Adaptive fuzzy controller based early detection and prevention of asymmetrical faults in power systems

The inclusion of smart electronic devices and the integration of a large number of renewable energy resources (RERs) make network monitoring and control complicated in a power system network. The scenario becomes worse when asymmetrical faults occur in a system at multiple intervals of time, which may lead to blackouts if proper remedial actions are not taken on time. In order to countermeasure these disturbances, short term load forecasting using neural networks is performed in this paper to observe nonlinear behavior due to asymmetrical faults in the demand profile of a power system. Furthermore, the artificial intelligence-based fuzzy controller (AIFC) is implemented in real-time to detect those fluctuations that are not monitored through traditional load forecasting techniques. AIFC continuously observes the demand profile and provides uninterrupted power by incorporating RERs in case of unexpected disturbances to stabilize the system. To validate the effectiveness of the proposed algorithm, it is tested on a well known Danish power transmission system, i.e., the Western Denmark transmission system, which contains 464 nodes and 162 transmission lines. Results show that the proposed algorithm stabilizes the system 8.5 times faster than the existing state of the art works.