Distributed photovoltaic power generation system non-stationary signal denoising method based on dual strategies

Abstract

The invention belongs to the field of electric digital data processing, and relates to a distributed photovoltaic power generation system non-stationary signal denoising method based on dual strategies, which comprises the following steps: step 1, based on an obtained original signal, generating a target intrinsic mode function through variational mode decomposition; 2, removing the intrinsic mode function with the maximum total composite entropy value from the target intrinsic mode functions to obtain effective signals; 3, identifying abnormal data through discrete data comparison according to the effective signal, and removing and replacing the abnormal data to obtain a new effective signal; and starting to circulate from the step 1 until an iteration cut-off condition is reached, and generating a de-noising signal. The problems that the non-stationary signal denoising effect is poor and the signal fidelity is low are solved.

Description

Technical Field

[0001] The invention relates to the field of electrical digital data processing, and specifically discloses a non-stationary signal denoising method for a distributed photovoltaic power generation system based on a dual strategy. Background Art

[0002] With the rapid development of renewable energy technology, distributed photovoltaic power generation has become increasingly important in the global energy structure. However, during the operation of distributed photovoltaic power generation systems, the collected voltage, current and other signals are often interfered by noise, which also interferes with the output signal. The noise includes broadband noise caused by electromagnetic interference, measurement errors, sensor noise, etc., as well as abnormal data caused by mechanical failures and the switching process of power electronic devices.

[0003] During the operation of distributed photovoltaic power generation systems, existing signal denoising technologies usually only process certain types of noise, and have certain limitations in noise processing for non-stationary signals. The main disadvantages are as follows: 1. Insufficient abnormal data processing. Traditional signal denoising methods, such as wavelet transform and empirical mode decomposition, are mainly used to remove broadband noise such as white noise, but they are ineffective in removing spikes and sudden changes in abnormal data caused by faults, switching processes of power electronic devices, etc.

[0004] 2. Single noise removal capability. Statistical methods, such as median filtering and dimensionality reduction analysis, are usually used to detect and remove abnormal data, but they are ineffective in removing broadband noise such as randomly distributed white noise.

[0005] 3. It is difficult to take into account signal fidelity. Existing signal denoising methods often affect the intrinsic characteristics of the signal, such as affecting the frequency distribution, resulting in low accuracy of signal analysis and signal control.

[0006] Therefore, how to propose an efficient and robust denoising method for the non-stationary signal characteristics of distributed photovoltaic power generation systems has become an urgent problem to be solved. Summary of the invention

[0007] The purpose of the present invention is to provide a non-stationary signal denoising method for a distributed photovoltaic power generation system based on a dual strategy, so as to solve the problems of poor effect of non-stationary signal denoising and low signal fidelity.

[0008] The specific scheme of the present invention is as follows: The non-stationary signal denoising method of distributed photovoltaic power generation system based on dual strategy includes: Step 1: Based on the acquired original signal, generate the target intrinsic mode functions through variational mode decomposition; Step 2: Eliminate the eigenmode function with the largest total value of composite entropy from the target eigenmode functions to obtain a valid signal; Step 3: Identify abnormal data by comparing discrete data based on valid signals, remove and replace abnormal data to obtain new valid signals; The denoised signal is generated by looping from step 1 until the iteration cutoff condition is reached.

[0009] In some embodiments, based on the acquired original signal, a target number of intrinsic mode functions are generated by variational mode decomposition, including: Set the initial decomposition level and initial penalty factor of variational mode decomposition; Generate a fitness function value based on the original signal and the initial intrinsic mode functions obtained; According to the fitness function value, the initial number of decomposition layers and the initial penalty factor are optimized to obtain the target number of decomposition layers and the target penalty factor; Based on the target decomposition level and target penalty factor, the original signal is subjected to variational mode decomposition to generate the target intrinsic mode functions.

