Artificial Intelligence Based Transient Stability Detection of IEEE 9 Bus System
Keywords:
PMU, GSA, VSA, TSA, IEEE 9-BusAbstract
The proposed approach focuses on developing an AI-based system utilizing event data to detect transient stability, with a specific emphasis on time-series measurements. The algorithm will be designed to account for various factors such as noise, measurement delays, line outages, and the integration of variable renewable energy sources (VREs). To facilitate high-fidelity data acquisition, phasor measurement units (PMUs) will be utilized to provide time-series information at a high sampling rate. Additionally, the impact of varying numbers of PMUs will be examined through simulation. The algorithm will be trained using a synthetic dataset generated by a MATLAB-based algorithm to simulate PMU measurement data. The IEEE bus test system will be employed to evaluate the algorithm's performance under different loading conditions.
The results of the study are expected to demonstrate the effectiveness of the proposed scheme in detecting stable and unstable transient stability conditions solely based on the magnitude and angle of bus voltages, without requiring detailed system parameter information. Furthermore, the proposed AI-based approach is anticipated to offer improved accuracy in transient stability detection across all scenarios. Importantly, the computational efficiency of the AI-based method will be compared to conventional approaches, highlighting potential advantages in terms of reduced computation time. Overall, this research aims to advance the field of transient stability assessment in power systems through the application of artificial intelligence techniques.
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