Utilization of Artificial Neural Networks to Improve the Accuracy of a Hybrid Power System Model

Atef, M. and Abdullah, M.F. and Khatib, T. and Romlie, M.F. (2019) Utilization of Artificial Neural Networks to Improve the Accuracy of a Hybrid Power System Model. [["eprint_typename_conference\_item" not defined]]

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An improvement for a new hybrid power system model is presented. The improvement considers the most accurate model that gives the exact energy output from the Solar photovoltaic (SPV) system to give more accurate result about the perfect size of the PV in the hybrid photo-voltaic and gas turbine generator (GTG) (H-PVGTG) system. This result will affect the size of both the battery bank and the GTG units. The values must justify the technical requirements of the system reliability. This value is recommended to be 0.01 in Malaysia, and it is known as the Loss of Load Probability (LLP). The main goal of the research is to get the most accurate system size with the lowest Annualized Total Life-Cycle Cost (ATLCC). The mathematical model (Math-M) that has been used in the optimization algorithm saved more than 38 from the operating cost of the power system that is used to supply the power to Universiti Teknologi PETRONAS (UTP). However, it has an error in the power output compared with the actual site power output. Due to the high operating cost of GTG system compared even with the grid supply in Malaysia, Tenaga Nasional Berhad (TNB). This paper proposed an Artificial Intelligent (AI) model to overcome the increase in the operating cost with lower power output error than the Math-M. The main challenge of the mathematical model was the low accuracy as it has +6.09 error than the actual power output of the SPV system and that is why a black box model (BB-M) has been trained to overcome this problem. A comparison between the BB-M, Math-M, GTG system, and TNB has been presented in this paper. The result concluded that BB-M has more accuracy than Math-M if compared with the actual power output of SPV system. © 2019 IEEE.

Item Type:["eprint_typename_conference\_item" not defined]
Impact Factor:cited By 1
Uncontrolled Keywords:Artificial intelligence; Errors; Hybrid systems; Image enhancement; Life cycle; Loss of load probability; Neural networks; Operating costs; Solar power generation, Artificial intelligent; ATLCC; Hybrid power systems; Optimization algorithms; Solar photovoltaics; System reliability; Technical requirement; Total life cycle costs, Thermoelectric power
ID Code:24907
Deposited By: Ms Sharifah Fahimah Saiyed Yeop
Deposited On:27 Aug 2021 06:37
Last Modified:27 Aug 2021 06:37

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