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This paper describes and uses an ant colony meta-heuristic optimization method to solve the redundancy optimization problem. This problem is known as total investment-cost minimization of series-parallel power system configuration. Redundant components are included to achieve a desired level of availability. System availability is represented by a multi-state availability function. The power systems components are characterized by their performance (capacity), availability and cost. These components are chosen among a list of products available on the market. The proposed meta-heuristic seeks to the best minimal cost power system configuration with desired availability. To estimate the series-parallel power system availability, a fast method based on universal moment generating function (UMGF) is suggested. The ant colony approach is used as an optimization technique. An example of electrical power system is presented.

-This paper combines the universal generating function UGF with harmony search (HSO) meta-heuristic optimization method to solve a preventive maintenance (PM) problem for series-parallel system. In this work, we consider the situation where system and its components have several ranges of performance levels. Such systems are called multi-state systems (MSS). To enhance system availability or (reliability), possible schedule preventive maintenance actions are performed to equipments and affect strongly the effective age. The MSS measure is related to the ability of the system to satisfy the demand. The objective is to develop an algorithm to generate an optimal sequence of maintenance actions providing system working with the desired level of availability or (reliability) during its lifetime with minimal maintenance cost rate. To evaluate the MSS system availability, a fast method based on UGF is suggested. The harmony search approach can be applied as an optimization technique and adapted to this PM optimization problem.

Various control techniques using Advanced Super-conducting Magnetic Energy Storage (ASMES) aimed at improving power system stability have been proposed. As fuzzy controller has proved its value in some applications, the number of investigations employing fuzzy controller with ASMES has been greatly increased over recent period. Nevertheless, it is sometimes difficult to specify the rule base for some plants, or the need can arise for tuning the rule-base parameters if the plant changes. In order to solve such problems the Fuzzy Model Reference Learning Controller (FMRLC) is proposed. This paper investigates multi-inputs/multi-outputs FMRLC for time-variant nonlinear system. This provides the motivation for adaptive fuzzy control, whereby the focus is placed on the automatic on-line synthesis and tuning of fuzzy controller parameters (i.e., the use of on-line data for continuous learning of the fuzzy controller which ensures that the performance objectives are met). The simulation results show that the proposed robust controller is able to work with nonlinear power system (i.e., single machine connected at infinite bus), under various fault conditions and significant disturbances

The global wind energy capacity has increased rapidly in this last decade and became the fastest developing renewable energy technology. But unbalances in wind energy are highly impacting the energy conversion and this problem can be resolved by a doubly Fed Induction Generator (DFIG) which is one of the most commonly deployed large grid-attached wind turbine systems. The objective of this paper is to study this system, as the first the mathematical model is developed in the coordinates of Park d-q, then the power transfer between the stator and the network is realized by action on the rotor signals via a bidirectional converter. Independent control of active-reactive power is provided by conventional controllers (PIs). Then, we realized the implementation of the control by an orientation of the rotor flux (FOC) which presents an attractive solution to have an operation with the best performances of the Machine, in the applications with variable speeds. The analysis of the simulation results under the Matlab / Simulink environment of this control approach clearly shows that the system perfectly follows the reference values and, consequently, provides a good static and dynamic performance of the machine under study. 2 turbin total g J J J J K