International Journal of Power Electronics and Drive Systems (IJPEDS) Vol. 14, No. 2, June 2023, pp. 1098~1109 ISSN: 2088-8694, DOI: 10.11591/ijpeds.v14.i2.pp1098-1109  1098 Journal homepage: http://ijpeds.iaescore.com Fault detection using acceptance ratio analysis on polycrystalline grid-connected photovoltaics system Nurmalessa Muhammad 1,2 , Fiona Roland 1 , Hedzlin Zainuddin 1,2 1 Department of Physics and Materials, Faculty of Applied Sciences, Universiti Teknologi MARA, Shah Alam, Malaysia 2 Solar Photovoltaic Energy Conversion Technology Research and Application (SPECTRA), Universiti Teknologi MARA, Shah Alam, Malaysia Article Info ABSTRACT Article history: Received Sep 9, 2022 Revised Jan 5, 2023 Accepted Jan 19, 2023 Around the world, electricity generation from PV photovoltaic systems is increasing, achieving 10-20% PV system efficiency. However, PV systems degrade due to the technology and the operating conditions and become worse in tropical climate countries. Hence, degradation is one of the key performance indicators for the reliability assessment of a PV system. This paper presents the acceptance ratio (AR) analysis grid-connected photovoltaic (GCPV) located on the campus of the Universiti Teknologi MARA, Shah Alam, Malaysia as the key performance indicators. A comparative analysis of the actual and predicted AC Power and AR of the polycrystalline GCPV system is carried out over monitoring of a one-year period. MATLAB software is chosen to simulate the output power using actual data. Malaysian Standard MS2692:2020 has noted that the AR value must ≥ 0.9 to classify as accepted in testing and commissioning tests and AR < 0.9 has been indicated as a non-accepted GCPV system. The results of acceptance ratio (AR), yield (Y), specific yield (SY), and performance ratio (PR) show that almost half of the AR’s data results show below 0.9 with the performance ratio of PV systems was less than 75%, indicating that the systems needed to be completely replaced. Keywords: AC power Acceptance ratio Grid-connected Photovoltaic system Polycrystalline This is an open access article under the CC BY-SA license. Corresponding Author: Nurmalessa Muhammad Department of Physics and Materials, Faculty of Applied Sciences, Universiti Teknologi MARA 40450, Shah Alam, Selangor, Malaysia Email: nurmalessa@uitm.edu.my 1. INTRODUCTION On growing environmental issues, solar energy has been widely used due to its inexhaustible and environmentally sustainable benefits [1]−[4]. The primary source of solar energy is called solar power [5]. Due to its reliability and minimal maintenance requirements, a significant number of photovoltaic (PV) systems have been deployed around the world in recent years [5]−[7]. The projected lifespan of the PV modules is typically around 20-25 years [8]. However, due to their unreliable estimation of the module's expected performance, several solar PV modules show poor performance in the field [9], [10]. The factors that contributed to the poor performance of the PV system may be faults or anomalies present in the system [11]−[14]. There have been different kinds of research on fault detection. These studies include fault detection using cables to capture losses using mathematical diagnostic methods [15], output ratio (PR) measurement, voltage, and current observation [16], [17] array and grid power loss analysis [18]−[20], artificial neural network [4], [21]-[23] environment conditions [8], [9], temperature variation [24]−[27] and experimental method [17]. A few other fault detection techniques have been introduced on the