CN109884465B - Unidirectional ground fault positioning method based on signal injection method - Google Patents
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Abstract
The invention discloses a unidirectional ground fault positioning method based on a signal injection method, which comprises the following steps in sequence: 1) Installing n fault indicators on each power transmission line to segment the power transmission line, wherein the size of n is determined by the length of the line; 2) Determining a fault section of the power transmission line by using a double-frequency ratio positioning method; 3) And calculating the distance from the head end of the power transmission line to the fault point. The unidirectional grounding fault positioning method based on the signal injection method can realize the positioning of the frequency ratio fault of the double-frequency injection signal, determine the fault area, reliably position and efficiently, utilize the artificial fish swarm optimization BP neural network model to carry out offline correction on the fault distance, effectively compensate the initial ranging result, improve the ranging precision and achieve the purpose of accurately finding the fault position.
Description
Technical Field
The invention relates to the technical field of power grid fault diagnosis, in particular to a unidirectional ground fault positioning method based on a signal injection method.
Background
At present, with the expansion of the power grid scale, the power load is increased year by year, the structure of the power distribution network is more complicated, and the power safety of the power distribution network is more and more concerned. The low-and-medium-voltage distribution network mostly adopts a small grounding current system, and the proportion of single-phase grounding faults in the accidents of the low-and-medium-voltage distribution network is the largest. Faults are always unavoidable, so that the power system is ensured to run stably, the power safety of people is ensured, the quick processing and protection of the faults are critical, the fault position is determined, and the determination of the fault distance is particularly important for the reliable and safe running of the power system.
For the problem of determining the grounding point of the power distribution network, the method solves the problems in two steps in early years: a successive pulling method is adopted to find out a fault line, and then an operator is used for inspecting the line to visually determine a fault point. The pull method requires experimental brake pulling and brake feeding of one line to determine which line fails, and the method has no selectivity and can cause unnecessary power interruption of power load in a non-failure area; the distribution network architecture is more and more complicated along with the expansion of the power grid, the visual inspection line inspection positioning method consumes considerable manpower and material resources, the power failure time is longer after the fault occurs, the automation level is low, and the two methods can not adapt to the production requirement of the high automation of the current generation power grid, and are further contrary to the development trend of the distribution network construction at the present stage.
Whether domestic or foreign, single-phase earth fault positioning distance measuring devices are mainly aimed at long and simple high-voltage transmission lines, and for positioning devices of medium-low voltage distribution networks, line selecting devices are mature, the distance measuring devices are not widely applied, and most of the distance measuring devices are only subjected to theoretical research.
Disclosure of Invention
The invention aims to provide a unidirectional ground fault positioning method based on a signal injection method.
For this purpose, the technical scheme of the invention is as follows:
a unidirectional ground fault positioning method based on a signal injection method comprises the following steps in sequence:
1) Installing n fault indicators on each power transmission line to segment the power transmission line, wherein the size of n is determined by the length of the line;
2) Determining a fault section of the power transmission line by using a double-frequency ratio positioning method;
3) And calculating the distance from the head end of the power transmission line to the fault point.
Further, the step 2) includes the following steps:
2-1) applying a frequency f to the transmission line 1 Calculating the current at the head end of the line and the current at each fault indicator;
2-2) applying a frequency f to the transmission line 2 Calculating the current at the head end of the line and the current at each fault indicator;
2-3) determining the application frequency to be f 1 Head-end current and application frequency of signal source are f 2 If the ratio of the head-end currents of the signal sources is equal to the ratio of the frequencies of the applied signal sources, if the judgment result is yes, no unidirectional grounding fault exists on the power transmission line, if the judgment result is thatIf the result is NO, the next step is carried out;
2-4) sequentially judging the application frequency as f 1 Is f 2 If the ratio of the fault indicator currents of the signal source is equal to the ratio of the frequency of the applied signal source, if the judgment is no, the next group of comparison is continued; if the judgment result is yes, the unidirectional grounding fault point on the power transmission line is positioned between the fault indicator and the last fault indicator.
Further, after the unidirectional ground fault points are found in the step 2-4), the ground fault is eliminated, and then the step 2) is repeated until all the unidirectional ground fault points on the transmission line are found.
