Unpacking solar-project data analytics
Just over a decade ago, Sandia National Laboratories founded the PV Performance Modeling Collaborative (PVPMC). PVPMC is increasing transparency and accuracy in PV system performance modeling, according to Sandia, with the organization helping to bring together stakeholders to improve modeling practices. Joshua Stein, senior scientist at Sandia, thinks it is time for a similar effort to shed light onto data analytics for PV plant performance monitoring.
“There is a lack of standardization and harmonization in the way people are talking about fault analytics and monitoring,” he explained to pv magazine. Currently, some companies are offering services that include data analytics and fault identification and classification based on monitoring data from PV power plants. However, these companies each use their own set of failure definitions, which means that it is very difficult to compare and contrast the value of these services.
“Imagine if every doctor or hospital you visited used their own set of diagnoses and clinical practices.” said Stein. “It would be very difficult and confusing to get a second opinion. Most mature industries standardize their definitions of faults and mitigation procedures. Solar PV is not quite there yet.”
For one thing, the introduction of machine learning techniques to automate fault detection in PV systems has further muddied the picture. Stein was careful to clarify that he is not against machine learning. “Automated ML-based fault detection is beneficial in that it can lead to highly scalable analytics,” said Stein.
Rethinking KPIsTechnical key performance indicators (KPIs) are essential for evaluating PV power plants, from the development stage to contractual agreements between asset owners and O&M providers.
“While IEC and ASTM standards define some KPIs, such as performance ratio (PR) and capacity tests, their calculation methods often vary or rely on user interpretation, leading to uncertain results,” said Stein’s Sandia Labs colleague Marios Theristis.
“For instance, there is significant flexibility in data handling, which can introduce biases that impact contracts and financial decisions. Without clear KPI definitions and harmonized calculations, contracts may be unfairly affected – not due to actual under-performance or over-performance, but because of calculation bias,” he added.
Bad data is data that is not useful because it has been obtained through inadequate means. It contains errors or it isn’t clean. This in turn creates problems for PV stakeholders who rely on it to tell them what their system’s actual performance is.
Standard industry practice is for asset owners to subcontract an O&M provider to take care of operation and maintenance. This means a contractual agreement dictating the power plant is being maintained appropriately and meeting certain KPIs.
“If you have bad data quality, then there will be some bias introduced in the KPI estimation. This bias can be positive, or it can be negative. In a hypothetical scenario where the PR bias is negative, then the O&M provider would be unfairly paying penalties caused by data quality, and not under-performance,” said Theristis.
This also works in reverse where an O&M provider could benefit, due to bias caused by data quality and not plant over-performance. Theristis continued: “In some cases, the operational PR is compared to the pre-construction PR generated by a PV design software, leading to an apples-to-oranges comparison: Is the PR difference due to an overly optimistic pre-construction simulation or actual underperformance?”
Program transparencyTheristis has begun a new program to tackle these issues, the PV O&M Analytics Collaborative (PVMAC). He introduced the project at the EU PVSEC conference held in Vienna in September 2024.
“Our objective is to create a collaborative network to improve transparency in PV analytics software and services, engage with monitoring/analytic companies, O&M providers, asset owners, insurers, and help these stakeholders come together to agree on what standards are needed and improve the overall market’s transparency,” he said.
The team is also using Sandia Labs’ supercomputers to run simulations across the United States to reduce uncertainties in KPI estimation.
PVMAC is still very new, but over the next few months the researchers will meet with industry to carry out interviews and tests such as blind analytic comparisons. This involves giving stakeholders real or synthetic performance data and asking them to identify and classify faults and calculate KPIs.
“Blind comparisons are a great way to evaluate the state of the industry practice and the consistency between different providers.
“We expect that these comparisons will highlight discrepancies in how different companies define faults and calculate KPIs,” said Stein.
“We plan to host dedicated sessions on O&M analytics, failure and KPI harmonization at our upcoming PVPMC workshops,” added Theristis.
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