Machine-Learning Solar Tracking Technology Nudges PV Field Production Nearer Optimum Levels

Solar energy products and services developers and vendors continue to leverage the latest in distributed information and communications technology (ICT) in bids to drive further declines in the cost and boost the productivity of solar energy systems. Development and use of an expanding range of machine-to-machine (M2M) communications and "Internet of Things" devices – wireless network sensors and "smart," network-connected inverters, meters and other devices – along with high-reliability wireless/mobile networking and cloud software- and infrastructure-as-a-service (SaaS and IaaS) platforms are enabling vendors and their customers to collect, analyze and act upon continuous streams of digital data and approach ideal maximum electrical power and energy production while coincidentally minimizing installation, operations and maintenance costs. With more than nine gigawatts (GWs) worth of its products installed on five continents, in 1991 Fremont, California-based NEXTracker published a groundbreaking white paper describing a new algorithm that improved solar tracking and resulted in gains of around three percent in solar PV facility production. While that methodology continues to be applied in nearly all solar energy tracking systems today, NEXTracker is pushing the technological envelope out further. On July 11, the company introduced its latest innovation to the market, a "first-of-its-kind intelligent, self-adjusting tracker control system for solar power plants."