Evaluation of MPPT Algorithms for Solar PV Systems with Machine Learning and Metaheuristic Techniques
Bibliographic record
Abstract
Optimizing the performance of photovoltaic (PV) systems, which are a key component of renewable energy systems, is highly dependent on efficient maximum power point tracking (MPPT) algorithms, particularly under varying operating conditions such as fluctuating irradiance and temperature.This study contributes to the field by presenting a comprehensive comparative analysis of conventional, hybrid, and machine learning (ML)-based MPPT techniques, identifying their strengths, limitations, and suitability for enhancing PV performance.The evaluation was based on critical performance metrics, including maximum current Imax, maximum voltage Vmax, and maximum power Pmax.The results highlight the superiority of hybrid and ML-based methods over conventional approaches, demonstrating their ability to achieve greater optimization and efficiency.For example, the ANN-GA algorithm achieved the highest Pmax of 99.2186 W, showcasing the effectiveness of combining neural networks with evolutionary algorithms.Other hybrid methods, such as RF-PSO and ANN-GA-PSO, also demonstrated high levels of performance, successfully optimizing both current and voltage for improved power quality.These findings underscore the importance of selecting MPPT algorithms based on the specific operational requirements and characteristics of PV systems.The study also emphasizes the need for continued development and refinement of hybrid and ML-based MPPT techniques, as they have demonstrated exceptional potential for achieving optimal PV efficiency under diverse conditions.This work advances the understanding of MPPT algorithms and provides valuable insights for improving PV technology.By facilitating the transition to more effective and environmentally friendly energy systems, the findings contribute to global efforts toward sustainable energy solutions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".