Voltage sag disturbance feature extraction method and system

Abstract

The invention provides a voltage sag disturbance feature extraction method and system, and the method comprises the steps: enabling a variational mode decomposition algorithm parameter set to correspond to the position of a tuna in a tuna swarm optimization algorithm, and carrying out the random initialization of the position of each tuna in the tuna swarm optimization algorithm, the envelope entropy of the modal component obtained by decomposing the original voltage signal by the parameter group is used as a fitness function, and a tuna swarm optimization algorithm is adopted to carry out optimization solution on the parameter group corresponding to the tuna position to obtain a target optimization parameter group; variational mode decomposition is carried out on the original voltage signal based on the target optimization parameter group to obtain a mode component, a root mean square value of the mode component is calculated, a sag characteristic amplitude corresponding to the original voltage signal is generated, and a tuna swarm optimization algorithm and a variational mode decomposition algorithm are combined to obtain a sag characteristic amplitude corresponding to the original voltage signal; therefore, the voltage sag features can be extracted quickly and accurately.

Description

Technical Field

[0001] The present invention relates to the technical field of power quality of electric power system, and in particular to a method and system for extracting voltage sag disturbance characteristics. Background Art

[0002] As the automation and intelligence of industrial equipment and building electrical systems continue to increase, voltage sags have an increasingly serious impact on the daily operations of commercial and industrial users. Voltage sags are a common problem in power quality and can be triggered by a variety of factors, such as the start-up of a motor, the switching operation of a transformer, or a short circuit in a circuit. The production interruptions and efficiency reductions caused by these sag events are becoming increasingly obvious, and the economic losses caused, both directly and indirectly, are increasing. Relevant statistics show that currently more than 80% of power quality problems are related to voltage sags. Voltage sag feature extraction is the basis for voltage sag management, so how to quickly and accurately extract voltage sag features has become a new research hotspot.

[0003] With the rapid development of artificial intelligence, genetic algorithms have been continuously improved. Based on the social behaviors of different species, a variety of intelligent group optimization algorithms have been proposed at home and abroad and combined with traditional algorithms, effectively improving the accuracy of traditional algorithms. The VMD algorithm (i.e., variational mode decomposition algorithm) has been highly praised by the academic community for its excellent mode separation performance, and many researchers have conducted in-depth discussions on its parameter setting issues. When solving this parameter optimization challenge, intelligent optimization strategies are often used. Some scholars have tried to use the particle swarm optimization (PSO) method to adjust the number of modes K and the penalty factor α in the VMD algorithm. Although this method has achieved results in improving the separation accuracy of VMD, it has limitations in local search and may not achieve the ideal search accuracy.

[0004] The TSO algorithm is a swarm intelligence optimization technology that imitates the two main foraging patterns of tuna - spiral feeding and parabolic feeding. Compared with traditional particle swarm optimization technology, the TSO algorithm has fewer parameter requirements, stronger global search capabilities and higher optimization accuracy, which enable it to achieve better results in parameter optimization tasks. At present, the TSO algorithm has been widely used in many parameter optimization fields.

[0005] Therefore, how to quickly and accurately extract voltage sag characteristics is of great significance. Summary of the invention

[0006] In order to solve the problem that the prior art cannot quickly and accurately extract voltage sag features, the present invention proposes a method and system for extracting voltage sag disturbance features.

[0007] In a first aspect, a method for extracting voltage sag disturbance features is provided, comprising:

[0008] Corresponding a parameter group of the variational mode decomposition algorithm to the position of tuna in the tuna school optimization algorithm, and randomly initializing the position of each tuna in the tuna school optimization algorithm, wherein the parameter group includes the number of modes and the penalty parameter;

[0009] The envelope entropy of the modal component obtained by decomposing the original voltage signal based on the parameter group is used as the fitness function, and the parameter group corresponding to the tuna position is optimized and solved by using the tuna school optimization algorithm to obtain the target optimization parameter group;

[0010] Performing variational modal decomposition on the original voltage signal based on the target optimization parameter group to obtain modal components;

[0011] The root mean square value of the modal component is calculated to generate a sag characteristic amplitude corresponding to the original voltage signal.

