Electric energy quality analysis method, system, equipment and medium

Abstract

The invention provides an electric energy quality analysis method, system and device and a medium. The analysis method comprises the following steps: acquiring an electric energy signal; inputting the electric energy signal into an analysis model to extract a time-frequency characteristic of the electric energy signal; and determining the quality of the electric energy signal based on the time-frequency characteristics. And extracting video features of the electric energy signals through the analysis model. The analysis model is based on S transformation, the electric energy signal is divided into a high frequency domain and a low frequency domain, time-frequency feature extraction is carried out on the disturbance signal in the different frequency domains, the accuracy of feature extraction can be effectively improved, and then the accuracy of electric energy quality analysis based on the time-frequency features is improved.

Description

Technical Field

[0001] The present invention relates to the field of electric power, and in particular to a method, system, equipment and medium for analyzing power quality. Background Art

[0002] As the most widely used, most convenient and cleanest process energy, electricity plays a vital role in all aspects of human society and has become an important indicator for measuring the economic development of a region.

[0003] As a special commodity provided by the power system to power users, the power quality (Power Quality, PQ) should be fully guaranteed. The quality of power has become an important indicator of the level of operation and management of the power system. Detecting, controlling and improving power quality is a necessary condition for enhancing the overall benefits of the national economy and building a stable and high-quality power grid.

[0004] In the actual operation of the power system, electric energy is disturbed by various factors. The electric energy reaching the power user will deviate from the sinusoidal waveform and be distorted, resulting in various power quality disturbance (PQD) signals, which in turn cause various power quality problems. However, there is currently a lack of a reliable and effective method to accurately analyze power quality. Summary of the invention

[0005] The present invention aims at the problem that there is a lack of a reliable and effective method in the prior art that can accurately analyze the power quality, and proposes a power quality analysis method, system, equipment and medium.

[0006] In a first aspect, an embodiment of the present invention provides a method for analyzing power quality, the method comprising:

[0007] Acquire power signal;

[0008] Inputting the electric energy signal into the analysis model to extract the time-frequency characteristics of the electric energy signal;

[0009] Determine the quality of the power signal based on time-frequency characteristics;

[0010] The analytical model includes:

[0011]

[0012] Where x(t) is the electric energy signal; f0 is the fundamental frequency; f is the signal frequency; |f means taking the absolute value; t is the time variable; τ is the time shift factor; τ controls the specific position of the Gaussian window on the time axis;

[0013] The window functions of the analysis model include:

[0014]

[0015] Among them, λ 1,2 is the adjustment factor, λ 1,2 ={λ1,λ2}.

[0016] In one embodiment, the sampling time interval of the analysis model is T s , the number of sampling points of the analysis model is N; the time shift factor τ = T s ;Signal frequency f=k / NT s ;Electric energy signal x(t)=x(nT s )(n=0,1,2,...N-1);

[0017] The analytical model also includes:

[0018]

[0019] Among them, X(k+r) is the discrete Fourier spectrum, and G(r,k) is the Gaussian window function.

[0020] In one embodiment, the analysis model further includes:

[0021] IDRST(m,k)=|IDRST(m,k)|e jφ(m,k) ;

[0022] Where |IDRST(m,k)| is the amplitude and φ(m,k) is the phase angle.

[0023] In one embodiment, different parameters are 1,2 ={λ1,λ2}, the energy concentration function is expressed as:

[0024]

[0025] Among them, the dimension size of the matrix IDRST(m,n) is M×N.

[0026] In one embodiment, the hyperparameters of the analytical model are optimized via a genetic algorithm.

[0027] In one embodiment, the fitness function of the genetic algorithm includes:

[0028] fit = 1 / ECM(λ1,λ2).

[0029] In one embodiment, the crossover probability P of the genetic algorithm is c The adaptive adjustment formula is

[0030]

[0031] Among them, fc min 、fcavg 、fc max Respectively represent the minimum, average and maximum fitness in the population performing the crossover operation; fc is the minimum fitness of the two parent individuals to be crossovered; P c 1. P c2 , P c3 are the maximum crossover probability, average crossover probability and minimum crossover probability respectively, and satisfy P c1 >P c2 >P c3 ∈(0,1);

[0032] And / or, the mutation probability P of the genetic algorithm m Adaptive adjustment to

[0033]

[0034] Among them, fm min 、fm avg 、fm max represent the minimum, average and maximum fitness of the population performing the mutation operation; fm is the minimum fitness of the two parent individuals after the crossover operation; P m1 ,P m2 , P m3 are the maximum mutation probability, average mutation probability and minimum mutation probability respectively.

