Enhancing the Dynamic Performance of Hybrid Photovoltaic-Battery DC Microgrid Through Piece-Wise Affine Model-Based Controller With Mode Transition Function
Bibliographic record
Abstract
Solar energy utilization, in conjunction with battery systems, within stand-alone DC microgrid systems represents a significant trend. In such isolated network configurations, the voltage of the Direct Current (DC) bus experiences fluctuations: it increases when the islanded DC microgrid receives excess energy and decreases during periods of energy scarcity. Ensuring the appropriate operational mode for photovoltaic (PV) panels is crucial for maintaining the DC bus voltage within specified operational limits, guaranteeing high electrical quality, whether the power is supplied directly or converted to AC form. Furthermore, achieving balanced power sharing between the PV and the energy storage system enhances the efficiency of the energy storage workload. This study proposes a transition function to facilitate seamless switching of the PV panel’s operational mode between Maximum Power Point Tracking (MPPT) and voltage-controlled modes. This transition function is applied to the PV side converter, which adjusts the duty cycle value of both MPPT and droop mode outputs based on the microgrid’s energy adequacy condition, as determined by the DC bus voltage readings. Moreover, this study introduces a Duty Cycle Range Divider (DCRD) algorithm to derive the converter’s Piece-wise Affine (PWA) model. Subsequently, a linear quadratic regulator (LQR) controller, designed based on the PWA model, is employed alongside the transition function to enhance the DC microgrid’s dynamic performance. A similar LQR controller is applied to the battery-side converter with a battery State of Charge (SoC)-based droop control to balance the power-sharing. The proposed control strategy stabilizes the DC bus voltage and ensures a seamless response during transitions in the PV system’s operating mode. The efficacy of this strategy is validated through MATLAB Simulink simulations and laboratory-scale experiments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".