Improved Fault Classification in Series Compensated Transmission Line: Comparative Evaluation of Chebyshev Neural Network Training Algorithms

Performance evaluation of machine learning for fault selection in power transmission lines

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.

Hybrid multi-mode machine learning-based fault diagnosis strategies with application to aircraft gas turbine engines

In this work, a novel data-driven fault diagnostic framework is developed by using hybrid multi-mode machine learning strategies to monitor system health status. The coexistence of multi-mode and concurrent faults and their adverse coupling effects pose serious limitations for developing reliable diagnostic methodologies. A novel framework is proposed by exploiting inherent embedded health information contained in the I/O sensor data. The proposed hybrid strategies consist of optimal integration of recurrent neural network-based feature generation and self-organizing map diagnostic modules. To construct reliable fault diagnostic modules, a systematic clustering and modeling methodology is developed that has two primary advantages: (i) it does not require any a priori knowledge of data set characteristics or system mathematical model, and (ii) it does address and resolve the key limitations and challenges in conventional self-organizing map approaches. The effectiveness of our proposed framework is validated by utilizing sensor data including healthy and various degradation modes in application to compressor and turbine of an aircraft gas turbine engine. Comparisons with other machine learning-based methods in the literature are provided to demonstrate the performance and superiority of our proposed framework in fault diagnostic accuracy, false alarm rates, and in dealing with multi-mode and concurrent fault scenarios.

A Novel Fault Classification Technique in Series Compensated Transmission Line using Ensemble Method

The paper presents a novel approach for classification of faults in Extra High Voltage (EHV) transmission line with series compensation. The proposed algorithm utilizes single end currents extracted from three phases of a transmission line given to a fault detection and classification system. A detailed model is constructed to analyze fault patterns occurring on a dual feed system with multiple series compensation provided on EHV transmission line. The algorithm uses a full cycle of post-fault currents. A Multiresolution Analysis Wavelet-based decomposition technique is used to provide a joint time frequency analysis. Extensive study is done by designing different fault classifiers using Support Vector Machine (SVM) with different feature vector groups. The SVMs with different parameters are compared to show performance of SVMs in given feature space of faults. A new algorithm is proposed with Ensemble method having group of different classifiers, namely, Artificial Neural Network (ANN), K-Nearest Neighborhood (KNN) and SVM. A combination of feature selection with Ensemble Classifiers is trained and tested on a wide range of fault patterns. A significant improvement in performance is obtained with different combination of features in Ensemble Classifier using subspace partition method.The proposed Ensemble Classifier provides an accuracy of 99.5%.

A Review of Fault Diagnosing Methods in Power Transmission Systems

Transient stability is important in power systems. Disturbances like faults need to be segregated to restore transient stability. A comprehensive review of fault diagnosing methods in the power transmission system is presented in this paper. Typically, voltage and current samples are deployed for analysis. Three tasks/topics; fault detection, classification, and location are presented separately to convey a more logical and comprehensive understanding of the concepts. Feature extractions, transformations with dimensionality reduction methods are discussed. Fault classification and location techniques largely use artificial intelligence (AI) and signal processing methods. After the discussion of overall methods and concepts, advancements and future aspects are discussed. Generalized strengths and weaknesses of different AI and machine learning-based algorithms are assessed. A comparison of different fault detection, classification, and location methods is also presented considering features, inputs, complexity, system used and results. This paper may serve as a guideline for the researchers to understand different methods and techniques in this field.

A Novel Equivalent Model of Active Distribution Networks Based on LSTM

Dynamic behaviors of distribution networks are of great importance for the power system analysis. Nowadays, due to the integration of the renewable energy generation, energy storage, plug-in electric vehicles, and distribution networks turn from passive systems to active ones. Hence, the dynamic behaviors of active distribution networks (ADNs) are much more complex than the traditional ones. The research interests how to establish an accurate model of ADNs in modern power systems are drawing a great deal of attention. In this paper, motivated by the similarities between power system differential algebraic equations and the forward calculation flows of recurrent neural networks (RNNs), a long short-term memory (LSTM) RNN-based equivalent model is proposed to accurately represent the ADNs. First, the adoption reasons of the proposed LSTM RNN-based equivalent model are explained, and its advantages are analyzed from the mathematical point of view. Then, the accuracy and generalization performance of the proposed model is evaluated using the IEEE 39-Bus New England system integrated with ADNs in the study cases. It reveals that the proposed LSTM RNN-based equivalent model has a generalization capability to capture the dynamic behaviors of ADNs with high accuracy.