes and the number of hidden layer nodes according to the following modes:
taking the number of feeder lines plus one dimension corresponding to the zero sequence voltage amplitude as the dimension number of input data of the BP neural network, and setting the number of nodes of an input layer of the BP neural network according to the dimension number of the input data;
and calculating the hidden layer node number of the BP neural network according to the input layer node number.
The calculation formula for calculating the number of input layer nodes of the BP neural network by using the number of feeder lines is as follows:
n＝k+1
wherein n is the number of nodes of the input layer, and k is the number of feeder lines.
In the preferred embodiment of the present invention, the calculation formula for calculating the number of hidden nodes of the BP neural network by using the number of nodes of the input layer is as follows:
wherein, I is the number of hidden nodes, n is the number of input nodes, and a is a constant.
In the preferred embodiment provided by the present invention, the inputting the collected zero sequence voltage amplitude of the transformer substation and the effective value of the zero sequence current at the beginning of each feeder line in a cycle after the real-time zero sequence voltage mutation moment of the transformer substation into a pre-trained fault identification model comprises:
and respectively inputting the collected zero sequence voltage amplitude of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the real-time zero sequence voltage mutation moment of the transformer substation into corresponding nodes of an input layer of a pre-trained BP neural network.
As will be appreciated by one skilled in the art, 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 application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A single-phase earth fault identification method for a power distribution network is characterized by comprising the following steps:
when the value of the real-time zero-sequence voltage of the transformer substation in the power distribution network, which is greater than the effective value of the normal phase voltage, exceeds a threshold value;
inputting the collected zero sequence voltage amplitude value of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the real-time zero sequence voltage mutation moment of the transformer substation into a pre-trained fault recognition model, and judging whether the power distribution network has a single-phase earth fault;
the fault identification model is obtained by training by taking the zero sequence voltage amplitude of each historical moment of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the zero sequence voltage mutation moment as input data and taking the running condition corresponding to whether the single-phase earth fault occurs as output data.
2. The method of claim 1, wherein the training of the fault recognition model comprises:
determining a fault identification model structure based on the number of feeder lines of a transformer substation in a single-phase earth fault identification area of a power distribution network;
constructing a sample set by using the zero sequence voltage amplitude of each historical moment of a transformer substation in a single-phase earth fault identification area of a power distribution network, the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the zero sequence voltage mutation moment and the running condition of whether a single-phase earth fault occurs at the same moment, wherein the historical moments comprise: historical fault time, historical normal operation time and historical unbalanced operation time;
dividing the sample set into a training set and a testing set according to a preset proportion;
training the fault recognition model structure by adopting a machine learning method based on a training set;
and verifying by using the trained fault recognition model structure based on the test set to determine a fault recognition model.
3. The method of claim 2, wherein the predetermined ratio is:
training data accounts for 70% to 98%, and test data accounts for 30% to 2%.
4. The method of claim 3, wherein the training data percentage is 90% and the test data percentage is 10%.
5. The method of claim 2, wherein the machine learning method comprises: BP neural network learning algorithm.
6. The method of claim 5, wherein the fault identification model structure comprises determining a number of input level nodes and a number of hidden level nodes as follows:
taking the number of feeder lines plus one dimension corresponding to the zero sequence voltage amplitude as the dimension number of input data of the BP neural network, and setting the number of nodes of an input layer of the BP neural network according to the dimension number of the input data;
and calculating the hidden layer node number of the BP neural network according to the input layer node number.
7. The method of claim 6, wherein the calculation of the number of nodes of the input layer of the BP neural network using the number of feeder lines is performed by:
n＝k+1
wherein n is the number of nodes of the input layer, and k is the number of feeder lines.
8. The method of claim 6, wherein the calculation of the number of hidden nodes of the BP neural network using the number of input layer nodes is performed by:
wherein, I is the number of hidden nodes, n is the number of input nodes, and a is a constant.
9. The method of claim 6, wherein inputting the collected transformer substation zero sequence voltage amplitude and the effective value of the zero sequence current at the beginning of each feeder line in a cycle after the real-time zero sequence voltage mutation moment of the transformer substation into a pre-trained fault recognition model comprises:
and respectively inputting the collected zero sequence voltage amplitude of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the real-time zero sequence voltage mutation moment of the transformer substation into corresponding nodes of an input layer of a pre-trained BP neural network.
10. The utility model provides a distribution network single-phase earth fault recognition device which characterized in that includes:
the identification module is used for calling the judgment module when the numerical value of the real-time zero-sequence voltage of the transformer substation in the power distribution network, which is greater than the effective value of the normal phase voltage, exceeds a threshold value;
the judging module is used for inputting the collected zero sequence voltage amplitude of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the real-time zero sequence voltage mutation moment of the transformer substation into a pre-trained fault recognition model and judging whether the power distribution network has a single-phase ground fault;
the fault identification model is obtained by training by taking the zero sequence voltage amplitude of each historical moment of the transformer substation and the effective value of the zero sequence current of the initial end of each feeder line in a cycle after the zero sequence voltage mutation moment as input data and taking the running condition corresponding to whether the single-phase earth fault occurs as output data.
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