CN112710923B - Data-driven single-phase earth fault line selection method based on post-fault steady-state information - Google Patents
Data-driven single-phase earth fault line selection method based on post-fault steady-state information Download PDFInfo
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Abstract
The invention provides a data-driven single-phase earth fault line selection method based on post-fault steady-state information. The method comprises two parts of off-line training and on-line application, wherein target distribution network topology information and network parameters are obtained firstly, and a corresponding model is established in simulation software; secondly, combining the post-fault steady-state current effective value and the state of a contact switch as characteristics, using the fault line number as a label, obtaining sufficient sample data sets through simulation, training a line selection model by using a machine learning method, and testing to obtain a network error-prone line selection area; and finally, inputting the sample characteristics acquired by the actual power distribution network into the model trained offline, outputting a line selection result and confidence, comparing the line selection result with the line selection area prone to error, and giving a prompt if the line selection result belongs to the line selection area prone to error. The method can accurately select the line aiming at the single-phase grounding fault of the distribution network with the ungrounded neutral point, and can still achieve good identification effect under the condition that the fault is grounded through high resistance.
Description
Technical Field
The invention belongs to the field of power systems, and particularly relates to a data-driven single-phase earth fault line selection method based on post-fault steady-state information, which is suitable for a neutral ungrounded distribution network.
Background
In order to ensure the reliability of power supply, when the capacitance current of the whole system does not exceed a certain value, a neutral point ungrounded mode is mostly adopted. When a single-phase earth fault occurs in a power distribution network with a non-grounded neutral point, the fault current is small, the current protection cannot act, time is provided for fault elimination, but the difficulty of fault line selection is also caused, and particularly when the fault point is in contact with non-ideal media such as branches, sandy soil, asphalt, cement and the like, a high-resistance earth fault is formed, and the difficulty of single-phase earth fault line selection is further increased.
The problem of single-phase earth fault line selection of a neutral point ungrounded system is a long-standing challenge problem, and numerous expert and scholars propose various line selection methods which can be generally divided into three types: the method comprises a fault characteristic-based line selection method, a fault characteristic-free line selection method and a comprehensive line selection method. The first method uses the steady state characteristic and the transient state characteristic after the fault as main research objects, realizes the line selection by detecting the characteristic difference of different feeder lines, and comprises a fundamental wave group ratio amplitude comparison method using the zero sequence current fundamental wave amplitude and polarity comparison of each feeder line as the line selection basis, a quintuple harmonic method based on zero sequence current odd harmonics, an active component method extracting active components from the zero sequence current of each line as the line selection basis, a fundamental wave transient state expansion method based on the amplitude and polarity of the transient state zero mode characteristic current, a negative sequence current method based on the detection of fault negative sequence current and other methods. The method extracts different characteristics based on the current after the fault, formulates a fault criterion, generally shows good line selection effect on the basis of a small transition resistor and a high-precision current transformer, and has advantages. The second method adopts active means, and mainly comprises the following two methods: one is to inject high-frequency signals from the secondary side by utilizing the PT on the bus side, and then special signal monitoring is carried out at the outlet of each line to determine a fault line; and the other is that a neutral point after a fault is connected with a medium resistance resistor, so that the system is temporarily converted into a high-current grounding system, and then line selection is realized. The two methods solve the problem of unobvious fault characteristics from the principle, are suitable for the power distribution network with the neutral point grounded through the arc suppression coil, but the former has higher cost and is still influenced by the transition resistance, and the latter can generate larger impact on the original power distribution network and possibly cause further expansion of faults. The third method introduces other theories to solve the problem of line selection on the basis of synthesizing various fault characteristics. The learners use the fuzzy theory, integrate multiple criteria, give respective weight coefficients to different methods, and select lines according to the final scores. In addition, methods such as artificial neural network and deep learning are adopted to perform large-sample learning on the internal relation between the fault characteristics and the fault line so as to realize line selection. The third method has better performance than the first two methods because of the advantages of the method and the sample number, but the complexity of the process and the requirements on high-precision measurement equipment such as PMU (phasor measurement unit) greatly improve the application threshold.
