This paper presents microgrid-distributed energy resources (DERs) for a rural standalone system. It is made up of a solar photovoltaic (solar PV) system, battery energy storage system (BESS), and a wind turbine coupled to a permanent magnet synchronous generator (WT-PMSG). The DERs are controlled by maximum power point tracking (MPPT)-based proportional integral (PI) controllers for both maximum power tracking and error feedback compensation. The MPPT uses the perturb and observe (P&O) algorithm for tracking the maximum power point of the DERs. The PI gains are tuned using the Ziegler–Nichols method. The developed system was built and simulated in MATLAB/Simulink under two conditions—constant load, and step-load changes. The controllers enabled the BESS to charge even during conditions of varying load and other environmental factors such as change of irradiance and wind speed. The reference was tracked extremely well by the output voltage of the DC microgrid. This is useful research for electrifying the rural islanded areas which are too far from the grid.

Design of a Hybrid Energy System with Energy Storage for Standalone DC Microgrid Application

Real-world knowledge is represented by a knowledge graph that provides assistance for various applications built on the basis of artificial intelligence. Awareness of the neighbourhood is obtained from the individuals and relationships of the Knowledge Graph. High-dimensional data analysis is a difficult task in many applications and this article discusses the dimensionality by specifying a limited collection of features that implies high-dimensional data without visible or substantial data loss. An unsupervised learning approach based on learning that uses the neural network principle and learns the features using the graph. The Positive Feature Selection approach using the Neural Network (PFSNN) approach in this paper defines features using a graph where the classification is carried out by the NN process and analyses the output of the proposed system. The efficiency of the PFSNN is evaluated by contrasting it with existing classification methods and using different datasets. Performance is measured using the classification performance metrics and it is defined from the observation that the proposed PFSNN algorithm has the best outcome.

Feature Selection and Classification using a Positive Learning Approach Focused on Graph and Neural Network

Users can interchange data with others and remotely store their data on the cloud using cloud storage services. The integrity of data saved in the cloud should be ensured through remote data integrity audits. An electronic health record system is one popular example of a well-known cloud storage system where a cloud file may include unique sensitive data. When a cloud file is shared, private information shouldn’t be exposed. Sensitive information can be realized if the entire shared file is encrypted, but other users cannot access it. How to share sensitive material that is hidden in remote data integrity audits is still up in the air. In this method, data blocks that match sensitive information in the file are sanitized, and the data block signatures are changed to ones that are appropriate for the cleaned-up file. Throughout the integrity auditing process, these signatures are used to verify the accuracy of the cleaned file. Since sensitive information is concealed, our solution permits the sharing and usage of a cloud-stored file by others while maintaining the capacity to conduct remote data integrity audits efficiently.

Integrity Auditing for Secure Cloud Storage on Sensitive Data Protection

Real-world knowledge is represented by a knowledge graph that provides assistance for various applications built on the basis of artificial intelligence. Awareness of the neighbourhood is obtained from the individuals and relationships of the Knowledge Graph. High-dimensional data analysis is a difficult task in many applications and this article discusses the dimensionality by specifying a limited collection of features that implies high-dimensional data without visible or substantial data loss. An unsupervised learning approach based on learning that uses the neural network principle and learns the features using the graph. The Positive Feature Selection approach using the Neural Network (PFSNN) approach in this paper defines features using a graph where the classification is carried out by the NN process and analyses the output of the proposed system. The efficiency of the PFSNN is evaluated by contrasting it with existing classification methods and using different datasets. Performance is measured using the classification performance metrics and it is defined from the observation that the proposed PFSNN algorithm has the best outcome. 

Users can interchange data with others and remotely store their data on the cloud using cloud storage services. The integrity of data saved in the cloud should be ensured through remote data integrity audits. An electronic health record system is one popular example of a well-known cloud storage system where a cloud file may include unique sensitive data. When a cloud file is shared, private information shouldn’t be exposed. Sensitive information can be realized if the entire shared file is encrypted, but other users cannot access it. How to share sensitive material that is hidden in remote data integrity audits is still up in the air. In this method, data blocks that match sensitive information in the file are sanitized, and the data block signatures are changed to ones that are appropriate for the cleaned-up file. Throughout the integrity auditing process, these signatures are used to verify the accuracy of the cleaned file. Since sensitive information is concealed, our solution permits the sharing and usage of a cloud-stored file by others while maintaining the capacity to conduct remote data integrity audits efficiently. 

Cluster-based attacks prevention algorithm for autonomous vehicles using machine learning algorithms

Regulation of load voltage and frequency is very important in standalone wind energy system under fluctuating wind speeds and changing load conditions. This paper presents vector control strategy for load side inverter in coordination with battery energy storage system for maintaining power balance, voltage and frequency regulation in stand-alone wind-energy system under different operating conditions. The robustness of the proposed control strategy to improve the dynamic performance of standalone PMSG under transient operating conditions is validated using MATLAB/SIMULINK environment.

Power quality improvement in standalone batteryintegrated wind energy system

Improved properties can be found in aluminum alloys containing silicon carbide reinforcement particles. This work studies the machinability of Al 6063 reinforced with silicon carbide particles with wire electrical discharge machining. To attain a high material removal rate, wire EDM constraints such as current (I), pulse-on time (Ton), wire speed (Ws), voltage 
 
 
 
 
 
 I
 
 
 v
 
 
 
 
 
 , and pulse-off time (Toff) can be adjusted with precision. Taguchi L16 orthogonal arrays are used to design the experiments and statistical methods are used to examine. These process characteristics had a significant impact on the overall rate of return, with a 28.2% impact on the MRR, 23.04% impact on the MRR, and 22.86% impact on the MRR. We achieved MRRs of 65.21 mg/min for samples containing 5% and 10% SiCp at optimal conditions, respectively. Linear regression was used to create the statistical model, which then used confirmation trials to verify its accuracy in predicting MRR (R -73.65%). The statistical model is used to estimate MRR based on various process parameter settings.

N. Vani Annapurna Bhavani

Papers

Cluster-based attacks prevention algorithm for autonomous vehicles using machine learning algorithms

Power quality improvement in standalone batteryintegrated wind energy system

Modeling and Optimization of MRR in Wire Electrical Discharge Machining of Silicon Particle-Reinforced AA6063 Composite