Electric energy quality rapid disturbance detection method based on improved K-S conversion

Abstract

The invention discloses a power quality rapid disturbance detection method based on improved K-S conversion. The method comprises the following steps: obtaining a discrete sampling sequence of power grid detection nodes; performing fast Fourier transform to obtain a signal frequency spectrum; iterative cyclic filtering processing is carried out on the signal frequency spectrum, and the maximum value of the filtered frequency spectrum is calculated to serve as a frequency feature point; carrying out improved K-S transformation on the signal to obtain a transformed matrix; extracting a fundamental frequency part from the transformed matrix to obtain a section of one-dimensional time signal representing a waveform of a fundamental wave in signal duration, calculating a maximum value, a minimum value and a sudden change time point to obtain time domain disturbance characteristics, and detecting disturbance amplitudes, starting time and ending time of voltage sag, voltage sag, voltage interruption and flicker; and determining the frequency, the amplitude and the duration of each frequency component according to the transformed matrix, and detecting frequency domain disturbance including harmonic waves and transient oscillation.

Description

Technical Field

[0001] The present invention belongs to the field of power quality composite disturbance detection, and particularly relates to a power quality rapid disturbance detection method based on improved KS transformation. Background Art

[0002] The demand for electric energy continues to grow, and higher requirements are placed on the guarantee of power quality. Good power quality is not only related to the safety and economic operation of the power grid, but also directly affects the national economic benefits. However, with the continuous changes in the power grid structure, large-scale access to nonlinear loads, new energy generation and power electronic equipment, power quality issues are becoming more and more complicated. Phenomena such as voltage fluctuations and harmonic pollution occur frequently, threatening the normal operation of equipment and may cause serious economic losses. In response to this challenge, it is crucial to quickly and accurately identify and classify power quality disturbances (PQD).

[0003] The power quality disturbance detection method can be roughly divided into time domain analysis, frequency domain analysis, and time-frequency domain analysis according to the different transformation domains. Among them, time domain analysis and frequency domain analysis are only effective for a single type of disturbance, such as time domain disturbance and frequency domain disturbance. For example, the time domain analysis method cannot detect harmonic disturbances in the frequency domain, so the time-frequency joint analysis method was born. Domestic and foreign researchers have obtained many high-quality results in this field, such as short-time Fourier transform, wavelet transform, S transform and other methods. Among the above methods, short-time Fourier transform cannot be used for practical application of power quality disturbance detection due to its fixed window width, spectrum leakage, fence effect and large amount of calculation; the accuracy of wavelet transform is highly dependent on the selected mother wavelet and decomposition scale, and the presence of noise will deteriorate its disturbance detection ability; S transform, as the inheritance and development of short-time Fourier transform and wavelet transform, is the most popular disturbance detection method at present, but its Gaussian window characteristics and huge amount of calculation make it difficult to directly apply it in practice.

[0004] In summary, with the massive access of distributed energy and the high power electronics of power systems today, a rapid power quality disturbance detection method is needed to accurately detect power quality composite disturbance signals. Summary of the invention

[0005] In order to solve the problem that it is difficult to accurately detect power quality composite disturbances, the present invention proposes a power quality rapid disturbance detection method based on improved KS transformation, which can be applied to power quality composite disturbance detection in modern high-proportion renewable energy power electronics systems. The technical solution of the present invention is as follows:

[0006] A method for rapid power quality disturbance detection based on improved KS transformation comprises the following steps:

[0007] Step 1: Obtain voltage signals from the power grid detection nodes and discretize them to obtain discrete sampling sequences;

[0008] Step 2: Perform fast Fourier transform on the discrete sampling signal to obtain the signal spectrum;

[0009] Step 3: Perform iterative loop filtering on the signal spectrum to remove the noise that interferes with the extraction of characteristic frequencies, and obtain the maximum value of the filtered spectrum as the frequency characteristic point;

[0010] Step 4: Perform an improved KS transformation on the signal to obtain a transformed matrix. The rows of the matrix represent the frequency components contained in the measured signal, and the columns represent the changes in the amplitude of each frequency component of the signal over time. The modulus of the matrix is ​​the amplitude of the frequency component. When forming the matrix, only the rows where the characteristic frequencies are screened out after the iterative loop filtering process in step 3 are calculated, and the remaining non-frequency characteristic rows are set to 0;