[0035] In a second aspect, an embodiment of the present invention provides a power quality analysis system, the analysis system comprising:

[0036] An acquisition module, used for acquiring electric energy signals;

[0037] An extraction module, used for inputting the electric energy signal into the analysis model to extract the time-frequency characteristics of the electric energy signal;

[0038] A determination module, used to determine the quality of the electric energy signal based on the time-frequency characteristics;

[0039] The analytical model includes:

[0040]

[0041] Where x(t) is the electric energy signal; f0 is the fundamental frequency; f is the signal frequency; |f means taking the absolute value; t is the time variable; τ is the time shift factor; τ controls the specific position of the Gaussian window on the time axis;

[0042] The window functions of the analysis model include:

[0043]

[0044] Among them, λ 1,2 is the adjustment factor, λ1,2 ={λ1,λ2}.

[0045] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement a method for analyzing power quality as described in any one of the first aspects.

[0046] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, in which at least one instruction or at least one program is stored, and the at least one instruction or at least one program is loaded and executed by a processor to implement a method for analyzing power quality as described in any one of the first aspects.

[0047] The present invention provides a method, system, device and medium for analyzing power quality, which have the following technical effects: extracting video features of power signals through an analysis model. The analysis model is based on S transformation, divides the power signal into two different frequency domains, high and low, and extracts time-frequency features of disturbance signals in different frequency domains, which can effectively improve the accuracy of feature extraction, and thus improve the accuracy of power quality analysis based on time-frequency features.

[0048] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The accompanying drawings constituting a part of the present invention are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the accompanying drawings:

[0050] Figure 1 A schematic diagram of a single power quality disturbance signal is provided for an exemplary embodiment of the present invention;

[0051] Figure 2 A schematic diagram of a power quality disturbance signal composited from two power quality disturbance signals provided for an exemplary embodiment of the present invention;

[0052] Figure 3 A schematic diagram of a power quality disturbance signal composited from three power quality disturbance signals provided for an exemplary embodiment of the present invention;

[0053] Figure 4 A flow chart of a method for analyzing power quality provided by an exemplary embodiment of the present invention;

[0054] Figure 5A schematic diagram of a time-frequency feature extraction result of a power quality disturbance signal of a voltage sag provided by an exemplary embodiment of the present invention;

[0055] Figure 6 A schematic diagram of a time-frequency feature extraction result of a power quality disturbance signal which is a harmonic signal provided by an exemplary embodiment of the present invention;

[0056] Figure 7 A schematic diagram of a time-frequency feature extraction result of a power quality disturbance signal of voltage sag+harmonic+voltage oscillation provided by an exemplary embodiment of the present invention;

[0057] Figure 8 A structural diagram of a power quality analysis system provided by an exemplary embodiment of the present invention;

[0058] Fig. 9 A structural diagram of an electronic device provided by an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0059] The present invention will be described in detail below with reference to the accompanying drawings and in combination with embodiments. It should be noted that the embodiments and features in the embodiments of the present invention can be combined with each other without conflict.

[0060] The following detailed description is an exemplary description, which is intended to provide further detailed description of the present invention. Unless otherwise specified, all technical terms used in the present invention have the same meaning as those generally understood by those skilled in the art to which the present invention belongs. The terms used in the present invention are only for describing specific embodiments, and are not intended to limit the exemplary embodiments according to the present invention.

[0061] Power quality includes voltage quality, current quality, power supply quality and power consumption quality. Ideally, power is a standard industrial frequency sinusoidal voltage with rated amplitude and rated frequency. In a three-phase AC power system, the voltage and current of each phase should also meet the following requirements: equal amplitude, symmetrical phase and 120° difference. However, in the actual operation of the power system, power is disturbed by various factors. The power reaching the power user will deviate from the sinusoidal waveform and be distorted, and various power quality disturbance signals will appear, which will lead to various power quality problems. There are many reasons for power quality problems. On the one hand, new energy represented by wind power generation and photovoltaic power generation is being distributed and integrated into the power grid in large quantities, resulting in increased instability of the power grid. On the other hand, a large number of nonlinear, impact and unbalanced loads are put into operation in the power system, making the power grid environment increasingly harsh. The extensive use of power electronic equipment such as railway electrification, electrochemical industrial electrolysis equipment, and industrial steelmaking arc furnaces not only causes the insulation aging and life reduction of power equipment, but also causes a certain degree of pollution to the power grid, causing power quality problems such as voltage deviation, voltage fluctuation, and harmonics.

