Automation & Digitalization In Solar O&M
India’s renewable energy sector, especially the solar segment, is increasingly moving towards automation and digitalization when it comes to operations and maintenance (O&M). According to the Ministry of New and Renewable Energy, India anticipates having 67.07 GW of installed solar power by the end of March 2023. Operations and maintenance (O&M) are one of the main drivers of this increase. Initially, operations and maintenance were mainly concerned with site management, such as cleaning modules and cutting grass; recently, it has expanded its reach to ensure promised output generation through technological advancement.
The advancements are mostly around predictive maintenance services.
Traditional O&M models with a large dedicated team for each asset aren’t a practical approach in the longer run. As the process of monitoring data becomes more complex a manual model would not be able to meet the real-time demands. The focus of both asset owners and O&M teams is now turning towards incorporating more automation and digitalization in their O&M practices.
Automation and digitalization of O&M in the solar sector include drones, robotics, artificial intelligence (AI)-enabled monitoring and analysis, advanced fault diagnostics techniques, and intelligent remote monitoring software. In addition to making the job more efficient, such tools and O&M solar services ensure higher quality and greater reliability.
Furthermore, such advanced tools can significantly reduce O&M costs due to their lower manpower requirements, even though a small initial investment is needed. More energy is generated, equipment life is extended, and project downtime is reduced through better O&M. The best O&M will ultimately lead to cost benefits for asset owners through increased revenue, operational savings, and reduced maintenance costs.
Furthermore, operators and owners are increasingly investing in O&M-specific software and advanced analytics. By doing so, they are able to reduce operational costs and increase data quality by moving away from more time-consuming spreadsheet-based analysis methods.
Automation and Digitalization
By 2024, 1,243 GWdc of cumulative solar PV installations will be expected to be installed globally. The whole value chain, including the operations and maintenance (O&M) sector, is under pressure due to expiring incentives and other factors. Between 80% and 90% of the O&M cost for solar power plants is labor-related. As a result, asset owners are turning more and more to automation to manage the complexity of massive solar power facilities spread across hundreds of acres of land while also reducing O&M costs for their fleet.
O&M is one of the largest expense areas for most renewable energy developers. Therefore, the ability to predict when equipment and projects will fail is crucial to reducing operational costs and saving time. Smart devices with predictive capabilities can be used in O&M processes, aiding clients by detecting problems before they occur, reducing risk, and avoiding penalties.
Digitalization and automation of Solar Operations and Maintenance have already led to considerable benefits in reduced downtime, and improved reliability and efficiency. Automation can also help reduce costs while ensuring the smooth running of power plants. Developers and O&M service providers can use the latest technology to ensure a high level of safety, efficiency, and reliability by setting up remote monitoring systems with real-time updates. Performing predictive maintenance and condition-based diagnostics has become the standard for utilities, renewable energy developers, O&M solar service providers, and end consumers.
Fault detection and rectification can be effectively performed with automation and digitalization. Real-time remote monitoring helps prevent equipment or project downtime by predicting faults and diagnosing their causes. Developers and O&M service providers are increasingly using advanced asset management software with AI-enabled monitoring platforms for predictive O&M as they can analyze data from several projects and take corrective measures based on the findings.
To ensure higher reliability and efficiency, predictive maintenance technologies have evolved and advanced greatly. Using cloud computing, O&M teams can access data based on specified critical parameters from anywhere in the world through cloud-based remote monitoring systems. Using data loggers, this data can be transmitted to cloud-based Internet of Things (IoT) platforms, where it can be viewed in raw form, aggregated, or visually displayed, making it easier for projects or even standalone equipment to be monitored. The FutrOS platform, developed by Futr Energy, provides proactive maintenance of solar power plants to help track their performance and predict failures.
Drones for Inspection: Drone inspection is anticipated to replace manual inspection as solar power projects in India grow in size and scope. According to estimations, drones can examine every module in a 2 MW solar power plant in 15 minutes, but performing the same task manually would take more than three hours2. Drones are becoming increasingly popular for inspections in solar plants due to their ability to access hard-to-reach areas, provide high-resolution imaging, and reduce inspection time and cost. In solar plants, drones can be used to inspect and survey large arrays of solar panels, monitor the condition of the panels and identify any faults or damage, and check the performance of the energy storage system. The use of drones equipped with cameras and sensors can provide real-time data and images that can be analyzed and used to optimize plant performance and improve maintenance processes.
Digital Twin: A digital twin in a solar plant refers to a virtual representation of the physical solar plant and its components, including solar panels, inverters, and energy storage systems. This digital replica can be used to monitor and analyze the performance of the solar plant in real-time, predict and optimize energy production, and simulate various scenarios to make informed decisions about operation and maintenance. The digital twin integrates data from various sources, such as sensors and weather forecasts, to provide a comprehensive view of the solar plant’s operation and performance. This information can be used to optimize energy production, identify potential problems before they occur, and improve the overall efficiency and reliability of the solar plant.
Role of AI and Machine Learning
In a 2017 research, PWC predicted that by the year 2030, AI could be worth up to $15.7 trillion to the global economy3. AI is being used by the worldwide solar energy industry to bring solar power closer to grid parity. Artificial Intelligence (AI) and Machine Learning (ML) are playing an increasingly important role in the optimization and management of solar plants. A web-based digital twin (DT) helps to provide operators with intelligence and insights through several AI-driven capabilities.
Some of the real-world applications of AI and ML in solar plants include:
- Performance Optimization: AI algorithms can analyze data from various sources, such as weather forecasts, energy production, and equipment health, to predict energy production and optimize plant performance.
- Fault Detection and Diagnosis: ML models can be trained to detect and diagnose faults in solar panels and other equipment, reducing downtime and maintenance costs.
- Predictive Maintenance: By analyzing data from sensors and equipment, AI algorithms can predict when maintenance is required, reducing downtime and increasing efficiency.
- Yield Forecasting: ML algorithms can be used to forecast energy production based on factors such as weather conditions and historical data.
- Image Recognition: Drones equipped with cameras can be used to collect images of solar panels and identify faults or damage, which can then be analyzed using computer vision and image recognition algorithms.
Overall, the integration of AI and ML technologies in solar plants is helping to improve the efficiency, reliability, and profitability of these systems.
Originally published at futr.energy/knowledge