[0012] Preferably, performing variational modal decomposition on the original voltage signal based on the target optimization parameter group to obtain modal components includes:

[0013] Perform variational mode decomposition on the original voltage signal to generate initial mode components;

[0014] Performing a Hilbert transform on the initial modal components to obtain transformed initial modal components;

[0015] Reconstructing the transformed initial modal components based on the optimized penalty factors in the target optimization parameter group and the Lagrangian factors in the variational modal decomposition algorithm to generate reconstructed initial modal components;

[0016] The reconstructed initial modal components are iteratively updated based on an alternating direction multiplier algorithm until a preset iterative update condition is met to obtain the modal components.

[0017] Preferably, the iterative update of the reconstructed initial modal component is as shown in the following calculation formula:

[0018]

[0019] in, represents the Kth modal component of the n+1th iteration, f(ω) represents the Fourier transform of the original voltage signal, and u i (ω) represents the Fourier transform of the i-th modal component, ω represents the frequency, ω k represents the center frequency, λ represents the Lagrangian factor, α represents the penalty factor, and λ(ω) represents the Fourier transform of the Lagrangian factor.

[0020] Preferably, the calculating the root mean square value of the modal component to generate the sag characteristic amplitude corresponding to the original voltage signal includes:

[0021] Obtaining the voltage value of the modal component, and calculating the root mean square voltage value of the modal component based on a root mean square formula;

[0022] The moment when the root mean square voltage value is less than the preset voltage sag trigger value point is determined as the voltage sag starting point, and the moment when the root mean square voltage value is greater than the preset voltage sag trigger value point is determined as the voltage sag end point;

[0023] Taking the difference between the voltage sag starting point and the voltage sag end point as the voltage sag duration, and calculating the root mean square value of the modal component of the voltage signal during the voltage sag duration;

[0024] The voltage operation root mean square value of the voltage signal modal component during normal operation of the power system is obtained, and the sag characteristic amplitude corresponding to the original voltage signal is generated based on the ratio of the voltage operation root mean square value to the root mean square value of the voltage signal modal component during the voltage sag duration.

[0025] Preferably, the root mean square voltage value is calculated as follows:

[0026]

[0027] Where V(x) represents the voltage value of the modal component, V rms (i) represents the RMS voltage value of the modal component of the i-th tuna individual, and N represents the sliding window length.

[0028] Preferably, the calculation formula of the temporary dip characteristic amplitude is as follows:

[0029]

[0030] Among them, ΔU represents the characteristic amplitude of the temporary sag, represents the RMS value of the modal component of the voltage signal during the duration of the voltage sag, It represents the RMS value of the modal component of the voltage signal when the power system is operating normally.

[0031] Preferably, the preset voltage sag trigger value point satisfies the following calculation formula:

[0032] V rms (t b ) <V trig

[0033] V rms (t e )>V trig

[0034] Among them, the V trig Indicates the preset voltage sag trigger value point, V rms (t b ) indicates the starting point of voltage sag t b The voltage value, V rms (t e ) indicates the voltage sag end point t e Voltage value.

[0035] In a second aspect, the present invention provides a voltage sag disturbance feature extraction system, comprising:

[0036] An initialization module, used to correspond the parameter group of the variational mode decomposition algorithm to the position of the tuna in the tuna school optimization algorithm, and randomly initialize the position of each tuna in the tuna school optimization algorithm, wherein the parameter group includes the number of modes and the penalty parameter;

[0037] An optimization module, used to use the envelope entropy of the modal component obtained by decomposing the original voltage signal based on the parameter group as a fitness function, and use a tuna school optimization algorithm to optimize and solve the parameter group corresponding to the tuna position to obtain a target optimized parameter group;

[0038] A decomposition module, used for performing variational modal decomposition on the original voltage signal based on the target optimization parameter group to obtain modal components;

[0039] The calculation module is used to calculate the root mean square value of the modal component and generate the sag characteristic amplitude corresponding to the original voltage signal.

[0040] Preferably, the decomposition module is further used for:

[0041] Perform variational mode decomposition on the original voltage signal to generate initial mode components;

[0042] Performing a Hilbert transform on the initial modal components to obtain transformed initial modal components;

[0043] Reconstructing the transformed initial modal components based on the optimized penalty factors in the target optimization parameter group and the Lagrangian factors in the variational modal decomposition algorithm to generate reconstructed initial modal components;

[0044] The reconstructed initial modal components are iteratively updated based on an alternating direction multiplier algorithm until a preset iterative update condition is met to obtain the modal components.

