Power quality disturbance signal denoising method, device, equipment and medium

Abstract

The present application relates to the technical field of power quality disturbance signal noise reduction, and discloses a power quality disturbance signal noise reduction method, a power quality disturbance signal noise reduction device, a power quality disturbance signal noise reduction equipment and a medium, 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 noise reduction processing on the wavelet coefficients to obtain de-noised wavelet coefficients; and reconstructing the signal based on the de-noised wavelet coefficients to obtain a noise-reduced power quality disturbance signal. In this way, the wavelet coefficients are subjected to noise reduction processing, noise interference is reduced, and the influence of noise interference on the accuracy of power quality disturbance identification 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 affect 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:

[0006] obtaining a power quality disturbance signal in a noisy environment;

[0007] wavelet decomposing the power quality disturbance signal to obtain J-layer wavelet coefficients of the power quality disturbance signal;

[0008] using a preset improved adaptive threshold algorithm and an improved threshold function algorithm to perform noise reduction processing on the wavelet coefficients to obtain denoised wavelet coefficients;

[0009] based on the denoised wavelet coefficients, reconstructing the signal to obtain a noise-reduced signal of the power quality disturbance signal.

[0010] 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:

[0011] a signal acquisition module for obtaining a power quality disturbance signal in a noisy environment;

[0012] a signal decomposition module for wavelet decomposing the power quality disturbance signal to obtain J-layer wavelet coefficients of the power quality disturbance signal;

[0013] The de-noising processing module is configured to perform de-noising processing on the wavelet coefficients by using a preset improved adaptive threshold algorithm and an improved threshold function algorithm to obtain de-noised wavelet coefficients.

[0014] The signal reconstruction module is configured to perform signal reconstruction based on the de-noised wavelet coefficients to obtain a de-noised signal of the power quality disturbance signal.

[0015] To achieve the above object, the third aspect of the present application provides a computer readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the steps of the method according to the first aspect.

[0016] To achieve the above object, the fourth aspect of the present application provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, which, when executed by the processor, causes the processor to perform the steps of the method according to the first aspect.

[0017] The present application has the following advantages:

[0018] The present application provides a de-noising method of a power quality disturbance signal, which comprises the following steps: obtaining 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 de-noising processing on the wavelet coefficients by using a preset improved adaptive threshold algorithm and an improved threshold function algorithm to obtain de-noised wavelet coefficients; and performing signal reconstruction based on the de-noised wavelet coefficients to obtain a de-noised signal of the power quality disturbance signal. In this way, the wavelet coefficients are de-noised to reduce noise interference and reduce the influence of noise interference on the identification accuracy of the power quality disturbance. BRIEF DESCRIPTION OF DRAWINGS

[0019] 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 embodiments 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.

[0020] In the drawings:

[0021] Figure 1 A flow chart of a de-noising method of a power quality disturbance signal in an embodiment of the present application;

[0022] Figure 2 A structural block diagram of a power quality disturbance identification system in an embodiment of the present application;

[0023] Figure 3A model structure block diagram of a one-dimensional convolutional neural network and a one-dimensional residual neural network in an embodiment of the present application;

[0024] Figure 4 A model structure block diagram of a multi-head attention structure in an embodiment of the present application;

[0025] Figure 5 A structure block diagram of a noise reduction device for power quality disturbance signals in an embodiment of the present application;

[0026] Figure 6 A structure block diagram of a computer device in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The technical solutions in the embodiments of the present application will be clearly 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 those skilled in the art without creative labor fall within the scope of protection of the present application.

[0028] 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 improving the adaptive threshold and the 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.

[0029] Please refer to Figure 1 , Figure 1 A flowchart of a noise reduction method for power quality disturbance signals in an embodiment of the present application is shown in Figure 1 The method includes the following steps:

[0030] 101. Obtain a power quality disturbance signal in a noisy environment;

[0031] 102. wavelet-decomposing the power quality disturbance signal to obtain J-layer wavelet coefficients of the power quality disturbance signal;

[0032] 103. denoising the wavelet coefficients by using a preset improved adaptive threshold algorithm and an improved threshold function algorithm to obtain denoised wavelet coefficients;

[0033] 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.

