P.K. Dash
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A quantum approach to synthetic minority oversampling technique (SMOTE) Open
The paper proposes the Quantum-SMOTE method, a novel solution that uses quantum computing techniques to solve the prevalent problem of class imbalance in machine learning datasets. Quantum-SMOTE, inspired by the Synthetic Minority Oversamp…
Identification of multiple power quality disturbances in hybrid microgrid using deep stacked auto-encoder based bi-directional LSTM classifier Open
In recent years microgrid technology has created widespread interest for the integration of renewable energy sources into main utility grid to supply clean energy to the end users. However, the use of power electronic equipments, electroni…
A new approach to instantaneous power based control of DFIG using active disturbance rejection control Open
This paper presents the design and control of a wind energy converter system that uses a doubly fed induction generator. It introduces a model that incorporates instantaneous active and reactive power as state variables to represent both t…
A novel control strategy of a variable-speed doubly-fed-induction-generator-based wind energy conversion system Open
This paper aims to address the issue of control of a variable-speed wind turbine based on doubly-fed induction generators. In this work, an effort is made to extract the maximum efficiency from a doubly-fed induction generator-based variab…
Real-time validation of optimal energy management in DC microgrids by using modified rejection controller based improved sparrow search algorithm Open
For optimal energy management in the DC microgrid, there are two major challenges, such as minimization of operational cost and balancing the power flow. Most of the benchmark techniques are used to develop energy management systems (EMS) …
Performance and engine exhaust study of a CI engine in dual fuel mode using diethyl ether as cetane enhancer additive Open
There is a wide range of biomass feedstock readily accessible for harvesting energy in the form of solid, liquid, and gaseous fuels that are used in thermal power production approaches.
Improved deep mixed kernel randomized network for wind speed prediction Open
Forecasting wind speed is an extremely complicated and challenging problem due to its chaotic nature and its dependence on several atmospheric conditions. Although there are several intelligent techniques in the literature for wind speed p…
Effect of Yogic Lifestyle on Engineering Students Open
In today's competitive life, everyone wants to outdo each other, and engineering students are no exception. Engineering students are always under the pressure of heavy course load, practicals and assignments not being completed on time. Mo…
An improvised nature-inspired algorithm enfolded broad learning system for disease classification Open
Deep analysis of genomic data reveals that many deadly diseases are generated due to genetic mutation. To make the health care system more robust, a machine learning researcher’s prime intention is to classify the genomic data more efficie…
A hybrid neural network and optimization algorithm for forecasting and trend detection of Forex market indices Open
An Autoencoder (AE) is an independent feature extractor from data samples and a deep network can be obtained by stacking several AEs. This paper presents a novel hybrid stacked Autoencoder-based Deep Kernel-based Random Vector Functional L…
An Integrated Nature-Inspired Algorithm Hybridized Adaptive Broad Learning System for Disease Classification Open
Classification of different deadly diseases using machine learning algorithms helps in making the health care system more robust. This, not only reduces the human errors during diagnosis of disease due to inexperience but also helps the ph…
A new protection scheme for PV-wind based DC-ring microgrid by using modified multifractal detrended fluctuation analysis Open
This paper presents fault detection, classification, and location for a PV-Wind-based DC ring microgrid in the MATLAB/SIMULINK platform. Initially, DC fault signals are collected from local measurements to examine the outcomes of the propo…
Influential Gene Selection From High-Dimensional Genomic Data Using a Bio-Inspired Algorithm Wrapped Broad Learning System Open
The classification of high dimensional gene expression/ microarray data always plays an important role in various disease diagnoses and drug discovery. To avoid the curse of high dimensionality, the selection of the most influential genes …
Review for "Research of a combined wind speed model based on multi‐objective ant lion optimization algorithm" Open
Get your research seenMake an impact with these nine promotional tools. SEO• Use relevant keywords to make your title and abstract clear and easy to search for.• Off-page SEO strategies, like link building, can help get your paper seen. Co…
FPGA‐based favourite skin colour restoration using improved histogram equalization with variable enhancement degree and ensemble extreme learning machine Open
This paper presents skin color enhancement based on favorite skin color to agree with user‐defined favorite skin color using improved histogram equalization with variable enhancement degree (IHEwVED) and machine learning methods. The skin …
Islanding detection in photovoltaic based DC micro grid using adaptive variational mode decomposition and detrended fluctuation analysis Open
This study presents a novel approach using adaptive variational mode decomposition with detrended fluctuation analysis to detect the islanding disturbances for photovoltaic based DC micro grid. DC parameters are simple to estimate in compa…
An Efficient Machine Learning Approach for Accurate Short Term Solar Power Prediction Open
Solar based electricity generations have experienced strong and impactful growth in recent years. The regulation, scheduling, dispatching, and unit commitment of intermittent solar power is dependent on the accuracy of the forecasting meth…
Multi‐kernel‐based random vector functional link network with decomposed features for epileptic EEG signal classification Open
This study proposes an improved hybrid model built with empirical mode decomposition (EMD) features combined with weighted multi‐kernel random vector functional link network (WMKRVFLN) where the kernel parameters are optimised with an effi…
FPGA implementation of adaptive p‐norm filter for non‐stationary power signal parameter estimation Open
A p‐norm extreme learning machine (ELM) based on sparsity constraint is presented in this study for tracking of fundamental frequency, harmonic and dc in current power signals which finds application in phasor measurement units for wide ar…
Short-term mixed electricity demand and price forecasting using adaptive autoregressive moving average and functional link neural network Open
A new hybrid adaptive autoregressive moving average (ARMA) and functional link neural network (FLNN) trained by adaptive cubature Kalman filter (ACKF) is presented in this paper for forecasting day-ahead mixed short-term demand and electri…
Adaptive fractional integral terminal sliding mode power control of UPFC in DFIG wind farm penetrated multimachine power system Open
With an aim to improve the transient stability of a DFIG wind farm penetrated multimachine power system (MPN), an adaptive fractional integral terminal sliding mode power control (AFITSMPC) strategy has been proposed for the unified power …
A fast time-frequency response based differential spectral energy protection of AC microgrids including fault location Open
This paper proposes a pattern recognition based differential spectral energy protection scheme for ac microgrids using a Fourier kernel based fast sparse time-frequency representation (SST or simply the sparse S-Transform). The average and…
Adaptive threshold based new active islanding protection scheme for multiple PV based microgrid application Open
A new adaptive active islanding protection scheme is presented for a multiple photovoltaic (PV) based microgrid environment, where islanding detection parameters are estimated in adaptive manner according to operational PV penetration leve…