IEA PVPS Press Release
View this email in your browser

International Energy Agency
Photovoltaic Power Systems Programme
Paris, France, 19 October 2021 – The Task in charge of Performance, Operation and Reliability of Photovoltaic Systems (Task 13) of the IEA PVPS has recently published a new report detailed below.
The Use of Advanced Algorithms in PV Failure Monitoring

This report provides an introduction to the emerging field of Statistical Performance Monitor-ing for photovoltaic (PV) systems and a survey of the development of these fault detection systems and their applications. 
This survey found four primary methods used for identifying faults: (i) identifying faulty elec-trical signatures, (ii) comparing historical performance to actual performance, (iii) comparing predicted performance to actual performance and (iv) comparing the relationships between different PV systems or subsystems. The four approaches used for identifying faults include applying machine learning algorithms, statistical tests, specifying computational rules and generating simulations using models. 
As shown in Figure 1, from the research papers studied, it shows that Asia is leading the world in studying and developing PV fault detection systems followed by Europe. The popularity of different parameters used by fault detection systems by developers include current and/or voltage (AC or DC) (25%), irradiance (19%), temperature (17%) and IV curve data (12%).
The study also found clear machine learning algorithm preferences. Among the papers stud-ied artificial neural networks are the most popular (30%), followed by K Nearest Neighbors (10%), fuzzy systems (8%) and support vector machines and linear regression (7%). 

In addition to explaining the statistical algorithms in effect and studying the approaches used for identifying faults, this paper also reviewed the different sources of data used by PV fault detection systems. Research has found that PV fault detection input data comes from a va-riety of devices and sources including sensors connected at the site, commercial weather stations, inverters, optimizers and IV curve tracers. Depending on the device system architec-ture, different parameters are available at different frequencies and accuracies.  
It appears from this study that a machine learning training strategy using training data close in time to testing data provides better results and that performance data and environmental data seem to be on par with each other for some machine learning algorithms regarding accuracy of the outcome. 
In comparing 8 of the 22 of the summarized algorithms in a head-to-head competition where each was fed the same data from a live PV system it was found that different algorithms have very different sensitivities. 

About the IEA PVPS Task 13
Task 13 was established in 2010 within the IEA PVPS Programme in order to continue to research activities on performance and quality issues started in the former Task 2. It is today one of the most respected Tasks within the programme, with contributors from all over the world.
The Task is co-managed by Ulrike Jahn and Boris Farnung, both from VDE (Germany)

Contacts for Further Information:
Ulrike Jahn, Task 13 Operating Agent -
Boris Farnung, Task 13 Operating Agent -
The International Energy Agency (IEA), founded in 1974, is an autonomous body within the framework of the Organization for Economic Cooperation and Development (OECD). The Technology Collaboration Program (TCP) was created with a belief that the future of energy security and sustainability starts with global collaboration. The program is made up of thousands of experts across government, academia, and industry dedicated to advancing common research and the application of specific energy technologies.
The IEA Photovoltaic Power Systems Programme (IEA PVPS) is one of the TCP’s within the IEA and was established in 1993. The mission of the programme is to “enhance the international collaborative efforts which facilitate the role of photovoltaic solar energy as a cornerstone in the transition to sustainable energy systems.” In order to achieve this, the Programme’s participants have undertaken a variety of joint research projects in PV power systems applications. The overall programme is headed by an Executive Committee, comprised of one delegate from each country or organisation member, which designates distinct. ‘Tasks,’ that may be research projects or activity areas. This report has been prepared under Task 1, which deals with market and industry analysis, strategic research and facilitates the exchange and dissemination of information arising from the overall IEA PVPS Programme. 

The IEA PVPS participating countries are Australia, Austria, Belgium, Canada, Chile, China, Denmark, Finland, France, Germany, Israel, Italy, Japan, Korea, Malaysia, Mexico, Morocco, the Netherlands, Norway, Portugal, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, and the United States of America. The European Commission, Solar Power Europe, the Smart Electric Power Alliance (SEPA), the Solar Energy Industries Association and the Copper Alliance are also members. 
Copyright © 2020 IEA PVPS, All rights reserved.

Our mailing address is:

Want to change how you receive these emails?
You can update your preferences or unsubscribe from this list

This email was sent to <<Email Address>>
why did I get this?    unsubscribe from this list    update subscription preferences
IEA PVPS · Postbox 5025 · Rheine 48419 · Germany

Email Marketing Powered by Mailchimp