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In recent years, the reconfiguration of the distribution network has been proclaimed as a method for realizing power savings, with virtually zero cost. The current trend is to design distribution networks with a mesh network structure, but to operate them radially. This is achieved by the establishment of an appropriate number of switchable branches which allow the realization of a radial configuration capable of supplying all of the normal defects in the box of permanent defect. The purpose of this article is to find an optimal reconfiguration using a Meta heuristic method, namely the particle swarm optimization method (PSO), to reduce active losses and voltage deviations by taking into account certain technical constraints. The validity of this method is tested on a 33-IEEE test network and the results obtained are compared with the results of basic load flow.

The main purpose of this paper is to design a regulator which enables a power system to track reference signals precisely and to be robust in the presence of uncertainty of system parameters and disturbances. The performances of the proposed controllers (NEWELM and NIMC) are based neural adaptive control and simulated on a two-bus test system and compared with a conventional PI controller with decoupling (PI-D). The studies are performed based on well known software package MATLAB/Simulink tool box. Flexible Alternating Current Transmission System devices (FACTS) are power electronic components. Their fast response offers potential benefits for power system stability enhancement and allows utilities to operate their transmission systems even closer to their physical limitations, more efficiently, with improved reliability, greater stability and security than traditional mechanical switching technology. The most used component of FACTS systems is the Unified Power Flow Controller (UPFC). According to high importance of power flow control in transmission lines, new controllers are designed based on the Elman Recurrent Neural Network (NEWELM) and Neural Inverse Model Control (NIMC) with adaptive control.

The aim of this article is to apply the Particle Swarm Optimization (PSO) method to find the best location for the wind turbine in the radial distribution network. The optimal location is found using the loss sensitivity factor. By respecting the constraints of the active power transmitted in the branches and the limits of the voltages modules for all the nodes. The validity of this method is tested on a 33-IEEE test network and the results obtained are compared with the results of basic load flow.

Electricity consumption is increasing gradually and this trend will continue in the future. In addition, rapid network control systems using the resources offered by power electronics and control microelectronics have been recently studied and developed, and are currently in normal application for some, for others, in pilot applications or as prototypes. This paper attempts to show that these systems are referred to by the general acronym flexible alternative current transmission systems (FACTS) similarly dethroned the traditional systems while offering better solutions and solving the energy quality problem such as the hybrid system (unified power flow controller (UPFC), or universal phase shifter regulator (UPSR)) which opens up new perspectives for more efficient operation of networks by continuous and rapid action on the various parameters of the network (voltage, phase shift, and impedance); thus, the power transits will be better controlled and the voltages better held, which will make it possible to increase the stability margins or tend towards the thermal limits of the lines. In this work, we used a classic control (PI-decoupled) and others while offering more flexibility of control thanks to the development of strategies identification/control based on generalized predictive control (GPC) with neural network to ensure robust control with advanced algorithms.

Voltage and reactive power become one of the most critical aspects to monitor and sustain at an appropriate level in vast alternative current networks to ensure good functionality, load satisfaction, network integrity, and service quality. The network’s spending should be obtained by calculating the load flow or applying more controllable variables to it, defined as the optimization power flow. In this vein, this paper focuses on improving the network’s voltage profile, reactive power generation, and losses. The study begins with load flow estimation in various situations using the Newton–Raphson process, followed by an optimization capacity calculation using the simplified Reduced Gradient power flow method. For each system, four cases are simulated, one with and one without the SVC (Static Var Compensator) device, which is positioned in an ideal position on the IEEE 57 bus network. The confirmation of the data is carried out using the MATLAB program. A distinction is made between the two approaches based on the SVC device’s position and its reactive power generation into the network. The simulation results indicate that the generalized Reduced Gradient optimization approach (GRG-OPF) offers reasonable efficiency, quick iteration in the simulation results, the best power budget with minimal active/reactive losses and the best voltage profile specifically in the case where SVC reactive power is taken as a variant into the GRG-OPF method.