Inefficiencies related to solar PV plant management and O&M can be handled using effective digitization, Artificial Intelligence, and automation practices. Read to know how predictive maintenance can empower solar asset managers

Solar Photovoltaic is the most preferred alternative clean energy asset. In the last couple of years, it has seen a massive deployment across the world with an average annual capacity addition of 180 GW which is expected to triple by 2030. The growing capacity of solar photovoltaics is a great news but contrarily, it is also making the solar asset management a tedious job and, in the existing ecosystem, it is bringing down the operational efficiency of asset maintenance which is subsequently limiting the overall performance level of the solar PV plants. This is a worrying phenomenon for investors. The existing asset management tools and Standard Operating Procedures (SPOs) are limited and not effective enough to utilize the possible potential of the solar energy assets. Some of the major problems related to solar plant’s operation and maintenance can be summarized below:

  1. Unable to detect the early signs of equipment failure or faults in the solar PV system which leads to system under-performance because of unknown reasons till it becomes significant enough to attract the plant/asset manager’s attention. But by this time the asset has already lost a substantial revenue.
  2. High manual dependence on the O&M process limits the O&M teams from acting on time which leads to a higher turn-around time from the Fault Appearing to Fault Resolved. In solar industry, in some cases, we have seen a fault resolution period of up to several months.
  3. The existing PV Asset Management practices are not optimized enough to maximize clean energy production and revenue. In most cases, the solar plant maintenance schedules depend on the convenience of the ground staff which leads to inefficient asset maintenance, system underperformance, and high O&M expenses.

world's first real-time analytics software - HelioCloud

We strongly believe inefficiencies related to solar asset management and O&M can be handled by introducing effective digitization, Artificial Intelligence, and automation practices. In our efforts, we have introduced An Autopilot for the solar PV industry. The Autopilot works as a virtual support assistance to the Asset Managers, Performance Managers, and Plant Managers. It continuously reads the plant performance data, weather data, satellite data, and any other 3rd party data that is beneficial for doing the performance assessment and root cause identification in real-time. Autopilot has a robust understanding of the solar PV system engineering and it uses Machine Learning to develop incremental intelligence over a period of time which makes it incredibly accurate and fast in terms of detecting the early signatures of system underperformance, predicting potential faults, quantifying the impact of existing/upcoming faults/issues and dynamically scheduling the on-ground interventions by technical work-force (human and machines). With Autopilot we can see an overall improvement in solar assets’ performance by 15% (over their lifetime) and a reduction in O&M and Asset management costs by more than 50%. 

As a stepping stone into the future of solar photovoltaics and other alternative clean energy assets, we believe if machines can manage themselves and can autonomously operate thousands of miles away in the extraterrestrial space then why can’t solar plants run autonomously using software like Autopilot. We see a vision to put each and every solar energy and other clean energy asset in Autopilot mode once they are commissioned.

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