Gaussian PI Controller Network Classifier for Grid-Connected Renewable Energy System

Multi-port converters are considered as exceeding earlier period decade owing to function in a combination of different energy sources in a single processing unit.Renewable energy sources are playing a significant role in the modern energy system with rapid development.In renewable sources like fuel combustion and solar energy,the generated voltages change due to their environmental changes.To develop energy resources,electric power generation involved huge awareness.The power and output voltages are plays important role in our work but it not considered in the existing system.For considering the power and voltage,Gaussian PI Controller-Maxpooling Deep Convolutional Neural Network Classifier(GPIC-MDCNNC)Model is introduced for the grid-connected renewable energy system.The input information is collected from two input sources.After that,input layer transfer information to hidden layer 1 fuzzy PI is employed for controlling voltage in GPIC-MDCNNC Model.Hidden layer 1 is transferred to hidden layer 2.Gaussian activation is employed for determining the output voltage with help of the controller.At last,the output layer offers the last value in GPIC-MDCNNC Model.The designed method was confirmed using one and multiple sources by stable and unpredictable input voltages.GPIC-MDCNNC Model increases the performance of grid-connected renewable energy systems by enhanced voltage value compared with state-of-the-art works.The control technique using GPIC-MDCNNC Model increases the dynamics of hybrid energy systems connected to the grid.

Performance evaluation of natural esters and dielectric correlation assessment using artificial neural network(ANN)

The performance of correlation between the dielectric parameters of Baobab Oil(BAO)and Mongongo Oil(MGO)is evaluated using Artificial Neural Network(ANN).The BAO and MGO naturally own high Unsaturated Fatty Acids(UFAs)and are highly biodegradable.The temperature studies and dielectric studies are carried out and found that the Natural Esters(NEs)show a reliable performance over mineral oil-based Transformer Oil(TO).Further the endurance test,Partial Discharge Inception Voltage(PDIV)repetition rate and drop after 30 days,dielectric measurements are done as per the standards of IEC(International Electrotechnical Commission)and ASTM(American Society for Testing and Materials).The NEs show stable performance under PDIV and show minimum repetition rate when compared to the TO.The C10H22 or Kerosene(KER)and NEs mixture prove that the NE-based transformer fluids show lesser tendency to hydro peroxidation.The C10H22 acts as a thinning agent and reduces the ageing rate of the NEs,and this leads to slower rate of water saturation.This in turn increases the thermal conductivity of the oil and nearly a 30-days thermal ageing of the oil samples at 90°C shows better strength of liquid insulation.The performance of association between the dielectric properties like breakdown voltage and water content,dissipation factor and thermal conduc-tivity prove that the NEs show consistent performance and is a better substitute for the mineral oil-based TO.