[0062] Power quality disturbance signals include but are not limited to voltage swell, voltage sag, voltage interruption, harmonics, voltage oscillation transients and voltage flicker.

[0063] Voltage swell, also known as voltage bump or voltage surge, refers to an electromagnetic disturbance phenomenon in which the root mean square value of the power frequency voltage rises to 1.1-1.8 (pu) and lasts for 0.5 cycles to 60 seconds, while the system frequency remains at the nominal value. The main causes of voltage swell are: circuit system short circuit fault recovery, industrial heavy load stop or removal, capacitor bank switching, etc.

[0064] Voltage sag is also called voltage drop or voltage sag. It is the opposite of voltage swell. It is an electromagnetic disturbance phenomenon in which the root mean square value of the power frequency voltage drops to 0.1-0.9 (pu) and lasts for 0.5 cycles to 60 seconds, while the system frequency remains at the nominal value. The main causes of voltage sag are: circuit system short circuit, industrial heavy load starting or switching, etc.

[0065] Voltage interruption refers to a power quality disturbance in which the root mean square value of the power frequency voltage is lower than 0.1 (pu), the duration is 0.5 cycle to 60 seconds, and the system frequency remains at the nominal value. The main causes of voltage interruption are: power system failure, mechanical equipment failure, control equipment failure, etc.

[0066] Harmonics refer to the components whose frequencies are integer multiples of the fundamental frequency obtained by Fourier series decomposition of periodic non-sinusoidal alternating current. The main causes of harmonics are: nonlinear load injection, transformer no-load or over-excitation, and closing inrush current when switching capacitors in the power grid.

[0067] Voltage oscillation transient refers to the occurrence of non-power frequency voltage or current pulses during steady-state operation. It often exists as unipolar pulses or spikes of either polarity. The main causes of transient oscillation are: lightning, poor grounding, inductive load switching, etc.

[0068] Voltage flicker refers to a phenomenon in which the power frequency voltage root mean square value changes periodically within 0.95-1.05 (pu) and causes the human eye to perceive the fluctuation of the illumination of the white weaving lamp. The main causes of voltage flicker are: fluctuating load injection, system short circuit fault, automatic switching of backup power supply, etc.

[0069] A PQD signal composed of any single type of PQD signal in the above examples is generally referred to as a single PQD signal, and a PQD signal composed of two or more PQD signals is generally referred to as a composite PQD signal. Figure 1 is an example of a single PQD signal, Figure 2-Figure 3 is an example of a composite PQD signal.

[0070] Only by accurately analyzing the time and frequency of PQD signals and efficiently classifying disturbance types can we further locate the source of dynamic PQD signals and analyze their causes, so as to correctly evaluate dynamic power quality and control grid pollution.

[0071] Based on this, an exemplary embodiment of the present invention provides a method for analyzing power quality, see Figure 1 The embodiment of the present invention provides a method for analyzing power quality, the method comprising:

[0072] S11. Acquire electric energy signal.

[0073] Among them, the power signal describes the signal of power transmission and consumption in the power system, and the power signal in this embodiment can preferably be a power quality disturbance signal, that is, a signal of power quality problems caused by various reasons in the power system in the above example.

[0074] S12. Inputting the electric energy signal into the analysis model to extract the time-frequency characteristics of the power quality disturbance signal.

[0075] Among them, time-frequency characteristics refer to the performance characteristics of power quality disturbances in the power system in the two dimensions of time and frequency. These characteristics can help us analyze and understand the nature and impact of disturbances, and provide important information for power quality monitoring, fault diagnosis, preventive maintenance, etc.

[0076] S13. Determine the quality of the electric energy signal based on the time-frequency characteristics.

[0077] In actual application, the time-frequency characteristics can be used to monitor the power quality of the power system in real time to ensure the stable operation of the power system.

[0078] In one embodiment, the analysis model in the steps of this embodiment preferably uses an improved double resolution S-transform (IDRST) algorithm to extract the time-frequency information of the power quality disturbance signal. Based on the S-transform, IDRST divides the disturbance signal into two different frequency domains, high and low, and performs time-frequency analysis on the power quality disturbance signal in different frequency domains.