[0034] The power quality disturbance signal can be a power quality disturbance waveform signal. The wavelet coefficients of the signal are obtained by wavelet decomposition, the wavelet threshold of the wavelet coefficients is obtained by using an improved adaptive threshold algorithm, and the denoised wavelet coefficients are obtained by using the wavelet threshold and the wavelet coefficients.

[0035] In a feasible implementation manner, step 103 comprises steps A01 to A03:

[0036] A01. determining a noise standard deviation based on the J-layer wavelet coefficients and a preset standard deviation algorithm ;

[0037] 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 .

[0038] A02. determining a wavelet threshold corresponding to each layer of wavelet coefficients by using a peak and ratio correction factor of each layer of wavelet coefficients, the noise standard deviation, and a preset improved adaptive threshold algorithm;

[0039] It should be noted that the threshold is used as a demarcation between noise and a real signal in splitting wavelet detail coefficients. The traditional general threshold is:

[0040] (1)

[0041] (2)

[0042] is the wavelet threshold, is the number of signal sampling points, is each layer of wavelet coefficients, The standard deviation of the noise is used to estimate the overall noise of the signal. Since the universal threshold is fixed, and the noise distribution is random, using the same threshold in other decomposition layers would lead to excessive removal of coefficients from the true signal. Therefore, improvements are made to address the shortcomings of this threshold setting.

[0043] A wavelet threshold is calculated for each layer. The wavelet threshold corresponding to the wavelet coefficients of each layer is determined by using the peak-to-sum ratio correction factor of the wavelet coefficients of each layer, the noise standard deviation, and a preset improved adaptive thresholding algorithm. .

[0044] For example, the improved adaptive threshold algorithm is as follows:

[0045] (3)

[0046] (4)

[0047] (5)

[0048] In the formula, The standard deviation of noise. This represents the wavelet coefficient in the k-th direction of the i-th level wavelet decomposition; This represents the median of the absolute values ​​of all wavelet coefficients. For the first j Layer wavelet threshold; The standard deviation of the noise. The number of signal sampling points. For the first Wavelet threshold of the layer For the first The noise standard deviation of the layer To represent the peak and ratio correction factor, It is the natural logarithm. For the first The ratio of peak value to sum value in layer wavelet coefficients. For the first The length of the layer wavelet coefficients.

[0049] By introducing Estimate the noise standard deviation of wavelet coefficients layer by layer to reduce the impact of noise standard deviation. The overall estimation introduces errors. This formula reduces the value of the first-layer threshold while increasing the threshold values ​​of subsequent layers, thus more effectively preserving the wavelet coefficients of the true signal.

[0050] In one feasible implementation, before step A02, the method further includes: utilizing the lengths of the wavelet coefficients at each layer respectively. L j The ratio of peak value to sum value in wavelet coefficientsP SRj , determine the peak and ratio correction factor of each layer wavelet coefficient .

[0051] A03, using each layer of the wavelet threshold, improve the threshold function algorithm to denoise the wavelet coefficient, determine the denoising wavelet coefficient.

[0052] Then, using each layer of the wavelet threshold, improve the threshold function algorithm to denoise the wavelet coefficient, determine the denoising wavelet coefficient, specifically, the improved threshold function algorithm includes the first denoising algorithm, the second denoising algorithm and the third denoising algorithm.

[0053] Specifically, step 103 includes the following steps:

[0054] B01, using the preset adjustable parameter determination rule to determine the adjustable parameter of each layer wavelet coefficient, the adjustable parameter determination rule at least includes the higher the decomposition layer number, the lower the adjustable parameter;

[0055] For each layer wavelet coefficient and wavelet threshold, the following processing is performed:

[0056] B02, if the wavelet coefficient is greater than or equal to the wavelet threshold, using the first denoising algorithm, the adjustable parameter, the wavelet coefficient and the wavelet threshold, to obtain the denoising wavelet coefficient;

[0057] B03, if the absolute value of the wavelet coefficient is less than the wavelet threshold, using the second denoising algorithm, the adjustable parameter, the wavelet coefficient and the wavelet threshold, to obtain the denoising wavelet coefficient;

[0058] B04, if the wavelet coefficient is less than or equal to the negative value of the wavelet threshold, using the third denoising algorithm, the adjustable parameter, the wavelet coefficient and the wavelet threshold, to obtain the denoising wavelet coefficient.

