A dynamic power quality disturbance signal detection method, device and medium

Abstract

The application discloses a dynamic power quality disturbance signal detection method, device and medium, and the method steps comprise the following steps: S01, acquiring a dynamic power quality disturbance signal to be detected; S02, dividing a signal frequency band, and performing time-frequency analysis on the dynamic power quality disturbance signal by using an improved variable resolution S transform method; the improved variable resolution S transform method is an S transform based on a Gaussian window, and a Gaussian window scale factor is used to optimize the Gaussian window function; S03, performing a synchronous squeezing rearrangement operation on the improved variable resolution S transform result, so that energy components scattered in adjacent frequency intervals are aggregated to real frequency axis positions, and a time-frequency matrix after synchronous squeezing rearrangement is obtained; and S04, detecting signal components of the time-frequency matrix after synchronous squeezing rearrangement. The application can reduce energy diffusion and realize high-precision detection of non-stationary multi-scale complex dynamic power quality disturbance signals.

Description

Technical Field

[0001] This invention relates to the field of power quality detection and analysis technology, and in particular to a method, device and medium for detecting dynamic power quality disturbance signals. Background Technology

[0002] With the continuous grid connection of numerous renewable energy sources such as photovoltaics and wind power, nonlinear and impulsive loads are constantly being introduced, making dynamic power quality disturbance signals in modern power grids increasingly complex and variable. Dynamic power quality disturbances can affect the lifespan of power equipment, reduce power efficiency, and pose numerous challenges to the stable and efficient operation of new power systems. Compared to single, stable power quality disturbance signals, complex dynamic power quality disturbance signals typically appear in a composite, non-stationary form, with disturbance signals overlapping and intertwining. This directly affects the difficulty and accuracy of disturbance signal detection. Therefore, high-precision detection and analysis of complex dynamic power quality disturbance signals is crucial to ensuring the stability and reliability of new power systems.

[0003] Existing technologies for power quality disturbance detection mainly include Fast Fourier Transform (FFT), Short-Time Fourier Transform (SFT), Wavelet Transform, and S-transform and their improved methods. Among these, FFT significantly shortens the Discrete Fourier Transform (DFT) time and has strong engineering practicality, but it cannot reflect the time-domain characteristics of disturbance signal changes. SFT compensates for the shortcomings of FFT in processing time-domain information through windowing, achieving time-frequency localization analysis; however, the fixed window function severely limits its time-frequency resolution, making it difficult to simultaneously capture the time-domain details of high-frequency disturbance signals and the frequency-domain details of low-frequency disturbance signals. Wavelet Transform overcomes the fixed resolution limitation of SFT through adaptive time-frequency windows, enabling multi-resolution analysis of dynamic power quality disturbance signals, but it faces challenges such as difficulty in selecting wavelet basis functions and susceptibility to noise interference.

[0004] As a combination of wavelet transform and short-time Fourier transform, the S-transform can be regarded as a phase correction of wavelet transform. By using a Gaussian window whose width changes with frequency as the transform kernel function, it avoids the defect of fixed window function in short-time Fourier transform and solves the problem of lack of phase information in wavelet transform. However, the traditional Gaussian window function has weak energy concentration and large spectral analysis errors in different frequency bands during time-frequency analysis of disturbance signals. If applied to solve the problem of detecting multi-scale non-stationary complex dynamic power quality disturbance signals in new power systems, the detection accuracy will still be low. Summary of the Invention

[0005] The technical problem to be solved by the present invention is as follows: In view of the above-mentioned problems existing in the prior art, the present invention provides a method, device and medium for detecting dynamic power quality disturbance signals, which can significantly enhance the energy concentration and adaptive variable resolution characteristics of S-transform based on Gaussian window, improve the time resolution and frequency resolution of disturbance signals in different frequency bands, reduce energy diffusion, and thus achieve high-precision detection of non-stationary multi-scale complex dynamic power quality disturbance signals.

[0006] To solve the above-mentioned technical problems, the technical solution proposed by this invention is as follows:

[0007] A method for detecting dynamic power quality disturbance signals, comprising the following steps:

[0008] Step S01. Signal Acquisition: Acquire the dynamic power quality disturbance signal to be detected, wherein the dynamic power quality disturbance signal includes a single type of disturbance signal and a composite form of disturbance signal;

[0009] Step S02. Time-Frequency Analysis: The signal frequency band is divided according to the fundamental frequency of the disturbance signal. The dynamic power quality disturbance signal is analyzed in time and frequency using an improved variable resolution S-transform method to obtain the improved variable resolution S-transform result. The improved variable resolution S-transform method is to use an S-transform based on a Gaussian window and optimize the Gaussian window function using a frequency-band optimized Gaussian window scaling factor. The frequency-band optimized Gaussian window scaling factor is a Gaussian window scaling factor formed by using a first time-frequency resolution adjustment factor in the low-frequency band to narrow the Gaussian window width in the corresponding time domain, and a Gaussian window scaling factor formed by using a second time-frequency resolution adjustment factor in the high-frequency band to narrow the Gaussian window width in the corresponding frequency domain. The first time-frequency resolution adjustment factor is smaller than the second time-frequency resolution adjustment factor.