[0079] Specifically include:

[0080]

[0081] Among them, x(t) is the power quality disturbance signal; f0 represents the fundamental frequency; f is the signal frequency; |f represents the absolute value; t is the time variable; τ is the time shift factor; τ controls the specific position of the Gaussian window on the time axis.

[0082] The window functions of the analysis model include:

[0083]

[0084] Among them, λ 1,2 is the adjustment factor, λ 1,2 ={λ1,λ2}.

[0085] Define the scale factor σ of IDRST IDRST (f)

[0086]

[0087] The sampling time interval of the analysis model is T s , the number of sampling points of the analysis model is N; the time shift factor τ = T s ;Signal frequency f=k / NT s ; Power quality disturbance signal x(t)=x(nT s )(n=0,1,2,...N-1);

[0088] The analytical model also includes:

[0089]

[0090] Among them, X(k+r) is the discrete Fourier spectrum, and G(r,k) is the Gaussian window function.

[0091] Generally speaking, IDRST exists in the form of a complex matrix, and discrete IDRST can be expressed as:

[0092] IDRST(m,k)=|IDRST(m,k)|e jφ(m,k) (5);

[0093] Where |IDRST(m,k)| is the amplitude and φ(m,k) is the phase angle.

[0094] For simplicity, the amplitude of IDRST applied in the time-frequency analysis task is A (m,k) is set to |IDRST A (m,k)|=|IDRST(m,k)|. Different parameters have different effects on λ 1,2 ={λ1,λ2}, the energy concentration function is expressed as:

[0095]

[0096] Among them, the dimension size of the matrix IDRST(m,n) is M×N.

[0097] The optimization goal of the Gaussian window adjustment factor parameters is to maximize the energy concentration function of IDRST, that is, arg max(ECM(λ1,λ2)), thereby obtaining the highest time-frequency resolution. The time resolution of the low-frequency interval corresponding to parameter λ1 should be high enough to distinguish the moment when the disturbance occurs. The frequency resolution of the high-frequency interval corresponding to parameter λ2 should also be high enough to meet the high-frequency resolution. In order to quickly obtain the optimized window parameters, the range of variation of parameters λ1 and λ2 is constrained, and the time resolution is set to meet:

[0098] σ low ≤σ(f)≤σ up (7);

[0099] Among them, σ low =lT s And l>1, which determines the lower limit of time resolution; σ up Determine the upper limit of time resolution. Combining equation (3) and equation (7), we can further obtain the constraints of λ1 and λ2 as follows:

[0100]

[0101] Among them, f min and f max denote the minimum and maximum frequencies of the low-frequency band and the high-frequency band, respectively. Finally, the parameter optimization problem can be summarized as

[0102]

[0103] Therefore, the solution of the two adjustment factors is converted into a nonlinear optimization problem as shown in formula (9).

[0104] In one embodiment, the hyperparameters of the analysis model are optimized by genetic algorithm. Genetic algorithm (GA) is a global optimization search algorithm formed by simulating the genetic and evolutionary process of organisms in the natural environment, which has high robustness.

[157] However, the crossover probability P in the standard GA c and mutation probability P m Fixed, it is easy to converge to the local optimal value early when solving multi-parameter optimization, and it is difficult to achieve the optimal search effect. The proposed AGA is based on GA, uses the energy aggregation function of discrete IDRST as the fitness function, and introduces linear function and Sigmoid function to complete the crossover probability P c With mutation probability P m The adaptive adjustment of parameters can improve the efficiency of parameter optimization.

[0105] The fitness function of the genetic algorithm includes:

[0106] fit = 1 / ECM(λ1,λ2)(10).

[0107] The maximum energy concentration problem is converted into a fitness minimum problem by taking the inverse. The parameter search performance of AGA is improved in the following two aspects: (1) In the initial stage of population evolution, in order to maintain the diversity of the population, the crossover probability is adjusted by a linear function to slowly decrease, so as to ensure that individuals with lower fitness still retain a higher crossover probability and avoid parameter optimization from falling into the local optimum; (2) In the later stage of population evolution, the Sigmoid function is introduced to adjust the crossover and mutation probabilities, and the crossover and mutation probability values ​​are reduced to near the minimum fitness, so as to retain the excellent individuals in the population as much as possible and improve the optimization accuracy.