[0059] Through the comparison result between each layer wavelet coefficient and the wavelet threshold of the layer where it is located, the denoising algorithm of the layer wavelet coefficient is determined, so as to obtain the denoising wavelet coefficient of the layer, and realize the adaptive denoising of the wavelet coefficient.

[0060] It should be noted that the definition of the noisy signal is , which is composed of a pure signal and a noise signal , that is:

[0061] (6)

[0062] Traditional soft threshold function is defined as:

[0063] (7)

[0064] wherein, is a step function, is a wavelet threshold.

[0065] Conventional hard threshold function is defined as:

[0066] (8)

[0067] In order to have the advantages of both and and make the signal retain more detail information after denoising, an improved threshold function algorithm is constructed, and self-adaptive adjustment is realized through adjustable parameter .

[0068] Exemplarily, the improved threshold function algorithm is as follows:

[0069] (9)

[0070] wherein, is a wavelet coefficient, is a wavelet threshold, is an adjustable parameter, is a first denoising algorithm; is a second denoising algorithm; is a third denoising algorithm; is a denoising wavelet coefficient of the wavelet coefficient x .

[0071] According to the energy distribution characteristics of each decomposition layer of the wavelet transform and , a mathematical model of is established:

[0072] (10)

[0073] wherein, , are the energies of the layer decomposition and respectively. And , the rest , the value of on 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 layer threshold function deviate , and most of the noise coefficients are filtered out; in the high decomposition layer, a smaller value is selected to deviate , and the information of local abrupt points is better retained.

[0074] 104. reconstructing the signal based on the denoised wavelet coefficients to obtain a denoised signal of the power quality disturbance signal.

[0075] Finally, the signal is reconstructed using the denoised wavelet coefficients, such as using the inverse wavelet transform to restore the signal, to obtain a denoised signal of the power quality disturbance signal.

[0076] Further, the denoised signal can be subjected to pre-trained deep learning model for disturbance identification to identify the disturbance type of the signal.

[0077] Specifically, reference can be made to Figure 2 , Figure 2 is a structural block diagram of an identification system for 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 to capture and fuse features at different levels and angles, extract and select 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.

[0078] Wherein, steps 101 to 104 are realized by setting Figure 2 the data input module and the signal denoising module shown in, and the disturbance identification can be realized by setting Figure 2 the feature extraction module, the feature fusion module and the classification module shown in.

[0079] Wherein, the denoised 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.

[0080] It can be understood that the deep learning model shown in the present application is a model for identifying power quality disturbances, which is a trained model. The training sample used for training of the model includes the corresponding relationship of a plurality of power quality disturbance signals and disturbance type labels. The training sample is used to let the original deep learning model learn the relationship between the signal and the label until the deep learning model can output the correct label corresponding to the signal according to the signal, so as to obtain the power quality disturbance identification model which can identify the disturbance type of the signal. The specific training process is not described herein and can refer to the training process of the existing deep learning model when performing a classification task.

[0081] 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. For details, refer to the following content.

[0082] S1, extracting features of the denoised signal by 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;

[0083] After obtaining the power quality disturbance signal, the preset one-dimensional convolutional neural network and the one-dimensional residual neural network can be used to extract features of the power quality disturbance signal to obtain a target time feature vector of the power quality disturbance signal, thereby obtaining preliminary disturbance features. The one-dimensional convolutional neural network and the one-dimensional residual neural network are used to process the denoised power quality disturbance signal, reduce the dimension of the power quality disturbance data, and extract a time feature vector (PQDs) with high distinguishability. and ). Figure 2 and Figure 3 , Figure 3 is a model structure block diagram of a one-dimensional convolutional neural network and a one-dimensional residual neural network in an embodiment of the present application. The feature extraction module shown in Figure 2 and Figure 3 is used to extract features of the signal.