Disclosure of Invention
The invention aims to provide a data-driven single-phase earth fault line selection method based on post-fault steady-state information aiming at the defects of the prior art, and the method is characterized by utilizing a steady-state current effective value and a network interconnection switch state after a fault occurs to perform single-phase earth fault line selection of a neutral point ungrounded power distribution network.
Therefore, the invention adopts the following technical scheme:
step 1: a target power distribution network with ungrounded neutral points is built in Simulink in MATLAB, line parameters are input, and the variation range of the system power supply operation mode, the variation range of each load, the on-off rule of an interconnection switch and the size range of a single-phase grounding transition resistor are obtained;
step 2: the method specifically comprises the following steps of setting a power distribution network operation mode:
(2-1) randomly setting equivalent impedance within the variation range of the system power supply operation mode;
(2-2) randomly setting the load size in each load variation range;
(2-3) randomly setting the state of each interconnection switch according to the on-off rule of the interconnection switches;
and 3, step 3: setting a single-phase earth fault, and carrying out simulation calculation to obtain a sample data set; the sample data set consists of sample characteristics and sample labels; the sample characteristics consist of the steady-state current effective value I' of each line after the fault and the on-off state BK of the interconnection switch together; the sample label is a fault line number, and the number of the sample label is 0, which indicates that no fault occurs; the method comprises the following steps:
(3-1) the single-phase earth fault can occur at any position of any three-phase line;
(3-2) randomly taking a value of the transition resistance in a set range;
and 4, step 4: repeating the step 2 and the step 3 until a sufficient number of sample data sets are obtained;
and 5: the method comprises the following steps of training a data-driven single-phase fault line selection model of the ungrounded neutral point power distribution network by using a sample data set, and comprises the following steps:
(5-1) dividing the sample set into a training set and a testing set, obtaining class weight by comparing the number of the classes of the samples in the training set, and substituting the class weight into a selected machine learning algorithm; according to the invention, the LightGBM algorithm is found to be most suitable through comparison of four machine learning algorithms of RF, SVM, XGboost and LightGBM. After a machine learning algorithm selects an optimal hyper-parameter combination through k-fold cross validation, a fault line selection model is obtained through training;
(5-2) testing and training by using the test set to obtain a fault route selection model, counting misjudgment conditions of the fault route selection model in the test set, determining a line region with high misjudgment frequency (a line with obvious more misjudgment times), classifying misjudgment samples, and selecting auxiliary solutions one by one; the invention discovers that:
the misjudgment samples can be divided into two types, namely specific misjudgment and random misjudgment; the first type of misjudgment accounts for the most, and is about 3/4 of all misjudgments, but the misjudgments are only generated with small probability when the transition resistance is more than 500 ohms, generally, the faults are misjudged as that the network has no faults, and the misjudgments can be effectively reduced by adjusting and enlarging the fault transition resistance range (increasing 300-500 ohms) in the simulation process and additionally arranging monitoring equipment (PMU, mu PMU) in the error-prone line region; the second type of misjudgment is 1/4 with a smaller occurrence probability of all misjudgment, and even if the sample is expanded, the second type of misjudgment is difficult to avoid, and the second type of misjudgment can be assisted by setting a confidence threshold value by utilizing the class confidence output by the model.