[0045] Preferably, the reconstructed initial modal component is iteratively updated in the calculation module as shown in the following calculation formula:

[0046]

[0047] in, represents the Kth modal component of the n+1th iteration, f(ω) represents the Fourier transform of the original voltage signal, and u i (ω) represents the Fourier transform of the i-th modal component, ω represents the frequency, ω k represents the center frequency, λ represents the Lagrangian factor, α represents the penalty factor, and λ(ω) represents the Fourier transform of the Lagrangian factor.

[0048] Preferably, the calculation module is further used for:

[0049] Obtaining the voltage value of the modal component, and calculating the root mean square voltage value of the modal component based on a root mean square formula;

[0050] The moment when the root mean square voltage value is less than the preset voltage sag trigger value point is determined as the voltage sag starting point, and the moment when the root mean square voltage value is greater than the preset voltage sag trigger value point is determined as the voltage sag end point;

[0051] Taking the difference between the voltage sag starting point and the voltage sag end point as the voltage sag duration, and calculating the root mean square value of the modal component of the voltage signal during the voltage sag duration;

[0052] The voltage operation root mean square value of the voltage signal modal component during normal operation of the power system is obtained, and the sag characteristic amplitude corresponding to the original voltage signal is generated based on the ratio of the voltage operation root mean square value to the root mean square value of the voltage signal modal component during the voltage sag duration.

[0053] Preferably, the calculation of the root mean square voltage value in the calculation module is as follows:

[0054]

[0055] Where V(x) represents the voltage value of the modal component, V rms (i) represents the RMS voltage value of the modal component of the i-th tuna individual, and N represents the sliding window length.

[0056] Preferably, the calculation formula of the temporary sag characteristic amplitude in the calculation module is as follows:

[0057]

[0058] Among them, ΔU represents the characteristic amplitude of the temporary sag, represents the RMS value of the modal component of the voltage signal during the duration of the voltage sag, It represents the RMS value of the modal component of the voltage signal when the power system is operating normally.

[0059] Preferably, the preset voltage sag trigger value point in the calculation module satisfies the following calculation formula:

[0060] V rms (t b ) <V trig

[0061] V rms (t e )>V trig

[0062] Among them, the V trig Indicates the preset voltage sag trigger value point, V rms (t b ) indicates the starting point of voltage sag t b The voltage value, V rms (t e ) indicates the voltage sag end point t e Voltage value.

[0063] In another aspect, the present application further provides an electronic device, comprising: at least one processor and a memory; the memory and the processor are connected via a bus;

[0064] The memory is used to store one or more programs;

[0065] When the one or more programs are executed by the at least one processor, the voltage sag disturbance feature extraction method as described above is implemented.

[0066] On the other hand, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed, the voltage sag disturbance feature extraction method as described above is implemented.

[0067] Compared with the prior art, the present invention has the following beneficial effects:

[0068] The present invention provides a method and system for extracting characteristics of voltage sag disturbance. The method corresponds a variational mode decomposition algorithm parameter group to the position of tuna in a tuna school optimization algorithm, and randomly initializes the position of each tuna in the tuna school optimization algorithm, and then decomposes the parameter group into an original voltage signal to obtain an envelope entropy of a modal component as a fitness function, adopts the tuna school optimization algorithm to optimize and solve the parameter group corresponding to the tuna position, obtains a target optimization parameter group, and then performs variational mode decomposition on the original voltage signal based on the target optimization parameter group to obtain the modal component, calculates the root mean square value of the modal component, generates a sag characteristic amplitude corresponding to the original voltage signal, and then realizes rapid and accurate extraction of voltage sag characteristics by combining the tuna school optimization algorithm with the variational mode decomposition algorithm. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1 It is a flow chart of the voltage sag disturbance feature extraction method of the present invention;

[0070] Figure 2 A parameter group updating flow chart of a voltage sag characteristic disturbance feature extraction method of the present invention;

[0071] Figure 3 A voltage sag feature extraction flow chart of the voltage sag disturbance feature extraction method of the present invention;

[0072] Figure 4 It is a structural schematic diagram of the voltage sag disturbance feature extraction system of the present invention;

[0073] Figure 5 The figure is a schematic diagram of the structure of an electronic device of the present invention. DETAILED DESCRIPTION

[0074] The present invention proposes a method and system for extracting voltage sag disturbance features. The method integrates a tuna swarm optimization algorithm and a variational mode decomposition algorithm to extract the voltage sag characteristic amplitude. Compared with the traditional particle swarm optimization method, the method can more accurately extract the voltage sag characteristic amplitude, and compared with the traditional variational mode decomposition technology, it is better in improving the accuracy of the decomposition results and effectively reduces the modal aliasing phenomenon.

