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Ensuring the secure operation of power systems has become an important and critical matter during the present time, along with the development of large, complex and load-increasing systems. Security constraints such as the thermal limits of transmission lines and bus-voltage limits must be satisfied under all of a system's operational conditions. An alternative solution to improve the security of a power system is the employment of Flexible Alternating-Current Transmission Systems (FACTS). FACTS devices can reduce the flows of heavily loaded lines, maintain the bus voltages at desired levels, and improve the stability of a power network. The Unified Power Flow Controller (UPFC) is a versatile FACTS device that can independently or simultaneously control the active power, the reactive power and the bus voltage; however, to achieve such functionality, it is very important to determine the optimal location of the UPFC device, with the appropriate parameter setting, in the power system. In this paper, a genetic algorithm (GA) method is applied to determine the optimal location of the UPFC device in a network for the enhancement of the power-system loadability and the minimization of the active power loss in the transmission line. To verify our approach, simulations were performed on the IEEE 14 Bus, 30 Bus, and 57 Bus test systems. The proposed work was implemented in the MATLAB platform.

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In order to conceive command systems for welding equipment based on intelligence techniques similar to human thinking; it is better to use artificial intelligence methods, for example: Genetic algorithms and particle swarm optimization. Freshly, this latter has received increased attention in many research fields. This paper discuss the application of particle swarm optimization algorithm to optimize the welding process parameters and obtain a better Width of Head Affected Zone (WHAZ) in the welding machine which is gas metal arc welding. The effect of four main welding variables in the gas metal arc welding process, namely welding speed, welding voltage, nozzle-to-plate distance and wire feed speed on the WHAZ are studied. A source code is developed in MATLAB 8.3 to perform the optimization.

Systems based on artificial intelligence, such as particle swarm optimization and genetic algorithm have received increased attention in many research areas. One of the main objectives in the gas metal arc welding (GMAW) process is to achieve maximum depth of penetration (DP) as a characteristic of quality and stiffness. This article has examined the application of particle swarm optimization algorithm to obtain a better DP in a GMAW and compare the results obtained with the technique of genetic algorithms. The effect of four main welding variables in GMAW process which are the welding voltage, the welding speed, the wire feed speed and the nozzle-to-plate distance on the DP have been studied. For the implementation of optimization, a source code has been developed in MATLAB 8.3. The results showed that, in order to obtain the upper penetration depth, it is necessary that: the welding voltage, the welding speed and the nozzle-to-plate distance must be at their lowest levels; the wire feed speed at its highest level.

<p><span lang="EN-US">Nowadays, the large number of shunt active power filters (SAPF) is installed in many grid networks to eliminate the source currents harmonics and enhance power quality. These filters are installed in different places according to the filtration requirements. The connection between SAPF and grid network has a negative effect during the open-circuit fault of the insulated gate bipolar transistor (IGBT) switch of the SAPF. This paper proposes the application of the new diagnostic method based on the trigonometric circle and mean value variations techniques to the early detection and precise location of the open-circuit fault of the IGBT switches, and the inclusion of the modified reconfigurable inverter topology to allow the perfect continuity of the filter currents, and improve the diagnostic of the open-circuit fault. A single-sided amplitude spectrum technique (SSAS) is applied on the source currents to get the THDi% value. The obtained simulation results prove, the great success of the proposed diagnostic method, the ability of the modified reconfigurable inverter to be adapted to the grid network, the short response time between the diagnosis and the reconfiguration process is about 7 ms which is very sufficient to guarantee the rapid continuity of the shunt active power filter.</span></p>