CN111856324A - Fault detection method and device for traction network feeder line - Google Patents
Fault detection method and device for traction network feeder line Download PDFInfo
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Abstract
The application provides a fault detection method and device for a traction network feeder line, relates to the technical field of electric power safety, and solves the problem of misoperation of traction network feeder line protection caused by oscillating current. The method comprises the following steps: collecting a current value of a traction network feeder line, and judging whether the traction network feeder line has a short-circuit fault according to the current value and a preset algorithm; if the short-circuit fault of the traction network feeder line is determined, counting a current value set in a preset time period, and performing phase space reconstruction on the current value set according to a time sequence to generate a target phase space; determining the correlation dimension of the data points in the target phase space, and calculating the characteristic value of the correlation dimension; and when the characteristic value is determined to be larger than the preset threshold value, sending a tripping signal to the circuit breaker. The embodiment of the application is applied to detecting the faults of the traction network feeder line.
Description
Technical Field
The embodiment of the application relates to the technical field of electric power safety, in particular to a method and a device for detecting faults of a traction network feeder line.
Background
In a traction network for supplying power to urban rail transit, a traction network feeder is a wire for transmitting electric energy to an electric train by a transformer substation, so that the protection of the traction network feeder is very important for guaranteeing the safe operation of the urban rail transit.
At present, the current change rate and current increment protection, also called as DDL protection, is mainly used as main protection of a traction network feeder line, so that the maximum load current and the short-circuit current of a traction network can be effectively identified. The principle is that when the current of a traction network feeder line is determined to be larger than a preset threshold value according to the current change rate, the current increase duration time and the current increment, a protection device sends a tripping signal to a circuit breaker, and the safety of urban rail transit is guaranteed.
However, the application of the fifth generation mobile communication technology (5th-generation, 5G) promotes the great improvement of the carrying capacity of urban rail transit, and simultaneously causes short-time oscillation current to appear in the traction network. The oscillating current is highly similar to the short-circuit current of the traction network in waveform characteristics, so that misoperation of the traction network feeder line protection is easily caused by a DDL protection mode, and the circuit breaker is tripped. Frequent tripping not only causes troubles for the operation of urban rail transit, but also influences the riding experience of passengers.
Disclosure of Invention
The application provides a fault detection method and device for a traction network feeder line, and solves the problem of misoperation of traction network feeder line protection caused by oscillating current.
In a first aspect, the present application provides a fault detection method for a traction network feeder line, which is applied to a fault detection device for a traction network feeder line, and the method includes: the fault detection device of the traction network feeder line collects the current value of the traction network feeder line and judges whether the traction network feeder line has a short-circuit fault according to the current value and a preset algorithm. And if the short-circuit fault of the traction network feeder line is determined, the fault detection device of the traction network feeder line counts a current value set in a preset time period, and performs phase space reconstruction on the current value set according to a time sequence to generate a target phase space. And then, a fault detection device of the traction network feeder line determines the correlation dimension of the data points in the target phase space and calculates the characteristic value of the correlation dimension. And when the characteristic value is determined to be larger than the preset threshold value, the fault detection device of the traction network feeder line sends a tripping signal to the circuit breaker.
The preset time period comprises the moment when the short-circuit fault happens to the traction network feeder line. The target phase space includes N data points, each data point corresponding to one of a set of current values.
In the above scheme, the fault detection device for the traction network feeder line firstly performs preliminary judgment on the operation state of the traction network feeder line by using the current value and the preset algorithm to obtain a preliminary judgment result. And when the primary judgment result is a short-circuit fault, judging the running state of the traction network feeder line again by using the current value set, the phase space reconstruction algorithm and other algorithms in the preset time period again, and finally determining whether the current causing the primary judgment result is the oscillation current or the short-circuit current to obtain a secondary judgment result. And finally determining that the short-circuit fault occurs on the feeder line of the traction network when the result of the secondary judgment is determined to be the short-circuit current, and sending a tripping signal to the circuit breaker. Therefore, the possible short-circuit fault of the traction network feeder line is determined through the primary judgment result, and then the oscillation current and the short-circuit current are identified through the secondary judgment result. The fault detection method and the fault detection device avoid the false operation of the traction network feeder line protection caused by the oscillation current in the traction network feeder line, so that the circuit breaker is tripped, the fault detection accuracy of the traction network feeder line is improved, and the riding experience of passengers is further improved.
In a second aspect, the present application provides a fault detection device for a traction network feeder, the protection device comprising: and the acquisition module is used for acquiring the current value of the traction network feeder. And the counting module is used for judging whether the traction network feeder line has the short-circuit fault according to the current value and a preset algorithm, and counting the current value set in a preset time period if the traction network feeder line is determined to have the short-circuit fault. And the generating module is used for carrying out phase space reconstruction on the current value set according to a time sequence to generate a target phase space. And the determining module is used for determining the correlation dimension of the data points in the target phase space. And the calculation module is used for calculating the characteristic value of the correlation dimension. And the sending module is used for sending a tripping signal to the circuit breaker when the characteristic value is determined to be greater than the preset threshold value.
