Multiple-Model Adaptive Control Approach

Abstract

6.10 SummaryIn this chapter, the application of a multiple-model adaptive control scheme for robust damping of inter-area oscillations in power system using a TCSC is demonstrated. The lack of robustness of the conventional controllers under varying operating conditions leads to the motivation behind adopting such an adaptive strategy. A recursive Bayesian approach is used for computing the current probability of each model being close to the post-disturbance behavior of the system and the results are used to determine the subsequent control actions. The control output of each individual controller is assigned a weight based on the computed probability of each model and the resulting control action is the probability-weighted average of the control moves of individual controllers. The algorithm is shown to work satisfactorily for the study system under two different test cases where the model corresponding to the post-disturbance behavior is either present or not present in the model bank. When the model is present, the recursive Bayesian approach is able to identify the proper model within a few iterative steps and switch to the appropriate controller accordingly. On the other hand, when the exact model is removed from the bank, the scheme performs an appropriate blending of the remaining control moves to achieve reasonably similar performance as before. This highlights the potential applicability of the MMAC scheme for large practical power systems where the dynamics are unlikely to be governed by a single model. Under such situations, the key to the success of the MMAC scheme is the rate of convergence of the probabilities, which in turn, is governed by the proper choice of convergence factor C f and artificial cut-off β min. This chapter provides an outline on the variation pattern of the computed weights for different values of C f and β min and attempts to set a tentative guideline for choosing them, depending on the situation.