277 research outputs found

    A Comparison of Heuristic Methods for Optimum Power Flow Considering Valve Point Effect

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    Optimum Power Flow (OPF) is one of the key considerations for planning, generation control and management of electric utility. Hence it is of major importance to solve OPF with minimum cost within reasonable computing time. This paper presents solutions of OPF with Valve Point Effect (OPF-VPE) using Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Artificial Bee Colony (ABC). When steam valve starts to open in a turbine it changes generation curve. The valve point effect is considered by adding sine component to the quadratic cost function for OPF-VPE. Also, penalty function is added for generator violations. The common parameters of algorithms such as population size and the iteration number are selected same values for the comparison of algorithms for solving OPFVPE. Specific parameters are stated and used for each algorithm. The heuristic algorithms are examined on IEEE-30 bus system and convergence curves are demonstrated with the system results. Performances of each algorithm are discussed as regards optimizing fuel cost, iteration time and other system results

    Optimal power flow solution with current injection model of generalized interline power flow controller using ameliorated ant lion optimization

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    Optimal power flow (OPF) solutions with generalized interline power flow controller (GIPFC) devices play an imperative role in enhancing the power system’s performance. This paper used a novel ant lion optimization (ALO) algorithm which is amalgamated with LĂ©vy flight operator, and an effectual algorithm is proposed named as, ameliorated ant lion optimization (AALO) algorithm. It is being implemented to solve single objective OPF problem with the latest flexible alternating current transmission system (FACTS) controller named as GIPFC. GIPFC can control a couple of transmission lines concurrently and it also helps to control the sending end voltage. In this paper, current injection modeling of GIPFC is being incorporated in conventional Newton-Raphson (NR) load flow to improve voltage of the buses and focuses on minimizing the considered objectives such as generation fuel cost, emissions, and total power losses by fulfilling equality, in-equality. For optimal allocation of GIPFC, a novel Lehmann-Symanzik-Zimmermann (LSZ) approach is considered. The proposed algorithm is validated on single benchmark test functions such as Sphere, Rastrigin function then the proposed algorithm with GIPFC has been testified on standard IEEE-30 bus system

    Optimization of the Thyristor Controlled Phase Shifting Transformer using PSO Algorithm

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    The increase of power system demand leads to the change in voltage profile, reliability requirement and system robustness against disturbance. The voltage profile can be improved by providing a source of reactive power through the addition of new power plants, capacitor banks, or implementation of Flexible AC Transmission System (FACTS) devices such as Static VAR Compensator (SVC), Unified Power Flow Control (UPFC), Thyristor Controlled Series Capacitor (TCSC), Thyristor Controlled Phase Shifting Transformer (TCPST), and many others. Determination of optimal location and sizing of device injection is paramount to produce the best improvement of voltage profile and power losses reduction. In this paper, optimization of the combined advantages of TCPST and TCSC has been investigated using Particle Swarm Optimization (PSO) algorithm, being applied to the 30-bus system IEEE standard. The effectiveness of the placement and sizing of TCPST-TCSC combination has been compared to the implementation of capacitor banks. The result showed that the combination of TCPST-TCSC resulted in more effective improvement of system power losses condition than the implementation of capacitor banks.  The power losses reduction of 46.47% and 42.03% have been obtained using of TCPST-TCSC combination and capacitor banks respectively. The TCPST-TCSC and Capacitor Bank implementations by using PSO algorithm have also been compared with the implementation of Static VAR Compensator (SVC) using Artificial Bee Colony (ABC) Algorithm. The implementation of the TCSC-TCPST compensation with PSO algorithm have gave a better result than using the capacitor bank with PSO algorithm and SVC with the ABC algorithm

    A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

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    Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms

    New Metaheuristic Algorithms for Reactive Power Optimization

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    Optimal reactive power dispatch (ORPD) is significant regarding operating the practice safely and efficiently. The ORPD is beneficial to recover the voltage profile, diminish the losses and increase the voltage stability. The ORPD is a complicated optimization issue in which the total active power loss is reduced by detecting the power-system control variables, like generator voltages, tap ratios of tap-changer transformers, and requited reactive power, ideally. This study offers new approaches based on Shuffled Frog Leaping Algorithm (SFLA) and Tree Seed Algorithm (TSA) to solve the best ORPD. The results of the approaches are offered set against the current results studied in the literature. The recommended algorithms were tested by IEEE-30 and IEEE-118 bus systems to discover the optimal reactive power control variables. It was observed that the obtained results are more successful than the other algorithms

    Resolving forward-reverse logistics multi-period model using evolutionary algorithms

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    © 2016 Elsevier Ltd In the changing competitive landscape and with growing environmental awareness, reverse logistics issues have become prominent in manufacturing organizations. As a result there is an increasing focus on green aspects of the supply chain to reduce environmental impacts and ensure environmental efficiency. This is largely driven by changes made in government rules and regulations with which organizations must comply in order to successfully operate in different regions of the world. Therefore, manufacturing organizations are striving hard to implement environmentally efficient supply chains while simultaneously maximizing their profit to compete in the market. To address the issue, this research studies a forward-reverse logistics model. This paper puts forward a model of a multi-period, multi-echelon, vehicle routing, forward-reverse logistics system. The network considered in the model assumes a fixed number of suppliers, facilities, distributors, customer zones, disassembly locations, re-distributors and second customer zones. The demand levels at customer zones are assumed to be deterministic. The objective of the paper is to maximize the total expected profit and also to obtain an efficient route for the vehicle corresponding to an optimal/near optimal solution. The proposed model is resolved using Artificial Immune System (AIS) and Particle Swarm Optimization (PSO) algorithms. The findings show that for the considered model, AIS works better than the PSO. This information is important for a manufacturing organization engaged in reverse logistics programs and in running units efficiently. This paper also contributes to the limited literature on reverse logistics that considers costs and profit as well as vehicle route management

    Optimal power flow using Hybrid Particle Swarm Optimization and Moth Flame Optimizer approach

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    In this study, the most common problem of the current power system named optimal power flow (OPF) is optimized using the recently hybrid meta-heuristic optimization technique Particle Swarm Optimization-Moth Flame Optimizer (PSO-MFO) algorithm. Hybrid PSO-MFO is an incorporation of PSO used for exploitation stage and MFO for exploration stage in an uncertain environment. The position and velocity of the particle are restructured according to Moth and flame location in each iteration. The hybrid PSO-MFO technique is carried out to solve the OPF problem. The performance of this technique is deliberated and evaluated on the standard IEEE 30-bus and IEEE 57-bus test system. The problems considered in the OPF are fuel cost reduction, Voltage stability enhancement and Active power loss minimization. The results obtained with hybrid PSO-MFO technique is compared with original PSO and MFO
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