Power Quality Disturbances Research Articles

A Hybrid Signal Processing Method Combining Mathematical Morphology and Walsh Theory for Power Quality Disturbance Detection and Classification

A Hybrid Signal Processing Method Combining Mathematical Morphology and Walsh Theory for Power Quality Disturbance Detection and Classification

A Temporal Ensembling Based Semi-Supervised Graph Convolutional Network for Power Quality Disturbances Classification

A Temporal Ensembling Based Semi-Supervised Graph Convolutional Network for Power Quality Disturbances Classification

Complex Power Quality Disturbance Recognition Research Based on Deep Complementary Fusion of 2-D Coding Transition

Complex Power Quality Disturbance Recognition Research Based on Deep Complementary Fusion of 2-D Coding Transition

Power Quality Disturbance Identification Basing on Adaptive Kalman Filter and Multi-Scale Channel Attention Fusion Convolutional Network

Power Quality Disturbance Identification Basing on Adaptive Kalman Filter and Multi-Scale Channel Attention Fusion Convolutional Network

High-Precision Intelligent Identification of Complex Power Quality Disturbances Based on Improved KST and CNNs

High-Precision Intelligent Identification of Complex Power Quality Disturbances Based on Improved KST and CNNs

Effect of Rectangular Voltage Fluctuations on Currents of Single-Phase Induction Motors

Various power quality disturbances commonly occur in a power system, including voltage fluctuations. These can be analysed as simultaneous occurrences of voltage subharmonics and interharmonics of various frequencies – components of frequency less than the fundamental one or not being its integer multiple. This work deals with the impact of voltage fluctuations on currents of a single-phase induction motor. The results of the research on currents are presented for rectangular amplitude modulation and are compared with the investigation results under sinusoidal modulation and the appearance of a single voltage subharmonic.

A Novel Methodology for Microgrid Power Quality Disturbance Classification Using URPM-CWT and Multi-Channel Feature Fusion

A Novel Methodology for Microgrid Power Quality Disturbance Classification Using URPM-CWT and Multi-Channel Feature Fusion

Dynamic Power Quality Disturbance Classification in Grid-Integrated PV Systems: Leveraging Clark Transformed Modal Voltage and Subspace Weighted KNN

Dynamic Power Quality Disturbance Classification in Grid-Integrated PV Systems: Leveraging Clark Transformed Modal Voltage and Subspace Weighted KNN

Deep learning-based power quality disturbance detection and classification in smart grid

Deep learning-based power quality disturbance detection and classification in smart grid

Deep Learning-Based Power Quality Disturbance Detection and Classification in Smart Grid

Deep Learning-Based Power Quality Disturbance Detection and Classification in Smart Grid

Identification of power quality disturbance characteristic based on deep learning

Identification of power quality disturbance characteristic based on deep learning

Performance Evaluation of Discrete Wavelet Transform and Machine Learning Based Techniques for Classifying Power Quality Disturbances

Performance Evaluation of Discrete Wavelet Transform and Machine Learning Based Techniques for Classifying Power Quality Disturbances

Real Time Intelligent Detection of PQ Disturbances With Variational Mode Energy Features and Hybrid Optimized Light GBM Classifier

Real Time Intelligent Detection of PQ Disturbances With Variational Mode Energy Features and Hybrid Optimized Light GBM Classifier

Intrusion detection system in a hardware in the loop scenario

Detecting perturbation in electrical signals is crucial to assure power quality (PQ) and protect equipment connected to an electrical network. Some of the information generated by disturbances analysis can be communicated using Internet of Things (IoT) protocols, but it is vital to verify the integrity of the data. As a result of the lack of security presented by IoT devices, the proposal of this work is based on the implementation of an algorithm based on IDS that allows identifying attacks or failures in the security of an IoT-based PQ disturbance detection system implemented in a BeagleBone Black Wireless. The Sparta and Sqlmap programs were used as tools to perform tests and detect security flaws in the BeagleBone, verifying essential operations of the IDS.

