Neelanga Thelasingha

Bio: Neelanga Thelasingha is an academic researcher from University of Peradeniya. The author has contributed to research in topics: Computer science & AC power. The author has an hindex of 1, co-authored 2 publications receiving 33 citations.

Second Order Volterra Filter for Appliance Modelling

Abstract: Availability of large quantities of residential electrical consumption data is bringing considerable attention towards load monitoring, load forecasting, load disaggregation and demand response. Load modelling is the first and most essential step in achieving all the above said tasks. Even though many appliance modelling schemes are presented in the literature, no considerably influential work has been done on modelling appliances under voltage fluctuating environment. Motivated by this fact, we present the design and analysis of a Volterra based appliance modelling scheme which can be used in a voltage fluctuating environment. Principles of Volterra filter, least mean square algorithm for Volterra filter coefficient approximation and applicability of Volterra filter for appliance modelling are discussed. Further, a case study is presented to validate and identify the performance of the model using a data set obtained from a real household. Obtained results show that, Volterra filter can be utilized as an efficient tool for appliance modelling in a supply voltage fluctuating environment. Finally, how Volterra filter modelling can be extended to achieve the non intrusive load monitoring task is discussed.

Acceleration of Heuristic Motion Planning for Unmanned Aerial Vehicles

Abstract: In this paper, we propose a heuristic-based fast motion planning framework which can be readily incorporated by the on-board path planner of Unmanned Aerial Vehicles (UAVs) to generate safe and efficient trajectories while traversing through challenging environments cluttered with obstacles. The proposed planning technique is effective for the scenarios where the exact obstacle locations need to be detected during flight and the obstacle detection range is limited by degraded environmental conditions like fog. Unlike many kinematic based planning strategies, the generated planned trajectories can be tracked effectively as they preserve the dynamics of the UAV. The planning problem is graphically represented by discretizing input and state spaces to facilitate usage of discrete search algorithms. We also propose a heuristic calculation strategy based on dynamics relaxation to accurately encode the obstacle. The Bellman optimality condition is used to modify the heuristic to facilitate faster search. This faster planning contributes to requiring a reduced minimum obstacle detection range for receding horizon planning. The proposed algorithm has been compared against an off-the-shelf nonlinear program solver and the proposed method produced superior planning times and feasible trajectories avoiding collisions. Further, we analyzed the sub-optimality of the planned trajectories and the minimum obstacle detection range required for the receding horizon planning framework.

NILM Techniques for Intelligent Home Energy Management and Ambient Assisted Living: A Review

Abstract: The ongoing deployment of smart meters and different commercial devices has made electricity disaggregation feasible in buildings and households, based on a single measure of the current and, sometimes, of the voltage. Energy disaggregation is intended to separate the total power consumption into specific appliance loads, which can be achieved by applying Non-Intrusive Load Monitoring (NILM) techniques with a minimum invasion of privacy. NILM techniques are becoming more and more widespread in recent years, as a consequence of the interest companies and consumers have in efficient energy consumption and management. This work presents a detailed review of NILM methods, focusing particularly on recent proposals and their applications, particularly in the areas of Home Energy Management Systems (HEMS) and Ambient Assisted Living (AAL), where the ability to determine the on/off status of certain devices can provide key information for making further decisions. As well as complementing previous reviews on the NILM field and providing a discussion of the applications of NILM in HEMS and AAL, this paper provides guidelines for future research in these topics.