[0108] Based on the above optimization policy, the crossover probability P of the genetic algorithm c The adaptive adjustment formula is

[0109]

[0110] Among them, fc min 、fc avg 、fc max Respectively represent the minimum, average and maximum fitness in the population performing the crossover operation; fc is the minimum fitness of the two parent individuals to be crossovered; P c 1. P c2 , P c3 are the maximum crossover probability, average crossover probability and minimum crossover probability respectively, and satisfy P c1 >P c2 >P c3 ∈(0,1);

[0111] And / or, the mutation probability P of the genetic algorithm m Adaptive adjustment to

[0112]

[0113] Among them, fm min 、fm avg 、fm max represent the minimum, average and maximum fitness of the population performing the mutation operation; fm is the minimum fitness of the two parent individuals after the crossover operation; P m1 ,P m2 , P m3 are the maximum mutation probability, average mutation probability and minimum mutation probability respectively.

[0114] As an example, the parameter settings of the genetic algorithm can refer to the following table, but are not limited to this:

[0115]

[0116] P and G shown in the table represent the population size and the number of iterations, respectively. According to the above table and equations (10), (11), and (12), the specific steps of parameter optimization based on AGA are summarized as follows:

[0117] The first step is to initialize the population. Set the evolutionary generation counter g = 0, set the population parameters according to Table 2.3, and initialize the population parameters.

[0118] The second step is fitness calculation. The energy aggregation of discrete IDRST is used as the fitness function of AGA to calculate the fitness of each individual in the population.

[0119] The third step is parent selection. Elite selection is performed based on individual fitness, and individuals with high fitness are used as parents for subsequent genetic operations.

[0120] The fourth step is adaptive crossover operation. The selected pairs of parent individuals are crossovered based on formula (2.21) to generate new individuals.

[0121] Step 5: Adaptive mutation operation. Perform mutation operation on parent individuals based on formula (12) to generate new individuals. After selection, crossover and mutation, the population obtains a new sub-population. Calculate the fitness of the sub-population and prepare for the next genetic operation.

[0122] Step 6, determine the termination condition: if the evolutionary algebraic counter g≤G, then the counter +1 and go to the second step; if g>G, then the individual obtained in this iteration is output as the optimal solution and the calculation is terminated.

[0123] In one embodiment, the characteristics of the above analysis model are described as follows:

[0124] (1) Linear additivity

[0125] Let x(t),y(t)∈L 2 (R), and their IDRST are IDRST x (τ,f) and IDRST y (τ, f), m and n are arbitrary constants. Let z(t) = mx(t) + ny(t), then when the signal is in the low frequency band, that is, f≤1.5f0, the following equation holds:

[0126]

[0127] When the signal is in the high frequency band, that is, f>1.5f0, the equation is satisfied:

[0128]

[0129] Combining equations (13) and (14), it can be seen that IDRST satisfies linear additivity and is a linear time-frequency transformation. The IDRST after signal addition is equal to the sum of the IDRSTs of each signal separately. Based on this, when the signal is superimposed with the same additive noise interference, there is no cross-term, and it has strong noise resistance to signals containing additive noise.

[0130] (2) Time-shift property

[0131] Let the IDRST of x(t) be IDRSTx(τ,f), and let z(t) = x(t - t0). Then when the signal is in the low-frequency band, that is, f ≤ 1.5f0, we have:

[0132]

[0133] Let m = t - t0, then equation (2.25) satisfies the equation:

[0134]

[0135] When the signal is in the high-frequency band, that is, f > 1.5f0, we have:

[0136]

[0137] Let m = t - t0, then equation (2.25) satisfies the equation:

[0138]

[0139] From equations (15) to (18), it can be seen that IDRST has the time-shift property. When the IDRST of the signal x(t) is IDRSTx(τ,f), the IDRST result after it is translated along the time axis by t0 in the time domain is

[0140] (3) Variable-resolution property

[0141] IDRST divides the frequency-domain part of the power quality disturbance signal into two sub-intervals, the low-frequency band and the high-frequency band, with the 1.5-fold fundamental frequency as the dividing line. There are two adjustment factors λ1 and λ2 corresponding to different intervals respectively. When the power quality disturbance signal is in the low-frequency band interval, that is, f ≤ 1.5f0, at this time, the adjustment factor λ1 > |f|, and the window function is narrower and more concentrated in the time domain. Therefore, the time resolution in the low-frequency band is higher. When the power quality disturbance signal is in the high-frequency band interval, that is, f > 1.5f0, at this time, the adjustment factor λ1 < |f|, and the window function is wider in the time domain, making the frequency resolution in the high-frequency band higher.