[0010] Step S03. Synchronous squeezing and rearrangement: Perform synchronous squeezing and rearrangement on the improved variable resolution S-transform result to aggregate the energy components scattered in adjacent frequency ranges to the true frequency axis position, and obtain the time-frequency matrix after synchronous squeezing and rearrangement;

[0011] Step S04. Signal detection: Detect the signal components of the dynamic power quality disturbance signal to be detected based on the time-frequency matrix after synchronous extrusion and rearrangement.

[0012] Further, in step S02, the calculation expression for the Gaussian window scaling factor of the frequency band optimization is:

[0013] ,

[0014] in, The Gaussian window scaling factor represents the frequency band optimization. For the frequency of the disturbance signal, The fundamental frequency of the disturbance signal. This corresponds to the low-frequency band. Corresponding to the high-frequency band, , , , These are the time-frequency resolution adjustment factors, , This is a time resolution adjustment factor used to adjust the high and low frequency bands. , , , This is a frequency resolution adjustment factor used to adjust the high and low frequency bands. , , , This is a smoothing factor used to smooth the shape change of the window function when transitioning from low frequency to high frequency.

[0015] Further, in step S02, the expression for optimizing the Gaussian window function using the frequency-band optimized Gaussian window scaling factor is:

[0016] ,

[0017] in, This indicates the optimization of the Gaussian window function. These represent time and the frequency of the disturbance signal, respectively. Indicates the time shift factor;

[0018] Using an optimized Gaussian window function The S-transform is modified to obtain the calculation expression for the improved variable resolution S-transform:

[0019] ,

[0020] in, This represents the improved variable resolution S-transform. This represents the continuous dynamic power quality disturbance signal to be detected.

[0021] Furthermore, in step S02, the problem of finding the maximum energy concentration degree is transformed into finding the corresponding minimum fitness degree by using the reciprocal of the maximum energy concentration degree as the fitness function. An adaptive genetic algorithm is used to determine the optimal solution of each time resolution adjustment factor in the frequency band optimized Gaussian window scaling factor. The energy concentration degree is calculated based on the improved variable resolution S-transform result.

[0022] Further, step S03 includes:

[0023] Calculate the results of the improved variable resolution S-transform The instantaneous frequency of the phase gradient , Indicates time frequency. Indicates the time shift factor:

[0024] Improved variable resolution S-transform results Time-frequency energy Squeezed along the frequency axis to the instantaneous frequency The position is used to obtain the time-frequency matrix after synchronous squeezing and rearrangement. , This represents the frequency variable after compression.

[0025] Furthermore, the instantaneous frequency of the phase gradient of the improved variable resolution S-transform result is calculated. The expression is:

[0026] ,

[0027] Synchronous compression rearrangement of time-frequency matrix The calculation expression is:

[0028] ,

[0029] in, This is the Dirac function.

[0030] Further, in step S03, the discrete time-frequency matrix after synchronous squeezing and rearrangement is obtained through discretization processing. The steps include:

[0031] Improved variable resolution S-transform results Discretization characterization is performed to obtain the discrete improved variable resolution S-transform results. :

[0032] ,

[0033] in, and These represent the set sampling rate and the number of sampling points, respectively. Represents the discrete Fourier transform of the sampled signal. This represents the Fourier spectrum of the optimized Gaussian window function. For time indexing, For frequency index, For the summation variable;

[0034] Discrete Improved Variable Resolution S-Transform Results Synchronous compression is performed to obtain discrete phases along the time direction. The derivative is:

[0035]

[0036] in, Indicates the sampling interval;

[0037] Calculate discrete instantaneous frequency :

[0038] ,

[0039] in, Indicates frequency point, Indicates frequency resolution;

[0040] Based on discrete instantaneous frequency Conversion to obtain discrete frequency index :

[0041] ,

[0042] Where, round represents the rounding operation;

[0043] Discrete improved variable resolution S-transform results Time-frequency energy Redistribute to position along the frequency axis The discrete time-frequency matrix after synchronous compression and rearrangement is obtained. :

[0044] ,

[0045] in, These are frequency-dependent weights.

