A method for identifying fault signals in power systems

Abstract

The invention discloses a method for identifying a fault signal of an electric power system, comprising the steps of sampling and preprocessing a signal of the electric power system; decomposing the preprocessed signal by using a Hippel transform strategy, and reconstructing the decomposed signal by adding white noise to obtain a reconstructed signal; calculating the permutation entropy of the reconstructed signal; if the permutation entropy is less than a threshold value T, taking the interval corresponding to the permutation entropy as the interval where the state of the electric power system changes; otherwise, continuing to reconstruct the signal; setting a reference point for the interval where the state of the electric power system changes, calculating a signal-to-noise ratio, and locating the fault signal according to the signal-to-noise ratio; the invention effectively filters out noise interference and solves the problem that a small signal cannot be identified by preprocessing the signal of the electric power system; reducing the reconstruction error by adding white noise; and realizing accurate identification of the fault signal by combining a permutation entropy algorithm.

Description

Technical Field

[0001] The present invention relates to the technical field of power system fault analysis, and in particular to a method for identifying power system fault signals. Background Art

[0002] The power system is an electricity production and consumption system consisting of power generation, transmission, transformation, distribution and consumption. It converts primary energy from nature into electricity through power generation devices, and then supplies electricity to various users through transmission, transformation and distribution. It mainly includes two categories: power generation equipment and power supply equipment. Power generation equipment mainly includes power station boilers, steam turbines, gas turbines, water turbines, generators, point motors, transformers, etc. Power supply equipment mainly includes transmission lines of various voltage levels, mutual inductors, contactors, etc.

[0003] In the actual operation of the power system, due to the influence of chemical, electrical, thermal or mechanical, external damage and natural disasters, or due to internal defects in the materials or parts used, hidden dangers are buried in the equipment manufacturing process. Therefore, it is particularly important to know the fault detection of power equipment. At present, there are mainly technical means such as ultrasonic detection technology and visual observation method to detect power equipment faults. However, these detection methods have problems such as complex operation, slow speed and inaccurate detection results. Summary of the invention

[0004] The purpose of this section is to summarize some aspects of embodiments of the present invention and briefly introduce some preferred embodiments. Some simplifications or omissions may be made in this section and the specification abstract and the invention title of this application to avoid blurring the purpose of this section, the specification abstract and the invention title, and such simplifications or omissions cannot be used to limit the scope of the present invention.

[0005] In view of the above existing problems, the present invention is proposed.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: including sampling and preprocessing the power system signal; decomposing the preprocessed signal using the Hible transform strategy, and reconstructing the decomposed signal by adding white noise to obtain a reconstructed signal; calculating the permutation entropy of the reconstructed signal; if the permutation entropy is less than a threshold value T, taking the interval corresponding to the permutation entropy as the interval where the power system state changes; otherwise, continuing to reconstruct the signal; setting a reference point for the interval where the power system state changes, calculating the signal-to-noise ratio, and locating the fault signal according to the signal-to-noise ratio.

[0007] As a preferred solution of the method for identifying fault signals of power systems described in the present invention, the preprocessing includes filtering out noise in power system signals by a filtering module and performing signal conversion by a conversion module; amplifying the converted signals by a Darlington tube to obtain a signal r(n).

[0008] As a preferred solution of the method for identifying fault signals of power system described in the present invention, wherein: the filtering module includes a diode and an RC low-pass filter; the power system signal x(n) is input to the diode, and the RC low-pass filter is driven to filter out the low-frequency signal in the power system signal, and the obtained signal spectrum F(e jω )as follows:

[0009] F(e jω ) = DTFT[x(n)] × DTFT[h(n)];

[0010] Among them, e jω is a complex exponential sequence, DTFT is a discrete time Fourier transform, h(n) is the RC low-pass filter, and the resistor and capacitor of the RC low-pass filter are made of metal copper and metal aluminum, respectively.

[0011] As a preferred solution of the method for identifying fault signals of power systems described in the present invention, the conversion module includes an inverter and a tri-state buffer; the inverter is used to invert the signal output by the filter module; and the inverted signal is then frequency-converted by the tri-state buffer. The converted signal spectrum D(e jω )for;

[0012]

[0013] Among them, q is the frequency shift amount and δ is the unit pulse.

[0014] As a preferred solution of the method for identifying fault signals of power systems of the present invention, the decomposition includes performing two Hilbert transforms on the signal r(n) according to the following formula to complete the decomposition:

[0015]

[0016] Among them, * is the convolution symbol, z(n) is the decomposed signal, j is the imaginary unit, and t is the time series.

