BHGSO:Binary Hunger Games Search Optimization Algorithm for Feature Selection Problem

In machine learning and data mining,feature selection(FS)is a traditional and complicated optimization problem.Since the run time increases exponentially,FS is treated as an NP-hard problem.The researcher’s effort to build a new FS solution was inspired by the ongoing need for an efficient FS framework and the success rates of swarming outcomes in different optimization scenarios.This paper presents two binary variants of a Hunger Games Search Optimization(HGSO)algorithm based on V-and S-shaped transfer functions within a wrapper FS model for choosing the best features from a large dataset.The proposed technique transforms the continuous HGSO into a binary variant using V-and S-shaped transfer functions(BHGSO-V and BHGSO-S).To validate the accuracy,16 famous UCI datasets are considered and compared with different state-of-the-art metaheuristic binary algorithms.The findings demonstrate that BHGSO-V achieves better performance in terms of the selected number of features,classification accuracy,run time,and fitness values than other state-of-the-art algorithms.The results demonstrate that the BHGSO-V algorithm can reduce dimensionality and choose the most helpful features for classification problems.The proposed BHGSO-V achieves 95%average classification accuracy for most of the datasets,and run time is less than 5 sec.for low and medium dimensional datasets and less than 10 sec for high dimensional datasets.

Development of Vehicle-to-Grid Systemto Regulate the System Parameters of Microgrid

This paper proposes a vehicle-to-grid(V2G)system interfaced with a microgrid that is effective at regulating frequency on a microgrid over a 24-h cycle.A microgrid is designed and divided into four components.The first component is a diesel generator,which is used to act as the base power generator.The second component consists of renewable energy(RE)power plants,which include solar photovoltaic(PV)and wind plants.The third component is a V2G system.The last component is the load connected to the microgrid.A microgrid is designed to be of sufficient size to represent a community of one thousand households during the day period of low consumption in the spring or fall seasons.A hundred electric vehicles(EVs)are modeled as base models to realize a 1:10 ratio for cars to households,which indicates a possible scenario in the near future.Detailed analysis of the active power,reactive power,voltage,frequency,and current is carried out.It is established that the proposed design of the V2G andmicrogrid effectivelymaintains systemparameters such as frequency and voltage within permissible limits with an error of less than 1%.Further,transient deviations in these parameters are limited to within 5%.A microgrid with V2G devices regulates the system frequency by mitigating load demand through coordinated control of conventional generation,solar PV plant generation,wind plant generation,power exchange with the microgrid network,and electric vehicles.The proposed microgrid and V2G are efficient for energy management and mitigation of intermittency and variability of RE power with improved performance.Variations in system parameters have been investigated by changing the operating scenario,and it has been determined that the error is limited to less than 5%.The study is effectively realized in the MATLAB/Simulink environment.

Multi-Objective Grey Wolf Optimization Algorithm for Solving Real-World BLDC Motor Design Problem

The first step in the design phase of the Brushless Direct Current(BLDC)motor is the formulation of the mathematical framework and is often used due to its analytical structure.Therefore,the BLDC motor design problem is considered to be an optimization problem.In this paper,the analytical model of the BLDC motor is presented,and it is considered to be a basis for emphasizing the optimization methods.The analytical model used for the experimentation has 78 non-linear equations,two objective functions,five design variables,and six non-linear constraints,so the BLDC motor design problem is considered as highly non-linear in electromagnetic optimization.Multi-objective optimization becomes the forefront of the current research to obtain the global best solution using metaheuristic techniques.The bio-inspired multi-objective grey wolf optimizer(MOGWO)is presented in this paper,and it is formulated based on Pareto optimality,dominance,and archiving external.The performance of theMOGWO is verified on standard multi-objective unconstraint benchmark functions and applied to the BLDC motor design problem.The results proved that the proposedMOGWO algorithm could handle nonlinear constraints in electromagnetic optimization problems.The performance comparison in terms of Generational Distance,inversion GD,Hypervolume-matrix,scattered-matrix,and coverage metrics proves that the MOGWO algorithm can provide the best solution compared to other selected algorithms.The source code of this paper is backed up with extra online support at https://premkumarmanoharan.wixsite.com/mysite and https://www.mathworks.com/matlabcentral/fileexchange/75259-multiobjective-non-sorted-grey-wolf-mogwo-nsgwo.

Identification and Classification of Multiple Power Quality Disturbances Using a Parallel Algorithm and Decision Rules

A multiple power quality(MPQ)disturbance has two or more power quality(PQ)disturbances superimposed on a voltage signal.A compact and robust technique is required to identify and classify the MPQ disturbances.This manuscript investigated a hybrid algorithm which is designed using parallel processing of voltage with multiple power quality(MPQ)disturbance using stockwell transform(ST)and hilbert transform(HT).This will reduce the computational time to identify theMPQdisturbances,whichmakes the algorithm fast.A MPQ identification index(IPI)is computed using statistical features extracted from the voltage signal using the ST and HT.IPI has different patterns for various types of MPQ disturbances which effectively identify the MPQ disturbances.A MPQ time location index(IPL)is computed using the features extracted from the voltage signal using ST and HT.IPL effectively identifies the initiation and end of PQ disturbances and thereby locates the MPQ events with respect to time.Classification of MPQ disturbances is performed using decision rules in both the noise-free and noisy environments with a 20 dB noise to signal ratio(SNR).The performance of the proposed hybrid algorithm using ST and HT with rule-based decision tree(RBDT)is better compared to the ST and RBDT techniques in terms of accuracy of classification of MPQ disturbances.MATLAB software is used to perform the study.