[0046] Furthermore, frequency-dependent weights The calculation expression is:

[0047]

[0048] in, , This is a frequency resolution adjustment factor used to adjust the high and low frequency bands. , , , This is a smoothing factor used to smooth the shape change of the window function during the transition from low to high frequencies. , This is a time resolution adjustment factor used to adjust the high and low frequency bands. , , The fundamental frequency of the disturbance signal. This is a floor operation.

[0049] A computer device includes a processor and a memory, the memory being used to store a computer program, and the processor being used to execute the computer program to perform the method described above.

[0050] A computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0051] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0052] 1. This invention divides the disturbance signal into frequency bands and uses an improved variable-resolution S-transform method to perform time-frequency analysis. In the low-frequency band, a Gaussian window scaling factor formed by a first time-frequency resolution adjustment factor is used to narrow the width of the Gaussian window in the corresponding time domain. In the high-frequency band, a Gaussian window scaling factor formed by a second time-frequency resolution adjustment factor is used to narrow the width of the Gaussian window in the corresponding frequency domain. This enables adaptive resolution adaptation for signals in different frequency bands, overcomes the limitation that time resolution and frequency resolution are difficult to balance in both high and low frequency bands, and makes the time-frequency energy more concentrated in the true time-frequency position of the disturbance signal. This effectively solves the problems of energy diffusion and time-frequency diagram blurring in traditional methods and improves time-frequency resolution.

[0053] 2. This invention achieves synchronous compression and rearrangement by combining the improved variable resolution S-transform results with synchronous compression fusion, which can improve the time-frequency energy concentration and reduce energy diffusion, thereby enabling high-precision detection of non-stationary multi-scale complex dynamic power quality disturbance signals.

[0054] 3. This invention achieves the detection of dynamic power quality disturbance signals by combining variable resolution adjustment and synchronous squeezing operation. It can fully explore the characteristics of complex dynamic power quality disturbance signals with multiple superimposed components, and can simultaneously identify disturbance characteristics of different scales and frequency bands, thereby accurately distinguishing different types of complex dynamic power quality disturbance signals. Attached Figure Description

[0055] Figure 1 This is a schematic diagram illustrating the implementation process of the dynamic power quality disturbance signal detection method in this embodiment.

[0056] Figure 2 This is a schematic diagram illustrating the implementation process of time-frequency analysis using the improved variable resolution S-transform method in this embodiment.

[0057] Figure 3 This is a schematic diagram illustrating the implementation process of using a genetic algorithm to optimize window parameters in this embodiment. Detailed Implementation

[0058] The present invention will be further described below with reference to the accompanying drawings and specific preferred embodiments, but this does not limit the scope of protection of the present invention.

[0059] This invention divides the disturbance signal into frequency bands and employs an improved variable-resolution S-transform method for time-frequency analysis. This improved variable-resolution S-transform method, based on the Gaussian window-based S-transform, optimizes the Gaussian window function using a frequency-band optimized Gaussian window scaling factor. A smaller first time-frequency resolution adjustment factor is used in the low-frequency band to narrow the Gaussian window width in the corresponding time domain, while a larger second time-frequency resolution adjustment factor is used in the high-frequency band to widen the Gaussian window width in the corresponding time domain and narrow it in the frequency domain. This allows for adaptive resolution adaptation for signals in different frequency bands, overcoming the limitation of simultaneously achieving high and low frequency resolution. It concentrates time-frequency energy more precisely at the true time-frequency location of the disturbance signal, effectively solving the problems of energy diffusion and time-frequency image blurring in traditional methods, thus improving time-frequency resolution. Simultaneously, by performing synchronous squeezing and rearranging operations on the improved variable-resolution S-transform results, synchronous squeezing and fusion are achieved, which improves time-frequency energy concentration and reduces energy diffusion, thereby enabling high-precision detection of non-stationary, multi-scale, complex, dynamic power quality disturbance signals. Especially for complex dynamic power quality disturbance signals with multiple superimposed components, this invention, by combining variable resolution adjustment and synchronous squeezing operation, can fully explore the characteristics of complex dynamic power quality disturbance signals, enabling the simultaneous identification of disturbance characteristics at different scales and frequency bands, thereby providing accurate data support for power quality assessment and management in new power systems.

[0060] like Figure 1 As shown, the steps of the dynamic power quality disturbance signal detection method in this embodiment include:

[0061] Step S01. Signal Acquisition: Acquire the dynamic power quality disturbance signal to be detected. The dynamic power quality disturbance signal includes single-type disturbance signals and composite-type disturbance signals.

