Detection and Discrimination of Islanding and Faults in distribution system with Distributed Generation by using Wavelet based Alienation approach

This paper presents a wavelet transform based alienation technique for the protection of a radial 5 bus  distribution system integrated with four wind type doubly fed Induction generators (DFIG).This technique is used to detect islanding condition and faults, classification of faults and their discrimination. Islanding is simulated at point of common coupling (PCC) and faults are simulated at each DG bus of the network. Daubechies wavelet transform has been used to decompose the current signals to get approximate coefficients. The Alienation coefficients of these approximate decompositions are termed as islanding and fault indexes. These indexes have been compared with predetermined threshold to detect islanding and faults. The same threshold value is utilized to discriminate transients associated with islanding and fault. Alienation coefficients at each bus over a half cycle window clearly detect both islanding and fault. Testing of the proposed algorithm has been carried out for various angles of incidence. Hence, the proposed algorithm is more effective and successful for finding the islanding condition as well as faults in distribution system with distributed generation

Comprehensive Review on Detection and Classification of Power Quality Disturbances in Utility Grid With Renewable Energy Penetration

The global concern with power quality is increasing due to the penetration of renewable energy (RE) sources to cater the energy demands and meet de-carbonization targets. Power quality (PQ) disturbances are found to be more predominant with RE penetration due to the variable outputs and interfacing converters. There is a need to recognize and mitigate PQ disturbances to supply clean power to the consumer. This article presents a critical review of techniques used for detection and classification PQ disturbances in the utility grid with renewable energy penetration. The broad perspective of this review paper is to provide various concepts utilized for extraction of the features to detect and classify the PQ disturbances even in the noisy environment. More than 220 research publications have been critically reviewed, classified and listed for quick reference of the engineers, scientists and academicians working in the power quality area

A comprehensive review of power quality mitigation in the scenario of solar PV integration into utility grid

Solar photo-voltaic (SPV) is a major contributor of renewable energy (RE) sources, which plays an important role in tackling climate change, reducing the cost of energy, improving the reliability of power supply, and providing access to energy. SPV is a major hope for “access to energy” for remote populations which are deprived of the conventional grid due to economical and feasibility issues. These issues are to be tackled in due course of time to enhance the reliability of the supply. The Extension of the grid to these areas weakens the strength of the grid. This results in a scenario of PV integration into a weak AC grid. However, solar integration into a weak AC grid provides power quality (PQ) challenges that limit the penetration levels. The other components which limit penetration levels include non-linear loads, dynamic loads, variable irradiations and partial shading etc. Various DFACTS devices in association with different conventional, adaptive and AI-based algorithms have been proposed in this article to mitigate PQ challenges associated with a weak grid to enhance penetration levels of Solar PV. This article provides a comprehensive review of various power quality challenges associated with SPV penetration and PQ mitigation techniques involving various DFACTS devices and control algorithms such as conventional control, adaptive control, and AI-based control algorithms. More than 130 research articles have been rigorously assessed, categorized, and listed in this article for quick reference for the advantage of engineers and academicians working in this area of research

Solving bi-objective economic-emission load dispatch of diesel-wind-solar microgrid using African vulture optimization algorithm

Microgrid is a localised power generation infrastructure designed to provide continuous and reliable power supply to a small, specific region. The increasing concern towards environmental sustainability has resulted in the prioritisation of non-emitting Renewable Energy Sources (RESs) while optimal sizing of microgrid. Optimal sizing of generation units at minimum cost with minimum emission satisfying various practical constraints is a challenging bi-objective optimization problem of power system known as Economic-Emission Load Dispatch (EELD). Metaheuristic approaches are predominantly used to solve the EELD problem. This article explores the advanced metaheuristic methods to solve EELD problem and proposes application of African Vulture Optimization Algorithm (AVOA) to subsequently address the EELD problem of a microgrid combining diesel, wind, and solar energy sources based on field data of a specific location in Jaisalmer, India. AVOA emulates the foraging and navigation patterns of vultures, incorporating effective exploration and exploitation characteristics. The effectiveness of AVOA is first validated using three standard test systems of 10, 6 (IEEE30-bus), and 40 units with/without transmission losses, prior applying it for microgrid. The obtained results are compared with several other popular optimization techniques to establish the efficacy of proposed method. Further, AVOA is employed to analyse the impact of individual RESs on microgrid's cost and emissions across three distinct generation scenarios. The viability score is employed to evaluate the efficacy of all techniques along with other significant performance indices. Statistical data tests such as ANOVA, Wilcoxon, and robustness are employed to assess the statistical confidence of the AVOA. Additionally, a multi-comparison post-hoc TukeyHSD test is introduced which proves the superiority of AVOA. Results establish AVOA as the most effective solution for addressing the EELD problem in microgrid (all sources), with significant reduction of 5.25% and 33.09% in cost (323318.21$/day) and emission (of 2433.95 Tons/day) respectively compared to the closest competitive method

Isolation And Identification Of Bacteria From Solid Waste Deposit Sites For The Degradation Of Pollutants

In and around the Treatment Storage and Disposal Facility (TSDF), Duindigal, Ramky site, Hyderabad, Telangana, ten samples were collected. To assess the ability of the bacterial isolates to remove contaminants, the following parameters were chosen for this study: pH, TDS, BOD, COD, TN, and TP. Two of the ten isolates showed promising degradation capabilities. The 16S rRNA gene sequence was used to apply genotypic techniques to the two strains that performed the best. Alcaligenes faecalis and Brevibacterium iodinum are the two bacterial strains that we found using NCBI BLAST analysis. To optimize the growth rate of the isolates, some parameters like pH, temperature, incubation time and peptone concentrations were optimized and got enhanced results. These findings have expanded the possibility of identifying industrially significant bacteria from solid waste disposal sites, and these isolates may be an essential source for the identification of enzymes with practical applications in industry

Isolation And Identification Of Bacteria From Solid Waste Deposit Sites For The Degradation Of Pollutants

In and around the Treatment Storage and Disposal Facility (TSDF), Duindigal, Ramky site, Hyderabad, Telangana, ten samples were collected. To assess the ability of the bacterial isolates to remove contaminants, the following parameters were chosen for this study: pH, TDS, BOD, COD, TN, and TP. Two of the ten isolates showed promising degradation capabilities. The 16S rRNA gene sequence was used to apply genotypic techniques to the two strains that performed the best. Alcaligenes faecalis and Brevibacterium iodinum are the two bacterial strains that we found using NCBI BLAST analysis. To optimize the growth rate of the isolates, some parameters like pH, temperature, incubation time and peptone concentrations were optimized and got enhanced results. These findings have expanded the possibility of identifying industrially significant bacteria from solid waste disposal sites, and these isolates may be an essential source for the identification of enzymes with practical applications in industry