[0075] In order to better understand the present invention, the content of the present invention is further described below in conjunction with the accompanying drawings and embodiments.

[0076] Embodiment 1:

[0077] A voltage sag disturbance feature extraction method, such as Figure 1 As shown, including:

[0078] Step 1: Corresponding the parameter group of the variational mode decomposition algorithm to the position of the tuna in the tuna school optimization algorithm, and randomly initializing the position of each tuna in the tuna school optimization algorithm, wherein the parameter group includes the number of modes and the penalty parameter;

[0079] Step 2: using the envelope entropy of the modal component obtained by decomposing the original voltage signal based on the parameter group as the fitness function, and using the tuna school optimization algorithm to optimize and solve the parameter group corresponding to the tuna position to obtain the target optimized parameter group;

[0080] Step 3: performing variational modal decomposition on the original voltage signal based on the target optimization parameter group to obtain modal components;

[0081] Step 4: Calculate the root mean square value of the modal component to generate the sag characteristic amplitude corresponding to the original voltage signal.

[0082] In this embodiment, the random initialization of the position of each tuna in the tuna school optimization algorithm in step 1 can be achieved by the following calculation formula:

[0083]

[0084] in, represents the i-th tuna individual in the j-th iteration, Random is a random vector of 0 or 1, u represents the upper bound of the search space, l represents the lower bound of the search space, and N is the population size.

[0085] In this embodiment, the process of selecting the fitness function in step 2 includes the following contents:

[0086] For the traditional VMD algorithm, the selection of parameters K and α will affect the final decomposition result. In order to improve the accuracy of decomposition, the present invention uses the tuna foraging algorithm to optimize these two parameters and selects the fitness function as the minimum envelope entropy (E min ). Among them, the smaller the envelope entropy is, the more characteristic information there is in the modal component (IMF) and the less noise component there is. min The specific calculation formula is:

[0087]

[0088] In the above formula, a(i) is the VMD decomposition modal vector after Hilbert transform, n is the total number of modal vectors, and p is i represents the normalized form of a(i), E min Indicates the minimum value of envelope entropy.

[0089] In this embodiment, in step 2, the tuna school optimization algorithm is used to optimize and solve the parameter group corresponding to the tuna position, and the process of obtaining the target optimized parameter group specifically includes the following steps:

[0090] Step 1: spiral foraging. For each tuna, select this foraging strategy to execute or optimize the strategy in step 2.

[0091]

[0092] α1=a+(1-a)×k / k max (6)

[0093] α2=(1-a)-(1-a)×k / k max (7)

[0094] β=e b*l ×cos2πb (8)

[0095]

[0096] in, represents the i-th individual generated in the j-th iteration, random represents a random point, best represents the best point, and k is the number of iterations (k max is the maximum number of iterations), α1 represents the weight coefficient that controls the movement trend of tuna individuals toward the best tuna individuals, represents a randomly generated reference point in the kth iteration search space, α2 represents the weight coefficient that controls the movement trend of the tuna individual toward the previous tuna individual, a represents a constant, β is the distance parameter between the current individual and other individuals, l is the spiral parameter, and b is a random number between 0 and 1.

[0097] Step 2: Parabolic foraging or choose step 1 strategy for optimization

[0098]

[0099] Among them, TF is 1 or -1, Represents the current best individual position, q represents the parameter that changes adaptively with the number of iterations, which determines the development range of the population.

[0100] Step three, continuously iteratively update the optimal position of the tuna according to step one or step two, and return the best result (i.e., the target optimization parameter group) after the final setting conditions are met.

[0101] In this embodiment, in step 3, the process of performing variational modal decomposition on the original voltage signal based on the target optimization parameter group to obtain modal components includes:

[0102] Perform variational mode decomposition on the original voltage signal to generate initial mode components;

[0103] Performing a Hilbert transform on the initial modal components to obtain transformed initial modal components;

[0104] Reconstructing the transformed initial modal components based on the optimized penalty factors in the target optimization parameter group and the Lagrangian factors in the variational modal decomposition algorithm to generate reconstructed initial modal components;

[0105] The reconstructed initial modal components are iteratively updated based on an alternating direction multiplier algorithm until a preset iterative update condition is met to obtain the modal components.

