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Load Frequency Control in Interconnected Power System by Nonlinear Term and Uncertainty Considerations by Using of Harmony Search Optimization Algorithm and Fuzzy-Neural Network

机译:通过使用和谐搜索优化算法和模糊神经网络,通过非线性期限和不确定性考虑互联电力系统中的负载频率控制

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Load frequency control is one of the most important issues in the power systems that many researches have been done for improving this problem until now, but most of the researches have not considered the power system model that is close to reality and nonlinear factors such as generation rate constraints, governor dead-hand, boiler dynamic and time delays. Also because of the power system dynamic changes continuously, the uncertainty is undeniable in the system. In this article by consideration of these nonlinear effects and uncertainty, first the PI control factors are found by using the harmony search algorithm then by consideration of the uncertainty, optimized factors for PI controller are found each time and by forming a table that determines inputs and outputs for fuzzy-neural network, the designing and training of the fuzzy-neural network is done that has capability of controller coefficients regulation in various dynamics of system and presence of uncertainty. The proposed method by a fuzzy controller that is designed based on certified knowledge, has been compared and shown that has a very good performance such as lower settling time, higher speed, better damping, and steady-stale error equal to 0.
机译:负载频率控制在电力系统中,许多研究已为改善这个问题到现在为止做过的最重要的问题之一,但大多数研究没有考虑到电力系统模型是贴近现实和非线性因素,如代率的限制,省长死手,锅炉动态和时间延迟。同时由于电力系统动态连续变化,不确定性是系统不可否认的。在本文中通过考虑这些非线性效应和不确定性,第一PI控制因子通过使用和声搜索算法然后通过考虑不确定性的发现,对PI控制器优化的因素每次被发现并通过形成确定输入和一个表为模糊神经网络中,设计和模糊神经网络的训练输出完成,其具有在系统和不确定性的存在的各种动态控制器系数调节的能力。所提出的方法通过基于认证的知识而设计的模糊控制器,进行了比较,并示出具有非常良好的性能,例如低的稳定时间,更高的速度,更好的阻尼和稳定陈旧误差等于0。

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