Detection and Classification of Power Quality Disturbances in Smart Grids Using Artificial Intelligence Methods

This paper presents a novel power quality disturbance detection and classification method of distribution power system based on complex wavelet transform (WT) and radial basis function (RBF) neural network. The complex supported orthogonal wavelets is employed to extract the feature information of disturbance signal, and finally proposed to explore several novel wavelet combined information (CI) to analyze the disturbance, superior to real wavelet analysis result. The feature obtained from WT coefficients are inputted into RBF network for power quality disturbance pattern classification. The power quality disturbance classification model is established and the synthesized method of recursive orthogonal least squares algorithm (ROLSA) with improved givens transform is used to fulfill the network structure parameters. By means of choosing enough samples to train the recognition model, the type of disturbance can be obtained when signal representing fault is inputted to the trained network. The simulation results demonstrate that the complex WT combined with RBF network are more sensitive to signal singularity, and found to be significant improvement for acquiring signal feature information.

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.

This paper offers a systematic literature review of real-time detection and classification of Power Quality Disturbances (PQDs). A particular focus is given to voltage sags and notches, as voltage sags cause huge economic losses while research on voltage notches is still very incipient. A systematic method based on scientometrics, text similarity and the analytic hierarchy process is proposed to structure the review and select the most relevant literature. A bibliometric analysis is then performed on the bibliographic data of the literature to identify relevant statistics such as the evolution of publications over time, top publishing countries, and the distribution by relevant topics. A set of articles is subsequently selected to be critically analyzed. The critical review is structured in steps for real-time detection and classification of PQDs, namely, input data preparation, preprocessing, transformation, feature extraction, feature selection, detection, classification, and characterization. Aspects associated with the type of disturbance(s) addressed in the literature are also explored throughout the review, including the perspectives of those studies aimed at multiple PQDs, or specifically focused on voltage sags or voltage notches. The real-time performance of the reviewed tools is also examined. Finally, unsolved issues are discussed, and prospects are highlighted.

Power quality gains more and more attentions because disturbances in power quality may damage equipment security, power availability and system reliability in power system. Detection and classification of the power quality disturbances is the first step before taking measures to lessen their harmful effects. Common methods to classify power quality disturbances includes signal processing methods, machine learning methods and deep learning methods. Signal processing methods are good at feature extraction, while machine learning methods and deep learning methods are expert in multi-classification tasks. Via combing their respective advantages, this paper proposes a combined method based on variational mode decomposition and convolutional neural networks, which needs a small quantity of samples but achieves high classification precision. The proposed method is proved to be a qualified and competitive scheme for the detection and classification of power quality disturbances.

The increasing penetration of renewable energy into power systems has intensified transient power quality (PQ) disturbances, demanding efficient detection and classification methods to enable timely operational decisions. This paper introduces a hybrid framework combining an Improved Adaptive S-Transform (IAST) with a Random Forest (RF) classifier to address these challenges. The IAST employs a globally adaptive Gaussian window as its kernel function, which automatically adjusts window length and spectral resolution based on real-time frequency characteristics, thereby enhancing time–frequency localization accuracy while reducing algorithmic complexity. To optimize computational efficiency, window parameters are determined through an energy concentration maximization criterion, enabling rapid extraction of discriminative features from diverse PQ disturbances (e.g., voltage sags and transient interruptions). These features are then fed into an RF classifier, which simultaneously mitigates model variance and bias, achieving robust classification. Experimental results show that the proposed IAST–RF method achieves a classification accuracy of 99.73%, demonstrating its potential for real-time PQ monitoring in modern grids with high renewable energy penetration.

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.

This paper aims to represent a proposition of an innovative and novel methodology applicable in detecting and classifying the power quality disturbances present in the supply to the induction motor. In all practicality, considering circumstantial real world applications, induction motors are usually operated on load. If the supply voltage is varied in any way, it would adversely affect the normal operation of the motor. In the present work, a healthy induction motor is subjected to power quality disturbances like balanced voltage sag, balanced voltage swell, unbalanced voltage sag and unbalanced voltage swell. For the purpose of detecting these power quality disturbances, discrete wavelet transform is applied to the stator current of the induction motor. The stator current wavelet coefficients are fed as input to the neural network for the classification purpose. Radial basis neural network and feed forward neural network have been independently trained and tested. The observation about the feedforward network having higher performance efficiency as compared to the radial basis network, has been seen.