[0106] In this embodiment, in step 3, the reconstructed initial modal component is iteratively updated as shown in the following calculation formula:

[0107]

[0108] in, represents the Kth modal component of the n+1th iteration, f(ω) represents the Fourier transform of the original voltage signal, and u i (ω) represents the Fourier transform of the i-th modal component, ω represents the frequency, ω k represents the center frequency, λ represents the Lagrangian factor, α represents the penalty factor, and λ(ω) represents the Fourier transform of the Lagrangian factor.

[0109] In this embodiment, the root mean square value of the modal component is calculated in step 4 to generate the sag characteristic amplitude corresponding to the original voltage signal, including:

[0110] Obtaining the voltage value of the modal component, and calculating the root mean square voltage value of the modal component based on a root mean square formula;

[0111] The moment when the root mean square voltage value is less than the preset voltage sag trigger value point is determined as the voltage sag starting point, and the moment when the root mean square voltage value is greater than the preset voltage sag trigger value point is determined as the voltage sag end point;

[0112] Taking the difference between the voltage sag starting point and the voltage sag end point as the voltage sag duration, and calculating the root mean square value of the modal component of the voltage signal during the voltage sag duration;

[0113] The voltage operation root mean square value of the voltage signal modal component during normal operation of the power system is obtained, and the sag characteristic amplitude corresponding to the original voltage signal is generated based on the ratio of the voltage operation root mean square value to the root mean square value of the voltage signal modal component during the voltage sag duration.

[0114] In this embodiment, the calculation formula of the root mean square voltage value in step 4 is as follows:

[0115]

[0116] Where V(x) represents the voltage value of the modal component, V rms (i) represents the RMS voltage value of the modal component of the i-th tuna individual, and N represents the sliding window length.

[0117] In this embodiment, the calculation formula of the temporary drop characteristic amplitude in step 4 is as follows:

[0118]

[0119] Among them, ΔU represents the characteristic amplitude of the temporary sag, represents the RMS value of the modal component of the voltage signal during the duration of the voltage sag, It represents the RMS value of the modal component of the voltage signal when the power system is operating normally.

[0120] In this embodiment, the preset voltage sag trigger value point in step 4 satisfies the following calculation formula:

[0121] V rms (t b ) <V trig

[0122] V rms (t e )>V trig

[0123] Among them, the V trig Indicates the preset voltage sag trigger value point, V rms (t b ) indicates the starting point of voltage sag t b The voltage value, V rms (t e ) indicates the voltage sag end point t e Voltage value.

[0124] Embodiment 2:

[0125] Figure 2 A parameter group updating flow chart of a voltage sag characteristic disturbance feature extraction method of the present invention; Figure 3 The voltage sag feature extraction flow chart of the voltage sag disturbance feature extraction method of the present invention is as follows: Figure 2 as well as Figure 3 The voltage sag feature extraction of the present invention is described in detail.

[0126] Step S1, initialize the position vector of the tuna school, select the fitness function, and update the individual position. The original voltage signal is decomposed using the TSO algorithm. In this process, the number of modes K and the penalty parameter α are optimized by applying the TSO algorithm to preliminarily obtain each modal component and its corresponding center frequency. This step includes the following steps:

[0127] In step S11, the mode number k and the penalty factor α are used as the initial position vector [k, α] (i.e., the parameter group) of the tuna, and the envelope entropy E is used as the fitness function.

[0128] In step S12, the individual fitness value of tuna is calculated by the aforementioned formulas (1) and (2) and the values ​​of α1, α2 and q are updated by the aforementioned formulas (6), (7) and (11).

[0129] Step S13, compare the value of random with the set random probability constant z, if random is less than z, go to S14, if random is greater than or equal to z, go to S16.

[0130] Step S14, using formula (3) to update the tuna position vector.

[0131] Step S15, if the current iteration number is lower than the set maximum iteration number (k < k max ), then return to Step S12. If the current iteration number is greater than or equal to the set maximum iteration number (k ≥ k max ), then go to Step S18.

[0132] Step S16, if random < 0.5, go to S17. If random ≥ 0.5, update the tuna position information according to formula (10) and then go to S15.

[0133] Step S17, calculate k / k max , if k / k max < random, update the tuna position information according to formula (4) and return to Step S15; if k / k max ≥ random, then update the tuna position information according to formula (5) and return to Step S15.

[0134] Step S18, output the optimal position vector [k, α] (i.e., the target optimization parameter group), and the algorithm ends.

