Faults in any components of PV system shall lead to performance degradation and if prolonged, it can leads to fire hazard. This paper presents an approach of early fault detection via acquired historical data sets of gridconnected PV (GCPV) systems. The approach is a developed algorithm comprises of failure detection on AC power by using Acceptance Ratio (AR) determination. Specifically, the implemented failure detection stage was based on the algorithm that detected differences between the actual and predicted AC power of PV system. Furthermore, the identified alarm of system failure was a decision stage which performed a process based on developed logic and decision trees. The results obtained by comparing two types of GCPV system (polycrystalline and monocrystalline silicon PV system), showed that the developed algorithm could perceive the early faults upon their occurrence. Finally, when applying AR to the PV systems, the faulty PV system demonstrated 93.38% of AR below 0.9, while the fault free PV system showed only 31.4% of AR below 0.9.

This paper presents the Acceptance Ratio (AR) analysis for three different grid-connected photovoltaic (GCPV) systems working under the Malaysia tropical climate. AR is a ratio between actual AC power, PAC_actual, and predicted AC power, PAC_predicted. According to Malaysian Standard MS2692:2020, the AR value must ≥ 0.9 to classify as accepted in testing and commissioning test. In contrast, a rate < 0.9 indicates a non-accepted GCPV system. Historical data of the AC power output, solar irradiance, and module temperature from January 1 to December 31, 2019, were used for the analysis. The analysis procedure was carried out using Matlab and Microsoft Excel software. The analysis covers the AC power analysis and the AR analysis based on the threshold of 0.9. The plotted monthly AC power graph shows that all systems have lower than 15 % differences between actual and predicted AC power. On the AR analysis, System 1 was found to show early fault indicator with a monthly cumulative percentage of AR < 0.9 ranges from 34 % to 71 %, meanwhile System 2 and System 3 have a lower cumulative percentage of AR < 0.9 ranges from 5 % to 19 %. This result suggested that only System 2 and 3 are fault-free GCPV systems and working in good condition. The outcome of this study has succeeded in providing preliminary AR analysis results for three GCPV systems located in Malaysia. This study would help to evaluate AR threshold reliability to indicate an early fault of a GCPV system.

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This paper presents the prediction of grid-connected photovoltaic (GCPV) system installed at Green Energy Research Center, Universiti Teknologi MARA, Shah Alam, Malaysia located at latitude of 2 °N 101°E. By using Mathematical approach and climate variations of Malaysia such as module temperature and solar irradiance, the prediction of power systems performance parameters was analyzed. The parameter of the study is limited to 26 consecutive days with filter data of 80W/m 2 irradiance. This study conducted by using monocrystalline and polycrystalline solar cell technologies. The actual and the predicted data measurement of these solar cell technologies were analyzed. The empirical models were compared according to the coefficient of determination (R 2) and percentage error. MathCAD software was used in order to calculate the prediction and detail analysis of electrical parameters. Finally, the results show a good accuracy between actual and prediction data.

The failure detection in a grid-connected photovoltaic (PV) system has become an important aspect of solving the issue of the reduced energy output in the PV system. One of the methods in detecting failure is by using the threshold-based method to compute the ratio of actual and predicted DC array current and DC string voltage value. This value will be applied in the failure detection algorithm by using power loss analysis and may reduce the time, cost and labour needed to measure the quality of the energy output of the PV system. This study presented the threshold value of DC array current and DC string voltage to be implemented in the algorithm of fault detection in grid-connected photovoltaic (PV) system under the Malaysian climate. Data from the PV system located at Green Energy Research Center (GERC) was recorded in 12 months interval using the monocrystalline PV modules. The actual data was recorded using five minutes interval for 30 consecutive days. The prediction of the data was calculated using the mathematical method. The threshold value was determined from the ratio between actual and predicted data. The results show that the DC array current threshold value, σ is 0.9816. While, DC string voltage threshold value, λ is 0.9261. The proposed value may be beneficial for the determination of threshold value for regions with the tropical climate.