[0010] In some embodiments, generating a fitness function value based on the original signal and the acquired initial intrinsic mode functions includes: Obtain the initial intrinsic mode functions through variational mode decomposition according to the original signal; The original signal and the initial eigenmode functions are equally divided into N The segment signals are respectively obtained as the segment signals of the original signal and the segment signals of each intrinsic mode function; Based on each segment signal of the original signal and each segment signal of each intrinsic mode function, composite entropy of each segment signal of the original signal and composite entropy of each segment signal of each intrinsic mode function are generated respectively; According to the composite entropy of each segment of the original signal and the composite entropy of each segment of the eigenmode function, the composite entropy correlation coefficient between the original signal and each eigenmode function is obtained respectively; The fitness function value is generated based on the composite entropy correlation coefficient between the original signal and each intrinsic mode function.

[0011] In some embodiments, generating composite entropies of each segment of the original signal and composite entropies of each segment of the intrinsic mode function based on each segment of the original signal and each segment of the intrinsic mode function includes: Based on each segment of the original signal, the envelope entropy and sample entropy of each segment of the original signal are obtained respectively; The envelope entropy and sample entropy of each segment of the original signal are weighted and summed to generate the composite entropy of each segment of the original signal; Based on each segment signal of each intrinsic mode function, the envelope entropy and sample entropy of each segment signal of each intrinsic mode function are obtained respectively; The envelope entropy and sample entropy of each segment of each intrinsic mode function signal are weighted and summed to generate the composite entropy of each segment of each intrinsic mode function signal.

[0012] In some embodiments, generating a fitness function value based on a composite entropy correlation coefficient between an original signal and each intrinsic mode function includes: The minimum value of the composite entropy correlation coefficient between the original signal and the intrinsic mode function is obtained from the composite entropy correlation coefficient between the original signal and each intrinsic mode function; The fitness function value is generated based on the minimum value of the composite entropy correlation coefficient between the original signal and the intrinsic mode function.

[0013] In some embodiments, optimizing the initial number of decomposition layers and the initial penalty factor according to the fitness function value to obtain the target number of decomposition layers and the target penalty factor includes: The initial number of decomposition layers and the initial penalty factor are used as input variables of the swarm optimization algorithm, and the fitness function value is used as the optimization fitness of the swarm optimization algorithm. The target number of decomposition layers and the target penalty factor are obtained through iterative optimization of the swarm optimization algorithm.

[0014] In some embodiments, removing the eigenmode function with the largest total composite entropy value from the target eigenmode functions to obtain a valid signal includes: According to the target intrinsic mode functions, the intrinsic mode function with the largest total value of composite entropy is obtained; The eigenmode function with the largest total compound entropy value is eliminated from the target eigenmode functions, and the sum of all the eigenmode functions before the eigenmode function with the largest total compound entropy value and all the eigenmode functions after the eigenmode function with the largest total compound entropy value is taken as the effective signal.

[0015] In some embodiments, identifying abnormal data by comparing discrete data according to the effective signal, removing and replacing the abnormal data to obtain a new effective signal includes: Set the threshold coefficient, compare all discrete data in the valid signal one by one, and determine whether each discrete data does not belong to the valid data interval. The valid data interval is [the difference between the mean value of the valid signal and the product of the standard deviation of the valid signal multiplied by the threshold coefficient, and the sum of the mean value of the valid signal and the product of the standard deviation of the valid signal multiplied by the threshold coefficient]; If it is determined that a certain discrete data does not belong to the valid data interval, the certain discrete data is identified as abnormal data; a certain discrete data in the valid signal is eliminated, and a certain discrete data is replaced to obtain a new valid signal.

[0016] In some embodiments, replacing a discrete data includes: Replace a discrete data with the average of the previous discrete data and the next discrete data.

[0017] In some embodiments, the iteration cutoff condition includes that the maximum value of the total composite entropy of the intrinsic mode function of the new valid signal is less than a first threshold and there is no abnormal data.

