&ldquo;IIT Jodhpur is committed to impart quality education to facilitate the creation of qualified, competent and responsible human resources to meet the emerging technological challenges of the world. It is working towards creating an equitable learning platform that is positive, accessible and effective in the face of the modern societal and scientific advancements.

Flexibility in curriculum, in all its academic programs, encompassing elements of teaching, research, creativity, innovation and entrepreneurship is the most unique feature of IIT Jodhpur. The Department of Science and Technology (DST), under the National Mission on Cyber Physical Systems, has identified IIT Jodhpur as a Technology &amp; Innovation Hub in the area of Augmented Reality and Virtual Reality. The institute has also been nominated as the coordinator for the Jodhpur City and Knowledge Cluster by the Office of Principal Scientific Adviser, Government of India.

The sprawling 852-acre IIT Jodhpur campus is a majestic tribute to the deep-rooted diverse cultural heritage of the region. The campus is an architectural masterpiece, which is slated to become an international exemplar of sustainability with net-zero energy, and advanced water and waste management strategies.&rdquo;

IIT Jodhpur

This paper proposes a protection scheme based on Wavelet Multi Resolution Analysis and Artificial Neural Networks which detects and classifies various faults like Single phasing, Under voltage, Unbalanced supply, Stator Turn fault, Stator Line to Ground fault, Stator Line to Line fault, Broken bars and Locked rotor of a three-phase induction motor. The three phase Induction Motor is represented by a universal model which is valid for a wide range of frequencies. The same has been simulated using MATLAB/Simulink software and tested for various types of motor faults. The wavelet decomposition of three-phase stator currents is carried out with Bi-Orthogonal 5.5 (Bior5.5). The maximum value of the absolute peak value of the highest level (d1) coefficients of three-phase currents is defined as fault index which is compared with a predefined threshold to detect the fault. The normalized fourth level approximate (a4) coefficients of these currents are fed to a Feedforward neural network to classify various faults. The normalized peak d1 coefficients of three-phase currents are fed to another Feedforward neural network to identify the faulty phase of stator internal faults. The algorithm has been tested for various incidence angles and proved to be simple, reliable and effective in detecting and classifying the various faults and also in identifying the faulty phase of stator. © 2011 IEEE.

World University root node

Department of Electrical Engineering

Proceedings - 2011 Annual IEEE India Conference: Engineering Sustainable Solutions, INDICON-2011

Wavelet - ANN based fault diagnosis in three phase induction motor

In this paper, wavelet transform technique is applied to detect fault in the transmission line with flexible alternating current transmission (FACTS) device. Presence of FACTS device changes the system impedance and hence makes it difficult to detect faults on the line which may result into maloperation of relay. Three phase currents are monitored at both ends of the transmission line using global positioning system synchronizing clock. Wavelet transforms, which is very fast and sensitive to high frequency signal is used to extract transients in these line currents for fault detection. Fault index is calculated based on the sum of local and remote end detail coefficients and compared with threshold value to detect the fault. Proposed technique is tested for various faults and fault inception angles with and without static synchronous compensator (STATCOM) device. Simulation results are presented showing the selection of proper threshold value for fault detection. © 2011 IEEE.

Detection of transmission line faults in the presence of STATCOM using wavelets

This paper proposes a protection scheme based on Wavelet Multi Resolution Analysis and Artificial neural network which detects and classifies various possible stator winding fault of a three-phase induction motor such as inter turn faults, line to ground faults and line to line faults. The wavelet decomposition of three-phase stator currents is carried out with Bior5.5. The maximum value of absolute peak dl coefficients of three-phase currents is defined as fault index which is compared with a predefined threshold to detect the fault. The normalized peak dl coefficients of these currents are fed to a Feedforward neural network to classify various faults. The algorithm has been tested for various incidence angles and proved to be simple, reliable and effective in detecting and classifying the various stator winding faults. © 2010 IEEE.

