A traveling wave detection method and system

Abstract

The present application discloses a traveling wave detection method and system, the method comprising collecting power signals according to a set sampling frequency, comparing the power signals of the current cycle with the power signals of the previous cycle, performing transient traveling wave signal detection when the comparison result is greater than a starting threshold, performing wavelet transform on the power signals of the current cycle to obtain traveling wave head information, and updating the starting threshold according to the number of traveling wave detections in the current cycle. Through the implementation of the present invention, the traveling wave detection starting threshold is adaptively adjusted according to the result of the wavelet transform, so that the detection device can capture the traveling wave more accurately, with both low computational complexity and high precision.

Description

Technical Field

[0001] The present invention belongs to the technical field of traveling wave detection, and in particular relates to a traveling wave detection method and system. Background Art

[0002] With the access of large-scale distributed energy and the rapid construction of new power systems, power electronic equipment such as photovoltaics, wind power, electric vehicles, and energy storage in distribution networks are increasing, and the penetration of distributed energy is increasing, which may lead to bidirectional power flow under normal operation. At the same time, in the case of a short circuit, the short-circuit current of power electronic equipment such as inverters is limited, resulting in a reduction in fault current. How to accurately obtain the fault location in the case of a weak fault will be a key consideration in the construction of new power systems. Even at a high penetration rate, high-frequency fault information, such as traveling waves, will be generated after a fault occurs. Therefore, obtaining the fault location through traveling waves is an important research direction for the current new power system.

[0003] However, the acquisition, sensitivity and reliability of traveling waves are a pair of contradictions that are difficult to adjust. If the traveling wave threshold is set too low, that is, a slight fluctuation is considered to be a traveling wave fault, it may cause a lot of traveling waves to be generated on site when there is no fault, resulting in a huge amount of data; if the traveling wave threshold is set too high, a large current fluctuation is required for the detection device to sense it, which may result in the device being unable to capture the traveling wave in time when a real fault occurs. Summary of the invention

[0004] Based on this, the present invention aims to propose a traveling wave detection method and system to achieve adaptive adjustment of the threshold for starting traveling wave detection, so as to overcome the defects of the prior art.

[0005] The present invention provides a traveling wave detection method, comprising:

[0006] Collect power signals according to a set sampling frequency;

[0007] Compare the power signal of the current cycle with that of the previous cycle, and perform transient traveling wave signal detection when the comparison result is greater than the start threshold;

[0008] Perform wavelet transform on the power signal of the current cycle to obtain the traveling wave head information;

[0009] The start threshold is updated according to the number of detections of the traveling wave in the current cycle.

[0010] Further, comparing the power signal of the current cycle with the power signal of the previous cycle includes:

[0011] The comprehensive value of the power signal within a cycle is calculated according to the following expression:

[0012] Where p(t) represents the comprehensive value of the power signal, x a(t), x b (t), x c (t) represents the power signals of the three phases A, B and C respectively;

[0013] Compare the comprehensive value of the power signal of the current cycle with that of the previous cycle.

[0014] Further, updating the start threshold according to the number of detections of the traveling wave in the current cycle includes:

[0015] Update the startup threshold according to the following calculation formula:

[0016] Among them, p' set represents the updated value of the start threshold, p set represents the currently unupdated start threshold, and M represents the number of detections of traveling waves in the current cycle.

[0017] Furthermore, performing wavelet transform on the power signal of the current cycle to obtain the traveling wave header information includes:

[0018] The time t represents the translation coefficient of the wavelet transform, and the wavelet transform of the power signal of the current cycle is performed according to the following expression:

[0019] (W ψ x(t))(s,t)=x(t)*ψ s (t), where x(t) represents the power signal, s represents the scale factor, and ψ s (t) represents the wavelet function, and the operator * represents convolution;

[0020] The singularity index Lipischitz is used to detect signal mutations in wavelet transform results. The location where the modulus maximum value increases is the traveling wave head information.

[0021] Furthermore, the wavelet transform includes:

[0022] Use spline function to solve wavelet basis function ψ(t);

[0023] The wavelet basis function ψ(t) is scaled by a scale factor of s to obtain the wavelet function ψ s (t);

[0024] For the wavelet function ψ s (t) Perform Z transform;

[0025] For the wavelet transform (W ψ x(t))(s,t) performs Z transformation;

[0026] Inverse the wavelet transform after Z transformation.

