Noise reduction method and noise reduction device for power quality disturbance signal, equipment and medium

Abstract

The invention relates to the technical field of noise reduction of electric energy quality disturbance signals, and discloses a noise reduction method and device for electric energy quality disturbance signals, equipment and a medium, and the method comprises the steps: obtaining the electric energy quality disturbance signals in a noisy environment; performing wavelet decomposition on the power quality disturbance signal to obtain a J-layer wavelet coefficient of the power quality disturbance signal; performing noise reduction processing on the wavelet coefficient by using a preset improved adaptive threshold algorithm and an improved threshold function algorithm to obtain a denoised wavelet coefficient; and performing signal reconstruction based on the de-noising wavelet coefficient to obtain a de-noised signal of the power quality disturbance signal. In this way, the wavelet coefficient is subjected to noise reduction processing, noise interference is reduced, and the influence of the noise interference on the electric energy quality disturbance recognition accuracy is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of noise reduction of power quality disturbance signals, and in particular to a noise reduction method and device for power quality disturbance signals, equipment and a medium. BACKGROUND

[0002] In the power system, power quality disturbances (PQDs) such as voltage sag, voltage swell, harmonics, flicker, etc. frequently occur, which seriously affects the safe and stable operation of power equipment. Due to the complexity of the actual power grid environment, the collected power quality disturbance signals are often disturbed by strong noise, which causes the signal disturbance characteristics to be covered, and brings great difficulty to the subsequent disturbance identification, classification and fault diagnosis.

[0003] Therefore, the noise interference currently affects the disturbance identification accuracy, and the disturbance identification accuracy needs to be improved. SUMMARY

[0004] The main purpose of the present application is to provide a noise reduction method and device for power quality disturbance signals, equipment and a medium, which can solve the problem of noise interference affecting the disturbance identification accuracy in the prior art.

[0005] To achieve the above-mentioned purpose, the first aspect of the present application provides a noise reduction method for power quality disturbance signals, which comprises: obtaining a power quality disturbance signal in a noisy environment; wavelet decomposing the power quality disturbance signal to obtain J-layer wavelet coefficients of the power quality disturbance signal; using a preset improved adaptive threshold algorithm and an improved threshold function algorithm to perform noise reduction processing on the wavelet coefficients to obtain de-noised wavelet coefficients; based on the de-noised wavelet coefficients, reconstructing the signal to obtain a noise-reduced signal of the power quality disturbance signal.

[0006] To achieve the above-mentioned purpose, the second aspect of the present application provides a noise reduction device for power quality disturbance signals, which comprises: a signal acquisition module for obtaining a power quality disturbance signal in a noisy environment; a signal decomposition module for wavelet decomposing the power quality disturbance signal to obtain J-layer wavelet coefficients of the power quality disturbance signal; a de-noising processing module for using a preset improved adaptive threshold algorithm and an improved threshold function algorithm to perform noise reduction processing on the wavelet coefficients to obtain de-noised wavelet coefficients; a signal reconstruction module for reconstructing the signal based on the de-noised wavelet coefficients to obtain a noise-reduced signal of the power quality disturbance signal.

[0007] To achieve the above object, the third aspect of the present application provides a computer readable storage medium, which stores a computer program, and the computer program, when executed by a processor, causes the processor to perform the steps of the method according to the first aspect.

[0008] To achieve the above object, the fourth aspect of the present application provides a computer device, which comprises a memory and a processor, and the memory stores a computer program, and the computer program, when executed by the processor, causes the processor to perform the steps of the method according to the first aspect.

[0009] By adopting the embodiment of the present application, the following beneficial effects are achieved: The present application provides a power quality disturbance signal denoising method, the method comprising: obtaining a power quality disturbance signal in a noisy environment; wavelet decomposing the power quality disturbance signal to obtain J-layer wavelet coefficients of the power quality disturbance signal; using a preset improved adaptive threshold algorithm and an improved threshold function algorithm to perform denoising processing on the wavelet coefficients to obtain denoised wavelet coefficients; and reconstructing a signal based on the denoised wavelet coefficients to obtain a denoised power quality disturbance signal. In this way, the wavelet coefficients are denoised, the noise interference is reduced, and the influence of noise interference on the power quality disturbance identification accuracy is reduced. BRIEF DESCRIPTION OF DRAWINGS

[0010] In order to more clearly illustrate the technical solutions of the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiment or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor based on these drawings.

