New Article Series: The Top 4 Solar Asset Monitoring Challenges—and What You Can Do About Them
The Top 4 Solar Asset Monitoring Challenges—and What You Can Do About Them: Series Introduction
Let’s be honest: Developing and implementing robust and scalable commercial asset monitoring software applications isn’t easy. And, while we have many smart people working to develop software applications for PV monitoring and performance optimization, the solar industry faces some big challenges when it comes to taking advantage of what’s currently available and preparing for innovations to come.
Why? Well, whether the core monitoring methods use first principle physical models, artificial intelligence or machine learning, they all rely on a foundation of good quality process data. This foundation of trustworthy data can be very difficult to achieve—especially when you’re talking solar assets. And that’s just the beginning.
For the past 35 years, I’ve developed plant monitoring applications, so I know first-hand how difficult it can be to get them to work effectively. I’ve also picked up a fair amount of knowledge when it comes to overcoming these challenges, and that’s what this article series is about.
I’ll start by laying out the top four challenges of developing and implementing asset monitoring software. Then, in the second half of the series, I’ll discuss ways to address these challenges.
The 4 Top Solar Asset Monitoring Challenges and How to Overcome Them
- The challenge of perfect operating data (and how to overcome it)
- The challenge of scale (and how to overcome it)
- The challenge of granularity (and how to overcome it)
- The challenge of selecting the right model (and how to overcome it)
Any one of these challenges can cause a solar monitoring application to fall flat on its face—and once operators lose confidence in the application, it’s doomed. Is there hope for any solar monitoring application? What can be done to overcome these challenges?
At Power Factors we believe these problems can be addressed and overcome, but a new approach is needed. The solar asset class is distinct from traditional power generation equipment and from wind power assets. The quality and granularity of data sensors is different, and the energy conversion process takes place in a hermetically sealed semiconductor package with no moving parts. How, then, do you approach performance monitoring for this type of asset?
If renewables are going to change the way the world is powered, we need to know how they are performing relative to bank models and expected performance. Just because it's challenging doesn’t mean it’s not worth doing.
Thank you for reading along. Looking forward to your questions and ideas.
Steve Hanawalt is an EVP and Co-Founder at Power Factors. For more information, please contact us at info@pfdrive.com or visit our website.
Data science
4yHi Steve, thank you for these tremendously insightful articles. I have a basic question as I'm learning about the industry - when would an asset manager conduct an aerial inspection of the asset? Does that typically occur when the performance software's results indicate there is already a problem but the problem itself cannot be identified remotely? Or do inspections happen on a regular basis independently of the performance assessment derived from modeling?