Multisector machines reveal a high fault-tolerant capability, since failure events can be isolated by de-energizing the faulty sector, while the healthy ones contribute in delivering the required power. This article is focused on the thermal analysis of multisector three-phase machines in healthy and faulty operations. First, a 3-D lumped parameter thermal network (LPTN) of a single sector is developed and finetuned against experimental data, through a genetic algorithm for identifying the uncertain parameters. According to the operating conditions, the varying housing surface temperature affects the heat exchanged to the ambient. Hence, an analytical formula is proposed to adjust the natural convection coefficient value depending on the operating condition. Then, the 3-D LPTN, modeling the whole machine, is built aiming at investigating the thermal behavior during faulty conditions. Finally, the complete 3-D LPTN is employed for predicting the machine thermal performance under several faulty conditions. Furthermore, the current overload experienced by the healthy sector (in order to keep the same torque level as during the pre-fault operation) is determined, in accordance with the magnet wire thermal class. The effectiveness of the 3-D LPTN in predicting the temperature is experimentally demonstrated.

中文翻译：

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多扇区三相电机的热模型方法


多扇区机器具有高容错能力，因为可以通过断开故障扇区的电源来隔离故障事件，而健康的扇区则有助于提供所需的电力。本文重点关注多部门三相电机在正常运行和故障运行时的热分析。首先，通过遗传算法识别不确定参数，开发了单个扇区的 3D 集总参数热网络 (LPTN)，并根据实验数据进行了微调。根据工作条件，不同的外壳表面温度会影响与环境的热量交换。因此，提出了一个解析公式来根据运行条件调整自然对流系数值。然后，构建了对整个机器进行建模的 3-D LPTN，旨在研究故障条件下的热行为。最后，采用完整的 3-D LPTN 来预测几种故障条件下的机器热性能。此外，根据漆包线耐热等级确定正常扇区所经历的电流过载（以便保持与故障前操作期间相同的扭矩水平）。实验证明了 3-D LPTN 在预测温度方面的有效性。