[0027] Furthermore, using the spline function to solve the wavelet basis function ψ(t) includes:

[0028] The third-order B-spline function is obtained by performing three convolutions on the 0-order B-spline function. The convolution process is performed according to the following expression:

[0029] β m represents the m-order B-spline function, β0 represents the 0-order B-spline function, and the operator * represents convolution;

[0030] Taking the first-order derivative of the third-order B-spline function, we get the wavelet basis function ψ(t) as follows:

[0031]

[0032] Furthermore, the wavelet transform also includes:

[0033] For the wavelet function ψ s (t) is discretized, and t = nT, j represents the discretization scale coefficient, T represents the sampling period, n takes an integer, and the discrete wavelet function is expressed as

[0034]

[0035] Furthermore, for the wavelet function ψ s (t) Z transform is performed according to the following expression

[0036]

[0037] Furthermore, for the wavelet transform (W ψ The Z transform of x(t))(s,t) is performed as follows

[0038]

[0039] Furthermore, the inverse expression of the Z transform is

[0040]

[0041] W j x(k) represents the wavelet transform value of the kth point at scale j.

[0042] The present invention also provides a traveling wave detection system, comprising:

[0043] A power signal acquisition unit, used for acquiring power signals according to a set sampling frequency;

[0044] A traveling wave detection start judgment unit is used to compare the power signal of the current cycle with that of the previous cycle, and to perform transient traveling wave signal detection when the comparison result is greater than the start threshold;

[0045] A traveling wave analysis unit, used for performing wavelet transform on the power signal of the current cycle to obtain traveling wave header information;

[0046] The start threshold updating unit is used to update the start threshold according to the number of occurrences of the traveling wave in the current cycle.

[0047] It can be seen from the above technical solutions that the present invention has the following beneficial effects:

[0048] The present invention provides a traveling wave detection method and system, wherein the method compares the power signals of the current cycle with the previous cycle, starts the traveling wave detection only when the comparison result is greater than the starting threshold, uses wavelet transform to obtain the traveling wave header information, and updates the starting threshold according to the number of traveling wave detection times. The start of transient traveling wave detection is adjusted in real time according to the result of wavelet transform, and traveling wave detection is not performed when the starting conditions are not met, thereby saving calculation amount and calculation resources; the starting threshold is adaptively adjusted so that the detection device can capture the traveling wave more accurately, and has both low calculation amount and high precision. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0050] Figure 1 Implementation flow chart of a traveling wave detection method provided by an embodiment of the present invention

[0051] Figure 2 Schematic diagram of wavelet transform implementation provided by an embodiment of the present invention

[0052] Figure 3 A structural block diagram of a traveling wave detection system provided by an embodiment of the present invention DETAILED DESCRIPTION

[0053] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0054] See also Figure 1 This embodiment provides a traveling wave detection method, which can adaptively adjust the start threshold of traveling wave detection, thereby reducing the amount of calculation and improving detection accuracy.

[0055] The method of this embodiment includes:

[0056] The power signal is collected according to the set sampling frequency.

[0057] In this embodiment, full-cycle traveling wave detection is adopted, and a comprehensive value is used to represent the power signal within one cycle, which is specifically expressed as Where p(t) represents the comprehensive value of the power signal, x a (t), x b (t), x c (t) represents the sampled power signals of the three phases A, B, and C respectively.

[0058] In a further embodiment, the power signal x(t) may be a current signal or a voltage signal.

[0059] The power signals of the current cycle are compared with those of the previous cycle, and transient traveling wave signal detection is performed when the comparison result is greater than the start threshold.

[0060] The start criterion given in this embodiment can be specifically expressed as [p(t)-p(tT)]>p set , p(tT) represents the comprehensive value of the power signal in the previous cycle, p set Indicates the startup threshold.

[0061] Under normal circumstances, traveling wave detection will not be started. Only signals that meet the start criteria will trigger traveling wave detection. The data density of transient traveling wave detection is relatively small, which can effectively reduce the amount of calculation.

[0062] Perform wavelet transform on the power signal of the current cycle to obtain the traveling wave head information.

[0063] The start threshold is updated according to the number of detections of the traveling wave in the current cycle.

[0064] When the traveling wave signal is frequently detected and captured within a cycle, it is considered that the current start threshold is set too low and the threshold needs to be adjusted. In this embodiment, the method of updating the start threshold is adjusted in real time according to the result of wavelet transformation. The specific adjustment is performed according to the following calculation formula

[0065] Among them, p' set represents the updated value of the start threshold, p set represents the currently unupdated start threshold, and M represents the number of detections of traveling waves in the current cycle.

