Optimal Protection Coordination for Grid-Connected and Islanded Microgrids Assisted by the Crow Search Algorithm: Application of Dual-Setting Overcurrent Relays and Fault Current Limiters
Bibliographic record
Abstract
This paper introduces a two-stage protection coordination framework designed for grid-connected and islanded microgrids (MGs) that integrate distributed generations (DGs) and energy storage systems (ESSs). The first stage focuses on determining the optimal location and sizing of DGs and ESSs within the islanded MG to ensure a stable and reliable operation. The objective is to minimize the combined annual investment and expected operational costs while adhering to the optimal power flow equations governing the MG, which incorporates both DGs and ESSs. To account for the inherent uncertainties in load and DG power generation, scenario-based stochastic programming (SBSP) is used to model these variations effectively. The second stage develops the optimal protection coordination strategy for both grid-connected and islanded MGs, aiming to achieve a rapid and efficient protective response. This is achieved by optimizing the settings of dual-setting overcurrent relays (DSORs) and determining the appropriate sizing of fault current limiters (FCLs), using operational data from the MG’s daily performance. The goal is to minimize the total operating time of the DSORs in both primary and backup protection modes while respecting critical constraints such as the coordination time interval (CTI) and the operational limits of DSORs and FCLs. To solve this complex optimization problem, the Crow Search Algorithm (CSA) is employed, ensuring the derivation of reliable and effective solutions. The framework is implemented on both 9-bus and 32-bus MGs, demonstrating its practical applicability and evaluating its effectiveness in real-world scenarios. The proposed method achieves an expected total daily relay operation time of 1041.36 s for the 9-bus MG and 1282 s for the 32-bus MG. Additionally, the optimization results indicate a reduction in maximum voltage deviation from 0.0073 p.u. (grid-connected mode) to 0.0038 p.u. (islanded mode) and a decrease in daily energy loss from 1.0114 MWh to 0.9435 MWh. The CSA solver outperforms conventional methods, achieving a standard deviation of 1.13% and 1.21% for two optimization stages, ensuring high reliability and computational efficiency. This work not only provides valuable insights into the optimization of MG protection coordination but also contributes to the broader effort of enhancing the reliability and economic viability of microgrid systems, which are becoming increasingly vital for sustainable energy solutions in modern power grids.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".