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Mohamed Imran A

Bio: Mohamed Imran A is an academic researcher from VIT University. The author has contributed to research in topics: Distributed generation & Capacitor. The author has an hindex of 2, co-authored 2 publications receiving 251 citations.

Papers
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Journal ArticleDOI
TL;DR: A new approach to find the optimal location and size of DG with an objective of minimizing network power losses, operational costs and improving voltage stability is presented.
Abstract: Optimal location and size of distributed generation (DG) in the distribution system play a significant role in minimizing power losses, operational cost and improving voltage stability. This paper presents a new approach to find the optimal location and size of DG with an objective of minimizing network power losses, operational costs and improving voltage stability. Loss sensitivity factor is used to identify the optimal locations for installation of DG units. Bacterial Foraging Optimization Algorithm (BFOA) is used to find the optimal size of DG. BFOA is a swarm intelligence technique which models the individual and group foraging policies of the Escherichia coli bacteria as a distributed optimization process. The technical constraints of voltage and branch current carrying capacity are included in the assessment of the objective function. The proposed method has been tested on IEEE 33-bus and 69-bus radial distribution systems with various load models at different load levels to demonstrate the performance and effectiveness of the technique.

240 citations

Proceedings ArticleDOI
19 Jun 2014
TL;DR: In this article, a new combined technique for minimizing the power loss in distribution system by optimal distributed generation (DG) installation together with capacitance placement is presented, where sensitivity analysis is used to identify the optimal candidate locations of DGs and capacitor placement.
Abstract: This paper presents a new combined technique for minimizing the power loss in distribution system by optimal Distributed Generation (DG) installation together with capacitor placement. Sensitivity analysis is used to identify the optimal candidate locations of DGs and capacitor placement. Bacterial Foraging Optimization Algorithm (BFOA) is applied to find the optimal size of DGs and capacitors. BFOA is a swarm intelligence technique which models the individual and group foraging policies of the E. coli bacteria as a distributed optimization process. The technical constraints of voltage and branch current carrying capacity are included in the assessment of the objective function. Different cases of DG and capacitor placement are considered to assess the performance of the proposed method. Proposed method has been tested on IEEE 33-bus radial distribution system and the results obtained are encouraging.

71 citations


Cited by
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Journal ArticleDOI
TL;DR: In this article, an extensive literature survey on Hybrid Renewable Energy Systems (HRES) and state-of-the-art application of optimization tools and techniques to microgrids, integrating renewable energies is presented.
Abstract: Fast depleting fossil fuels and the growing awareness for environmental protection have led us to the energy crisis. Hence, efforts are being made by researchers to investigate new ways to extract energy from renewable sources. ‘Microgrids’ with Distributed Generators (DG) are being implemented with renewable energy systems. Optimization methods justify the cost of investment of a microgrid by enabling economic and reliable utilization of the resources. This paper strives to bring to light the concept of Hybrid Renewable Energy Systems (HRES) and state of art application of optimization tools and techniques to microgrids, integrating renewable energies. With an extensive literature survey on HRES, a framework of diverse objectives has been outlined for which optimization approaches were applied to empower the microgrid. A review of modelling and applications of renewable energy generation and storage sources is also presented.

538 citations

Journal ArticleDOI
TL;DR: Fireworks Algorithm is used to simultaneously reconfigure and allocate optimal DG units in a distribution network using a new swarm intelligence based optimization algorithm conceptualized using the fireworks explosion process of searching for a best location of sparks.

281 citations

Journal ArticleDOI
TL;DR: In this paper, a review of the classical and heuristic approaches for optimal sizing and placement of DG units in distribution networks and study their impacts on utilities and customers is presented, and an attempt has also been made to compare the analytical (classical) and meta-heuristic techniques for optimal size and siting of DG in distribution network.
Abstract: To extract the maximum potential advantages in light of environmental, economical and technical aspects, the optimum installation and sizing of Distributed Generation (DG) in distribution network has always been challenging for utilities as well as customers. The installation of DG would be of maximum benefit where setting up of central power generating units are not practical, or in remote and small areas where the installation of transmission lines or availability of unused land is out of question. The objective of optimal installation of DG in distribution system is to achieve proper operation of distribution networks with minimization of the system losses, improvement of the voltage profile, enhanced system reliability, stability and loadability etc. In this respect analytical (classical) methods, although well-matched for small systems, perform adversely for large and complex objective functions. Unlike the analytical (classical) methods, the intelligent techniques for optimal sizing and siting of DGs are speedy, possess good convergence characteristics, and are well suited for large and complex systems. However, to find a global optimal solution of complex multi-objective problems, a hybrid of two or more meta-heuristic optimization techniques give more effective and reliable solution. This paper presents the fundamentals of DG and DG technologies review the classical and heuristic approaches for optimal sizing and placement of DG units in distribution networks and study their impacts on utilities and customers. An attempt has also been made to compare the analytical (classical) and meta-heuristic techniques for optimal sizing and siting of DG in distribution networks. The present study can contribute meaningful knowledge and assist as a reference for investigators and utility engineers on issues to be considered for optimal sizing and siting of DG units in distribution systems.

266 citations

Journal ArticleDOI
TL;DR: In this article, Ant Lion Optimization Algorithm (ALOA) is proposed for optimal location and sizing of distributed generation (DG) based renewable sources for various distribution systems.

256 citations

Journal ArticleDOI
01 Dec 2016-Energy
TL;DR: In this article, the authors proposed Ant Lion Optimization Algorithm (ALOA) for optimal allocation and sizing of renewable DG sources in various distribution networks, where the most candidate buses for installing DG are suggested using Loss Sensitivity Factors (LSFs).

216 citations