[0135] Step S2, perform VMD decomposition according to the optimized number of modes and penalty factor to obtain mode components. By repeatedly adjusting the mode components u k , the central frequency w k and the Lagrange multiplier λ until a predetermined iteration criterion is reached, and finally generate the intrinsic mode function (IMF). This step includes the following steps:

[0136] Step S21, perform mode decomposition on the original voltage signal f(x) to obtain the mode component G(x) (i.e., the initial mode component), and perform Hilbert transform on G(x) to obtain the single-sided spectrum:

[0137] [δ(x)+j / (πx)]u(x) (12)

[0138]

[0139]

[0140] where δ(x) is the impulse function, j represents the imaginary number, and u(x) represents the unit step function.

[0141] Add an exponential function to adjust formula (12) to the corresponding base frequency band:

[0142]

[0143] where ω k is the central frequency.

[0144] Step S22, adding the penalty factor α obtained in step S1 and the Lagrangian factor λ to reconstruct each modal component of the demodulated signal, and the obtained Lagrangian multiplier is

[0145]

[0146] Step S23, introduce ADMM algorithm into formula (12) to find the optimal solution, and we can get

[0147]

[0148] Step S24, solving the VMD algorithm, specifically as follows:

[0149] Step S241, initialization parameters (u k ,ω k ,λ), let n=n+1, and use formula (18) and formula (19) to calculate u k ,ω k Perform K iterations, where K is the K value optimized in step S1, and u k is the amplitude of the modal component.

[0150] Step S242, update λ, and determine whether the following conditional formula (20) is satisfied. If satisfied, the process ends. Otherwise, the process proceeds to step S241 after the parameters are initialized until the iteration ends.

[0151]

[0152] Among them, ε is the set termination condition.

[0153] Step S3, calculate the root mean square value of the modal component, and extract the amplitude and duration of the sag. The RMS detection method is usually used to analyze the power or amplitude of the signal. The root mean square value is the square root of the average of the square values ​​of the signal, which can provide a measure of the signal strength. In practical applications, the RMS detection method is often used in power systems: measuring the root mean square value of the voltage or current of the power grid to evaluate the power quality. By calculating the root mean square (RMS) value of the modal component, the characteristic amplitude of the voltage sag is extracted. This step includes the following steps:

[0154] Step S31, obtain the voltage value V(x) of the modal component obtained in the above step, and calculate the root mean square value V of the value rms

[0155]

[0156] Where N is the sliding window length.

[0157] Step S32, setting the voltage sag trigger value point V trig , if the detection device obtains V rmsLess than V trig It is considered that a voltage sag has occurred, and the starting point of the voltage sag is determined, and the starting point is t b , the end point is t e , which satisfies the following conditions:

[0158] V rms (t b ) <V trig (twenty two)

[0159] V rms (t e )>V trig (twenty three)

[0160] Step S33, the duration of the voltage sag can be obtained from the start time and the end time obtained in the previous step, and is set to T:

[0161] T=t e -t b (twenty four)

[0162] Step S34, assuming that the root mean square voltage of the power system is When the voltage drops temporarily Calculate the voltage sag amplitude ΔU.

[0163]

[0164] Embodiment 3:

[0165] The present invention based on the same inventive concept also provides a voltage sag disturbance feature extraction system, such as Figure 4 As shown, including:

[0166] An initialization module, used to correspond the parameter group of the variational mode decomposition algorithm to the position of the tuna in the tuna school optimization algorithm, and randomly initialize the position of each tuna in the tuna school optimization algorithm, wherein the parameter group includes the number of modes and the penalty parameter;

[0167] An optimization module, used to use the envelope entropy of the modal component obtained by decomposing the original voltage signal based on the parameter group as a fitness function, and use a tuna school optimization algorithm to optimize and solve the parameter group corresponding to the tuna position to obtain a target optimized parameter group;

[0168] A decomposition module, used for performing variational modal decomposition on the original voltage signal based on the target optimization parameter group to obtain modal components;

[0169] The calculation module is used to calculate the root mean square value of the modal component and generate the sag characteristic amplitude corresponding to the original voltage signal.

[0170] Preferably, the decomposition module is further used for:

[0171] Perform variational mode decomposition on the original voltage signal to generate initial mode components;

[0172] Performing a Hilbert transform on the initial modal components to obtain transformed initial modal components;

[0173] Reconstructing the transformed initial modal components based on the optimized penalty factors in the target optimization parameter group and the Lagrangian factors in the variational modal decomposition algorithm to generate reconstructed initial modal components;

[0174] The reconstructed initial modal components are iteratively updated based on an alternating direction multiplier algorithm until a preset iterative update condition is met to obtain the modal components.

