Power quality optimization technology for photovoltaic nanoscale electronic grid-connected systems based on FNN algorithm

With the continuous maturity and widespread application of photovoltaic power generation technology, the power quality of photovoltaic grid-connected systems has become a significant factor affecting the power grid’s stability and the lifespan of equipment. This research aims to improve the power quality in photovoltaic nanoscale electronic grid-connected systems. Through in-depth analysis of harmonic problems in grid-connected systems, a harmonic prediction and governance means using a fuzzy neural network algorithm was put forward. To improve the accuracy and adaptability of predictions, the long short-term memory network was combined with a fuzzy neural network to form an improved deep learning model. Comparative experiments confirmed that the research prediction model exhibited advantages in training convergence, prediction accuracy, and various error evaluation indicators, with an accuracy of up to 96.24% and an R2 value as high as 0.9998. It effectively reduced harmonic content by 2.4% and significantly improved the harmonic compensation effect. Based on the above-mentioned model, dynamic compensation governance for harmonics was implemented. By predicting the harmonic content in grid-connected electricity in real-time and taking corresponding measures for compensation, the adverse effects of harmonics on the entire grid-connected system were effectively reduced. The deep learning-based power quality optimization technology developed in this research can accurately predict harmonic problems in photovoltaic nanoscale electronic grid-connected systems. Moreover, it can provide efficient compensation governance solutions and help improve the entire power grid’s energy efficiency and operational stability.