Green energy, in its purest form, rests on the capacity for huge amounts of solar and wind energy and is entirely renewable. This makes it a natural fit for controlled environments like utilities. However, much like the mining industry, utilities have largely remained away from innovations and upgrades when compared to more nimble industries. As a result, large-scale green energy transformation has been slow and unwieldy at worst.
Solar O&M is becoming a high-priority focus area in the field of solar renewable energy. According to a study of one year of implementation of AI, It was found that the average energy yield for project sites with AI systems is 0.16 kWh/kWp/day (4.7%) higher than for sites without. The accuracy for defect detection is 99.2%, while the accuracy for failure mode diagnosis is 92.3%1. AI(Artificial Intelligence) and automation can minimize these services costs by reducing human intervention in real time. Apart from this, solar power plants that are built with advanced efficient solar panels need regular technical checks for their effectiveness to stay intact. However, with the high cost of O&M changing the scenario, we often notice new ways to bring down costs. Futr Energy has a series of smart O&M solutions like solar control panel monitoring, digital twin mapping, and many more that can help you get on track while keeping your assets sustainable and under control.
In this article, we will walk you through how O&M asset management software combined with AI technology maximizes the generation of solar plants.
How does an AI work to fix solar O&M issues?
The PWC company projected in a 2017 study that by 2030, AI could contribute up to $15.7 trillion to the global economy, which only serves to highlight the tremendous potential of this technology and the development of Industry 4.02. In recent times AI is the latest innovation in grid technology. It can be utilized to make grid devices perform optimally ensuring real-time adaptation to changes in their environment. The concept involves AI engines that collect information from internal and external databases such as sensors, supported by ML(Machine Learning) at the local and device level.
In the solar industry, the information derived from internal and external data sources such as sensors is analyzed to determine risks and dysfunctionality. In such instances, AI engines activate a specific control method to resolve the issue.
Traditional Solar operational and maintenance (O&M) functions are being replaced by Modern asset management systems (MAS) at a great pace. The reasoning behind the switch is that AI-enabled Machine learning (ML) can be used to monitor, operate and optimize solar energy assets for maximized power generation and savings with minimal human intervention.
This ensures DERs (Distributed Energy Resources) become smarter over time as the learning is derived from real data and rule-based solutions. In addition to it, modern AIs use intelligent machine-to-machine communication called edge analytics which actively synchronizes and optimizes traditional and new DERs, with each other and with the power grid. This active synchronization capability ensures that all assets under AI control work together to meet individual and system-wide goals while reducing cost and risk while increasing reliability, quality, and efficiency.
1. AI in solar plant equipment control: AI plays a significant role in maintaining the solar plants by modelling the upcoming power supply/demand on a real-time basis to predict future output accurately and dynamically. Rather than encountering random spikes, your solar plant will have the ability to predict and optimize the conditions of its plant to meet future demands accurately. The traditional approach followed in the utility industry relies upon historical data mining to identify trends in weather, power supply/demand, and device control. This approach ensures sufficient energy available at all times but has limitations due to inherent inaccuracies whereas the AI-driven approach triggers real-time data modelling of weather and power supply/demand, the device control can yield insights that are superior to those available using historical information.
2. AI in weather forecasting: AI plays a significant role in weather forecasting. It can break down huge volumes of verifiable and continuous information from satellites, climate stations, and IoT gadgets to perceive designs and foresee climate that could affect sunlight-based creation. This data can permit control makers to alter as needed. In 2018, Peter Dueben and Peter Bauer from ECMWF published a report on utilizing the organization’s most recent reanalysis (ERA5) at a resolution of around 500 km to forecast future 500 hPa geopotential height3. This was the start of ECMWF’s investigation into the subject of creating ML-based weather predictions. Besides, AI can be utilized for prescient upkeep by learning calculations to spot irregularities and decide when a board or an inverter is going to fall flat.
3. Real-time monitoring of the Solar PV system: Manually checking solar panels is considered a time-consuming and expensive process, with only certain parts of the plant being checked. Real-time solar performance monitoring platforms enable the identification of changes and breakdowns in system performance by sending warnings so that owners may take action and improve efficiency. The top performance monitoring systems often save at least 2-5% of PV system production back into your packet4. By digitizing maintenance and routine inspections, Futr OS has significantly reduced the cost of ownership for solar PV plants. Futr OS monitors the performance of your solar PV systems in real-time, it collects data, analyzes it, and sends an alarm notification if a fault is detected. Ensuring that problems get detected before they occur – and helps make maintenance 20% more effective than manual reporting alone. Usage of Futr OS informs the stakeholders about the power their plant is generating on a daily basis and most importantly comes up with an understanding of where the energy is flowing.
Along with the development and improvement of AI technologies, O&M media analysis tools have become increasingly important. This is due to the increasing complexity of data, which can be challenging for inexperienced analysts to interpret. The amalgamation of AI and O&M technology has facilitated identifying patterns in the system data and generated actionable insights while saving time and money compared to manually driven processes.
