Fault Detection and Classification in Medium Voltage DC Shipboard Power Systems With Wavelets and Artificial Neural Networks
Fault Detection and Classification in Medium Voltage DC Shipboard Power Systems With Wavelets and Artificial Neural Networks
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DOI：
10.1109/tim.2014.2313035
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发表时间：
2014-11-01
影响因子：
5.6
通讯作者：
Ponci, Ferdinanda
中科院分区：
文献类型：
--
作者：
Li, Weilin;Monti, Antonello;Ponci, Ferdinanda
提出了一种将小波变换（WT）多分辨率分析（MRA）技术与人工神经网络（ann）相结合的船用中压直流电力系统故障检测与分类方法。考虑到未来全电动船舶的MVDC系统提出了一系列新的挑战，特别是本文所讨论的故障检测和分类问题。本文采用WT-MRA和Parseval定理提取不同断层的特征。选取故障信号在不同分辨率下的能量变化作为特征向量。经过分析比较，我们选择了Daubechies 10 （db10）小波和scale 9作为小波函数和分解层次。然后，根据提取的特征，采用人工神经网络对故障类型进行自动分类。对不同类型的故障，如直流母线和交流侧的短路故障以及接地故障进行了分析和测试，以验证该方法的有效性。利用数字模拟器对这些故障进行了实时仿真，并用MATLAB对数据进行了初步分析。案例研究是一个概念上的MVDC SPS模型，仿真结果表明该模型具有较好的分类精度。最后，在一个实时平台上对所提出的故障检测算法进行了实现和测试，为将来的实际应用奠定了基础。
This paper proposes a fault detection and classification method for medium voltage DC (MVDC) shipboard power systems (SPSs) by integrating wavelet transform (WT) multiresolution analysis (MRA) technique with artificial neural networks (ANNs). The MVDC system under consideration for future all-electric ships presents a range of new challenges, in particular the fault detection and classification issues addressed in this paper. The WT-MRA and Parseval's theorem are employed in this paper to extract the features of different faults. The energy variation of the fault signals at different resolution levels are chosen as the feature vectors. As a result of analysis and comparisons, the Daubechies 10 (db10) wavelet and scale 9 are the chosen wavelet function and decomposition level. Then, ANN is adopted to automatically classify the fault types according to the extracted features. Different fault types, such as short circuit faults on both dc bus and ac side, as well as ground fault, are analyzed and tested to verify the effectiveness of the proposed method. These faults are simulated in real time with a digital simulator and the data are then initially analyzed with MATLAB. The case study is a notional MVDC SPS model, and promising classification accuracy can be obtained according to simulation results. Finally, the proposed fault detection algorithm is implemented and tested on a real-time platform, which enables it for future practical use.