Power Quality Disturbances Classification Using Compressive Sensing and Maximum Likelihood

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.

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 deals with a modified technique for the recognition of single stage and multiple power quality (PQ) disturbances. An algorithm based on Stockwell's transform and artificial neural network-based classifier and a rule-based decision tree is proposed in this paper. The analysis and classification of single stage PQ disturbances consisting of both events and variations such as sag, swell, interruption, harmonics, transients, notch, spike, and flicker are presented. Moreover, the proposed algorithm is also applied on multiple PQ disturbances such as harmonics with sag, swell, flicker, and interruption. A database of these PQ disturbances based on IEEE-1159 standard is generated in MATLAB for simulation studies. The proposed algorithm extracts significant features of various PQ disturbances using S-transform, which are used as input to this hybrid classifier for the classification of PQ disturbances. Satisfactory results of effective recognition and classification of PQ disturbances are obtained with the proposed algorithm. Finally, the proposed method is also implemented on real-time PQ events acquired in a laboratory to confirm the validity of this algorithm in practical conditions.

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.

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.

A new method classifying power quality disturbances (PQD) based on random matrix transform (RMT) and sparse representation classification (SRC) by L1-minimization is presented. First, the PQD signals are characterized by random matrix lower-dimensional projection based on compressive sensing theory. Then, every test sample from feature vectors is represented as a sparse linear combination of training samples. The PQD type assign to the object class that minimizes the residual between test sample and its sparse representation by solving L1-minimization problem. RMT feature extraction method is extremely efficient to generate and independent of the training dataset. Compared with support vector machine (SVM), the SRC algorithm needs neither training process nor combination of two-class classifiers for multiclass classification. Simulation results show that the proposed feature extraction and classification method has high classification correct ratio in strong noise condition.

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.

This paper develops a knowledge-based neural network (KBNN) for the classification of power quality (PQ) disturbances. Initially, the tunable-q wavelet transform (TQWT) is employed for the extraction of the 50 Hz component from a voltage signal with any sort of disturbance. This is achieved by varying the quality factor of wavelet according to the signal information. The KBNN is a combined model of neural network and rule-based approach. This paper explores the potential of the KBNN for classification of the most common power quality disturbances. The efficacy of the KBNN approach is evaluated on a wide range of time-varying signals with noise, fundamental frequency deviation, and variation in signal parameters. The performance analysis elucidates the efficiency and robustness of the proposed approach using KBNN classifier for classification of the normal and eight PQ disturbances.

The classification of power quality (PQ) disturbances to improve the PQ is an important issue in utilities and industrial factories. In this paper, an approach to classify PQ disturbances is presented. First, a signal containing one of the PQ disturbances, like sag, swell, flicker, interruption, transient, or harmonics, is evaluated using the proposed approach. Afterwards, S-transform and TT-transform are applied to the signal and an artificial neural network is used to recognize the disturbance using S-transform and TT-transform data, like the variance and mean values of S-transform and TT-transform matrices. The main features of the proposed approach are the real-time and very fast recognition of the PQ disturbances. Finally, the proposed method's results are compared with the support vector machine and k-nearest neighbor classification methods to verify the results. The results show the effectiveness of this state-of-the-art approach.

The power quality (PQ) disturbance signals have the characteristics of short duration and strong randomness, and often form complex disturbances, which make the disturbance signals difficult to detect and identify. In this paper, EMD algorithm is introduced to decompose the PQ disturbance signals and calculate the intrinsic mode function (IMF) of the disturbance signals. Then, Hibert transform are performed for each IMF to obtain the characteristic information of the disturbance signal. EMD transform is used to detect the type, duration, frequency and amplitude of PQ disturbances. To verify the effectiveness of the algorithm, several kinds of PQ disturbance signals are simulated with transient harmonic, voltage interruption, voltage drop and voltage surge and complex disturbances. Experimental results show that the algorithm can accurately detect power quality interferences. This paper provides a new method for the detection of PQ disturbances and a new idea for the power management.

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

Power Quality (PQ) monitoring in a systematic and automated way is the important issue to prevent detrimental effects on power system. The development of new methods for the automatic classification of PQ disturbances is at present a major concern. This paper presents a novel approach of automatic detection and classification of single and hybrid PQ disturbances using Discrete Wavelet Transform (DWT) and Modular Probabilistic Neural Network (MPNN). The automatic classification of the PQ disturbances consists of three stages i) data generation, ii) feature extraction and iii) disturbance classification. The data is generated by synthetic models of single and hybrid PQ disturbance signals based on IEEE 1159 standard. DWT with multiresolution analysis was applied for feature extraction from the PQ waveforms. The entropy and energy features extracted from the detail and approximation coefficients were applied as the training and testing data to MPNN in order to accomplish the automatic classification process. The effectiveness of the proposed algorithm has been validated by using a typical real-time underground distribution network in Malaysia which was simulated in PSCAD/EMTDC power system software to generate PQ disturbances. The simulation results show that the classifier has an excellent performance in terms of accuracy and reliability even in the case of PQ signals under noisy condition.

Optimal feature selection using modified cuckoo search for classification of power quality disturbances

To address the inadequate time-frequency analysis of the S-transform and the low accuracy of traditional Power Quality Disturbance (PQD) classification methods, this paper presents a classification method based on Iterative Filtering Kaiser Window S-Transform (IFKST) and Newton-Raphson-Based Optimizer to optimize Extreme Gradient Boosting (NRBO-XGBoost). Firstly, the iterative loop filtering interval positioning algorithm extracts frequency feature points of disturbance signal. Then the K-S transform is applied to each feature point. Finally, 17 time-frequency features are extracted from the IFKST resulting matrix, and the hyperparameters of Extreme Gradient Boosting are optimized using the Newton-Raphson-based optimizer. The 17 disturbance features are then input into the model to achieve PQDs classification. Experimental results show that this method can classify 21 types of power quality disturbances with high accuracy in noisy environments.

Identifying power quality (PQ) disturbances is an important prerequisite for developing mitigation measures to improve PQ. However, the coupling of multiple PQ disturbances in the noise condition makes it difficult to achieve effective feature extraction and classification. This article proposes a novel method to identify multiple PQ disturbances by integrating improved TQWT with XGBoost algorithm. The improved TQWT is proposed to automatically select the proper tuning parameters by screening the spectral information of PQ signals. Then, the improved TQWT is used to decompose PQ disturbances into sub-bands for further feature extraction. Optimum feature selection and classification are implemented in XGBoost. Classification accuracies of 26 categories of synthetic PQ disturbances under different noisy levels are tested and compared with existing methods. Results indicate that the proposed method is efficient and noise-resistant, and the classification accuracy can reach 97.63% under 20 dB noise, and keep above 99% under lower level noise.