Mukesh Pushkarna

Bio: Mukesh Pushkarna is an academic researcher from GLA University. The author has contributed to research in topics: AC power & Photovoltaic system. The author has an hindex of 4, co-authored 8 publications receiving 37 citations.

Correction to “Optimal Design of Passive Power Filter Using Multi-Objective Pareto-Based Firefly Algorithm and Analysis Under Background and Load-Side’s Nonlinearity”

Abstract: In this paper, the optimal designing of passive power filter (PPF) is formulated as a multi-objective optimization (MOO) problem under several constraints of system’s performance indices (PIs) such as individual as well as total harmonic distortion (THD) in the line current and the point of common coupling’s (PCC) voltage, distribution line’s ampacity under harmonic currents overloading, steady-state voltage profile, load power factor (PF) and a few associated with the filter itself. The optimal design parameters of a third-order damped filter are simultaneously determined for achieving maximum PF at the PCC while keeping system’s other indices such as total demand distortion (TDD) in the line current, total voltage harmonic distortion (TVHD) at the PCC and total filter cost (FC) incurred at a minimum by obtaining a best-compromised solution using the newly proposed multi-objective Pareto-based firefly algorithm (pb-MOFA). A novel MOO approach inspired by the modified firefly algorithm and Pareto front is established in order to deal with PPF design problems. The extension of MOFA is considered for producing the Pareto optimal front and various conclusions are drawn by analysing the trade-offs among the objectives. The efficiency and accuracy of the proposed pb-MOFA, in solving the concerned MOO problem, is validated by comparing an obtained solution and three computed PIs viz. convergence metric (CM), generational distance (GD) and diversity metric (DM) with those obtained from popular multi-objective Pareto-based PSO (pb-MOPSO), non-dominated sorting genetic algorithm (NSGA-II) and recently introduced multi-objective slime mould algorithm (MOSMA). The need for true Pareto front (TPF) is served by the one obtained by Monte Carlo method. At last, the impacts of different background voltage distortion (BVD) levels and load-side’s nonlinearity levels (NLLs) on filter performance are analysed.

Influence of Artificial and Natural Cooling on Performance Parameters of a Solar PV System: A Case Study

Abstract: Contribution of renewable energy in overall power generation is eagerly welcomed by all nations to mitigate the carbon emission. Solar Photovoltaic based power generation is a rapid progressing technology. Although drop in efficiency due to rise in Photovoltaic (PV) module temperature, is yet a significant loss which is highly site dependent. The most common approach does not include natural wind cooling effect while others are not commonly applied to estimate the module temperature during performance evaluation, which leads to error in forecasting, large area requirement for same power generation, more money investment as well as large payback period. Temperature and natural wind cooling highly affects the PV module performance, thus it becomes important to study and evaluate the performance of PV module in local conditions. In this work an attempt is made to observe the effect of natural cooling on PV module performance. The case study includes the performance ratio for simulation and experimental conditions considering artificial cooling. On another hand performance ratio is also evaluated for simulation and experimental conditions considering natural cooling. This study evaluates various errors, invested cost, annual units, annual recovery, payback time and return on investment to emphasize on local site dependent performance. An improved performance for various performance parameters is observed considering the natural cooling effect.

Microgrid Implementation in Unbalanced Nature of Feeder using Conventional Technique

Abstract: This paper introduces a micro-grid implementation in a real time model of the IEEE 13-node test feeder to establish a quantified real-time platform. Solar photovoltaic electricity and wind turbines used in micro grids are considered as non-conventional energy sources. The main of this research work is to understand the useful concept for incorporating environmental friendly sources and analysis, where micro-grid performs along with the micro-grid response and the system reliability response as required. At the relevant bus, the renewable sources (i.e. PV system & wind generator) are linked to the imbalance the radial feeder. Micro-grid is a system that interacts with resources across the central grid through the distribution system but it can operate independently. Using the Matlab Simulink software to model a micro-grid with a three-phase unbalanced power supply together with battery as a storage device. The results demonstrate a significant improvement in the voltage and current profile of different time-shifting buses.

An improved SRF based control algorithm for D-STATCOM under abnormal source voltage

Abstract: This paper describes an improved SRF (Synchronous Reference Frame) based control technique or algorithm for time varying power flow control and optimum load compensation of non-linear loading under abnormal or disturbed source voltage by D-STATCOM designed for three-phase three wire systems. The improved algorithm is based on the active power separation and imaginary symmetrical components of voltage and has been compared with conventional IRP based algorithm to show the preciseness in finding the reference current for a DSTATCOM under abnormal i.e. asymmetric and distorted source voltage. Individually the algorithms were implemented and simulated under MATLAB/Simulink. The simulation results also show which algorithm can accurately detect the harmonics and reactive component of load current even under the asymmetric and distorted source voltage condition. Additionally by the control loop of reactive power of improved algorithm, load reactive power can be compensated as well as regulated under variations of load. Thus this additional control arrangement can help system operators improve overall system performances.