[0018] Compared with the prior art, the present invention has the following advantages and beneficial effects: The invention obtains composite entropy by weighted summation of envelope entropy and sample entropy, generates composite entropy correlation coefficients between the original signal and each intrinsic mode function based on composite entropies of each segment of the original signal and composite entropies of each segment of each intrinsic mode function, obtains fitness function value according to the minimum value of composite entropy correlation coefficients between the original signal and each intrinsic mode function, performs optimization based on the fitness function value to obtain target intrinsic mode functions, removes intrinsic mode functions with the largest total composite entropy value from the target intrinsic mode functions to obtain effective signals, identifies abnormal data through discrete data comparison according to the effective signals, removes and replaces the abnormal data to obtain new effective signals, and generates denoised signals until an iteration cut-off condition is reached, thereby improving the denoising effect and signal fidelity of non-stationary signals; not only can broadband noise be effectively and robustly removed, but also abnormal data can be removed, while fully retaining the intrinsic characteristics of the original signal, maintaining the integrity of the original signal, improving the accuracy of signal analysis and signal control, and being suitable for the complex operating environment of distributed photovoltaic systems. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of a non-stationary signal denoising method for a distributed photovoltaic power generation system based on a dual strategy in Example 1 of the present invention.

[0020] Figure 2 This is a flow chart of generating a target number of intrinsic mode functions in Example 1 of the present invention.

[0021] Figure 3 This is a schematic diagram of the original signal in Example 2 of the present invention.

[0022] Figure 4 It is a schematic diagram of effective signals in embodiment 2 of the present invention.

[0023] Figure 5 This is a schematic diagram of a new valid signal in Embodiment 2 of the present invention.

[0024] Figure 6 Schematic diagram of a denoised signal in Example 2 of the present invention.

[0025] Reference symbols: A-current, S-time. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0027] Example 1 A non-stationary signal denoising method for distributed photovoltaic power generation system based on dual strategy, such as Figure 1 As shown, the following steps are included: S1, obtain the original signal; Get the original signal from the distributed photovoltaic power generation system S ori , the original signal S ori There are T discrete data.

[0028] S2, generating target intrinsic mode functions through variational mode decomposition based on the original signal; like Figure 2 As shown, the target intrinsic mode functions are generated, including: S21, initializing variational mode decomposition parameters; The variational mode decomposition parameters include the number of decomposition layers and the penalty factor. The initial number of decomposition layers of variational mode decomposition is set to K , the initial penalty factor is α .

[0029] S22, obtaining initial intrinsic mode functions through variational mode decomposition according to the original signal; The initial eigenmode function is K IMF 1 , IMF 2 , ..., IMF K .

[0030] S23, the original signal and each eigenmode function of the initial eigenmode function are equally divided into N The segment signals are respectively obtained as the segment signals of the original signal and the segment signals of each intrinsic mode function; N The calculation formula is: , in, T is the number of discrete data in the original signal, W is the window length, S is the window sliding step size, Indicates rounding up.

[0031] S24, generating composite entropy of each segment of the original signal based on each segment of the original signal; Based on each segment of the original signal, the envelope entropy and sample entropy of each segment of the original signal are obtained respectively; The envelope entropy and sample entropy of each segment of the original signal are weighted and summed to generate the composite entropy of each segment of the original signal.

[0032] The original signal j The envelope entropy and sample entropy of the segment signal are weighted and summed to generate the original signal. j The composite entropy of the original signal j The formula for the composite entropy of a segment signal is: , in, CE ( S ori, j ) is the original signal j The composite entropy of the segment signal, EE ( S ori, j ) is the original signal j Segment signal envelope entropy, SE ( S ori, j ) is the original signal j Segment signal sample entropy, w 1 The original signal j The weighting coefficient of the segment signal envelope entropy, w 2 The original signal j The weighting coefficient of the entropy of the segment signal sample, and w 1 + w 2 =1, j =1,2,……, N .

[0033] S25, generating composite entropy of each segment signal of each intrinsic mode function based on each segment signal of each intrinsic mode function; Based on each segment signal of each intrinsic mode function, the envelope entropy and sample entropy of each segment signal of each intrinsic mode function are obtained respectively; The envelope entropy and sample entropy of each segment of each intrinsic mode function signal are weighted and summed to generate the composite entropy of each segment of each intrinsic mode function signal.

