Sridharan Naveen Venkatesh

Bio: Sridharan Naveen Venkatesh is an academic researcher from VIT University. The author has contributed to research in topics: Deep learning & Fault (power engineering). The author has co-authored 1 publications.

A combined approach of convolutional neural networks and machine learning for visual fault classification in photovoltaic modules

Abstract: Fault diagnosis plays a significant role in enhancing the useful lifetime, power output, and reliability of photovoltaic modules (PVM). Visual faults such as burn marks, delamination, discoloration...

Photovoltaic Module Fault Detection Based on Deep Learning Using Cloud Computing

Abstract: Te performance of photovoltaic modules (PVMs) degrades due to the occurrence of various faults such as discoloration, snail trail, burn marks, delamination, and glass breakage. Tis degradation in power output has created a concern to improve PVM performance. Automatic inspection and condition monitoring of PVM components can handle performance-related issues, especially for installed capacity where no trained personnel are available at the location.Tis paper describes a deep learning-based technique involving convolutional neural networks (CNNs) to extract features from aerial images obtained from unmanned aerial vehicles (UAVs) and classify various types of fault occurrences using cloud computing and Internet of things (IoT).Te algorithm used demonstrates a binary classifcation with high accuracy by comparing individual faults with good condition. Efcient and efective fault detection can be observed from the results obtained.

Automatic Inspection of Photovoltaic Power Plants Using Aerial Infrared Thermography: A Review

Abstract: In recent years, aerial infrared thermography (aIRT), as a cost-efficient inspection method, has been demonstrated to be a reliable technique for failure detection in photovoltaic (PV) systems. This method aims to quickly perform a comprehensive monitoring of PV power plants, from the commissioning phase through its entire operational lifetime. This paper provides a review of reported methods in the literature for automating different tasks of the aIRT framework for PV system inspection. The related studies were reviewed for digital image processing (DIP), classification and deep learning techniques. Most of these studies were focused on autonomous fault detection and classification of PV plants using visual, IRT and aIRT images with accuracies up to 90%. On the other hand, only a few studies explored the automation of other parts of the procedure of aIRT, such as the optimal path planning, the orthomosaicking of the acquired images and the detection of soiling over the modules. Algorithms for the detection and segmentation of PV modules achieved a maximum F1 score (harmonic mean of precision and recall) of 98.4%. The accuracy, robustness and generalization of the developed algorithms are still the main issues of these studies, especially when dealing with more classes of faults and the inspection of large-scale PV plants. Therefore, the autonomous procedure and classification task must still be explored to enhance the performance and applicability of the aIRT method.

Detection of Compound Faults in Ball Bearings Using Multiscale-SinGAN, Heat Transfer Search Optimization, and Extreme Learning Machine

Abstract: Intelligent fault diagnosis gives timely information about the condition of mechanical components. Since rolling element bearings are often used as rotating equipment parts, it is crucial to identify and detect bearing faults. When there are several defects in components or machines, early fault detection becomes necessary to avoid catastrophic failure. This work suggests a novel approach to reliably identifying compound faults in bearings when the availability of experimental data is limited. Vibration signals are recorded from single ball bearings consisting of compound faults, i.e., faults in the inner race, outer race, and rolling elements with a variation in rotational speed. The measured vibration signals are pre-processed using the Hilbert–Huang transform, and, afterward, a Kurtogram is generated. The multiscale-SinGAN model is adapted to generate additional Kurtogram images to effectively train machine-learning models. To identify the relevant features, metaheuristic optimization algorithms such as teaching–learning-based optimization, and Heat Transfer Search are applied to feature vectors. Finally, selected features are fed into three machine-learning models for compound fault identifications. The results demonstrate that extreme learning machines can detect compound faults with 100% Ten-fold cross-validation accuracy. In contrast, the minimum ten-fold cross-validation accuracy of 98.96% is observed with support vector machines.

Deep learning-based ensemble model for classification of photovoltaic module visual faults

Abstract: ABSTRACT Fault occurrences in photovoltaic (PV) modules can hinder the performance of the system, resulting in reduced lifetime and performance of the modules. PV module (PVM) faults if unmonitored can affect the power transmission through the system, thereby creating short circuits that can be hazardous. Unmanned aerial vehicle (UAV)-based monitoring is one of the most common and widely adopted techniques to detect faults in PVM. Visual images of PVM contain the necessary information about the faults, but sometimes, it becomes difficult even for expert professional to work on large amount of image data. Automatic classification of PVM faults using deep learning techniques can help in providing improved analysis and instantaneous results. The present study adopts renowned deep convolution neural network (CNN) models such as MobileNet V2, Inception V3, and Xception for the classification of PVM. The aforementioned models were trained individually, and the classification performances of the models were observed to be 97.03%, 95.55%, and 92.27%, respectively. A hybrid deep ensemble model is proposed in the study that merges all the aforementioned models. The proposed model produced classification accuracy higher than each of the individual model with a value of 99.04%. Automatic classification using deep ensemble model can help in the accurate identification of faults in PVM from images acquired through UAV. Consequently, this computer-aided and quick diagnosis can eliminate the downtime and fire hazards.

Hybrid Wavelet–CNN Fault Diagnosis Method for Ships’ Power Systems

Abstract: Three-phase induction motors (IMs) are considered an essential part of electromechanical systems. Despite the fact that IMs operate efficiently under harsh environments, there are many cases where they indicate deterioration. A crucial type of fault that must be diagnosed early is stator winding faults as a consequence of short circuits. Motor current signature analysis is a promising method for the failure diagnosis of power systems. Wavelets are ideal for both time- and frequency-domain analyses of the electrical current of nonstationary signals. In this paper, the signal data are obtained from simulations of an induction motor for various stator winding fault conditions and one normal operating condition. Our main contribution is the presentation of a fault diagnostic system based on a hybrid discrete wavelet–CNN method. First, the time series of the currents are processed with discrete wavelet analysis. In this way, the harmonic frequencies of the faults are successfully captured, and features can be extracted that comprise valuable information. Next, the features are fed into a convolutional neural network (CNN) model that achieves competitive accuracy and needs significantly reduced training time. The motivations for integrating CNNs into wavelet analysis results for fault diagnosis are as follows: (1) the monitoring is automated, as no human operators are needed to examine the results; (2) deep learning algorithms have the potential to identify even more indistinguishable and complex faults than those that human eyes could.