The importance of Integrating wind power generation into electric power grids has rapidly progressed over the past decade. But the intermittency of wind power presents a special challenge for utility system operations as well as the market structure mechanisms. The problem arises from the uncertainty and variability in wind resources that causes fluctuations in the output of wind power generators. This paper presents a short-term wind speed prediction using linrealized time series model. Wind data are first collected from a weather station in ten minute resolution for a period of one year followed by a fitted two Weibull distribution parameters model being estimated from regression analysis on the logarithms of wind speed data. Transformation from Weibull into normal distribution is then held and linear predictive coefficients calculated using finite impulse response filter (FIR) and infinite impulse response filter (IRR) are evaluated for the normalized wind speed random process. Results of 10 minute ahead, one hour ahead, 12 hours ahead and 24 hours ahead wind speed predictions are presented and model accuracy in each of these time-ahead prediction scale are discussed. Also a remarkable observation of the independencies between future and historical wind speed data allows a state space representation model using discrete Markov Process to best represent the stochastic behavior of wind speed signal. In doing so, optimum quantization parameters are first done for both Weibull and normal wind speed distributions and a transition probability matrices are evaluated in each case showing smooth state transition levels in wind data.Index Terms-wind speed, short term prediction, filter design, optimum quantization, transition probability, and Markov Process.

The two-coupled distillation column process is a physically complicated system in many aspects. Specifically, the nested interrelationship between system inputs and outputs constitutes one of the significant challenges in system control design. Mostly, such a process is to be decoupled into several input/output pairings (loops), so that a single controller can be assigned for each loop. In the frame of this research, the Brain Emotional Learning Based Intelligent Controller (BELBIC) forms the control structure for each decoupled loop. The paper's main objective is to develop a parameterization technique for decoupling and control schemes, which ensures robust control behavior. In this regard, the novel optimization technique Bacterial Swarm Optimization (BSO) is utilized for the minimization of summation of the integral time-weighted squared errors (ITSEs) for all control loops. This optimization technique constitutes a hybrid between two techniques, which are the Particle Swarm and Bacterial Foraging algorithms. According to the simulation results, this hybridized technique ensures low mathematical burdens and high decoupling and control accuracy. Moreover, the behavior analysis of the proposed BELBIC shows a remarkable improvement in the time domain behavior and robustness over the conventional PID controller.

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