[0084] Referring to Figure 2 and Figure 3 , the use of the preset one-dimensional convolutional neural network and the one-dimensional residual neural network to extract features of the power quality disturbance signal to obtain a target time feature vector of the power quality disturbance signal includes i, ii, and iii:

[0085] i, extracting features of 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;

[0086] In an implementable manner, the one-dimensional convolutional neural network comprises a plurality of convolutional layers and a fully connected layer connected in series; and the step i comprises: inputting the power quality disturbance signal into the convolutional layers and the fully connected layer, and sequentially processing the power quality disturbance signal through the convolutional layers and the fully connected layer to obtain a first time feature vector of the power quality disturbance signal.

[0087] ii. performing feature extraction on the noise-reduced signal based on the one-dimensional residual neural network to obtain a second time feature vector of the power quality disturbance signal;

[0088] In an implementable manner, the one-dimensional residual neural network comprises a plurality of convolutional layers, a residual connection layer and a fully connected layer connected in series; and the step ii comprises: inputting the power quality disturbance signal into the convolutional layers to obtain a signal feature after convolutional processing; inputting the power quality disturbance signal and the signal feature after convolutional processing into the residual connection layer to obtain a signal feature after residual connection; and inputting the signal feature after residual connection into the fully connected layer to obtain a second time feature vector.

[0089] iii. performing 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.

[0090] It should be noted that the feature splicing in the feature extraction module is performed by using a multiplication fusion (element-by-element multiplication of exponentialized features based on

[0091] ; (11)

[0092] In the formula, B is a component of B is a component of

[0093] The advantage of multiplication fusion is that it can comprehensively consider the interaction between each pair of feature elements. Multiplication fusion has better robustness when dealing with abnormal values (especially extremely large or extremely small values), because multiplication is less likely to cause rapid growth of numerical values.

[0094] 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;

[0095] ​​​​​Specifically, step S2 includes: using the multi-head attention mechanism, capturing and fusing the features of the target time feature vector at different levels and angles to obtain a fused signal feature. Figure 2 and Figure 4 , Figure 4 is a model structure block diagram of a multi-head attention structure in an embodiment of the present application.

[0096] Through Figure 2 and Figure 4 the feature fusion module: introduce a multi-head attention mechanism, capture and fuse features at different levels and angles, extract and select appropriate features, and input them into the classification module.

[0097] It should be noted that the multi-head attention mechanism is an extended form of the attention mechanism, which 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, and the attention weight is calculated and weighted averaged, and the calculation process is as follows:

[0098] Suppose there is a set of PQD feature data , and a query vector is given, the relevance of each input and is calculated by the scoring function , and then the relevance score output is normalized by the Softmax function to obtain the attention distribution corresponding to the PQD, and finally the input data is weighted and summed by the attention distribution to obtain the output result , the calculation formula is:

[0099] (12)

[0100] (13)

[0101] The attention scoring function , is the dimension of the input vector.

[0102] Compared with the conventional attention mechanism, the multi-head attention mechanism (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, so as to analyze the current input information from different angles, and the multi-head attention structure is as shown in Figure 4 .

[0103] It mainly consists of three steps: First, input the extracted features. And then Perform a linear transformation, to Mapped to query space respectively Key space Sum value space Then, the scaled dot product and the Softmax function are used to calculate each attention distribution, and the attention distributions are weighted and summed to obtain the corresponding output. Finally, the multiple output results are concatenated using a splicing method. The formulas are shown in equations (14) to (18).

[0104] (14)

[0105] (15)

[0106] (16)

[0107] (17)

[0108] (18)

[0109] In the formula, C represents the fused signal characteristics. It is the i-th query vector q The weighted summation result over all keys; , , respectively query space Q Key space K Value space V The linear transformation parameters; A matrix consisting of the dimensions of each key; is a vector of elements in the key space; It is a vector of elements in the value space; This is a transpose transformation; q For query vector, The number of query vectors; For feature concatenation function; H is the dimension after linear transformation; H is the target time feature vector. ; It is the softmax function; Used to calculate the i query vectors and the j Key vectors The correlation between them.

[0110] 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.

[0111] Finally, the fusion signal feature C is input to the classifier for classification to identify the disturbance type, that is, setting Figure 2 The classification module is shown in the figure, and the function of the classification module is to receive the fusion feature and perform classification and identification.

