Reinforcement learning approach for optimizing load shedding and controller parameter tuning in active islanded power systems
Bibliographic record
Abstract
The rising frequency of extreme weather events associated with climate change has made the resilient design an important feature for power systems. Incorporating the capability of parts of the power system to operate as active decentralized systems is one way of increasing the resiliency and enhancing the system reliability. This thesis addresses two significant issues related to decentralized active networks. The first issue concerns the challenge of maintaining the balance between generation and load within an islanded system through under-frequency load shedding (UFLS) during islanding events. The optimal design of UFLS schemes in active networks presents a complex problem, especially with the high penetration of inverter-based resources (IBR) that exhibit complex dynamics and low inertia. It is also important to ensure that the system operates effectively at any given operating point, necessitating the consideration of multiple operating points in the design process. Conventional schemes and control methods may not be sufficient and identifying a suitable scheme can be both time-consuming and labor-intensive, often requires conducting many simulations at various operating points. This thesis presents an optimal load shedding method in active decentralized power systems using reinforcement learning. The effectiveness of these proposed methodologies is demonstrated through the implementation of a UFLS system on a segment of the Manitoba Hydro power network. The second significant issue addressed in this context is the tuning of controllers for active, decentralized systems. These controllers need to be adjusted to function optimally across a wide range of operating conditions, both in grid-connected and islanded modes. This poses numerous challenges due to the systems' complexity and dynamic nature and traditional heuristic controller tuning methods often prove inadequate in managing such complexities. In response, this thesis proposes a novel approach that extends the ideas of deep reinforcement learning to tune controllers across multiple operating scenarios. The validity of the proposed method is illustrated through tuning of Proportional-Integral (PI) controllers for battery energy storage systems within the modeled systems. While these developed methods are showcased on a section of the network, it can be expanded to larger power networks, providing scalability to the operation of power systems.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".