CN104198893B - Adaptive failure electric current detecting method - Google Patents
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Abstract
A kind of adaptive failure electric current detecting method, it is characterised in that described fault current detection method is predicted to current signal using sef-adapting filter, using predicted value as reference frame, and sets threshold value.The predicted value of the current signal that the current signal value that current transformer is detected is exported with sef-adapting filter compares, if the deviation between the current signal value and predicted value of the reality that current transformer is detected exceedes threshold value set in advance, then it is assumed that produce failure.The present invention is applied to the current failure diagnosis of power system and the devices such as power equipment.
Description
Technical Field
The invention relates to a method for detecting a fault current of a power grid.
Background
With the ever increasing size of power grids, the grid fault currents are also increasing, and some places have already begun to exceed the maximum breaking limit of circuit breakers. The state detection of the fault current is a direct basis for the action of the circuit breaker, and the quick and effective fault current judgment can save precious time for the action of the circuit breaker.
The conventional fault current judgment is based on a certain threshold value, namely, when the current exceeds a certain preset value, a fault is considered to occur, and the circuit breaker is required to be opened. This method has the following problems: firstly, generally, in order to reduce misjudgment, a threshold value is set to be higher, and a certain time is required from fault detection to breaker action, so that a fault current peak value is generated when the breaker acts; secondly, the output of the sensor is easily interfered by the external environment, and the interference pulse may cause the current signal to exceed the set threshold value, so as to cause the misoperation of the circuit breaker.
In order to reduce the damage of fault current to the maximum extent, a rapid and reliable fault current detection method becomes an important content for the research and development of modern power grid and power equipment relay protection devices. By utilizing a digital technology and analyzing the detected current signal, the defect of direct hardware circuit threshold value detection can be effectively avoided. Aiming at the detection of sine wave fault current of an alternating current power grid, various methods are proposed at home and abroad: the method needs to detect that the current waveform is a stable sine wave, and needs to carry out more multiplication and division, so that the calculation amount is larger; the method is similar to the two-point product method, has a slightly high response speed, and also requires the detection current to be a stable sine wave; a third step of differential method, which differentiates the signal, Is easy to be interfered and has large calculation amount (ABB Is-technical handbook of flow restrictors, http:// www.abb.com.cn/product/db0003db 004279/c125739900636470c1256988 c0055343. aspx); fourthly, a half-cycle absolute value integral algorithm, wherein the method needs to acquire a signal of a half sine wave, so that the time delay is large; and fifthly, a fourier algorithm, which distinguishes signals by performing fourier transform on signals of half or one cycle, and has large delay and low rapidity (guo glong, relay protection of power system (second edition), advanced education press, 2011).
The above methods all require the measured signal to be a stable sine wave, and in practice, the current of the power grid always contains multiple harmonics, and the amplitude and the phase of the harmonics fluctuate, so the application range of the above methods is limited to a certain extent, and the reliability is limited.
In addition, some researches adopt a neural network to realize fault current detection, wherein wavelet transformation is combined with a neural network classifier, the characteristics of the fault current are extracted by using the wavelet transformation, and then the characteristics are input into the neural network to identify the fault (Zhaoyang, et al, a neural network fault current detection method for a hybrid circuit breaker, academic newspaper of Harbin Ringji university, 2011, 16 (1): 53-56). However, this kind of method needs a large number of fault samples to train the neural network, and actually, the forms of faults are very different, and it is difficult to train the neural network with samples of all fault classes. Furthermore, the neural network is computationally expensive and has low real-time performance, and the simulation results of the above documents require 3ms to identify a fault.
Therefore, how to make the most accurate fault judgment in the shortest time by using the least detected current signal samples becomes a problem to be mainly solved for power system relay protection.
Disclosure of Invention
The invention aims to solve the problem of rapid and reliable detection of the fault current of the power system and provides a reliable self-adaptive fault current detection method. The method is suitable for detecting the fault current of the power grid and the power equipment in the alternating current and direct current power system.