The preset time period comprises the moment when the short-circuit fault happens to the traction network feeder line. The target phase space includes N data points, each data point corresponding to one current value of a set of current values.
In a third aspect, the present application provides a fault detection apparatus for a traction network feeder, including a processor, where when the fault detection apparatus for a traction network feeder operates, the processor executes a computer to execute instructions, so that the fault detection apparatus for a traction network feeder performs the fault detection method for a traction network feeder as described above.
In a fourth aspect, the present application provides a computer-readable storage medium comprising instructions which, when executed on a computer, cause the computer to perform the method of fault detection of a traction network feeder as described above.
In a fifth aspect, the present application provides a computer program product comprising instruction code for performing the method of fault detection of a trailed network feeder as described above.
It can be understood that any one of the processing devices, computer-readable storage media, or computer program products for concurrent services of the internet of things provided above is used to execute the method provided above, and therefore, the beneficial effects that can be achieved by the processing devices, the computer-readable storage media, or the computer program products can refer to the beneficial effects of the method provided above and the solutions in the following specific embodiments, and are not described herein again.
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In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed to be used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a waveform diagram of an oscillating current of a traction network feeder according to an embodiment of the present application;
fig. 2 is a waveform diagram of a short-circuit current of a traction network feeder according to an embodiment of the present application;
fig. 3 is a schematic hardware structure diagram of a fault detection apparatus for a feeder of a traction network according to an embodiment of the present application;
fig. 4 is a schematic flowchart of a method for detecting a fault of a feeder line of a traction network according to an embodiment of the present application;
FIG. 5 is a graph illustrating a current rate of change protection characteristic according to an embodiment of the present disclosure;
fig. 6 is a graph illustrating a current increment protection characteristic provided by an embodiment of the present application;
FIG. 7 is a graph illustrating an autocorrelation function of an oscillating current according to an embodiment of the present disclosure;
FIG. 8 is a graph illustrating an autocorrelation function of a short circuit current according to an embodiment of the present disclosure;
FIG. 9 is a graph illustrating an associated integral function of oscillating current in different dimensions according to an embodiment of the present application;
FIG. 10 is a graph illustrating an associated integral function of short circuit current in different dimensions according to an embodiment of the present application;
fig. 11 is a graph illustrating an associated integral function of an oscillating current of a traction network feeder according to an embodiment of the present application;
fig. 12 is a graph illustrating a correlation integral function of a short-circuit current of a traction network feeder according to an embodiment of the present application;
fig. 13 is a schematic structural diagram of a fault detection apparatus for a traction network feeder according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. 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 application.
In the embodiments of the present application, words such as "exemplary" or "for example" are used to mean serving as an example, instance, or illustration. Any embodiment or design described herein as "exemplary" or "e.g.," is not necessarily to be construed as preferred or advantageous over other embodiments or designs. Rather, use of the word "exemplary" or "such as" is intended to present concepts related in a concrete fashion.
In the following, the terms "first", "second" are used for descriptive purposes only and are not to be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the embodiments of the present application, "a plurality" means two or more unless otherwise specified.
In a traction network for supplying power to urban rail transit, a traction network feeder is a wire for transmitting electric energy to an electric train by a transformer substation, so that the protection of the traction network feeder is very important for guaranteeing the safe operation of the urban rail transit. At present, current change rate and current increment protection, also called DDL protection, are mainly used as main protection of a traction network feeder line, and the DDL protection can effectively identify the maximum load current and the short-circuit current of the traction network. The principle is that when the current of a traction network feeder line is determined to be larger than a preset threshold value according to the current change rate, the current increase duration time and the current increment, a protection device sends a tripping signal to a circuit breaker, and the safety of urban rail transit is guaranteed.
However, the application of the 5G technology promotes the great improvement of the carrying capacity of urban rail transit, and simultaneously causes short-time oscillation current to appear in the traction network. The oscillating current is highly similar in waveform characteristics to the traction network short circuit current.
For example, fig. 1 records a waveform plot of the oscillating current pulling the network feeder within 200 ms. Referring to fig. 1, at-200 ms, the current value of the oscillating current is 500A; at-155 ms, the current value of the oscillation current is 1000A; at-110 ms, the current value of the oscillation current is 500A; at-155 ms, the current value of the oscillation current is 1000A; when the current value is-75 ms, the current value of the oscillation current is 1000A; when the current value is-20 ms, the current value of the oscillation current is 500A; at 0ms, the current value of the oscillation current is 4500A. Where 0ms represents the current time. Figure 2 records a waveform of the short circuit current of the traction network feeder within 350 ms. Referring to FIG. 2, at-200 ms, the current value of the short-circuit current is-200A; when the short-circuit current is-100 ms, the current value of the short-circuit current is-200A; at 0ms, the current value of the short-circuit current is-200A; at 25ms, the current value of the short-circuit current is 11800A; at 50ms, the current value of the short-circuit current is 0A; at 150ms, the current value of the short-circuit current is 0A. Where 0ms represents the time when the short circuit occurs.