[0142] Figure 5-Figure 7 Schematic diagram for video signal extraction in this embodiment:

[0143] Figure 5 Display voltage sag signal and its time-frequency characteristics. According to Figure (c), the time domain characteristic curve clearly shows that the corresponding time domain amplitude of the signal first decreases and then rises along the time axis, and can accurately locate the start and end time of the disturbance signal, with a time resolution of about 0.02s. The amplitude of the disturbance signal drops by 0.5 (pu), which is consistent with the amplitude drop of the signal disturbance in Figure (a). According to Figure (d), the frequency domain characteristic curve has a peak with an amplitude of l (pu) at the base frequency of 50Hz, indicating that the signal has no other frequency components except the base frequency, which is consistent with the signal shown in Figure (a).

[0144] Figure 6 This is a two-dimensional view of the time-frequency results of the disturbance signal. The yellow area represents the fundamental frequency characteristics. Through this two-dimensional view, it can be clearly identified that there are two harmonic components at 150Hz and 250Hz. Figure 6 (d) The frequency domain characteristic curve shows that in addition to the peak with an amplitude of l (pu) at the fundamental frequency of 50 Hz, there are two peaks at 150 Hz and 250 Hz, and the amplitude of each peak is between 0.2-0.4 (pu), which is consistent with the display in Figure (b), indicating that the signal has two harmonic components.

[0145] according to Figure 7 In addition to the fundamental frequency characteristics in the yellow area and the two blue harmonic components that extend through the time axis, the two-dimensional view also shows that there is a voltage oscillation transient component at 300Hz. Figure 7 (c) The time domain characteristic curve clearly captures the voltage sag disturbance and locates the start and end time of the signal. Figure 7 (d) In addition to the peak at the fundamental frequency, there are also peaks at 150 Hz, 250 Hz, and 300 Hz, indicating that the signal has these three high-frequency components, which is consistent with the signal setting.

[0146] from Figure 5-Figure 7 It can be seen that IDRST has good time-frequency resolution. Through IDRST, the time domain characteristic curve and frequency domain characteristic curve of the disturbance signal can be extracted respectively, and the time resolution response is 0.02s, which can complete the time-frequency analysis task of power quality disturbance signal.

[0147] In addition, this embodiment also extracts time-frequency features of the power quality disturbance signal through IDRST under noise-free interference and white noise interference, and it is found that the effects are better than other algorithms.

[0148] In this embodiment, the adaptive genetic algorithm is used to realize the adaptive optimization of the IDRST window parameters, and the energy concentration is introduced to evaluate the energy concentration degree of the disturbance signal. The IDRST in this embodiment has good energy concentration. When dealing with the time-frequency analysis task of a single disturbance signal, it can effectively distinguish various disturbances and accurately identify the time-frequency information such as the start and end time and amplitude of various disturbances; when dealing with the time-frequency analysis task of a composite disturbance signal, the IDRST can identify the time-frequency characteristics of each disturbance component that constitutes various composite disturbances from the complex disturbance time-frequency information, and has a high resolution for the time-frequency information including the start and end time and amplitude of the disturbance; various power quality disturbance signals are superimposed with different degrees of Gaussian white noise interference and IDRST is performed. The results show that the IDRST still has a high time-frequency resolution when the disturbance is affected by noise, which verifies the robustness of the IDRST. According to the characteristics and advantages of the proposed method, the IDRST can be used in the time-frequency analysis task of the power quality disturbance signal to realize the extraction of characteristic information of the power quality disturbance signal.

[0149] To extract the time-frequency information of dynamic PQD. Based on S transform, IDRST divides the disturbance signal into two different frequency domains, high and low, and performs time-frequency analysis on the disturbance signal in different frequency domains. In order to further improve the accuracy of time-frequency resolution, in the window parameter optimization link, an adaptive genetic algorithm with the maximum concentration of signal energy as the fitness function and the crossover probability and mutation probability that can be automatically adjusted is proposed to achieve the optimal setting of window parameters in different frequency domains, so that the IDRST method has better time-frequency resolution accuracy for dynamic PQD.

[0150] An exemplary embodiment of the present invention provides a power quality analysis system, see Figure 8 , the analysis system includes:

[0151] The acquisition module 21 is used to acquire the electric energy signal.

[0152] The extraction module 22 is used to input the electric energy signal into the analysis model to extract the time-frequency characteristics of the electric energy signal.