[0011] Step 5: Preset the range of fundamental voltage amplitude variation when voltage sag or voltage interruption occurs; extract the fundamental frequency part from the transformed matrix to obtain a one-dimensional time signal, which represents the waveform of the fundamental wave during the signal duration. Calculate its maximum value, minimum value, and mutation time point to obtain the time domain disturbance characteristics, and detect the disturbance amplitude, start time, and end time of voltage sag, voltage swell, voltage interruption, and flicker;

[0012] Step 6: Determine the frequency, amplitude, and duration of each frequency component based on the transformed matrix, i.e., the harmonic frequencies f1, f2, f3, ...; the corresponding harmonic amplitudes A1, A2, A3, ...; and the frequency component existence time t1, t2, t3, ..., and detect frequency domain disturbances including harmonics and transient oscillations.

[0013] Furthermore, let the discrete sampling sequence obtained in step 1 be x s (nT s ), where T s is the sampling interval of the power grid sampling device, n is the sampling point number; in step 2, the discrete sampling signal x s (nT s ) is subjected to fast Fourier transform to obtain the signal spectrum X0(m):

[0014]

[0015] In the formula, m, n = 0, 1, ..., N-1, and N is the total number of sampling points.

[0016] Furthermore, the method of step 3 is:

[0017] Perform iterative loop filtering on the signal spectrum:

[0018]

[0019] In the formula, l = 1, 2; d = 0, 1, 2, ...; m = 2, 3, ..., N-3, find the maximum value of the above formula, and set the threshold η to remove the noise that interferes with the characteristic frequency extraction, and select the characteristic frequency point m that satisfies the following formula: i :

[0020] X d+1 (m i -1) <X d+1 (m i )>X d+1 (m i +1)

[0021] X d+1 (m i )>η

[0022] Record the obtained frequency characteristic point m i location.

[0023] Furthermore, in step 4, the signal is subjected to an improved KS transformation, and the method for obtaining the transformed matrix is ​​as follows:

[0024] The transformation formula for improving the KS transformation of the signal is as follows

[0025]

[0026] Among them, ω G (τ-t,f) is the Gaussian window function, τ is the oscillation attenuation factor, The parameter η1 is used to adjust the rate of change of the window shape with the frequency f, η2 is used to adjust the window shape at the fundamental frequency, and I0(·) is the first kind of modified zero-order Bessel function.

[0027] Furthermore, in step 5, when the fundamental voltage amplitude increases by 10% to 80%, it is defined as a voltage swell; when the fundamental voltage amplitude shows a sinusoidal oscillation, it is defined as a voltage flicker disturbance; when the fundamental voltage amplitude decreases by 10% to 90% during the disturbance, it is defined as a voltage sag; and when it decreases by 90% to 100%, it is defined as a voltage interruption.

[0028] Furthermore, in step 6, the fundamental frequency is 50 Hz. When one or more frequency components other than the fundamental frequency component appear between 0 Hz and 700 Hz and exist in the entire time domain of the input signal, it is defined as the occurrence of harmonic disturbance; and when a frequency component between 700 Hz and 1600 Hz appears and lasts only for 0.01s to 0.06s, it can be identified as the occurrence of transient oscillation disturbance.

[0029] Furthermore, the composite disturbance is a superposition of voltage sag, voltage swell, voltage interruption, voltage flicker, harmonics and transient oscillation disturbances, and is detected by judging its characteristics according to the judgment method in step 5 and step 6. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 Voltage sag waveform

[0031] Figure 2 The voltage rise waveform

[0032] Figure 3 Voltage interruption waveform

[0033] Figure 4 Voltage flicker waveform

[0034] Figure 5 The harmonic disturbance waveform

[0035] Figure 6 Transient oscillation waveform

[0036] Figure 7 Voltage interruption + harmonic disturbance + transient oscillation waveform

[0037] Figure 8 Comparison of frequency responses of 50 Hz and 500 Hz window functions

[0038] Fig. 9 Kaiser window variable β time domain waveform

[0039] Fig.10 Frequency feature point extraction results

[0040] Fig.11 Improve KS transformation detection results

[0041] Fig.12 50Hz fundamental frequency amplitude curve extraction results

[0042] Fig.13 Disturbance frequency domain amplitude diagram DETAILED DESCRIPTION

[0043] Firstly, the technical solution and technical feasibility of the present invention are explained and verified below.

[0044] Step 1: Establish mathematical models of 21 different power quality disturbance signals.

