Automation aims to decrease reliance on human labour across organisational tasks, prioritising the delegation of simpler, repetitive duties to free up human resources for tasks that demand uniquely manual skills. Within the renewable energy sector, integrating automation into the design and development processes of components offers unprecedented efficiency enhancements. Automation introduces heightened precision to manufacturing processes, mitigating the risk of defects and ensuring consistent quality.
Automation in wind energy
The wind sector has embraced automation and digitalisation, leveraging real-time data for fault detection, and streamlining operations and maintenance (O&M) of wind power plants. A primary focus within the wind industry has been the reduction of O&M costs, which constitute a significant portion of the expense of running a wind farm. Rising O&M expenses, often attributed to component failures and logistical challenges, are being addressed through technological innovations and specialised services. While the initial investments required for smart technologies may be higher, they ultimately contribute to lowering overall costs through enhanced fault detection and simplified maintenance procedures.
Remote monitoring with AI and ML
Wind turbines, unlike solar power plants, have many moving parts which can lead to breakdowns. To address this, advanced remote monitoring tools are crucial, tracking performance and enabling timely fault diagnoses. These tools, integrated with artificial intelligence (AI) and machine learning (ML), facilitate predictive maintenance and efficient handling of large data sets. Automation further enhances output efficiency and reduces costs. AI also improves forecasting by analysing environmental data continuously, leading to better planning and operational efficiency. Additionally, AI helps in executing maintenance activities by uncovering patterns for future repairs and optimising schedules. Real-time turbine performance monitoring and automated inspection are other use cases of AI. Despite initial costs, the long-term gains are significant, and with ongoing innovation, wind power O&M is expected to undergo substantial transformation in the near future.
Digital twins
Digital twin technology is increasingly being explored in the wind O&M sector, akin to its adoption in other infrastructure fields. Essentially, a digital twin represents a virtual replica of a physical wind power plant, encompassing processes, systems, conditions and equipment accessible via a virtual interface. The advantages of digital twins are manifold, ranging from minimising downtime through remote monitoring to reducing production costs and enhancing profit margins. This facilitates proactive processes and reduces the necessity for on-site personnel, offering real-time remote monitoring capabilities that aid in predicting equipment failures and enhancing operational efficiency. Moreover, it assists in determining the remaining lifespan of components, aiding in replacement scheduling and optimisation of generation.
Robots
Various robots are now being deployed at wind power project sites to automate specific operations. For instance, crawling robots inspect structure surfaces for faults using radiation, while driving robots manage the supply chain, from transporting equipment to unloading and installation. Robotics and automation are revolutionising critical aspects of wind turbine manufacturing. Automation has significantly reduced the time required for painting, sanding and polishing turbine blades from days to hours, enabling increased production output. Robots ensure uniform paint distribution and precision, preventing balancing issues compared to manual application. Manufacturing wind towers would be arduous without robot assistance, especially when welding structural components due to their complex geometry. Robots excel in tasks such as sharpening gear edges, trimming blade flash, drilling and cutting blade roots with remarkable accuracy. Their large and contoured surfaces are crucial for ensuring consistent production quality and reducing the likelihood of frequent breakdowns in turbine systems.
Drones
Aerial drones offer more comprehensive insights than ground crews and are frequently employed for site assessment and O&M tasks. Although the upfront expenses associated with fully automated drones might be substantial, they significantly contribute to operational time and cost savings. By integrating these autonomous drones with AI and advanced analytics, further enhancements in O&M efficiency can be achieved.
Thermal camera for blade inspection
Thermal cameras serve as invaluable tools for inspecting wind turbine components, including both electrical and mechanical systems. By detecting temperature variations, thermal imaging enables operators to identify potential malfunctions before they occur. Hotspots indicative of impending failures are visible in thermal images, aiding in the early detection of issues. These cameras can also pinpoint gearbox and motor problems such as shaft misalignment, as well as electrical issues like loose connections and uneven loads.
