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Multi-objective GA with fuzzy decision making for security enhancement in power system

机译:模糊决策的多目标遗传算法在电力系统安全中的应用

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Power system security enhancement is a major concern in the operation of power system. In this paper, the task of security enhancement is formulated as a multi-objective optimization problem with minimization of fuel cost and minimization of FACTS device investment cost as objectives. Generator active power, generator bus voltage magnitude and the reactance of Thyristor Controlled Series Capacitors (TCSC) are taken as the decision variables. The probable locations of TCSC are pre-selected based on the values of Line Overload Sensitivity Index (LOSI) calculated for each branch in the system. Multi-objective genetic algorithm (MOGA) is applied to solve this security optimization problem. In the proposed GA, the decision variables are represented as floating point numbers in the GA population. The MOGA emphasize non-dominated solutions and simultaneously maintains diversity in the non-dominated solutions. A fuzzy set theory-based approach is employed to obtain the best compromise solution over the trade-off curve. The proposed approach has been evaluated on the IEEE 30-bus and IEEE 118-bus test systems. Simulation results show the effectiveness of the proposed approach for solving the multi-objective security enhancement problem.
机译:电力系统安全性的提高是电力系统运行中的主要问题。本文将安全性增强的任务表述为以燃料成本最小化和FACTS设备投资成本最小化为目标的多目标优化问题。发电机有功功率,发电机母线电压幅值和晶闸管控制串联电容器(TCSC)的电抗作为决策变量。根据为系统中每个分支计算的线路过载灵敏度指数(LOSI)的值,预​​先选择TCSC的可能位置。应用多目标遗传算法(MOGA)解决了该安全性优化问题。在拟议的遗传算法中,决策变量以遗传算法种群中的浮点数表示。 MOGA强调非主导解决方案,同时保持非主导解决方案的多样性。采用基于模糊集理论的方法来获得折衷曲线上的最佳折衷解决方案。该提议的方法已经在IEEE 30总线和IEEE 118总线测试系统上进行了评估。仿真结果表明,该方法解决了多目标安全性增强问题。

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