F-LMS Adaptive Filter Based Control Algorithm with Power Management Strategy for Grid Integrated Rooftop SPV-BES System

• Published in: IEEE transactions on sustainable energy Vol. 14; no. 2; pp. 1 - 12
• Main Authors: Modi, Gaurav, Singh, Bhim
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2023; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive control; Adaptive Filtering; Adaptive filters; Algorithms; Battery Energy Storage; Control algorithms; Control theory; Electrical loads; Energy Management Strategy; Energy storage; Filtering theory; Harmonic analysis; Islanded Operation; Load; Overvoltage at PIC; Peak load; Photovoltaics; Power converters; Power harmonic filters; Power management; Power Quality; Regulators; Solar Photovoltaic; Synchronization; Voltage control
• ISSN: 1949-3029; 1949-3037
• DOI: 10.1109/TSTE.2022.3231684

The power quality performance of a grid-integrated rooftop solar photovoltaic (SPV) system is significantly affected by the local nonlinear loads. Further, during peak solar insolation and load demand, the SPV system experiences overvoltage and undervoltage at its point of interconnection. Both these issues infringe the IEEE std. 519 and 1547. Therefore, this paper presents a fractional least mean square (F-LMS) based control with the power management strategy (PMS) for the SPV-battery energy storage (BES) system. This presented control uses the SPV system's power converter to compensate load currents locally. By this means, the grid voltages and currents remain distortion-free. The introduced F-LMS algorithm is used as a pre-filter for load current. It estimates the load current fundamental component more accurately and swiftly than the existing LMS method. The designed PMS supervises the SPV array generation, BES power, and grid power to cater to both over and undervoltage issues and keeps the PIC voltage amplitude within the IEEE std. 1547 limits. In addition, this work enables the autonomous operation of SPV system during grid disruption, enhancing power resilience. The efficacy of the introduced work is experimentally verified.