A New Metaheuristic Optimization Algorithms for Brushless Direct Current Wheel Motor Design Problem

The Equilibrium Optimizer(EO),Grey Wolf Optimizer(GWO),and Whale Optimizer(WO)algorithms are being recently developed for engineering optimization problems.In this paper,the EO,GWO,and WO algorithms are applied individually for a brushless direct current(BLDC)design optimization problem.The EO algorithm is inspired by the models utilized to find the system’s dynamic state and equilibrium state.The GWO and WO algorithms are inspired by the hunting behavior of the wolf and the whale,respectively.The primary purpose of any optimization technique is to find the optimal configuration by maximizing motor efficiency and/or minimizing the total mass.Therefore,two objective functions are being used to achieve these objectives.The first refers to a design with high power output and efficiency.The second is a constraint imposed by the reality that the motor is built into the wheel of the vehicle and,therefore,a lightweight is needed.The EO,GWO,and WOA algorithms are then utilized to optimize the BLDC motor’s design variables to minimize the motor’s total mass or maximize the motor efficiency by simultaneously satisfying the six inequality constraints.The simulation is carried out using MATLAB simulation software,and the simulation results prove the dominance of the proposed algorithms.This paper also suggests an efficient method from the proposed three methods for the BLDC motor design optimization problem.

IRKO:An Improved Runge-Kutta Optimization Algorithm for Global Optimization Problems

Optimization is a key technique for maximizing or minimizing functions and achieving optimal cost,gains,energy,mass,and so on.In order to solve optimization problems,metaheuristic algorithms are essential.Most of these techniques are influenced by collective knowledge and natural foraging.There is no such thing as the best or worst algorithm;instead,there are more effective algorithms for certain problems.Therefore,in this paper,a new improved variant of a recently proposed metaphorless Runge-Kutta Optimization(RKO)algorithm,called Improved Runge-Kutta Optimization(IRKO)algorithm,is suggested for solving optimization problems.The IRKO is formulated using the basic RKO and local escaping operator to enhance the diversification and intensification capability of the basic RKO version.The performance of the proposed IRKO algorithm is validated on 23 standard benchmark functions and three engineering constrained optimization problems.The outcomes of IRKO are compared with seven state-of-the-art algorithms,including the basic RKO algorithm.Compared to other algorithms,the recommended IRKO algorithm is superior in discovering the optimal results for all selected optimization problems.The runtime of IRKO is less than 0.5 s for most of the 23 benchmark problems and stands first for most of the selected problems,including real-world optimization problems.

An Algorithm for the Protection of Distribution Feeders Using the Stockwell and Hilbert Transforms Supported Features

Faults’recognition in the distribution feeders(DFs)is extremely important for improving the reliability of the distribution system.Therefore,this paper proposes a technique to identify the faults on the DF using the Stockwell Transform(ST)dependent variance feature and Hilbert transform(HT)by utilizing current signals.By element to element multiplication of the H-index,we compute using HT aided decompositions of current waveforms and VS-index,and calculate through ST aided decomposition of current waveforms.By utilizing the decision rules,various faults are classified.Different faults studied in this work are line to ground,double line,double line to ground and 3-Φto ground.For high fault impedance,this technique is effectively utilized.Furthermore,variations in the fault incidence angles are also utilized to test the performance of the proposed technique.To perform the proposed algorithm,a IEEE-13 bus system is developed in MATLAB/Simulink software.The algorithm effectively classified the faults with accuracy greater than 98%.The algorithm is also successfully validated on the IEEE-34 bus test system.Furthermore,the algorithm was successfully validated on the practical power system network.It is recognized that the developed method performed better than the discrete Wavelet transform(DWT)and ruled decision tree based protection scheme reported in various literature.

Comprehensive Overview of Multi-agent Systems for Controlling Smart Grids

Agents are intelligent entities that act flexibly and autonomously and make wise decisions based on their intelligence and experience.A multi-agent system(MAS)contains multiple,intelligent,and interconnected collaborating agents for solving a problem beyond the ability of a single agent.A smart grid(SG)combines advanced intelligent systems,control techniques,and sensing methods with an existing utility power network.For controlling smart grids,various control systems with different architectures have already been developed.MAS-based control of power system operations has been shown to overcome the limitations of time required for analysis,relaying,and protection;transmission switching;communication protocols;and management of plant control.These systems provide an alternative for fast and accurate power network control.This paper provides a comprehensive overview of MASs used for the control of smart grids.The paper provides a wide-spectrum view of the status of smart grids,MAS-based control techniques and their implementation for the control of smart grids.Use of MASs in the control of various aspects of smart grids-including the management of energy,marketing energy,pricing,scheduling energy,reliability,network security,fault handling capability,communication between agents,SG-electrical vehicles,SG-building energy systems,and soft grids—have been critically reviewed.More than a hundred publications on the topic of MAS-based control of smart grids have been critically examined,classified,and arranged for fast reference.

Recognition of complex and multiple power quality disturbances using wavelet packet-based fast kurtogram and ruled decision tree algorithm

This paper introduces an algorithm based on wavelet packet supported fast kurtogram and decision rules for the identification and classification of complex power quality(PQ)disturbances.Features are extracted from the signals using fast kurtogram,envelope of filtered voltage signal and amplitude spectrum of squared envelop.Proposed algorithm can be implemented for the recognition of the complex PQ disturbances,which include the combination of voltage sag and harmonics,voltage momentary interruption(MI)and oscillatory transient(OT),voltage MI and harmonics,voltage sag and impulsive transient(IT),voltage sag,OT,IT and harmonics.Proposed work has been performed using the MATLAB software.Performance of the algorithm is compared with performance of algorithm supported by discrete wavelet transform(DWT)and fuzzy C-means clustering(FCM).