[0066] It can be seen from the expression that if the number of traveling wave detections is too many, the start threshold will be increased accordingly, and the threshold can be adaptively adjusted with continuous wavelet transform operations.

[0067] Another embodiment of the present invention is described below. The process of wavelet transform is improved on the basis of the above embodiment. Figure 2 shown.

[0068] Wavelet transform is a time-frequency domain analysis method that can accurately describe the characteristics of signals at different locations and frequency bands, making it an effective tool for identifying and analyzing transient signals such as mutations. The traditional continuous wavelet transform can be expressed as

[0069]

[0070] in, is the wavelet function The complex conjugate of , a, b∈R, a≠0, a is called the scaling coefficient, which represents the scaling of the basic wavelet ψ(t); b is called the translation coefficient, which represents the translation of the basic wavelet ψ(t).

[0071] When a signal is discontinuous at a certain point or a certain derivative is discontinuous, the function is said to have a singularity at that point. Singularity detection can be used to describe the mutation of the signal. Specifically, in wavelet transform, the modulus maximum at different scales is obtained through wavelet transform. The singularity detection of the signal not only gives the moment when the signal mutates, but also gives a description of the severity of the signal change. Specifically, for noise signals, the modulus maximum of the wavelet transform decreases as the scale increases; while for mutation signals, the modulus maximum increases as the scale increases. This is also the basis for obtaining the head information of the traveling wave through wavelet transform.

[0072] The traditional wavelet transform is solved by the Mallat algorithm, which is based on multi-resolution analysis and realizes the decomposition and reconstruction of the signal, but the signal is scaled to 2. j When performing wavelet transform, it is necessary to calculate each scale value in the middle. As the scale increases, the amount of calculation increases exponentially, which makes it difficult to meet the requirements of the synchronous phasor measurement device for efficient algorithms. In synchronous phasor measurement, it is only necessary to take the modulus maximum of a specific point of a scale or several scales of the wavelet transform, and the wavelet transform value of each point of each scale of the signal is not concerned, and there is no need for reconstruction. Based on this, this embodiment proposes an improvement on the wavelet transform algorithm for identifying transient processes of power signals in response to the specific needs of synchronous phasor measurement, thereby realizing adaptive switching calculation mode.

[0073] The time t is used to replace the translation coefficient in the traditional wavelet transform, and the wavelet transform in this embodiment can be expressed as (W ψ x(t))(s,t)=x(t)*ψ s (t), where x(t) represents the power signal, s represents the scale factor, and ψ s (t) represents the wavelet function and the operator * represents convolution.

[0074] Wavelet function ψ s(t) is obtained by scaling the wavelet basis function ψ(t) using the scale factor s, that is,

[0075] Specifically, this embodiment uses a spline function to solve the wavelet basis function ψ(t).

[0076] Spline function is a kind of function that is piecewise smooth and has a certain smoothness at the connection points. It is widely used in the field of numerical analysis. Among them, B-spline function can detect the singularity of the signal well and is easy to implement by computer. It is considered to be one of the best functions for constructing wavelet transform. The 0th order B-spline function is the characteristic function on the unit interval [0,1]. The nth order central B-spline function can be generated by repeatedly convolving the 0th order B-spline function, that is, β m represents the m-order B-spline function, β0 represents the 0-order B-spline function, and the operator * represents convolution.

[0077] In this embodiment, the first-order derivative of the third-order B-spline function is selected as the wavelet basis function. The third-order B-spline function β3 can be obtained by performing three convolutions on the 0-order B-spline function. The first-order derivative of β3 is solved to obtain the wavelet basis function

[0078]

[0079] Since the wavelet function ψ s (t) is the expansion of the wavelet basis function ψ(t) on the scale factor s. s (t) is discretized, and t = nT, j represents the discretization scale coefficient, T represents the sampling period, n takes an integer, and the discrete wavelet function is expressed as

[0080]

[0081] In order to eliminate a large number of intermediate calculation processes and save computing resources, this embodiment performs the wavelet function ψ s (t) Perform Z transform and get the following expression

[0082]

[0083] Then the wavelet transform (W ψ The Z transform of x(t))(s,t) gives the following expression

[0084]

[0085] The inverse of the wavelet transform after Z transform is

[0086]

[0087] W jx(k) represents the wavelet transform value of the kth point at scale j.

[0088] The singularity index Lipischitz is used to detect signal mutations in wavelet transform results. The location where the modulus maximum value increases is the traveling wave head information.