The self-adaptive fault current detection method is based on the following principle:
the invention is based on the microcomputer relay protection device commonly used in the current power system. The microcomputer relay protection device collects current and voltage data of a power grid on one hand, and is provided with a filter to resolve the collected data in real time on the other hand, and whether a fault occurs or not is judged. The invention adopts the adaptive filter to replace the filter in the existing microcomputer relay protection device of the power system, and predicts the current value at the next moment according to the historical operating data. The deviation ratio between the actual current value and the predicted value is smaller in the normal operation fluctuation range of the power system, and if the power system breaks down, the deviation between the current value actually detected by a current transformer in the microcomputer relay protection device and the predicted value is larger. By using this feature, normal current fluctuations can be distinguished from faults. The invention adopts the self-adaptive filter to predict the current signal, takes the predicted value as the reference and sets the threshold value. And comparing the current signal value detected by the current transformer with a predicted value output by the adaptive filter, and if the deviation between the actual current signal value detected by the current transformer and the predicted value exceeds a preset threshold value, determining that a fault occurs. In addition, the output of the current transformer may be interfered to cause a situation that the deviation between the detection value and the predicted value is large, and the deviation caused by interference in an actual system is far larger than the deviation of the system in normal operation and fault operation, so that the current transformer can be distinguished by setting different threshold values.
The self-adaptive fault current detection method comprises the following steps:
(1) the current transformer output signal value x acquired by the current transformer at the current time ttWith the output value of the adaptive filterThe comparison is carried out in such a way that,
wherein t is the data acquisition time, xtFor the current transformer output value collected at time t,the output value of the adaptive filter at the time t;l、hare respectively preset constants and satisfyl<h(ii) a The symbol | represents the absolute value. The above formula indicates three cases:
a. output value x of current transformer at time ttAnd adaptive filter output valueThe difference between them does not exceed the set valuelWhen the current is normal, the current of the power grid is considered to be normal;
b. output value x of current transformer at time ttAnd adaptive filter output valueThe difference between the two is not less than the set valuelBut is less than the set valuehWhen the power grid is considered to be in fault, the breaker needs to be immediately operated;
c. output value x of current transformer at time ttAnd adaptive filter output valueThe difference between them is greater than the set valuehThe output of the current transformer is considered to be subjected to external interference, and the signal is a false signal and can be ignored.
(2) If the situation is judged to belong to the situation b, a fault occurrence signal is immediately sent to a relay of the relay protection system by the microcomputer relay protection device; if the current transformer output data is judged to belong to the condition a or the condition c, the fault is not generated, and the output of the adaptive filter at the moment is calculated according to the collected current transformer output data:
wherein,
in the formula, symbolIndicating transpose, sign, of the solved matrix or vectorMeaning that the inverse of the matrix or vector is solved, respectively represent the values corresponding to the symbol variables at the time points of "t", "t-1" and "t + 1". λ is a positive number close to 1 but less than 1, and λ can be 0.99;the noise variance of the output signal of the current transformer can be obtained by the characteristic parameters of the current transformer; i is a d-dimensional unit square matrix;setting an initial value as a unit square matrix; wt＝[wtwt-1… wt-d-1]TThe vector is a d-dimensional weight value vector of the adaptive filter, the initial value of the vector can be selected as a zero vector, and d is the order of the filter;for an adaptive filter d-dimensional input vector, at a sampling time t<d is selected to be 0, and has:
(3) collecting current transformer data x at t +1 momentt+1X is to bet+1And (3) the output of the adaptive filter in the step (2)And (4) subtracting, and judging according to the formula in the step (1). And repeatedly iterating calculation and judgment until the fault is detected. And (3) at the moment, according to the processing mode in the step (2), the microcomputer relay protection device gives a fault occurrence signal to a relay of the relay protection system.
The microcomputer relay protection device is an important part of a power system and is well known to those skilled in the art. The microcomputer relay protection device comprises a data acquisition system, a CPU main system, a switching value output system and the like; the data acquisition system consists of a current transformer, a voltage transformer and an analog quantity conversion unit, wherein the primary sides of the current transformer and the voltage transformer are directly connected to a protected power line to measure the current value and the voltage value of the protected power line in real time, and the secondary sides of the current transformer and the voltage transformer are connected with the analog quantity conversion unit and are converted into corresponding digital quantity by the analog quantity conversion unit; the output bus of the data acquisition system is directly connected with the CPU main system, the digital quantity converted by the analog quantity conversion unit is sent to the CPU main system, and the CPU main system processes and analyzes the acquired current value, voltage value and the like and provides corresponding operation instructions; the switching value output system comprises an isolation circuit, a relay and the like, the output of the CPU main system is directly connected with the isolation circuit of the switching value output system, and an operation instruction given by the CPU main system is sent to the relay of the switching value output system after passing through the isolation circuit of the switching value output system; the relay of the switching value output system is directly connected with the actuating mechanism of the circuit breaker in the power line, and the actuating mechanism of the circuit breaker performs corresponding actions through the switch of the relay.
