Real Time Sustainable Power Quality Analysis of Non-Linear Load under Symmetrical Conditions

Babu, Bandla Pavan; Indragandhi, V.; Teekaraman, Yuvaraja; Kuppusamy, Ramya; Nikolovski, Srete

doi:10.3390/en15010057

Cited by 12 publications

(3 citation statements)

References 15 publications

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“…In this approach, the eradication of network reliability improvements and overloading effects in the FACTS strategy gets restricted. [3] insisted the e cient optimal power ow compensation through an electrical power grid operation and blackout prevention that demands on the power supply properly. The issues of optimal power ow (OPF) require the proper power systems with the differentiating levels, which are gets dispatch.…”

Section: Related Workmentioning

confidence: 99%

Improvement of Power Quality for Smart Microgrid Using Fuzzy Based ANN with Microcontroller

Stanley¹,

Pavalarajan²

2022

Preprint

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Renewable and dispersed forms of energy are also the evolving usage of power networks that have generated major problems for effective electricity efficiency, sustainable energy utilization and voltage stabilization. This encompasses clusters of Multi Renewable energy with micro grids, interfaced through power electronics converters with Fuzzy controller. To Design Multi Renewable energy (PV, Fuel Cell and Battery) system power Quality Controller. In such systems, increasing the dispersion of power electronics-depending on the energy storages, and distributed generations provides a huge for controlling power quality. The PV power is enhanced by means of Enhanced moth-flame optimization algorithm (EMFO) based MPPT technique. In this work, an effectiveness of storing smart hybrid Multi Renewable energy with micro grids is presented with the use of fuzzy controller based ANN (Artificial neural network). In proposed system DC to AC inverter is done with the use of Fuzzy controller based pulse generating process and the power quality factor are presented. Also, the fuzzy controller is capable of handling hard constriction of the process variables in an explicit manner. In this, the manipulated variable is employed. In addition, the hardware analysis was done using PIC microcontroller the performance estimation is employed and the outcomes attained are represented.

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Section: Related Workmentioning

confidence: 99%

Improvement of Power Quality for Smart Microgrid Using Fuzzy Based ANN with Microcontroller

Stanley¹,

Pavalarajan²

2022

Preprint

View full text Add to dashboard Cite

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“…The acceptable limits are defined by the IEEE and IEC international standards, such as IEEE 1159-2019 [1] and IEC 61000-2-2 [2]. Although measurement and event detection methods have been proposed by these international standards, PQ remains an active research topic, especially in the area of signal processing, with the development and proposal of new algorithms and techniques [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Power Quality Transient Detection and Characterization Using Deep Learning Techniques

2023

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Power quality issues can affect the performance of devices powered by the grid and can, in severe cases, permanently damage connected devices. Events that affect power quality include sags, swells, waveform distortions and transients. Transients are one of the most common power quality disturbances and are caused by lightning strikes or switching activities among power-grid-connected systems and devices. Transients can reach very high magnitudes, and their duration spans from nanoseconds to milliseconds. This study proposed a deep-learning-based technique that was supported by convolutional neural networks and a bidirectional long short-term memory approach in order to detect and characterize power-quality transients. The method was validated (i.e., benchmarked) using an alternative algorithm that had been previously validated according to a digital high-pass filter and a morphological closing operation. The training and performance assessments were carried out using actual power-grid-measured data and events.

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“…A combined application of chirp mode pursuit and Grasshopper Optimization Algorithm has been reported in reference [25]. Real-time PQ analysis is conducted in reference [26].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Approach Based on Principal Component Analysis for Power Quality Event Classification Using Support Vector Machines

et al. 2022

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Power quality has emerged as a sincere denominator in the planning and operation of a power system. Various events affect the quality of power at the distribution end of the system. Detection of these events has been a major thrust area in the last decade. This paper presents the application of Support Vector Machine (SVM) in classifying the power quality events. Well-known signal processing techniques, namely Hilbert transform and Wavelet transform, are employed to extract the potential features from the observation sets of voltages. Supervised architecture consisting of SVM has been constructed by tuning the parameters of SVM by various algorithms. It has been observed that Augmented Crow Search Algorithm (ACSA) yields the best accuracy compared to other contemporary optimizers. Further, Principal Component Analysis (PCA) is employed to choose the most significant features from the available features. On the basis of PCA, three different models of tuned SVMs are constructed. Comparative analysis of these three models, along with recently published approaches, is exhibited. Results are validated by the statistical one-way analysis of variance (ANOVA) method. It is observed that SVM, which contains attributes from both signal-processing techniques, gives satisfactory results.

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Real Time Sustainable Power Quality Analysis of Non-Linear Load under Symmetrical Conditions

Cited by 12 publications

References 15 publications

Improvement of Power Quality for Smart Microgrid Using Fuzzy Based ANN with Microcontroller

Improvement of Power Quality for Smart Microgrid Using Fuzzy Based ANN with Microcontroller

Power Quality Transient Detection and Characterization Using Deep Learning Techniques

A Hybrid Approach Based on Principal Component Analysis for Power Quality Event Classification Using Support Vector Machines

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