Software Development for Optimum Allocation of Power System Elements Based on Genetic Algorithm (original) (raw)
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The utilization of genetic algorithm (GA) in tackling engineering problems has been a major issue arousing the curiosity of researcher and practitioner system and engineering research, operation research and management sciences in last few years. The various improvement occurs in it year by year, and researches has been done over genetic algorithm to improve its limitation and to process well. In view of this, this paper present a state-of-the-art survey of application of GA technique in engineering with focus on system power optimization using GA in last few years to understand what changes has been done till now and its improvement of various papers that are searched. The scope of the paper is centered between the years 2000-2016.
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A Decade Survey of Engineering Applications of Genetic Algorithm in Power System Optimization
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The utilization of Genetic Algorithms (GA) in tackling engineering problems has been a major issue arousing the curiosity of researchers and practitioners in the area of systems and engineering research, operations research and management sciences in the past decades. The limitations on the use of conventional methods and stochastic search paved the way to wide applications of GA optimization techniques in tackling problems related to engineering and sciences. In view of this, this paper presents a state-of-the-art survey of applications of GA technique in engineering with focus on system power optimization using GA in the last decade. Hence, the scope of this paper is centred between the years 2003-2013.
Om Prakash Mahela & Sheesh Ram Ola, 2013
Electric transmission system is the intermediate stage in the transfer of electrical power from the central generating station to the consumers. Minimization of active power losses is one of the essential aims for any electric utility, due to its importance in improvement of system properties towards minimum production cost and to support increased load requirement. The voltage at buses reduces and loss increases due to insufficient amount of reactive power in the network. The reactive power requirement is provided by the shunt capacitor banks. Optimal capacitor placement in the transmission system has been studied for a long time. It is an optimization problem which has an objective to define the optimal sizes and allocations of capacitors to be installed. In this paper we have studied the possibility of reducing the value of real power losses for global system transmission lines by choosing the best location to install shunt capacitors using Genetic Algorithm to calculate the optimal allocation and sizing considering the value of real power losses with injection of reactive power as an indicator of the ability of reducing losses at load buses. The results are tested on IEEE 6-bus system.
A combination of MADM and genetic algorithm for optimal DG allocation in power systems
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Distributed Generation (DG) can help in reducing the cost of electricity to the customer, relieve network congestion, provide environmentally friendly energy close to load centers as well as promote system technical characteristics such as loss reduction, voltage profile enhancement, reserve mitigation and many other factors. Furthermore, its capacity is also scalable and it can provide voltage support at distribution level. The planning studies include penetration level and placement evaluation which are influenced directly by DG type. Most of the previous publications in this field chose one or two preferred parameter as basic objective and implement the optimizations in systems. But due to small size of DGs output, placement according to one or two of just technical parameters usually leads to more theoretical results and with incorporation of less DG resources. Furthermore, optimization of one parameter might degrade another system attribute. In this paper a multi-objective placement and penetration level of Distributed Generators (DGs) is examined, concerning both technical and economical parameters of power system using Genetic Algorithm (GA) combined with Multi-Attribute Decision Making (MADM) method. In fact, by using GA best plans for system with incorporation of DG are determined. For approaching such aim, 4 technical parameters of system, including total losses, buses voltage profile, lines capacity limits and total reactive power flow, are consider with appropriate priorities applied to each objective. In the next step, Analytic Hierarchy Process (AHP) along with Data Envelopment Analysis (DEA) is used as a multi attribute decision making technique to form a decision making framework for selecting the best capacity and place of DG units. The attributes are defined as technical and economical parameters. The technical parameters are the voltages on the buses, the reactive power and losses in the transmission lines and the economical parameters are the emi- - ssions, congestion and capital cost. The proposed approach is illustrated by case studies on IEEE 30 bus distribution system which demonstrate significant improvement in optimization through this procedure.