The relay protection system is an essential part for the operation of a power system and mainly comprises a microcomputer relay protection device and a circuit breaker. The microcomputer relay protection device is responsible for monitoring the state of the protected power line in real time, and once the abnormality of the protected power line is detected, a corresponding action instruction value is given to the circuit breaker; the circuit breaker is directly connected in series with a protected power line, the circuit breaker is in a conducting state when the protected power line normally runs, and when the protected power line is abnormal, the microcomputer relay protection device controls the circuit breaker to be disconnected.
The relay is a component which must be adopted for driving the power system switch equipment. Because the starting and stopping of the power equipment such as the circuit breaker and the like need high power, and the control system of the microcomputer relay protection device can only output weak current signals, the power of the weak current signals is not enough to drive the power equipment such as the circuit breaker and the like, a relay is needed to convert the weak current signal instruction with low power into a high-power signal capable of driving the power equipment such as the circuit breaker and the like.
The self-adaptive fault current detection method is characterized in that a self-adaptive filter is adopted to predict a current signal, a predicted value is used as a reference basis, and if the deviation between the current signal value actually detected by a current transformer and the predicted value exceeds a preset threshold value, a fault is considered to be generated. The adaptive filter of this method differs from a conventional adaptive filter in that its calculation only requires the use of historical data and does not rely on a reference signal. The conventional adaptive filter must have a reference signal in the calculation process, and the reference signal is difficult to obtain in a practical system. The method provides a new effective technical means for solving the problem of rapid and accurate detection of fault current, and is suitable for current fault diagnosis of devices such as power systems, power equipment and the like.
Drawings
FIG. 1 is a block diagram of an adaptive fault current detection method;
FIG. 2 is a current diagram at a primary grid short-circuit fault;
FIG. 3 is a graph of prediction of grid current by an adaptive filter;
FIG. 4 is a graph of the deviation between the adaptive filter prediction value and the actual detection value;
FIG. 5 is a graph of the sensed values for normal grid current but disturbed sensor output;
fig. 6 is a graph showing the deviation between the predicted value and the detected value when the sensor output is disturbed.
Detailed Description
The invention is further described with reference to the following figures and detailed description.
Fig. 1 is a block flow diagram of an adaptive fault current detection method. When the detection system starts to operate, various parameters in the method are initialized, wherein the parameters comprise a forgetting factor lambda of 0.99, and the noise variance is given according to parameters of a current transformer manualAssigning values, setting the length d of the filter to be 5, setting I to be a unit square matrix of d rows and d columns, and setting an intermediate variableSetting an initial weight vector W of an adaptive filter0Setting an initial input vector X of the adaptive filter for a zero vector of d rows and 1 column0Setting the initial output of the adaptive filter for the zero vector of the d rows and 1 columnSetting threshold values according to characteristics of power systemlAndhsuch asl＝100，h＝1000。
And after the parameter initial value is set, starting a judgment process.
Firstly, collecting the output value of the current transformer at the current time t, and assigning the output value to xt；
Then, x istWith the output of the filter at time t-1Comparing if the deviation between the two is less thanlIf the current is within the normal range, the power system operates normally, and the output x of the current transformer is outputtAssign to intermediate parameterIf there is a gap between the twoIs not less thanlThen, the judgment is continued to see if the deviation value exceeds another threshold valuehIf the deviation does not exceedhWhen the power system is considered to be in fault, a fault signal is output, and the output x of the current transformer is outputtAssign to intermediate parameterIf the deviation exceedshThe output of the current transformer is considered to be interfered, and the output of the adaptive filter at the time t-1 is considered to be interfered at the momentAssign to intermediate parameter
Further, the adaptive filter utilizes historical data and an intermediate parameter at time tThe predicted value for the current at time t +1 is calculated according to the following formula:
wherein,
wherein λ is 0.99;the noise variance of the output signal of the current transformer is d is 5, I is a 5 × 5-dimensional unit square matrix,is the 5-dimensional input vector of the adaptive filter.
