Dig4morE Uses AI to Improve Yields from Solar Plants

15 March 2022 – Using artificial intelligence (AI) to fully exploit the potential of solar plants is the main aim of the collaborative project Dig4morE, a cooperation between the Helmholtz Institute Erlangen-Nürnberg and photovoltaic companies SunSniffer, Aquila Capital, and Sunset Energietechnik. The project partners aim to develop a method that uses AI to identify measures of optimizing the plants in a quick and cost-effective manner. The process only requires monitoring data, which are generated during everyday operation. The German Federal Ministry for Economic Affairs and Energy (BMWi) will provide the project with funding worth more than € 2 million over a period of three years.

Solaranlage (Symbolbild)
Forschungszentrum Jülich / Sascha Kreklau

Using machine learning, researchers in the Dig4morE project aim to identify performance deficits or defects at an early stage. This is to be made possible by a new process that enables performance deficits to be read out directly in situ from the monitoring data of the individual modules. To develop the algorithms, Sunsniffer, Aquila Capital, and Sunset Energietechnik provide data from eleven of their solar parks, which are located throughout Europe.

The extensive analyses across the entire continent take account of the different operating conditions in the relevant climate zones. Various problems arise for the solar modules depending on the solar plant type and its environment. “In Hesse, which is situated in central Germany, different factors are at play compared to the west coast of Portugal, where strong winds cause the modules to vibrate,” explains Dr. Claudia Buerhop-Lutz from the Helmholtz Institute Erlangen-Nürnberg, an institute of Forschungszentrum Jülich. “The algorithms need to be trained in such a way that the various deficits can be distinguished on the basis of fundamental data such as electricity, voltage, and temperature.”

Towards the end of this year, the first results are set to be available and will be used to derive best practice examples and recommendations for action. Solar plant operators will then be able to use these to identify deficits and defects at an early stage, for example to budget for maintenance work such as cleaning measures.

An earlier study conducted by the Helmholtz Institute Erlangen-Nürnberg showed just how great the potential for optimization is. It revealed that around 8 % of European solar modules are not running at full capacity. “Alongside incorrectly set or defective modules, environmental influences such as dust, pollen, bird droppings, and tall-growing trees and grasses can lead to the plants supplying less electricity than they are capable of,” says Dr. Buerhop-Lutz.

Using state-of-the-art measuring technology, it is already possible to detect modules that are either defective or not being fully utilized by means of thermographic analyses, for example. However, this procedure is expensive and complex. Large-scale solar parks are typically analysed with the aid of drones in the sky. The use of AI measuring instruments, as proposed by the Dig4more project, aims to enable more cost-effective and comprehensive analyses.

Prof. Brabec, head of the High Throughput Methods in Photovoltaics department, stresses: “We regard the application of high-throughput measuring methods as being key to the sustainable operation of solar parks. The best possible yields and lifespans for solar fields can only be ensured through a combination of measurement technology, which can be used to quickly characterize large amounts of solar modules, and artificial intelligence.”

Further information:

dig4morE - Digitized O&M Management for Securing Yields of Solar Plants

Helmholtz Institute Erlangen-Nürnberg

Contact:

Dr. Claudia Buerhop-Lutz
Helmholtz Institute Erlangen-Nürnberg for Renewable Energy Production (IEK-11)
Tel: +49 9131-9398100
Email: c.buerhop-lutz@fz-juelich.de

Press contact::

Tobias Schlößer
Corporate Communications
Tel: +49 2461 61-4771
Email: t.schloesser@fz-juelich.de

Project partners

Helmholtz Institute Erlangen-Nürnberg

The Helmholtz Institute Erlangen-Nürnberg for Renewable Energy (HI ERN) researches and develops material- and process-based solutions for a climate-neutral, sustainable, and cost-effective utilization of renewable energy. The institute focuses on research into electrochemical energy conversion in order to develop innovative hydrogen and solar technologies.

HI ERN is at the heart of a close collaboration between Forschungszentrum Jülich, Helmholtz-Zentrum Berlin für Materialien und Energie, and Friedrich-Alexander Universität Erlangen-Nürnberg. The aim of the cooperation is to closely link the excellent material, energy, and process research of the partner institutions. The partners’ collaboration is based on the areas of innovative materials and processes for photovoltaic energy systems and hydrogen as a storage and carrier medium for energy produced in a carbon-neutral manner. HI ERN is making an important contribution to the energy transition through its interdisciplinary cooperation.

Sunsniffer

SunSniffer is a spin-off of a medium-sized photovoltaic construction company. Through its monitoring of the solar plants, the company’s biggest task became replacing cost-intensive in situ analyses with power electronics at module level and artificial intelligence.
SunSniffer has developed three electronic components for solar modules:

1.) An inexpensive module sensor for measuring module efficiency
2.) RSD technology to switch the module off in case of fire
3.) Optimization technology for modules in shaded surroundings

In the field of software, scalable data pool technology with artificial intelligence is available to generate usable data in real time. The technology converts highly technical information into feasible, easy-to-understand analyses, for example the percentage by which solar modules lose their ability to generate electricity over time. This makes it possible to identify at all times whether a module is outside of warranty and whether it is worth replacing it.

Losses caused by degradation, shading, and soiling are calculated right down to the individual modules. The system is capable of suggesting maintenance work and the precise yields that this will result in. By using SunSniffer, operating costs for photovoltaic plants can be reduced by 50 % and yields increased by 7 %.

Aquila Capital

Aquila Capital is an investment and industrial development company focused on generating and managing essential assets on behalf of its clients. By investing in clean energy and sustainable infrastructure Aquila Capital contributes to the global energy transition and strengthens the world’s infrastructure backbone. The company initiates, develops, and manages these essential assets along the entire value chain and lifetime.

Currently Aquila Capital manages around € 13 billion on behalf of institutional investors worldwide, across wind, solar, and hydropower energy. Assets of more than 12 GW capacity and over 2 million square metres of sustainable real estate and green logistics projects have been completed or are under development. The company has around 600 employees from 48 nations, operating in 15 offices in 13 countries worldwide.

Sunset Energietechnik GmbH

High-power photovoltaic modules “made in Germany” form the core business of Sunset Energietechnik GmbH. Founded in 1979, the Adelsdorf-based firm is likely the oldest solar company in Germany and specializes in the production of crystalline high-performance modules in various designs. The modules are produced and supplied worldwide in a carbon-neutral manner by its subsidiary Sunset Solar GmbH&Co KG in Löbichau/Gera with a capacity of up to 50 MWp per year. In contrast to many other photovoltaic manufacturers, Sunset also produces modules tailored to customer requirements in addition to specialist modules for various applications (BiPV, ViPV; different climate and exposure zones). Sunset designs, configures, and assembles photovoltaic systems in all size classes, ranging from several kWp to MWp.

Last Modified: 14.11.2022