[0011] Among them: Figure 1 A flow chart of a power quality disturbance signal denoising method in an embodiment of the present application; Figure 2 A structural block diagram of a power quality disturbance identification system in an embodiment of the present application; Figure 3 A model structural block diagram of a one-dimensional convolutional neural network and a one-dimensional residual neural network in an embodiment of the present application; Figure 4 A model structural block diagram of a multi-head attention structure in an embodiment of the present application; Figure 5 A structural block diagram of a power quality disturbance signal denoising device in an embodiment of the present application; Figure 6 A structural block diagram of a computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0012] The technical solutions in the embodiments of the present application will be apparently and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0013] It should be noted that in order to solve the problems of redundancy in feature extraction and low recognition accuracy in traditional power quality disturbance recognition in a strong noise environment, the present application proposes a noisy power quality disturbance recognition method based on deep learning feature fusion. First, the power quality disturbance waveform signal in a noisy environment is obtained, and then the noisy disturbance signal is denoised by using an improved adaptive threshold and an improved threshold function algorithm. Second, one-dimensional convolutional neural network and one-dimensional residual neural network are used to extract features from the denoised power quality disturbance signal, and multiplication fusion method is used for feature splicing to strengthen the spatial correlation of features. Then, the multi-head attention mechanism is introduced to capture and fuse features at different levels and angles, and to extract and select appropriate features. Finally, the classification module is used to classify the features to obtain the final classification result. The noisy power quality disturbance recognition model proposed in the present application integrates multiple feature information, which can effectively reduce the influence of noise on the classification result, improve the model classification performance and generalization ability, and has better performance in classification accuracy and noise resistance, providing effective support for power fault diagnosis in power systems.

[0014] Please refer to Figure 1 , Figure 1 The flow chart of the power quality disturbance signal denoising method in the embodiments of the present application is shown in Figure 1 The method comprises the following steps: 101. Obtain a power quality disturbance signal in a noisy environment; 102. Wavelet decompose the power quality disturbance signal to obtain J-layer wavelet coefficients of the power quality disturbance signal; 103. Use a preset improved adaptive threshold algorithm and an improved threshold function algorithm to denoise the wavelet coefficients to obtain denoised wavelet coefficients; It should be noted that the power quality disturbance signal in a noisy environment is obtained, the power quality disturbance signal is wavelet decomposed to obtain J-layer wavelet coefficients of the power quality disturbance signal, and the wavelet coefficients are denoised by using a preset improved adaptive threshold algorithm and an improved threshold function algorithm to obtain denoised wavelet coefficients.

[0015] The power quality disturbance signal can be a power quality disturbance waveform signal, wavelet coefficients of the signal are obtained through wavelet decomposition, a wavelet threshold of the wavelet coefficients is obtained by using an improved adaptive threshold algorithm, and the wavelet threshold and the wavelet coefficients are used for noise reduction processing to obtain denoised wavelet coefficients.

[0016] In a feasible implementation manner, the step 103 comprises steps A01 to A03: A01, determining a noise standard deviation based on the J-layer wavelet coefficients and a preset standard deviation algorithm ; It should be noted that a noise standard deviation is calculated for each layer , and specifically, a noise standard deviation is determined based on the J-layer wavelet coefficients and a preset standard deviation algorithm .

[0017] A02, respectively using a peak and ratio correction factor of each layer of wavelet coefficients, the noise standard deviation and a preset improved adaptive threshold algorithm to determine a wavelet threshold corresponding to each layer of wavelet coefficients It should be noted that the threshold is used as a demarcation between noise and real signal in the split wavelet detail coefficient, wherein a traditional general threshold is: (1) (2) is the wavelet threshold, is the number of signal sampling points, is each layer of wavelet coefficients, is the standard deviation of noise, used for estimating the noise of the whole signal. Since the general threshold is fixed, and the distribution of noise is random, the fixed threshold used in other decomposition layers will cause the coefficients of the real signal to be removed too much. Therefore, the threshold setting is improved based on the deficiency.

[0018] A wavelet threshold is calculated for each layer, and a wavelet threshold corresponding to each layer of wavelet coefficients is determined by respectively using a peak and ratio correction factor of each layer of wavelet coefficients, the noise standard deviation and a preset improved adaptive threshold algorithm .