In fig. 1, the current value of the oscillation current is 500A at-20 ms of the oscillation current, and is 4500A at 0ms of the oscillation current, which takes 20 ms. In fig. 2, the short-circuit current has a current value of-200A at 0ms, 11800A at 25ms, and 25 ms. It can be seen that both the oscillating current and the short-circuit current have the characteristic that the instantaneous current value becomes larger, and the time duration for the instantaneous current value to become larger is basically the same.
In the DDL protection algorithm, when the current change rate is greater than a first threshold value and is kept for a first preset time period, protection is effective, and the circuit breaker is indicated to be tripped. Therefore, when the oscillation current occurs to the feeder line of the traction network, the waveform characteristics of the oscillation current are highly similar to those of the short-circuit current, so that the protection is effective, and the tripping of the circuit breaker is indicated. Therefore, the DDL protection method is easy to cause misoperation of the traction network feeder protection, thereby causing the circuit breaker to trip. Frequent tripping not only causes troubles for the operation of urban rail transit, but also influences the riding experience of passengers.
In order to solve the problems, the application provides a method and a device for detecting the fault of a traction network feeder line. The fault detection method comprises the following steps: the fault detection device of the traction network feeder line collects the current value of the traction network feeder line and judges whether the traction network feeder line has a short-circuit fault according to the current value and a DDL protection algorithm. And if the short-circuit fault of the traction network feeder line is determined, the fault detection device of the traction network feeder line counts a current value set in a preset time period, and performs phase space reconstruction on the current value set according to a time sequence to generate a target phase space. And then, a fault detection device of the traction network feeder line determines the correlation dimension of the data points in the target phase space and calculates the characteristic value of the correlation dimension. And when the characteristic value is determined to be larger than the preset threshold value, the fault detection device of the traction network feeder line sends a tripping signal to the circuit breaker. The method avoids the false protection of the traction network feeder line caused by the oscillation current in the traction network feeder line, and improves the accuracy of the fault detection of the traction network feeder line.
In a specific implementation, the fault detection device of the traction network feeder has the components as shown in fig. 3. Fig. 3 is a fault detection apparatus for a traction network feeder according to an embodiment of the present application, and the apparatus may include a processor 302, where the processor 302 is configured to execute an application program code, so as to implement a fault detection method for a traction network feeder in the present application.
The processor 302 may be a Central Processing Unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more ics for controlling the execution of programs in accordance with the present disclosure.
As shown in fig. 3, the fault detection device of the traction network feeder may further include a memory 303. The memory 303 is used for storing application program codes for executing the scheme of the application, and the processor 302 is used for controlling the execution.
The memory 303 may be, but is not limited to, a read-only memory (ROM) or other type of static storage device that may store static information and instructions, a Random Access Memory (RAM) or other type of dynamic storage device that may store information and instructions, an electrically erasable programmable read-only memory (EEPROM), a compact disc read-only memory (CD-ROM) or other optical disk storage, optical disk storage (including compact disc, laser disc, optical disc, digital versatile disc, blu-ray disc, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. The memory 303 may be separate and coupled to the processor 302 via a bus. The memory 303 may also be integrated with the processor 302.
As shown in fig. 3, the fault detection apparatus of the traction network feeder may further include a communication interface 301, wherein the communication interface 301, the processor 302, and the memory 303 may be coupled to each other, for example, via a bus 304. The communication interface 301 is used for information interaction with other devices, for example, information interaction between a fault detection device supporting a traction network feeder and other devices is supported.
It is noted that the configuration of the device shown in fig. 3 does not constitute a limitation of the fault detection means of the traction network feeder, which may comprise more or less components than those shown in fig. 3, or a combination of some components, or a different arrangement of components, in addition to those shown in fig. 3.
The following describes a fault detection method for a traction network feeder provided by an embodiment of the present application through fig. 4 to 12, with reference to a waveform of an oscillating current of the traction network feeder shown in fig. 1, a waveform of a short-circuit current of the traction network feeder shown in fig. 2, and a fault detection device for the traction network feeder shown in fig. 3.
Fig. 4 is a schematic flowchart of a method for detecting a fault of a feeder line of a traction network according to an embodiment of the present application. Referring to fig. 4, the method for detecting the fault of the traction network feeder line comprises the following steps.
401. The fault detection device of the traction network feeder line collects the current value of the traction network feeder line.
Specifically, an analog-to-digital converter (ADC) may be used to acquire the current value of the traction network feeder, or an associated current acquisition sensor may be used to acquire the current value of the traction network feeder, for example, a hall sensor.
402. And if the short-circuit fault of the traction network feeder line is determined according to the current value and a preset algorithm, counting the current value set in a preset time period.
