Dynamic state estimation of generators using spherical simplex unscented transform-based unbiased minimum variance filter

摘要

Dynamic state estimation is essential in case of various monitoring, control, and protection strategies that are designed based on the state-space model. Kalman filter-based estimation algorithms are mainly used to estimate these states locally using the input and output measurements of the generator. However, in the case of wide-area power system control and protection strategies, remote estimation of these states is required. This remote estimation relies upon phasor measurement units for measurement signals, which are limited to output measurements such as voltage, current, and frequency. For Kalman filter-based techniques, apart from the output, input measurements such as field and torque input are also required to estimate the states. This study proposes an input invariant filter technique using unbiased minimum variance filter and spherical simplex unscented transform for remote estimation of generator states using the limited phasor measurement unit measurements. The estimation is performed in the absence of mechanical input torque and field voltage measurements using a minimum set of sigma points. The performance of the filter under various transient conditions and in the presence of measurement errors are analysed and compared with existing techniques.