Browsing by Author "Jayamaha, DKJS"

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item: Conference-Abstract
Open conductor fault detection
(2017) Jayamaha, DKJS; Madhushani, IHN; Gamage, RSSJ; Tennakoon, PPB; Lucas, JR; ayatunga, U
Detection of open live conductors is a challenging task in the power system operation since it acts as a high impedance fault. High impendence faults (HIF) characteristically has a very low current value which is often not detectable using conventional over current protection devices. Several methods of detecting these HIF have been put forward .These methods involve spectral analysis of currents, algorithms based on residual current analysis and detection of unbalanced voltages produced by the fault at the end of distribution line. This paper presents a method of detecting open conductor faults based on the sequence current component. Here the ratio of negative sequence current to positive sequence current is used as the detecting parameter and fault is detected through the unbalanced resulted in the system with the occurrence of the fault.
item: Conference-Abstract
SoC based multi-mode battery energy management system for dc microgrids
Jayasena, KNC; Jayamaha, DKJS; Lidula, NWA; Rajapakse, AD
Renewable based DC microgrids are being widely deployed due to its increased efficiency compared to AC networks. An energy storage system helps to cater power flow imbalances due to the intermittent nature of renewable energy sources and varying load conditions. An adaptive Battery Energy Management System (BEMS) to ensure efficient operation of the battery storage system is proposed in this study. The state of charge (SoC) level management is of great importance for the prolong battery life, minimizing the capacity fade and avoiding over draining of the battery storage. Monitoring SoC level, DC bus voltage regulation and ability to provide backup power are the main considerations in designing the proposed BEMS. Performance of the proposed control algorithm was evaluated using PSCAD/EMTDC simulation results, and is presented in this paper.
item: Article-Full-text
Wavelet-Multi resolution analysis based ANN architecture for fault detection and localization in DC microgrids
(IEEE, 2019) Jayamaha, DKJS; Lidula, NWA; Rajapakse, AD
DC microgrids present an effective power system solution for increased integration of renewable sources while providing clear benefits, such as high efficiency and simpler control. However, the protection of DC networks still remains a challenge due to strict time limits for fault interruption imposed by fast rising fault currents in DC systems, and absence of frequency and phasor information. This paper introduces a technique for fast detection and isolation of the faults in the DC microgrids without de-energizing the whole network. In the proposed algorithm, branch current measurements are sampled and Wavelet transform is applied to capture the characteristic changes in the current signals caused by network faults. The temporal variations in the relative wavelet energy within the frequency bands are acquired to construct the feature vector for classification. Artificial neural networks are used as the classifier as it provides a soft criterion for fault detection, featuring smart fault detection capability. The relatively fast calculation time of artificial neural networks makes it a good candidate for this application, due to the strict time restrictions inherited in DC fault isolation. To evaluate the performance, a comprehensive study on the proposed scheme is presented. The results demonstrate the effectiveness of the proposed scheme in terms of fast and reliable fault detection and inbuilt accurate fault localization capability.