Performance Study of Load Power Management and Control based on ANN and MPC for Standalone PV-Battery DC Off-Microgrid
Bibliographic record
Abstract
In this paper, a Load Power Management (LPM) Strategy, Artificial Neural Network (ANN) and Model Predictive Control (MPC) are developed for a standalone DC off-microgrid based on Photovoltaic (PV) system and Battery Energy System (BES). The State of Charge (SoC) of the BES and the generated power from the PV, are used as inputs for the LPM The connected loads are classified into three categories with different priorities, where the LPM manages the loads by disconnecting and reconnecting them to ensure uninterruptible power supply to the selected critical loads. Furthermore, an ANN is employed to achieve MPPT from the PV system, and the MPC is used to regulate the voltage and control the current of the BES. The performance of the proposed system and its control strategies are evaluated using Matlab/Simulink simulation.
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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 | 0.000 |
| 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".