[0175] Preferably, the reconstructed initial modal component is iteratively updated in the calculation module as shown in the following calculation formula:

[0176]

[0177] in, represents the Kth modal component of the n+1th iteration, f(ω) represents the Fourier transform of the original voltage signal, and u i (ω) represents the Fourier transform of the i-th modal component, ω represents the frequency, ω k represents the center frequency, λ represents the Lagrangian factor, α represents the penalty factor, and λ(ω) represents the Fourier transform of the Lagrangian factor.

[0178] Preferably, the calculation module is further used for:

[0179] Obtaining the voltage value of the modal component, and calculating the root mean square voltage value of the modal component based on a root mean square formula;

[0180] The moment when the root mean square voltage value is less than the preset voltage sag trigger value point is determined as the voltage sag starting point, and the moment when the root mean square voltage value is greater than the preset voltage sag trigger value point is determined as the voltage sag end point;

[0181] Taking the difference between the voltage sag starting point and the voltage sag end point as the voltage sag duration, and calculating the root mean square value of the modal component of the voltage signal during the voltage sag duration;

[0182] The voltage operation root mean square value of the voltage signal modal component during normal operation of the power system is obtained, and the sag characteristic amplitude corresponding to the original voltage signal is generated based on the ratio of the voltage operation root mean square value to the root mean square value of the voltage signal modal component during the voltage sag duration.

[0183] Preferably, the calculation of the root mean square voltage value in the calculation module is as follows:

[0184]

[0185] Where V(x) represents the voltage value of the modal component, V rms (i) represents the RMS voltage value of the modal component of the i-th tuna individual, and N represents the sliding window length.

[0186] Preferably, the calculation formula of the temporary sag characteristic amplitude in the calculation module is as follows:

[0187]

[0188] Among them, ΔU represents the characteristic amplitude of the temporary sag, represents the RMS value of the modal component of the voltage signal during the duration of the voltage sag, It represents the RMS value of the modal component of the voltage signal when the power system is operating normally.

[0189] Preferably, the preset voltage sag trigger value point in the calculation module satisfies the following calculation formula:

[0190] V rms (t b ) <V trig

[0191] V rms (t e )>V trig

[0192] Among them, the V trig Indicates the preset voltage sag trigger value point, V rms (t b ) indicates the starting point of voltage sag t b The voltage value, V rms (t e ) indicates the voltage sag end point t e Voltage value.

[0193] Embodiment 4:

[0194] like Figure 5 As shown, the present invention also provides an electronic device, which may be a computer device, a single-chip device, an intelligent mobile device, etc. The electronic device in this embodiment may include a processor, a memory, a transceiver component, etc. The memory, the processor, and the transceiver component are connected via a bus; the memory may be used to store an execution program, and an exemplary execution program may include instructions; the processor is used to execute the instructions stored in the memory. The memory may also be used to store data, which may be called and / or modified when the instructions are executed.

[0195] The processor may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, which is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in a storage medium to implement the corresponding method flow or corresponding functions, so as to implement the steps of a voltage sag disturbance feature extraction method in the above-mentioned embodiment.

[0196] Example 5

[0197] Based on the same inventive concept, the present invention also provides a readable storage medium, specifically an electronic device readable storage medium (Memory), which is a memory device in the electronic device for storing programs and data. It can be understood that the storage medium here can include both the built-in storage medium in the electronic device and the extended storage medium supported by the electronic device. The storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space, and these instructions can be one or more execution programs (including program codes). It should be noted that the storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk storage. The processor loads and executes one or more instructions stored in the storage medium, which can implement the steps of a voltage sag disturbance feature extraction method in the above embodiment.

[0198] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0199] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0200] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0201] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0202] The above are merely embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention are included in the scope of the claims of the present invention to be approved.

Claims

1. A method for extracting voltage sag disturbance features, characterized in that: include: Corresponding a parameter group of the variational mode decomposition algorithm to the position of tuna in the tuna school optimization algorithm, and randomly initializing the position of each tuna in the tuna school optimization algorithm, wherein the parameter group includes the number of modes and the penalty parameter; The envelope entropy of the modal component obtained by decomposing the original voltage signal based on the parameter group is used as the fitness function, and the parameter group corresponding to the tuna position is optimized and solved by using the tuna school optimization algorithm to obtain the target optimization parameter group; Performing variational modal decomposition on the original voltage signal based on the target optimization parameter group to obtain modal components; The root mean square value of the modal component is calculated to generate a sag characteristic amplitude corresponding to the original voltage signal.

