针对海上风电场多分支集电线路故障定位大都依赖于多测点的问题,提出一种基于卷积神经网络（CNN）的集电线路故障选线方法,基于稀疏量测利用局部连接实现集电线路故障选线。该方法以少量节点电流信号作为特征量,建立以稀疏样本的CNN初始网络损失最小为目标的量测位置优化模型,利用离散二进制粒子群（BPSO）算法进行模型求解得出最优量测位置。算例分析表明,所提方法可在稀疏量测下以较高精度实现故障选线,对采样频率要求较低,不受故障起始角、故障电阻、故障位置等因素的影响,且对量测噪声具有较好的鲁棒性。
Abstract
To solve the problem that the fault location of multi-branch collecting lines of offshore wind farm mostly depends on multiple measurement points, a method of fault line selection of collecting lines based on convolutional neural network （CNN） is proposed, which uses local connection based on sparse measurement to realize fault line selection of collector lines. In this method, a small number of node current signals are taken as the characteristic quantity, and a measurement position optimization model is established with the goal of minimizing the initial CNN network loss of sparse samples. The binary particle swarm optimization （BPSO） algorithm is used to solve the model and obtain the optimal measurement position. The example analysis shows that the proposed method can achieve fault line selection with high accuracy under sparse measurements, with low sampling frequency requirements, and is not affected by factors such as fault starting angle, fault resistance, and fault location. It also has good robustness to measurement noise.
关键词
海上风电场 /
集电线路 /
卷积神经网络 /
离散二进制粒子群优化算法 /
故障选线 /
量测位置
Key words
offshore wind farms /
collector lines /
convolutional neural network /
binary particle swarm optimization algorithm /
fault line selection /
measurement position
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参考文献
[1] PLIEGO MARUGÁN A, GARCÍA MÁRQUEZ F P. Advanced analytics for detection and diagnosis of false alarms and faults: a real case study[J]. Wind energy, 2019, 22(11): 1622-1635.
[2] AGARWAL S, SWETAPADMA A, PANIGRAHI C, et al.Fault analysis method of integrated high voltage direct current transmission lines for onshore wind farm[J]. Journal of modern power systems and clean energy, 2019, 7(3): 621-632.
[3] 贾科, 顾晨杰, 毕天姝, 等. 基于压缩感知技术的大型光伏电站汇集系统故障定位研究[J]. 中国电机工程学报, 2017, 37(12): 3480-3489.
JIA K, GU C J, BI T S, et al.Research on the compressive sensing based fault location within the collection system of a large-scale photovoltaic power plant[J]. Proceedings of the CSEE, 2017, 37(12): 3480-3489.
[4] 贾科, 董雄鹰, 李论, 等. 基于稀疏电压幅值量测的配电网故障测距[J]. 电网技术, 2020, 44(3): 835-845.
JIA K, DONG X Y, LI L, et al.Fault location for distribution network based on transient sparse voltage amplitude measurement[J]. Power system technology, 2020, 44(3): 835-845.
[5] 贾科, 李论, 杨哲, 等. 基于贝叶斯压缩感知理论的配网故障定位研究[J]. 中国电机工程学报, 2019, 39(12): 3475-3485.
JIA K, LI L, YANG Z, et al.Research on distribution network fault location based on Bayesian compressed sensing theory[J]. Proceedings of the CSEE, 2019, 39(12): 3475-3485.
[6] 罗深增, 李银红, 陈博, 等. 计及PMU最优配置的输电线路广域自适应故障定位算法[J]. 中国电机工程学报, 2016, 36(15): 4134-4143.
LUO S Z, LI Y H, CHEN B, et al.An adaptive wide area fault location algorithm for transmission lines with optimal PMU placement[J]. Proceedings of the CSEE, 2016, 36(15): 4134-4143.
[7] 罗深增, 李银红, 石东源. 广域测量系统可观性概率评估及其在PMU优化配置中的应用[J]. 电工技术学报, 2018, 33(8): 1844-1853.
LUO S Z, LI Y H, SHI D Y.Wide area monitoring system observability probabilistic evaluation and it's application in optimal PMU placement[J]. Transactions of China Electrotechnical Society, 2018, 33(8): 1844-1853.
[8] KORKALI M, ABUR A.Optimal deployment of wide-area synchronized measurements for fault-location observability[J]. IEEE transactions on power systems, 2013, 28(1): 482-489.
[9] ROZENBERG I, BECK Y, ELDAR Y C, et al.Sparse estimation of faults by compressed sensing with structural constraints[J]. IEEE transactions on power systems, 2018, 33(6): 5935-5944.
[10] 陶维青, 肖松庆, 秦明辉, 等. 含有限PMU的主动配电网故障定位[J]. 太阳能学报, 2022, 43(4): 112-120.
TAO W Q, XIAO S Q, QIN M H, et al.Fault location of active distribution network with limited PMU[J]. Acta energiae solaris sinica, 2022, 43(4): 112-120.
[11] 高淑萍, 左俊杰, 宋国兵, 等. 基于电流故障分量变化特征的三端柔性直流配电网线路保护方法[J]. 太阳能学报, 2023, 44(8): 139-148.
GAO S P, ZUO J J, SONG G B, et al.Line protection method for three terminal flexible DC distribution network based on variation characteristics of current fault components[J]. Acta energiae solaris sinica, 2023, 44(8): 139-148.
[12] WANG X D, GAO X, LIU Y M, et al.Stockwell-transform and random-forest based double-terminal fault diagnosis method for offshore wind farm transmission line[J]. IET renewable power generation, 2021, 15(11): 2368-2382.
[13] 王晓东, 王若瑾, 刘颖明, 等. 基于EMD-MDT的直流微电网线路故障检测[J]. 太阳能学报, 2022, 43(11): 522-528.
WANG X D, WANG R J, LIU Y M, et al.Line fault monitoring of DC microgrid based on EMD-MDT[J]. Acta energiae solaris sinica, 2022, 43(11): 522-528.
基金
国家自然科学基金（52007124）; 辽宁省揭榜挂帅科技攻关专项（2021JHI/10400009）
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