The preset time period comprises the moment when the short-circuit fault occurs in the traction network feeder line.
Optionally, the preset algorithm is a current change rate and current increment protection algorithm, which is also called a DDL protection algorithm.
Specifically, after the fault detection device of the traction network feeder line collects the current value of the traction network feeder line, the collected current value is preliminarily judged by using a DDL (distributed data link layer) protection algorithm to obtain a preliminary judgment result. The DDL protection algorithm comprises current change rate protection and current increment protection.
For example, FIG. 5 provides a graphical illustration of a current rate of change protection characteristic. Referring to fig. 5, a first curve 501, a second curve 502, a first time a, and a second time b are included. The first time a is the time when the current change rate protection of the first curve 501 and the second curve 502 is started, and the second time b is the time when the delay phase of the current change rate protection of the first curve 501 and the second curve 502 is ended.
At a first time a, the current change rates of the first curve 501 and the second curve 502 are both greater than a protection setting start value (not shown in the figure), and the current change rate protection is started, at this time, both the first curve 501 and the second curve 502 enter a delay stage. At the second time b, the delay phase is ended, and at this time, the current change rate of the first curve 501 is still greater than the starting value set for protection, and it is determined that the short-circuit fault occurs in the traction network feeder line corresponding to the first curve 501. If the current change rate of the second curve 502 is smaller than the protection setting starting value, it is determined that the traction network feeder corresponding to the second curve 502 is normal.
As another example, fig. 6 provides a graphical illustration of a current delta protection characteristic. Referring to fig. 6, the first current increment F includes a third curve 601, a fourth curve 602, a fifth curve 603, a sixth curve 604, a seventh curve 605ΔiSecond current increment EΔiThird time t1, fourth time t2, fifth time t3, sixth time t4, seventh time t5 and seventh time t 6.
Wherein the first current increment FΔiAnd the return value of the current increment protection is represented, namely when the current increment does not reach the return value of the current increment protection and lasts for a certain time period, the current increment protection is closed and does not act any more. Second current increment EΔiIndicating the setting value of the current increment protection, i.e. when the current increment reaches the setting value of the current increment protection and continues for a certain timeAnd determining that the short-circuit fault occurs in the curve corresponding to the current increment. First current increment FΔiLess than a second current increment EΔi. The third time t1 is the time when the current increment protection of the third curve 601, the fourth curve 602, the fifth curve 603, the sixth curve 604, and the seventh curve 606 is started (also the time when the current rate of change protection is started), and the fourth time t2 is the time when the delay phase of the current increment protection is finished.
For the third curve 601, at the third time t1, the current change rate of the third curve 601 is greater than the protection setting start value (not shown in the figure), and the current increment protection is started. Although in the partial period from the third time t1 to the fourth time t2, the current increment of the third curve 601 is larger than the second current increment EΔiHowever, the third curve 601 does not reach the fourth time t2, and therefore, it is determined that the feeder line of the towing network corresponding to the third curve 601 is normal.
For the fourth curve 602, at the third time t1, the current change rate of the fourth curve 602 is greater than the protection setting start value (not shown in the figure), and the current increment protection is started. By a fourth time t2, the current increment of the fourth curve 602 is greater than the second current increment EΔiTherefore, it is determined that the traction network feeder corresponding to the fourth curve 602 has a short-circuit fault, and the current delta protection initiates a protection action at the fourth time t 2.
For the fifth curve 603, at the third time t1, the current change rate of the fifth curve 603 is greater than the protection setting start value (not shown in the figure), and the current increment protection is started. By a fourth time t2, the current increment of the fifth curve 603 is smaller than the second current increment EΔiHowever, at the sixth time t4, the current increment of the fifth curve 603 is larger than the second current increment EΔiTherefore, at the sixth time t4, it is determined that the short-circuit fault occurs in the traction network feeder corresponding to the fifth curve 603, and the protection action is initiated by the current increment protection at the sixth time t 4.
For the sixth curve 604, at the third time t1, the current change rate of the sixth curve 604 is greater than the protection setting start value (not shown), and the current increment protection is started. By a fourth time t2, a sixth curve 604 is less than a second current increment EΔiAnd at a fifth time t3, if the current change rate is smaller than the starting value of the protection setting, it is determined that the protection of the current change rate returns to the delay stage, and at a seventh time t5, when the end time of the protection of the current change rate returning to the delay stage is reached, it is determined that the trailed grid feeder corresponding to the sixth curve 604 is normal. However, at the seventh time t6, the current change rate of the sixth curve 604 is again greater than the protection setting activation value, and at the seventh time t6, the current delta protection is reactivated.
For the seventh curve 605, at the third time t1, the current change rate of the seventh curve 605 is greater than the protection setting start value (not shown in the figure), and the current increment protection is started. However, in the time period from the third time t1 to the seventh time t6, the current increment of the seventh curve 605 is smaller than the first current increment FΔiThen the towing net feeder corresponding to the seventh curve 605 is determined to be normal.
