Transient Power Quality Disturbance Research Articles

Power Quality Transient Disturbance Diagnosis Based on Dynamic Large Convolution Kernel and Multi-Level Feature Fusion Network

Power quality is an important metric for the normal operation of a power system, and the accurate identification of transient signals is of great significance for the improvement of power quality. The diverse types of power system transient signals and strong characteristic coupling brings new challenges to the analysis and identification of power system transient signals. In order to enhance the identification accuracy of transient signals, one method of power system transient signal identification is proposed based on a dynamic large convolution kernel and multilevel feature fusion network. First, the more fine-grained and more informative features of the transient signals are extracted by the dynamic large convolution kernel feature extraction module. Then, the multi-scale local features are adaptively fused by the multilevel feature fusion module. Finally, the fused features are reduced in dimension by the fully connected layer in the classification module and fed into the SoftMax layer for transient signal type detection. The proposed method can effectively improve the small receptive field problem of convolutional neural networks and the lack of ability of Transformer network in extracting local context information. Compared with five other power quality transient disturbance identification models, the experimental results show that the proposed method has better diagnostic accuracy and anti-noise capability.

Power Quality Disturbance Detection Based on Partitioning-Improved Empirical Wavelet Transform

Power quality is an important indicator to measure the quality of power supply in power grids. With the large evasion accession of new energy sources, its randomness and volatility cause frequent transient power quality problems in power grids. With the large evasion accession of new energy sources, its randomness and volatility cause frequent transient power quality problems in power grids. Aiming at the detection of transient power quality disturbance, this paper proposes the empirical wavelet algorithm which improves the adaptive division. This paper improves the spectrum processing, frequency point selection and frequency band division to improve the adaptive division ability of the algorithm under complex perturbation signals. Combined with the Hilbert transform, a perturbation detection algorithm is proposed based on an improved empirical wavelet algorithm to extract the transient characteristics of the perturbation signal. Through software simulation, this method has good adaptive ability in the face of composite power mass transient disturbance and can accurately extract the transient characteristics of the signal.

Simulation and FPGA Implementation of Transient Power Quality Disturbance Detection

With the access to new energy in the smart grids and the massive use of nonlinear and unbalanced large loads, the problem of power quality becomes more and more serious. At present, the transient power quality disturbance problem has been paid more attention to. The research on transient power quality is relatively late, and various detection methods and technologies are not mature. It is necessary to use more efficient methods to detect and identify transient power quality to improve the safety of electricity use. This paper proposes to detect transient power quality disturbance by wavelet transform. In this paper, the original signal modelling of five kinds of power quality problems such as voltage sag and voltage swell. The 9/7 wavelet lifting coefficient is optimized in this paper and is carried out on the MATLAB software platform to improve the processing speed of the algorithm and realize parallel processing. The simulation results show that the improved lifting wavelet processing can effectively detect the transient power quality disturbance.

Denoising Transient Power Quality Disturbances Using an Improved Adaptive Wavelet Threshold Method Based on Energy Optimization

Noise significantly reduces the detection accuracy of transient power quality disturbances. It is critical to denoise the disturbance. The purpose of this research is to present an improved wavelet threshold denoising method and an adaptive parameter selection strategy based on energy optimization to address the issue of unclear parameter values in existing improved wavelet threshold methods. To begin, we introduce the peak-to-sum ratio and combine it with an adaptive correction factor to modify the general threshold. After calculating the energy of each layer of wavelet coefficient, the scale with the lowest energy is chosen as the optimal critical scale, and the correction factor is adaptively adjusted according to the critical scale. Following that, an improved threshold function with a variable factor is proposed, with the variable factor being controlled by the critical scale in order to adapt to different disturbance types’ denoising. The simulation results show that the proposed method outperforms existing methods for denoising various types of power quality disturbance signals, significantly improving SNR and minimizing MSE, while retaining critical information during disturbance mutation. Meanwhile, the effective location of the denoised signal based on the proposed method is realized by singular value decomposition. The minimum location error is 0%, and the maximum is three disturbance points.