[0045] Assuming that the input signal detected by the sampling device in the power system is x(t), the normal voltage signal model can be established as:

[0046] x(t)=Asin(ω0t)

[0047] Where A is the standard voltage amplitude, t is the time value, angular frequency ω0 = 2πf0, and the alternating current frequency in China is 50 Hz. Therefore, in subsequent modeling, f0 = 50 Hz is uniformly used. The single disturbances in the power system are divided into voltage sags, voltage swells, voltage interruptions, voltage flickers, harmonics, and transient oscillations. The modeling of the above six single disturbances is as follows:

[0048] Voltage sag:

[0049] x(t) = [1 - A[ε(t - t1) - ε(t - t2)]]sin(ω0t)

[0050] The voltage sag is defined as the rapid drop of the effective value of the supply voltage to 90% - 10% of the rated value. In the formula, ε(t) is the step function, and its defined expression is

[0051]

[0052] t1 is the starting time of the voltage sag, t2 is the ending time of the voltage sag, A is the depth of the voltage sag, and the parameter value range is 0.1 < A < 0.9, T < t2 - t1 < 9T. Here, T is the length of one cycle, which is 0.02 s.

[0053] Voltage swell:

[0054] x(t) = [1 + A[ε(t - t1) - ε(t - t2)]]sin(ω0t)

[0055] The voltage swell is defined as the rapid rise of the effective voltage value to 110% - 180% of the rated value. In the formula, t1 is the starting time of the voltage swell, t2 is the ending time of the voltage swell, A is the height of the voltage swell, and the parameter value range is 0.1 < A < 0.8, T < t2 - t1 < 9T. Here, T is the length of one cycle, which is 0.02 s.

[0056] Voltage interruption:

[0057] x(t) = [1 - A[ε(t - t1) - ε(t - t2)]]sin(ω0t)

[0058] The voltage interruption is defined as the rapid drop of the effective voltage value to 90% - 100% of the rated value. In the formula, t1 is the starting time of the voltage interruption, t2 is the ending time of the voltage interruption, A is the depth of the voltage interruption, and the parameter value range is 0.9 < A < 1, T < t2 - t1 < 9T. Here, T is the length of one cycle, which is 0.02 s.

[0059] Voltage flicker:

[0060] Voltage flicker is the slow sinusoidal periodic change of the amplitude of the sinusoidal voltage signal with time. Its mathematical modeling is

[0061] x(t) = [1 + Asin(2πf f t)]sin(ω0t)

[0062] where A is the flicker amplitude, generally taking 0.1 < A < 0.2, and f f is the flicker frequency, with a value range of 5 Hz < f f < 25 Hz harmonics:

[0063]

[0064] Harmonic disturbance is the presence of non-linear components with frequencies higher than the fundamental frequency in the power system, which is mathematically represented as the sum of multiple sine waves. In the formula, A i is the amplitude of each harmonic, is the initial phase angle of each harmonic, i is the harmonic order. Harmonics in the power system are usually considered as integer multiples of the fundamental frequency, so i = 1, 2, 3, …, n.

[0065] Transient oscillation:

[0066]

[0067] where A is the oscillation amplitude, taking 0.1 - 0.8; t1 and t2 are the start and end times of the oscillation, 0.5T < t2 - t1 < 3T; τ is the oscillation decay factor, with a value range of 25 - 330, and f is the oscillation frequency, generally taking 700 - 1600 Hz.

[0068] Step 2: Replace the window function of the S transform and improve its parameters.

[0069] Define the expression of the S transform as

[0070]

[0071] x(t) is the input power quality disturbance signal, ω G (τ - t, f) is the Gaussian window function, and its expression is:

[0072]

[0073] where σ(f) is the window width adjustment factor of the Gaussian window function, used to adjust the main lobe width of the window function.

[0074] Define the expression of the Kaiser window function as

[0075]

[0076] β is the adjustment factor of the Kaiser window function, used to adjust the ratio between the main lobe and the side lobes, and I0(·) is the first kind of modified zero-order Bessel function,

[0077] Improve the adjustment factor β and change the Kaiser window function to

[0078]

[0079] in The parameter η1 is mainly used to adjust the rate of change of the window shape with the frequency f, and η2 can more conveniently adjust the window shape at the base frequency. Both are constant parameters.

