Dynamic state estimation for power grids with unconventional measurements

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonState estimation problem for power systems has long been a fundamental issue that demands a variety of methodologies dependent on the system settings. With recent introduction of advanced devices of phasor measurement units (PMUs) and dedicated communication networks, the infrastructure of power grids has been greatly improved. Coupled with the infrastructure improvements are three emerging issues for the state estimation problems, namely, the coexistence of both traditional and PMU measurements, the incomplete information resulting from delayed, missing and quantized measurements due to communication constraints, and the cyber-attacks on the communication channels. Three challenging problems are faced when dealing with the three issues in the state estimation program of power grids: 1) how to include the PMU measurements in the state estimator design, 2) how to account for the phenomena of incomplete information occurring in the measurements and design effective state estimators resilient to such phenomena, and 3) how to identify the system vulnerability in state estimation scheme and protect the estimation system against cyber-attacks. In this thesis, with the aim to solve the above problems, we develop several state estimation algorithms which tackle the issues of mixed measurements and incomplete information, and examine the cyber-security of the dynamic state estimation scheme. • To improve the estimation performance of power grids including PMU measurements, a hybrid extended Kalman filter and particle swarm optimization algorithm is developed, which has the advantages of being scalable to the numbers of the installed PMUs and being compatible with existing dynamic state estimation software as well. • Two kinds of network-induced phenomena, which leads to incomplete information of measurements, are considered. Specifically, the phenomenon of missing measurements is assumed to occur randomly and the missing probability is governed by a random variable, and the quantized nonlinear measurement model of power systems is presented where the quantization is assumed to be of logarithmic type. Then, the impact of the incomplete information on the overall estimation performance is taken into account when designing the estimator. Specifically, a modified extended Kalman filter is developed which is insensitive to the missing measurements in terms of acceptable probability, and a recursive filter is designed for the system with quantized measurements such that an upper bound of the estimation error is guaranteed and also minimized by appropriately designing the filter gain. • With the aim to reduce or eliminate the occurrence of the above-mentioned network-induced phenomena, we propose an event-based state estimation scheme with which communication transmission from the meters to the control centre can be greatly reduced. To ensure the estimation performance, we design the estimator gains by solving constrained optimization problems such that the estimation error covariances are guaranteed to be always less than a finite upper bound. • We examine the cyber-security of the dynamic state estimation system in power grids where the adversary is able to inject false data into the communication channels between PMUs and the control centre. The condition under which the attacks cause unbounded estimation errors is found. Furthermore, for system that is vulnerable to cyber-attacks, we propose a system protection scheme through which only a few (rather than all) communication channels require protection against false data injection attacks