[0112] The power quality disturbance identification model provided in 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.

[0113] The method has better performance in classification accuracy and noise resistance, and provides effective support for power fault diagnosis in power systems.

[0114] The application provides a noise reduction method for a power quality disturbance signal, 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 noise reduction 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 noise-reduced signal of the power quality disturbance signal. Through the above method, the wavelet coefficients are subjected to noise reduction processing, noise interference is reduced, and the influence of noise interference on the identification accuracy of the power quality disturbance is reduced.

[0115] Please refer to Figure 5 , Figure 5 The structure block diagram of the noise reduction device for the power quality disturbance signal in the embodiment of the application is shown in the figure. Figure 5 The device comprises:

[0116] The signal acquisition module 501 is configured to acquire a power quality disturbance signal in a noisy environment.

[0117] The 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.

[0118] The noise reduction processing module 503 is configured to perform noise reduction processing on the wavelet coefficients by using a preset improved adaptive threshold algorithm and an improved threshold function algorithm to obtain denoised wavelet coefficients.

[0119] The signal reconstruction module 504 is configured to perform signal reconstruction based on the denoised wavelet coefficients to obtain a noise-reduced signal of the power quality disturbance signal.

[0120] It should be noted that,Figure 5 The functions of each module of the device are similar to those of the device shown in the above embodiment, and thus are not described here again. Figure 1 The content of each step of the method is similar to that of the method shown in the above embodiment, and thus is not described here again, and can be referred to in the above embodiment. Figure 1 The content of each step of the method is similar to that of the method shown in the above embodiment.

[0121] The present application provides a kind of electric energy quality disturbance signal's noise reduction device, device includes: signal acquisition module: for obtaining the electric energy quality disturbance signal in noisy environment;Signal decomposition module: for the wavelet decomposition of the electric energy quality disturbance signal, obtains the J layer wavelet coefficient of the electric energy quality disturbance signal;Denoising processing module: for using the wavelet coefficient of the improved adaptive threshold algorithm and the improved threshold function algorithm to carry out denoising processing, obtains denoising wavelet coefficient;Signal reconstruction module: for based on the denoising wavelet coefficient carries out signal reconstruction, obtains the denoising signal of the electric energy quality disturbance signal.By the above manner, wavelet coefficient is carried out denoising processing, reduces noise interference, reduces the influence of noise interference on electric energy quality disturbance identification accuracy.

[0122] Figure 6 The internal structure diagram of the computer device in one embodiment is shown.The computer device can be terminal or server specifically.As shown in the figure, Figure 6 The computer device includes processor, memory and network interface connected by system bus.Wherein, memory includes non-volatile storage medium and internal memory.The non-volatile storage medium of the computer device stores operating system, and can also store computer program, which is executed by processor, can make processor realize the above method.The internal memory can also store computer program, which is executed by processor, can make processor execute the above method.Those skilled in the art can understand that Figure 6 The structure shown in the figure is only the block diagram of part of the structure related to the scheme of the present application, and does not constitute the limitation of the computer device to which the scheme of the present application is applied, and specific computer device can include more or less components than those shown in the figure, or combine certain components, or have different component arrangement.

[0123] In one embodiment, a computer device is proposed, including memory and processor, the memory stores computer program, the computer program is executed by the processor, makes the processor execute the steps of the method as shown in Figure 1 The steps of the method as shown in the figure.

[0124] In one embodiment, a computer readable storage medium is proposed, which stores computer program, the computer program is executed by processor, makes the processor execute the steps of the method as shown in Figure 1 The steps of the method as shown in the figure.

[0125] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The program can be stored in a non-volatile computer readable storage medium, and when the program is executed, the processes of the above-mentioned embodiment methods can be included. Any reference to memory, storage, database or other medium used in the embodiments provided in 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.

[0126] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist, it should be considered as the scope of the present application.