[0080] Replace the Gaussian window in the S transform with the improved Kaiser window to obtain the improved KS transform expression:

[0081]

[0082] Step 3: Extract the frequency feature points of the disturbance signal

[0083] Discretize the disturbance signal x(t) to obtain the discrete disturbance signal x s (nT s ), where T s is the sampling interval of the power grid sampling device, n is the sampling point number, and for the discrete signal x s (nT s ) is used to perform fast Fourier transform to obtain the signal spectrum X0(m),

[0084]

[0085] In the formula, m, n = 0, 1, ..., N-1, and N is the total number of sampling points.

[0086] Perform iterative loop filtering on the signal spectrum

[0087]

[0088] In the formula, l = 1, 2; d = 0, 1, 2, ...; m = 2, 3, ..., N-3, find the maximum value of the above formula, and set a suitable threshold η to remove the noise that interferes with the characteristic frequency extraction, and select the characteristic frequency point m that satisfies the following formula: i

[0089] X d+1 (m i -1) <X d+1 (m i )>X d+1 (m i +1)

[0090] X d+1 (m i )>η

[0091] Record the obtained frequency characteristic point mi location.

[0092] Step 4: Perform KS transformation on the feature points to detect power quality composite disturbances

[0093] The mathematical modeling of 21 disturbance signals was carried out in Matlab software, and 30dB Gaussian white noise was added to it. The disturbance signals were sampled to simulate the real power grid sampling process. The improved KS transformation expression obtained in step 2 and the convolution theorem can be deduced.

[0094]

[0095] Where X(f+v) is the frequency spectrum of the disturbance signal after frequency shift, W GK (v, α, β) is the spectrum of the improved Kaiser window function, so the process can be quickly calculated by fast Fourier transform and inverse Fourier transform.

[0096] According to the disturbance signal spectrum, the disturbance signal frequency shift matrix X is formed (M+1)×N

[0097]

[0098] The value of M is floor(N / 2). Similarly, the window function spectrum matrix is ​​constructed. The Hadamard product (corresponding element multiplication) is performed on the two, while skipping the non-characteristic frequency parts. The obtained matrix is ​​subjected to inverse fast Fourier transform to quickly obtain the result matrix of the improved KS transform. The rows of the matrix represent the changes of the signal frequency components over time. The power quality disturbance signal can be detected by observing the matrix data.

[0099] The method for rapid power quality disturbance detection based on improved KS transformation proposed by the present invention is described in detail below in conjunction with the accompanying drawings and embodiments.

[0100] (1) Establish mathematical models of 21 different power quality disturbance signals

[0101] According to the above mathematical model, six single disturbance waveforms were established using Matlab. Figures 1 to 6 As shown, a composite disturbance can be constructed. Generally speaking, voltage sag, voltage swell, voltage interruption and voltage flicker can be defined as time domain disturbances, while harmonics and transient oscillations can be defined as frequency domain disturbances. The composite disturbance of the power system is composed of time domain disturbances and frequency domain disturbances. The 21 different power quality disturbance signals are shown in Table 1. Now, a typical example of composite disturbance is given, and the others are not repeated. For example, voltage interruption + harmonic disturbance + transient oscillation disturbance, and its waveform is as follows: Figure 7 .

[0102] Table 1

[0103] C1 Temporary Drop C8 Harmonics + Interruptions C15 oscillation + temporary rise C2 Interrupt C9 harmonic + temporary rise C16 flicker + harmonics + temporary rise C3 Temporary Rise C10 Harmonics + Oscillations C17 flicker + harmonics + sag C4 Harmonic C11 flicker + harmonics C18 harmonics + oscillation + temporary rise C5 Flicker C12 flicker + temporary rise C19 harmonics + oscillation + temporary dip C6 transient oscillation C13 flicker + sag C20 harmonics + oscillation + interruption C7 harmonic + dip C14 oscillation + dip C21 harmonics + oscillation + flicker

[0104] (2) Replace the window function of the S transform and improve its parameters.

[0105] The expression of S transform is

[0106]

[0107] The original S transform uses a Gaussian window whose window width varies with frequency. The main lobe width of the spectrum window gradually widens as the signal frequency f increases. In other words, the time resolution of the S transform gradually increases as the signal frequency f increases. Therefore, the S transform has the characteristics of strong frequency resolution in the low-frequency band and high amplitude detection accuracy in the high-frequency band. However, when detecting time-domain disturbances such as voltage sags in power systems, better time resolution and amplitude detection accuracy are required. Detecting medium- and high-frequency disturbances such as harmonics and oscillations requires better frequency resolution. The effect brought by the Gaussian window is completely opposite to the demand. Therefore, here we consider using the Kaiser window instead of the Gaussian window for S transform. The frequency response comparison of the two at 50 Hz and 500 Hz is shown in the figure below. Figure 8 As shown in the figure, it can be seen that at the low frequency of 50 Hz, the Kaiser window has a main lobe width similar to that of the Gaussian window, but can obtain lower side lobe attenuation than the Gaussian window, which can effectively suppress spectrum leakage; at the high frequency of 500 Hz, the Kaiser window can obtain a narrower main lobe width than the Gaussian window, which can obtain a higher frequency resolution to detect frequency domain disturbances.

