Power Quality Disturbances Detection Based on EMD

A novel ensemble neural network structure is presented for automatic classification of power quality disturbances. Power quality (PQ) disturbances analysis is the focus of power quality control. The characteristics of PQ disturbances include short duration, variety of types and so on. Power quality disturbances classification is the foundation of power quality control automation. Different types of Neural network, such as BP neural networks, RBF neural networks and probabilistic neural network etc, is already applied in the area of PQ disturbances classification and recognition. The researches about the neural network for PQ disturbances recognition are mainly focused on the optimizing for the signal type of neural network. But the accuracy rate of the classification is still needed to be improved. Ensemble and hybrid algorithms research is currently flourishing in pattern classification machine learning and decision sciences. Compare to traditional NN, the ensemble and hybrid NN classifier achieves higher classification rate. In this paper, a novel PQ classification system using S-transform and ensemble and hybrid NN is designed. There are 2 stages in the novel system. Firstly, the PQ disturbances signals are transformed by S-transform and the subset of features extracted from the result of S-transform is used as the input vector of the ensemble and hybrid NN. Secondly in the pattern classification process, BP network and RBF neural network are utilized as two classification agents. Through choosing different parameters and different samples, every agent includes a group of neural networks. The classification results, generated by different agents, are fuzzified into fuzzy numbers. The centroid of all fuzzy numbers is compared with the threshold. Finally we obtain the classification results. In the simulation, 6 types of disturbances signals which are simulated by Matlab 7.0 use for test the new classification system. Simulation result shows that when the new system has better classification rate especially at the high noising environment.

This paper presents a novel hybrid approach combining Gramian Angular Summation Field (GASF) method with a convolutional neural network (CNN) to classify power quality disturbances. Firstly, a 1-D Power quality disturbance signal is transformed into a 2-D image file using GASF. Subsequently, CNN is implemented for features extraction and image classification. In this work, the synthetic power quality (PQ) disturbances are considered including nine single disturbances and five mixed disturbances. Further, to capture multi-scale aspects of power quality disturbances problem and reduce overfitting, a unit is designed using 2-D convolutional, pooling, and batch-normalization layers. The classification study is further supported by experimental signals obtained on a prototype setup of PV system. The obtained results demonstrate the efficiency and reliability of the proposed method. The proposed method is compared with the other advanced CNNs and other conventional methods to illustrate its effectiveness.

Power quality disturbances (PQDs) can significantly affect the reliability of electrical power systems, leading to potential equipment damage and operational inefficiencies. Accurate classification of these disturbances is essential for ensuring continuous and reliable service. The study proposes a deep transfer learning (TL) approach for PQD classification. In the proposed work, various single and multiple PQD signals pertaining to 15 different classes have been generated using mathematical models of PQDs adhering to the guidelines of the IEEE 1159 and IEC 61000-4-30 standards. Time-domain PQD signals are first converted into 2D color images using continuous wavelet transform (CWT). These images are then used to re-train modified pre-trained models such as GoogleNet, SqueezeNet, ResNet-18, and ShuffleNet on synthetic PQD data. Various single and combined PQDs are then classified using trained models. Moreover, the performance of the trained models is evaluated with the PQD signals containing noise of various signal-to-noise ratios (SNR), as well as PQD signals collected from the experimental setup in the laboratory. The results signify that the GoogleNet model exhibits consistent performance for classifying PQDs under various conditions, achieving classification accuracy of 99.8% for synthetic noiseless signals, 98.87% for signals with 20 dB SNR, and 98.89% for signals acquired through experimental setup. Furthermore, the trained models were tested using real-world PQD signals, comprising 26 sag and 42 impulse signals. The GoogleNet model achieved the highest classification accuracy, correctly identifying 23 sag and 34 impulse events, thereby demonstrating real-world applicability and robustness of the proposed approach.

The electric utilities and end users of power system network have become more concerned about power quality issues due to technical and financial consequences that have resulted from electric power quality disturbances. The power quality monitoring technology has an effective on analyzing power quality related problems. This paper presents a novel study combining wavelet transform with pattern recognition technique to investigate voltage stability using for power quality events. The wavelet transformation possesses capabilities of time and frequency domain localizations, achieving a great impetus in signal singularity detection. The statistics-based denoising method is designed to filter the random noise and impulse noise in power quality disturbance signals, incorporating the advantages of wavelet transform to extract signal feature meanwhile restraining various noises. The wavelet decomposition coefficients as feature vector of neural network are presented for extracting disturbance signal. The neural network provides a means of determining a degree of belief for each identified disturbance waveform. The performance of the proposed approach is studied and a proper combination of wavelet transformation and neural network is identified.

