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Grey Wolf Optimizer based MPPT Control of Centralized Thermoelectric Generator Applied in Thermal Power Stations

机译:基于灰狼优化器的集中式热电发电机MPPT控制在火力发电厂中的应用

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This study attempts to develop a grey wolf optimizer (GWO) based maximum power point tracking (MPPT) control of centralized thermoelectric generator (TEG) applied in thermal power stations under non-uniform temperature distribution (NTD) condition. Since only one converter is employed in centralized TEG systems, the total costs of implementation and maintenance is minimal among different structures. Nevertheless, numerous maximum power points (MPPs) often emerge under NTD condition, in which conventional MPPT techniques might be easily trapped at multiple local MPPs (LMPPs) thus the overall efficiency is inevitably low. In order to effectively search the global MPP (GMPP), GWO is proposed in this paper, which can achieve an appropriate balance between the exploration and exploitation under NTD condition. In contrast with perturb and observe (P&O) and particle swarm optimization (PSO), two case studies, namely, start-up test and step change of temperature, are employed to prove the efficiencies and benefits of GWO, respectively.
机译:本研究试图开发基于灰狼优化器(GWO)的集中式热电发电机(TEG)的最大功率点跟踪(MPPT)控制,该集中式热电发电机应用于不均匀温度分布(NTD)条件下的火力发电厂。由于在集中式TEG系统中仅使用一个转换器,因此不同结构之间的实施和维护总成本最小。然而,在NTD条件下,经常会出现许多最大功率点(MPP),在这种情况下,传统的MPPT技术可能很容易被困在多个本地MPP(LMPP)处,因此总效率不可避免地较低。为了有效地搜索全球MPP(GMPP),提出了GWO,可以在NTD条件下实现勘探与开发之间的适当平衡。与扰动和观测(P&O)和粒子群优化(PSO)相反,分别采用两个案例研究(即启动测试和温度阶跃变化)来证明GWO的效率和益处。

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