Finally, the output value of the adaptive filter is returned for the next comparison. And (5) re-collecting the sensor output at the t +1 moment, and performing a new round of comparison and judgment, and repeating the steps.
Fig. 2 shows a current diagram during a primary grid short-circuit fault. It can be seen that the current value increases sharply when the grid fails, but the fundamental frequency characteristic of the power system is still 50Hz, and the increase in current is mainly due to the decrease in grid system impedance at the time of short circuit.
Fig. 3 is a diagram illustrating prediction of the grid current by the adaptive filter. In comparison with fig. 2, it can be seen that the predicted current value is normally substantially identical to the actual current value.
Fig. 4 is a graph showing the deviation between the prediction value and the actual detection value of the adaptive filter. It can be seen that normally the deviation is not more than 5A. When the grid fails, the predicted current may increase sharply at the first sampling point after the failure. The current sampling frequency is 100kHz, settingl100, immediately giving a fault early warning signal at 0.02ms after the fault; if the conventional threshold detection method is adopted, and 5kA is taken as a threshold, 0.46ms is needed.
Fig. 5 is a graph showing the detected values when the grid current is normal but the sensor output is disturbed. The current transformer mainly has induced voltage as the output interference, and is characterized by large peak value and short duration.
Fig. 6 is a graph showing the deviation between the predicted value and the detected value when the output of the current transformer is disturbed. The cause of the disturbance is that the detected value is suddenly changed due to disturbance, which is larger than the failure current prediction deviation value shown in fig. 4. However, since the network contains a certain inductive component both in normal and in fault, the actual network current cannot suddenly change. The abrupt change causes the output of the adaptive filter to change significantly beyond a threshold valuelThe interference can be distinguished from the normal fault current according to this feature, 1000.
Claims (1)
1. A self-adaptive fault current detection method is characterized in that a self-adaptive filter is adopted to predict a current signal, the predicted value is used as a reference basis, and a threshold value is set; comparing a current signal value detected by a current transformer with a predicted value of a current signal output by an adaptive filter, and if the current transformer detects that the deviation between an actual current signal value and the predicted value exceeds a preset threshold value, determining that a fault occurs; the method comprises the following specific steps:
(1) collected at the current time tOutput value x of current transformertWith the output value of the adaptive filterAnd (3) comparison:
wherein t is the data acquisition time, xtFor the current transformer output value collected at time t,the output value of the adaptive filter at the time t;l、hare respectively preset constants and satisfyl<h(ii) a The symbol | represents the absolute value; the above formula indicates three cases:
a. output value x of current transformer at time ttAnd adaptive filter output valueThe difference between them is less than the set valuelWhen the current is normal, the current of the power grid is considered to be normal;
b. output value x of current transformer at time ttAnd adaptive filter output valueThe difference between the two is not less than the set valuelBut is less than the set valuehWhen the power grid is considered to be in fault, the breaker needs to be immediately operated;
c. output value x of current transformer at time ttAnd adaptive filter output valueThe difference between the two is greater than or equal to the set valuehThe output of the current transformer is considered to be subjected to external interference, and the signal is a false signal which can be ignored;
(2) if the situation is judged to belong to the situation b, the microcomputer relay protection device immediately gives a fault occurrence signal to a relay of the relay protection system; if the current transformer output value is judged to belong to the condition a or c, the fault is not generated, and the output value of the adaptive filter at the moment is calculated according to the acquired output value of the current transformer:
wherein,
in the formula, symbolIndicating transpose, sign, of the solved matrix or vectorMeaning that the inverse of the matrix or vector is solved, respectively representing the values corresponding to the symbol variables at the moments of't','t-1' and't + 1'; lambda is a positive number close to 1 but less than 1, and lambda is 0.99;the noise variance of the output signal of the sensor can be obtained by the characteristic parameters of the sensor; i is a d-dimensional unit square matrix;setting an initial value as a unit square matrix; wt＝[wtwt-1… wt-d-1]TThe vector of d-dimension weight value of the self-adaptive filter is selected as zero vector for initial value, and d is the order of the filter;for an adaptive filter d-dimensional input vector, at a sampling time t<d is selected to be 0, and has:
(3) for time t +1Output value x of current transformert+1The current transformer outputs a value xt+1And (3) comparing the output value of the adaptive filter in the step (2)Subtracting, and judging according to the formula in the step (1); and repeatedly iterating calculation and judgment until the fault is detected.
