Decentralized Adaptive Quantized Excitation Control for Multi-Machine Power Systems by Considering the Line-Transmission Delays

In this paper, a neural decentralized adaptive quantized dynamic surface control scheme is proposed for a class of large-scale multi-machine power systems with static var compensator (SVC) and unknown line-transmission time delays. The main contributions of the proposed method are summarized as follows: 1) a decentralized dynamic surface quantized control scheme with simple structure is proposed for the large-scale multi-machine systems with SVC, where the 'explosion of complexity' problem in backstepping method and the complexities introduced by SVC are overcome; 2) the unknown line-transmission time delays between different generators are considered and dealt with by introducing the finite-cover lemma with radial basis function neural networks (RBFNNs) approximator, which leads to the arbitrarily small L∞ ^a tracking performance; 3) the strong nonlinearities, uncertain parameters and external disturbances of the system are considered and the number of the estimated parameters is greatly reduced by estimating the weight vector norm of neural networks instead of estimating the weighted vector itself. It is proved that all the signals in the control system are ultimately uniformly bounded^b and can be made arbitrarily small. Simulation results show the validity of the proposed method.^aHere, the L∞ norm is defined as x ∞ =Δ t≥q 0 x(t) and we say x L∞ when x ∞ exists.^bHere, we say x(t) is ultimately uniformly bounded if there exist positive constants b and c, independent of t₀ ≥ 0, and for every a\in (0,c), there is T=T(a,b), independent of t₀, such that x(t₀)x(t)b, t t₀+T.