Abstract
High voltage direct current (HVDC) transmission systems play an increasingly important role in long-distance power transmission. Realizing accurate and timely fault location of transmission lines is extremely important for the safe operation of power systems. With the development of modern data acquisition and deep learning technology, deep learning methods have the feasibility of engineering application in fault location. The traditional single-terminal traveling wave method is used for fault location in HVDC systems. However, many challenges exist when a high impedance fault occurs including high sampling frequency dependence and difficulty to determine wave velocity and identify wave heads. In order to resolve these problems, this work proposed a deep hybrid convolutional neural network (CNN) and long short-term memory (LSTM) network model for single-terminal fault location of an HVDC system containing mixed cables and overhead line segments. Simultaneously, a variational mode decomposition–Teager energy operator is used in feature engineering to improve the effect of model training. 2D-CNN was employed as a classifier to identify fault segments, and LSTM as a regressor integrated the fault segment information of the classifier to achieve precise fault location. The experimental results demonstrate that the proposed method has high accuracy of fault location, with the effects of fault types, noise, sampling frequency, and different HVDC topologies in consideration.
Highlights
Renewable power generation has been widely used in recent years
The voltage source converter (VSC)-High voltage direct current (HVDC) transmission system with mixed lines in Figure 1 is constructed on the power system simulation software PSCAD/EMTDC
A deep convolutional neural network (CNN)-long short-term memory (LSTM) method was proposed to locate the fault in HVDC systems with mixed cables and overhead lines
Summary
Renewable power generation has been widely used in recent years. High voltage direct current (HVDC) transmission systems can provide high power transmission capability over long distances. (1) CNN-LSTM was used to solve many shortcomings of the single-ended traveling wave method, including high sampling frequency, difficulty to determine wave velocity and identify wave heads when an HIF occurs It provides high precision and strong robustness to fault types, noise, sampling frequency, and different HVDC topologies in fault location. (2) VMD-TEO was used for feature engineering, which made the characteristics of the fault signals obvious It reduced the dependence of deep learning on the number of samples to a certain extent, thereby improving the learning efficiency and accuracy of CNN-LSTM. Feature engineering is mainly through decomposing the fault voltage and current signals at terminal M into several intrinsic mode function (IMF) [32,33] components by VMD and analyzing the first IMF component (IMF1) to obtain. Dropout layers (D1, D2) are set as 0.3 and 0.2, respectively
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