[0017] As a preferred solution of the method for identifying fault signals of power systems of the present invention, the signal reconstruction includes adding two positive and negative white noise pairs with equal amplitudes and opposite signs to the decomposed signal to generate a reconstructed signal z′(n):

[0018]

[0019] Where N represents N decomposed signals, i represents the i-th decomposed signal, and noise i (t) is the white noise corresponding to the i-th decomposed signal.

[0020] As a preferred solution of the method for identifying fault signals of power systems described in the present invention, the calculation of the permutation entropy of the reconstructed signal includes:

[0021]

[0022] Among them, H p (m) is the permutation entropy of the reconstructed signal, P a is the probability that index a appears in the reconstructed signal; M is the number of indices.

[0023] As a preferred solution of the method for identifying fault signals in a power system according to the present invention, the method comprises: calculating the signal-to-noise ratio SNR:

[0024]

[0025] If SNR(t) takes the maximum value at a certain moment and SNR(t)>3, then it is determined that this moment is the moment when the power system fails;

[0026] in, are the means of the subsequences before and after the selected benchmark point, s 1 、s 2 is the corresponding standard deviation.

[0027] The beneficial effects of the present invention are as follows: the present invention pre-processes the power system signal to effectively filter out noise interference and solve the problem that small signals cannot be identified; by adding white noise, the reconstruction error is reduced; and by combining the permutation entropy algorithm, accurate identification of fault signals is achieved. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative labor. Among them:

[0029] Figure 1 A schematic diagram of the structure of a filter module of the method for identifying a fault signal in a power system according to the first embodiment of the present invention;

[0030] Figure 2 The figure is a flow chart of the method for identifying fault signals in a power system according to the first embodiment of the present invention. DETAILED DESCRIPTION

[0031] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0032] In the following description, many specific details are set forth to facilitate a full understanding of the present invention, but the present invention may also be implemented in other ways different from those described herein, and those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0033] Secondly, the term "one embodiment" or "embodiment" as used herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The term "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive with other embodiments.

[0034] The present invention is described in detail with reference to schematic diagrams. When describing the embodiments of the present invention, for the sake of convenience, the cross-sectional diagrams showing the device structure will not be partially enlarged according to the general scale, and the schematic diagrams are only examples, which should not limit the scope of protection of the present invention. In addition, in actual production, the three-dimensional dimensions of length, width and depth should be included.

[0035] At the same time, in the description of the present invention, it should be noted that the directions or positional relationships indicated by the terms "upper, lower, inner and outer" are based on the directions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific direction, be constructed and operated in a specific direction, and therefore cannot be understood as limiting the present invention. In addition, the terms "first, second or third" are only used for descriptive purposes and cannot be understood as indicating or implying relative importance.

[0036] In the present invention, unless otherwise clearly specified and limited, the terms "install, connect, connect" should be understood in a broad sense, for example: it can be a fixed connection, a detachable connection or an integral connection; it can also be a mechanical connection, an electrical connection or a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0037] Example 1

[0038] Reference Figures 1-2, which is the first embodiment of the present invention, provides a method for identifying a fault signal of a power system, comprising:

[0039] S1: Sample the power system signal and perform preprocessing.

[0040] Specifically, the preprocessing steps are as follows: (1) using a filtering module to filter out noise in the power system signal;

[0041] Reference Figure 1 , the filtering module includes a diode and an RC low-pass filter;

[0042] The power system signal x(n) is input to the diode, and the diode charges the capacitor by driving the RC low-pass filter. Then the low-frequency signal in the power system signal is filtered out by the RC low-pass filter, and the signal spectrum F(e jω )as follows:

[0043] F(e jω ) = DTFT[x(n)] × DTFT[h(n)];

[0044] Among them, e jω is a complex exponential sequence, DTFT is a discrete time Fourier transform, and h(n) is the RC low-pass filter.

[0045] Preferably, the resistor and capacitor of the RC low-pass filter are made of metal copper and metal aluminum, respectively, so as to reduce the influence of parasitic resistance and skin effect on the filter module and improve the noise reduction performance.

[0046] (2) Perform signal conversion through a conversion module;

[0047] The conversion module includes an inverter and a tri-state buffer;

[0048] The inverter is used to invert the signal output by the filter module;

[0049] Then the inverted signal is frequency converted through a tri-state buffer (the frequency conversion range is set to 3.2GHz to 3.2835GHz). The converted signal spectrum D(e jω )for;

[0050]

[0051] Among them, q is the frequency shift amount and δ is the unit pulse.

[0052] (3) Use a Darlington tube to amplify the converted signal to obtain the signal r(n).

[0053] The Darlington transistor of this embodiment is composed of NPN and PNP transistors. Assume that the three electrodes of the NPN transistor T1 are C1, B1, and E1 respectively, and the three electrodes of the PNP transistor T2 are C2, B2, and E2 respectively, wherein C1 is connected to B2, and E1 is connected to C2. The corresponding relationship of the pins of the equivalent transistor CBE is: C=E2, B=B1, and E=E1 (i.e., C2).

