# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 4 Results
### Experimental Set Up  
In this experimental set-up, diverse illuminance conditions were applied to assess the performance of PV systems, with the parameters of the utilized PVs detailed in Table 4. The table provides a comprehensive overview of critical PV parameters essential for accurate modeling and optimization. The experiment's focus is on understanding how the PVs respond to varying illuminance levels, shedding light on their efficiency and robustness under different environmental scenarios. By systematically varying illuminance conditions, the study aims to capture the nuanced behavior of the PV systems and evaluate their real-world adaptability. The parameters outlined in Table 4, such as the diode ideality factor (\(n\)), series resistance (\(R_{s}\)), and photogenerated current (\(I_{ph,ST}\)), play pivotal roles in determining the system's response to changing illuminance. The ensuing analysis and results will provide valuable insights into the dynamic behavior of PV systems, contributing to a broader understanding of their performance in varying environmental contexts.  
The comprehensive modeling procedure involves acquiring crucial measurements from an array of devices, meters, and sensors to accurately assess the performance of PV systems. The key objective is to identify potential errors in PV operations by comparing real PV values with the expected MPP values. Real PV values are determined through the utilization of meters and MPPT, while the expected PV values are estimated using a genetic algorithm.  
For an accurate simulation of PV system operation, the GA relies on input values from various outdoor sensors and PV modeling. These inputs include temperature measurements obtained through type J thermocouples [36] attached to the PV collectors, providing insights into temperature variations during operation. Additionally, an optical sensor [37] is employed to simultaneously monitor the brightness of the surrounding area, contributing to a comprehensive understanding of the environmental conditions affecting PV performance. A pyrometer sensor is incorporated to measure solar radiation, offering reliable data on solar energy incidence.  
Simultaneously, the monitoring system utilizes a multimeter to track the voltage and current values of the PV collectors, providing real-time data on electrical parameters. Simultaneously, a wattmeter is employed to monitor the power produced by the solar cells, offering insights into the overall energy generation of the PV system. To convert analog signals into digital values for precise measurements, an analog-to-digital converter (ADC) is employed, facilitating the retrieval of accurate and digitized data. The final digital outputs include the actual electric qualities, namely \(V_{act}\) (voltage), \(I_{act}\) (current), and power \(P_{act}\), forming the basis for the subsequent analysis and comparison with the expected values generated by the genetic algorithm. This meticulous data acquisition process ensures a comprehensive and accurate assessment of the PV system's real-time performance and aids in the identification of any deviations or errors in its operation.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**PV Parameter** & **Value** & **Unit** \\ \hline \(n\) & 0.988 & unitless \\ \(R_{p}\) & 249.678 & \(O_{lm}\) \\ \(R_{s}\) & 0.384 & \(O_{lm}\) \\ \(I_{sc,ST}\) & 5.17 & A \\ \(I_{ph,ST}\) & 5.178 & A \\ \(V_{cc,ST}\) & 43.99 & V \\ \(\mu_{L_{c}}\) & 0.00415 & \(I/^{\circ}\)C \\ \(\mu_{V_{sc}}\) & \(-\)0.03616 & \(V/^{\circ}\)C \\ \(N_{s}\) & 72 & unitless \\ \(I_{s}\) & \(1.784\times 10^{-10}\) & A \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters of the PVs used in the experiments.