Step 6: and (4) applying a fault line selection model. Acquiring characteristic data from the actual power distribution network, inputting the model trained in the step 5 in an off-line mode after data preprocessing, and outputting a line selection result, wherein the line selection result comprises the following steps:
(6-1) acquiring the current effective value and the contact switch state of each line of the power distribution network through sampling of an SCADA system; if the obtained data is missing, filling the missing data by using a special value filling method (NaN);
(6-2) inputting the data subjected to missing filling into the fault line selection model established in the step 5 to obtain a class probability matrix Y output by the model, determining a line selection result j, and outputting a corresponding class confidence degree P j The output formula is as follows:
Y＝[P 0 P 1 … P i … P n ]
wherein n is the number of the lines of the power distribution network; p i Is the probability of the fault occurring in the i line; first column P 0 Expressed as a probability of no failure, so Y has n +1 classes in common; the sum of all the class probability values is 1; j is the result of line selection, i.e. line number, P j The category confidence corresponding to the line selection result can be output together as reference information;
(6-3) whether a fault occurs or not can be known according to the line selection result (whether j is equal to 0), and if the fault does not occur (j is equal to 0), the next sampling is continued; if judging that a fault occurs (j is not equal to 0), judging the output class confidence level, if the class confidence level is greater than a threshold value, judging that the result is reliable, and directly outputting the line selection result and the corresponding confidence level; otherwise, comparing the output line selection result j with the error-prone line region determined offline, and if the line selection result j is not in the error-prone region, outputting the line selection result and the corresponding confidence coefficient; if the line selection result j belongs to the error-prone line, the possible deviation range is given while the line selection result and the corresponding confidence coefficient are output, and the fault position is assisted to be identified.
The category confidence threshold of step 6 may be selected to be 0.98.
According to the method, a large number of fault samples are generated off line, the three-phase steady-state current effective value of a line after a fault and the state of a contact switch are selected as training characteristics, a fault line selection model of a neutral point ungrounded power distribution network is established by adopting a data driving method, and the model is applied on line; by the aid of two methods of category confidence auxiliary judgment and error-prone line marking, model misjudgment is effectively reduced, and accuracy of single-phase earth fault line selection of the ungrounded neutral point power distribution network is improved.
Drawings
FIG. 1 is a flow chart of the line selection result processing of the present invention.
Fig. 2 is a simulation model diagram of a neutral point ungrounded distribution network in the invention.
Detailed Description
Reference is made to the accompanying drawings. The invention discloses a data-driven single-phase earth fault line selection method based on post-fault steady-state information, which comprises the following steps of:
step 1: and (3) constructing a target distribution network with ungrounded neutral points in the MATLAB/Simulink, inputting line parameters, and acquiring the variation range of the system power supply operation mode, the variation range of each load, the on-off rule of the contact switch and the size range of the single-phase grounding transition resistance.
And 2, step: the method comprises the following steps of setting a power distribution network operation mode specifically:
(1) randomly setting equivalent impedance within the variation range of the system power supply operation mode;
(2) randomly setting the load size in each load variation range;
(3) and randomly setting the state of each interconnection switch according to the on-off rule of the interconnection switch.
And step 3: and setting single-phase earth faults, and carrying out simulation calculation to obtain a sample data set. The sample set consists of sample features and sample labels. The sample characteristics of the invention are composed of the steady-state current effective value I' of each line after the fault and the on-off state BK of the interconnection switch; the sample label is the number of the faulty line, and the number 0 indicates that no fault occurs. The method comprises the following steps:
(1) the single-phase earth fault can occur at any position of any three-phase line;
(2) randomly taking a value of the transition resistance in a set range;
and 4, step 4: and (5) repeating the step 2 and the step 3 until a sufficient number of sample sets are obtained.
And 5: and (3) carrying out data-driven single-phase fault line selection model training on the ungrounded neutral point power distribution network by using the sample set, and providing some measures for improving the line selection accuracy of the model. The method comprises the following steps:
(1) and dividing the sample set into a training set and a testing set, obtaining the class weight by comparing the number of the sample classes in the training set, and bringing the class weight into the selected machine learning algorithm. According to the invention, the LightGBM algorithm is found to be most suitable through comparison of four machine learning algorithms of RF, SVM, XGboost and LightGBM. After the optimal hyper-parameter combination is selected through k-fold cross validation, the algorithm is trained to obtain a fault line selection model;
(2) and (3) utilizing a fault line selection model obtained by test training of the test set, counting misjudgment conditions of the model in the test set, determining a line region with high misjudgment frequency (a line with obvious more misjudgment times), classifying misjudgment samples, and selecting auxiliary solutions one by one. The invention discovers that:
the misjudgment samples can be divided into two categories, namely specific misjudgment and random misjudgment. The first type of misjudgment accounts for the most, and is about 3/4 of all misjudgments, but the misjudgments are only generated with small probability when the transition resistance is more than 500 ohms, generally, the faults are misjudged as that the network has no faults, and the misjudgments can be effectively reduced by adjusting and enlarging the fault transition resistance range (increasing 300-500 ohms) in the simulation process and additionally arranging monitoring equipment (PMU, mu PMU) in the error-prone line region; the percentage of the second type of misjudgment is about 1/4 of all misjudgments, the occurrence probability is small, even if the sample is enlarged, the sample is still difficult to avoid, and the type confidence coefficient output by the model can be utilized to assist the judgment by setting a confidence threshold.