[0108] Define the Kaiser window function expression as

[0109]

[0110] β is the Kaiser window function adjustment factor, which is used to adjust the ratio between the main lobe and the side lobe. I0(@) is the first kind of modified zero-order Bessel function. Where β is a constant function, and the Kaiser window β time domain waveform is as follows: Fig. 9 As shown in Figure 2, in order to enable Kaiser to have adaptive window width adjustment capability in power quality detection, the adjustment factor β is improved and the Kaiser window function is changed to

[0111]

[0112] in The parameter η1 is mainly used to adjust the rate of change of the window shape with the frequency f, and η2 can more conveniently adjust the window shape at the base frequency. Both are constant parameters. In practical applications, it is only necessary to control α(f) between 90 and 100 to obtain good detection effects.

[0113] (3) Extracting the frequency characteristic points of the disturbance signal

[0114] The S transform requires a lot of calculations, mainly because each row of the KS matrix contains a characteristic frequency. However, in actual working conditions, most frequency points cannot provide effective information. If only the frequency characteristic points can be calculated, the calculation efficiency will be greatly improved. Therefore, this paper adopts an iterative filtering method to extract frequency characteristics. First, the disturbance signal x(t) is discretized to obtain the discrete disturbance signal x s (nT s ), where T s is the sampling interval of the power grid sampling device, n is the sampling point number, and for the discrete signal x s (nT s ) is used to perform fast Fourier transform to obtain the signal spectrum X0(m),

[0115]

[0116] In the formula, m, n = 0, 1, ..., N-1, and N is the total number of sampling points.

[0117] Perform iterative loop filtering on the signal spectrum

[0118]

[0119] In the formula, l = 1, 2; d = 0, 1, 2, ...; m = 2, 3, ..., N-3, find the maximum value of the above formula, and set a suitable threshold η to remove the noise that interferes with the characteristic frequency extraction, and select the characteristic frequency point m that satisfies the following formula: i

[0120] X d+1 (m i -1) <X d+1 (m i )>X d+1 (m i +1)

[0121] X d+1 (m i )>η

[0122] Record the obtained frequency characteristic point m i After completing the above steps, the frequency feature points will be recorded in the KS transformation function in an indexed manner. When performing the final matrix calculation, only the feature part is calculated and the non-feature part is skipped. Taking the voltage sag + 357th harmonic + transient oscillation composite disturbance signal as an example, the result of extracting the frequency feature points through iterative filtering is as follows: Fig.10 As shown, the red dots are frequency feature points. It can be seen that the result retains the frequency features more accurately and greatly reduces the amount of calculation.

[0123] (4) Perform KS transformation on feature points to detect power quality composite disturbances

[0124] From the improved KS transformation expression obtained in step 2 and the convolution theorem, we can deduce

[0125]

[0126] Where X(f+v) is the frequency spectrum of the disturbance signal after frequency shift, W GK (v, α, β) is the spectrum of the improved Kaiser window function, so the process can be quickly calculated by fast Fourier transform and inverse Fourier transform. The detection results of voltage interruption + harmonic + transient oscillation disturbance are as follows: Fig.11 As shown, the 50Hz fundamental frequency amplitude curve ( Fig.12 ) is extracted and observed separately. It can be seen that there is a voltage interruption disturbance from 0.08 to 0.14 seconds. Observe the frequency domain disturbance amplitude diagram ( Fig.13 ) It can be seen that there are third, fifth and seventh harmonics in the whole process, and there is a 1kHz transient oscillation from 0.04 to 0.06 seconds, which can detect the complex disturbance more accurately.

[0127] In summary, the present invention proposes a method for rapid power quality disturbance detection based on improved KS transformation, which can be applied to power quality composite disturbance detection in modern high-proportion renewable energy power electronics systems.