[0019] For example, the improved adaptive threshold algorithm is as follows: (3) (4) (5) In the formula, is the noise standard deviation, indicates a wavelet coefficient in the kth direction in the ith layer of wavelet decomposition; represents the median of the absolute values ​​of all wavelet coefficients, For the j layer wavelet threshold; is the standard deviation of the noise, is the number of signal sampling points, For the The wavelet threshold of the layer, For the The noise standard deviation of the layer, To express the peak sum ratio correction factor, is the natural logarithm, For the The ratio of the peak value to the sum value in the layer wavelet coefficients, For the The length of the layer wavelet coefficients.

[0020] By introducing Estimate the noise standard deviation of the wavelet coefficients in each layer layer by layer to reduce the noise standard deviation The error caused by the overall estimation. This formula not only reduces the value of the first layer threshold, but also increases the threshold values ​​of the subsequent layers, which can more effectively retain the wavelet coefficients of the real signal.

[0021] In a feasible implementation, before step A02, the method further includes: using the length of each layer of wavelet coefficients L j The ratio of the peak value to the sum value in the wavelet coefficients P SRj , determine the peak-sum ratio correction factor of each layer of wavelet coefficients .

[0022] A03. Utilize the wavelet threshold of each layer and the improved threshold function algorithm to reduce noise on the wavelet coefficients to determine denoised wavelet coefficients.

[0023] Then, the wavelet coefficients are denoised using the wavelet threshold of each layer and an improved threshold function algorithm to determine denoised wavelet coefficients. Specifically, the improved threshold function algorithm includes a first denoising algorithm, a second denoising algorithm and a third denoising algorithm.

[0024] Specifically, step 103 includes the following steps: B01. Determine the adjustable parameters of each layer of wavelet coefficients using a preset adjustable parameter determination rule, wherein the adjustable parameter determination rule at least includes that the higher the number of decomposition layers, the lower the adjustable parameters; For each layer of wavelet coefficients and wavelet thresholds, the following processing is performed: B02. If the wavelet coefficient is greater than or equal to the wavelet threshold, obtain denoised wavelet coefficients using a first denoising algorithm, the adjustable parameters, the wavelet coefficients, and the wavelet threshold; B03. If the absolute value of the wavelet coefficient is less than the wavelet threshold, obtain denoised wavelet coefficients using a second denoising algorithm, the adjustable parameter, the wavelet coefficient, and the wavelet threshold; B04. If the wavelet coefficient is less than or equal to the negative value of the wavelet threshold, obtain denoised wavelet coefficients using a third denoising algorithm, the adjustable parameters, the wavelet coefficients, and the wavelet threshold.

[0025] By comparing the wavelet coefficients of each layer with the wavelet threshold of the layer where it is located, the denoising algorithm of the wavelet coefficients of the layer is determined, thereby obtaining the denoised wavelet coefficients of the layer and realizing adaptive denoising of the wavelet coefficients.

[0026] It should be noted that the noisy signal is defined as , which consists of a pure signal and noise signal Composition, namely: (6) Traditional soft threshold function Defined as: (7) Where, is a step function, is the wavelet threshold.

[0027] Traditional hard threshold function Defined as: (8) In order to have both and The advantages of this method are to retain more detailed information after signal noise reduction, and to construct an improved threshold function algorithm. Achieve adaptive adjustment.

[0028] Exemplarily, the improved threshold function algorithm is as follows: ; (9) Where, are the wavelet coefficients, is the wavelet threshold, is an adjustable parameter, is the first denoising algorithm; is the second denoising algorithm; is the third denoising algorithm; is the wavelet coefficient x The denoised wavelet coefficients.

[0029] according to and Energy distribution characteristics of each decomposition layer of wavelet transform Mathematical model of the wavelet transform: (10) In the formula: , are the energy of the first decomposition layer and respectively. The rest , the value of each decomposition layer can be calculated, and its range is [1, 11]. In the low decomposition layer of the wavelet transform, a larger value is selected to make the threshold function of the layer biased , and most of the noise coefficients are filtered out; in the high decomposition layer, a smaller value is selected to be biased , and the information of the local abrupt points is better preserved.

[0030] 104. Based on the denoised wavelet coefficients, the signal is reconstructed to obtain the denoised signal of the power quality disturbance signal.

[0031] Finally, the denoised wavelet coefficients are used for signal reconstruction, such as using wavelet inverse transform to restore the signal, to obtain the denoised signal of the power quality disturbance signal.

[0032] Further, the denoised signal can be used for disturbance identification by using a pre-trained deep learning model to identify the disturbance type of the signal.

