Browsing by Author "Jeewandara, JMDS"

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item: SRC-Report
Develop a micro grid control platform for sustainable energy management in distribution network
(2018) Attanayaka, AMS; Jeewandara, JMDS; Karunadasa, JP; Hemapala, KTMU
The Senate Research Committee (SRC) promotes research by giving funding for research. In addition to the monthly stipend payment, the allocated payments for publications, hardware purchasing, and transportation is highly imperative to achieve the proposed objectives successfully within the time duration. Moreover, guidance of the supervisor who provided the SRC grant for the research is also essential factor to meet the requirements of the research. Microgrids and Battery Energy Storage Systems play a major role in the sustainable and clean energy sector. In this study, an accurate battery model is developed which can represent the electrical and the thermal behavior of the battery and all the battery parameters are investigated experimentally by implementing a test bench for the proposed model and moreover, each of the parameters is validated theoretically by developing a MATLAB simulation. To improve the accuracy of the battery parametrization, battery state of charge (SOC) level is estimated via machine learning algorithms. According to the results, the accuracy of the comprehensive electro-thermal battery model based on electrical and thermal parameters is at a satisfactory level and proves that designing such a model that achieves excellent accuracy and realistic behavior in real-time platform simulators. There are six different time series models are used to estimate the SOC level and according to the performance, auto regressive (AR) model and seasonal auto regressive integrated moving average (SARIMA) models are the best machine learning models for SOC level estimation when battery parametrization.
item: Conference-Full-text
Development of a steady state voltage regulation method for power distribution networks
(IEEE, 2022-07) Jeewandara, JMDS; Sanju, KAY; Samarawickrama, SB; Fernando, MAJ; Senaka, AK; Rathnayake, M; Adhikariwatte, V; Hemachandra, K
Distributed generation (DG) can be known as the power generating technologies that are installed at or close to the power consumption locations and it consists of a significant number of advantages for the power system in which it would reduce the transmission loss to a greater extent. On the contrary, it will be the cause of fluctuating the voltage on the distribution line. Many countries are inclined to observe the over voltage and under voltage fluctuations due to the high integration of inductive loads and distributed generation such as solar and wind power systems. Consequently, the active distribution system is supposed to be properly managed and coordinated. This study is mainly focusing on minimizing the voltage variation of a standard IEEE 33 bus bar system with the efficient placing of custom power devices on it. Moreover, voltage variation when distribution generations and inductive loads are presented on the bus bar system is studied. According to the results, it has been seen that the voltage variation in the existing distribution network can be minimized by placing a static var compensator at optimal locations.
item: Conference-Full-text
Experimental study on efficiency enhancement of concentrated solar photo-voltaic systems with convex lenses and cooling
(IEEE, 2020-07) Samaraweera, P; Jeewandara, JMDS; Gammampila, GUS; Keerthanan, J; Jayathunga, JVUP; Rodrigo, AS; Weeraddana, C; Edussooriya, CUS; Abeysooriya, RP
Low efficiency can be identified as one of the main drawbacks of solar photo-voltaic systems. The main two factors that affect the efficiency of the existing solar photovoltaic cells are the operating temperature and the solar irradiance received by the solar cell. This paper presents an efficiency enhanced solar photo-voltaic system, which concentrates the solar irradiance through convex lenses and at the same time, cools the solar cells using a forced flow of mineral turpentine. The evaluation of the test results shows an enhancement of power output and efficiency by 60% & 32% respectively by both cooling and concentrating.
item: Conference-Full-text
IoT based building energy management system
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Hettiarachchi, DG; Jaward, GMA; Tharaka, VPV; Jeewandara, JMDS; Hemapala, KTMU; Abeykoon, AMHS; Velmanickam, L
The ever-growing demand for energy and uncertainty of supply lead towards a major crisis in the energy sector, especially in building energy management. In case of power outages it is crucial to utilize the scarce power sources for the most vulnerable cause of demand. Furthermore, it is evident that due to the lack of monitoring and automation present in building energy management systems, a considerable percentage of energy wastage gets reported. Thus the need for a proper load forecasting methodology has arisen in the recent past. Researchers have formulated statistical methods and machine learning based models to facilitate energy forecasting for future periods. This paper addresses the load forecasting challenge by proposing an IoT (Internet of Things) based energy management system that incorporates an XGBoost (Extreme Gradient Boost) machine learning model to forecast energy consumption. The energy management system consists of a user-friendly central dashboard that acts as a mediator between a NodeMCU device and a cloud-hosted database with the aforementioned machine learning model. The paper concludes with a summarized discussion on the research.
item: Conference-Full-text
SOC level estimation of lithium-ion battery based on time series forecasting algorithms for battery management system
(Institute of Electrical and Electronics Engineers, Inc., 2021-09) Jeewandara, JMDS; Karunadasa, JP; Hemapala, KTMU; Abeykoon, AMHS; Velmanickam, L
To fulfill a reliable battery management system, a precise state of charge (SOC) estimation method for a battery energy storage system should be developed. This study makes two contributions to the battery management system. First, a combined electro-thermal battery model is proposed. To identify the electrical and thermal battery parameters, constant current -constant voltage (CC-CV) charge, constant current (CC) discharge, and pulse discharge tests should be performed on the lithium-ion battery cells and each of the above experiments, battery SOC level should be estimated precisely. The second study of this research is the development of the SOC level estimation method by using time series forecasting algorithms. In this study, six kinds of models are used in real-time, and each of the models is evaluated with the performance indices and the computational time, and finally, forecast diagrams are graphically represented for each of the experiments.