[0062] In this embodiment, 18 dynamic power quality disturbance signal models are established, including 7 single signals: normal signal, voltage swell, voltage droop, voltage interruption, voltage transient oscillation, harmonics, and voltage flicker; and 11 complex dynamic power quality disturbance signals composed of these signals, namely, voltage swell + voltage transient oscillation, voltage droop + voltage transient oscillation, voltage interruption + voltage transient oscillation, voltage swell + harmonics, voltage droop + harmonics, voltage interruption + harmonics, harmonics + voltage flicker, voltage swell + voltage transient oscillation + harmonics, voltage droop + voltage transient oscillation + harmonics, voltage interruption + voltage transient oscillation + harmonics, and voltage flicker + voltage transient oscillation + harmonics. The disturbance signal sampling frequency... f s The fundamental frequency is 6400Hz. f 0 represents 50Hz, and the number of sampling points is... N It is 1280.

[0063] Specifically, MATLAB can be used to randomly generate 1000 samples for each type of dynamic power quality disturbance signal. At the same time, considering that actual dynamic power quality disturbance signals are often superimposed with noise, 30dB Gaussian white noise is also superimposed on each type of disturbance signal.

[0064] Step S02. Time-Frequency Analysis: The signal frequency band is divided according to the fundamental frequency of the disturbance signal, and the time-frequency analysis of the dynamic power quality disturbance signal is performed using an improved variable resolution S-transform method to obtain the improved variable resolution S-transform result. The improved variable resolution S-transform method is to use an S-transform based on a Gaussian window and optimize the Gaussian window function using a frequency-band optimized Gaussian window scaling factor. The frequency-band optimized Gaussian window scaling factor is a Gaussian window scaling factor formed by using a first time-frequency resolution adjustment factor in the low-frequency band to narrow the Gaussian window width in the corresponding time domain, and a Gaussian window scaling factor formed by using a second time-frequency resolution adjustment factor in the high-frequency band to narrow the Gaussian window width in the corresponding frequency domain. The first time-frequency resolution adjustment factor is smaller than the second time-frequency resolution adjustment factor.

[0065] Considering that actual dynamic power quality disturbance signals are often composed of a mixture of multiple low-frequency components (such as sudden rises and falls) and high-frequency components (such as transients), based on the Heisenberg uncertainty principle, the scaling factor used in the S-transform is difficult to accurately analyze signals in different frequency bands simultaneously, thus causing insufficient time-frequency resolution. This embodiment optimizes the Gaussian window scaling factor in the Gaussian window-based S-transform by frequency band: at low frequencies, a smaller time-frequency resolution adjustment factor (first time-frequency resolution adjustment factor) is used to narrow the Gaussian window width in the time domain, significantly improving its time resolution and enhancing the detection accuracy of low-frequency disturbance components such as sudden rises and falls; at high frequencies, a larger time-frequency resolution adjustment factor (first time-frequency resolution adjustment factor) is used to narrow the Gaussian window width in the frequency domain, significantly enhancing its frequency resolution and improving the detection accuracy of high-frequency disturbance components such as transients. By using the above frequency division optimization method, the problem of difficulty in achieving both low-frequency and high-frequency resolution in the traditional S-transform can be effectively overcome, improving the time-frequency resolution characteristics of the S-transform. This enables the targeted extraction of feature information from mixed disturbance signals, thereby improving the detection accuracy of dynamic power quality disturbance signals.

[0066] Specifically, based on the original Gaussian window scaling factor of the S-transform, a first time-frequency resolution adjustment factor can be formed at low frequencies by optimizing the window parameters to reduce the original Gaussian window scaling factor of the S-transform. This narrows the Gaussian window width in the frequency domain at low frequencies, significantly enhancing its frequency resolution and improving the detection accuracy of transient and other high-frequency disturbance components. At high frequencies, a second time-frequency resolution adjustment factor can be formed by adjusting the window parameters to increase the original Gaussian window scaling factor of the S-transform. This narrows the Gaussian window width in the frequency domain at high frequencies, significantly enhancing its frequency resolution and improving the detection accuracy of transient and other high-frequency disturbance components.

[0067] Specifically, assuming a dynamic power quality disturbance signal is randomly selected. Its continuous S-transform expression is:

[0068] (1)

[0069] In the formula, For signal frequency, For time shift factor, The Gaussian window function can be defined as follows:

[0070] (2)

[0071] In the formula, The traditional scaling factor is expressed as:

[0072] (3)

[0073] This embodiment uses a frequency-band optimized Gaussian window scaling factor to optimize the Gaussian window function. Optionally, the calculation expression for the frequency-band optimized Gaussian window scaling factor is as follows:

[0074] (4)

[0075] in, The Gaussian window scaling factor represents the frequency band optimization. For frequency, The fundamental frequency of the disturbance signal. This corresponds to the low-frequency band. Corresponding to the high-frequency band, , , , These are the time-frequency resolution adjustment factors, p 1, p 2 is the time resolution adjustment factor used to adjust the low-frequency and high-frequency bands. , , , This is a frequency resolution adjustment factor used to adjust the high and low frequency bands. , , , This is a smoothing factor, with a value between 0 and 1, used to smooth the shape change of the window function when transitioning from low frequency to high frequency.