[0034] The first i The eigenmode function j The envelope entropy and sample entropy of the segment signal are weighted and summed to generate the first i The eigenmode function jThe composite entropy of the segment signal, i The eigenmode function j The formula for the composite entropy of a segment signal is: , in, CE (IMF i, j ) is the i The eigenmode function j The composite entropy of the segment signal, EE (IMF i, j ) is the i The eigenmode function j Segment signal envelope entropy, SE (IMF i, j ) is the i The eigenmode function j Segment signal sample entropy, w 1 For the i The eigenmode function j The weighting coefficient of the segment signal envelope entropy, w 2 For the i The eigenmode function j The weighting coefficient of the entropy of the segment signal sample, and w 1 + w 2 =1, i =1,2,……, K , j =1,2,……, N .

[0035] S26, obtaining composite entropy correlation coefficients between the original signal and each intrinsic mode function according to composite entropy of each segment of the original signal and composite entropy of each segment of the intrinsic mode function; The original signal and i The formula for the composite entropy correlation coefficient between the eigenmode functions is: , in, r i is the original signal and i The composite entropy correlation coefficient between the eigenmode functions, CE ( S ori, j ) is the composite entropy of the jth segment of the original signal, is the average value of the composite entropy of each segment of the original signal, CE (IMF i, j ) is the i The eigenmode function j The composite entropy of the segment signal, For the i The average value of the composite entropy of each segment of the intrinsic mode function, i =1,2,……, K , j =1,2,……, N .

[0036] S27, generating a fitness function value based on the composite entropy correlation coefficient between the original signal and each intrinsic mode function; The minimum value of the composite entropy correlation coefficient between the original signal and the intrinsic mode function is obtained from the composite entropy correlation coefficient between the original signal and each intrinsic mode function; The fitness function value is generated based on the minimum value of the composite entropy correlation coefficient between the original signal and the intrinsic mode function.

[0037] The formula of the fitness function is: .

[0038] Among them, e is a natural constant.

[0039] S28, optimizing the initial number of decomposition layers and the initial penalty factor according to the fitness function value to obtain the target number of decomposition layers and the target penalty factor; The initial decomposition level K and the initial penalty factor α As the input variable of the swarm optimization algorithm, the fitness function value is used as the optimization fitness of the swarm optimization algorithm, and the target decomposition layer number is obtained through the iterative optimization of the swarm optimization algorithm. K best and the target penalty factor α best .

[0040] S29. Based on the target decomposition level and the target penalty factor, the original signal is subjected to variational mode decomposition to generate a target number of intrinsic mode functions.

[0041] Using the target decomposition level K best and the target penalty factor α best For the original signal S ori Perform variational mode decomposition to generate the target intrinsic mode functions. The target intrinsic mode function refers to K best IMF 1,best , IMF 2,best ,……, .

[0042] S3, removing the eigenmode function with the largest total value of composite entropy from the target eigenmode functions to obtain a valid signal; According to the target intrinsic mode functions, the intrinsic mode function with the largest total value of composite entropy is obtained; The eigenmode function with the largest total compound entropy value is eliminated from the target eigenmode functions, and the sum of all the eigenmode functions before the eigenmode function with the largest total compound entropy value and all the eigenmode functions after the eigenmode function with the largest total compound entropy value is taken as the effective signal.

[0043] No. i The formula for the total value of the composite entropy of the eigenmode functions is: , in, SCE (IMF i ) is the i The total value of the composite entropy of the eigenmode functions, CE (IMF i, j ) is the i The eigenmode function j The composite entropy of the segment signal, i =1,2,……, K best , j =1,2,……, N .

[0044] The eigenmode function with the largest total value of composite entropy is M The intrinsic mode functions, M The calculation formula is: , in, SCE (IMF i ) is the i The total value of the composite entropy of the eigenmode functions, i =1,2,……, K best .