[0153] A determination module 23, used to determine the quality of the electric energy signal based on the time-frequency characteristics;

[0154] The analytical model includes:

[0155]

[0156] Where x(t) is the electric energy signal; f0 is the fundamental frequency; f is the signal frequency; |f means taking the absolute value; t is the time variable; τ is the time shift factor; τ controls the specific position of the Gaussian window on the time axis;

[0157] The window functions of the analysis model include:

[0158]

[0159] Among them, λ 1,2 is the adjustment factor, λ 1,2 ={λ1,λ2}.

[0160] In one embodiment, the sampling time interval of the analysis model is T s , the number of sampling points of the analysis model is N; the time shift factor τ = T s ;Signal frequency f=k / NT s ;Electric energy signal x(t)=x(nT s )(n=0,1,2,...N-1);

[0161] The analytical model also includes:

[0162]

[0163] Among them, X(k+r) is the discrete Fourier spectrum, and G(r,k) is the Gaussian window function.

[0164] In one embodiment, the analysis model further includes:

[0165] IDRST(m,k)=|IDRST(m,k)|e jφ(m,k) ;

[0166] Where |IDRST(m,k)| is the amplitude and φ(m,k) is the phase angle.

[0167] In one embodiment, different parameters are 1,2 ={λ1,λ2}, the energy concentration function is expressed as:

[0168]

[0169] Among them, the dimension size of the matrix IDRST(m,n) is M×N.

[0170] In one embodiment, the hyperparameters of the analytical model are optimized via a genetic algorithm.

[0171] In one embodiment, the fitness function of the genetic algorithm includes:

[0172] fit = 1 / ECM(λ1,λ2).

[0173] In one embodiment, the crossover probability P of the genetic algorithm is c The adaptive adjustment formula is

[0174]

[0175] Among them, fcmin 、fc avg 、fc max Respectively represent the minimum, average and maximum fitness in the population performing the crossover operation; fc is the minimum fitness of the two parent individuals to be crossovered; P c 1. P c2 , P c3 are the maximum crossover probability, average crossover probability and minimum crossover probability respectively, and satisfy P c1 >P c2 >P c3 ∈(0,1);

[0176] And / or, the mutation probability P of the genetic algorithm m Adaptive adjustment to

[0177]

[0178] Among them, fm min 、fm avg 、fm max represent the minimum, average and maximum fitness of the population performing the mutation operation; fm is the minimum fitness of the two parent individuals after the crossover operation; P m1 ,P m2 , P m3 are the maximum mutation probability, average mutation probability and minimum mutation probability respectively.

[0179] The system and method embodiments in the embodiments of the present invention are based on the same inventive concept.

[0180] An exemplary embodiment of the present invention further provides an electronic device, see Fig. 9 The electronic device 300 may have relatively large differences due to different configurations or performances, and may include one or more central processing units (CPU) 310 (the processor 310 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic battery FPGA), a memory 330 for storing data, and one or more storage media 320 (such as one or more mass storage devices) for storing application programs 323 or data 322. Among them, the memory 330 and the storage medium 320 can be short-term storage or permanent storage. The storage medium 320 stores at least one instruction, at least one program, code set or instruction set, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by the processor to implement the above method.

[0181] Furthermore, the CPU 310 may be configured to communicate with the storage medium 320 to execute a series of instruction operations in the storage medium 320 on the electronic device 300. The electronic device 300 may also include one or more power supplies 360, one or more wired or wireless network interfaces 30, one or more input and output interfaces 340, and / or one or more operating systems 321, such as Windows Server™, Mac OS X™, Unix™, Linux™, FreeBSD™, etc.

[0182] The input / output interface 340 may be used to receive or send data via a network. The specific example of the network may include a wireless network provided by a communication provider of the electronic device 300. In one example, the input / output interface 340 includes a network adapter (Network Interface Controller, NIC), which may be connected to other network devices via a base station so as to communicate with the Internet. In one example, the input / output interface 340 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0183] It can be understood by those skilled in the art that Fig. 9 The structure shown is only for illustration and does not limit the structure of the above electronic device. Fig. 9 More or fewer components as shown, or with Figure 4 Different configurations shown.

[0184] An embodiment of the present invention also provides a computer-readable storage medium, which can be set in a server to store at least one instruction, at least one program, code set or instruction set related to implementing a method in a method embodiment. The at least one instruction, the at least one program, the code set or instruction set is loaded and executed by the processor to implement the above method.