And if the short-circuit fault of the traction network feeder line is determined according to the preliminary judgment result, counting a current value set in a preset time period. The preset time period comprises the moment when the short-circuit fault occurs in the traction network feeder line. And if the primary judgment result is normal, not starting secondary judgment, and continuously performing primary judgment on the current value of the traction network feeder line again by using a DDL (distributed data link layer) protection algorithm in the next period.
403. And the fault detection device of the traction network feeder line carries out phase space reconstruction on the current value set according to the time sequence to generate a target phase space.
Wherein the target phase space includes N data points, each data point corresponding to a current value of the set of current values.
Firstly, a power supply system of the traction network is a strong nonlinear system, which determines that a nonlinear signal processing method must be adopted in a state feature extraction method of the traction network. The fractal theory is an important theoretical branch for analyzing a nonlinear dynamic system, and can explain the evolution law of a dynamic system through the internal relation between local parts and the whole body; the method can also unify simplicity, complexity, order, disorder, certainty, randomness, contingency and the like, and provides a simple and effective mathematical tool for the analysis of complex phenomena. The traction network feeder line current signal has obvious disordered and complex morphological characteristics, so that the irregularity and the complexity of the feeder line current signal can be accurately measured by adopting the correlation dimension based on the fractal theory.
The fractal dimension is the core of fractal theory and is defined as:
when q is different, DqRepresenting different fractal dimensions. Wherein, when q is 2, D2Representing the correlation dimension.
In the application, the correlation dimension of the current value set of the traction network feeder is calculated by using an embedded space method. Specifically, the fault detection device of the traction network feeder performs phase space reconstruction on the current value set in a preset time period according to a time sequence to generate a target phase space.
For example, { xkX (k Δ t), k 1, 2.. N } is a set of current values arranged in chronological order, where x iskDenotes a current value in the current value set, τ ═ k Δ t denotes a delay time, and N denotes the number of current values in the current value set.
Thus, if x is desiredkEmbedding into m-dimensional Euclidean space RmThen, first, the delay time τ and the embedding dimension m need to be determined.
Specifically, the selection manner of the delay time τ includes a main period relationship method, a minimum mutual information standard method, an autocorrelation function method, and the like. For example, when the autocorrelation function method is used, the current value set of the present application is a single-variable discrete time series, and thus, the autocorrelation function is defined as
Where N represents the number of current values in the set of current values, i.e. the length of the time sequence,
xirepresents a certain current value of the set of current values,
representing the average of the current values in the set of current values.
In the calculation of the autocorrelation function RxxAnd (tau), selecting the value of the delay time tau to meet the requirements of (1) small autocorrelation among time sequence elements and (2) no loss of original dynamic system information contained in the time sequence. In general, when the autocorrelation function satisfies the condition Rxx(τ)＜1-e-1The delay time τ is considered to substantially satisfy the requirement, where e is the euler constant.
For example, fig. 7 provides a graphical illustration of the autocorrelation function of the oscillating current. Referring to fig. 7, before 14 to 15ms, the curve change of the autocorrelation function of the oscillation current is irregular and can contain the original dynamic system information, and after 14 to 15ms, the curve change of the autocorrelation function of the oscillation current is regular, and it is considered that the original dynamic system information is basically lost. Therefore, according to the conditions required to be met by selecting the value of the delay time tau and the curve of the autocorrelation function of the oscillation current, the selection range of the delay time tau is determined to be 14-15 ms.
Since the oscillating current and the short-circuit current in the traction network feeder need to be identified in the present application, the situation of the short-circuit current also needs to be considered when determining the delay time τ. For example, fig. 8 provides a graphical illustration of an autocorrelation function of the short circuit current. Referring to fig. 8, before 13 to 14ms, the curve change of the autocorrelation function of the short-circuit current is irregular and can contain the original dynamic system information, and after 13 to 14ms, the curve change of the autocorrelation function of the short-circuit current is regular, and the original dynamic system information is considered to be basically lost. Therefore, according to the conditions required to be met by selecting the value of the delay time tau and the curve of the autocorrelation function of the short-circuit current, the selection range of the delay time tau is determined to be 13-14 ms.
From the above description, the delay time τ is determined to be 14ms in the present application.
In addition, the embedding dimension m can be selected by a successive approximation method, a false neighbor method, a C-C algorithm, a wobble product method and the like. For example, when a successive approximation method is adopted, the embedding dimension is gradually increased from 8 to 20 according to the step size of 1, the correlation dimension value of the feeder current of the traction network under different dimensions is calculated until the correlation dimension integral of the signal basically does not change within a certain scale range, and the embedding dimension calculated in this way is the optimal value.
