基于遗传算法的飞轮储能电机多工况效率优化

doi:10.19799/j.cnki.2095-4239.2024.0249

摘要/Abstract

摘要：

由于热泵系统具有电热转换效率高、电机转速控制便捷、系统灵活调节能力强的特点，其运用于电热耦合系统可实现热力系统与电力系统的协同运行支撑，而配备高速飞轮电机的电热耦合热泵系统具备大惯量、长延时特征，在电热协同运行领域具备更强的支撑能力。但现有飞轮储能电机多基于额定工作点进行效率优化，难以实现全工作周期内综合效率最优。为此，本工作基于电热协同实际工作场景得到单个完整工作周期下飞轮电机的运行工况变化，提出基于遗传算法的飞轮电机多工况效率优化方法。首先，建立适用于高速电机的效率计算模型，并根据热泵运行场景计算出单次工作下飞轮电机转速曲线；其次，确定待优化变量，通过参数灵敏度分析去除关联度较小参数以减少计算量，并根据实际工况提出电机综合效率指标以量化电机在全工作周期内的效率表现；最后，基于遗传算法寻优得到最优工作点，优化后综合效率增加0.34%，全速范围内电机效率均得到提升，单个工作周期能量损耗下降38.7 kJ，较优化前能量损耗降低14.8%。优化结果证明此方法可提高飞轮电机全工作周期内的运行效率，降低热泵运行过程中的能耗损失，提升飞轮储能系统的经济性。

关键词: 电热协同, 电热耦合, 高速永磁同步电机, 多工况效率优化, 遗传算法, 损耗计算

Abstract:

The heat pump system's high electric heating conversion efficiency, convenient motor speed control, and strong flexibility makes it an effective solution for integrating thermal power systems in electrothermal coupling applications. When equipped with a high-speed flywheel motor, the electrothermal coupling heat pump system benefits from large inertia and long delay characteristics and has enhanced support for coordinated electrothermal operations. However, existing flywheel energy storage motors are mostly optimized based on the rated working points, and it is difficult to achieve an optimal comprehensive efficiency during the entire working cycle. Therefore, based on the actual working scenario of electric heating cooperation, this study obtained the changes in theoperating conditions of the flywheel motor under a single complete working cycle and proposed a multi-operating efficiency optimization method for the flywheel motor based on a genetic algorithm. First, an efficiency calculation model for a high-speed motor was established, and the speed curve of a flywheel motor under a single operation was calculated based on the running scene of a heat pump. Second, the variables to be optimized were determined, and the parameters with lower degrees of correlation were removed using parameter sensitivity variables to reduce the amount of computation. A comprehensive efficiency index of the motor was proposed based on actual working conditions to quantify the efficiency of the motor over the entire working cycle. Finally, the optimal working point was obtained using a genetic algorithm. After optimization, the overall efficiency increased by 0.34%, the motor efficiency improved within the full speed range, and the energy loss in a single working cycle decreased by 38.7 kJ, which is 14.8% lower than that before optimization. The optimization results showed that the proposed method could improve the operating efficiency of the flywheel motor during the entire working cycle, reduce the energy loss during heat pump operation, and enhance the efficiency of the flywheel energy storage system.

Key words: electrothermal coordination, electrothermal coupling, high speed permanent magnet synchronous motor, efficiency optimization in multi-operating conditions, genetic algorithm, loss calculation

中图分类号:

TM 301

朱迪, 赵杨阳, 艾邓鑫, 张利, 周咏. 基于遗传算法的飞轮储能电机多工况效率优化[J]. 储能科学与技术, 2024, 13(10): 3582-3592.

Di ZHU, Yangyang ZHAO, Dengxin AI, Li ZHANG, Yong ZHOU. Efficiency optimization of PMSM in flywheel energy storage under multiple working conditions based on genetic algorithm[J]. Energy Storage Science and Technology, 2024, 13(10): 3582-3592.

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高速永磁电机		原始电机
性能指标	数值	结构参数	数值
额定功率/kW	130	定子外径/mm	166
额定电压/V	380	转子外径/mm	240
转速运行区间/(r/min)	10000~20000	转子内径/mm	182
额定转矩/(N∙m)	62.1	永磁体厚度/mm	5
额定效率	＞90%	极数	4
功率因数	＞0.9	槽数	24

输入参数x_i	原始值	变化范围
转子外径D_ro/mm	240	230~250
定子外径D_so/mm	166	155~177
定子内径D_si/mm	64	54~74
气隙长度δ_air/mm	3	2~4
永磁体厚度h_PM/mm	5	4~6
槽口宽B_s0/mm	1.7	1~10
槽宽B_s1/mm	10.2	9.18~11.22
槽底宽B_s2/mm	3.4	3~8
槽深h_s/mm	26.9	24.21~29.59
极弧系数α_p	0.6	0.5~0.9
轴向长度L/mm	93	80~106

转速/(r/min)	参考转矩/(N∙m)	转矩约束条件/(N∙m)
10000	121.43	[115.36，127.50]
12500	98.92	[93.97，103.87]
15000	83.31	[79.14，87.48]
17500	71.89	[68.30，75.48]
20000	63.2	[60.04，66.36]

典型工作点j	转速/(r/min)	t_j /s	λ_j
1	10000	44.01	0.345
2	12500	9.96	0.078
3	15000	11.95	0.094
4	17500	13.94	0.109
5	20000	47.72	0.374

参数	ρ_s(η₁)	ρ_s(η₂)	ρ_s(η₃)	ρ_s(η₄)	ρ_s(η₅)	ρ_s(η_g )
D_ro	0.02	0.03	0.02	0.02	0.03	0.0245
D_so	0.14	0.13	0.13	0.14	0.14	0.1383
D_si	-0.09	-0.07	-0.06	-0.06	-0.06	-0.0711
δ_air	0.36	0.39	0.38	0.37	0.36	0.3653
h_PM	0.16	0.03	-0.03	-0.07	-0.09	0.0134
B_s0	-0.75	-0.77	-0.77	-0.78	-0.78	-0.7679
B_s1	-0.01	0.02	-0.03	0.01	0.02	0.0039
B_s2	0	0.03	0.04	0.05	0.05	0.0303
h_s	-0.19	-0.18	-0.18	-0.17	-0.17	-0.1786
α_p	0.32	0.18	0.13	0.10	0.09	0.1812
L	0.02	0.01	0.00	0.00	-0.01	0.0039