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Ant Colony Optimization for Maximum Loadability Search in Voltage Control Study

机译:蚁群优化在电压控制研究中的最大可加载性搜索

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Several blackout occurrences in many part of the world had indicated the importance of voltage stability studies. These events could be caused by line or generator outages, stressed condition, change of loads and load shedding. The occurrence of voltage collapse is very much dependent upon the maximum permissible load that can be supported at a particular load bus. Any attempt to increase the load beyond this point could force the entire system into instability, leading to voltage collapse. This would indicate that the power system physically could not support the amount of the connected load. This paper presents the application of Ant Colony Optimization (ACO) technique for searching the optimal point of maximum loadability point at a load bus. The optimal point identified using this technique in the off-line mode can assist the power system operators to perform pilot study prior to intended load increment in their transmission system. Comparative studies performed with respect to evolutionary programming (EP) and automatic voltage stability analysis (AVSA) algorithm had indicated the merit of the proposed technique. The capability of the developed ACO engine in solving the non-graphical optimization problems has been identified as the strength of the proposed technique.
机译:世界上许多地方的几种停电事件表明了电压稳定性研究的重要性。这些事件可能是由线路或发电机中断,压力条件,负载变化和负载脱落引起的。电压折叠的发生非常依赖于可以在特定负载总线上支撑的最大允许负载。任何增加载荷的尝试都可能强迫整个系统稳定,导致电压崩溃。这表明电力系统物理无法支持连接负载的量。本文介绍了蚁群优化(ACO)技术在负载总线上搜索最大可加载点最佳点的应用。在离线模式下使用该技术识别的最佳点可以帮助电力系统运营商在其传输系统中之前在预期的负载增量之前执行导频研究。对进化编程(EP)和自动电压稳定性分析(AVSA)算法进行的比较研究表明了所提出的技术的优点。开发的ACO发动机在解决非图形优化问题方面的能力被识别为所提出的技术的强度。

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