Decentralized estimation and control for power systems

Abstract

This thesis presents a decentralized alternative to the centralized state-estimation and control technologies used in current power systems. Power systems span over vast geographical areas, and therefore require a robust and reliable communication network for centralized estimation and control. The supervisory control and data acquisition (SCADA) systems provide such a communication architecture and are currently employed for centralized estimation and control of power systems in a static manner. The SCADA systems operate at update rates which are not fast enough to provide appropriate estimation or control of transient or dynamic events occurring in power systems. Packet-switching based networked control system (NCS) is a faster alternative to SCADA systems, but it suffers from some other problems such as packet dropouts, random time delays and packet disordering. A stability analysis framework for NCS in power systems has been presented in the thesis considering these problems. Some other practical limitations and problems associated with real-time centralized estimation and control are computational bottlenecks, cyber threats and issues in acquiring system-wide parameters and measurements.

The aforementioned problems can be solved by a decentralized methodology which only requires local parameters and measurements for estimation and control of a local unit in the system. The cumulative effect of control at all the units should be such that the global oscillations and instabilities in the power system are controlled. Such a decentralized methodology has been presented in the thesis. The method for decentralization is based on a new concept of `pseudo-inputs' in which some of measurements are treated as inputs. Unscented Kalman filtering (UKF) is applied on the decentralized system for dynamic state estimation (DSE). An extended linear quadratic regulator (ELQR) has been proposed for the optimal control of each local unit such that the whole power system is stabilized and all the oscillations are adequately damped. ELQR requires DSE as a prerequisite. The applicability of integrated system for dynamic estimation and control has been demonstrated on a model 16-machine 68-bus benchmark system.