Further, the method for calculating the distance from the head end of the power transmission line to the fault point in the step 3) includes the following steps:
3-1) collecting data; obtaining the capacitive current of the grounding section to the ground according to the whole length of the transmission line, the position of the fault indicator and the collected zero sequence current of the non-grounding section, and collecting the grounding phase characteristic signal current of the non-grounding section, namely the inductive reactance shunt of the non-grounding section;
3-2) calculating measured impedance Z in the ground region m ；
Wherein:the voltage is the voltage at the bus outlet or the head end of the grounding line; />The current is at the bus outlet or at the head end of the grounding line; />Capacitive current to ground for the ground line; />Inductive reactance shunt for the non-grounded area of the grounding line; z m I is the measured impedance Z m Is a modulus of the model.
3-3) calculating the measured inductive reactance X in the ground region m ；
Wherein: z m I is the measured impedance Z m Is a modulus of (2); l is the fault line inductance; l is inductance of each kilometer of the line; d, d mf Is the theoretical fault distance;
3-4) calculating the theoretical fault distance d from the measured inductive reactance in the ground region mf And corrects the theoretical fault distance.
Further, when the theoretical fault distance is corrected in the step 3-4), an artificial fish school optimization BP neural network model is adopted for correction, and the actual fault distance is obtained after the output of the artificial fish school optimization BP neural network model is overlapped with the theoretical fault distance; the construction method of the artificial fish swarm optimization BP neural network model comprises the following steps:
(1) determining the topology structure of the BP neural network: the number of layers of the BP network is the number of hidden layers except the input layer and the output layer and the number of neurons of each layer;
(2) initializing a fish school: according to BP network structure, setting initial weight and threshold value between neurons randomly, and using the initial weight and threshold value as initial position of artificial fish, determining artificial fish number, forming initial fish swarm;
(3) setting parameters of AFSA: visual field, trial number Try-number in foraging behavior, step length, maximum iteration number MAXGEN and congestion factor delta;
(4) calculating the concentration of food in a water area: calculating a network error of the BP network under the conditions of an initial weight and a threshold value, and taking the reciprocal of the network error as the food concentration of the artificial fish in the fish swarm;
(5) performing artificial fish behavior criteria: searching food through foraging, clustering and rear-end collision of the artificial fish, and timely updating the position of the artificial fish with the highest food concentration;
(6) determining optimal weights and thresholds: the artificial fish position with the highest food concentration finally explored by the fish shoal is used as a new initial weight and a threshold value to be assigned to the BP network;
(7) BP training and prediction: inputting training samples, calculating forward network errors according to the BP network training step, reversely correcting weights and threshold values again according to the errors, continuously calculating the forward network errors, continuously reciprocating until the errors meet the precision requirement, and ending training to obtain an artificial fish swarm optimization BP neural network model; and (5) after training is completed, performing simulation prediction by using a test sample.
Further, the artificial fish swarm optimization BP neural network model is input into the injection signal frequency, the line distribution capacitance, the ground loop inductance, the ground resistance and the theoretical fault distance.
Compared with the prior art, the unidirectional grounding fault positioning method based on the signal injection method can realize the frequency ratio fault positioning of the double-frequency injection signal, determine the fault area, reliably position and efficiently, utilize the artificial fish swarm optimization BP neural network model to carry out offline correction on the fault distance, effectively compensate the initial ranging result, improve the ranging precision and achieve the aim of accurately finding the fault position.
Drawings
Fig. 1 is a schematic diagram of signal injection ranging.
Fig. 2 is a simplified model diagram of the tracking and positioning principle of the single-phase signal injection method.
FIG. 3 is a flowchart of an AFSA-BP neural network algorithm.
FIG. 4 is a schematic diagram of an AFSA-BP neural network model distance correction strategy.
FIG. 5 is a graph comparing the prediction error of the AFSA-BP neural network with the initial error.
FIG. 6 is a graph showing the comparison of errors before and after correction of the AFSA-BP neural network.
FIG. 7 is a graph showing the comparison of the results before and after correction of the AFSA-BP neural network.
Detailed Description
The invention will now be further described with reference to the accompanying drawings and specific examples, which are in no way limiting.
The unidirectional ground fault positioning method based on the signal injection method comprises the following steps in sequence:
1) Installing n fault indicators on each power transmission line to segment the power transmission line, wherein the size of n is determined by the length of the line;
2) Determining a fault section of the power transmission line by using a double-frequency ratio positioning method;
3) And calculating the distance from the head end of the power transmission line to the fault point.