[0127] 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, a number of modifications and improvements can be made, which are all within the protection 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 denoising power quality disturbance signals, characterized in that, The method includes: Acquire power quality disturbance signals under noisy environments; The power quality disturbance signal is decomposed using wavelet decomposition to obtain the J-level wavelet coefficients of the power quality disturbance signal; The wavelet coefficients are denoised using a preset improved adaptive threshold algorithm and an improved threshold function algorithm to obtain denoised wavelet coefficients. The signal is reconstructed based on the denoised wavelet coefficients to obtain the denoised signal of the power quality disturbance signal; The step of 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 includes: Based on the J-layer wavelet coefficients and the preset standard deviation algorithm, the noise standard deviation is determined; The wavelet threshold corresponding to each layer of wavelet coefficients is determined by using the peak-to-ratio correction factor of each layer of wavelet coefficients, the noise standard deviation, and the preset improved adaptive threshold algorithm. The wavelet coefficients are denoised using the wavelet threshold and improved threshold function algorithm for each layer to determine the denoised wavelet coefficients; The step of determining the wavelet threshold corresponding to each layer of wavelet coefficients by utilizing the peak-to-ratio correction factor of each layer of wavelet coefficients, the noise standard deviation, and a preset improved adaptive threshold algorithm, further includes: The peak-to-sum ratio correction factor for each layer of wavelet coefficients is determined by using the length of each layer of wavelet coefficients and the ratio of peak value to sum value in the wavelet coefficients. The improved threshold function algorithm includes a first denoising algorithm, a second denoising algorithm, and a third denoising algorithm. The step of using the wavelet threshold and the improved threshold function algorithm at each layer to denoise the wavelet coefficients and determine the denoised wavelet coefficients includes: The adjustable parameters of the wavelet coefficients at each level are determined using a preset adjustable parameter determination rule. The adjustable parameter determination rule includes at least the rule that the higher the number of decomposition levels, the lower the adjustable parameters. For each layer of wavelet coefficients and wavelet threshold, the following processing is performed: If the wavelet coefficients are greater than or equal to the wavelet threshold, then the denoised wavelet coefficients are obtained using the first denoising algorithm, the adjustable parameters, the wavelet coefficients, and the wavelet threshold. If the absolute value of the wavelet coefficient is less than the wavelet threshold, then the denoised wavelet coefficient is obtained by using the second denoising algorithm, the adjustable parameter, the wavelet coefficient, and the wavelet threshold. If the wavelet coefficients are less than or equal to the negative value of the wavelet threshold, then the denoised wavelet coefficients are obtained using the third denoising algorithm, the adjustable parameters, the wavelet coefficients, and the wavelet threshold. The improved threshold function algorithm is as follows: ； In the formula, These are wavelet coefficients. It is a wavelet threshold. It is an adjustable parameter. This is the first denoising algorithm; This is the second denoising algorithm; This is the third denoising algorithm; wavelet coefficients x The denoised wavelet coefficients; The standard deviation algorithm is as follows: ； In the formula, The standard deviation of noise. This represents the wavelet coefficient in the k-th direction of the i-th level wavelet decomposition; This represents the median of the absolute values ​​of all wavelet coefficients; The improved adaptive threshold algorithm is as follows: ； ； In the formula, For the first j Layer wavelet threshold; For the first j Layer noise standard deviation; This represents the number of signal sampling points. Indicates the first j Peak and ratio correction factors of the layer; P SRj For the first The ratio of peak values ​​to sum values ​​in layer wavelet coefficients; L j For the first The length of the layer wavelet coefficients.

2. A noise reduction device for power quality disturbance signals, characterized in that, The method of claim 1 is applied to the apparatus, wherein the apparatus comprises: Signal acquisition module: used to acquire power quality disturbance signals in noisy environments; Signal decomposition module: used to perform wavelet decomposition on the power quality disturbance signal to obtain the J-level wavelet coefficients of the power quality disturbance signal; Denoising module: used to perform denoising on the wavelet coefficients using a preset improved adaptive threshold algorithm and improved threshold function algorithm to obtain denoised wavelet coefficients; Signal reconstruction module: used to reconstruct the signal based on the denoised wavelet coefficients to obtain the denoised signal of the power quality disturbance signal.

3. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, the processor performs the steps of the method as described in claim 1.

4. A computer device, comprising a memory and a processor, characterized in that, The memory stores a computer program that, when executed by the processor, causes the processor to perform the steps of the method as described in claim 1.

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