山东大学学报 (工学版) ›› 2023, Vol. 53 ›› Issue (4): 140-148.doi: 10.6040/j.issn.1672-3961.0.2022.308
范海雯1,郝旭东2,赵康2,邢法财2,蒋哲2,李常刚1
FAN Haiwen1, HAO Xudong2, ZHAO Kang2, XING Facai2, JIANG Zhe2, LI Changgang1
摘要： 为提升含分布式光伏配电网静态等值的运行方式适应性,提出一种基于卷积神经网络(convolutional neural networks,CNN)的含分布式光伏配电网静态等值方法。考虑源荷不确定性及相关性,基于核密度估计和Copula函数生成光伏、负荷功率场景并计算配网潮流。针对各单一运行方式下的等值问题,构造含分布式光伏配电网的等值模型,采用粒子群优化(particle swarm optimization, PSO)辨识变压器和线路参数。为提高模型参数计算效率,提出一种基于CNN的含分布式光伏配电网静态等值参数估计模型。在某省配电网算例下验证了所提方法的有效性。相较于其他方法,基于CNN的静态等值考虑了源荷功率的波动性及相关性,且提高了等值参数辨识效率,能够应用于静态等值参数的在线计算。
中图分类号:
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