[0033] Specifically, refer to Figure 2 , Figure 2 is a structural block diagram of an identification system of a power quality disturbance in an embodiment of the present application; Figure 1 The denoising method shown in Figure 2 The identification system shown in, wherein Figure 2 It is shown that: the data input module: acquires the power quality disturbance waveform signal in the noisy environment; and inputs it into the signal denoising module; the signal denoising module: denoises the noisy disturbance signal by improving the adaptive threshold and the improved threshold function algorithm; and inputs the denoised power quality disturbance signal into the feature extraction module; the feature extraction module: processes the denoised power quality disturbance signal, extracts features of the denoised power quality disturbance signal using one-dimensional convolutional neural network and one-dimensional residual neural network, and uses multiplication fusion method for feature splicing to strengthen the spatial correlation of the features; and inputs the feature-extracted power quality disturbance signal into the feature fusion module; the feature fusion module: introduces a multi-head attention mechanism, simultaneously captures and fuses features at different levels and angles, extracts and selects appropriate features; and inputs it into the classification module; the classification module: uses a Softmax classifier to classify the features to obtain the final classification result.

[0034] Wherein, the steps 101 to 104 are implemented by setting Figure 2 The disturbance identification can be realized by setting Figure 2 The feature extraction module, the feature fusion module and the classification module are shown.

[0035] Wherein, the noise-reduced signal is input into a deep learning model for power quality disturbance identification. Deep learning can automatically learn features from raw data, which can overcome the limitations of traditional methods and simple machine learning methods, thereby more effectively identifying noisy power quality disturbance signals. Therefore, the present application proposes a noisy power quality disturbance identification method based on deep learning feature fusion, which can further improve the disturbance identification accuracy, has faster convergence speed, smaller fluctuation amplitude, stronger anti-noise performance, and higher recognition accuracy in different noise environments.

[0036] It can be understood that the deep learning model shown in the present application is a power quality disturbance identification model, which is a trained model. The training samples used for training of the model include the corresponding relationship between a plurality of power quality disturbance signals and disturbance type labels. The training samples are used to let the original deep learning model learn the relationship between the signals and the labels until the deep learning model can output the correct label corresponding to the signal based on the signal, and obtain a power quality disturbance identification model that can identify the disturbance type of the signal. The specific training process is not described in detail, and can refer to the training process of the existing deep learning model when performing a classification task.

[0037] Wherein, the deep learning model at least includes a one-dimensional convolutional neural network, a one-dimensional residual neural network, a multi-head attention network and a classifier, which will be described in detail below.

[0038] S1, using a preset one-dimensional convolutional neural network and a one-dimensional residual neural network to extract features from the noise-reduced signal to obtain a target time feature vector of the power quality disturbance signal; After obtaining the power quality disturbance signal, the preset one-dimensional convolutional neural network and one-dimensional residual neural network can be used to extract features from the power quality disturbance signal to obtain a target time feature vector of the power quality disturbance signal, and obtain preliminary disturbance features. Wherein, the noise-reduced power quality disturbance signal is processed, and the one-dimensional convolutional neural network and the one-dimensional residual neural network are used to reduce the dimension of the noise-reduced power quality disturbance signal and extract a time feature vector (PQDs) with high distinguishability. And ) can be referred to Figure 2 And Figure 3 , Figure 3A one-dimensional convolutional neural network and a one-dimensional residual neural network are shown in FIG. Figure 2 and Figure 3 The feature extraction module shown extracts features from the signal.

[0039] See Figure 2 and Figure 3 , the power quality disturbance signal is feature extracted using a preset one-dimensional convolutional neural network and a one-dimensional residual neural network to obtain a target time feature vector of the power quality disturbance signal, including i, ii, and iii: i. performing feature extraction on the power quality disturbance signal based on the one-dimensional convolutional neural network to obtain a first time feature vector of the power quality disturbance signal; In one feasible implementation, the one-dimensional convolutional neural network includes multiple convolutional layers and fully connected layers connected in series; then the step i includes: inputting the power quality disturbance signal into the convolutional layer and the fully connected layer, and processing the convolutional layer and the fully connected layer in sequence to obtain a first time feature vector of the power quality disturbance signal.

