Iot based sustainable wind green energy for smart cites using fuzzy logic based fractional order darwinian particle swarm optimization

Abstract

In view of the fast growing countries, most of the people prefer to live in smart cities. Green and sustainable energy is an important factor for creating a smart system because only an integrated smart grid system can be an alternative solution from all the traditional methods. Wind energy is one of the renewable energy sources growing in popularity because of its many advantages such as lower cost of production, sustainability, and being environmentally friendly. There is a specific ratio for each wind turbine, at which the extracted power is maximum. To work at this optimal ratio, Wind Energy Conversion System (WECS) is essential to adjust the power converter duty cycle and rapidly maximize the energy of wind in the system. In this paper, a Maximum Power Point Tracking (MPPT) controller is proposed to achieve utmost available wind power and the controller parameters are optimized by Fractional Order Darwinian Particle Swarm Optimization (FODPSO) based on Fuzzy Logic Controller (FLC) techniques. A wind turbine has a variety of sensors for monitoring and measuring the state of the turbine which are connected to the internet using Internet of Things (IoT). This paper proposed an IoT based communication framework for the purpose of reliable communication between wind turbines and control centre. The proposed FODPSO tunes the FLC continuously for generate duty cycle and adjust the converter switches propositional to the variations in wind speed. This FODPSO technique used in the controller gives high speed and reliability as well as excellent accuracy to track maximum wind power. Proposed FODPSO-FLC extracts 20% and 13% more power than existing FLC and PSO-FLC respectively. Finally Proposed FODPSO-FLC reduces 20% and 9% THD than existing FLC and PSO-FLC respectively. The obtained results show that the new IOT solution can accurately predict and control the wind turbine state.