Combined morphology and SVM-based fault feature extraction technique for detection and classification of transmission line faults

Abstract

A transmission line is the main commodity of power transmission network through which power is transmitted to the utility. These lines are often swayed by accidental breakdowns owing to different random origins. Hence, researchers try to detect and track down these failures at the earliest to avoid financial prejudice. This paper offers a new realtime mathematical morphology based approach for fault feature extraction. The morphological open-close-median filter is exploited to wrest unique fault features which are then fed as an input to support vector machine to detect and classify the short circuit faults. The acquired graphical and numerical results of the extracted fault features affirm the potency of the offered scheme. The proposed scheme has been verified for different fault cases simulated on high-voltage transmission line modelled using ATP/EMTP with varying system constraints. The performance of the stated technique is also validated for fault detection and classification in real-field transmission lines. The results state that the proposed method is capable of detecting and classifying the faults with adequate precision and reduced computational complexity, in less than quarter of a cycle.

Convolutional-Neural-Network Assisted Segmentation and SVM Classification of Brain Tumor in Clinical MRI Slices

“…The ML approaches implemented in earlier research offered a satisfactory classification result on the benchmark as well as the clinical grade MRI slices (94.51%) when a binary classification is employed to categorize the MRI slices into healthy/disease class [10]. The DL supported approaches helped to achieve a better result during the binary as well as the multi-class categorization of the brain MRI slices [7,8]. The earlier works on BT detection confirmed that, the DL schemes implemented with the DF and combined DF and HF (DF+HF) will offer a better detection accuracy.…”

“…Let us consider there exist feature vectors is implemented as ature vectors GBM FV is with a value he FA then performs t and computes the in Equation ( 7):. − LGGa FV GBMa FV , (7) 268 in HF between features is (8) other FA parameter information can be accessed from [34].…”

“…Selected feature (8) The fitness function then assigned as in Equation ( 9 then the corresponding feature is selected and a new feature vector is formed. The proposed procedure is graphically presented in Figure 5 and the FA with optimally assigned parameters will help to identify the new feature vector.…”

“…The screening of the BT can be performed using signal-assisted-methodologies (recording and examining the electroencephalogram) and image-assisted-procedures (recording the brain section using radiological imaging). The BT detection with imaging practice helps to reach a healthier diagnosis compared to signal supported techniques [8][9][10].…”

Rolling Bearing Fault Diagnosis Based on Support Vector Machine Optimized by Improved Grey Wolf Algorithm

“…During research on fault identification, with the continuous optimization and development of deep learning and intelligent optimization algorithms in recent years, swarm intelligence optimization algorithms have been effectively applied to rolling bearing fault diagnosis, which has become a hotspot in this field. Artificial neural networks [ 12 ], backpropagation (BP) neural networks [ 13 ], and support vector machines (SVMs) [ 14 ] are common machine learning algorithms. An artificial neural network is prone to overfitting, a BP neural network tends to fall into local optimality [ 15 , 16 ], and an SVM effectively solves the above problems.…”