Analysis of Power Quality Disturbances Using Spectrogram and S-transform

This paper presents the implementation of linear time-frequency distribution (TFD) techniques to analyze power quality signals. The power quality signals are swell, sag, interruption, transient, harmonic, interharmonic, notching and normal voltage based on IEEE Std. 1159-1995. The time-frequency analysis techniques selected are spectrogram and Gabor transform. From the time-frequency representation (TFR) obtained, the signal parameters are estimated to identify the signal characteristics. Identified signal characteristics that are used as input to a classifier includes the average of root mean square (RMS) voltage, total waveform distortion, total harmonic distortion and total nonharmonic distortion and duration of swell, sag, interruption and transient. From the linear time-frequency analysis techniques used, the optimal technique is chosen in terms of accuracy, computation complexity and memory size.

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) problem is the deviation of voltage and current from their standard waveforms. Penetration of renewable energy (RE) sources such as wind and solar photovoltaic (PV) system into the utility network affects the quality of power supplied. This paper presents the detection of power quality disturbances in the distribution network with various scenarios in the presence of doubly fed induction generator based wind power generation system. The features extracted by multi-resolution analysis of the voltage signal captured at the point of common coupling (PCC) based on Stockwell's Transform have been utilized for the detection of PQ disturbances. The study has been performed on the IEEE 13 bus test system modified to incorporate the wind power generation. The results have been validated in MATLAB/Simulink environment.

This paper discusses the implementation of time-frequency analysis techniques to analyze power quality disturbances. The approached methods are spectrogram and Gabor transform algorithms. Signal parameters such as time marginal and frequency marginal are extracted from the time-frequency distributions. The parameters are analyzed in terms of correctness measurement of root mean square (RMS), total harmonic distortion (THD), total waveform distortion (TWD) and total interharmonic distortion (TnHD) values. Power quality events that are analyzed are swell, sag, interruption, harmonic, interharmonic, transient, notching and normal voltage. The results show that Gabor transform provides better performance in terms of correctness of parameters measurement, window length, frequency resolution and memory size.

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.

Quantum algorithms have demonstrated extraordinary potential across numerous fields, offering significant advantages in solving practical problems. Power Quality Disturbances (PQDs) have always been a critical factor affecting the stability and safety of electrical power systems, and accurately detecting and identifying PQDs is crucial for ensuring reliable system operation. This paper explores the application of quantum algorithms in the field of power quality and proposes a novel method using Quantum Support Vector Machines (QSVM) to detect and identify PQDs, which marks the first application of QSVM in PQD analysis. The QSVM model employed involves three main stages: quantum feature mapping, quantum kernel computation, and model training. Quantum feature mapping uses quantum circuits to map classical data into a high-dimensional Hilbert space, enhancing feature separability. Quantum kernel computation calculates the inner products between features for model training. Rigorous theoretical and experimental analyses validate our approach. This method achieves a time complexity of O(N2log(N))\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$O(N^{2} \\log (N))$\\end{document}, superior to classical SVM algorithms. Simulation results show high accuracy in PQDs detection, achieving a 100% detection rate and a 96.25% accuracy rate in single PQD identification. Experimental outcomes demonstrate robustness, maintaining over 87% accuracy even with increased noise levels, confirming its effectiveness in PQDs detection and identification.

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 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.

With the increasingly penetration of nonlinear loads in the power system, power quality (PQ) has become a significant issue for the power utilities and end users. In order to improve the PQ, the PQ detection is essential. In this paper, a new method for detecting the PQ disturbances via empirical wavelet transform (EWT) and Hilbert (HT) is proposed. Firstly, EWT is applied to the signal for obtaining different modes. Then the instantaneous amplitude and frequency of each mode are calculated by using the HT. By applying it to two stationary signals and two non-stationary signals, the efficiency of the proposed method is evaluated. With no frequency aliasing like the S transform (ST), the proposed method presents more accurate results than the ST.

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.

Detection and classification of multiple power quality disturbances in Microgrid network using probabilistic based intelligent classifier

The power quality domain in power system networks has received more attention recently because of the increase in sensitive equipment, non-linear load disturbances, and the utilization of Renewable Energy Sources (RES). In case of distributed generation, which could occur from consumers to utilities, power quality quantifies the suitability of electric power supplied from the utilities to the commercial, industrial, and residential users. A minimum of half of the total disruptions to power quality are related to voltage quality. This research concentrated an amplitude-frequency estimation based methods, Power quality Disturbance Classification and Filtering techniques for harmonic mitigation. The enhancement of power quality in a three-phase nonlinear load power system is the subject of this research. Shunt Active Power Filter (SAPF) is used in real-time applications for fault detection and harmonic suppression. The suggested work uses a digital signal processor (DSP) (28335) and a shunt active power filter (SAPF) to achieve power quality disturbance detection and harmonic reduction on hardware system. In addition to applying shunt compensation devices for harmonic detection, the designed hardware model is tested for power quality disturbance categorization of voltage sag, harmonics, and interruption and Total Harmonic Distortion (THD) is 4.3% in this case. The overall implementation of the hardware using -Fuzzy algorithm is found to be satisfactory.

The recommended power quality (PQ) indices in electric power systems are defined based on the discrete Fourier transform (DFT). DFT is mostly evaluated applying fast Fourier transform (FFT) algorithm which requires signals to be periodic in nature. Hence, it evaluates PQ indices accurately for stationary PQ disturbances only. However, in case of nonstationary PQ disturbances signal is aperiodic and FFT results in erroneous assessment due to spectral leakage phenomena. Furthermore, FFT cannot provide time information of the PQ indices as computation is performed in frequency domain only. Which is significant shortcoming of the FFT when dealing with time-varying PQ disturbances to extract dynamic signature of the signal. Thereby, this paper presents a framework of redefining PQ indices for stationary and nonstationary PQ disturbances employing time-frequency distribution (TFD) method. Among all TFDs, reduced interference distribution (RID) represents the most suitable properties for analyzing time-varying PQ disturbances. Hence, in this work, it is employed to reformulate PQ indices typically used in electric power systems. The results of two synthetic examples considering stationary and nonstationary PQ disturbances imply that the RID-based redefined PQ indices are closer to the actual values. Also, they provide more accurate results than the existing transient PQ indices and traditional FFT-based method.

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 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.