For example, FIG. 9 provides a graphical illustration of the associated integral function of the oscillating current in different dimensions. Referring to fig. 9, the curve of the associated integral function of the oscillation current becomes increasingly dense as the embedding dimension increases from 8 to 20 by a step size of 1. Fig. 10 provides a graphical illustration of the associated integration function for short circuit current in different dimensions. Referring to fig. 10, when the embedding dimension is gradually increased from 8 to 20 by a step size of 1, the curve of the associated integration function of the short-circuit current is also gradually dense. And as can be derived from fig. 9 and 10, as the embedding dimension increases to 16, the correlation integral function values begin to overlap or parallel within a certain scale range, and the calculated correlation dimension value tends to be stable; the correlation dimension value of the oscillation signal is larger than the correlation dimension calculated during short-circuit fault.
Considering that too large an embedding dimension would result in a large number of calculations and too small would affect the accuracy of the result, the embedding dimension m is determined to be 16 in the present application, according to the above description.
And after the embedding dimension m and the delay time tau are determined, carrying out phase space reconstruction on the current value set according to the time sequence to generate a target phase space.
For example, { xkX (k Δ t), k 1, 2,. N } is a current value set arranged in time series, and is embedded in the m-dimensional euclidean space RmIn and set kτWhen 2, the target phase space X is formed as
Wherein x iskRepresenting the current values and, at the same time, the data points in the target phase space X, N representing the number of data points,
τ denotes a delay time, and Δ t denotes a time interval of the statistical current value.
404. And determining the correlation dimension of the data points in the target phase space by the fault detection device of the traction network feeder line.
Specifically, the fault detection device of the traction network feeder firstly determines the correlation integral function of the data points in the target phase space according to a second formula.
Wherein the second formula satisfies
Wherein, Cm(r) represents a function value of the correlation integral function, N represents the number of data points in the target phase space, H (r-r)ij) Is a Hervesaide function, and
representing the target distance, the target distance is the sum of the distances between the target data point and other N-1 data points, the target data point is any data point in the target phase space, xiAnd xjRepresenting two different data points in the target phase space,
τ denotes a delay time, Δ t denotes a time interval of statistical current values, l denotes the number of data points in the target phase space, and l is N-1. r represents the radius of the hyper-sphere in the target phase space, and min (r)ij)≤r≤max(rij)。
And then, the fault detection device of the traction network feeder line determines the correlation dimension of the data points in the target phase space according to a third formula and a correlation integral function.
The third formula satisfies
Wherein D is2Representing the correlation dimension, r the hypersphere radius in the target phase space, N the data points in the target phase spaceNumber of (2), Cm(r) represents a function value of the correlation integral function.
For example, fig. 11 provides a graphical illustration of a correlation integral function of the oscillating current of the traction network feed. Where the delay time τ is 14ms and the embedding dimension m is 16. Referring to fig. 11, the curve of the associated integral function of the oscillating current includes a curve 1101 of the associated integral function of the oscillating current and a first fitted curve 1102 obtained by fitting the operating condition of the curve 1101 of the associated integral function of the oscillating current, where a slope of the first fitted curve 1102 is an associated dimension of the curve 1101 of the associated integral function of the oscillating current.
As another example, fig. 12 provides a graphical illustration of a correlation integral function of the short circuit current of the traction network feed line. Where the delay time τ is 14ms and the embedding dimension m is 16. Referring to fig. 12, the curve of the integral function related to the short-circuit current includes a curve 1201 of the integral function related to the short-circuit current and a second fitted curve 1202 obtained by fitting the condition of the curve 1201 of the integral function related to the short-circuit current, where a slope of the second fitted curve 1202 is a correlation dimension of the curve 1201 of the integral function related to the short-circuit current.
Optionally, the correlation dimension value of the oscillating current satisfies [1.05, 1.32], and the correlation dimension value of the short-circuit current satisfies [0.032, 0.040 ].
405. And the fault detection device of the traction network feeder calculates the characteristic value of the correlation dimension according to a first formula.
The first formula satisfies J ═ D21, where J represents the eigenvalue of the correlation dimension and D2 represents the correlation dimension.
406. And if the characteristic value is larger than the preset threshold value, the fault detection device of the traction network feeder line sends a tripping signal to the circuit breaker.
Optionally, the preset threshold is set to 0.8.
In the above scheme, the fault detection device for the traction network feeder line firstly performs preliminary judgment on the operation state of the traction network feeder line by using the current value and the preset algorithm to obtain a preliminary judgment result. And when the primary judgment result is a short-circuit fault, judging the running state of the traction network feeder line again by using the current value set, the phase space reconstruction algorithm and other algorithms in the preset time period again, and finally determining whether the current causing the primary judgment result is the oscillation current or the short-circuit current to obtain a secondary judgment result. And finally determining that the short-circuit fault occurs on the feeder line of the traction network when the result of the secondary judgment is determined to be the short-circuit current, and sending a tripping signal to the circuit breaker. Therefore, the possible short-circuit fault of the traction network feeder line is determined through the primary judgment result, and then the oscillation current and the short-circuit current are identified through the secondary judgment result. The fault detection method and the fault detection device avoid the false operation of the traction network feeder line protection caused by the oscillation current in the traction network feeder line, so that the circuit breaker is tripped, the fault detection accuracy of the traction network feeder line is improved, and the riding experience of passengers is further improved.