Proceedings of the 2010 5th IEEE Conference on Industrial Electronics and Applications, ICIEA 2010

Wavelet ANN based stator internal faults protection scheme for 3-phase induction motor

This paper deals with application of wavelet transform to detect busbar faults and to provide backup protection for external faults. The detail coefficients of a source CT current, feeder current and Zone-2 current are obtained over a narrow moving window. The fault indexes of differential and source CT current signals obtained are compared with their respective threshold values to detect the internal faults. In the event of feeder and Zone-2 faults, the d-coefficients of differential current have a time shift compared to that of source current. Further the external fault is located by comparing the polarities of peak d-coefficient of source, feeder and Zone-2 currents. The scheme is tested for different types of external and internal faults with variation in incidence angles and fault impedance. The proposed scheme detects internal faults and locates external faults even in the presence of current transformer saturation. © 2008 IEEE.

2008 IEEE International Conference on Sustainable Energy Technologies, ICSET 2008

A transient current based bus zone protection scheme using wavelet transform

This paper deals with the application of wavelet transform for the detection of busbar faults and providing backup for external faults. In the event of internal faults the transient behavior of feeder and zone-2 currents is opposite to that of zone-1 source current. In the event of feeder or zone-2 faults, the corresponding currents exhibit similar transient behavior as that of source current. The transients of these current signals are analyzed using wavelet based multi resolution analysis to obtain detailed coefficients over a narrow moving window. The fault indexes of each current signal, defined based on these detailed coefficients, are utilized to detect the internal faults as well as external faults. The scheme is tested for different types of internal and external faults with various incidence angles and fault impedance. The proposed scheme discriminates internal faults from external faults even in the presence of current transformer saturation.

IEEE Region 10 Annual International Conference, Proceedings/TENCON

This article proposes a protection scheme for turbo generators to detect stator ground faults in particular closer to neutral. The scheme considers total generated voltage of the machine and makes use of wavelet multi-resolution analysis. A fault index is defined in terms of peak and valley values of the highest-level wavelet decomposition, which is compared with a threshold value to discriminate from other transients, like sudden unbalance, in load. To identify the faulty coil, an ANN employing back propagation algorithm is used with the lowest level wavelet coefficients as inputs. The results of digital simulation are presented for different fault impedances and fault locations.

Electric Power Components and Systems

Wavelet ANN based stator ground fault protection scheme for turbo generators

This paper proposes a protection scheme for turbo generators to detect stator ground faults in particular closer to neutral. The scheme considers total generated voltage of the machine and makes use of Wavelet Multi Resolution Analysis. A Fault Index is defined in terms of peak and valley values of the highest-level wavelet decomposition, which is compared with a threshold value to discriminate from other transients like sudden unbalance in load. The results of digital simulation are presented for different fault impedances and fault locations. ©2007 IEEE.

ICIIS 2007 - 2nd International Conference on Industrial and Information Systems 2007, Conference Proceedings

A new wavelet based ground fault protection scheme for turbo generators

This paper deals with application of Wavelet Transform for detection of busbar faults and to discriminate them from external faults. The detail coefficients of differential current and those of a source CT current are obtained over a narrow moving window. The Fault indexes of both current signals obtained are compared with their respective threshold values to detect the internal faults. In the event of external faults the d-coefficients of differential current have a time shift compared to that of source current due to saturation of CT and this is used to discriminate the external faults from internal faults. The scheme is tested for different types of external and internal faults with variations in incidence angles and fault impedances. The proposed scheme is proved to be fast, stable and reliable in detecting the internal faults and discriminating them from external faults. © 2006 IEEE.

A new Wavelet Transform based busbar protection

This paper deals with the application of wavelet transforms for the detection, classification and location of faults on Transmission lines. A Global Positioning System synchronizing clock is used to sample three phase voltage and current signals at both the ends of the transmission line over a moving window length of half cycle. The current signals are analyzed with Bior2.2 wavelet to obtain detail coefficients of single decompositions. Fault Indices are calculated based on the sum of local and remote end detail coefficients, and compared with threshold values to detect and classify the faults. For estimation of fault location Feed Forward Artificial Neural Networks are employed, which make use of third level approximate decompositions of the voltages and currents of local end obtained with Bior4.4 wavelet. Two types of neural networks are proposed, one for locating Phase Faults and the other for Ground Faults. The proposed algorithm is tested for different locations and types of faults as well as for various incidence angles and fault impedances. The algorithm is proved to be efficient and effective in detecting, classifying and locating faults. © 2006 IEEE.