Browsing by Author "Siano, P"

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    Investigating the Effects of Selecting Different Slack Bus on Power Systems
    Sabati, A; Basaran, K; Bayindir, R; Padmanaban, S; Siano, P; Leonowicz, Z
    The load-flow study is a numerical analysis of the flow of electric power in an interconnected system in power engineering. Simplified notation such as the one-line diagram and per-unit system is used for power-flow study and focuses on various features of AC power parameters, such as voltages, voltage angles, real power and reactive power. It analyses the power systems in normal steady-state operation. So, the load flow studies play a major role in analyzing of the power systems. Generally, power system buses are categorized into three classes named load bus, power grid bus and slack bus or swing bus. In fact, slack buses in the power system are chosen among PV buses and also voltage value and phase angle of slack buses must be set 1 and 0, respectively. In this study, analysis of power flow was performed on six bus bar system using Gauss Seidel method. The study analyzed the change of effects that come from choosing different slack buses to determine the critical stress points, voltage stability, in the multi-bus systems. At the beginning of the each power flow analysis, different busses which have generator were determined as the slack bus and the analysis was performed without increasing the load. The effects of changes in critical points were examined at the end of the all possible analysis.
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    Systematic literature review of photovoltaic output power forecasting
    Basaran, K; Bozyigit, F; Siano, P; Taser, PY; Kilinç, D
    Since the harmful effects of climate warming on our planet were first observed, the use of renewable energy resources has been significantly increasing. Among the potential renewable energy sources, photovoltaic (PV) system installations keep continuously increasing world-wide due to its economic and environmental contributions. Despite its significant benefits, the inherent variability of PV power generation due to meteorological parameters can cause power management/planning problems. Thus, forecasting of PV output data (directly or indirectly) in an accurate manner is a critical task to provide stability, reliability, and optimisation of the grid systems. In considering the literature reviewed, there are various research items utilizing PV output power forecasting. In this study, a systematic literature review based on the search of primary studies (published between 2010 and 2020), which forecast PV power generation using machine learning and deep learning methods, is reported. The studies are evaluated based on the PV material used, their approaches, generated outputs, data set used, and the performance evaluation methods. As a result, gaps and improvable points in the existing literature are revealed, and suggestions which include novelties are offered for future works.