The Application Of Fuzzy Predictive Control To AGC System In Communication Network

In deregulated environment, the networked automatic generation control (NAGC) system is exposed to more sudden, larger changes and communication delay every now and then. Considering the nonlinearity and communication delay, a fuzzy predictive control strategy based on fuzzy T-S model is proposed in this paper. After deregulation, the influence of the nonlinearity becomes more obvious, which may cause large amount of over shoot or long-lasting oscillation on the valve position of the governor. Under this circumstance, the former linearized model is not a good representation of the power system in deregulation. In addition, AGC system and other ancillary systems, nowadays run on a common communication platform once the wide area open communication network is set up. Such widely distributed and interconnected communication systems promote the generation of networked AGC (NAGC), in which system communication delay and packet loss are inevitable. Motivated by the above two problems in the deregulated NAGC system, fistly, this paper constructed fuzzy T-S model of the valve position limits, which convert the nonlinear system into the superimposition of a series of linear systems. The advantage of fuzzy T-S model is that it not only accurately decribed the nonlinear system, but also make successful use of the fruitful linear control theories in the nonlinear system. Secondly, to compensate the random time delay and data dropouts induced by communication network, the generalized predictive control (GPC) is applied to the AGC system through building buffers in the actuator node. The GPC strategy predicted the optimal control signal, while buffers storage the latest optimal or suboptimal control signal, compensating the influence of time delay and data dropouts. At the end of the paper, the method combining fuzzy T-S modeling and predictive control is used in a two-area thermal AGC system with bilateral contract and DPM, and the simulation results have shown that this control method can make the frequency deviation, the area control error (ACE) and the tie-line power flow rapidly converge to zero or the allowed range. The simulation results indicated that the fuzzy predictive control strategy could accurately describe the nonlinearity and compensate the influence of the time delay and data dropouts, with good stability, rapidity and accuracy.