[0054] Preferably, the amplification factor of the Darlington tube is very high, and it can amplify very small signals to facilitate subsequent signal decomposition and processing.

[0055] S2: Decompose the preprocessed signal using the Hible transform strategy, and reconstruct the decomposed signal by adding white noise to obtain the reconstructed signal.

[0056] (1) Decomposition

[0057] The signal r(n) is decomposed by performing two Hilbert transforms according to the following formula:

[0058]

[0059] Among them, * is the convolution symbol, z(n) is the decomposed signal, j is the imaginary unit, and t is the time series.

[0060] (2) Reconstruction

[0061] White noise is a pair of positive and negative white noises with equal amplitudes and opposite signs. In this embodiment, white noise is added to the decomposed signal to generate a reconstructed signal z′(n):

[0062]

[0063] Where N represents N decomposed signals, i represents the i-th decomposed signal, and noise i (t) is the white noise corresponding to the i-th decomposed signal.

[0064] Preferably, this embodiment reduces the reconstruction error by adding white noise to the decomposed signal.

[0065] S3: Calculate the permutation entropy of the reconstructed signal.

[0066]

[0067] Among them, H p (m) is the permutation entropy of the reconstructed signal, P a is the probability that index a appears in the reconstructed signal; M is the number of indices.

[0068] It should be noted that any reconstructed signal has a corresponding index number:

[0069] Z(M)={a 1 , a 1 , …, a n}

[0070] Among them, Z(M) is the index number sequence.

[0071] Permutation Entropy H p The size of (m) represents the randomness of the signal. The smaller the value, the more regular the time series is; the larger the value, the more disordered the time series is.

[0072] S4: If the permutation entropy is less than the threshold value T, the interval corresponding to the permutation entropy is used as the interval where the power system state changes; otherwise, continue to reconstruct the signal.

[0073] S5: Setting a reference point for the interval where the power system state changes, calculating a signal-to-noise ratio, and locating a fault signal according to the signal-to-noise ratio.

[0074] In the interval {z′(n)}, reference points are continuously set for each time interval where n>L, and the signal-to-noise ratio SNR is calculated:

[0075]

[0076] If SNR(t) takes the maximum value at a certain moment and SNR(t)>3, it is determined that the moment is the moment when a fault occurs in the power system, and the fault signal can be located.

[0077] in, are the means of the subsequences before and after the selected benchmark point, s 1 、s 2 is the corresponding standard deviation.

[0078] Example 2

[0079] In order to verify and illustrate the technical effects adopted in this method, this embodiment selects a traditional technical solution and adopts this method for comparative testing, and compares the test results by means of scientific demonstration to verify the real effect of this method.

[0080] In order to verify that the present method has higher fault signal recognition accuracy than the traditional technical solution, in this embodiment, the traditional technical solution and the present method are used to perform fault detection comparison on the transformer respectively.

[0081] Three transformers were tested for each method, and the test results (residual percentage) obtained by each method were statistically analyzed. The results are shown in the following table.

[0082] Table 1: Fault signal recognition results.

[0083]

[0084] In summary, compared with traditional technical solutions, this method can more accurately identify fault signals.

[0085] It should be appreciated that embodiments of the present invention may be implemented or enforced by computer hardware, a combination of hardware and software, or by computer instructions stored in a non-transitory computer-readable memory. The method may be implemented in a computer program using standard programming techniques - including a non-transitory computer-readable storage medium configured with a computer program, wherein the storage medium so configured causes the computer to operate in a specific and predefined manner - according to the methods and drawings described in the specific embodiments. Each program may be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, if desired, the program may be implemented in an assembly or machine language. In any case, the language may be a compiled or interpreted language. In addition, the program may be run on a programmed ASIC for this purpose.

[0086] Furthermore, the operations of the processes described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The processes described herein (or variations and / or combinations thereof) may be performed under the control of one or more computer systems configured with executable instructions, and may be implemented as code (e.g., executable instructions, one or more computer programs, or one or more applications) that is executed collectively on one or more processors, by hardware, or a combination thereof. The computer program includes a plurality of instructions that may be executed by one or more processors.

