Roles of dynamic state estimation in power system modeling, monitoring and operation

Power system dynamic state estimation (DSE) remains an active research area. This is driven by the absence of accurate models, the increasing availability of fast-sampled, time-synchronized measurements, and the advances in the capability, scalability, and affordability of computing and communications. This paper discusses the advantages of DSE as compared to static state estimation, and the implementation differences between the two, including the measurement configuration, modeling framework and support software features. The important roles of DSE are discussed from modeling, monitoring and operation aspects for today's synchronous machine dominated systems and the future power electronics-interfaced generation systems. Several examples are presented to demonstrate the benefits of DSE on enhancing the operational robustness and resilience of 21st century power system through time critical applications. Future research directions are identified and discussed, paving the way for developing the next generation of energy management systems and novel system monitoring, control and protection tools to achieve better reliability and resiliency.Departamento de Energía de EE. UU TPWRS-00771-202

Wide-area control of power systems through delayed network communication

Like general network communication, there are network-induced delays, data packet dropout and disordering in the communication of wide-area measurement systems. What impact do these factors have on the control of wide-area closed-loop power systems? This study aims at developing methods in order to take these factors into account in control of wide-area power systems. First, a networked control system model is constructed for widearea closed-loop power systems; in this model, network-induced delays, data packet dropout, and disordering are captured by timevarying delays in wide-area measurement systems. Then, linear matrix inequality based methods are applied to design a controller for better power system performance using wide-area information as feedback signals. The controller can tolerate network-induced delays, data packet dropout, and disordering in the communication of wide-area measurement systems. Finally, we give some simulation results showing the effectiveness of our approach. © 2011 IEEE

Robust Adaptive Nonlinear Kalman Filter for Synchronous Machine Parameter Calibration

This paper proposes a robust and adaptive nonlinear Kalman filter for synchronous machine parameter calibration. The key idea is to develop the polynomial chaos-based analysis of variance (ANOVA) method for suspicious parameter detection. ANOVA allows us to derive a set of adaptive weights that can be used to address local parameter optimality issue when performing joint state and parameter estimation. It is shown that if erroneous parameters have strong correlations, the widely used methods that augment state and parameter for joint estimation will lead to large biases. By contrast, thanks to the derived adaptive weights for the suspicious parameters, the proposed method can effectively deal with the parameter dependence, yielding much better calibration results. In addition, the robustness of the proposed method enables us to filter non-Gaussian noise. Simulations carried out on the IEEE 39-bus system validate the effectiveness and robustness of the proposed approach