Match-Level Fusion of Finger-Knuckle Print and Iris for Human Identity Validation Using Neuro-Fuzzy Classifier

Abstract: Biometrics is the term for measuring human characteristics. If the term is divided into two parts, bio means life, and metric means measurement. The measurement of humans through different computational methods is performed to authorize a person. This measurement can be performed via a single biometric or by using a combination of different biometric traits. The combination of multiple biometrics is termed biometric fusion. It provides a reliable and secure authentication of a person at a higher accuracy. It has been introduced in the UIDIA framework in India (AADHAR: Association for Development and Health Action in Rural) and in different nations to figure out which biometric characteristics are suitable enough to authenticate the human identity. Fusion in biometric frameworks, especially FKP (finger–knuckle print) and iris, demonstrated to be a solid multimodal as a secure framework. The proposed approach demonstrates a proficient and strong multimodal biometric framework that utilizes FKP and iris as biometric modalities for authentication, utilizing scale-invariant feature transform (SIFT) and speeded up robust features (SURF). Log Gabor wavelet is utilized to extricate the iris feature set. From the extracted region, features are computed using principal component analysis (PCA). Both biometric modalities, FKP and iris, are combined at the match score level. The matching is performed using a neuro-fuzzy neural network classifier. The execution and accuracy of the proposed framework are tested on the open database Poly-U, CASIA, and an accuracy of 99.68% is achieved. The accuracy is higher compared to a single biometric. The neuro-fuzzy approach is also tested in comparison to other classifiers, and the accuracy is 98%. Therefore, the fusion mechanism implemented using a neuro-fuzzy classifier provides the best accuracy compared to other classifiers. The framework is implemented in MATLAB 7.10.

Hosting capacity enhancement of renewable-based distributed generation in harmonically polluted distribution systems using passive harmonic filtering

Abstract: In spite of being economically viable for numerous applications, renewable energy cannot realise its true penetration potential until some of the barriers are not tackled with excellence. Such barriers are harmonic distortion, distribution cable’s ampacity and voltage rise limits, those put bound on the maximum allowable penetration level of renewable energy. This paper formulates the enhancement of power quality-constrained hosting capacity (HC) as a multi-objective optimization (MOO) problem under several constraints of system’s performance indices (PIs). The considered performance indices are individual order and total harmonic distortion in the line current and point of common coupling (PCC)’s voltage, load power factor (PF), distribution line’s ampacity and steady-state voltage profile. In the formulated multi-objective optimization model, an optimal design of a third-order damped filter and size of the distributed generation (DG) unit is simultaneously determined for achieving maximum hosting capacity and power factor at the PCC while keeping system’s other indices such as total voltage harmonic distortion (TVHD) and total filter cost (FC) incurred at a minimum by obtaining a best-compromised solution using the newly proposed Pareto-based multi-objective firefly algorithm (MOFA). The extension of the multi-objective firefly algorithm is considered for producing the Pareto optimal front and various conclusions are drawn by analysing the trade-offs among the objectives by plotting the same on different 2-axis planes. The optimization efficiency and superiority of the proposed multi-objective firefly algorithm based hosting capacity enhancement approach is validated by comparing the results with those obtained by popular multi-objective PSO (MOPSO) and non-dominated sorting genetic algorithm (NSGA-II) under similar objectives. Also, the results of the proposed methodology are compared with those achieved from one of the most recently introduced hosting capacity enhancement approaches in literature. Eventually, the impacts of different background voltage distortion (BVD) levels and load-side’s nonlinearity levels (NLLs) on filter performance, particularly filter cost as well as enhanced hosting capacity, are analysed and various conclusions are drawn.

Correction to “Optimal Design of Passive Power Filter Using Multi-Objective Pareto-Based Firefly Algorithm and Analysis Under Background and Load-Side’s Nonlinearity”

Abstract: In this paper, the optimal designing of passive power filter (PPF) is formulated as a multi-objective optimization (MOO) problem under several constraints of system’s performance indices (PIs) such as individual as well as total harmonic distortion (THD) in the line current and the point of common coupling’s (PCC) voltage, distribution line’s ampacity under harmonic currents overloading, steady-state voltage profile, load power factor (PF) and a few associated with the filter itself. The optimal design parameters of a third-order damped filter are simultaneously determined for achieving maximum PF at the PCC while keeping system’s other indices such as total demand distortion (TDD) in the line current, total voltage harmonic distortion (TVHD) at the PCC and total filter cost (FC) incurred at a minimum by obtaining a best-compromised solution using the newly proposed multi-objective Pareto-based firefly algorithm (pb-MOFA). A novel MOO approach inspired by the modified firefly algorithm and Pareto front is established in order to deal with PPF design problems. The extension of MOFA is considered for producing the Pareto optimal front and various conclusions are drawn by analysing the trade-offs among the objectives. The efficiency and accuracy of the proposed pb-MOFA, in solving the concerned MOO problem, is validated by comparing an obtained solution and three computed PIs viz. convergence metric (CM), generational distance (GD) and diversity metric (DM) with those obtained from popular multi-objective Pareto-based PSO (pb-MOPSO), non-dominated sorting genetic algorithm (NSGA-II) and recently introduced multi-objective slime mould algorithm (MOSMA). The need for true Pareto front (TPF) is served by the one obtained by Monte Carlo method. At last, the impacts of different background voltage distortion (BVD) levels and load-side’s nonlinearity levels (NLLs) on filter performance are analysed.

