Design and Simulation of Renewable Energy Resources for Micro Grid Based Rural Electrification in Ethiopia
Abiy Mokennen Weldegiyorgis,
Ravikumar Hiremath,
Derje Shiferaw
Issue:
Volume 10, Issue 4, July 2021
Pages:
51-59
Received:
2 June 2021
Accepted:
9 July 2021
Published:
22 July 2021
Abstract: Ethiopia is a developing country where the majority of the community lives in rural areas without electricity from the grid because of unfavorable condition of the remote area. It is necessary to supply the energy needs of this rural population for better advantages; by integrates multiply stand-alone renewable energy sources. Further, the power management of these renewably energy systems is a vital. On this research we deals with modeling & simulation of photovoltaic, micro-hydro and, storage based power generation system in MATLAB/Simulink. The power generated from these combined three renewable energy sources through intelligent decision serves for selected kebele loads. This kebele (selected village) has 5.46KWhr/m2/day amount yearly average solar radiation and 12.241l/s average flow rate. 64KW primary peak load was considered for 180 model households. The optimization result of HOMER 10KW PV, 50.4KW micro-hydro, and 18KW fuel cell optimal design is developed for electrifying the study area, for the investment cost, total present cost and unit cost of $160,780, $269,054 $0.059 respectively. Then to use the three energy resources efficiently, fuzzy logic controller based intelligent decision was used for monitoring the type and amount of resources available, as per the demand and available sources.
Abstract: Ethiopia is a developing country where the majority of the community lives in rural areas without electricity from the grid because of unfavorable condition of the remote area. It is necessary to supply the energy needs of this rural population for better advantages; by integrates multiply stand-alone renewable energy sources. Further, the power ma...
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Early Anomaly Detection for Power Systems Based on Kullback-Leibler Divergence Using Factor Model Analysis
Qing Feng,
Ghadir Radman,
Xuebin Li
Issue:
Volume 10, Issue 4, July 2021
Pages:
60-73
Received:
3 August 2021
Accepted:
21 August 2021
Published:
30 August 2021
Abstract: Real-time anomaly detection is a critical monitoring task for power systems. Most studies of power network detection fail to identify small fault signals or disturbances that might lead to damages or system-wide blackout. This work presents a methodology for analyzing high-dimensional PMU data and detecting early events for large-scale power systems in a non-Gaussian noise environment. Also, spatio-temporal correlations of PMU data are explored and determined by the factor model for anomaly detection. Based on random matrix theory, the factor model monitors the variation of spatio-temporal correlations in PMU data and estimates the number of dynamic factors. Kullback-Leibler Divergence is employed to measure the deviation between two spectral distributions: the empirical spectral distribution of the covariance matrix of residuals from online monitoring data and its theoretical spectral distribution determined by the factor model. Using IEEE 57-bus, IEEE 118-bus, and Polish 2383-bus systems, three different case studies demonstrate that the proposed method is more effective in identifying early-stage anomalies in high-dimensional PMU data collected from large-scale power networks. Performance evaluations validate that this method is sensitive and robust to small fault signals compared with other statistical approaches. The proposed method is a data-driven approach that doesn’t require any prior knowledge of the topology of power networks.
Abstract: Real-time anomaly detection is a critical monitoring task for power systems. Most studies of power network detection fail to identify small fault signals or disturbances that might lead to damages or system-wide blackout. This work presents a methodology for analyzing high-dimensional PMU data and detecting early events for large-scale power system...
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