In the embodiment of the present application, the functional modules of the fault detection apparatus for the feeder line of the traction network may be divided according to the above method embodiment, for example, each functional module may be divided corresponding to each function, or two or more functions may be integrated into one processing module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. It should be noted that, in the embodiment of the present application, the division of the module is schematic, and is only one logic function division, and there may be another division manner in actual implementation.
The fault detection device of the traction network feeder is used for executing the steps executed in the method shown in the figure 4. The fault detection device for the traction network feeder line provided by the embodiment of the application can comprise modules corresponding to corresponding steps.
Fig. 13 shows a schematic diagram of a possible configuration of the fault detection device for a feeder of a traction network, in the case of using functional modules divided for each function. As shown in fig. 13, the fault detection apparatus for the feeder line of the traction network includes an acquisition module 131, a statistics module 132, a generation module 133, a determination module 134, a calculation module 135, and a sending module 136.
And the acquisition module 131 is used for acquiring the current value of the traction network feeder. For example, in conjunction with fig. 4, the acquisition module 131 is configured to execute step S401 in fig. 4. And the counting module 132 is configured to count a current value set in a preset time period if it is determined that the short-circuit fault occurs in the traction network feeder according to the current value acquired by the acquisition module 131 and a preset algorithm. The preset time period comprises the moment when the short-circuit fault occurs in the feeder line of the traction network. For example, in conjunction with fig. 4, the statistic module 132 is configured to execute step S402 in fig. 4. A generating module 133, configured to perform phase space reconstruction on the current value set counted by the counting module 132 according to a time sequence, so as to generate a target phase space. The target phase space includes N data points, each data point corresponding to one current value of a set of current values. For example, in conjunction with fig. 4, the generating module 133 is configured to execute step S403 in fig. 4. A determining module 134, configured to determine the correlation dimension of the data points in the target phase space generated by the generating module 133. For example, in conjunction with fig. 4, the determining module 134 is configured to execute step S404 in fig. 4. A calculating module 135, configured to calculate the feature value of the associated dimension determined by the determining module 134 according to the first formula. For example, in conjunction with fig. 4, the calculation module 135 is configured to execute step S405 in fig. 4. A sending module 136, configured to send a trip signal to the circuit breaker if it is determined that the characteristic value calculated by the calculating module 135 is greater than the preset threshold. For example, in conjunction with fig. 4, the sending module 136 is configured to execute step S406 in fig. 4.
Optionally, the determining module 134 is specifically configured to: and determining the associated integral function of the data points in the target phase space according to a second formula. And determining the correlation dimension of the data points in the target phase space according to a third formula and a correlation integral function.
Optionally, the second formula satisfies
Wherein, Cm(r) represents a function value of the correlation integral function, N represents the number of data points in the target phase space, H (r-r)ij) Is the hervesseld function.
Representing the target distance, the target distance is the sum of the distances between the target data point and other N-1 data points, and the target data point is any one of the target phase spacesData points, xiAnd xjRepresenting two different data points in the target phase space,
τ represents a delay time, Δ t represents a time interval of the statistical current value, l represents the number of data points in the target phase space, and l is N-1; r represents the radius of the hyper-sphere in the target phase space, and min (r)ij)≤r≤max(rij)。
Optionally, the third formula satisfies
Wherein D is2Representing the correlation dimension, r the radius of the hypersphere in the target phase space, N the number of data points in the target phase space, Cm(r) represents a function value of the correlation integral function.
Alternatively, the first formula satisfies J ═ D21, where J represents the eigenvalue of the correlation dimension and D2 represents the correlation dimension.
Optionally, the preset algorithm is a current change rate and current increment protection algorithm.
Another embodiment of the present application further provides a computer-readable storage medium, which stores instructions that, when executed on a fault detection apparatus of a traction network feeder, perform the steps in the fault detection method of the traction network feeder according to the embodiment shown in fig. 4.
In another embodiment of the present application, there is also provided a computer program product comprising computer executable instructions stored in a computer readable storage medium. The processor of the fault detection device of the traction network feeder may read the computer-executable instructions from the computer-readable storage medium, and the processor executing the computer-executable instructions causes the fault detection device of the traction network feeder to perform the steps in the fault detection method of the traction network feeder of the embodiment shown in fig. 4.
All relevant contents of each step related to the above method embodiment may be referred to the functional description of the corresponding functional module, and the function thereof is not described herein again.