2. The method according to claim 1, characterized in that The performing variational modal decomposition on the original voltage signal based on the target optimization parameter group to obtain modal components includes: Perform variational mode decomposition on the original voltage signal to generate initial mode components; Performing a Hilbert transform on the initial modal components to obtain transformed initial modal components; Reconstructing the transformed initial modal components based on the optimized penalty factors in the target optimization parameter group and the Lagrangian factors in the variational modal decomposition algorithm to generate reconstructed initial modal components; The reconstructed initial modal components are iteratively updated based on an alternating direction multiplier algorithm until a preset iterative update condition is met to obtain the modal components.

3. The method according to claim 2, characterized in that The iterative update of the reconstructed initial modal component is shown in the following calculation formula: in, represents the Kth modal component of the n+1th iteration, f(ω) represents the Fourier transform of the original voltage signal, and u i (ω) represents the Fourier transform of the i-th modal component, ω represents the frequency, ω k represents the center frequency, λ represents the Lagrangian factor, α represents the penalty factor, and λ(ω) represents the Fourier transform of the Lagrangian factor.

4. The method according to claim 1, characterized in that: The calculating the root mean square value of the modal component to generate the sag characteristic amplitude corresponding to the original voltage signal includes: Obtaining the voltage value of the modal component, and calculating the root mean square voltage value of the modal component based on a root mean square formula; The moment when the root mean square voltage value is less than the preset voltage sag trigger value point is determined as the voltage sag starting point, and the moment when the root mean square voltage value is greater than the preset voltage sag trigger value point is determined as the voltage sag end point; Taking the difference between the voltage sag starting point and the voltage sag end point as the voltage sag duration, and calculating the root mean square value of the modal component of the voltage signal during the voltage sag duration; The voltage operation root mean square value of the voltage signal modal component during normal operation of the power system is obtained, and the sag characteristic amplitude corresponding to the original voltage signal is generated based on the ratio of the voltage operation root mean square value to the root mean square value of the voltage signal modal component during the voltage sag duration.

5. The method according to claim 4, characterized in that The calculation formula of the root mean square voltage value is as follows: Where V(x) represents the voltage value of the modal component, V rms (i) represents the RMS voltage value of the modal component of the i-th tuna individual, and N represents the sliding window length.

6. The method according to claim 4, characterized in that The calculation formula of the temporary sag characteristic amplitude is as follows: Among them, ΔU represents the characteristic amplitude of the temporary sag, represents the RMS value of the modal component of the voltage signal during the duration of the voltage sag, It represents the RMS value of the modal component of the voltage signal when the power system is operating normally.

7. The method according to claim 4, characterized in that The preset voltage sag trigger value point satisfies the following calculation formula: V rms (t b )<V trig V rms (t e )>V trig Among them, the V trig Indicates the preset voltage sag trigger value point, V rms (t b ) indicates the starting point of voltage sag t b The voltage value, V rms (t e ) indicates the voltage sag end point t e Voltage value.

8. A voltage sag disturbance feature extraction system, characterized in that: include: An initialization module, used to correspond the parameter group of the variational mode decomposition algorithm to the position of the tuna in the tuna school optimization algorithm, and randomly initialize the position of each tuna in the tuna school optimization algorithm, wherein the parameter group includes the number of modes and the penalty parameter; An optimization module, used to use the envelope entropy of the modal component obtained by decomposing the original voltage signal based on the parameter group as a fitness function, and use a tuna school optimization algorithm to optimize and solve the parameter group corresponding to the tuna position to obtain a target optimized parameter group; A decomposition module, used for performing variational modal decomposition on the original voltage signal based on the target optimization parameter group to obtain modal components; The calculation module is used to calculate the root mean square value of the modal component and generate the sag characteristic amplitude corresponding to the original voltage signal.

9. An electronic device, characterized in that: include: at least one processor and memory; The memory and the processor are connected via a bus; The memory is used to store one or more programs; When the one or more programs are executed by the at least one processor, the voltage sag disturbance feature extraction method according to any one of claims 1 to 7 is implemented.

10. A readable storage medium, characterized in that: An execution program is stored thereon, and when the execution program is executed, the voltage sag disturbance feature extraction method according to any one of claims 1 to 7 is implemented.

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