Performance improvement of Hybrid System based DFIG-Wind/PV/Batteries connected to DC and AC grid by applying Intelligent Control

Abstract

One of the main causes of CO2 emissions is the production of electrical energy. Therefore, many researchers goal’s is to develop renewable power systems. This paper proposes a new intelligent control development of hybrid PV–Wind-Batteries. Neuro-Fuzzy Direct Power Control (NF-DPC) is invested in order to enhance system performance and generated currents quality. An improved MPPT algorithm based on Fuzzy Controller (FC) is invested for PV power optimization. In addition, a new Modified Fuzzy Direct Power Control (MF-DPC) is developed and applied to the grid side converter to control the active and reactive power by monitoring the involved active power flow and providing a unit power factor by imposing a zero reactive power. An Energy Management Algorithm (EMA) is developed to maintain energy balance, meet the DC load demand, mitigate fluctuations caused by weather condition variations (wind speed and solar irradiance), and minimize battery overcharge and deep discharge. To test the proposed hybrid microgrid system operation, the different parts of the system are modeled, the wind turbine associated to the DFIG, the photovoltaic system as well as the battery storage system. Furthermore, the associated power converters with their control strategies are also presented. Global system simulation, using MATLAB/Simulink, is carried out to validate the effectiveness of both EMA and control techniques. The obtained results show significant reduction of active/reactive power ripples and THD by about 64%, 72%, and 50%, respectively. The EMA ability to manage the energy flow, produced and requested by the load. The THD rate of all injected currents is less than 4%, meaning that the proposed controls will increase the used equipments’ life span, minimize their maintenance and then reduce the hybrid power system cost.