Methodology based on higher‐order statistics and genetic algorithms for the classification of power quality disturbances

The present work proposes a methodology for the detection and classification of some transient power quality disturbances. It uses a genetic algorithm for estimating the amplitude, frequency, and phase of the fundamental component in an optimum way to suppress it from an electric signal. By using this pre-processing strategy, it is possible to remove a single frequency component instead of removing a whole frequency band as other methodologies do. Once the fundamental component is suppressed, it is possible to perform a better identification of an anomalous condition in a signal because its high energy does not hide the presence of a disturbance. After the suppression, the proposed methodology computes three higher-order statistics (variance, kurtosis, and sixth-order cumulant) from the resulting signal and uses them as inputs for a fuzzy-based classifier. Higher-order statistics are selected because they are insensible to the presence of Gaussian noise adding robustness to the proposed methodology. Experimentation is performed using both synthetic and real signals. Real signals come from a photovoltaic generation plant and a hospital facility, both located in Spain. Results prove that the proposed methodology allows enhancing the results delivered by other methodologies up to 30%.

An Efficient and Accurate Method for Detection and Classification of Power Quality Disturbances

Aiming to the problem of large computation amount and high redundancy of traditional S‐transform methods available in the detection and classification of transient power quality disturbances, an efficient and accurate method for the detection and classification is proposed. First, the maximum power spectrum dynamics method is used to extract the main frequency points. Then, a called modified incomplete S‐transform proposed in the paper is performed on the main frequency points so as to obtain a modulus time‐frequency matrix involving the characteristic information of disturbances. The modified incomplete S‐transform is implemented by introducing a bi‐Gaussian window function with two parameters to replace the Gaussian window function of the traditional S‐transform. And then, the characteristic quantities for detecting power quality disturbance and identifying the classification of the disturbances are extracted based on the matrix. Finally, the disturbance parameters such as the amplitude, start time, and stop time of the disturbances are calculated based on the relevant theories and rules, and also the classification of the disturbances is identified by extracting some important characteristic quantities. A great number of simulation experiments are conducted. The results obtained show that by the method, the start time, stop time, and amplitude changes of power quality disturbance signals can be fast and accurately detected, and the classification of the disturbances can be fast and accurately identified, and that the method has the characteristics of less operation time and strong anti‐interference ability. © 2020 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Research on Power Quality Disturbance Detection Method Based on Improved Ensemble Empirical Mode Decomposition

With the increasing proportion of various unbalanced loads in the power grid, power quality is seriously challenged. It is of great significance to effectively detect, analyze, and evaluate the power quality problems. First, this paper introduces the current situation of power quality (PQ) disturbance detection methods. It summarizes that the current PQ disturbance detection methods include Wavelet Transform (WT), Hilbert–Huang Transform (HHT), and Ensemble Empirical Mode Decomposition (EEMD). EEMD has a better detection accuracy, but its running time is longer. Therefore, to reduce the running time of the EEMD algorithm, this paper proposed two improvements: increasing the screening threshold and selecting piecewise cubic Hermite interpolation polynomial fitting. At the same time, the mathematical models of transient power quality disturbance and harmonic were established for comparative verification. The experimental results showed that the improved Ensemble Empirical Mode Decomposition (IEEMD) algorithm greatly reduced the running time of the algorithm on the premise of ensuring the detection accuracy. Hence, the improvement of this paper is of great significance for the industrial application of the EEMD algorithm.

A Study on Power Quality Transient Disturbance Location Method

Due to periodic variation of power signal and various disturbances present during sampling process, resulting background gradient affects the accuracy of disturbance detection. In order to solve this problem, this paper proposes a power quality transient disturbance location method based on morphological edge detection, and introduces a soft threshold quantitative evaluation method to improve its accuracy. Firstly, an evaluation method of filtering effect is proposed, and original signal is filtered by adaptively choosing size of structuring elements. Then, Top-Hat transform of morphological gradient is performed by flat structuring elements to suppress background gradient, and the location result is initially obtained. Finally, combined with soft threshold processing method, the location of power quality transient disturbance is realized. Simulation analysis of a series of power quality transient disturbances shows that this algorithm has accurate location results, strong capacity of resisting disturbance and good versatility.

Identification of Power Quality Transient Disturbance Based on S Transform and Wavelet Transform

With the rapid advancement of the country's industrialization, intelligence, and information technology, the nonlinearity, asymmetry, and impact of power system load are becoming increasingly prominent. On the other hand, users' demands for power quality are increasing. The national grid inevitably faces severe challenges. The power quality problem in circuit systems is particularly important. The type identification of power quality transient disturbances is of great significance for analyzing power quality problems and solving power quality problems. In this paper, a method based on S transform and wavelet transform for transient power quality disturbance identification is proposed. Firstly, the wavelet signal is used to denoise the power signal. Then, after obtaining the feature vector of the power signal by the S transform and the wavelet transform, The Relieff algorithm is used to obtain the feature weights of the feature vectors generated by S transform and wavelet transform and to select features. Finally, training and classification are performed using the Support Vector Machine (SVM) classifier. The experimental results show that the power quality transient disturbance classification method proposed in this paper can accurately identify the type of disturbance and have strong robustness, and fully meet the requirements of the relevant industry for the accuracy of power quality transient disturbance recognition.