[0076] As shown in equation (4), with The signal frequency band is divided into boundaries, with frequencies higher than [the specified frequency]. Divided into high frequency and lower frequency Divided into low frequency, when the frequency is higher When using As a Gaussian window scaling factor When the frequency is lower Use at that time As a Gaussian window scaling factor This allows for a significant improvement in time resolution at low frequencies by optimizing window parameters to narrow the Gaussian window width in the time domain, thereby enhancing the detection accuracy of low-frequency disturbance components such as sudden rises and falls. At high frequencies, adjusting window parameters to narrow the Gaussian window width in the frequency domain significantly enhances frequency resolution, improving the detection accuracy of high-frequency disturbance components such as transients. This overcomes the difficulty in simultaneously achieving high-frequency and low-frequency time resolution in the S-transform, thus improving the detection accuracy of dynamic power quality disturbance signals.

[0077] Specifically, the Gaussian window scaling factor optimized using the above frequency bands is used. The expression for optimizing the Gaussian window function is:

[0078] (5)

[0079] in, This indicates the optimization of the Gaussian window function. Representing time and frequency respectively. This represents the time shift factor.

[0080] Then, an optimized Gaussian window function is adopted. The S-transform is modified to obtain the calculation expression for the improved variable resolution S-transform:

[0081] (6)

[0082] in, This represents the improved variable resolution S-transform. This represents the continuous dynamic power quality disturbance signal to be detected.

[0083] like Figure 2 As shown, after receiving a complex dynamic power quality disturbance signal, the first step is to... To divide the signal frequency band, if Construct a low-frequency Gaussian window scaling factor To adaptively adjust the low-frequency band window adjustment parameters, if Construct a high-frequency Gaussian window scaling factor By adaptively adjusting the high-frequency window adjustment parameters, an improved variable resolution S-transform is formed according to equations (5) and (6) to perform time-frequency analysis on complex dynamic power quality disturbance signals.

[0084] Step S03. Synchronous squeezing and rearrangement: Perform synchronous squeezing and rearrangement on the improved variable resolution S-transform result to aggregate the energy components scattered in adjacent frequency intervals to the true frequency axis position, and obtain the time-frequency matrix after synchronous squeezing and rearrangement.

[0085] This embodiment, based on the improved variable resolution S-transform, further combines the synchronous squeezing operation to adaptively rearrange the time-frequency matrix obtained by the improved variable resolution S-transform. This can aggregate energy components scattered in adjacent frequency intervals to the true frequency axis position, thereby significantly compressing the energy diffusion range in the time-frequency domain, significantly improving the time-frequency aggregation degree of the Gaussian window function in dynamic disturbance signal analysis, and improving the time-frequency energy aggregation performance of the optimized Gaussian window function.

[0086] As an optional implementation, the steps for implementing the synchronous extrusion rearrangement operation include:

[0087] Calculate the results of the improved variable resolution S-transform The instantaneous frequency of the phase gradient :

[0088] Improve the variable resolution S-transform results Time-frequency energy Squeezed along the frequency axis to the instantaneous frequency The position is used to obtain the time-frequency matrix after synchronous squeezing and rearrangement. , This represents the frequency variable after compression.

[0089] In a specific application embodiment, the instantaneous frequency of the phase gradient of the improved variable resolution S-transform result is calculated. The expression is:

[0090] (7)

[0091] Then the time-frequency matrix after synchronous squeezing and rearrangement The calculation expression is:

[0092] (8)

[0093] in, The Dirac function is the core of the "squeeze" operation. The frequency variable after compression is the frequency coordinate dimension after the synchronous "compression" operation.

[0094] Furthermore, during the synchronous squeezing and rearrangement operation, the discrete time-frequency matrix after synchronous squeezing and rearrangement can be obtained through discretization to facilitate subsequent processing. Detailed steps include:

[0095] Step S301. Analyze the improved variable resolution S-transform results. Discretization is performed to facilitate processing, resulting in a discrete improved variable-resolution S-transform. :

[0096] (9)

[0097] in, and These are setting the sampling rate and the number of sampling points, respectively. Represents the discrete Fourier transform of the sampled signal. This represents the Fourier spectrum of the optimized Gaussian window function. For time indexing, For frequency index, For the summation variable, , , The value range is 0~ N -1.

[0098] For IST( m , k This can be further expressed in terms of magnitude and phase, as shown in the following equation:

[0099] (10)

[0100] In the formula, and They represent IST( m , k The magnitude and phase of ).

