Digital O&M

Growing need for remote monitoring and automation

The renewable energy sector in India, particularly the solar segment, is increasingly moving towards automation and digitalisation, with a special focus on operations and maintenance (O&M). In the initial years, O&M activities were limited to site management, involving module cleaning and grass cutting. Later, the scope of O&M was extended to the optimal use of balance of system and delivery of the promised generation. O&M players have also started investing in the automation of predictive maintenance services.

There is a strong business case for the automation of services in the solar sector. One, automation helps in reducing manpower costs. The low tariffs discovered in the recent solar auctions have squeezed the margins of O&M players as well. Automation will help them maintain profitability. Two, O&M players can deploy technology to predict the future of components and assess plant performance in order to avoid penalties. To integrate renewable energy with the grid, various state governments have come up with strict scheduling, forecasting and deviation settlement regulations, which penalise over- and under-generation of electricity from renewable energy plants. These two factors have encouraged O&M players to invest in the automation of predictive maintenance services.

Automation and digitalisation can assist in predictive maintenance on the field as well as in offices. While the use of drones, robots and smart sensors is decentralised, remote monitoring systems are set up at a centralised location. These systems monitor the solar plant from a remote location in real time and help in taking appropriate and quick actions in case of any untoward incident. Remote monitoring also helps identify underperforming components, operate automatic ticketing systems and detect soiling losses.

Inspire Clean Energy, a Mumbai-based O&M service company, has developed a software platform called EIRA. The model of EIRA is based on the three-second and three-click concept, wherein a client is made aware about the status of solar plants in three clicks. EIRA logs into every project at 15-minute intervals to check the performance parameters of each inverter, energy meter and transformer. If a flaw is detected, it automatically generates a ticket and sends it to the nearest engineer. As soon as the problem is solved, a report is sent to the client. The platform also has a dashboard with smart tiles. The tiles stay green in normal conditions and turn red if the corresponding inverter is down. It takes just three seconds for an investor to get this status and three clicks for the operator to procure more information about the generated ticket.

There are a number of such examples where advanced technologies are deployed to improve predictive maintenance. These technologies have matured from computer-based automated monitoring systems to drones, robots and wearables, and now finally to solutions based on artificial intelligence (AI) and internet of things (IoT). Over the past few years, various robotic and water-free module cleaning technologies have emerged. They minimise the need for a physical workforce and reduce water wastage. These technologies use AI to come up with optimal cleaning solutions, taking into account the inclination of solar panels, geographic location, wind direction and speed.

Aegeus Technologies has developed one such robotic water-free module cleaning product, Unicorn. It can start the cleaning process in three ways. One, sensors on solar panels can initiate the cleaning process when the dust on panels exceeds the threshold level. This is an automatic process that does not require any human intervention. Two, the cleaning process can be initiated manually through a cloud-based remote system. The product can be switched on and off from any device by an engineer. Three, the cleaning can be scheduled daily or weekly at a predetermined time. The product also sends a notification on the remote monitoring system if the cleaning process stops due to any unforeseen reason. The notification mentions the exact module where the cleaning process has stopped. Such notifications help O&M engineers easily find the cause of disruption in the cleaning process. While many robotic module cleaning products have been launched in the solar market in India, the widespread use of such products is yet to be seen.

Automated monitoring and big data analytics

The real-time monitoring of equipment performance and measurement of energy generation is necessary to ensure that the plant is performing at an optimum level. Further, plant yield needs to be remotely monitored in order to identify faults without hindering plant operations. Thus, supervisory control and data acquisition (SCADA) systems have become an inherent part of all solar plants. Small distributed plants are monitored remotely and not by a dedicated O&M team. They typically depend on web-based monitoring systems that are more economical than the large expensive SCADA systems. The main difference between SCADA and web-based systems is that the former provides remote control functions, in addition to monitoring and data collection functionalities.

Robotics, drones and wearables

Automation technologies are being used in the solar industry in the form of drones, robots and wearables in order to reduce manpower costs. Drones are excellent for site assessment and O&M, providing greater detail than ground crews. According to industry experts, drones can inspect all the modules in a 2 MW plant in about 15 minutes, while the same activity will require more than three hours if carried out manually. Moreover, thermal imaging cameras on drones can detect malfunctioning modules, specifically hotspots that reduce electricity generation. They can identify faulty modules or strings with great reliability and can save up to 30,000 hours of hazardous work each year. Meanwhile, crawling robots can get quite close to a structure’s surface, and use microwave and ultrasonic transmitters and receivers to penetrate equipment structures and reveal defects in materials.

Apart from drones and robots, IoT-enabled wearables such as watches, headphones and armbands are being used by many O&M players for remote monitoring of solar plants. While their use is currently limited to very small rooftop plants, their scope in large utility-scale plants is immense.

Cost-benefit analysis

While solar power automation systems have several benefits, they also entail significant costs. The cost largely depends on the level of automation. The use of robotics, drones and other automated tools for O&M is more expensive than asset management tools and machine learning applications. If applied on a certain minimum scale and across regions, it may prove to be cost effective in terms of total efficiency gains.

Investments in automation will help devise a more focused O&M strategy. In addition, it reduces replacement costs by extending the component life. However, it takes time to develop human resources with the right expertise.

Future outlook

A key positive trend is that industry players are now more aware about the scope of automation and digitalisation. In the O&M segment, automation has been a priority area to facilitate predictive maintenance. It has shown positive results with respect to decreasing manpower costs. As per industry estimates, the technical manpower requirement in solar plants is 5 MW per person and should reach 10 MW per person in the near future. As the O&M segment becomes more technology-driven, the composition of the total cost is likely to shift from personnel to digital in the years to come. As per estimates, the share of personnel in the total cost will come down to 17 per cent and the expenditure on digital equipment will comprise more than 50 per cent of the total cost by 2028. It is believed that if the current pace of automation is maintained, the majority of the O&M activities will be carried out through AI-enabled field assistants instead of manpower.

With positive results on the ground, O&M players are keen to incorporate futuristic technologies such as AI-based digital twins. These AI applications can reduce the time and effort required for planning and analysis. A replica of an actual physical asset or a digital twin will be created to be used as a benchmark for identifying faults through data anomalies. However, in the short term, the high cost associated with these technologies may pose a challenge, particularly for small players working on thin margins.

The need for remote monitoring systems has increased during the current crisis of Covid-19. Developers have been struggling to make sure that the workforce at the site and involved in the O&M of plants are able to work with full productivity. Once the lockdown ends, the remobilisation of the workforce will be a big challenge. Developers that had invested heavily in remote monitoring and digitalisation will face fewer issues while transitioning from these difficult times. The current experiences of developers will encourage them to invest more in automation going forward.

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