Transient power quality disturbance denoising and detection based on improved iterative adaptive kernel regression

The denoising and detection of transient disturbances are two important subjects for power quality monitoring and analysis. To effectively denoise and detect transient disturbances under noisy conditions, an improved iterative adaptive kernel regression method is proposed in this paper. The proposed method has advantages in that it does not need to estimate the noise variance or a filter threshold, and has both denoising and detection capabilities for transient disturbances. Simulation results demonstrate that the proposed method provides excellent denoising effects, which can not only suppress noise effectively but also preserve disturbance features of sudden change points well. Additionally, it provides good detection and location performance for single and combined transient disturbances, even under strong noise conditions. Finally, the effectiveness of the proposed method is further verified by using real disturbance data.

Power Quality Disturbance Classification Using the S-Transform and Probabilistic Neural Network

This paper presents a transient power quality (PQ) disturbance classification approach based on a generalized S-transform and probabilistic neural network (PNN). Specifically, the width factor used in the generalized S-transform is feature oriented. Depending on the specific feature to be extracted from the S-transform amplitude matrix, a favorable value is determined for the width factor, with which the S-transform is performed and the corresponding feature is extracted. Four features obtained this way are used as the inputs of a PNN trained for performing the classification of 8 disturbance signals and one normal sinusoidal signal. The key work of this research includes studying the influence of the width factor on the S-transform results, investigating the impacts of the width factor on the distribution behavior of features selected for disturbance classification, determining the favorable value for the width factor by evaluating the classification accuracy of PNN. Simulation results tell that the proposed approach significantly enhances the separation of the disturbance signals, improves the accuracy and generalization ability of the PNN, and exhibits the robustness of the PNN against noises. The proposed algorithm also shows good performance in comparison with other reported studies.

Research on the transient power quality disturbance detection based on HHT

Real time detection of transient power quality disturbance signal is an effective way to analyze and eliminate the fault of power grid and improve power quality. The traditional time frequency analysis method has some limitations and large errors in the detection of transient power quality disturbances. In order to improve the accuracy of real-time detection and analysis of power system signal which is interfered by complex disturbance signal, the power quality multi-disturbance signal is detected by the Hilbert-Huang transform method. And the simulation results are verified by MATLAB, and the simulation results show that the method is effective and accurate.

Research and Realization of Transient Disturbance Detection Algorithm Based Coiflet Wavelets and FPGA

This paper proposes a Coiflet wavelet algorithm based on FPGA to realize the realtime detection of power quality transient disturbance. As to the real-time problem of wavelet algorithm software applications, the algorithm is realized on the FPGA. When Coiflet wavelet is taken as basic functions, the effect of the detecting signal singularity is good. Because of its symmetry in the time-frequency domain the algorithm could avoid the signal distortion during decomposition and reconstruction, which is convenient for subsequent signal processing. By simulating test of several kinds of signal source, the begin-end time of signal transient disturbance is contrasted. The test results show that the method is more accurate and feasible on real-time detection of power quality transient disturbance.

A Novel Power Quality Disturbances Detection and Classification Method

Power quality disturbance detection and identification is the prerequisite and basis for the power quality management and control. This paper presents a new power quality disturbance detection and classification method. Firstly, the time-time transform is applied to power quality disturbance signal analysis. According to spectrum analysis results of the diagonal elements of time-time transform matrix, a preliminary judge about whether the disturbance signal contains harmonics and inter harmonic was given. For disturbances with non-harmonics, based on time-time transform modulus matrix diagonal sequence, the beginning and ending time of the disturbance is located, and the disturbance amplitude is calculated. For the disturbances which contain harmonics, time-time transform is perform twice to get the row mean value curve and the column mean value curve, which are required by disturbance time location and amplitude measurement. Finally, disturbance classification had realized by using rule tree. Simulation results reveal that this method is very robust and adaptable, which can identify transient power quality disturbance with minor magnitude under noisy environment, and the recognition rate is satisfactory.