Analysis and mitigation of PQ disturbances in grid connected system using fuzzy logic based IUPQC

Renewable energy integration introduces grid instability due to variable and intermittent sources like solar and wind, impacting reliability. This paper provides a thorough discussion of recent advancements and emerging trends in grid-integrated wind energy systems (GIWES) and grid-integrated solar energy systems (GISES). More than 70 research articles have been rigorously assessed and listed the technological and economic challenges. The increase in installations of grid-Integrating systems gives rise to challenges like as grid strain, peak shaving impacts, unpredictability of renewable energy sources (RES), and power quality disturbances. A variety of custom power devices, such as dynamic voltage restorers (DVR), static synchronous compensators (STATCOM), active power filters (APF), and unified power quality conditioners (UPQC), have gained popularity in response to these challenges. Among the various challenges, power quality disturbances, including voltage sag, swell, current and harmonics pose significant issues. To address these disturbances this work present a novel approach utilizing fuzzy logic (FL) to develop multi-feeder interline unified power-quality conditioners (MF-IUPQCs). The MF-IUPQC has three legs and three levels, each of which has four diode-clamped inverters. Switching is carried out through the use of space vector pulse width/duration modulation (SVPWM). Total harmonic distortion (THD) induced by nonlinear loads is reduced by the FLC-based MF-IUPQC, which also improves dynamic performance and offers a smooth DC-link voltage. The proposed control mechanism is implemented using MATLAB/Simulink. The fuzzy-based controller is compared to the industry-standard proportional-integral (PI) controller to determine its efficacy. Among them, the MF-IUPQC based on FLC delivers the smoothest voltage profile and the lowest THD.

Embedded Parallel Computing Platform for Real-Time Recognition of Power Quality Disturbance Based on Deep Network

Embedded Parallel Computing Platform for Real-Time Recognition of Power Quality Disturbance Based on Deep Network

Evolutionary and Swarm Based Optimization of Fit k-Nearest Neighbor Classifier for Classification of Power Quality Disturbances

In the electric network, integration of renewable energy sources and switching in large industrial loads and non-linear loads result in Power Quality Disturbances (PQDs). PQDs arise malfunctioning the sensitive equipment integrated with power grid; therefore, it is necessary to overcome the effects of poor power quality (PQ) in electric power supply. It is possible to monitor PQ with the advancement of signal processing and artificial intelligence automatically. In this paper, techniques of automatic detection and classification of PQDs are proposed using discrete wavelet transform–multi-resolution analysis (DWT–MRA) and evolutionary and swarm intelligence algorithms in renewable integrated power grid. Fit k-nearest neighbor (KNN) classifier along with DWT–MRA technique is used to classify the PQDs and it is optimized using evolutionary and swarm intelligence algorithms like, genetic algorithms, particle swarm optimization, and grey wolf optimization. Performance of algorithm is compared using accuracy of classification and confusion matrix. Further robustness of classifier is tested with Simulink model of IEEE bus test system that indicated the renewable integrated power grid environment.

Power quality disturbance mitigation in grid connected photovoltaic distributed generation with plug-in hybrid electric vehicle

In the last twenty years, electric vehicles have gained significant popularity in domestic transportation. The introduction of fast charging technology forecasts increased the use of plug-in hybrid electric vehicle and electric vehicles (PHEVs). Reduced total harmonic distortion (THD) is essential for a distributed power generation system during the electric vehicle (EV) power penetration. This paper develops a combined controller for synchronizing photovoltaic (PV) to the grid and bidirectional power transfer between EVs and the grid. With grid synchronization of PV power generation, this paper uses two control loops. One controls EV battery charging and the other mitigates power quality disturbances. On the grid connected converter, a multicarrier space vector pulse width modulation approach (12-switch, three-phase inverter) is used to mitigate power quality disturbances. A Simulink model for the PV-EV-grid setup has been developed, for evaluating voltage and current THD percentages under linear and non-linear and PHEV load conditions and finding that the THD values are well within the IEEE 519 standards.

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.

Effect of supply voltage variations on single-phase capacitor clamped multilevel inverter fed induction motor drive

This article presents the effect of supply voltage variations on single-phase capacitor-clamped multilevel inverter-fed induction motor drives. This research is tailored at determining the best torque value and speed to attain a stable state under input voltage variation and minimum time response to realize low percentage harmonic distortions. The effect of constant power quality disturbance harms the performance and behavior of asynchronous motors based on harmonic contents and other energy source integrations. The multilevel inverter has shown good performance in motor drives. This paper deals with the effect of input voltage variations on a single-phase multilevel capacitor-clamped inverter for asynchronous induction motor drives. A five-level capacitor-clamped inverter with an in-phase disposition pulse width modulation technique is adopted. Four high-frequency triangular carrier signals are generated and compared with a reference sinusoidal signal. As a result of this approach, the inverter switches firing signals are generated. The open-loop model is designed and simulated utilizing MATLAB/Simulink and results based on different values of supply voltage are presented. The current and voltage total harmonic distortions (THDs) obtained are 4.97% and 4.46% respectively at the best operating voltage of 400 V and at maximum torque of 47 Nm.