Backtracking Search Algorithm Based Fuzzy Charging-Discharging Controller for Battery Storage System in Microgrid Applications
Bibliographic record
Abstract
This paper presents an efficient fuzzy logic control system for charging and discharging of the battery energy storage system in microgrid applications. Energy storage system can store energy during the off-peak hour and supply energy during peak hours in order to maintain the energy balance between the storage and microgrid. However, the integration of battery storage system with microgrid requires a flexible control of charging-discharging technique due to the variable load conditions. Therefore, a comparative evaluation of the developed model is analyzed by considering controllers with fuzzy only and optimized fuzzy algorithms. In this paper, backtracking search algorithm based fuzzy optimization is introduced to evaluate the state of charge of the battery by optimizing the input and output fuzzy membership functions of rate of change of the state of charge and power balance. Backtracking search algorithm is chosen due to its high convergence speed, and it is good for searching and exploration process with exploiting capabilities. To validate the performance of the developed controller, the obtained results are compared to the results obtained with the particle swarm optimization based fuzzy and fuzzy only controllers, respectively. Results show that the backtracking search algorithm based fuzzy optimization outperforms the other control methods in terms of effectively manage the charging-discharging of the battery storage to ensure the desired outcome and reliable microgrid operation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".