Power quality (PQ) has received the attention of several research groups due to the impact of PQ disturbances (PQDs) and how they affect the operation of the electrical equipment connected to the grid, especially in industrial and healthcare facilities. The monitoring and analysis of PQD are generally performed with specialized measuring equipment, such as power analyzers, based on the standards. However, this equipment is not suited to perform continuous monitoring and classification of PQD, and it cannot be configured to perform further analysis of the monitored signal. Smart sensors, on the other hand, can provide the functionality that the standard equipment cannot, by integrating several signal processing modules that can be reconfigured using a reconfigurable technology, such as field programmable gate arrays (FPGAs). This paper presents the development of an FPGA-based smart sensor that integrates the processing cores of higher order statistics (HOS) to provide a signal analysis aimed to detect and quantify PQD on electrical installations and an artificial neural network to classify the PQD. Experimentation is performed on the electrical installation in hospital facilities. Results from the HOS processing of electrical signals show that these processing methodologies are suitable for the quantification and classification of PQD on electrical installations.

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.

A new deep learning method for classification of power quality disturbances using DWT-MRA in utility smart grid

In this paper, fractal theory and wavelet transform are combined to detect and classify self-extinguishing and fugitive scenarios of power quality disturbances (PQDs). After deciding whether the disturbance is simple or complex, the additional voltage is denoised through Discrete Wavelet Transform (DWT); the denoising process is adapted according to whether the distorted voltage contains oscillatory transients or not. At the detection stage, the grille fractal dimension of the DWT decomposition detail is computed. Then, a threshold is deduced to detect the start and end moments of the disturbance. The results reveal that the proposed detection scheme yields accurate location of PQDs even in the presence of high oscillatory transients. An algorithm based on geometric and statistical approaches is developed at the classification stage to recognize PQDs automatically. The geometric classification is based on Continuous Wavelet Transform (CWT), whereas the statistical classification is based on Multifractal Detrended Fluctuations Analysis (MFDFA) and an energy metric. The results prove that the combination of geometric and statistical classification can serve as an effective discrimination tool for PQDs. The major strength of the proposed approach is its ability to interpret the impact of each disturbance on the multifractal behavior of the nominal voltage, thus giving the possibility to draw the necessary generalizations for real-time applications.

This study presents a novel technique for automated power quality (PQ) disturbance detection and classification in power distribution system using cross‐correlation‐based approach in conjunction with fuzzy logic. The proposed method requires minimum number of features when compared with conventional approaches for identification of disturbances. Total 17 types of PQ disturbances including eight basic and nine combinations which are very close to real situations are considered for the classification. The scheme is immune to real life uncorrelated noises due to incorporation of cross spectrum analysis in the feature extraction stage. Experimentation under real operating conditions is carried out in the laboratory using data acquisition system in order to test the proposed technique. The proposed scheme is also applied in IEEE 33‐bus distribution system and validated by a real‐time simulator. The developed classifier achieved 100% accuracy and could comfortably outperform several contemporary methods for PQ disturbance classification.

This manuscript describes an algorithm based on Stockwell Transform and rule based decision tree for detection and classification of complex power quality disturbances. The multiple (complex) power quality (PQ) events are generated by various combinations of single stage PQ disturbances. These complex PQ disturbances are generated in conformity with IEEE Standard-1159 using the mathematical relations in MATLAB as per various combinations of voltage swell, voltage sag, momentary interruption (MI), oscillatory transient (OT), harmonics and impulsive transient (IT). These complex power quality signals are decomposed with the help of Stockwell transform to obtain S-matrix. Various plots are obtained from the S-matrix. Various features are extracted from these plots and given as input to the rule-based decision tree for classification of complex PQ disturbances. The effectiveness of proposed algorithm is established by testing 30 data sets of each complex PQ disturbance obtained by varying the parameters.

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.