Particle Swarm Optimization Based DC-Link Voltage Control for Two Stage Grid Connected PV Inverter

A particle Swarm optimization-based DC-link voltage control method is proposed for two stage grid connected PV inverters. A two-stage grid connected Photovoltaic Generator (PVG) is used to test the performance of artificial intelligence-based controller. The system test consists of PVG, a boost converter, a DC-link, an inverter, a LCL filter and the external grid. The objectives of the system controllers are three-fold. The first objective is to regulate PVG output voltage for Maximum Power Point Tracking (MPPT). The second objective is to keep the DC-link voltage of inverter constant. The third objective is to maximize the power factor of the inverter i.e. close to unity. To solve the first problem, a conventional MPPT technique known as Perturb & Observe (P&O) method is used which generates the best voltage reference for PVG. To track the reference voltage, backstepping control technique is used to regulate PVG output voltage. The regulation of DC-link voltage of the inverter is realized with PI controllers, the parameters of which are optimized by Particle Swarm Optimization (PSO) method. The third objective, power factor regulation, is also tackled using backstepping control. Conventional PI control is compared to the proposed artificial intelligence-based controller, and the proposed technique for optimizing parameters of PI has reduced the Ripple Factor of DC-link voltage from 6.0193% to 3.3218% compared to a simple PI control with manually chosen parameters.