Abstract
Learning methods have been increasingly used in power engineering to perform various tasks. In this paper, a fault selection procedure in double-circuit transmission lines employing different learning methods is accordingly proposed. In the proposed procedure, the discrete Fourier transform (DFT) is used to pre-process raw data from the transmission line before it is fed into the learning algorithm, which will detect and classify any fault based on a training period. The performance of different machine learning algorithms is then numerically compared through simulations. The comparison indicates that an artificial neural network (ANN) achieves remarkable accuracy of 98.47%. As a drawback, the ANN method cannot provide explainable results and is also not robust against noisy measurements. Subsequently, it is demonstrated that explainable results can be obtained with high accuracy by using rule-based learners such as the recently developed quantitative association rule mining algorithm (QARMA). The QARMA algorithm outperforms other explainable schemes, while attaining an accuracy of 98%. Besides, it was shown that QARMA leads to a very high accuracy of 97% for highly noisy data. The proposed method was also validated using data from an actual transmission line fault. In summary, the proposed two-step procedure using the DFT combined with either deep learning or rule-based algorithms can accurately and successfully perform fault selection tasks but indicating remarkable advantages of the QARMA due to its explainability and robustness against noise. Those aspects are extremely important if machine learning and other data-driven methods are to be employed in critical engineering applications.
Highlights
Transmission lines are a fundamental part of today’s power systems, as they ensure power supply to end consumers by connecting them to far-off large generation plants
– We demonstrate with several numerical examples, including real-world data that fault classification task can be solved by quantitative association rule mining algorithm (QARMA) with very high accuracy even when only one-end currents are available, or when the measurements are subject to high levels of noise
With either of the studied methods (DL or QARMA), once the model is generated based on historical data of the target system, the time taken to perform phase evaluation given a single-phase fault in the system is as small as 4 ms
Summary
Transmission lines are a fundamental part of today’s power systems, as they ensure power supply to end consumers by connecting them to far-off large generation plants. A reliable distance relaying protection system for transmission networks must have a high-accuracy fault selector for correct operations in any protective zone for fast trip decision-making. Others perform the fault selection with high accuracy, but they lack speed and even post-protective actions, and are not suitable for real-time protection and trip decision making based on the faulted loop (distance relays) [28]. These algorithms consider all measurements available, if not, approaches like shown in [21] can deal with missing values. The output components employed in this method are, for example, decaying memory function (as illustrated in Fig. 2), superimposed signals, or Fourier transforms
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