# A Novel Real-Time PV Error Handling Exploiting Evolutionary-Based Optimization
## 5 Conclusions  
In this paper, a novel evolutionary algorithm has been presented for real-time PV power optimization, introducing a comprehensive system that monitors and maintains the health of PV installations. The system provides a holistic overview of outdoor environmental conditions, continuously monitors PV voltage, power, and current, and predicts PV values in real time. Alerts and notifications are sent to stakeholders and end-users, enhancing the practical utility of the system.  
The power optimization process underwent a thorough analysis, leading to the development of a finely tuned GA. Through a systematic trial-and-error phase, optimal GA parameters were determined--\(p_{c}=0.8\), \(p_{m}=0.01\), and a population size of 60--resulting in a remarkable accuracy exceeding 98% when compared to actual PV values. This underscores the effectiveness of the proposed GA in optimizing PV power output.  
To ensure the operational integrity of PV modules or arrays, a robust comparison between actual and predicted voltage and current was implemented. The proposed error-handling procedure demonstrated efficiency during experimentation, promptly alerting end-users to potential PV malfunctions. A comparative analysis with other algorithms affirmed the accuracy of the genetic algorithm, boasting a 99% success rate and offering flexibility in parameter configuration.  
Looking ahead, future work will involve the comprehensive testing of additional PV parameters and diverse outdoor conditions to further enhance system efficiency. The refinement and analysis of the PV modeling approach will be prioritized to ensure the accuracy of PV error handling. Exploring alternative PV modeling techniques and conducting user satisfaction research will contribute to continual improvements, ensuring the system's effectiveness and enhancing the user experience.  
Conceptualization, A.D.; Methodology, A.D., A.P., K.G. and D.T. ([PERSON]); Software, A.D., A.P., D.T. ([PERSON]) and I.T.; Validation, A.D., A.P., K.G., D.T. ([PERSON]), I.T., C.-N.A., S.K., D.I. and D.T. ([PERSON]), I.T., C.-N.A., S.K., D.I. and D.T. ([PERSON]), I.T., C.-N.A., S.K., D.I. and D.T. ([PERSON]); Writing-original draft, A.D., A.P., K.G., D.T. ([PERSON]), I.T., C.-N.A., S.K., D.I. and D.T. ([PERSON]); Writing-review & editing, A.D., A.P., K.G., D.T. ([PERSON]), I.T., C.-N.A., S.K., D.I. and D.T. ([PERSON]); Visualization, K.G., D.T. ([PERSON]), I.T., C.-N.A., S.K., D.I. and D.T. ([PERSON]); Supervision,A.D., A.P. and S.K.; Project administration, A.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article  
This work is partially supported by the PRECEPT project, funded by the EU H2020 under grant agreement No. 958284. The authors express their sincere gratitude to [PERSON] for his valuable contribution in enhancing our understanding of the Lambert function in photovoltaic modeling. His assistance has been instrumental in advancing our research in this area, and we are truly grateful for his guidance and support.  
The authors declare no conflict of interest.