[0089] See also Figure 3 The following embodiment further provides a traveling wave detection system, which is used to implement the traveling wave detection method described in the above embodiment, including:

[0090] The power signal acquisition unit 201 is used to acquire the power signal according to the set sampling frequency;

[0091] The traveling wave detection start judgment unit 202 is used to compare the power signal of the current cycle with the power signal of the previous cycle, and perform transient traveling wave signal detection when the comparison result is greater than the start threshold;

[0092] The traveling wave analysis unit 203 is used to perform wavelet transform on the power signal of the current cycle to obtain the traveling wave header information;

[0093] The start threshold updating unit 204 is used to update the start threshold according to the number of occurrences of the traveling wave in the current cycle.

[0094] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A traveling wave detection method, characterized in that: include: Collect power signals according to a set sampling frequency; The comparison result is obtained by comparing the comprehensive value of the power signal of the current cycle with that of the previous cycle. The comprehensive value of the power signal in a single cycle is expressed as Where p(t) represents the comprehensive value of the power signal, x a (t), x b (t), x c (t) represents the power signals of the three phases A, B and C respectively; When the comparison result is greater than the start threshold, transient traveling wave signal detection is performed. The start threshold is updated according to the number of detections of traveling waves in the current cycle. Specifically, the start threshold is updated according to the following calculation formula: Among them, p′ set represents the updated value of the start threshold, p set represents the currently unupdated start threshold, M represents the number of detections of traveling waves in the current cycle; Perform wavelet transform on the power signal of the current cycle to obtain the traveling wave head information.

2. The traveling wave detection method according to claim 1, characterized in that: The step of performing wavelet transform on the power signal of the current cycle to obtain the traveling wave header information comprises: The time t represents the translation coefficient of the wavelet transform, and the wavelet transform of the power signal of the current cycle is performed according to the following expression: (W ψ x(t))(s,t)=x(t)*ψ s (t), where x(t) represents the power signal, s represents the scale factor, and ψ s (t) represents the wavelet function, and the operator * represents convolution; The singularity index Lipischitz is used to detect signal mutations in wavelet transform results. The location where the modulus maximum value increases is the traveling wave head information.

3. The traveling wave detection method according to claim 2, characterized in that: The wavelet transform comprises: Use spline function to solve wavelet basis function ψ(t); The wavelet basis function ψ(t) is scaled by a scale factor of s to obtain the wavelet function ψ s (t); For the wavelet function ψ s (t) Perform Z transform; For the wavelet transform (W ψ x(t))(s,t) performs Z transformation; Inverse the wavelet transform after Z transformation.

4. The traveling wave detection method according to claim 3, characterized in that: The method of solving the wavelet basis function ψ(t) by using the spline function comprises: The third-order B-spline function is obtained by performing three convolutions on the 0-order B-spline function. The convolution process is performed according to the following expression: β m represents the m-order B-spline function, β0 represents the 0-order B-spline function, and the operator * represents convolution; Taking the first-order derivative of the third-order B-spline function, we get the wavelet basis function ψ(t) as follows:

5. The traveling wave detection method according to claim 3, characterized in that: The wavelet transform also includes: For the wavelet function ψ s (t) is discretized, and t = nT, j represents the discretization scale coefficient, T represents the sampling period, n takes an integer, and the discrete wavelet function is expressed as 6. The traveling wave detection method according to claim 5, characterized in that: The wavelet function ψ s (t) Z transform is performed according to the following expression 7. The traveling wave detection method according to claim 6, characterized in that: The wavelet transform formula (W ψ The Z transform of x(t))(s,t) is performed as follows 8. A traveling wave detection system, characterized in that: include: A power signal acquisition unit, used for acquiring power signals according to a set sampling frequency; The traveling wave detection start judgment unit is used to compare the comprehensive value of the power signal of the current cycle with that of the previous cycle to obtain a comparison result. The comprehensive value of the power signal in a single cycle is expressed as Where p(t) represents the comprehensive value of the power signal, x a (t), x b (t), x c (t) represents the power signals of the three phases A, B and C respectively; When the comparison result is greater than the start threshold, transient traveling wave signal detection is performed. The start threshold is updated according to the number of detections of traveling waves in the current cycle. Specifically, the start threshold is updated according to the following calculation formula: Among them, p′ set represents the updated value of the start threshold, p set represents the currently unupdated start threshold, M represents the number of detections of traveling waves in the current cycle; The traveling wave analysis unit is used to perform wavelet transform on the power signal of the current cycle to obtain the traveling wave header information.

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