Open AccessAn Efficient Intelligent Power Detection Method for Photovoltaic System
Author(s) -
Ayman Mansour,
J. Abdallah,
Mohammad A. Obeidat
Publication year - 2020
Publication title -
international journal of circuits, systems and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.156
H-Index - 13
ISSN - 1998-4464
DOI - 10.46300/9106.2020.14.88
Subject(s) - photovoltaic system , renewable energy , decision tree , irradiance , electricity generation , computer science , solar irradiance , maximum power point tracking , electric power system , electricity , meteorology , population , power (physics) , environmental science , engineering , geography , electrical engineering , artificial intelligence , physics , demography , quantum mechanics , inverter , sociology , voltage
Jordan has experienced a significant increase in both peak load and annual electricity demand within the last decade due to the growth of the economy and population. Photovoltaic (PV) system is one of the most popular renewable energy source in Jordan. PV system is highly nonlinear with unpredictable behavior since it is always subject to many external factors such as severe weather conditions, irradiance level, sheds, temperature, etc. This makes it difficult to maintain maximum power production around its operation ranges. In this paper, an intelligent technique is used to predict and identify the working ability of the PV system under different weather factors in Tafila Technical University (TTU) in Jordan. It helps in optimizing power productions for different operation points. The PV system in Tafila with size 1 MWp PV generated 5.4 GWh since 2017. It saves about € 1.5 million in three years. A real power data from the PV system and a weather data from world weather online site of TTU location are used in this study. Decision tree technique is employed to identify the relation between the output power and weather factors. The results show that the system accuracy is 82.01% during the training phase and 93.425 % on the validation set.