[0101] Step S302. Analyze the discrete improved variable resolution S-transform result IST( m , k To further enhance energy concentration, synchronous compression is performed, and the discrete phase derivative along the time direction can be described as follows:

[0102] (11)

[0103] in, Indicates the sampling interval.

[0104] Step S303. Calculate the discrete instantaneous frequency. The calculation expression is:

[0105] (12)

[0106] in, Indicates frequency point, Indicates frequency resolution.

[0107] Step S304. Based on discrete instantaneous frequency Conversion to obtain discrete frequency index :

[0108] (13)

[0109] Here, round represents the rounding operation.

[0110] Step S304. Calculate the discrete improved variable resolution S-transform result. Time-frequency energy Redistribute to position along the frequency axis The discrete time-frequency matrix after synchronous compression and rearrangement is obtained. :

[0111] (14)

[0112] in, These are frequency-dependent weights.

[0113] As shown in equation (14), this embodiment transforms the discrete improved variable resolution S-transform result. Time-frequency energy Redistribute to position along the frequency axis In the process, frequency-dependent weights are introduced. The discrete time-frequency matrix after synchronous compression and rearrangement is obtained. In the discrete domain, frequency points Since the scale factor directly affects the energy density in the time-frequency domain—for example, the larger the scale factor, the higher the energy sensitivity of the time-frequency window, i.e., the stronger the energy perception, and vice versa—this embodiment introduces frequency-dependent weights. Compensating for sensitivity differences at different frequency points can solve the problem of energy "distortion" (i.e., the total energy does not match the original signal energy) caused by the "jump" of the frequency band window width difference, and ensure energy conservation.

[0114] In a specific application example, frequency-dependent weights The calculation expression is:

[0115] (15)

[0116] in, , This is a frequency resolution adjustment factor used to adjust the high and low frequency bands. , , , This is a smoothing factor used to smooth the shape change of the window function during the transition from low to high frequencies. , This is a time resolution adjustment factor used to adjust the high and low frequency bands. , , The fundamental frequency of the disturbance signal. This is a floor operation.

[0117] Compared to traditional synchronous extrusion operations, this embodiment introduces the aforementioned segmented processing frequency-dependent weights during the synchronous extrusion rearrangement process. This allows for the precise aggregation of energy components scattered across adjacent frequency ranges in the original time-frequency distribution to their true frequency axis positions, thereby significantly compressing the energy diffusion range in the time-frequency domain. This significantly improves the time-frequency aggregation of the Gaussian window function in dynamic disturbance signal analysis, further enhancing the time-frequency energy aggregation performance of the optimized Gaussian window function, while also achieving frequency compensation in the low-frequency band.

[0118] Step S04. Signal detection: Detect the signal components of the dynamic power quality disturbance signal to be detected based on the time-frequency matrix after synchronous squeezing and rearrangement.

[0119] In this embodiment, based on the discrete time-frequency matrix after synchronous squeezing and rearrangement The independent energy accumulation regions of different components of the disturbance signal, especially the composite disturbance signal, in the time-frequency matrix can be detected and analyzed. The detection results of the dynamic power quality disturbance signal are output, and the fitness value of the time-frequency analysis (the reciprocal of the energy accumulation degree) is recorded.

[0120] To adaptively adjust the window parameters, this embodiment focuses on improving time resolution in the low-frequency range and taking frequency resolution into account in the high-frequency range. Furthermore, it introduces the concept of maximum energy concentration. A quantitative evaluation of energy concentration in high and low frequency bands is performed. The maximum energy concentration indicates that the window adjustment parameters have reached their optimal value, representing the energy concentration degree. The expression can be represented as:

[0121] (16)

[0122] Specifically, with maximum energy concentration The reciprocal of the product is used as the fitness function, transforming the problem of finding the maximum energy concentration into finding the minimum fitness. An adaptive genetic algorithm is then used to determine the optimal solution for each time resolution adjustment factor in the Gaussian window scaling factor for frequency band optimization, thus optimizing each window parameter. For example... Figure 3As shown, each time-resolution adjustment factor in the Gaussian window scaling factor to be optimized is used as an individual in the population. In the adaptive genetic algorithm, real number encoding is used for encoding, and the fitness of different individuals is calculated based on the reciprocal of energy aggregation degree. The optimal solution is obtained through iterative evolution through genetic operations.

[0123] As an optional implementation, step S04 may further include adjusting the energy concentration based on... The root mean square error (RMSE) is used to evaluate the accuracy of dynamic power quality disturbance signal detection. For example, the signal amplitude of the time-frequency detection result is compared with the actual disturbance signal amplitude, and the detection error is further calculated using RMSE as a metric to evaluate the actual performance of the dynamic power quality disturbance detection algorithm.

