Decoding Digital Twin for solar plants
A Digital Twin looks-like, behaves-like, and connects-to a PV solar plant system with the goal of improving or optimising decision making processes.

Investment in the solar PV energy projects have increased in recent years and so has the use of advanced solutions for optimizing solar operations and maintenance like Digital Twin, edge computing, and IoT. We are decoding the concept of Digital Twin in this article and explain to you how this technology is helping to transform the way PV solar plants function and operate.

With the use of advanced IoT solutions and smart sensors, now we can get PV module-level data insights on any kind of solar PV asset. Detailed analysis of this data is used to design advanced solutions like Digital Twin.

Effective use of Digital Twin can help solar asset managers detect early patterns of system underperformance, which later can be used to predict potential faults, quantify the impact of existing/upcoming faults or losses. Thus, Digital Twinning techniques help solar developers to anticipate PV system’s future production, to plan predictive and corrective maintenance activities, and to improve bankability of future projects.

1. What is a PV plant Digital Twin?

A Digital Twin is a virtual model of a real system. While changing the input variables, a Digital twin helps to conduct experiments and to test hypothesis, as well as to predict the behavior of the system and to manage its life cycle. A Digital Twin also helps to prevent equipment failures, poor system design and minimizes operating costs. In the specific case of the solar PV industry, a Digital Twin is a digital copy of the PV plant, including all relevant design and location information, as well as information from the past; including major losses causes and performance indicators breakdown.

3. Why do Asset Managers need Digital Twin?

Digital Twin is a faster way of comparing, at specific conditions, PV plant’s current performance with its expected performance drawn from its digital copy. After a Digital Twin of a Solar PV Plant is created, the only required inputs, other than real-time plant data, are irradiance and temperature. Therefore, performance and losses analysis becomes faster and easier to perform. More advanced solar PV plant Digital Twins even include a parameters’ breakdown and trends to indicate root causes of faults or losses in real-time. Thus, making daily tasks easier for Asset Managers and increase the uptime of the PV solar plants.

Understanding Digital Twin for solar plants

2. How do you make a Digital Twin?

A Digital Twin is a virtual instance of a physical system (twin) that is continually updated with the latter’s performance, maintenance, and health status data throughout the PV solar power system’s life cycle. A Digital Twin looks-like, behaves-like, and connects-to a PV solar plant system with the goal of improving or optimising decision making for any time horizon. It should be localized by considering the geographical position of a PV plant, locally measured power and weather data, and power loss factors.

An ideal Digital Twin should be self-adaptive, that means, it should be able to modify and recalibrate its behaviour and likeness under changing operating and external conditions, such that it satisfies operational goals and constraints to accommodate possible future operational uncertainties.

Step by step process of how to create a Digital Twin for solar plants

3. How SmartHelio uses Digital Twin?

In general terms, creating a Digital Twin is a faster way to detect even the smallest of  deviations in performance and make sense out of different trends within the historical data. At SmartHelio, we create and localize Digital Twins of PV plants by adding design factors, geographical factors, and performance factors and trends. Our Digital Twin models automatically prioritizes maintenance activities to minimize O&M costs and maximize solar PV  plant’s uptime. It’s a blend of advanced technology, continuous learning with a custom tailored understanding of O&M costs and resources for each plant or company. Our Digital Twins do what a Digital Twin is actually supposed to do- directly derive actionable insights!

Authored by: Dorian Guzman  Co-authored by: Govinda Upadhyay, Shankaransh Srivastava

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