Power quality becomes a severe problem due to the increasing use of nonlinear electrical loads, complex electric power systems on smart grids, inverters in renewable power plants, and electronic control equipment. Power quality problems include variations in voltage or current such as sag, swell, flicker, spike, overvoltage, undervoltage, interruption, transient, harmonics, and frequency fluctuations. Research on power quality disturbances mostly applies signal processing and transformation methods such as Fast Fourier transform, S-transform, and Wavelet. In this paper, we use empirical mode decomposition methods and statistical parameters to analyze power quality disturbance signals. It gives more detailed characteristics of power quality disturbances. We conducted a six-step analysis to get a percentage of each power quality disturbance signal. The developed method provides a preliminary description of the power quality characteristics from the percentage values of RMS, range, and energy levels at the first IMF. The positive percentage indicates the existence of power quality disturbances contains flicker and swell, while negative indicates contain harmonics, sag, and transient.

ABSTRACTThis paper presents a newly developed compressive sensing (CS), maximum likelihood (ML), and rule-based method for classification of power quality disturbances (PQDs). The first PQD signals are sampled using compressive sampling method and reduced PQD signal dimensionally, and then the data are directly used as features for the classification. PQD signals are divided into two groups: at first, impulsive, oscillatory, harmonics notch, flicker and DC offset, and the second, sag, swell, and interruption. Compressive sensing maximum likelihood (CSML) classifier is used for the classification of the first group and the rule-based method is used for the classification of the second group. CSML classifier and the rule-based method are tested utilizing 1000 PQD events under noiseless and noisy environment. About 100% correct classification rate is achieved without noise and 99.8% correct classification result is obtained with noise. In both methods, noise was considered from 20 to 40 dB. Results show that the proposed CS and ML algorithms and rule-based method can be efficiently used for PQD classification.

An effective wavelet-based feature extraction method for classification of power quality disturbance signals

The widespread integration of renewable energy sources (RESs) into power systems using power electronics-based interface devices has led to a substantial rise in power quality (PQ) issues. There is an immediate requirement for effective monitoring, detection, and classification of power quality disturbances (PQDs) that is needed to take remedial measures and design planning of the system architecture. This study presents a hybrid approach with an objective for the feature extraction and classification of PQDs. The proposed hybrid approach is comprised of an optimized tunable-Q wavelet transform (OTQWT) for the feature extraction and incremental support vector machine (ISVM). A four-stage approach is suggested for the PQ detection and classification in this study. In the first stage, the various data are retrieved both in the form of synthetic data by mathematical formulations and real-time data with prototype design setup. In the second stage, regardless of the specified wavelet function, the PQD signals are decomposed into low-pass and high-pass sub-bands using the tunable-Q wavelet transform (TQWT). However, the utilization of default decomposition parameters to address nonstationary PQ signals may lead to information loss and reduced performance of the system. To avoid this limitation, an OTQWT as an enhanced technique to TQWT based on an Adaptive Particle Swarm Optimization (APSO) is suggested. A modified objective function based on the mean square error (MSE) is used to improve the decomposition process. In the third stage, an efficient classifier is suggested based on the ISVM. Lastly, to test and evaluate the performance of the proposed approach, twelve types of PQDs including noise and multiple occurrences are considered. The comparative analysis with other popular methods reflects the better performance of the proposed approach and justifies its use for PQ detection and classification purposes in real-time​ conditions.

Power quality events caused by renewable-energy integration are usually associated with complex disturbances; therefore, their type identification is the primary task of subsequent pollution control. This study proposes a novel three-step classification approach based on time-dependent spectral features (TDSFs) for the classification of complex power quality disturbances (PQDs). First, the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is adopted to decompose the PQDs into several intrinsic mode functions (IMFs). The related IMFs are selected by correlation coefficient and kurtosis. Then the eight eigenvalues of each IMF are extracted, including TDSFs. Finally, the eigenvalue dimension of each IMF is reduced by linear discriminant analysis (LDA). Moreover, the classifier of the adaptive k-nearest neighbor with excluding outliers (AdaKNNEO) can confirm the PQDs type. To verify the effectiveness of the proposed approach, a series of simulations and hardware experiments are conducted. The overall result shows robustness and high accuracy of the proposed method, and especially for the complex PQDs, it possesses the highest entirely correct of 96% compared to other advanced methods.

