A novel hybrid algorithm, incorporating Prairie Dog, INFO, Fission Fusion, and Naked Mole-Rat algorithms, has been developed to significantly improve the parameter estimation of solar photovoltaic (PV) systems. This innovative approach addresses the challenges faced by traditional optimization techniques, enhancing the performance of the Naked Mole-Rat Algorithm by preventing local optima stagnation and optimizing exploration and exploitation properties.
The Prairie INFO Fission Naked (PIFN) algorithm integrates several advanced techniques, making it more effective than existing methods. Researchers utilized this new algorithm to perform parametric estimations on various PV modules, including the RTC France Solar Cell and Photowatt-PWP201, demonstrating its capability to yield accurate results.
One of the standout features of the PIFN algorithm is its self-adaptive mechanism, achieved through five new mutation operators. These operators allow the algorithm to dynamically adjust its parameters, which significantly enhances its ability to refine solutions over multiple iterations. Statistically sound tests, including Friedman and Wilcoxon rank-sum tests, validated the superiority of PIFN compared to classical benchmark functions and other Meta-Heuristic algorithms.
“The PIFN algorithm achieved the lowest root-mean-square error value for multiple PV modules, indicating its robustness and efficiency,” the authors noted. By directly addressing the estimation of parameters like photovoltaic current and diode saturation current, the PIFN algorithm enhances predictive models for solar energy generation.
This research is particularly timely, as the integration of solar photovoltaic systems presents numerous challenges for modern power grids. Accurate modeling and parameter estimation are pivotal for optimizing the efficacy of these systems, especially considering their nonlinear behaviors and complex interactions.
Traditional optimization methods have struggled to deliver reliable results consistently due to the intricacies involved with these systems. The PIFN algorithm, on the other hand, not only solidifies a balance between exploration and exploitation but also circumvents the common pitfalls related to local optima stagnation.
The adoption of this holistic algorithm could revolutionize how researchers and engineers approach challenges within the energy sector, leading to improved efficiency and reliability in solar energy technology.
“The integration of the Newton-Raphson approach with the PIFN algorithm enhances the accuracy of parameter estimation,” the authors highlighted, emphasizing the combinatorial strength of their approach.
Future research endeavors may adapt the principles of the PIFN algorithm across various other complex optimization scenarios, paving the way for advanced methodologies within engineering and scientific research.
Through the development of the PIFN algorithm, the authors have opened up new avenues for exploring the optimization challenges inherent within solar photovoltaic systems, contributing knowledge valuable for both researchers and practitioners working within the renewable energy sphere.