Manoj Tripathy

Bio: Manoj Tripathy is an academic researcher from Indian Institute of Technology Roorkee. The author has contributed to research in topics: Fault (power engineering) & Inrush current. The author has an hindex of 13, co-authored 91 publications receiving 770 citations. Previous affiliations of Manoj Tripathy include Motilal Nehru National Institute of Technology Allahabad & Indian Institutes of Technology.

Power Transformer Differential Protection Based On Optimal Probabilistic Neural Network

Abstract: In this paper, the optimal probabilistic neural network (PNN) is proposed as the core classifier to discriminate between the magnetizing inrush and the internal fault of a power transformer. The particle swarm optimization is used to obtain an optimal smoothing factor of PNN which is a crucial parameter for PNN. An algorithm has been developed around the theme of the conventional differential protection of the transformer. It makes use of the ratio of voltage-to-frequency and amplitude of differential current for the determination of operating condition of the transformer. The performance of the proposed heteroscedastic-type PNN is investigated with the conventional homoscedastic-type PNN, feedforward back propagation (FFBP) neural network, and the conventional harmonic restraint method. To evaluate the developed algorithm, relaying signals for various operating condition of the transformer, including internal and external faults, are obtained by modeling the transformer in PSCAD/EMTDC. The protection algorithm is implemented by using MATLAB.

Probabilistic neural-network-based protection of power transformer

Abstract: An optimal probabilistic neural network (PNN) as a core classifier for fault detection and status indication of a power transformer has been presented. In this scheme, various operating conditions of a transformer are distinguished using signatures of the differential currents. The proposed differential protection scheme is implemented through two different structures of PNN, that is, one having one output and the other having five outputs. The developed algorithm is found to be stable against external fault, magnetising inrush, sympathetic inrush and over-excitation conditions for which relay operation is not required. For the test data of fault, it is found to operate successfully. The performance of proposed PNN and classical artificial neural network (ANN) has been compared. For evaluation of the developed algorithm, relaying signals for various operating conditions of a transformer are obtained by modelling the transformer in PSCAD/EMTDC. The algorithms are implemented using MATLAB. The results show the capability of PNN in terms of classification accuracy and speed in comparison to classical ANNs.

Recent techniques used in transmission line protection: A review

Abstract: This article presents a survey of the developments in digital relays for protection of transmission lines. For a modern power system, selective high speed clearance of faults on high voltage transmission lines is critical and this survey indicates the efficient and promising implementations for fault detection, classification and fault location in power transmission line protection. The work done in this area favor computerized relays, digital communication technologies and other technical developments, to avoid cascading failures and facilitate safer, secure and reliable power systems. Efforts have been made to include almost all the techniques and philosophies of transmission line protection reported in the literature up to October 2010. The focus of this article is on the most recent techniques, like artificial neural network, fuzzy logic, fuzzy-neuro, fuzzy logicwavelet based and phasor measurement unit-based concepts as well as other conventional methods used in transmission line protection.

An Innovative Pilot Relaying Scheme for Shunt-Compensated Line

Abstract: This paper presents a fault detection and classification scheme for a shunt [static var compensator (SVC)] compensated line. The proposed relaying scheme is based on the concept of superimposed sequence components-based integrated impedance (SSCII). For an internal fault, the magnitude of SSCII is small and for an external fault, it is very large. In the SVC compensated line, a fault forces SVC to vary its impedance. Superimposed components are injected due to this impedance variation, along with the fault components. These SVC-injected components are treated as fault-injected components and, therefore, even the sound phases are detected as faulty phases. To avoid such failure, fault-injected superimposed components have been extracted by using the modified prefault data, which is estimated according to SVC impedance variations. The superimposed components measured using the modified prefault data consist of fault-injected superimposed components only. The proposed scheme has been tested for all types of faults, different values of fault resistances, and several fault locations and SVC locations. The results demonstrate that the proposed scheme successfully detects and classifies the faults. Also, the proposed scheme is robust against variations in fault resistance, source impedance, and SVC location.

Pattern Recognition and Machine Learning

Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Enhanced probabilistic neural network with local decision circles: A robust classifier

Abstract: In recent years the Probabilistic Neural Network (PPN) has been used in a large number of applications due to its simplicity and efficiency. PNN assigns the test data to the class with maximum likelihood compared with other classes. Likelihood of the test data to each training data is computed in the pattern layer through a kernel density estimation using a simple Bayesian rule. The kernel is usually a standard probability distribution function such as a Gaussian function. A spread parameter is used as a global parameter which determines the width of the kernel. The Bayesian rule in the pattern layer estimates the conditional probability of each class given an input vector without considering any probable local densities or heterogeneity in the training data. In this paper, an enhanced and generalized PNN (EPNN) is presented using local decision circles (LDCs) to overcome the aforementioned shortcoming and improve its robustness to noise in the data. Local decision circles enable EPNN to incorporate local information and non-homogeneity existing in the training population. The circle has a radius which limits the contribution of the local decision. In the conventional PNN the spread parameter can be optimized for maximum classification accuracy. In the proposed EPNN two parameters, the spread parameter and the radius of local decision circles, are optimized to maximize the performance of the model. Accuracy and robustness of EPNN are compared with PNN using three different benchmark classification problems, iris data, diabetic data, and breast cancer data, and five different ratios of training data to testing data: 90:10, 80:20, 70:30, 60:40, and 50:50. EPNN provided the most accurate results consistently for all ratios. Robustness of PNN and EPNN is investigated using different values of signal to noise ratio (SNR). Accuracy of EPNN is consistently higher than accuracy of PNN at different levels of SNR and for all ratios of training data to testing data.

Estimation of Battery State of Health Using Probabilistic Neural Network

Abstract: In this study, a probabilistic neural network (PNN) is used to estimate the state of health (SOH) of Li-ion batteries. The accurate prediction of SOH can help avoid inconveniences or fatal accidents from the sudden malfunction of the battery. A total of 110 pieces of Li-Co batteries are used. Constant current/voltage recharging and constant current discharging are performed for the life-cycle test of the battery. The data obtained from the recharging and discharging electric characteristics as well as the life-cycle test of the battery are used to estimate the SOH of the battery. The test data show that the constant current charging time, the instantaneous voltage drop at the start of discharging, and the open circuit voltage are the most important characteristics for estimating the SOH of the battery. The PNN is trained using 100 pieces of batteries. The remaining 10 pieces are used to verify the feasibility of the proposed method. The effectiveness of the PNN training using a number of samples is discussed and analyzed. The results show that the average error of the prediction is 0.28% and the standard deviation is 1.15%. The computation time of the PNN is 62.5 ms. Thus, the proposed method can accurately estimate the SOH of the battery in a short period.