Abstract:
The primary aim of this research is to leverage the Open Network Operating System (ONOS) to develop an efficient and scalable process automation architecture for real-world windfarm operations. The study focuses on enhancing network management, data processing, and operational optimization within the windfarm infrastructure. This research utilizes ONOS as the centralized control platform for managing the windfarm's network infrastructure. The proposed system integrates network virtualization, real-time data processing, predictive analytics using LSTM networks, and optimization algorithms to improve wind turbine efficiency and fault management. MATLAB simulations are conducted to evaluate the performance and reliability of the system. The simulation results demonstrate significant improvements in network performance, including reduced latency and increased throughput. The fault detection and recovery mechanisms show enhanced system reliability. Real-time data processing and predictive analytics lead to optimized turbine settings and increased operational efficiency. The system also exhibits robust security measures and compliance with regulatory standards. The implementation of ONOS in windfarm process automation presents a viable solution for efficient and scalable network management. The findings highlight the potential for improved operational performance, reliability, and security in windfarm operations, contributing to the advancement of renewable energy technologies. The study's results offer valuable insights for future research and practical applications in the field of windfarm automation.