[0087] Further, the method can be implemented in any type of computing platform that is operably connected to a suitable computer, including but not limited to a personal computer, a minicomputer, a mainframe, a workstation, a network or distributed computing environment, a separate or integrated computer platform, or in communication with a charged particle tool or other imaging device, etc. Aspects of the present invention can be implemented in machine-readable code stored on a non-transitory storage medium or device, whether removable or integrated into a computing platform, such as a hard disk, an optical read and / or write storage medium, a RAM, a ROM, etc., so that it can be read by a programmable computer, and when the storage medium or device is read by the computer, it can be used to configure and operate the computer to perform the process described herein. In addition, the machine-readable code, or part thereof, can be transmitted via a wired or wireless network. When such media includes instructions or programs that implement the steps described above in conjunction with a microprocessor or other data processor, the invention described herein includes these and other different types of non-transitory computer-readable storage media. When programmed according to the methods and techniques described in the present invention, the present invention also includes the computer itself. The computer program can be applied to input data to perform the functions described herein, thereby converting the input data to generate output data stored in a non-volatile memory. The output information can also be applied to one or more output devices such as a display. In a preferred embodiment of the present invention, the converted data represents a physical and tangible object, including a specific visual depiction of the physical and tangible object produced on a display.

[0088] As used in this application, the terms "component", "module", "system", etc. are intended to refer to a computer-related entity, which can be hardware, firmware, a combination of hardware and software, software, or software in operation. For example, a component can be, but is not limited to: a process running on a processor, a processor, an object, an executable file, a thread in execution, a program, and / or a computer. As an example, an application running on a computing device and the computing device can both be components. One or more components can exist in a process and / or thread in execution, and a component can be located in a computer and / or distributed between two or more computers. In addition, these components can be executed from various computer-readable media having various data structures thereon. These components can communicate in a local and / or remote process manner, such as according to a signal having one or more data packets (e.g., data from a component that interacts with another component in a local system, a distributed system, and / or interacts with other systems in a signal manner through a network such as the Internet).

[0089] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for identifying fault signals in a power system, characterized in that: include, The power system signal is sampled, and the noise in the power system signal is filtered out by a filter module, and the signal is converted by a conversion module, and the converted signal is amplified by a Darlington tube to obtain a preprocessed signal r(n); wherein: the Darlington tube is composed of NPN and PNP, the filter module includes a diode and an RC low-pass filter, and the resistor and capacitor of the RC low-pass filter are respectively made of metal copper and metal aluminum, and the use of the filter module to filter out the noise in the power system signal includes: The power system signal x(n) is input to the diode, and the RC low-pass filter is driven to filter out the low-frequency signal in the power system signal to obtain a signal spectrum. The signal spectrum F(e jω ) is: F(e jω )=DTFT[x(n)]×DTFT[h(n)]; Among them, e jω is a complex exponential sequence, DTFT is a discrete time Fourier transform, h(n) is the RC low-pass filter, and the resistor and capacitor of the RC low-pass filter are made of metal copper and metal aluminum respectively; The conversion module includes an inverter and a tri-state buffer, and the signal conversion by the conversion module includes: The inverter is used to invert the signal output by the filter module; The inverted signal is frequency-converted by a tri-state buffer, and the frequency conversion range is 3.2~3.2835GHz to obtain a converted signal spectrum. The converted signal spectrum D(e jω ) is expressed as; Among them, q is the frequency shift amount, δ is the unit pulse; The preprocessed signal is decomposed using the Hible transform strategy, and two positive and negative white noise pairs with equal amplitudes and opposite signs are added to the decomposed signal to generate a reconstructed signal. The expression of the reconstructed signal z′(n) is: Where N represents the number of decomposed signals, i represents the i-th signal after decomposition, and noise i (t) is the white noise corresponding to the i-th decomposed signal; Calculate the permutation entropy of the reconstructed signal; If the permutation entropy is less than the threshold value T, the interval corresponding to the permutation entropy is used as the interval where the power system state changes; otherwise, continue to reconstruct the signal; A reference point is set for the interval where the power system state changes, and a signal-to-noise ratio is calculated, and the fault signal is located according to the signal-to-noise ratio.

2. The method for identifying a fault signal in a power system according to claim 1, wherein: The decomposition includes, The signal r(n) is decomposed by performing two Hilbert transforms according to the following formula: Among them, * is the convolution symbol, z(n) is the decomposed signal, j is the imaginary unit, and t is the time series.

3. The method for identifying a fault signal in a power system according to claim 2, wherein: Calculating the permutation entropy of the reconstructed signal includes, Among them, H p (m) is the permutation entropy of the reconstructed signal, P a is the probability that index a appears in the reconstructed signal; M is the number of indices.

4. The method for identifying fault signals in a power system according to claim 3, wherein: include, Calculate the signal-to-noise ratio SNR: If SNR(t) takes the maximum value at a certain moment and SNR(t)>3, then it is determined that this moment is the moment when the power system fails; in, , are the means of the subsequences before and after the reference point, s1 and s2 are the standard deviations of the subsequences before and after the reference point.

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