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| CN104915777B (en) * | 2015-06-12 | 2018-11-02 | 北京交通大学 | Relay protection criterion method based on the analysis of dynamic trend degree |
| CN107525969A (en) * | 2016-06-21 | 2017-12-29 | 中电普瑞科技有限公司 | A kind of self-adapting type electric harmonic analysis method for merging many algorithms |
| CN107294049B (en) * | 2017-06-19 | 2019-02-26 | 华中科技大学 | A kind of short circuit electric current quick predict and guard method and system |
| CN112034387B (en) * | 2020-09-08 | 2021-09-21 | 武汉大学 | Power transmission line short-circuit fault diagnosis method and device based on prediction sequence |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101533058A (en) * | 2009-04-24 | 2009-09-16 | 东北大学 | Power abnormal failure data analyzing device and diagnosing method |
| CN102279341A (en) * | 2011-07-23 | 2011-12-14 | 华北电力大学（保定） | Cage asynchronous motor rotor broken-bar fault detection method based on electronic stability program rotation invariant technology (ESPRIT) and pattern search algorithm (PSA) |
| CN202230172U (en) * | 2011-10-13 | 2012-05-23 | 李怡 | Electric power system fault detection device |
| CN103091606A (en) * | 2013-02-28 | 2013-05-08 | 绥化电业局 | Grounding fault detecting method for direct current system with high anti-interference capacity |
| CN103176128A (en) * | 2013-03-28 | 2013-06-26 | 华南理工大学 | Method and system for forcasting state of wind generating set and diagnosing intelligent fault |
| CN103336173A (en) * | 2013-01-29 | 2013-10-02 | 上海海维工业控制有限公司 | Genetic algorithm based self-adaption harmonic detection method |
| CN103929150A (en) * | 2014-03-27 | 2014-07-16 | 苏州大学 | Weight vector updating method for sub-band adaptive filter |
| CN103971163A (en) * | 2014-05-09 | 2014-08-06 | 哈尔滨工程大学 | Adaptive learning rate wavelet neural network control method based on normalization lowest mean square adaptive filtering |
Family Cites Families (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8336352B2 (en) * | 2010-01-25 | 2012-12-25 | Aclara Power-Line Systems, Inc. | Transient detector and fault classifier for a power distribution system |
| KR101118375B1 (en) * | 2010-09-07 | 2012-03-09 | 엘에스산전 주식회사 | Apparatus for swift determination of fault in electric power system |
-
2014
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Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101533058A (en) * | 2009-04-24 | 2009-09-16 | 东北大学 | Power abnormal failure data analyzing device and diagnosing method |
| CN102279341A (en) * | 2011-07-23 | 2011-12-14 | 华北电力大学（保定） | Cage asynchronous motor rotor broken-bar fault detection method based on electronic stability program rotation invariant technology (ESPRIT) and pattern search algorithm (PSA) |
| CN202230172U (en) * | 2011-10-13 | 2012-05-23 | 李怡 | Electric power system fault detection device |
| CN103336173A (en) * | 2013-01-29 | 2013-10-02 | 上海海维工业控制有限公司 | Genetic algorithm based self-adaption harmonic detection method |
| CN103091606A (en) * | 2013-02-28 | 2013-05-08 | 绥化电业局 | Grounding fault detecting method for direct current system with high anti-interference capacity |
| CN103176128A (en) * | 2013-03-28 | 2013-06-26 | 华南理工大学 | Method and system for forcasting state of wind generating set and diagnosing intelligent fault |
| CN103929150A (en) * | 2014-03-27 | 2014-07-16 | 苏州大学 | Weight vector updating method for sub-band adaptive filter |
| CN103971163A (en) * | 2014-05-09 | 2014-08-06 | 哈尔滨工程大学 | Adaptive learning rate wavelet neural network control method based on normalization lowest mean square adaptive filtering |
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