[0045] The formula for the effective signal is: , in, S eff is a valid signal, is the sum of all the eigenmode functions preceding the eigenmode function with the largest total composite entropy value, is the sum of all the eigenmode functions following the eigenmode function with the largest total composite entropy value, i =1,2,……, K best .

[0046] S4, identifying abnormal data by comparing discrete data according to the effective signal, eliminating and replacing the abnormal data to obtain a new effective signal; Set the threshold coefficient, compare all discrete data in the valid signal one by one, and determine whether each discrete data does not belong to the valid data interval. The valid data interval is [the difference between the mean value of the valid signal and the product of the standard deviation of the valid signal multiplied by the threshold coefficient, and the sum of the mean value of the valid signal and the product of the standard deviation of the valid signal multiplied by the threshold coefficient]; If it is determined that a certain discrete data does not belong to the valid data interval, the certain discrete data is identified as abnormal data; a certain discrete data in the valid signal is eliminated, and the certain discrete data is replaced to obtain a new valid signal; a certain discrete data is replaced with the average value of the previous discrete data and the next discrete data.

[0047] For effective signal S eff middle T Discrete data are compared one by one. If the t The discrete data does not belong to the valid data interval. t The expression for discrete data that does not belong to the valid data interval is: , in, S eff ( t ) is the first valid signal t discrete data, is the average value of the effective signal, The standard deviation of the effective signal, is the threshold coefficient; Then the t Discrete data are identified as abnormal data and used Replacement t discrete data, and obtain a new effective signal. The formula is: , in, is the first replaced signal in the valid signal t discrete data, S eff ( t -1) is the first valid signal t The previous discrete data of discrete data, S eff ( t +1) is the first valid signal t The next discrete data after the discrete data.

[0048] S5, judging whether the iteration cutoff condition is met, if it is judged that the iteration cutoff condition is not met, repeating steps S2 to S4, if it is judged that the iteration cutoff condition is met, ending the iteration to generate the denoised signal.

[0049] The iteration cutoff condition includes: the upper limit of the set iteration number, and the maximum value of the total value of the composite entropy of the intrinsic mode function of the new effective signal is less than the first threshold and there is no abnormal data.

[0050] The composite entropy is obtained by weighted summation of envelope entropy and sample entropy, and the composite entropy correlation coefficient between the original signal and each intrinsic mode function is generated based on the composite entropy of each segment of the original signal and the composite entropy of each segment of each intrinsic mode function. The fitness function value is obtained according to the minimum value of the composite entropy correlation coefficient between the original signal and each intrinsic mode function. The target intrinsic mode function is obtained by optimization based on the fitness function value, and the intrinsic mode function with the largest total composite entropy value is eliminated from the target intrinsic mode function to obtain the effective signal. According to the effective signal, abnormal data is identified by discrete data comparison, and the abnormal data is eliminated and replaced to obtain a new effective signal until the iteration cutoff condition is reached to generate a denoised signal, thereby improving the denoising effect and signal fidelity of non-stationary signals; it can not only effectively and robustly remove broadband noise, but also remove abnormal data, while fully retaining the intrinsic characteristics of the original signal, maintaining the integrity of the original signal, and improving the accuracy of signal analysis and signal control, which is suitable for the complex operating environment of distributed photovoltaic systems.

[0051] Example 2 A specific embodiment of the non-stationary signal denoising method for a distributed photovoltaic power generation system based on a dual strategy includes: Get the original current signal, such as Figure 3 As shown, there are 2500 discrete data in the original current signal.

[0052] Set the initial decomposition level of variational mode decomposition to 3 and the initial penalty factor to 600.

[0053] Based on the original current information, the initial decomposition level and the initial penalty, the initial intrinsic mode functions are obtained through variational mode decomposition. The initial intrinsic mode functions are 3 intrinsic mode functions.

[0054] Set the window length W The window sliding step is 500. S is 50, then the original current signal and the three intrinsic mode functions are divided into (2500-500) / 50=40 segments.