Streamflow prediction with large climate indices using several hybrid multilayer perceptrons and copula Bayesian model averaging

Abstract: Streamflow prediction help the modelers to manage water resources in watersheds. It gives essential information for flood control and reservoir operation. This study uses the copula-based -Bayesian model averaging (CBMA) as an improved version of the BMA model for predicting streamflow in the Golok River, the Kelantan River, the Lanas River, and the Nenggiri River of Malaysia. The CBMA corrected the assumption of the utilization of Gaussian distortion in the BMA. While the BMA used normal distribution for the variables, the CBMA uses different distribution and copula functions for the variables. This study works on the Archimedes optimization algorithm (AOA) to train the mutlilayer perceptron (MLP) model. The ability of the MLP-AOA model was benchmarked against the MLP-bat algorithm (BA), MLP-particle swarm optimization (MLP-PSO), and the MLP-firefly algorithm (MLP-FFA). The models used significant climate signals, namely, the southern oscillation index (SOI), El NiNo–Southern Oscillation (ENSO), North Atlantic oscillation (NAO), and the pacific decadal oscillation (PDO) as the inputs to the models. The Gamma test (GT) was coupled with the AOA to provide the hybrid GT for choosing the best inputs. The gamma test was used to determine the suitable lag times of the Nino 3.4, PDO, NAO, and SOI as the inputs. The novelty of the current paper includes introducing new hybrid MLP models, new gamma test for choosing the best input combination, the comprehensive uncertainty analysis of outputs, and the use of an advanced ensemble CBMA model for predicting streamflow. First, the outputs of the MLP-AOA, MLP-BA, MLP-FFA, MLP-PSO, and MLP were obtained, following which, the CBMA as an ensemble framework based on outputs of the hybrid and standalone MLP models was used to predict monthly streamflow. The CBMA at the training level, decreased the root mean square error (RMSE) of BMA, MLP-AOA, MLP-BA, MLP-FFA, MLP-PSO, and MLP models by 28%, 32%, 52%, 53 53%, and 55%, respectively. The CBMA at the training level of another station decreased the mean absolute error (MAE) of BMA, MLP-AOA, MLP-BA, MLP-FFA, MLP-PSO, and MLP models by 6.04, 29%,42%, 49%, 52%, and 52%, respectively. The Nash Sutcliff efficiency (NSE) of the CBMA at the training level was 0.94 while it was 0.92, 0.90, 0.85, 0.84, 0.82, and 0.80 for the BMA, MLP-AOA, MLP-BA, MLP-FFA, MLP-PSO, and MLP models. The RMSE of the MLP-AOA was reported 4.3%, 12%, 14%, and 17% lower than those of the MLP-BA, MLP-FFA, MLP-PSO, and MLP models, respectively. The current research showed the CBMA and the BMA models had high abilities for predicting monthly streamflow. The results of this current study indicated that the CBMA and BMA provided lower uncertainty the standalone MLP models. The general results indicated that the streamflows in the hotter months decreased and flood control is of higher priority during the other months.

Power Quality Concerns with Integration of RESs into the Smart Power Grid and Associated Mitigation Techniques

Abstract: Power distribution systems are advancing day today with the integration of renewable energy vindicated by growing energy demands and the sake of the global environment. But, the transformation of traditional grids into the smarter one has affected the power quality (PQ) and has given rise to new power quality concerns. The main cause, of these new power quality concerns, is the integration of renewable energy sources (RESs). Analysis of these power quality concerns is essential for predicting the stochastic effects on the operation of the modern power system and evaluating the appropriate solution. This paper presents an overview of power quality concerns arose as a result of an increase in grid integrated renewable energy systems and a mini review on state of the art solutions, in the literature, to alleviate those concerns. Besides, the opportunities for future research on mitigation techniques are also highlighted.

Design and Simulation of Hybrid DG System Fed Single-Phase Dynamic Voltage Restorer for Smart Grid Application

Abstract: The modern commercial and domestic devices are highly sensitive to the variation in system voltage profile. In proposed work, hybrid DG system fed single-phase dynamic voltage restorer (DVR) is pro...