And 6: and (5) applying a line selection model. Acquiring characteristic data from an actual power distribution network, inputting the characteristic data into the model trained offline in the step 5 after data preprocessing, and outputting a line selection result, wherein the method specifically comprises the following steps:
(1) and acquiring the current effective value and the interconnection switch state of each line of the power distribution network through sampling of the SCADA system. If the obtained data has missing, filling the missing data by using a special value filling method (NaN);
(2) inputting the data subjected to missing filling into the line selection model established in the step 5, obtaining a class probability matrix Y output by the model, determining a line selection result j, and outputting a corresponding class confidence coefficient P j The output formula is as follows:
Y＝[P 0 P 1 … P i … P n ]
wherein n is the number of lines of the power distribution network; p i Is the probability of the fault occurring in the i line; first column P 0 Expressed as a probability of no failure, so Y has a total of n +1 classes; the sum of all the class probability values is 1; j is the result of line selection, i.e. line number, P j The category confidence corresponding to the line selection result can be output together as reference information;
(3) whether a fault occurs or not can be known from the line selection result (whether j is equal to 0 or not), and if the fault does not occur (j is equal to 0) is judged, next sampling is continued; if judging that a fault occurs (j is not equal to 0), judging the output category confidence level, and if the category confidence level is greater than 0.98 (P) j Not less than 0.98), the result is considered to be reliable, and the line selection result and the corresponding confidence coefficient are directly output; otherwise, comparing the output line selection result j with the error-prone line region determined offline, and if the line selection result j is not in the error-prone region, outputting the line selection result and the corresponding confidence coefficient; if the line selection result j belongs to the error-prone line, the possible deviation range is given while the line selection result and the corresponding confidence coefficient are output, and the fault position is assisted to be identified.
In order to verify the feasibility and the effectiveness of the single-phase earth fault line selection method for the data-driven neutral point ungrounded power distribution network, simulation verification is performed by taking the power distribution network shown in fig. 2 as an example, the model modifies the grounding mode, the line impedance and the line capacitance of the original IEEE123 node system to a certain extent, and adopts 10.5kV as the system side power supply voltage. When a sample set is obtained through simulation, the single-phase earth fault corresponding to each group of samples occurs in any three phasesAt any position of a line, the transition resistance of the line randomly takes a value within a set range, the network load randomly fluctuates between 80% and 120%, the system impedance is randomly selected between 3+4j and 7+8j omega, one of the four interconnection switches is randomly disconnected, white noise interference of 30dB is added into the current, the three-phase line current effective value and interconnection switch conditions (0 represents disconnection, 1 represents closure) after each fault occurs are finally obtained as sample characteristics x, and the line number with the fault occurs is used as a label value y (the label is 0 when no fault occurs). Finally establishing a test sample set omega 0 ～Ω 14 As shown in table 1, wherein Ω 01 And omega 02 Is omega 0 Mutually exclusive subsets of (a).
TABLE 1 sample set case
When a single-phase earth fault occurs to a line, the line current changes, the effective value of the steady-state current is continuously reduced along with the increase of the fault transition resistance, the influence of load fluctuation, system impedance and noise on the current is increased, and the line selection accuracy of the algorithm is inevitably reduced. By using a base based on omega 01 LightGBM model trained from sample set, omega for transition resistance of different sections in Table 1 1 ～Ω 14 The sample set is tested, and the obtained line selection misjudgment rate is shown in table 2. As can be seen from Table 2, the model can accurately select lines for samples with the transition resistance of less than 500 Ω, and can control the misjudgment rate of less than 5% for samples with the transition resistance of 500-900 Ω.
