V. Ramesh

Bio: V. Ramesh is an academic researcher from VIT University. The author has contributed to research in topics: Smart grid & Economic dispatch. The author has an hindex of 12, co-authored 25 publications receiving 450 citations.

Optimal Allocation of Distributed Generation Using Hybrid Grey Wolf Optimizer

Abstract: Optimal allocation of distributed generation units is essential to ensure power loss minimization, while meeting the real and reactive power demands in a distribution network. This paper proposes a solution to this non-convex, discrete problem by using the hybrid grey wolf optimizer, a new metaheuristic algorithm. This algorithm is applied to IEEE 33-, IEEE 69-, and Indian 85-bus radial distribution systems to minimize the power loss. The results show that there is a considerable reduction in the power loss and an enhancement of the voltage profile of the buses across the network. Comparisons show that the proposed method outperforms all other metaheuristic methods, and matches the best results by other methods, including exhaustive search, suggesting that the solution obtained is a global optimum. Furthermore, unlike for most other metaheuristic methods, this is achieved with no tuning of the algorithm on the part of the user, except for the specification of the population size.

A Novel Selective Particle Swarm Optimization Approach for Combined Heat and Power Economic Dispatch

Abstract: This article presents a new, improved particle swarm optimization algorithm with a selection operator for the solution of the combined heat and power economic dispatch problem In this technique, starting with a large swarm of particles, only those particles whose fitness is above the scaled average fitness are selected in successive iterations, using a selection factor that is adjustable depending on the nature of the problem The method is illustrated using a test case The result compares favorably with other particle swarm optimization variants and other existing non-conventional methods

Combined heat and power economic dispatch problem using firefly algorithm

Abstract: Cogeneration units, which produce both heat and electric power, are found in many process industries. These industries also consume heat directly in addition to electricity. The cogeneration units operate only within a feasible zone. Each point within the feasible zone consists of a specific value of heat and electric power. These units are used along with other units, which produce either heat or power exclusively. Hence, the economic dispatch problem for these plants to optimize the fuel cost is quite complex and several classical and meta-heuristic algorithms have been proposed earlier. This paper applies the firefly algorithm, which is inspired by the behavior of fireflies which attract each other based on their luminosity. The results obtained have been compared with those obtained by other methods earlier and showed a marked improvement over the earlier methods.

Combined heat and power economic dispatch problem using the invasive weed optimization algorithm

Abstract: Cogeneration units which produce both heat and electric power are found in many process industries. These industries also consume heat directly in addition to electricity. The cogeneration units operate only within a feasible zone. Each point within the feasible zone consists of a specific value of heat and electric power. These units are used along with other units which produce either heat or power exclusively. Hence the economic dispatch problem for these plants optimizing the fuel cost is quite complex and several classical and meta-heuristic algorithms have been proposed earlier. This paper applies the invasive weed optimization algorithm which is inspired by the ecological process of weed colonization and distribution. The results obtained have been compared with those obtained by other methods earlier and showed a marked improvement over earlier ones.

Grey wolf optimizer: a review of recent variants and applications

Abstract: Grey wolf optimizer (GWO) is one of recent metaheuristics swarm intelligence methods. It has been widely tailored for a wide variety of optimization problems due to its impressive characteristics over other swarm intelligence methods: it has very few parameters, and no derivation information is required in the initial search. Also it is simple, easy to use, flexible, scalable, and has a special capability to strike the right balance between the exploration and exploitation during the search which leads to favourable convergence. Therefore, the GWO has recently gained a very big research interest with tremendous audiences from several domains in a very short time. Thus, in this review paper, several research publications using GWO have been overviewed and summarized. Initially, an introductory information about GWO is provided which illustrates the natural foundation context and its related optimization conceptual framework. The main operations of GWO are procedurally discussed, and the theoretical foundation is described. Furthermore, the recent versions of GWO are discussed in detail which are categorized into modified, hybridized and paralleled versions. The main applications of GWO are also thoroughly described. The applications belong to the domains of global optimization, power engineering, bioinformatics, environmental applications, machine learning, networking and image processing, etc. The open source software of GWO is also provided. The review paper is ended by providing a summary conclusion of the main foundation of GWO and suggests several possible future directions that can be further investigated.

A new fusion of grey wolf optimizer algorithm with a two-phase mutation for feature selection

Abstract: Because of their high dimensionality, dealing with large datasets can hinder the data mining process. Thus, the feature selection is a pre-process mandatory phase for reducing the dimensionality of datasets through using the most informative features and at the same time maximizing the classification accuracy. This paper proposes a new Grey Wolf Optimizer algorithm integrated with a Two-phase Mutation to solve the feature selection for classification problems based on the wrapper methods. The sigmoid function is used to transform the continuous search space to the binary one in order to match the binary nature of the feature selection problem. The two-phase mutation enhances the exploitation capability of the algorithm. The purpose of the first mutation phase is to reduce the number of selected features while preserving high classification accuracy. The purpose of the second mutation phase is to attempt to add more informative features that increase the classification accuracy. As the mutation phase can be time-consuming, the two-phase mutation can be done with a small probability. The wrapper methods can give high-quality solutions so we use one of the most famous wrapper methods which called k-Nearest Neighbor (k-NN) classifier. The Euclidean distance is computed to search for the k-NN. Each dataset is split into training and testing data using K-fold cross-validation to overcome the overfitting problem. Several comparisons with the most famous and modern algorithms such as flower algorithm, particle swarm optimization algorithm, multi-verse optimizer algorithm, whale optimization algorithm, and bat algorithm are done. The experiments are done using 35 datasets. Statistical analyses are made to prove the effectiveness of the proposed algorithm and its outperformance.

A survey on electric vehicle transportation within smart grid system

Abstract: The electrification of hybrid electric vehicle reduces the reliance of transportation on fossil fuels and reduces Green House Gas emissions. The economic and environmental benefits of the hybrid electric vehicles are greatly reshaping the modern transportation sector. The transportation electrification (TE) brings various challenges to the Smart Grid (SG), such as power quality, reliability, and control. Thus, there is a need to explore and reveal the key enabling technologies for TE. Moreover, the intermittent nature of Renewable Energy Resources (RER) based generation demands for efficient, reliable, flexible, dynamic, and distributed energy storage technologies. The Electrical Vehicles (EVs) storage battery is the promising solution in accommodating RER based generation within SG. The most efficient feature of transportation sector is Vehicle to Grid (V2G) concept that will help in storing the surplus energy and feeding back this energy to the main grid during period of high demands. The storage technology is an integral part of the SG that helps in attaining the proper utilization of RER. In this paper, our goal is to explore the TE sector and its impact on economy, reliability and eco-friendly system. We reviewed the V2G technology and their implementation challenges. We further reviewed various energy storage technologies deployed in EVs within SG, considering attention to their influence on the environment. Moreover, this paper presented a detailed overview of the on board and off board charging infrastructure and communication necessities for EV. The paper also investigated the current issues and challenges of energy storage technologies in EVs. The technical and economic benefits of storage technologies are also considered. Our analysis reviews the role of EVs in decarbonizing the atmosphere. Lastly, the survey explains the current regulation, Standard, and interfacing issues within SG.