TERIME: An improved RIME algorithm with enhanced exploration and exploitation for robust parameter extraction of photovoltaic models

Abstract: Parameter extraction of photovoltaic (PV) models is crucial for the planning, optimization, and control of PV systems. Although some methods using meta-heuristic algorithms have been proposed to determine these parameters, the robustness of solutions obtained by these methods faces great challenges when the complexity of the PV model increases. The unstable results will affect the reliable operation and maintenance strategies of PV systems. In response to this challenge, an improved rime optimization algorithm with enhanced exploration and exploitation, termed TERIME, is proposed for robust and accurate parameter identification for various PV models. Specifically, the differential evolution mutation operator is integrated in the exploration phase to enhance the population diversity. Meanwhile, a new exploitation strategy incorporating randomization and neighborhood strategies simultaneously is developed to maintain the balance of exploitation width and depth. The TERIME algorithm is applied to estimate the optimal parameters of the single diode model, double diode model, and triple diode model combined with the Lambert-W function for three PV cell and module types including RTC France, Photo Watt-PWP 201 and S75. According to the statistical analysis in 100 runs, the proposed algorithm achieves more accurate and robust parameter estimations than other techniques to various PV models in varying environmental conditions. All of our source codes are publicly available at https://github.com/dirge1/TERIME.

• The paper introduces TERIME, an improved meta-heuristic algorithm that balances exploration and exploitation for robust PV parameter extraction.
• It integrates a Differential Evolution mutation operator and randomization strategies, achieving RMSE reductions of 3.24% and 1.70% in complex models.
• Its consistent performance across Single, Double, and Triple-Diode models validates its reliability for optimizing PV system efficiency in real conditions.

Analyzing TERIME: Enhanced RIME Algorithm for PV Model Parameter Extraction

The paper "TERIME: An improved RIME algorithm with enhanced exploration and exploitation for robust parameter extraction of photovoltaic models" addresses a vital problem in photovoltaic (PV) systems: the accurate extraction of model parameters under various conditions. This process is crucial for optimizing and controlling PV systems, yet faces significant challenges due to the non-linear and implicit nature of PV model parameters, which are often not disclosed in manufacturers' data sheets.

The paper introduces TERIME, an advanced iteration of the RIME algorithm specifically designed to enhance the robustness and accuracy of parameter extraction in increasingly complex PV models. Traditional methods, including analytical and numerical approaches, often fall short due to their reliance on limited data or propensity to converge to local optima. Meta-heuristic algorithms, despite their advantages, experience diminishing effectiveness as PV model complexity increases. Therefore, there’s a need for an algorithm that balances exploration and exploitation effectively, which is precisely the gap TERIME aims to fill.

Algorithm Enhancements and Methodology

The TERIME algorithm integrates two primary strategies to improve the exploration and exploitation phases of the RIME meta-heuristic algorithm. A Differential Evolution (DE) mutation operator is employed during exploration to enhance population diversity, thereby increasing the algorithm's ability to escape local optima. Concurrently, TERIME utilizes a combination of randomization and neighborhood strategies during exploitation phases, balancing the depth and breadth of exploration to avoid premature convergence.

The paper notably compares TERIME's performance with various state-of-the-art algorithms, including MRIME, DO, and CLRao-1, across multiple PV models: Single-Diode Model (SDM), Double-Diode Model (DDM), and Triple-Diode Model (TDM). It validates TERIME's superior robustness and accuracy through statistical analysis over 100 iterations per model. Detailed assessments show TERIME consistently delivering lower Root Mean Square Error (RMSE) values, a definitive measure of model accuracy.

Results and Implications

TERIME demonstrates marked improvements over competing algorithms, particularly in its ability to maintain low RMSE values across varying environmental conditions. For RTC France and PWP 201 datasets, TERIME achieved significant improvements—3.24% and 1.70% in RMSE for the DDM and TDM respectively. The superior performance is repeated in the S75 module dataset across different irradiance levels and temperatures.

These results have substantial implications not only for enhancing the reliability of PV systems in practice but also for advancing theoretical models of PV parameter extraction. The use of TERIME could lead to more reliable operation and long-term maintenance strategies for PV systems, presenting a viable solution to address energy shortages and environmental concerns associated with fossil fuels.

Future Directions

The success of TERIME in improving PV parameter extraction highlights potential future directions in the refinement of meta-heuristic algorithms. Its exploitation strategy particularly opens avenues for evolving other algorithms to address high-dimensional optimization problems systematically.

Future research could focus on integrating more sophisticated learning mechanisms within TERIME, potentially involving adaptive or dynamic parameter settings that consider real-time environmental feedback. Continually evolving the balance between exploration and exploitation within the algorithm will remain key to addressing the complex challenges posed by real-world, volatile PV systems.

In conclusion, TERIME represents a significant advancement in the field of PV parameter extraction, offering robust solutions across diverse environmental conditions and model complexities. Its contributions are not just confined to the performance enhancements of PV systems but extend broader implications for the future development of optimization algorithms in science and engineering domains.