TABLE 2 relationship between transition resistance and model misjudgment rate
By using different random number seeds, the data set omega is processed 0 Dividing a training set and a test set for ten times, repeating model training and prediction for ten times, and counting the misjudgment of the line condition in each test setThe misjudgment type distribution is shown in table 3.
TABLE 3 number of misjudged sample classifications in each data set
As can be seen from Table 3, the influence caused by the first-type fault is eliminated by adopting the error-prone line monitoring method, so that the occurrence of 3/4 misjudgment can be reduced, the line selection effect is effectively improved, and the average line selection accuracy is improved from 89.4% to 97.6%.
Simulation results show that the method can accurately select the line aiming at the single-phase grounding fault of the ungrounded neutral point power distribution network, and can still achieve good identification effect under the condition that the fault is grounded through high resistance.
The above-mentioned embodiments, objects, technical solutions and advantages of the present invention are further described in detail, it should be understood that the above-mentioned embodiments are only examples of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (5)
1. The data-driven single-phase earth fault line selection method based on the post-fault steady-state information is characterized by comprising the following steps of:
step 1: a target power distribution network with ungrounded neutral points is built in Simulink in MATLAB, line parameters are input, and the variation range of the system power supply operation mode, the variation range of each load, the on-off rule of an interconnection switch and the size range of a single-phase grounding transition resistor are obtained;
and 2, step: the method comprises the following steps of setting a power distribution network operation mode specifically:
(2-1) randomly setting equivalent impedance within the variation range of the system power supply operation mode;
(2-2) randomly setting the load size in each load variation range;
(2-3) randomly setting the state of each interconnection switch according to the on-off rule of the interconnection switches;
and step 3: setting a single-phase earth fault, and carrying out simulation calculation to obtain a sample data set; the sample data set consists of sample characteristics and sample labels; the sample characteristics consist of the steady-state current effective value I' of each line after the fault and the on-off state BK of the interconnection switch together; the sample label is a serial number of a fault line, and the serial number is 0 to indicate that no fault occurs; the method comprises the following steps:
(3-1) a single-phase earth fault occurs at any position of any three-phase line;
(3-2) randomly taking a value of the transition resistance in a set range;
and 4, step 4: repeating the step 2 and the step 3 until a sufficient number of sample data sets are obtained;
and 5: the method comprises the following steps of training a data-driven single-phase fault line selection model of the ungrounded neutral point power distribution network by using a sample data set, and comprises the following steps:
(5-1) dividing the sample set into a training set and a testing set, obtaining class weights according to the proportion of the number of the classes of the samples in the training set, substituting the class weights into a selected machine learning algorithm, and training the machine learning algorithm to obtain a fault line selection model after selecting an optimal hyper-parameter combination through k-fold cross validation;
(5-2) testing and training by using the test set to obtain a fault route selection model, counting the misjudgment conditions of the fault route selection model in the test set, determining a line region prone to error, classifying misjudgment samples, and selecting auxiliary solutions one by one;
step 6: and (3) applying a fault line selection model, acquiring characteristic data from an actual power distribution network, inputting the model trained in the step (5) in an off-line mode after data preprocessing, and outputting a line selection result, wherein the line selection result comprises the following steps:
(6-1) acquiring the current effective value and the contact switch state of each line of the power distribution network through sampling of an SCADA system; if the obtained data has missing, filling the missing data by using a special value filling method (NaN);
(6-2) inputting the data subjected to missing filling into the fault line selection model established in the step 5 to obtain a class probability matrix Y output by the model, determining a line selection result j, and outputting a corresponding class confidence degree P j Of the output typeAs follows:
Y＝[P 0 P 1 …P i …P n ]
wherein n is the number of the lines of the power distribution network; p is i Is the probability of the fault occurring in the i line; first column P 0 Expressed as a probability of no failure, so Y has n +1 classes in common; the sum of all the class probability values is 1; j is the result of line selection, i.e. line number, P j Outputting the category confidence corresponding to the line selection result as reference information;
(6-3) judging whether a fault occurs according to the line selection result, and if the fault does not occur, continuing to sample for the next time; if the judgment is that the fault occurs, judging the output category confidence level, if the category confidence level is greater than a threshold value, judging that the result is reliable, and directly outputting the line selection result and the corresponding confidence level; if the category confidence is not greater than the threshold, comparing the output line selection result j with an off-line determined error-prone line region, and if the category confidence is not in the error-prone line region, outputting the line selection result and the corresponding confidence; if the line selection result j belongs to the error-prone line, the possible offset range is given while the line selection result and the corresponding confidence coefficient are output, and the fault position is assisted to be identified.
