Use of least-mean squares (LMS) adaptive filtering technique for estimating low-frequency electromechanical modes in power systems

The stability of heavily interconnected power systems is a primary concern in the power utility industry. Accurate knowledge of the low-frequency electromechanical modes in power systems gives vital information about the stability of the system. Current techniques for estimating electromechanical modes are computationally intensive and rely on complex system models. This research uses measurement-based techniques. Current measurement-based techniques typically require a ringdown from a disturbance. This paper involves the development of a least-mean squares (LMS) adaptive filtering technique to track these low-frequency electromechanical modes. This is a new approach in that the modes are tracked as ambient data arrives from power system monitors. The LMS adaptive filtering technique is applied to simulated data containing a stationary mode, a stationary mode with a fault, and a time-varying mode. The frequency and damping factor of the modes tracked with the LMS technique are compared with the actual modes. The results show how the LMS algorithm is able to track both a stationary and a moving mode in the noisy data.