Fig. 1 is a schematic diagram of signal injection ranging, in order to obtain more fault information and obtain more accurate measured impedance, 3-4 fault indicators (3 are taken as an example in the figure and the length of a line is determined in actual cases) are added on each power transmission line, so that the line is segmented. In the figure: i m The ground phase characteristic signal current is separated from the power frequency information; i C Capacitive current to ground for the faulty line; i L Inductive reactance shunt for the non-grounded area of the grounding line; i' m Grounding current for a grounding loop from a bus to a grounding point; d is the total length of the line.
Fig. 2 is a simplified model diagram of the tracking and positioning principle of the single-phase signal injection method.
According to FIG. 2, the measurement nodes on the transmission line are numbered from 0 to n, the line head end I 0 The current is the sum of the currents of all nodes on the line:
in the above formula:-current on the transmission line at node n; />-capacitance current to ground of each node; />-current at ground.
The capacitance of the transmission line to the ground is very small and is micro-farad, the capacitance resistance is quite large, and even for a cable line, the capacitance is larger, but the capacitance resistance is far larger than the resistance and the inductance. A significant portion of the current on the line is shunted through the capacitance to ground. If the impedance on the line is ignored, and the frequency f is applied 1 In the case of the signal source of (a), there are:
the effective value is:
the current after the ground point is:
effective value:
if the application frequency is f 2 The signal sources of (a) are as follows:
according to equations (5) and (7), the capacitive current after the ground point is proportional to the frequency of the injected signal; the expressions (3) and (6) show that the capacitive current in the fault region does not satisfy the proportional relationship due to the influence of the ground resistance. The capacitive current ratio after the double-frequency characteristic signals are injected into the non-fault region is the ratio of the frequencies of the non-fault region and the non-fault region is not satisfied, as shown in formulas (8) and (9).
Said step 2) comprises the steps of:
2-1) applying a frequency f to the transmission line 1 Calculating the current at the head end of the line and the current at each fault indicator;
2-2) applying a frequency f to the transmission line 2 Calculating the current at the head end of the line and the current at each fault indicator;
2-3) determining the application frequency to be f 1 Head-end current and application frequency of signal source are f 2 If the ratio of the head-end currents of the signal sources is equal to the ratio of the frequencies of the applied signal sources, if the judgment result is yes, no unidirectional grounding fault exists on the power transmission line, and if the judgment result is no, the next step is carried out;
2-4) sequentially judging the application frequency as f 1 Is f 2 If the ratio of the fault indicator currents of the signal source is equal to the ratio of the frequency of the applied signal source, if the judgment is no, the next group of comparison is continued; if the judgment result is yes, the unidirectional grounding fault point on the power transmission line is positioned between the fault indicator and the last fault indicator.
And 2-4) after the unidirectional grounding fault points are found, eliminating the grounding fault, and repeating the step 2) until all the unidirectional grounding fault points on the power transmission line are found.
The method for calculating the distance from the head end of the power transmission line to the fault point in the step 3) comprises the following steps:
3-1) collecting data; obtaining the capacitive current of the grounding section to the ground according to the whole length of the transmission line, the position of the fault indicator and the collected zero sequence current of the non-grounding section, and collecting the grounding phase characteristic signal current of the non-grounding section, namely the inductive reactance shunt of the non-grounding section;
3-2) calculating measured impedance Z in the ground region m ；
Wherein:the voltage is the voltage at the bus outlet or the head end of the grounding line; />The current is at the bus outlet or at the head end of the grounding line; />Capacitive current to ground for the ground line; />Inductive reactance shunt for the non-grounded area of the grounding line; z m I is the measured impedance Z m Is a modulus of the model.
3-3) calculating the measured inductive reactance X in the ground region m ；
Wherein: z m I is the measured impedance Z m Is a modulus of (2); l is the fault line inductance; l is inductance of each kilometer of the line; d, d mf Is the theoretical fault distance;
3-4) calculating the theoretical fault distance d from the measured inductive reactance in the ground region mf And corrects the theoretical fault distance.