Detection of power quality disturbance is most essential to ensure the good Power Quality (PQ). The power disturbance signal will reduce the reliability of power system and create some disadvantage on operation process. Aging of electrical device, incorrect measurement, devices malfunction are the consequence of this condition. The characteristic of power disturbance signals are non-stationary and the way to detect this disturbance needed a sample method. The term of non-stationary in signal processing is regularly used to define a process in which the spectrum is changing with time. One kind of methods for analyzed this problem is S-transform (ST). However, Due to this method have relatively fixed Gaussian window the S-transform cannot provide satisfactory time-frequency resolution for all types of disturbance signals. Modified S-transform (MST) provides more suitable means for achieving desired time-frequency for several PQ disturbances signals. This paper proposes power quality analysis (PQA) using modified S-transform (MST) method. Both the time and the frequency domain information of each PQ component are extracted by MST. Comparison between MST simulation result and ST is applied to validate the accuracy and efficiency of modified S-transform (MST) method algorithm. The result of simulation show that this method can accurately detect and show the power signal disturbance such as voltage sag, voltage swell, interruption, flicker, oscillatory transient, notch, spike, and harmonic.

This study introduces a novel unified framework for simultaneous denoising and compression of electric power system disturbance signals using sparse signal decomposition and reconstruction on overcomplete hybrid dictionary (OHD) matrix. In the proposed method, the power quality signal is first decomposed into deterministic sinusoidal components and non‐deterministic components using the OHD matrix, including discrete impulse dictionary (I), cosine dictionary (C), sine dictionary (S) and the ℓ1‐norm optimisation algorithm. Then, the hard‐thresholding, uniform threshold dead‐zone quantisation, modified index coding and Huffman coding techniques are used for compression of significant detail signal samples and approximation coefficients. To justify the selection of OHD matrix, four compression methods are implemented using the decomposition techniques based on the dictionaries Ψ = [I C S] and Ψ = [I C], the wavelet transform (WT) and the discrete cosine transform (DCT). The performance of each method is tested and validated using a wide variety of typical power quality disturbance (PQD) signals taken from the IEEE‐1159‐PQE and GIM–PQE databases and generated using the Microgrid model. The results show that the method with dictionary Ψ = [I C S] is capable of effectively compressing the PQD signals as well as suppressing the noise components in the signals.

: In this study, a method based on Stockwell transform (ST), ReliefF feature selection method and Multilayer Perceptron Algorithm (MPA) algorithm was developed for classification of Power Quality (PQ) disturbance signals. In the method, firstly, ST was applied to different PQ signals to obtain classification features. A total of 30 different classification features were obtained by taking different entropy values of the matrix obtained after ST and different entropy values of the PQ signals. The use of all of the classification features obtained causes the method to be complicated and the training/testing times to be prolonged. Therefore, so as to determine the effective ones among the classification features and to ensure high classification success with less classification features, ReliefF feature selection method was used in this study. PQ disturbances were classified by using 8 different classification features determined by ReliefF feature selection method and MPA. The simulation results show that the method provides a high classification success in a shorter training/testing time. At the same time, simulation results have shown that the method was successful on testing data with noise levels of 35 dB and above after only one training.

Multiple power quality disturbances analysis in photovoltaic integrated direct current microgrid using adaptive morphological filter with deep learning algorithm

This paper presents a method that combines discrete S-transform (DST) time-frequency distribution (TFD) and local binary pattern (LBP) based image analysis technique for classifying power quality (PQ) disturbances. The purpose of this combination is to extract discriminative features by utilizing from both capability of generating the compact TFD of a non-stationary signal and the efficient image representation capability of LBP. In the proposed method, DST based TFDs of PQ disturbance signals are considered as 2-D images. LBP histograms are used to extract the features from TF images. Initially, the uniform patterns in TF images are obtained by the LBP operator. Next, the occurrence histograms of these patterns are used to produce representative feature vectors that can capture the unique and salient characteristics of each disturbance. The classification performance of the proposed method is evaluated through total 2400 disturbance signals. The experimental results have shown that the rate of correct classification is about 98 % for the different PQ disturbances.

Accurate classification of power quality disturbance is the premise and basis for improving and governing power quality. A method for power quality disturbance classification based on time-frequency domain multi-feature and decision tree is presented. Wavelet transform and S-transform are used to extract the feature quantity of each power quality disturbance signal, and a decision tree with classification rules is then constructed for classification and recognition based on the extracted feature quantity. The classification rules and decision tree classifier are established by combining the energy spectrum feature quantity extracted by wavelet transform and other seven time-frequency domain feature quantities extracted by S-transform. Simulation results show that the proposed method can effectively identify six types of common single disturbance signals and two mixed disturbance signals, with fast classification speed and adequate noise resistance. Its classification accuracy is also higher than those of support vector machine (SVM) and k-nearest neighbor (KNN) algorithms. Compared with the method that only uses S-transform, the proposed feature extraction method has more abundant features and higher classification accuracy for power quality disturbance.