Measurements, Models, Systems and Design

531 s.

Power Loss Minimization for Distribution Networks with Load Tap Changing Using Genetic Algorithm and Environmental Impact Analysis

This paper presents an investigation of the IEEE 34 bus test system benefits with deployment of distribution static compensator (DSTATCOM) and distributed generation (DG) in the aspect of power loss minimization, bus voltage stability and greenhouse gas emission mitigation. Power loss minimization is carried out by adjusting tap changer positions of the load tap changing transformer with one of the well-known metaheuristic algorithms, Genetic Algorithm (GA). To check the voltage stability of the system after minimization, bus voltage profile index is developed. Similarly, environmental profile is evaluated by three different indices. The behaviour of the system is analysed for four different cases as follows. In Case 1, voltage and reactive power control is provided by capacitor banks. In Case 2, capacitor banks are replaced with DSTATCOM. In Case 3 and Case 4, Case 1 and Case 2 are reinvestigated in the presence of additional DG. All cases are evaluated with both traditional Newton- Raphson optimization algorithm and evolutionary-based GA optimization algorithm. The results indicate that GA optimization provides more energy savings than traditional optimization in all cases with bus voltage index within the allowed range. Besides voltage profile of the system in all cases with two algorithms supports the fact that evolutionary-based metaheuristics offer the best choices for a non-linear optimization problem in comparison with the traditional optimization methods. The overall results reveal that Case 4, test system with DSTATCOM and DG, is the best case which provides minimum power losses and a significant amount of emission savings with greenhouse payback time (GPBT) of 0.458 years

Design and Simulation of Multi Objective Power Flow Optimization in IEEE-30 Bus System using Modified Particle Swarm Optimization

Execution and Reliability of OPF algorithms is an important issue of research for gainful power structure control and orchestrating. Perfect Power Flow is driven for restricting the objective work. This objective limit can be single regarded target work or different objective limits. In the present research, we have executed perfect power stream in order to constrain the fuel cost while satisfying the constraints, for instance, the voltages, power yields of the generator kept inside embraced purpose of repression. Some other objective can be used reliant on utility's preferred position and needs. Many streamlined framework models have been combined in the past by various researchers for OPF issue, for instance, Linear Programming, Non Linear Programming, Quadratic Programming, Newton Based Techniques, Parametric Methods, and Interior Point Methods, etc. A wide variety of bleeding edge optimization methodologies like Evolutionary Programming, Genetic Algorithm, PSO Algorithm, etc are proposed recorded as a hard copy for handling OPF issue. In this proposition, we have executed improved particle swarm optimization algorithm to restrain cost limit while keeping goals inside acceptable most extreme. The adjustments in particle swarm optimization is finished by introducing the idea of quantum computing and optimization of quickening coefficients. The proposed algorithm is associated with IEEE-30 bus structure. Resuts indicated unrivaled execution of proposed algorithm as contrasted and contemporary techniques

NOVEL OPTIMAL COORDINATED VOLTAGE CONTROL FOR DISTRIBUTION NETWORKS USING DIFFERENTIAL EVOLUTION TECHNIQUE

This paper investigates a Distributed Generators (DG) connected to distribution networks offer multiple benefits for power networks and environments in the case of renewable sources. Nevertheless, if there is not an appropriate planning and control strategy, several issues, such as voltage rise problems and increased power losses, may happen. In order to overcome such disadvantages, in this paper, a coordinated voltage control method for distribution networks with multiple distributed generators is proposed. This method is based on a differential evolution DE approach to obtain the optimal setting points for each control component. Furthermore this proposed method considers both of time-varying load demand and production, leading to not only an improvement in the voltage profile but also to optimally minimize the active power loss

Design of Multivariate PID Controller for Power Networks Using GEA and PSO

The issue of proper modeling and control for industrial systems is one of the challenging issues in the industry. In addition, in recent years, PID controller design for linear systems has been widely considered. The topic discussed in some of the articles is mostly speed control in the field of electric machines, where various algorithms have been used to optimize the considered controller, and always one of the most important challenges in this field is designing a controller with a high degree of freedom. In these researches, the focus is more on searching for an algorithm with more optimal results than others in order to estimate the parameters in a more appropriate way. There are many techniques for designing a PID controller. Among these methods, meta-innovative methods have been widely studied. In addition, the effectiveness of these methods in controlling systems has been proven. In this paper, a new method for grid control is discussed. In this method, the PID controller is used to control the power systems, which can be controlled more effectively, so that this controller has four parameters, and to determine these parameters, the optimization method and evolutionary algorithms of genetics (EGA) and PSO are used.  One of the most important advantages of these algorithms is their high speed and accuracy. In this article, these algorithms have been tested on a single-machine system, so that the single-machine system model is presented first, then the PID controller components will be examined. In the following, according to the transformation function matrix and the relative gain matrix, suitable inputs for each of the outputs are determined. At the end, an algorithm for designing PID controller for multivariable MIMO systems is presented. To show the effectiveness of the proposed controller, a simulation was performed in the MATLAB environment and the results of the simulations show the effectiveness of the proposed controller