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Purpose: This study aims to focus on an adaptive method for fault detection and classification of fault types that trigger in three-phase transmission lines using artificial neural networks (ANNs). The proposed scheme can detect and classify several types of faults, including line-to-ground, line-to-line, double-line-to-ground, triple-line and triple-line-to-ground faults. Design/methodology/approach: The fundamental components of three-phase current and voltage were used as inputs in the ANNs. An analysis of the impact of variations in the fault resistance, fault type and fault inception time was conducted to evaluate the ANNs performance. The survey compares the performance of the multi-layer perceptron neural network (MLPNN) and Elman recurrent neural network trained with the backpropagation learning technique to improve each of the three phases of the fault detection and classification process. A detailed analysis validates the choice of the ANNs architecture based on the variation in the number of hidden neurons in each step. Findings: The mean square error, root mean square error, mean absolute error and linear regression are measured to improve the efficiency of the ANN models for both fault detection and classification. The results indicate that the MLPNN can detect and classify faults with a satisfactory performance. Originality/value: The smart adaptive scheme is fast and accurate for fault detection and classification in a single circuit transmission line when faced with different conditions and can be useful for transmission line protection schemes. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to use frequency response analysis, a powerful tool to detect the location and types of transformer winding faults. Proposing an effective intelligent approach for interpreting the frequency responses is the most crucial problem of this method and has created many challenges. Design/methodology/approach: Heat maps based on appropriate statistical indices have been supplied to depict the variations in the frequency responses associated with each fault type, fault location and fault extent along the windings. Also, after analyzing the results of artificial neural network (ANN) techniques, the generalized regression neural network method is introduced as the most effective solution for the classification of transformer winding faults. Findings: Using a comparative approach, the performance of the used indices and ANN techniques are evaluated. The results showed the proper performance of Lin's concordance coefficient (LCC) index and the amplitude (Amp) part of the frequency response. The proposed fitting percentage (FP) index can assist the intelligent classifiers in diagnosing the radial deformation (RD) fault with the highest accuracy considering all frequency response components in the classification procedure of winding faults. Practical implications: Various ANN techniques are used to detect and determine the type of four important faults of transformer winding, i.e. axial displacement, RD, disc space variation and short circuit. Various statistical indices, such as cross-correlation factor, LCC, standard difference area, sum of errors, normalized root-mean-square deviation and FP, are used to extract the features of the frequency responses to consider as the ANN inputs. In addition, different components of the frequency response, such as Amp, argument, real and imaginary parts are examined in this paper. To implement the proposed procedure, step by step, various types of winding faults with different locations and extents are applied on the 20 kV winding of a 1.6 MVA distribution transformer. Originality/value: Contributions have been made in identifying and diagnosing transformer winding defects through the use of appropriate algorithms for future research. [ABSTRACT FROM AUTHOR]

This paper considers the problem of fault classification when different fault classifiers are performed simultaneously. Based on spectral meta-learner (SML) proposed by Parisi et al., its recursive version, i.e., recursive SML (RSML) is developed for online combining the potentially conflicting classification information. Considering different statistical properties of faults occurring at different time intervals, the binary classification information is recursively utilized. By introducing a forgetting factor, the leading eigenvector of the estimate of the time-varying covariance matrix is used as the weight vector for each classifier. Rank-one modification is then used for calculating the eigenvector in order to reduce the online computational complexity. The performance of RSML is strictly analyzed in a statistical sense, including the effect of recursive calculation and conditional dependence of different classifiers on the weight vector. Compared with majority voting and SML, a higher balanced accuracy of RSML can be verified by the benchmark Tennessee Eastman process. [ABSTRACT FROM PUBLISHER]

This paper presents the Chebyshev neural network (ChNN) as an improved artificial intelligence technique for power system protection studies and examines the performances of two ChNN learning algorithms for fault classification of series compensated transmission line. The training algorithms are least-square Levenberg–Marquardt (LSLM) and recursive least-square algorithm with forgetting factor (RLSFF). The performances of these algorithms are assessed based on their generalization capability in relating the fault current parameters with an event of fault in the transmission line. The proposed algorithm is fast in response as it utilizes postfault samples of three phase currents measured at the relaying end corresponding to half-cycle duration only. After being trained with only a small part of the generated fault data, the algorithms have been tested over a large number of fault cases with wide variation of system and fault parameters. Based on the studies carried out in this paper, it has been found that although the RLSFF algorithm is faster for training the ChNN in the fault classification application for series compensated transmission lines, the LSLM algorithm has the best accuracy in testing. The results prove that the proposed ChNN-based method is accurate, fast, easy to design, and immune to the level of compensations. Thus, it is suitable for digital relaying applications. [ABSTRACT FROM PUBLISHER]

Abstract: This paper employs a digital signal processing (DSP) based frequency domain approach using wavelet multi-resolution analysis (MRA) to overcome difficulties such as fault inception angle, fault impedance and fault distance associated with conventional time domain approach employing voltage and current based measurements for fault classification in case of digital relaying of transmission line. The frequency domain approach for fault classification algorithm uses wavelet MRA technique to extract the features of the current signals based on harmonics generated at the instant of occurrence of fault due to abrupt change of currents in a three phase transmission line. Since choice of particular wavelet plays a vital role for extracting features of generated harmonics, therefore an attempt has been made in this proposed research to extensively investigate using 16 wavelets to establish the superiority of Db4 wavelet over other standard wavelets for accurate fault classification. [Copyright &y& Elsevier]

• A fault classification and isolation method based on SAE is proposed for complex system. • Deep learning model formed by AE with sparse and regular constraints can well extract features. • The fault isolation is conducted by calculating the contribution rate of each variable and choosing the largest one. • High fault classification and isolation accuracy of the proposed method is validated by some typical processes. Modern industrial systems are increasingly complex with the development of technology and equipment. Due to the variation of process loads and other factors, many processes often switch their operating modes, which results in the problem of multimode process monitoring. In addition, owing to the harsh industrial conditions, monitoring of multimode processes is difficult and challenging. In this paper, a fault classification and isolation method were proposed based on sparse stacked autoencoder network. In detail, a single autoencoder is trained one by one in an unsupervised way. Then, the hidden layer of each trained autoencoder is cascade connected to form a deep structure. Finally, the stacked autoencoder network is followed by a Softmax layer to realize the fault classification task. Since the deep structure can well learn and fit the nonlinear relationship in the process and perform feature extraction more effectively compare with other traditional methods, it can classify the faults accurately. After an in-depth exploration of the deep neural network, a novel fault isolation scheme is proposed. By calculating the contribution rate of each variable, the one with the largest contribution was identified as the fault location. In order to evaluate the efficiency of the proposed method, some numerical experiments were conducted on the continuous stirred tank heater (CSTH) benchmark process as well as two industrial cases including the wind turbines monitoring and the aluminum electrolysis process monitoring in comparison with several traditional methods. The experimental results show that the accuracy of the proposed method in the fault classification process is better than other methods and the fault isolation scheme can well locate the variable that caused the fault. [ABSTRACT FROM AUTHOR]