[0040] ii. performing feature extraction on the noise reduction signal based on the one-dimensional residual neural network to obtain a second time feature vector of the power quality disturbance signal; In a feasible implementation, the one-dimensional residual neural network includes several convolutional layers, residual connection layers and fully connected layers connected in series, then step ii includes: inputting the power quality disturbance signal into the convolutional layer to obtain the signal characteristics after convolution processing; inputting the power quality disturbance signal and the signal characteristics after convolution processing into the residual connection layer to obtain the signal characteristics after residual connection; inputting the signal characteristics after residual connection into the fully connected layer to obtain a second time feature vector.

[0041] iii. Perform feature splicing using the first time feature vector and the second time feature vector to obtain a target time feature vector of the power quality disturbance signal.

[0042] It should be noted that the feature extraction module uses multiplication fusion (based on The feature concatenation method is to use the exponential feature multiplication method (element-wise multiplication of the base). The formula for the multiplication fusion feature concatenation is: ; (11) Where: for The weight; for , B is the first time eigenvector, A is the second time eigenvector, is the target time feature vector.

[0043] The advantage of multiplication fusion is that it can comprehensively consider the interaction between each pair of feature elements. Multiplication fusion shows better robustness when dealing with abnormal values (especially extremely large or small values), because it is not easy to cause rapid growth of numerical values when multiplying.

[0044] S2, performing feature fusion processing on the target time feature vector by using a preset multi-head attention mechanism to obtain a fused signal feature; Specifically, step S2 includes: simultaneously capturing and fusing and splicing features of the target time feature vector at different levels and angles by using the multi-head attention mechanism to obtain a fused signal feature. For details, please refer to Figure 2 and Figure 4 , Figure 4 is a model structure block diagram of a multi-head attention structure in an embodiment of the application.

[0045] Through Figure 2 and Figure 4 the feature fusion module: introduce a multi-head attention mechanism, simultaneously capture features at different levels and angles and perform fusion, extract and select appropriate features, and input them into a classification module.

[0046] It should be noted that the multi-head attention mechanism is an extended form of the attention mechanism, which simultaneously captures features at different levels and angles by introducing multiple independent attention heads to improve the expression ability and generalization ability of the model. The soft attention mechanism is selected based on the deep learning feature fusion module, the attention weight is calculated and weighted average is performed, and the calculation process is as follows: Suppose there is a set of PQD feature data , and a query vector is given, the relevance of each input and is calculated by a scoring function , then the relevance scores are normalized by the Softmax function to obtain the attention distribution corresponding to the PQDs, and finally the input data is weighted and summed by the attention distribution to obtain the output result , the calculation formula is: (12) (13) The attention scoring function , is the dimension of the input vector.

[0047] Compared with the conventional attention mechanism, the multi-head attention (MA) can make the output of the attention layer contain representation information in different subspaces, thereby enhancing the expression ability of the model. It uses different query vectors to focus on different parts of the input information to achieve the purpose of analyzing the current input information from different angles. The multi-head attention structure is as shown in Figure 4 .

[0048] It mainly includes three steps: first, the extracted features are inputted, then linear transformation is performed on to map , key space and value space respectively to query space ; then the scaling dot product and the Softmax function are used to calculate each attention distribution, and the attention distribution is weighted and summed to obtain the corresponding output ; finally, the multiple output results are spliced by splicing. The formulas are shown in equations (14)-(18).

[0049] (14) (15) (16) (17) (18) In the formula, C is the fusion signal feature, is the weighted sum result of the i-th query vector q for all keys; , , are linear transformation parameters of query space Q , key space K and value space V respectively; is a matrix composed of the dimensions of each key; is an element vector in the key space; is an element vector in the value space; is a transpose transformation; q is a query vector, is the number of query vectors; is a feature splicing function; is the dimension number after linear transformation; H is the target time feature vector, ; is a softmax function; is used to calculate the i-thi a query vector and the j key vector between them.

[0050] S3, classifying the fusion signal feature by using a preset classifier to obtain a final classification result, wherein the final classification result is used to indicate a disturbance type of the power quality disturbance signal.

[0051] Finally, the fusion signal feature C is input to the classifier for classification to identify the disturbance type, that is, a classification module shown in FIG. Figure 2 The classification module receives the fusion feature and performs classification identification, and a Softmax classifier is used to classify the feature to obtain a final classification result.

[0052] The power quality disturbance identification model provided by the application comprehensively considers various feature information, which can effectively reduce the influence of noise on the classification result and improve the classification performance and generalization ability of the model.