2. The data-driven single-phase ground fault line selection method based on post-fault steady-state information as claimed in claim 1, wherein: a large number of fault samples are generated offline, three-phase steady-state current effective values and interconnection switch states of a line after a fault are selected as training characteristics, a data driving method is adopted to establish a fault line selection model of a neutral point ungrounded power distribution network, and online application of the model is realized; the model misjudgment is effectively reduced through two methods of category confidence auxiliary judgment and error-prone line marking.
3. The data-driven single-phase ground fault line selection method based on post-fault steady-state information as claimed in claim 1, wherein: in step 5, the machine learning algorithm adopts a LightGBM algorithm.
4. The data-driven single-phase ground fault line selection method based on post-fault steady-state information as claimed in claim 1, wherein: in step 5, the misjudgment samples are divided into two types, namely specific misjudgment and random misjudgment; the first type of misjudgment has the largest proportion, namely 3/4 of all misjudgments, but the misjudgments are only generated with small probability when the transition resistance is more than 500 ohms, the fault is misjudged as that the network has no fault, the fault transition resistance range in the simulation process is enlarged by adjustment, and monitoring equipment is additionally arranged in a fault-prone circuit area to effectively reduce the misjudgments; the second type of misjudgment accounts for 1/4 which is about all misjudgments, the occurrence probability is small, even if the sample is expanded, the sample is still difficult to avoid, and the type confidence coefficient output by the model is used for assisting judgment.
5. The data-driven single-phase ground fault line selection method based on post-fault steady-state information as claimed in claim 1, wherein: the category confidence threshold for step 6 is 0.98.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104833900A (en) * | 2015-05-11 | 2015-08-12 | 国家电网公司 | Integrated line selection method for small-current single-phase grounding fault |
| CN108931709A (en) * | 2018-07-24 | 2018-12-04 | 华北电力大学 | A kind of distribution network fault location method of high serious forgiveness |
| CN108983040A (en) * | 2018-05-30 | 2018-12-11 | 广东电网有限责任公司 | A kind of electrical power distribution network fault location method based on Bayesian analysis |
| CN109490704A (en) * | 2018-10-16 | 2019-03-19 | 河海大学 | A kind of Fault Section Location of Distribution Network based on random forests algorithm |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7742444B2 (en) * | 2005-03-15 | 2010-06-22 | Qualcomm Incorporated | Multiple other sector information combining for power control in a wireless communication system |
-
2020
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104833900A (en) * | 2015-05-11 | 2015-08-12 | 国家电网公司 | Integrated line selection method for small-current single-phase grounding fault |
| CN108983040A (en) * | 2018-05-30 | 2018-12-11 | 广东电网有限责任公司 | A kind of electrical power distribution network fault location method based on Bayesian analysis |
| CN108931709A (en) * | 2018-07-24 | 2018-12-04 | 华北电力大学 | A kind of distribution network fault location method of high serious forgiveness |
| CN109490704A (en) * | 2018-10-16 | 2019-03-19 | 河海大学 | A kind of Fault Section Location of Distribution Network based on random forests algorithm |
Non-Patent Citations (1)
| Title |
|---|
| 张国栋等.基于深度学习的小电流接地系统故障选线方法.《发电技术》.2019,(第06期), * |
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