[0055] According to the 40 segments of the original current signal and the 40 segments of each intrinsic mode function, the composite entropy correlation coefficient between the original current signal and the first intrinsic mode function is calculated to be -0.21488, the composite entropy correlation coefficient between the original current signal and the second intrinsic mode function is -0.01567, and the composite entropy correlation coefficient between the original current signal and the third intrinsic mode function is 0.05797. It can be seen that the composite entropy correlation coefficient between the original current signal and the first intrinsic mode function -0.21488 is the minimum value. Based on the composite entropy correlation coefficient between the original current signal and the first intrinsic mode function -0.21488, the fitness function value generated is e -0.21488 =0.80664.

[0056] The initial decomposition level 3 and the initial penalty factor 600 are used as the input variables of the swarm optimization algorithm, and the fitness function value 0.80664 is used as the optimization fitness of the swarm optimization algorithm. The target decomposition level 5 and the target penalty factor 993 are obtained through iterative optimization of the swarm optimization algorithm.

[0057] Based on the original current signal, the target decomposition level of 5 and the target penalty factor of 993, the target intrinsic mode functions are obtained through variational mode decomposition, and the target intrinsic mode functions are 5 intrinsic mode functions.

[0058] The eigenmode function with the largest total value of composite entropy is the fifth eigenmode function. The fifth eigenmode function is removed from the five eigenmode functions, and the sum of the remaining four eigenmode functions is taken as the effective signal, such as Figure 4 shown.

[0059] Set the threshold coefficient to 1.6. At this time, the mean value of the valid signal is -0.020, the standard deviation of the valid signal is 16.0860, and the valid data interval of the valid signal is [-25.7577, 25.7177].

[0060] The 2500 discrete data of the valid signal are compared one by one, and one abnormal data is found. It is removed and replaced to obtain a new valid signal, such as Figure 5 shown.

[0061] Repeat the above calculation process, set the upper limit of the iteration number of the iteration cutoff condition to 5 times, and generate the denoised signal when the iteration number reaches 5 times, such as Figure 6 shown.

[0062] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A non-stationary signal denoising method for distributed photovoltaic power generation system based on dual strategy, characterized in that: include: Step 1: Based on the acquired original signal, generate the target intrinsic mode functions through variational mode decomposition; Step 2: Eliminate the eigenmode function with the largest total value of composite entropy from the target eigenmode functions to obtain a valid signal; Step 3: Identify abnormal data by comparing discrete data based on valid signals, remove and replace abnormal data to obtain new valid signals; The denoised signal is generated by looping from step 1 until the iteration cutoff condition is reached.

2. The method for denoising non-stationary signals of distributed photovoltaic power generation systems based on dual strategies according to claim 1 is characterized in that: The method of generating a target number of intrinsic mode functions by variational mode decomposition based on the acquired original signal includes: Set the initial decomposition level and initial penalty factor of variational mode decomposition; Generate a fitness function value based on the original signal and the initial intrinsic mode functions obtained; According to the fitness function value, the initial number of decomposition layers and the initial penalty factor are optimized to obtain the target number of decomposition layers and the target penalty factor; Based on the target decomposition level and target penalty factor, the original signal is subjected to variational mode decomposition to generate the target intrinsic mode functions.

3. The non-stationary signal denoising method of distributed photovoltaic power generation system based on dual strategy according to claim 2 is characterized in that: The generating of the fitness function value based on the original signal and the obtained initial intrinsic mode functions includes: Obtain the initial intrinsic mode functions through variational mode decomposition according to the original signal; The original signal and the initial eigenmode functions are equally divided into N Segment signals, and obtain the segment signals of the original signal and the segment signals of each intrinsic mode function respectively; Based on each segment signal of the original signal and each segment signal of each intrinsic mode function, composite entropy of each segment signal of the original signal and composite entropy of each segment signal of each intrinsic mode function are generated respectively; According to the composite entropy of each segment of the original signal and the composite entropy of each segment of the eigenmode function, the composite entropy correlation coefficient between the original signal and each eigenmode function is obtained respectively; The fitness function value is generated based on the composite entropy correlation coefficient between the original signal and each intrinsic mode function.