SCADA
Supervisory control and data acquisition (SCADA) software is employed to oversee industrial processes by collecting real-time data from remote sites to manage equipment and conditions. This enables full remote control and supervision of entire wind parks and individual turbines. SCADA systems offer an extensive overview of critical wind turbine features, including temperatures, pitch angles, electrical parameters, rotor speeds and yaw system operation, to optimise performance.
Automation in solar energy
Manpower costs typically make up 60-70 per cent of the total O&M expenses for solar power plants, but this proportion is gradually declining due to the adoption of digital and automated tools. Much of the workforce in solar O&M is allocated to tasks such as module cleaning, inspection and vegetation management. Since solar panels rely on sunlight for electricity generation, it is crucial to regularly clean them to maintain optimal performance. Besides manual methods such as water spraying with hosepipes or using tractors, various mechanical, coating and electrostatic techniques are employed for solar photovoltaic panel cleaning. Mechanical methods include air-blowing, robots, water-blowing and ultrasonic vibration techniques.
Automated monitoring and big data analytics
Real-time monitoring of equipment performance and energy generation is crucial for ensuring optimal plant operation. SCADA systems enable remote monitoring of plant yield and identification of faults without disrupting operations. While large-scale plants utilise comprehensive SCADA systems, smaller distributed plants often rely on more cost-effective web-based monitoring systems. These web-based systems offer monitoring and data collection functions but lack the remote control capabilities of SCADA systems.
Sensors and IoT
Digitalisation in power plants begins with deploying IoT-enabled sensors to monitor parameters such as temperature, wind speed, solar irradiance and equipment health. These sensors must transmit data in real time and withstand harsh environments. A robust communications infrastructure, combining wired and wireless technologies such as fibre optics, satellites and cellular networks, is essential. Redundancy and cybersecurity ensure data integrity and system security. Power plants generate vast amounts of data that require effective management systems for collection, storage and analysis to provide actionable insights. Automation systems are necessary for remote monitoring and control, optimising energy output and managing energy storage. These systems must be reliable and secure against cyberthreats.
Robotics, drones and wearables
Automation technologies, including drones, robots and IoT-enabled wearables, are increasingly being employed in the solar industry to reduce manpower costs. Drones are particularly efficient for site assessment and O&M tasks, offering greater detail than ground crews. Thermal imaging cameras on drones can swiftly detect malfunctioning modules such as hotspots, saving significant time compared to manual inspections. Crawling robots equipped with microwave and ultrasonic transmitters can penetrate equipment structures to identify defects in materials. Additionally, IoT-enabled wearables such as watches and armbands enable remote monitoring of solar plants, although they are currently more prevalent in small rooftop installations. The potential for their application in large utility-scale plants remains significant.
Challenges in automation
The advancements in drones and robots hold significant promise for the power and utilities sector, but there are notable challenges that need to be addressed for their effective implementation and utilisation. For instance, high winds in wind farms can pose difficulties in controlling and operating drones. Cybersecurity threats, including data breaches, are a concern, as drones may capture sensitive information without adequate security measures. Additionally, the lack of supporting software for timely and accurate data transfer from drones to operators is a limitation. System integration is necessary to streamline the entire process from data collection to analysis. Insufficient capacity building and training, particularly in developing countries, hinder technology adoption. The high initial cost of drones and robots is also a deterrent for developers. To overcome these challenges, market players should develop digital platforms integrating drones and robotics with software for comprehensive inspection and maintenance. Financial assistance can incentivise greater adoption, while knowledge transfer and increased research can optimise technological solutions.
Conclusion
Robots and automation are indispensable in the manufacturing of renewable energy products due to the precision they offer, particularly for complex designs. Automation also enables faster and more efficient implementation of tasks, reducing labour and production costs. Increased adoption of robots and automation by renewable energy companies may pave the way for a transition from traditional energy sources such as petroleum to safer alternatives such as solar power. While robots currently cannot conduct machine repairs, AI assists in maintenance scheduling and optimising machine downtime, playing a crucial role in equipment upkeep within industrial settings.
Aastha Sharma