Research on islanding detection of solar power system based on new nanomaterials

As the energy problem becomes tenser, solar energy is used and researched increasingly. Traditional solar power generation photovoltaic panels have low power generation efficiency, high cost, and large size that is difficult to install. At present, a new type of nano-material coating has been developed in China, which can be applied to the surface of any building and can stably convert solar energy into electric energy. This technology reduces the cost of photovoltaic power generation systems, improves power generation efficiency, and makes solar power generation systems widely used. The reliability of the power system is improved greatly by the Solar power generation system based on nanotechnology, but it also increases the complexity of grid control. Bringing new failure problems to the system, the most common of which is the is-landing effect. The active frequency drift with positive feedback (AFDPF) is an effective anti-islanding detection method in a photovoltaic grid-connected system. But because of the parameters of this method, the initial chopping coefficient Cf 0 and the positive feedback coefficient k are fixed values. Improper setting of parameters will result in too large detection blind area, too slow detection speed, and too high total harmonic content of grid current. This paper proposed a particle swarm optimization (PSO)-based technique to obtain optimal parameters of the AFDPF anti-islanding detection method. A new method is proposed, which combines the particle swarm optimization algorithm with the positive feedback active frequency shift method. It takes the non-detection zone (NDZ) minimization as the objective function, the current harmonic content as the constraint condition, and performs the parameter Cf 0 and k Settings optimization. The simulation verification was carried out on a 10 kW single-phase photovoltaic power generation grid-connected system. The results show that under different loads, the improved algorithm can accurately and quickly detect the islanding effect and reduce the total harmonic content of the system.