K. Jamuna

Bio: K. Jamuna is an academic researcher from VIT University. The author has contributed to research in topics: Electric power system & Microgrid. The author has an hindex of 8, co-authored 21 publications receiving 261 citations. Previous affiliations of K. Jamuna include GKM College of Engineering and Technology & Indian Institute of Technology Madras.

Multi-objective biogeography based optimization for optimal PMU placement

Abstract: The paper proposes a multi-objective biogeography based optimization (MO-BBO) algorithm to design optimal placement of phasor measurement units (PMU) which makes the power system network completely observable. The simultaneous optimization of the two conflicting objectives such as minimization of the number of PMUs and maximization of measurement redundancy are performed. The Pareto optimal solution is obtained using the non-dominated sorting and crowding distance. The compromised solution is chosen using a fuzzy based mechanism from the Pareto optimal solution. Simulation results are compared with Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Non-dominated Sorting Differential Evolution (NSDE). Developed PMU placement method is illustrated using IEEE standard systems to demonstrate the effectiveness of the proposed algorithm.

Smart metering system

Abstract: The monitoring of the power quality helps to lower the energy costs and to prolong the machine's life. Smart metering is such a complete end to end solution which minimizes the several errors and helps in distributing Quality Power. It is an energy policy for consumers to provide them a user friendly face in dealing with utility (especially the Electricity) bills. It provides the users, a Digital Meter which displays the real time power consumption every time in very friendly and detailed format and a website to analyze their consumption and expenses on energy, using different types of graphs, tabulated and manipulated data. It not only comforts their users but also give relief to the distribution company by minimizing power losses by using automatic Power factor maintenance technique, and providing anti-power theft capability. It also gives a control of power distribution through which Distribution Company can limit the user from exceeding usage of power in specific time duration.

Biogeography based optimization for optimal meter placement for security constrained state estimation

Abstract: The paper presents a biogeography based optimization algorithm to design an optimal meter placement scheme which makes the power system network observable. The procedure consists of initially determining the optimal meter set under normal conditions, followed by the optimal reliable meter locations that are obtained under two types of contingencies termed as single meter failure or loss and single branch outages. This is achieved by modifying the derived scheme from the normal condition using penalty functions. The effectiveness of the meter placement algorithm is demonstrated for IEEE standard systems.

Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey

Abstract: Appliance Load Monitoring (ALM) is essential for energy management solutions, allowing them to obtain appliance-specific energy consumption statistics that can further be used to devise load scheduling strategies for optimal energy utilization. Fine-grained energy monitoring can be achieved by deploying smart power outlets on every device of interest; however it incurs extra hardware cost and installation complexity. Non-Intrusive Load Monitoring (NILM) is an attractive method for energy disaggregation, as it can discern devices from the aggregated data acquired from a single point of measurement. This paper provides a comprehensive overview of NILM system and its associated methods and techniques used for disaggregated energy sensing. We review the state-of-the art load signatures and disaggregation algorithms used for appliance recognition and highlight challenges and future research directions.

A regularity model-based multiobjective estimation of distribution algorithm with reducing redundant cluster operator

Abstract: A regularity model-based multiobjective estimation of distribution algorithm (RM-MEDA) has been proposed for solving continuous multiobjective optimization problems with variable linkages. RM-MEDA is a kind of estimation of distribution algorithms and, therefore, modeling plays a critical role. In RM-MEDA, the population is split into several clusters to build the model. Moreover, the fixed number of clusters is recommended in RM-MEDA when solving different kinds of problems. However, based on our experiments, we find that the number of clusters is problem-dependent and has a significant effect on the performance of RM-MEDA. Motivated by the above observation, in this paper we improve the clustering process and propose a reducing redundant cluster operator (RRCO) to build more precise model during the evolution. By combining RRCO with RM-MEDA, we present an improved version of RM-MEDA, named IRM-MEDA. In this paper, we also construct four additional continuous multiobjective optimization test instances. The experimental results have shown that IRM-MEDA outperforms RM-MEDA in terms of efficiency and effectiveness. In particular, IRM-MEDA performs on average 31.67% faster than RM-MEDA.

A Comprehensive Review on Solar Powered Electric Vehicle Charging System

Abstract: Electric vehicles (EVs) are becoming increasingly popular in many countries of the world. EVs are proving more energy efficient and environmental friendly than ICEVs. But the lack of charging stati...

Economic analysis and power management of a stand-alone wind/photovoltaic hybrid energy system using biogeography based optimization algorithm

Abstract: The stand-alone energy system having a photovoltaic (PV) panels or wind turbines have low reliability and high cost as compared with wind/PV hybrid energy system. In this study, Biogeography Based Optimization (BBO) algorithm is developed for the prediction of the optimal sizing coefficient of wind/PV hybrid energy system in remote areas. BBO algorithm is used to evaluate optimal component sizing and operational strategy by minimizing the total cost of hybrid energy system, while guaranteeing the availability of energy. A diesel generator is added to ensure uninterrupted power supply due to the intermittent nature of wind and solar resources. Due to the complexity of the hybrid energy system design with nonlinear integral planning, BBO algorithm is used to solve the problem. The developed BBO Algorithm has been applied to design the wind/ PV hybrid energy systems to supply a located in the area of Jaipur, Rajasthan (India). Conventional methods require calculation at every single combination of sizing, operation strategy and the data for each variation of component needs to be entered manually and execute separately. Results show that the hybrid energy systems can deliver energy in a stand-alone installation with an acceptable cost. It is clear from the results that the proposed BBO method has excellent convergence property, require less computational time and can avoid the shortcoming of premature convergence of other optimization techniques to obtain the better solution.