Nagireddy Ravi

Bio: Nagireddy Ravi is an academic researcher. The author has contributed to research in topics: Fault detection and isolation & Fault (power engineering). The author has an hindex of 4, co-authored 6 publications receiving 57 citations.

Internal fault detection techniques for power transformers

Abstract: This paper presents the methodologies for incipient fault detection in power transformers both off-line and on-line. An artificial neural network is used to detect faults off-line with dissolved gas analysis reports of transformers and whereas wavelet transforms are being used for on-line fault detection. The accuracy in fault detection through artificial neural networks is compared with Rogers ratio method using the analysis of experimental oil samples for power transformers of power companies in Andhra Pradesh, India. The Wavelet transform techniques have been developed with different mother wavelets to detect incipient faults and to distinguish between incipient fault and short circuit fault. Further their performances with different mother wavelets are compared.

Fault Detection Techniques for Power Transformers

Abstract: This paper presents the methodologies for incipient fault detection in Power transformers for off-line and on-line. An artificial neural network is used to detect off-line faults and whereas wavelet transforms are being used for on-line fault detection. The Dissolved Gas Analysis to detect incipient faults has been improved using artificial neural networks and is compared with Rogers ratio method with available samples of field information. The Wavelet transform techniques have been developed with different mother wavelets and their performances are compared. These have been used to detect incipient faults and also to distinguish between incipient fault and short circuit fault.

Statistical method for fault detection in synchronous generators

Abstract: This paper presents a new methodology to detect faults in synchronous generators. The proposed approach is based on principal component analysis. Principal component analysis is a multivariate statistical technique used for detecting fault transients and characterization of faults. The proposed fault detection scheme is able to distinguish internal and external faults. The proposed scheme will aid in development of an automatic protective relay for the synchronous generators.

A new protection algorithm for synchronous generators using artificial neural networks

Abstract: This paper presents a new protection scheme for synchronous generator using an artificial neural network. The proposed scheme is able to discriminate internal and external faults of synchronous generator, consists of feature extraction using eigenvalues and fault classification using artificial neural networks. The performance of algorithm was tested using artificial neural networks and simulation results show the proposed scheme is able to identify and classify all types of faults of synchronous generator. The proposed approach has the ability to detect the generator winding faults which are close to neutral.

Fault Detection in Synchronous Generators Based on Independent Component Analysis

Abstract: A novel technique for fault detection and classification in the synchronous generator is proposed. In this paper, a new statistical method based on independent component analysis is presented. The proposed fault detection scheme identifies external and internal faults of synchronous generator. This characterization of fault transients will aid in the development of a protection relay for synchronous generator and the proposed method is effective in detecting faults and has great potential in power engineering applications.

An intelligent system based on optimized ANFIS and association rules for power transformer fault diagnosis.

Abstract: This research work put forward an intelligent method for diagnosis and classification of power transformers faults based on the instructive Dissolved Gas Analysis Method (DGAM) attributes and machine learning algorithms. In the proposed method, 14 attributes obtained through DGAM are utilized as the initial and unprocessed inputs of Adaptive Neuro-Fuzzy Inference System (ANFIS). In this method, attribute selection and improved learning algorithm are utilized to enhance fault detection and recognition precision . In the propounded fault detection and classification method, the most instructive attributes obtained by DGAM are selected by association rules learning technique (ARLT). Using efficient enlightening attributes and eliminating tautological attributes lead to higher accuracy and superior operation. Furthermore, appropriate training of ANFIS has significant effect on its precision and robustness . Therefore, Black Widow Optimization Algorithm (BWOA) is applied to train the ANFIS. Having excellent exploration and extraction capability, fast convergence speed and simplicity is the main reason for choosing the BWOA as the learning algorithm. Two industrial datasets are utilized to test and evaluate the performance of the put forward method. The results show that the propounded diagnosis system has high accuracy, robust performance and short run time. Selecting the most educative attributes of DGAM, training ANFIS optimally, improving the robustness of ANFIS and increasing the classification accuracy are the main contribution of this paper in the field of power transformer fault detection and classification.

Application of Gene Expression Programming (GEP) in Power Transformers Fault Diagnosis Using DGA

Abstract: Accurate diagnosis of incipient faults in oil-filled power transformers is important in preventive maintenance of transformers. Dissolved gas analysis (DGA) is an effective tool to diagnose incipient transformer faults. The majority of the methods reported in literature to analyze DGA results lay more emphasis on user experience rather than mathematical formulation/justification. Furthermore, sometimes DGA results for a certain fault do not belong to any of the IEC/IEEE standard and cannot be categorized/diagnosed. To address these issues, we propose a new approach for DGA interpretation using gene expression programming (GEP). The proposed approach is employed for analysis of 552 DGA samples collected from transformers of Himachal Pradesh State Electricity Board, India, in conjunction with samples extracted from reliable literature. We use the aforementioned dataset to test and validate our proposed GEP model. We also compare the performance of our approach against other artificial intelligence-based techniques such as artificial neural network, fuzzy-logic, and support vector machine. Results and comparison against other soft computing approaches show relative superiority of GEP-based DGA interpretation in terms of classification accuracy.

Anomaly detection in wireless sensor networks using self-organizing map and wavelets

Abstract: Wireless Sensor Networks (WSNs) have been applied in agriculture monitoring to monitor and collect various physical attributes within a specific area. It is important to detect data anomalies to determine a suitable course of action. The underlying aim of this paper is therefore to propose an anomaly detection scheme which is able to detect anomalies accurately by means of exploiting both time and frequency characteristics of the data signals. The contribution of this paper centers on anomaly detection by using Discrete Wavelet Transform (DWT) combined with a competitive learning neural network called self-organizing map (SOM) in order to accurately detect abnormal data readings. Experiment results from synthetic and real data collected from a WSN show that the proposed algorithm outperforms the SOM algorithm by up to 18% and DWT algorithm by up to 35% in presence of bursty faults with marginal increase of false alarm rate.