It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Those of ordinary skill in the art would appreciate that the various illustrative modules, elements, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus, and method may be implemented in other ways. For example, the device embodiments described above are merely illustrative, e.g., multiple units or components may be combined or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
Units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application or portions thereof that substantially contribute to the prior art may be embodied in the form of a software product stored in a storage medium and including instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U disk, a removable hard disk, a read-only memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above description is only for the specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (14)
1. A fault detection method for a traction network feeder line is characterized by comprising the following steps:
collecting the current value of a traction network feeder line;
if the short-circuit fault of the traction network feeder line is determined according to the current value and a preset algorithm, counting a current value set in a preset time period; the preset time period comprises: determining the moment when the short-circuit fault occurs to the traction network feeder line;
performing phase space reconstruction on the current value set according to a time sequence to generate a target phase space; the target phase space comprises N data points, and each data point corresponds to one current value in the current value set;
determining the correlation dimension of the data points in the target phase space;
calculating a characteristic value of the correlation dimension according to a first formula;
and if the characteristic value is determined to be larger than the preset threshold value, sending a tripping signal to the circuit breaker.
2. The fault detection method of claim 1, wherein said determining the correlation dimensions of the data points in the target phase space comprises:
determining a correlation integral function of the data points in the target phase space according to a second formula;
and determining the correlation dimension of the data points in the target phase space according to a third formula and the correlation integral function.
3. The fault detection method of claim 2,
the second formula satisfies
Wherein, Cm(r) represents a function value of the correlation integral function, N represents the number of data points in the target phase space, H (r-r)ij) Is a Hervesseld function;
representing a target distance, wherein the target distance is the sum of the distances between a target data point and other N-1 data points, the target data point is any data point in the target phase space, xiAnd xjDifferent data points representing two of the target phase spaces,
τ represents a delay time, Δ t represents a time interval of a statistical current value, l represents the number of data points in the target phase space, and l is N-1; r represents the radius of the hypersphere in the target phase space, and min (r)ij)≤r≤max(rij)。
4. The fault detection method of claim 2,
the third formula satisfies
Wherein D is2Representing the correlation dimension, r representing the hypersphere radius in the target phase space, N representing the number of data points in the target phase space, Cm(r) represents a function value of the correlation integral function.
5. The fault detection method of claim 1,
the first formula satisfies J ═ D2-1|, where J represents the eigenvalue of the correlation dimension and D2 represents the correlation dimension.
6. The fault detection method of claim 1,
the preset algorithm is a current change rate and current increment protection algorithm.
7. A fault detection device for a traction network feeder, comprising:
the acquisition module is used for acquiring the current value of the traction network feeder;
the statistical module is used for counting a current value set in a preset time period if the short-circuit fault of the traction network feeder line is determined according to the current value acquired by the acquisition module and a preset algorithm; the preset time period comprises: determining the moment when the short-circuit fault occurs to the traction network feeder line;
the generation module is used for carrying out phase space reconstruction on the current value set counted by the counting module according to a time sequence to generate a target phase space; the target phase space comprises N data points, and each data point corresponds to one current value in the current value set;
the determining module is used for determining the correlation dimension of the data points in the target phase space generated by the generating module;
the calculation module is used for calculating the characteristic value of the correlation dimension determined by the determination module according to a first formula;
and the sending module is used for sending a tripping signal to the circuit breaker if the characteristic value calculated by the calculating module is determined to be greater than a preset threshold value.
8. The fault detection device according to claim 7, wherein the determination module is specifically configured to:
determining a correlation integral function of the data points in the target phase space according to a second formula;
and determining the correlation dimension of the data points in the target phase space according to a third formula and the correlation integral function.
9. The failure detection device according to claim 8,
the second formula satisfies
Wherein, Cm(r) represents a function value of the correlation integral function, N represents the number of data points in the target phase space, H (r-r)ij) Is a Hervesseld function;
representing a target distance, wherein the target distance is the sum of the distances between a target data point and other N-1 data points, the target data point is any data point in the target phase space, xiAnd xjDifferent data points representing two of the target phase spaces,
τ represents a delay time, Δ t represents a time interval of a statistical current value, l represents the number of data points in the target phase space, and l is N-1; r represents the radius of the hypersphere in the target phase space, and min (r)ij)≤r≤max(rij)。
10. The failure detection device according to claim 8,
the third formula satisfies
Wherein D is2Representing the correlation dimension, r representing the hypersphere radius in the target phase space, N representing the number of data points in the target phase space, Cm(r) represents a function value of the correlation integral function.
11. The failure detection device according to claim 7,
the first formula satisfies J ═ D2-1|, where J represents the eigenvalue of the correlation dimension and D2 represents the correlation dimension.
12. The failure detection device according to claim 7,
the preset algorithm is a current change rate and current increment protection algorithm.
13. A failure detection apparatus for a haul network feeder, comprising a processor, wherein when the failure detection apparatus for a haul network feeder is in operation, the processor executes computer-executable instructions to cause the failure detection apparatus for a haul network feeder to perform a method for detecting a failure of a haul network feeder as claimed in any one of claims 1 to 6.
14. A computer-readable storage medium comprising instructions that, when executed on a computer, cause the computer to perform the method of fault detection of a traction network feeder of any one of claims 1-6.
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