[0124] (17)

[0125] In the formula, and They represent the first time. i The true value and the measured value of each point.

[0126] To verify the effectiveness of the present invention, S-transform, synchronous extrusion S-transform, improved variable resolution S-transform, and the present invention combined with improved variable resolution S-transform and synchronous extrusion operation were used to detect 18 kinds of dynamic power quality disturbance signals. The average performance evaluation results of the ablation experiment are shown in Table 1.

[0127] Table 1: Comparison of Detection Performance for Different Disturbances

[0128]

[0129] In Table 1, 1 / E represents the reciprocal of the energy concentration degree; the smaller the value, the stronger the energy concentration performance. Table 1 shows that the energy concentration performance and detection error of the four detection methods exhibit a consistent trend: the higher the energy concentration performance, the smaller the detection error of the disturbance signal. Compared to the traditional S-transform, the synchronous squeezing S-transform reduces the 1 / E and RMSE values ​​through energy redistribution, verifying the effect of synchronous squeezing operation on improving energy concentration and detection accuracy. The improved variable resolution S-transform, through optimized window width adjustment strategy, outperforms the synchronous squeezing S-transform, demonstrating the adaptability advantage of variable resolution design to signals in different frequency bands. This invention, by further integrating synchronous squeezing operation on the basis of the improved variable resolution S-transform, achieves the highest energy concentration and detection accuracy, effectively realizing high-precision detection of complex dynamic power quality disturbance signals.

[0130] In summary, this invention optimizes the segmented design window width adjustment logic, uses a frequency-segment optimized Gaussian window scaling factor to optimize the Gaussian window function, and adaptively adjusts the window control parameters to form an improved variable resolution S-transform method for time-frequency analysis of dynamic power quality disturbance signals. Combined with the synchronous squeezing energy redistribution mechanism, it aggregates energy components dispersed in adjacent frequency ranges to the true frequency axis position, enabling adaptive resolution adaptation for signals in different frequency bands. This overcomes the limitation of difficulty in simultaneously achieving high and low frequency band time-frequency resolution, obtaining higher energy concentration and detection accuracy, and enabling precise detection and analysis of complex dynamic power quality disturbances.

[0131] This invention can be applied to the detection of dynamic power quality disturbance signals, and can also be used in related applications such as power system transient fault location and industrial electrical equipment condition diagnosis.

[0132] This embodiment further provides a computer device, including a processor and a memory, the memory for storing a computer program and the processor for executing the computer program to perform the method as described above.

[0133] It is understood that the method described in this embodiment can be executed by a single device, such as a computer or server, or it can be applied to a distributed scenario where multiple devices cooperate to complete the task. In a distributed scenario, one of the multiple devices may execute only one or more steps of the method described in this embodiment, and the multiple devices interact to complete the method. The processor can be implemented using a general-purpose CPU, microprocessor, application-specific integrated circuit, or one or more integrated circuits, and is used to execute relevant programs to implement the method described in this embodiment. The memory can be implemented using read-only memory (ROM), random access memory (RAM), static storage devices, and dynamic storage devices. The memory can store the operating system and other applications. When the method described in this embodiment is implemented through software or firmware, the relevant program code is stored in the memory and called and executed by the processor.

[0134] This embodiment further provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the method described above.

[0135] Those skilled in the art will understand that the above embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-readable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code. The present invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce implementations of the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 The computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable apparatus for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0136] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the invention. Therefore, any simple modifications, equivalent changes, and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention should fall within the protection scope of the present invention.