When the theoretical fault distance is corrected in the step 3-4), an artificial fish school optimization BP neural network model is adopted for correction, and the actual fault distance is obtained after the output of the artificial fish school optimization BP neural network model is overlapped with the theoretical fault distance; as shown in fig. 3 to 4, the construction method of the artificial fish school optimization BP neural network model is as follows:
(1) determining the topology structure of the BP neural network: the number of layers of the BP network is the number of hidden layers except the input layer and the output layer and the number of neurons of each layer;
(2) initializing a fish school: according to BP network structure, setting initial weight and threshold value between neurons randomly, and using the initial weight and threshold value as initial position of artificial fish, determining artificial fish number, forming initial fish swarm;
(3) setting parameters of AFSA: visual field, trial number Try-number in foraging behavior, step length, maximum iteration number MAXGEN and congestion factor delta;
(4) calculating the concentration of food in a water area: calculating a network error of the BP network under the conditions of an initial weight and a threshold value, and taking the reciprocal of the network error as the food concentration of the artificial fish in the fish swarm;
(5) performing artificial fish behavior criteria: searching food through foraging, clustering and rear-end collision of the artificial fish, and timely updating the position of the artificial fish with the highest food concentration;
(6) determining optimal weights and thresholds: the artificial fish position with the highest food concentration finally explored by the fish shoal is used as a new initial weight and a threshold value to be assigned to the BP network;
(7) BP training and prediction: inputting training samples, calculating forward network errors according to the BP network training step, reversely correcting weights and threshold values again according to the errors, continuously calculating the forward network errors, continuously reciprocating until the errors meet the precision requirement, and ending training to obtain an artificial fish swarm optimization BP neural network model; and (5) after training is completed, performing simulation prediction by using a test sample.
The artificial fish swarm optimization BP neural network model is input into the injection signal frequency, the line distribution capacitance, the ground loop inductance, the ground resistance and the theoretical fault distance.
And establishing a three-layer BP neural network structure model of 1 input layer, 1 hidden layer and one output layer. The number of neurons in the input layer is i=5, and the hidden layer takes j=20. Layer 1 neurons were output. The weight between the output layer and the hidden layer neurons is wjm; the weight between the hidden layer and the input layer neurons is wij; training input data of a sample, namely injection signal frequency, line distribution capacitance, ground loop inductance, ground resistance and theoretical fault distance; the expected output of the samples, i.e. the initial range error; and outputting a test sample, namely, a prediction result of the ASFA-BP neural network.
The setting of the artificial fish algorithm parameters is that the fish scale fishnum=100, the Visual field range=1.5, the crowding factor delta=0.618, the maximum Step size step=0.1, the probing times Try-number=100 and the maximum iteration times MAXGEN=200. And setting the initial weight and the threshold of the BP network as the initial position of the artificial fish, wherein the process of searching the highest food concentration by the artificial fish is the process of optimizing the weight threshold of the BP network.
As can be seen from FIG. 5, the ASFA-BP neural network has excellent nonlinear approximation performance, and the prediction error is almost the same as the initial error. The comparison of errors before and after correction of the AFSA-BP neural network in FIG. 6 shows that the ranging error is greatly reduced and basically approaches 0 after the compensation of the AFSA-BP neural network. FIG. 7 shows the comparison of the measured fault distances before and after correction of ASFA-BP neural network, the corrected curve almost coincides with the actual fault distance, and the accuracy is quite high. And the calculation result shows that the final ranging relative error is within 1%, and the ranging accuracy is quite high even if the grounding resistance exceeds 1000 omega.
Claims (5)
1. The unidirectional ground fault positioning method based on the signal injection method is characterized by comprising the following steps in sequence:
1) Installing n fault indicators on each power transmission line to segment the power transmission line, wherein the size of n is determined by the length of the line;
2) Determining a fault section of the power transmission line by using a double-frequency ratio positioning method; comprises the following steps:
2-1) applying a frequency f to the transmission line 1 Calculating the current at the head end of the line and the current at each fault indicator;
2-2) inThe frequency of application on the transmission line is f 2 Calculating the current at the head end of the line and the current at each fault indicator;
2-3) determining the application frequency to be f 1 Head-end current and application frequency of signal source are f 2 If the ratio of the head-end currents of the signal sources is equal to the ratio of the frequencies of the applied signal sources, if the judgment result is yes, no unidirectional grounding fault exists on the power transmission line, and if the judgment result is no, the next step is carried out;
2-4) sequentially judging the application frequency as f 1 Is f 2 If the ratio of the fault indicator currents of the signal source is equal to the ratio of the frequency of the applied signal source, if the judgment is no, the next group of comparison is continued; if the judgment result is yes, the unidirectional grounding fault point on the power transmission line is positioned between the fault indicator and the last fault indicator;
3) And calculating the distance from the head end of the power transmission line to the fault point.