[0053] The method has better performance in classification accuracy and noise resistance, and provides effective support for power fault diagnosis in power systems.

[0054] The application provides a power quality disturbance signal denoising method, which comprises the following steps: acquiring a power quality disturbance signal in a noisy environment; performing wavelet decomposition on the power quality disturbance signal to obtain J-layer wavelet coefficients of the power quality disturbance signal; performing denoising processing on the wavelet coefficients by using a preset improved adaptive threshold algorithm and an improved threshold function algorithm to obtain denoised wavelet coefficients; and performing signal reconstruction based on the denoised wavelet coefficients to obtain a denoised signal of the power quality disturbance signal.

[0055] Please refer to Figure 5 , Figure 5 The application provides a power quality disturbance signal denoising device, and a structure block diagram of the device is shown in FIG. Figure 5 The device comprises the following modules: A signal acquisition module 501 is configured to acquire a power quality disturbance signal in a noisy environment. A signal decomposition module 502 is configured to perform wavelet decomposition on the power quality disturbance signal to obtain J-layer wavelet coefficients of the power quality disturbance signal. A denoising processing module 503 is configured to perform denoising processing on the wavelet coefficients by using a preset improved adaptive threshold algorithm and an improved threshold function algorithm to obtain denoised wavelet coefficients. The signal reconstruction module 504 is configured to reconstruct a signal based on the denoised wavelet coefficients to obtain a denoised signal of the power quality disturbance signal.

[0056] It should be noted that, Figure 5 The functions of each module in the device are similar to those of the device Figure 1 The contents of each step in the method are similar to those of the method Figure 1 The contents of each step in the method are similar to those of the method

[0057] The present application provides a kind of power quality disturbance signal denoising device, device includes: signal acquisition module: for obtaining power quality disturbance signal in noisy environment;Signal decomposition module: for the wavelet decomposition of the power quality disturbance signal, obtains the J layer wavelet coefficient of the power quality disturbance signal;Denoising processing module: for using the improved adaptive threshold algorithm and improved threshold function algorithm to the wavelet coefficient denoising processing, obtains denoised wavelet coefficient;Signal reconstruction module: for reconstructing a signal based on the denoised wavelet coefficients to obtain a denoised signal of the power quality disturbance signal.By the above manner, wavelet coefficient is denoised, reduce noise interference, reduce the influence of noise interference on power quality disturbance identification accuracy.

[0058] Figure 6 The internal structure diagram of the computer device in one embodiment is shown.The computer device can be a terminal or a server specifically.As shown in the figure, Figure 6 The computer device includes a processor, a memory and a network interface connected by a system bus.The memory includes a non-volatile storage medium and an internal memory.The non-volatile storage medium of the computer device stores an operating system, and can also store a computer program, which is executed by the processor to enable the processor to implement the above method.The internal memory can also store a computer program, which is executed by the processor to enable the processor to execute the above method. Figure 6 The structure shown in the figure is only a block diagram of part of the structure related to the scheme of the present application, and does not constitute a limitation on the computer device to which the scheme of the present application is applied.The specific computer device can include more or fewer components than those shown in the figure, or combine certain components, or have a different component arrangement.

[0059] In one embodiment, a computer device is provided, which includes a memory and a processor, the memory stores a computer program, and the computer program is executed by the processor to enable the processor to execute the steps of the method as shown in Figure 1 The steps of the method as shown in

[0060] In one embodiment, a computer readable storage medium is provided, storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method as shown. Figure 1

[0061] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be completed by a computer program instructing related hardware, and the program can be stored in a non-volatile computer readable storage medium. When the program is executed, it can include the processes of the above-mentioned embodiments. Any reference to memory, storage, database or other medium used in the embodiments provided by the present application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0062] The technical features of the above embodiments can be combined in any way. In order to make the description concise, not all possible combinations of the technical features in the above embodiments are described, but as long as the combinations of the technical features do not contradict, they should be considered as the scope of the present application.

[0063] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent of the present application. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, some modifications and improvements can be made, which are all within the scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.​

Claims

1. A method for reducing noise of power quality disturbance signals, characterized in that: The method comprises: Obtain power quality disturbance signals in noisy environments; Performing wavelet decomposition on the power quality disturbance signal to obtain J-layer wavelet coefficients of the power quality disturbance signal; Performing noise reduction processing on the wavelet coefficients using a preset improved adaptive threshold algorithm and an improved threshold function algorithm to obtain denoised wavelet coefficients; Signal reconstruction is performed based on the denoising wavelet coefficients to obtain a noise reduction signal of the power quality disturbance signal.

