Intelligent mitigation of blackout in real-time microgrids: Neural Network Approach
| Autor: | Adeniyi A. Babalola, Rabie Belkacemi, Sina Zarrabian |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Engineering
Artificial neural network business.industry 020209 energy Testbed Blackout Control engineering 02 engineering and technology Cascading failure Electric power system Robustness (computer science) 0202 electrical engineering electronic engineering information engineering medicine Microgrid medicine.symptom business Power-system protection |
| Zdroj: | 2016 IEEE Power and Energy Conference at Illinois (PECI). |
| DOI: | 10.1109/peci.2016.7459213 |
| Popis: | In this paper, a novel application of Artificial Neural Networks (ANN) is deployed to prevent blackout in a microgrid after N-1-1 contingency condition. In fact, microgrids are vulnerable to disturbances and abnormal conditions due to their inherent small inertia. Therefore, stability of microgrids after a disturbance turns into a challenge in power systems. The key contribution of this paper is to utilize the artificial intelligence concept to prevent cascading failure practically at early stages and to make microgrids more reliable and robust by intelligent and adaptive re-dispatch of power. The proposed ANN control approach is tested on an experimental testbed microgrid. Experimental results verify the robustness, accuracy, and effectiveness of the ANN method for preventing cascading failure in addition to providing voltage and frequency stability after initiation of a disturbance. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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