[0185] Optionally, in this embodiment, the storage medium may be located in a network server among multiple network servers in a computer network. Optionally, in this embodiment, the storage medium may include, but is not limited to, various media that can store program codes, such as a USB flash drive, a read-only memory (ROM), a random access memory (RAM), a mobile hard disk, a magnetic disk, or an optical disk.

[0186] It is known from common technical knowledge that the present invention can be implemented by other embodiments that do not deviate from its spirit or essential features. Therefore, the embodiments of the above invention are only illustrative in all respects and are not exclusive. All changes within the scope of the present invention or within the scope equivalent to the present invention are included in the present invention.

[0187] 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.

[0188] 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.

[0189] 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 including 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.

[0190] 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.

[0191] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for analyzing power quality, characterized in that: The analysis method comprises: Acquire power signal; Inputting the electric energy signal into an analysis model to extract time-frequency characteristics of the electric energy signal; Determining the quality of the electric energy signal based on the time-frequency characteristics; The analysis model includes: Wherein, x(t) is the electric energy signal; f0 represents the fundamental frequency; f represents the signal frequency; |f| represents the absolute value; t represents the time variable; τ represents the time shift factor; and τ controls the specific position of the Gaussian window on the time axis; The window function of the analysis model includes: Among them, λ 1,2 is the adjustment factor, λ 1,2 ={λ1,λ2}.

2. The analysis method according to claim 1, characterized in that The sampling time interval of the analysis model is T s , the number of sampling points of the analysis model is N; the time shift factor τ=T s ; The signal frequency f = k / NT s ; The electric energy signal x(t)=x(nT s )(n=0,1,2,...N-1); The analytical model also includes: Among them, X(k+r) is the discrete Fourier spectrum, and G(r,k) is the Gaussian window function.

3. The analysis method according to claim 2, characterized in that The analytical model also includes: IDRST(m,k)=|IDRST(m,k)|e jφ(m,k) ; Where |IDRST(m,k)| is the amplitude and φ(m,k) is the phase angle.

4. The analysis method according to claim 1, characterized in that Different parameters have an effect on the 1,2 ={λ1,λ2}, the energy concentration function is expressed as: Among them, the dimension size of the matrix IDRST(m,n) is M×N.

5. The analysis method according to claim 1, characterized in that The hyperparameters of the analytical model are optimized by genetic algorithm.

6. The analysis method according to claim 5, characterized in that The fitness function of the genetic algorithm includes: fit = 1 / ECM(λ1,λ2).

7. The analysis method according to claim 5, characterized in that The crossover probability P of the genetic algorithm c The adaptive adjustment formula is Among them, fc min 、fc avg 、fc max Respectively represent the minimum, average and maximum fitness in the population performing the crossover operation; fc is the minimum fitness of the two parent individuals to be crossovered; P c 1. P c2 , P c3 are the maximum crossover probability, average crossover probability and minimum crossover probability respectively, and satisfy P c1 >P c2 >P c3 ∈(0,1); And / or, the mutation probability P of the genetic algorithm m Adaptive adjustment to Among them, fm min 、fm avg 、fm max represent the minimum, average and maximum fitness of the population performing the mutation operation; fm is the minimum fitness of the two parent individuals after the crossover operation; P m1 ,P m2 , P m3 are the maximum mutation probability, average mutation probability and minimum mutation probability respectively.

8. A power quality analysis system, characterized in that: The analysis system comprises: An acquisition module, used for acquiring electric energy signals; An extraction module, used for inputting the electric energy signal into an analysis model to extract the time-frequency characteristics of the electric energy signal; A determination module, configured to determine the quality of the electric energy signal based on the time-frequency characteristics; The analysis model includes: Wherein, x(t) is the electric energy signal; f0 represents the fundamental frequency; f represents the signal frequency; |f| represents the absolute value; t represents the time variable; τ represents the time shift factor; and τ controls the specific position of the Gaussian window on the time axis; The window function of the analysis model includes: Among them, λ 1,2 is the adjustment factor, λ 1,2 ={λ1,λ2}.

9. An electronic device, characterized in that: The electronic device includes a processor and a memory, wherein the memory stores at least one instruction, at least one program, a code set or an instruction set, and the at least one instruction, at least one program, a code set or an instruction set is loaded and executed by the processor to implement the power quality analysis method as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores at least one instruction or at least one program, and the at least one instruction or at least one program is loaded and executed by a processor to implement the power quality analysis method according to any one of claims 1 to 7.

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