Nonlinear Optimal Feedback Control and Stability Analysis of Solar Photovoltaic Systems
Bibliographic record
Abstract
The efficiency of solar photovoltaic (PV) systems is directly affected by the convergence and steady-state responses of the implemented control method. In this paper, considering the nonlinearity appearing in the model of the solar PV system, we employ a nonlinear optimal feedback control scheme to deal with the oscillations around the maximum power point (MPP) of the system, induced by the chattering phenomenon in the control. Taking into account the improved transient response and flexibility, brought by including the cross-weighting terms in the cost functional, we develop an optimal control framework with a nonquadratic cost for addressing the MPP tracking (MPPT) problem of the solar PV system. Exploiting the fact that a Lyapunov function candidate can be considered as the steady-state solution of the Hamilton-Jacobi-Bellman (HJB) equation, we obtained the optimal feedback controller via minimizing the resultant Hamiltonian. The stability analysis of the closed-loop system is done for the obtained control law with a guaranteed performance measure. Moreover, to enhance the practicality of the obtained control law, we present two procedures to implement the obtained control scheme under nonuniform insolation and as a model-free approach, separately. To demonstrate the merits of the proposed framework, the obtained optimal feedback control, together with the partial shading condition and model-free approach, is simulated under various weather conditions. The optimal approach illustrates an improved performance in terms of the convergence rate and the amplitude of oscillations around the MPP, compared to existing results in the literature.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".