Metaheuristic Optimization with Deep Learning Enabled Smart Grid Stability Prediction

Due to the drastic increase in global population as well as economy,electricity demand becomes considerably high.The recently developed smart grid(SG)technology has the ability to minimize power loss at the time of power distribution.Machine learning(ML)and deep learning(DL)models can be effectually developed for the design of SG stability techniques.This article introduces a new Social Spider Optimization with Deep Learning Enabled Statistical Analysis for Smart Grid Stability(SSODLSA-SGS)pre-diction model.Primarily,class imbalance data handling process is performed using Synthetic minority oversampling technique(SMOTE)technique.The SSODLSA-SGS model involves two stages of pre-processing namely data nor-malization and transformation.Besides,the SSODLSA-SGS model derives a deep belief-back propagation neural network(DBN-BN)model for the pre-diction of SG stability.Finally,social spider optimization(SSO)algorithm can be applied for determining the optimal hyperparameter values of the DBN-BN model.The design of SSO algorithm helps to appropriately modify the hyperparameter values of the DBN-BN model.A series of simulation analyses are carried out to highlight the enhanced outcomes of the SSODLSA-SGS model.The extensive comparative study reported the enhanced performance of the SSODLSA-SGS algorithm over the other recent techniques interms of several measures.

Field Weakening Control of a Separately Excited DC Motor Using Neural Network Optemized by Social Spider Algorithm

This paper presents the speed control of a separately excited DC motor using Neural Network (NN) controller in field weakening region. In armature control, speed controller has been used in outer loop while current controller in inner loop is used. The function of NN is to predict the field current that realizes the field weakening to drive the motor over rated speed. The parameters of NN are optimized by the Social Spider Optimization (SSO) algorithm. The system has been implemented using MATLAB/SIMULINK software. The simulation results show that the proposed method gives a good performance and is feasible to be applied instead of others conventional combined control methods.

Improved Hybrid Swarm Intelligence for Optimizing the Energy in WSN

In this current century,most industries are moving towards automation,where human intervention is dramatically reduced.This revolution leads to industrial revolution 4.0,which uses the Internet of Things(IoT)and wireless sensor networks(WSN).With its associated applications,this IoT device is used to compute the receivedWSN data from devices and transfer it to remote locations for assistance.In general,WSNs,the gateways are a long distance from the base station(BS)and are communicated through the gateways nearer to the BS.At the gateway,which is closer to the BS,energy drains faster because of the heavy load,which leads to energy issues around the BS.Since the sensors are battery-operated,either replacement or recharging of those sensor node batteries is not possible after it is deployed to their corresponding areas.In that situation,energy plays a vital role in sensor survival.Concerning reducing the network energy consumption and increasing the network lifetime,this paper proposed an efficient cluster head selection using Improved Social spider Optimization with a Rough Set(ISSRS)and routing path selection to reduce the network load using the Improved Grey wolf optimization(IGWO)approach.(i)Using ISSRS,the initial clusters are formed with the local nodes,and the cluster head is chosen.(ii)Load balancing through routing path selection using IGWO.The simulation results prove that the proposed optimization-based approaches efficiently reduce the energy through load balancing compared to existing systems in terms of energy efficiency,packet delivery ratio,network throughput,and packet loss percentage.