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首页> 外文期刊>Renewable Power Generation, IET >Optimal load sharing strategy for a wind/diesel/battery hybrid power system based on imperialist competitive neural network algorithm
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Optimal load sharing strategy for a wind/diesel/battery hybrid power system based on imperialist competitive neural network algorithm

机译：基于帝国主义竞争神经网络算法的风/柴/电混合动力系统最优负荷分配策略

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摘要

In this study, optimal load sharing strategy for a stand-alone hybrid power generation system that consists of wind turbine, diesel generator and battery banks is presented. The diesel generator is used to complement the intermittent output of the wind source whereas the battery is used to compensate for part of the temporary peak demand, which the wind and diesel generator cannot meet thus avoiding oversizing of the diesel generator. To optimise the performance of the system, imperialist competitive algorithm (ICA), ant colony optimisation (ACO) and particle swarm optimisation (PSO) are used to optimal load sharing. These algorithms are used to select the best available energy source so that the system has the best performance.To verify the system performance simulation studies have been carried out using forecasted data (load demand and wind speed). Accordingly, ICA, ACO and PSO are used to train a three-layer feed forward neural network. This trained artificial neural network is applied to short-term wind speed and load demand forecasting on a specific day in the Qazvin. The results show that the proposed control methods can reduce fuel consumption and increase the battery lifetime and battery ability to respond to real-time load turbulences simultaneously.
机译：在这项研究中，提出了一种由风力涡轮机，柴油发电机和电池组组成的独立混合发电系统的最优负荷分配策略。柴油发电机用于补充风源的间歇输出，而电池用于补偿部分临时峰值需求，这是风力和柴油发电机无法满足的，从而避免了柴油发电机的尺寸过大。为了优化系统性能，帝国主义竞争算法（ICA），蚁群优化（ACO）和粒子群优化（PSO）用于优化负载分配。这些算法用于选择最佳可用能源，从而使系统具有最佳性能。为了验证系统性能，已使用预测数据（负载需求和风速）进行了仿真研究。因此，ICA，ACO和PSO用于训练三层前馈神经网络。这个训练有素的人工神经网络可应用于Qazvin特定日期的短期风速和负荷需求预测。结果表明，所提出的控制方法可以减少燃料消耗，延长电池寿命，并能同时响应实时负载湍流。

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