[0128] Finally, 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 above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for rapid power quality disturbance detection based on improved KS transformation, comprising the following steps: Step 1: Obtain the voltage signal from the power grid detection node and discretize it to obtain a discrete sampling sequence; Step 2: Perform fast Fourier transform on the discrete sampling signal to obtain the signal spectrum; Step 3: Perform iterative loop filtering on the signal spectrum to remove the noise that interferes with the extraction of characteristic frequencies, and obtain the maximum value of the filtered spectrum as the frequency characteristic point; Step 4: Perform an improved KS transformation on the signal to obtain a transformed matrix. The rows of the matrix represent the frequency components contained in the measured signal, and the columns represent the changes in the amplitude of each frequency component of the signal over time. The modulus of the matrix is ​​the amplitude of the frequency component. When forming the matrix, only the rows where the characteristic frequencies are screened out after the iterative loop filtering process in step 3 are calculated, and the remaining non-frequency characteristic rows are set to 0; Step 5: Preset the range of fundamental voltage amplitude variation when voltage sag or voltage interruption occurs; extract the fundamental frequency part from the transformed matrix to obtain a one-dimensional time signal, which represents the waveform of the fundamental wave during the signal duration. Calculate its maximum value, minimum value, and mutation time point to obtain the time domain disturbance characteristics, and detect the disturbance amplitude, start time, and end time of voltage sag, voltage swell, voltage interruption, and flicker; Step 6: Determine the frequency, amplitude, and duration of each frequency component based on the transformed matrix, i.e., the harmonic frequencies f1, f2, f3, ...; the corresponding harmonic amplitudes A1, A2, A3, ...; and the frequency component existence time t1, t2, t3, ..., and detect frequency domain disturbances including harmonics and transient oscillations.

2. The method for rapid power quality disturbance detection according to claim 1, characterized in that: Suppose the discrete sampling sequence obtained in step 1 is x s (nT s ), where T s is the sampling interval of the power grid sampling device, n is the sampling point number; in step 2, the discrete sampling signal x s (nT s ) is subjected to fast Fourier transform to obtain the signal spectrum X0(m): In the formula, m, n = 0, 1, ..., N-1, and N is the total number of sampling points.

3. The method for rapid power quality disturbance detection according to claim 2, characterized in that: The method for step 3 is: Perform iterative loop filtering on the signal spectrum: In the formula, l = 1, 2; d = 0, 1, 2, ...; m = 2, 3, ..., N-3, find the maximum value of the above formula, and set the threshold η to remove the noise that interferes with the characteristic frequency extraction, and select the characteristic frequency point m that satisfies the following formula: i : X d+1 (m i -1)<X d+1 (m i )>X d+1 (m i +1) X d+1 (m i )>h Record the obtained frequency characteristic point m i location.

4. The method for rapid power quality disturbance detection according to claim 3, characterized in that: Step 4: Perform an improved KS transformation on the signal, and the method for obtaining the transformed matrix is: The transformation formula for improving the KS transformation of the signal is as follows Among them, ω G (τ-t,f) is the Gaussian window function, τ is the oscillation attenuation factor, The parameter η1 is used to adjust the rate of change of the window shape with the frequency f, η2 is used to adjust the window shape at the fundamental frequency, and I0(·) is the first kind of modified zero-order Bessel function.

5. The method for rapid power quality disturbance detection according to claim 1, characterized in that: In step 5, a rise of 10% to 80% in the fundamental voltage amplitude is defined as a voltage swell; a sinusoidal oscillation of the fundamental voltage amplitude is defined as a voltage flicker disturbance; a drop of 10% to 90% in the fundamental voltage amplitude during the disturbance is defined as a voltage sag; a drop of 90% to 100% is defined as a voltage interruption.

6. The method for rapid power quality disturbance detection according to claim 1, characterized in that: In step 5, the fundamental frequency is 50 Hz. When one or more frequency components other than the fundamental frequency component appear between 0 Hz and 700 Hz and exist in the entire time domain of the input signal, it is defined as the occurrence of harmonic disturbance; and when a frequency component between 700 Hz and 1600 Hz appears and lasts only 0.01s to 0.06s, it can be identified as the occurrence of transient oscillation disturbance.

7. The method for rapid power quality disturbance detection according to claim 1, characterized in that: The composite disturbance is the superposition of voltage sag, voltage swell, voltage interruption, voltage flicker, harmonics and transient oscillation disturbances, and is detected by judging its characteristics according to the judgment method in step 5 and step 6.

Images