With increasing installations of photovoltaic (PV) systems, interest in power forecasting has also increased. Inaccurate forecasts would result in substantial economic losses and system reliability issues. The correlation between weather variables and PV power is critical to ensure the efficient use of energy in PV systems. A key step toward accurate power forecasting is estimating the output from a PV system based on known environmental input data. In this research, all available weather data are used to predict the PV power. Meteorological and power data are then analyzed using a statistical approach to identify the order of significance of the input variables. Then, a predictive model is suggested as a function of irradiance, ambient temperature, wind speed, and relative humidity. The model produces a root mean square error of 4.957% and a mean absolute percentage error of 5.468% during the measurement period and over the entire range of irradiation.

Unlike conventional photovoltaic (PV) modules that generate power by absorbing light through the front side only, a bifacial PV module can generate power by absorbing light through the rear as well as the front, which would lead to an enhancement of power generation. Particularly, bifacial PV modules would have the advantage of lower power loss in shaded environments than monofacial PV modules, thanks to the light absorbed through the rear side. To predict the power of a bifacial PV module in a shaded environment, modeling is suggested by considering the shaded areas, the operational status of the bypass diodes, and the temperature of the bifacial PV module. To verify the power prediction of a bifacial PV module with a shaded area, modeled and measured powers are compared, showing error rates of 7.28%. From the results of the power loss experiments for bifacial and monofacial PV modules in shaded environments, it is confirmed that the bifacial PV module shows a relatively low power loss rate when compared with the monofacial PV module, with a power loss rate being 87.26% of the rate for the monofacial PV module.

Fault detection and repair of the components of photovoltaic (PV) systems are essential to avoid economic losses and facility accidents, thereby ensuring reliable and safe systems. This article presents a method to detect faults in a PV system based on power ratio (PR), voltage ratio (VR), and current ratio (IR). The lower control limit (LCL) and upper control limit (UCL) of each ratio were defined using the data of a test site system under normal operating conditions. If PR exceeded the set range, the algorithm considered a fault. Subsequently, PR and IR were examined via the algorithm to diagnose faults in the system as series, parallel, or total faults. The results showed that PR exceeded the designated range between LCL (0.93) and UCL (1.02) by dropping to 0.91–0.68, 0.88–0.62, and 0.66–0.33 for series, total, and parallel faults, respectively. Moreover, VR exceeded the LCL (0.99) and UCL (1.01) by 0.95–0.69 and 0.91–0.62 for series and total faults, respectively, but not under parallel faults condition. IR did not change in series and total faults but exceeded the range of LCL (0.93) and UCL (1.05) by dropping to 0.66–0.33. Thus, faults in PV systems can be detected and diagnosed by analyzing quantitative output values.

Considering the increasing interest in the realization of carbon-neutral and RE100 systems, and the expansion of the supply of renewable energy through large-scale floating photovoltaic systems (FPVs), this study designed large-scale bifacial FPVs for maximum power density. Moreover, the estimated power generation was compared and analyzed according to the installation methods of the conventional monofacial PV module facing south (Mono-S), bifacial PV module facing south (Bi-S), and bifacial PV module facing east and west (Bi-EW). Using the proposed design method, the power generation per unit area for FPVs was 17.87% to 36.08% higher than that of the conventional installation method. In addition, this method can contribute to gird stability by decreasing the peak power around noon and increasing power generation during low irradiation.

A distributed power generation system uses an intermittent power source that provides low power. Furthermore, it cannot be centrally controlled from the perspective of the system operator. The photovoltaic (PV) power generation system is installed with an energy storage system (ESS) to increase the power source quality compared to that possible with PV-only power generation. In this paper, an estimation algorithm is proposed for improving battery capacity to construct a suitable and economical system. In addition, an optimal algorithm is proposed that considers different factors. For example, the cost of constructing the battery and the amount of electric power sales reflecting the renewable energy certificate weighting factor of 5.0 are based on the cost of the battery construction, depth of battery discharge, charge/discharge cycle of the battery, cost of the energy conversion device, operation maintenance cost, and charge cycle of the battery. The optimized battery capacity is calculated by considering the correlation between PV power and meteorological parameters, construction cost, and the benefit of the amount of electricity sold, with an error rate of 5.75%.

The performance evaluation (PE) of the Photovoltaic(PV) system is an index representing the efficiency and reliability of the system. Most PE indicators evaluate the ratio of theoretically calculated power generation to actually measured power generation. The closer the ratio is to 1, the more ideal the PV system is. PV system varies depending on weather conditions and regional characteristics, especially on the types of sensors and measuring variables. Floating Photovoltaics(FPVs) and Marine photovoltaics(MPVs) vary with the environmental variables more as it is installed on the water and sea. In this paper, on the contrary to the existing PE methods, the most accurate regression model considering ambient temperature, relative humidity, and wind speed was used to predict the power in order to improve the accuracy. The optimal PE method for the PV system to easily and accurately detect failures of the PV system is proposed. Data from three FPVs in the same environment were analyzed for 1 year. The PV power prediction model including the wind speed and relative humidity was used to improve accuracy. The quality diagnosis was performed with an improved PE and the impact of various events can be represented through this. In this paper, the distribution of the corresponding Power Performance Index (PPI) values was analyzed using a descriptive statistic method, and indicators in terms of quality control were presented. The PV power generation state can be determined by the location of the average and median values of the boxplot. The fluctuation of PV power generation was identified using the size change of Inter Quartile Range (IQR), which represents the degree of data scattering. As a result, it was confirmed that failure occurred in the system when the IQR value is 2 times bigger than the normal IQR value.