Optimization of Welding Input Parameters Using PSO Technique for Minimizing HAZ Width in GMAW

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

Contribution of the two rectifiers reconfiguration to fault tolerance connected to the grid network to feed the GMAW through processor-in-the-loop

This study aims to propose a new diagnosis technique based on the Park’s vector and the polar coordinates of electric currents for the detection and location of open-circuit faults (OC) at the level of two rectifiers connected to the grid network to feed the Gas Metal Arc Welding process (GMAW). This diagnosis technique allows the early location of faulty switches (Thyristors) to overcome the negative effect of faulty rectifiers on welding current, welding voltage, and droplet diameter. For that, the reconfigurable rectifiers have been integrated to accomplish the welding process. The proposed diagnosis technique is applied to reconfigurable rectifiers connected to the GMAW system through numerical simulations using MATLAB/Simulink and real-time processor-in-the-loop (PIL) implementation via DSpace ds 1103 card. The simulation and PIL experimental results show similar trends and great success of the diagnosis technique and the two rectifiers reconfiguration for overcoming the open circuit faults and obtaining high welding quality while maintaining the work-piece and avoiding the distortions caused by the faulty rectifiers, which affecting the grid network and on the GMAW system at the same time

Improved PSO with Disturbance Term for Solving ORPD Problem in Power Systems

The essential purpose of an energy sys- tem is to provide electricity to its loads effectively and economically, as well as safely and reliably. Therefore, the solutions to the problems of Optimal Power Flow (OPF) and Optimal Reactive Power Dispatch (ORPD) to enable the efficient employment of various energy distributions should be found. Our work focuses on the ORPD issue; it can be formulated as a non-linear con- straint and with single or multiple objectives optimiza- tion problems. Minimizing total losses is one of the main objective functions to solve the ORPD problem. This paper presents the use of an improved particle swarm optimization -with a disturbance term- (called PSO-DT) algorithm, to find the solution of ORPD in the standard IEEE 30-bus power system for reduc- ing electrical power transmission losses. The obtained results demonstrate that the proposed method is more efficient and has a more extraordinary ability to get better solutions compared to the basic PSO method

Experimental Investigation of an Adaptive Fuzzy-Neural Fast Terminal Synergetic Controller for Buck DC/DC Converters

This study proposes a way of designing a reliable voltage controller for buck DC/DC converter in which the terminal attractor approach is combined with an enhanced reaching law-based Fast Terminal Synergetic Controller (FTSC). The proposed scheme will overcome the chattering phenomena constraint of existing Sliding Mode Controllers (SMCs) and the issue related to the indefinite time convergence of traditional Synergetic Controllers (SCs). In this approach, the FTSC algorithm will ensure the proper tracking of the voltage while the enhanced reaching law will guarantee finite-time convergence. A Fuzzy Neural Network (FNN) structure is exploited here to approximate the unknown converter nonlinear dynamics due to changes in the input voltage and loads. The Fuzzy Neural Network (FNN) weights are adjusted according to the adaptive law in real-time to respond to changes in system uncertainties, enhancing the increasing the system’s robustness. The applicability of the proposed controller, i.e., the Adaptive Fuzzy-Neural Fast Terminal Synergetic Controller (AFN-FTSC), is evaluated through comprehensive analyses in real-time platforms, along with rigorous comparative studies with an existing FTSC. A dSPACE ds1103 platform is used for the implementation of the proposed scheme. All results confirm fast reference tracking capability with low overshoots and robustness against disturbances while comparing with the FTSC

Design and performances improvement of an UWB antenna with DGS structure using a grey wolf optimization algorithm

In this article, we propose the design of a rectangular-shaped patch antenna suitable for ultra-wideband (UWB) applications and short and long–range Millimeter-Wave Communications. We begin with the design of a high-gain UWB rectangular patch antenna featuring a partial ground plane and operating within the 3.1–10.6 GHz bandwidth. Complementary Split Ring Resonators (CSRRs) are integrated on both sides of the structure to meet desired specifications. The resulting UWB antenna boasts an extended frequency bandwidth, covering 2.38–22.5 GHz (twice that of the original antenna), with a peak gain of 6.5 dBi and an 88% radiation efficiency. The grey wolf optimization technique (GWO) determines optimal structural dimensions. Validation of the antenna's performance is demonstrated through the strong agreement between measurement and simulation