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Swarm-inspired optimization has become very popular in recent years. Ant colony optimization (ACO) is successfully applied in the traveling salesman problem. Performance of the basic ACO for small problems with moderate dimensions and searching space is satisfactory. As the searching space grows exponentially in large-scale power systems generator planning, the basic ACO is not applicable for the vast size of the pheromone matrix of the ACO in practical-time and physical computer-memory limits. However, memory-bounded methods prune the least-promising nodes to fit the system in the computer memory. Therefore, the authors propose a memory-bounded version of ant colony optimization (MACO) with selected dynamic programming search in this paper for the scalable generator planning problem. This MACO solves the limitation of computer memory, and does not permit the system to grow beyond a bound on memory. In the memory-bounded ACO implementation, A * heuristic is introduced to increase local searching ability and the authors propose probabilistic nearest neighbor approach to estimate pheromone intensity for the forgotten value. Finally, the benchmark data and methods are used to show the effectiveness of the proposed method. Index Terms--[A.sup.*] heuristic, memory-bounded ant colony optimization (MACO), pheromone matrix, probabilistic nearest neighbor.

This paper presents a new approach to the fuzzy unit commitment problem using the absolutely stochastic simulated annealing method. In every iteration, a solution is taken with a certain probability. Typically in the simulated annealing minimization method, a higher cost feasible solution is accepted with temperature-dependent probability, but other solutions are accepted deterministically. However, in this paper, all the solutions, both higher and lower cost, are associated with acceptance probabilities, e.g., the minimum membership degree of all the fuzzy variables. Besides, the number of bits flipping is decided by the linguistic fuzzy control. Excess units with system-dependent distribution handle constraints efficiently and reduce overlooking the optimal solution. To reduce the economic load dispatch calculations, a sign bit vector is introduced with imprecise calculation of the fuzzy model as well. The proposed method is tested using the reported problem data sets. Simulation results are compared to previous reported results. Numerical results show an improvement in solution cost and time compared to the results obtained from powerful algorithms. Index Terms--Best heat rate, fuzzy logic, linguistic fuzzy control, sign vector, simulated annealing (SA), unit commitment (UC).

A digital H∞ controller for a permanent magnet synchronous generator (PMSG) based wind energy conversion system (WECS) is presented. Wind energy is an uncertain fluctuating resource which requires a tight control management. So, it is still an exigent task for the control design engineers. The conventional proportional-integral (PI) control is not ideal during high turbulence wind velocities, and the nonlinear behavior of the power converters. These are raising interest towards the robust control concepts. The robust design is to find a controller, for a given system, such that the closed-loop system becomes robust that assurance high-integrity and fault tolerant control system, robust H∞ control theory has befallen a standard design method of choice over the past two decades in industrial control applications. The robust H∞ control theory is also gaining eminence in the WECS. Due to the implementation complexity for the continuous H∞ controller, and availability of the high speedy micro-controllers, the design of a sample-data or a digital H∞ controller is very important for the realistic implementation. But there isn't a single research to evaluate the performance of the digital H∞ controller for the WECS. In this paper, the proposed digital H∞ controller schemes comprise for the both generator and grid interactive power converters, and the control performances are compared with the conventional PI controller and the fuzzy controller. Simulation results confirm the efficacy of the proposed method which are ensured the WECS stabilities, mitigate shaft stress, and improving the DC-link voltage and output power qualities [ABSTRACT FROM AUTHOR]