Harmonic analysis in distributed power system based on IoT and dynamic compressed sensing

Abstract

Massive distributed clean energy takes power electronic equipment as the grid​ access connector, which brings serious harmonic pollutions. To monitor the power quality in the massive distributed power systems, this paper proposes a real-time harmonic monitoring and analysis framework based on Internet of Things (IoT) and dynamic compressed sensing. In sensing node, the power quality signal is continuously sampled and compressed with sliding time window. In edge node, a homotopy optimization with fundamental filter (HO-FF) algorithm is designed. Along the homotopy path in the solution space, the current compressed data frame is iteratively recovered based on previous recovery result, hence the computational complexity is greatly reduced and the real-time performance of dynamic power quality signal recovery is improved. Besides, a fundamental filtering scheme is adopted to further improve the signal recovery accuracy and to avoid repeated recovery of fundamental component. Tests based on Siemens benchmark 0.4kV microgrid model show that, compared with existing methods, the proposed HO-FF algorithm could offer better harmonic detection accuracy with lower computational complexity.

Keywords

• IoT
• Distributed power system
• Power quality
• Harmonic monitoring
• Dynamic compressed sensing
• Homotopy optimization