By Steve Hanawalt
These days, you can’t avoid the hype touting all the ways advanced analytics (AA) optimizes the performance of solar and wind power assets. “Advanced analytics” is a broad term for a range of techniques and tools that takes data and uses it to generate valuable business insights, and make predictions and recommendations. For the purposes of this article, the umbrella term “advanced analytics” refers to a range of techniques and tools, including artificial intelligence (AI) and machine learning (ML).
If the buzz about advanced analytics is to be believed, you simply plumb your operating data up to a shiny new web service and the insights pour forth—saving you millions! But is it really that easy? What kind of real-world results can you expect? What data infrastructure and pre-processing do you already need to have in place for these applications to deliver reliable ROI? Before moving into advanced analytics, what about making standard industry analytics work properly? Are you confident you’re capturing all the asset performance improvement opportunities available?
Buyer beware—there are a lot of claims out there from software vendors and consultants looking to sell unproven or misapplied technology in the renewable energy space. On the other hand, some AA solutions generate significant opportunities for improvement in project returns—when properly applied. Therein lies the challenge: What is the right application of AA in the renewable energy space?
The Five Keys to Successful Implementation
To help find the answer, let’s look at the five keys of successful AA implementation in solar and wind:
When to Consider Advanced Analytics—and When Not To
Advanced analytics should be considered for anomalies that aren’t or can’t be detected using the plant SCADA or traditional monitoring applications. For example, you don’t need AA to tell you your wind turbine or solar inverter is offline, and you shouldn’t have to use AA to tell you that a tracker has stopped tracking. However, if your plant SCADA system isn’t pulling tracker control setpoint data, advanced analytics can detect that a tracker has stopped tracking. In other words, before you invest in moving up the asset optimization pyramid, make sure you have each of the five keys to a successful implementation.
Consider the typical utility-scale solar power plant: It has thousands, even millions of non-instrumented electric generators. Sensors well downstream of these generators don’t have the sensitivity to detect performance issues associated with these generators. So how can we know if our solar generators are performing optimally? We have three choices:
Advanced analytics solves other problems specific to our industry as well, such as using machine learning to detect subtle changes in a turbine’s power curve, or to catch the temperature derating of a solar inverter. Additionally, automated workflows incorporating AA can generate a work order with all details about failure mode, failure cause, and repair code recorded by the software. By integrating the event detection, insight, and action steps into an automated workflow, we can reap the benefits of advanced analytics.
To Buy or Not to Buy?
That’s the big question. Is advanced analytics worth the investment? It can be. But if you’re trying to solve the wrong problem, or if you don’t have the right data platform, business processes, and subject matter experts in place, you’re far better off investing your money getting the basics done right first.
Steve Hanawalt is co-founder and executive vice president of Power Factors. Power Factors consolidates multiple operational data sources, asset hierarchies, and metadata frameworks to create a single cloud-based remote asset management platform that works with today’s large-scale portfolios.
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