| Abstract: | Identification of transient stability state in real-time and maintaining stability through preventive control technology are challenging tasks for a large power system while integrating deregulation constraints. Widely employment of the phasor measurement units (PMUs) in a power system and development of wide area management systems (WAMS) give relaxation to monitoring, measurement and control hurdles. This paper focuses on two research objectives; the first is transient stability assessment (TSA) and second is selection of the appropriate member for the control operation in unstable operating scenario. A model based on the artificial machine learning and PMU data is constructed for achieving both the objectives. This model works through prompt TSA status with radial basis function neural network (RBFNN) and validates it with PMU data to determine the criticality level of the generators. To reduce the complexity of the model a transient stability index (TSI) is proposed in this paper. A RBFNN is used to determine the transient stability aspects like stability status of system, coherent group and criticality rank of generator and preventive control action, following a large perturbation. PMUs measure post-fault rotor angle values and these are used as input for training RBFNN. The proposed approach is demonstrated on the IEEE 10-generator 39-bus, 16-generator 68-bus and 50-generator 145-bus test power systems successfully and the effectiveness of the approaches is discussed. |
