A Nonlinear Dynamic State Estimation Approach to Synchronous Generator Using Cubature Kalman Filter

Information about the state value of a synchronous generator is an important element in control, monitoring, and fault detection of power system. However, the state value still cannot be obtained through direct measurements. Therefore, a method that can be used to estimate the state accurately from terminal measurement is needed. This research presents a Dynamic State Estimation (DSE) approach based on measurement data from the generator terminal using a Cubature Kalman Filter (CKF). The synchronous generator is modeled as a sub-transient model to accurately depict the behavior of synchronous generators. The results of the CKF estimator will be contrasted with actual state under a variety of circumstances. Prediction results using CKF provide precise state estimation, enabling monitoring and estimation of dynamic conditions with robustness confirmed through various scenarios, including line-to-ground fault, measurement noise, process noise, step input, unknown input, and parameter.