# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 1 Introduction
Integrating all PV parameters and methods to estimate the I-V curve, while also providing techniques for error handling, is a challenging task. The complexity further increases when using fusion and hybrid methods to ensure that the PV modeling output closely approximates the actual PV output, which is essential for optimizing the power process and, subsequently, error handling. In light of the challenges and concerns mentioned above, this paper proposes a novel approach to address the modeling and optimization of single-diode photovoltaic systems. Specifically, an analytical method is employed to model the photovoltaic system, while a state-of-the-art genetic algorithm is utilized to estimate the electric parameters of the system. The resulting real-time values of \(V_{act}\), \(I_{act}\), and \(P_{act}\) are then compared to the predicted values obtained from the genetic algorithm (\(V_{gen}\), \(I_{gen}\), and \(P_{gen}\)), which provides a means of identifying potential malfunctions or errors in the PV system or its sensing devices. By notifying the end-user of any significant deviations between the predicted and actual values, appropriate actions can be taken to prevent further issues and ensure the smooth and continuous operation of the photovoltaic system [10]. Overall, this approach holds great promise for addressing the challenges associated with accurate PV modeling and error handling in single-diode photovoltaic systems while eliminating the integration of complex IoT systems [11]. Recent studies and advancements in PV modeling have indicated that, for the specific task of estimating the I-V curve and optimizing the power process, the inclusion of certain parameters (e.g., air humidity, wind direction, wind speed) may not yield a significant improvement in accuracy [12]. The selected PV modeling method, while simplified in its consideration of operating conditions, has been validated against real-world data, demonstrating high accuracy and reliability [12]. Moreover, the decision to limit the number of parameters considered in the model is also pragmatic. By focusing on essential factors, the complexity of the model is reduced while minimizing associated costs. Additional parameters would require more sophisticated sensing devices and potentially lead to increased expenses in both installation and maintenance for real-time conditions. Within this context, the proposed method holds profound significance in the field of PV systems by introducing a novel approach to real-time monitoring and error detection. The fundamental concept revolves around the premise that, under optimal conditions, the PV modeling outputs, which estimate the electricity generation (represented by \(V_{gen}\), \(I_{gen}\), and \(P_{gen}\)), should closely align with the actual measurements of the PV system's performance (\(V_{act}\), \(I_{act}\), and \(P_{act}\)). Any substantial deviation between these two sets of values indicates the potential occurrence of errors or malfunctions within the PV system or its sensing devices. Exploiting a GA for parameter estimation, the method enables accurate and low-cost real-time monitoring of PV operations. This not only ensures the flawless functionality of the PV system but also provides a proactive mechanism for identifying and addressing issues promptly, thereby maximizing the overall efficiency and reliability of the photovoltaic installation. The contributions and novelty of this paper can be summarized as follows:  
* Development of a novel genetic algorithm for MPP optimization in single-diode photovoltaic systems. * Integration of the Lambert function into the optimization function of the genetic algorithm to ensure PV constraints are met. * Implementation of a real-time PV error detection system to detect and notify the end-user of any potential malfunction or deviation between real and predicted PV values. In the subsequent sections of this paper, a comprehensive analysis of photovoltaic (PV) output power estimation and fault diagnosis is presented.