4. The method for denoising non-stationary signals of distributed photovoltaic power generation systems based on dual strategies according to claim 3 is characterized in that: The method of generating composite entropies of each segment of the original signal and composite entropies of each segment of the intrinsic mode function based on each segment of the original signal and each segment of the intrinsic mode function comprises: Based on each segment of the original signal, the envelope entropy and sample entropy of each segment of the original signal are obtained respectively; the envelope entropy and sample entropy of each segment of the original signal are weighted and summed respectively to generate the composite entropy of each segment of the original signal; Based on each signal segment of each intrinsic mode function, the envelope entropy and sample entropy of each signal segment of each intrinsic mode function are obtained respectively; the envelope entropy and sample entropy of each signal segment of each intrinsic mode function are weighted and summed respectively to generate the composite entropy of each signal segment of each intrinsic mode function.

5. The method for denoising non-stationary signals of distributed photovoltaic power generation systems based on dual strategies according to claim 3 is characterized in that: The generating of the fitness function value based on the composite entropy correlation coefficient between the original signal and each intrinsic mode function includes: The minimum value of the composite entropy correlation coefficient between the original signal and the intrinsic mode function is obtained from the composite entropy correlation coefficient between the original signal and each intrinsic mode function; The fitness function value is generated based on the minimum value of the composite entropy correlation coefficient between the original signal and the intrinsic mode function.

6. The method for denoising non-stationary signals of distributed photovoltaic power generation systems based on dual strategies according to claim 2 is characterized in that: The step of optimizing the initial number of decomposition layers and the initial penalty factor according to the fitness function value to obtain the target number of decomposition layers and the target penalty factor includes: The initial number of decomposition layers and the initial penalty factor are used as input variables of the swarm optimization algorithm, and the fitness function value is used as the optimization fitness of the swarm optimization algorithm. The target number of decomposition layers and the target penalty factor are obtained through iterative optimization of the swarm optimization algorithm.

7. The method for denoising non-stationary signals of distributed photovoltaic power generation systems based on dual strategies according to claim 1, characterized in that: The step of removing the eigenmode function with the largest total composite entropy value from the target eigenmode functions to obtain a valid signal includes: According to the target intrinsic mode functions, the intrinsic mode function with the largest total value of composite entropy is obtained; The eigenmode function with the largest total compound entropy value is eliminated from the target eigenmode functions, and the sum of all the eigenmode functions before the eigenmode function with the largest total compound entropy value and all the eigenmode functions after the eigenmode function with the largest total compound entropy value is taken as the effective signal.

8. The method for denoising non-stationary signals of distributed photovoltaic power generation systems based on dual strategies according to claim 1, characterized in that: The method of identifying abnormal data by comparing discrete data according to the effective signal, and removing and replacing the abnormal data to obtain a new effective signal includes: Setting a threshold coefficient, comparing all discrete data in the valid signal one by one, and determining whether each discrete data does not belong to a valid data interval, wherein the valid data interval is [the difference between the mean value of the valid signal and the product of the standard deviation of the valid signal multiplied by the threshold coefficient, and the sum of the mean value of the valid signal and the product of the standard deviation of the valid signal multiplied by the threshold coefficient]; If it is determined that a certain discrete data does not belong to the valid data interval, the certain discrete data is identified as abnormal data; a certain discrete data in the valid signal is eliminated, and a certain discrete data is replaced to obtain a new valid signal.

9. The method for denoising non-stationary signals of a distributed photovoltaic power generation system based on a dual strategy according to claim 8, characterized in that: The replacing of a discrete data comprises: Replace a discrete data with the average of the previous discrete data and the next discrete data.

10. The method for denoising non-stationary signals of a distributed photovoltaic power generation system based on a dual strategy according to claim 1, characterized in that: The iteration cutoff condition includes that the maximum value of the total value of the composite entropy of the intrinsic mode function of the new effective signal is less than a first threshold and there is no abnormal data.

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