A New Approach for Online Transient Instability Assessment and Prediction via Tracing the Variation Trend of Synchronous Generator Energy

Transient Instability Prediction (TIP) of the synchronous generator offers an opportunity to mitigate out-of-step damages through early tripping or facilitating remedial actions to prevent instability. Analyzing transient stability is inherently complex due to the diverse components and controllers within the power system, and it must be conducted within a limited timeframe of less than one second. This study introduces an innovative method for predicting the instability of synchronous machines, focusing on the rate of generator energy variations. The energy is calculated using the integral of the electrical power of the generator measured over time. This energy increases during a fault and subsequently decreases in post-fault. The increase in the rate of change of energy in the energy-releasing area indicates that the machine is progressing toward an unstable condition. This novel criterion is implemented locally on each generator within the network, enabling effective prediction of transient instability through the sole measurement of the machine’s electrical power. The proposed approach is online, conceptual, and independent of fault data, network configuration, and system parameters. Furthermore, the reliability level of the proposed method can be determined based on the required accuracy level or prediction speed using a rule-based mechanism. Simulation results across various scenarios in the IEEE 39-bus and Iran power systems demonstrate that the proposed approach effectively predicts the instability condition quickly and precisely. The algorithm was implemented in MATLAB, and Power Factory software was employed as the stability laboratory.