A New Transient Power Quality Disturbances Detection Using Strong Trace Filter

This paper proposed new transient power quality disturbances (PQDs) detection using strong trace filter (STF). By appropriate filter model design, when there are stationary PQDs the STF works as the same as Kalman filter, while when there are transient disturbances the STF indicates each sudden change of the distorted waveform by the fading factor (FF). The FF can also reveal which parameter of the signal component is changing and its sensitivity to sudden change can be tuned by the soften factor easily to avoid noise influence. Besides, the STF is a small algorithm, which can be easily implemented on embedded system for real-time and time-varying detection. Both simulation and experiment suggest that the STF is a good solution for transient PQDs detection.

A Compressed Sensing Improvement Algorithm Based on Power Quality Transient Disturbance Signal

Traditional power quality signal samples are based on the Nyquist sampling theory. Because of the existence of disturbance signal for the presence of power, it requires two times higher than the sampling frequency of the original signal, resulting in many problems, such as a high cost of hardware. Compressed sensing algorithm abandoned the characteristics of Shannon theorem, using a lower sampling frequency and the less amount of the signal to reconstruct the signal, with the method of a loss compression, which can effectively solve this problem. A team in Beijing University of Chemical Technology has done a deep research in this direction and proposed the total variation gradient reduction algorithm, which has good effects on reduction. But the algorithm runs slower and needs higher sample volumes of signal. Therefore, this paper presents a modified algorithm based on Nesta algorithm to reduce the amount of data sampled of power quality signal, the complexity of the algorithm to improve the algorithm’s speed. The modified algorithm has a very important value in practical applications. This paper has carried out simulations in matlab, the results of the simulation show that this method is accurate and applied.

Lifting Wavelet Detection Method of Transient Power Quality Disturbance for Power System Connected with Micro Grid

Based on the power quality disturbance problems of the power system connected with micro grid, The detecting method of transient power quality disturbance is mainly studied, which based on lifting wavelet transform, after the analysis of lifting wavelet construction principle, the transient power quality disturbance detecting method based lifting db4 wavelet is put forward, the results of simulation and comparison analysis prove that the method can detect and locate the transient power quality disturbances quickly and accurately, so that ,an effective and feasible method is provided to the research of transient power quality disturbance problems brought by the connection between micro grid and power system.

Detection and Localization of Power Quality Disturbances Based on Complex Wavelet

电能质量扰动信号的检测与定位为分析扰动产生的原因提供依据。本文提出了一种基于复小波的电能质量扰动检测与定位方法，该方法利用离散复小波变换，提取扰动信号的复小波系数的幅值和相位信息，再利用幅值和相位的复合信息实现对5种暂态电能质量扰动信号的快速准确定位。针对噪声条件下的扰动特征不明显时的定位，提出了一种基于信号预处理的辅助定位方法。仿真表明，该方法能在噪声条件下实现对电能质量扰动信号的快速准确定位。 Detection and localization of power quality disturbance signals provide a basis to analyze the causes of the disturbance. This paper presents a complex wavelet-based power quality disturbance detection and localization. First of all, the amplitude and phase of complex wavelet coefficients are extracted by discrete complex wavelet transform, and then, the composite information of amplitude and phase is used to achieving fast and accurate positioning of 5 kinds of transient power quality disturbance signals. Under the condition of noise and disturbance characteristics is not obvious, this paper proposes an auxiliary positioning method based on signal preprocessing. Simulation shows, under the condition of noise, the method can achieve fast and exact positioning of power quality disturbance signals.

Denoising Power Quality Signal Using Savitzky-Golay Based on Virtual Instrument

In power quality monitoring system, there are unavoidably existing various kinds of noises in collected data，the presence of noise may result in increased false classification rate, denoising is an extremely important work for detection and classification of power quality disturbances. In order to improve the denoising result of power quality signal, an denoising method for power quality signal using Savitzky-Golay is proposed. Numerical results show that the proposed method can eliminate the influence of noise components and implement transient power quality disturbance detection and localization, thus providing good foundations for transient power quality disturbance monitoring under noise environment.

Study on Transient Power Quality Disturbance and Detection Method

Nowadays, in electrical energy, transient power quality disturbance issue is one of the “stubborn problems” that plagued the power sector and users. This paper focuses on analyzing the causes and effects of transient power quality disturbance, sequentially finds out scientific testing methods and sums up its control methods.