Claims

1. A method for detecting dynamic power quality disturbance signal, characterized in that the steps of The method comprises the following steps: Step S01. Signal acquisition: acquiring a dynamic power quality disturbance signal to be detected, wherein the dynamic power quality disturbance signal comprises a single type of disturbance signal and a composite form of disturbance signal; Step S02. Time-frequency analysis: dividing a signal frequency band according to a disturbance signal base frequency, and performing time-frequency analysis on the dynamic power quality disturbance signal by using an improved variable resolution S transform method to obtain an improved variable resolution S transform result, wherein the improved variable resolution S transform method is to use a Gaussian window-based S transform and use a frequency-band-optimized Gaussian window scale factor to optimize a Gaussian window function, and the frequency-band-optimized Gaussian window scale factor is a Gaussian window scale factor formed by using a first time-frequency resolution adjustment factor to narrow the width of a Gaussian window in a corresponding time domain and a Gaussian window scale factor formed by using a second time-frequency resolution adjustment factor to narrow the width of a Gaussian window in a corresponding frequency domain, and the first time-frequency resolution adjustment factor is smaller than the second time-frequency resolution adjustment factor; Step S03. Synchronous squeezing rearrangement: performing a synchronous squeezing rearrangement operation on the improved variable resolution S transform result to aggregate energy components dispersed in adjacent frequency intervals to a real frequency axis position to obtain a synchronous squeezing rearranged time-frequency matrix; Step S04. Signal detection: detecting signal components of the dynamic power quality disturbance signal to be detected according to the synchronous squeezing rearranged time-frequency matrix; In step S02, the calculation expression of the frequency-band-optimized Gaussian window scale factor is: ， wherein denotes a frequency band dependent Gaussian window scaling factor, is the perturbation signal frequency, is the perturbation signal fundamental frequency, corresponds to the low frequency band, corresponds to the high frequency band, , , , are time frequency resolution adjustment factors, respectively, , are time resolution adjustment factors for adjusting the high and low frequency band, respectively, , , , are frequency resolution adjustment factors for adjusting the high and low frequency band, respectively, , , , is a smoothing factor for smoothing the shape change of the low frequency to high frequency window function; In step S02, the expression of the Gaussian window function optimized by using the frequency-band-optimized Gaussian window scale factor is: ， wherein, denotes an optimized Gaussian window function, denotes time, respectively perturbation signal frequency, denotes a time shift factor; Using an optimized Gaussian window function The S transform is modified, and the calculation expression of the improved variable resolution S transform is obtained as follows: ， wherein, denotes an improved variant resolution S-transform, denotes a continuous dynamic power quality disturbance signal to be detected.

2. The method for dynamic power quality disturbance signal detection according to claim 1, characterized in that, In step S02, the reciprocal of the maximum energy aggregation degree is used as a fitness function to convert the problem of solving the maximum energy aggregation degree into the problem of solving the minimum corresponding fitness, and an adaptive genetic algorithm is used to determine the optimal solution of each time resolution adjustment factor in the frequency-band-optimized Gaussian window scale factor, wherein the energy aggregation degree is calculated according to the improved variable resolution S transform result.

3. The method for detecting dynamic power quality disturbance signal according to any one of claims 1-2, characterized in that, The step S03 comprises: Computing improved variable resolution s-transform results Instantaneous frequency of phase gradient , Indicates time frequency, Indicates time shift factor: improved variant resolution s-transform results time-frequency energy along the frequency axis to the position of the instantaneous frequency of the synchronous squeezed rearranged time-frequency matrix , squeezed frequency variable.

4. The method for dynamic power quality disturbance signal detection according to claim 3, characterized in that, computing the instantaneous frequency of the phase gradient of the improved variable resolution S-transform result The expression is: ， Synchronous extrusion rearranged time-frequency matrix The computational expression is; ， wherein is the Dirac function.

5. The method for detecting dynamic power quality disturbance signal according to any one of claims 1-2, characterized in that, In the step S03, the synchronous squeezing rearranged discrete time-frequency matrix is obtained by performing the synchronous squeezing rearrangement operation and the discretization processing on the improved variable resolution S transform result, and comprises: improved variable resolution S-transform result performing a discretization representation to obtain a discrete improved variable resolution S-transform result : ， wherein, with denote a set sampling rate and a set number of sampling points, respectively, denotes a discrete Fourier transform of the sampled signal, denotes an optimized Fourier spectrum of the Gaussian window function, is a time index, is a frequency index, is a summation variable; Discrete improved wavelet transform results Synchronous extrusion is performed to obtain a discrete phase in the time direction The derivative of the function wherein denotes the sampling interval; Computing a discrete instantaneous frequency : ， wherein denotes a frequency point, denotes a frequency resolution; According to the discrete instantaneous frequency The conversion obtains the discrete frequency index : ， Wherein, round represents the rounding operation; Discrete modified variable resolution s-transform results time-frequency energies redistributed along the frequency axis to positions synchronously squeezed and rearranged discrete time-frequency matrix : ， wherein is a frequency-dependent weight.

6. The method for dynamic power quality disturbance signal detection according to claim 5, characterized in that, Frequency-dependent weight The computational expression for the frequency-dependent weight is wherein , is a frequency resolution adjustment factor for adjusting the high and low frequency bands, , , , is a smoothing factor for smoothing the shape change of the low frequency to high frequency window function, , is a time resolution adjustment factor for adjusting the high and low frequency bands, , , is the fundamental frequency of the disturbance signal, is a floor operation.

7. A computer apparatus comprising a processor and a memory for storing a computer program, characterised in that, The processor is configured to execute the computer program to perform the method according to any one of claims 1-6.

8. A computer readable storage medium storing a computer program, characterized in that, The computer program is executed by the processor to implement the method according to any one of claims 1-6.

Images