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Design of reactive power optimization control for electromechanical system based on fuzzy particle swarm optimization algorithm

机译:基于模糊粒子群优化算法的机电系统无功功率优化控制设计

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摘要

Due to low precision and premature tendency of traditional particle swarm optimization, the reactive power optimization control of electromechanical system based on fuzzy particle swarm optimization algorithm was designed. The premise is to meet the constraints of operation conditions. The active network loss was reduced and the static reactive power optimization mathematical model of electromechanical system was constructed by changing the voltage and reactive power distribution of system. Meanwhile, the voltage did not exceed the limit, and the discrete control variables were limited by the maximum allowable action times, so that the dynamic reactive power optimization mathematical model of electromechanical system was built by minimizing the sum of network loss in twenty-four hours of a day. The particle swarm algorithm was optimized by adaptive adjustment strategy, and then the particle position of particle swarm optimization algorithm was updated. Moreover, the static and dynamic reactive power optimization mathematical model of electromechanical system was solved. Finally, the reactive power optimization control of the electromechanical system is realized. Experimental results show that the proposed method has high convergence performance, so it is able to realize the precise control of reactive power optimization for electromechanical system and eliminate the voltage exceeding specified limits of electromechanical system. In this way, the node voltage can always be within the specified range.
机译:由于传统粒子群优化的精度低,过早趋于,设计了基于模糊粒子群优化算法的机电系统的无功功率优化控制。前提是符合运营条件的约束。减少了有源网络损耗,通过改变系统的电压和无功功率分布来构建机电系统的静电无功功率优化数学模型。同时,电压不超过极限,并且离散控制变量受到最大允许动作时间的限制,从而通过最大限度地减少二十四小时的网络损失总和来构建机电系统的动态无功功率优化数学模型一天。通过自适应调整策略优化粒子群算法,然后更新了粒子群优化算法的粒子位置。此外,解决了机电系统的静态和动态无功优化数学模型。最后,实现了机电系统的无功功率优化控制。实验结果表明,该方法具有高收敛性能,因此能够实现机电系统对无功功率优化的精确控制,并消除电压超过机电系统的指定限制。以这种方式,节点电压始终可以在指定范围内。

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