2. The method for locating unidirectional ground faults based on a signal injection method of claim 1, wherein after the unidirectional ground fault points are found in the step 2-4), the ground faults are eliminated, and then the step 2) is repeated until all the unidirectional ground fault points on the transmission line are found.
3. The unidirectional ground fault location method of claim 2, wherein the method for calculating the distance from the head end of the transmission line to the fault point in step 3) comprises the following steps:
3-1) collecting data; obtaining the capacitive current of the grounding section to the ground according to the whole length of the transmission line, the position of the fault indicator and the collected zero sequence current of the non-grounding section, and collecting the grounding phase characteristic signal current of the non-grounding section, namely the inductive reactance shunt of the non-grounding section;
3-2) calculating measured impedance Z in the ground region m ；
Wherein:the voltage is the voltage at the bus outlet or the head end of the grounding line; />The current is at the bus outlet or at the head end of the grounding line;capacitive current to ground for the ground line; />Inductive reactance shunt for the non-grounded area of the grounding line; z m I is the measured impedance Z m Is a modulus of (2);
3-3) calculating the measured inductive reactance X in the ground region m ；
Wherein: z m I is the measured impedance Z m Is a modulus of (2); l is the fault line inductance; l is inductance of each kilometer of the line; d, d mf Is the theoretical fault distance;
3-4) calculating the theoretical fault distance d from the measured inductive reactance in the ground region mf And corrects the theoretical fault distance.
4. The unidirectional ground fault positioning method based on a signal injection method as claimed in claim 3, wherein when the theoretical fault distance is corrected in the step 3-4), an artificial fish swarm optimization BP neural network model is adopted for correction, and the actual fault distance is obtained after the output of the artificial fish swarm optimization BP neural network model is overlapped with the theoretical fault distance; the construction method of the artificial fish swarm optimization BP neural network model comprises the following steps:
(1) determining the topology structure of the BP neural network: the number of layers of the BP network is the number of hidden layers except the input layer and the output layer and the number of neurons of each layer;
(2) initializing a fish school: according to BP network structure, setting initial weight and threshold value between neurons randomly, and using the initial weight and threshold value as initial position of artificial fish, determining artificial fish number, forming initial fish swarm;
(3) setting parameters of AFSA: visual field, trial number Try-number in foraging behavior, step length, maximum iteration number MAXGEN and congestion factor delta;
(4) calculating the concentration of food in a water area: calculating a network error of the BP network under the conditions of an initial weight and a threshold value, and taking the reciprocal of the network error as the food concentration of the artificial fish in the fish swarm;
(5) performing artificial fish behavior criteria: searching food through foraging, clustering and rear-end collision of the artificial fish, and timely updating the position of the artificial fish with the highest food concentration;
(6) determining optimal weights and thresholds: the artificial fish position with the highest food concentration finally explored by the fish shoal is used as a new initial weight and a threshold value to be assigned to the BP network;
(7) BP training and prediction: inputting training samples, calculating forward network errors according to the BP network training step, reversely correcting weights and threshold values again according to the errors, continuously calculating the forward network errors, continuously reciprocating until the errors meet the precision requirement, and ending training to obtain an artificial fish swarm optimization BP neural network model; and (5) after training is completed, performing simulation prediction by using a test sample.
5. The method for locating unidirectional ground faults based on a signal injection method of claim 4, wherein the input of the artificial fish swarm optimization BP neural network model is injection signal frequency, line distributed capacitance, ground loop inductance, ground resistance and theoretical fault distance.
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| CN106936136A (en) * | 2017-04-24 | 2017-07-07 | 李清远 | A kind of all-pass wave filtering harmonic elimination structure and control method for medium voltage network system |
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| CN108903936A (en) * | 2018-07-03 | 2018-11-30 | 西安科技大学 | The Intelligent mining helmet control method merged based on human body information and environmental information |
| CN108879646A (en) * | 2018-07-11 | 2018-11-23 | 云南电网有限责任公司电力科学研究院 | A method of singlephase earth fault is solved based on phased arc suppression coil |
| CN109061400A (en) * | 2018-10-30 | 2018-12-21 | 国网江苏省电力有限公司电力科学研究院 | A kind of method for locating single-phase ground fault and its device based on transient current frequency range feature |
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