2. The noise reduction method according to claim 1, characterized in that: The denoising process is performed on the wavelet coefficients using a preset improved adaptive threshold algorithm and an improved threshold function algorithm to obtain denoised wavelet coefficients, including: Determining the noise standard deviation based on the J-layer wavelet coefficients and a preset standard deviation algorithm; Determine the wavelet threshold corresponding to each layer of wavelet coefficients by using the peak sum ratio correction factor of each layer of wavelet coefficients, the noise standard deviation and a preset improved adaptive threshold algorithm; The wavelet coefficients are denoised using the wavelet threshold of each layer and an improved threshold function algorithm to determine denoised wavelet coefficients.

3. The noise reduction method according to claim 2, characterized in that: The method of determining the wavelet threshold corresponding to each layer of wavelet coefficients by respectively utilizing the peak sum ratio correction factor of each layer of wavelet coefficients, the noise standard deviation and a preset improved adaptive threshold algorithm may also include: The peak-to-sum ratio correction factor of each layer of wavelet coefficients is determined by using the length of each layer of wavelet coefficients and the ratio of the peak value to the sum value in the wavelet coefficients.

4. The noise reduction method according to claim 2, characterized in that: The improved threshold function algorithm includes a first denoising algorithm, a second denoising algorithm and a third denoising algorithm. Then, the wavelet coefficients are denoised using the wavelet threshold and the improved threshold function algorithm at each layer to determine the denoised wavelet coefficients, including: Determining the adjustable parameters of each layer of wavelet coefficients using a preset adjustable parameter determination rule, wherein the adjustable parameter determination rule at least includes that the higher the number of decomposition layers, the lower the adjustable parameter; For each layer of wavelet coefficients and wavelet thresholds, the following processing is performed: If the wavelet coefficient is greater than or equal to the wavelet threshold, obtaining a denoised wavelet coefficient using a first denoising algorithm, the adjustable parameter, the wavelet coefficient and the wavelet threshold; If the absolute value of the wavelet coefficient is less than the wavelet threshold, a second denoising algorithm, the adjustable parameter, the wavelet coefficient and the wavelet threshold are used to obtain a denoised wavelet coefficient; If the wavelet coefficient is less than or equal to the negative value of the wavelet threshold, a denoised wavelet coefficient is obtained by using a third denoising algorithm, the adjustable parameter, the wavelet coefficient and the wavelet threshold.

5. The noise reduction method according to claim 4, characterized in that: The improved threshold function algorithm is as follows: ； Where, are the wavelet coefficients, is the wavelet threshold, is an adjustable parameter, is the first denoising algorithm; is the second denoising algorithm; is the third denoising algorithm; is the wavelet coefficient x The denoised wavelet coefficients.

6. The noise reduction method according to claim 2, characterized in that: The standard deviation algorithm is as follows: ； Where, is the noise standard deviation, Represents the wavelet coefficient in the kth direction in the i-th layer wavelet decomposition; Represents the median of the absolute values ​​of all wavelet coefficients.

7. The noise reduction method according to claim 4, characterized in that: The improved adaptive threshold algorithm is as follows: ； ； Where, For the j layer wavelet threshold; For the j layer noise standard deviation; is the number of signal sampling points; Indicates the j Peak-to-peak ratio correction factor for the layer; P SRj For the The ratio of the peak value to the sum value in the layer wavelet coefficients; L j For the The length of the layer wavelet coefficients.

8. A device for reducing noise of power quality disturbance signals, characterized in that: The device comprises: Signal acquisition module: used to obtain power quality disturbance signals in noisy environments; Signal decomposition module: used for performing wavelet decomposition on the power quality disturbance signal to obtain J-layer wavelet coefficients of the power quality disturbance signal; Denoising processing module: used for performing denoising processing on the wavelet coefficients by using a preset improved adaptive threshold algorithm and an improved threshold function algorithm to obtain denoised wavelet coefficients; Signal reconstruction module: used for performing signal reconstruction based on the denoising wavelet coefficients to obtain a noise reduction signal of the power quality disturbance signal.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

10. A computer device comprising a memory and a processor, characterized in that: The memory stores a computer program, and when the computer program is executed by the processor, the processor is caused to perform the steps of the method according to any one of claims 1 to 7.

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