2,320 research outputs found

    Application of Multi-Objective Evolutionary Optimization Algorithms to Reactive Power Planning Problem

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    This paper presents a new approach to treat reactive power (VAr) planning problem using multi-objective evolutionary algorithms. Specifically, Strength Pareto Evolutionary Algorithm (SPEA) and Multi-Objective Particle Swarm Optimization (MOPSO) approaches have been developed and successfully applied. The overall problem is formulated as a nonlinear constrained multi-objective optimization problem. Minimizing the total incurred cost and maximizing the amount of Available Transfer Capability (ATC) are defined as the main objective functions. The proposed approaches have been successfully tested on IEEE 14 bus system. As a result a wide set of optimal solutions known as Pareto set is obtained and encouraging results show the superiority of the proposed approaches and confirm their potential to solve such a large scale multi-objective optimization problem

    SVC device optimal location for voltage stability enhancement based on a combined particle swarm optimization-continuation power flow technique

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    The increased power system loading combined with the worldwide power industry deregulation requires more reliable and efficient control of the power flow and network stability. Flexible AC transmission systems (FACTS) devices give new opportunities for controlling power and enhancing the usable capacity of the existing transmission lines. This paper presents a combined application of the particle swarm optimization (PSO) and the continuation power flow (CPF) technique to determine the optimal placement of static var compensator (SVC) in order to achieve the static voltage stability margin. The PSO objective function to be maximized is the loading factor to modify the load powers. In this scope, two SVC constraints are considered: the reference voltage in the first case and the total reactance and SVC reactive power in the second case. To test the performance of the proposed method, several simulations were performed on IEEE 30-Bus test systems. The results obtained show the effectiveness of the proposed method to find the optimal placement of the static var compensator and the improvement of the voltage stability

    Paper plate machine

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    In recent years natural fibers appear to be the outstanding materials which come as the viable and abundant substitute for the expensive and nonrenewable synthetic fiber. Natural fibers like sugar cane, banana, , oil palm, kenaf and coir has been used as reinforcement in thermoplastic composite for applications in consumer goods, furniture, low cost housing and civil structures. Pineapple leaf fiber (PALF) is one of them that have also good potential as reinforcement in thermoplastic composite. It is the objective of the current research to characterize PALF and to investigate the effect of fiber treatment on the mechanical properties of PALF reinforced polypropylene (PP) composite. Figure 6.1 show the example of pineapple used

    A hybrid algorithm for voltage stability enhancement of distribution systems

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    This paper presents a hybrid algorithm by applying a hybrid firefly and particle swarm optimization algorithm (HFPSO) to determine the optimal sizing of distributed generation (DG) and distribution static compensator (D-STATCOM) device. A multi-objective function is employed to enhance the voltage stability, voltage profile, and minimize the total power loss of the radial distribution system (RDS). Firstly, the voltage stability index (VSI) is applied to locate the optimal location of DG and D-STATCOM respectively. Secondly, to overcome the sup-optimal operation of existing algorithms, the HFPSO algorithm is utilized to determine the optimal size of both DG and D-STATCOM. Verification of the proposed algorithm has achieved on the standard IEEE 33-bus and Iraqi 65-bus radial distribution systems through simulation using MATLAB. Comprehensive simulation results of four different cases show that the proposed HFPSO demonstrates significant improvements over other existing algorithms in supporting voltage stability and loss reduction in distribution networks. Furthermore, comparisons have achieved to demonstrate the superiority of HFPSO algorithms over other techniques due to its ability to determine the global optimum solution by easy way and speed converge feature

    Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems

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    Many areas in power systems require solving one or more nonlinear optimization problems. While analytical methods might suffer from slow convergence and the curse of dimensionality, heuristics-based swarm intelligence can be an efficient alternative. Particle swarm optimization (PSO), part of the swarm intelligence family, is known to effectively solve large-scale nonlinear optimization problems. This paper presents a detailed overview of the basic concepts of PSO and its variants. Also, it provides a comprehensive survey on the power system applications that have benefited from the powerful nature of PSO as an optimization technique. For each application, technical details that are required for applying PSO, such as its type, particle formulation (solution representation), and the most efficient fitness functions are also discussed
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