As wind power generation becomes increasingly mainstream in India, wind power plants are being tasked with providing ancillary services such as frequency support and voltage regulation to the grid, especially in high wind capacity states such as Karnataka, Andhra Pradesh, Gujarat and Tamil Nadu. In view of this added responsibility of grid security and stability, there is an even greater need to deploy advanced digital technologies at wind power plants.
Wind power plants generate a large amount of operational data, which can be harnessed through analytics to assist in their operations and maintenance (O&M). The digitalisation of wind power plants has several advantages for plant and transmission system operators. These include increased uptime, better O&M capabilities, reduction in the cost of generation and real-time grid support.
Technology has been the biggest driver for capital cost reduction and the scale-up of wind power generation capacity. It also reduces the cost of generation, leading to a fall in tariffs discovered as part of competitive reverse auctions. Wind power tariffs in the country are in the range of Rs 2.80-Rs 3 per kWh, down from around Rs 5 per kWh during the feed-in tariff regime. This has exerted greater pressure on operators to make their plants more efficient in order to improve profit margins. Digitalisation has provided them the opportunity to lower O&M costs, improve efficiency, increase generation, and thus drive revenues.
The smart quotient of wind power turbines has increased over the years. Post-installation, turbines are remotely monitored and controlled and a huge cache of data is generated that assists operators in running the plant smoothly and efficiently. A digitalised wind power plant allows the operator to leverage data and transform it into analytics through specified software platforms. Across the board, digitalisation allows power generation to be customised to suit the various assets – plant, farm or fleet – and gain a micro to macro perspective of their functioning.
The sensors installed in plant generators and transformers provide data at intervals as short as milliseconds, ensuring the maximum precision of data. The data gathered is moved to an analytics platform that uses real-time algorithms to develop actionable insights. The analytics are further used to run diagnostics on the turbine, drivetrain and other parts of the power plant. Meanwhile, the continuous monitoring of various parts is carried out with the help of real-time data. The insights provided by digitalised operations help reduce the downtime of the plant and its critical components.
Digitalisation has enabled power plant maintenance services to move from reactive maintenance, wherein rectification followed the event, to predictive maintenance. Advanced technology provides real-time access to data, which helps in predictive maintenance. This critical data is acquired through cloud services to gain insights for greater operational control. The digital asset management platforms help understand the issues that may exist within the system. Here, technologies such as machine learning are particularly useful as they understand the patterns of a healthy asset and any deviation can immediately be tracked and traced to the source. These deviations may not have an immediate impact on the performance of the wind power plant. However, if accumulated over time, these deviations may pose a serious risk. Therefore, predictive maintenance software platforms track the deviations and trigger a reaction to rectify the issue before it occurs.
Predictive maintenance also helps in providing operators an insight into maintenance services that may be required at a future point in time, thereby reducing the downtime of the asset. Meanwhile, knowledge of the precise point at which the issue may occur helps the personnel save time, increase productivity and efficiency, optimise costs, and mitigate possible risks. This has a direct impact on the revenues generated from the asset. Energy generation lost as a result of downtime can be costly. Thus, proactive O&M is required for anticipating faults.
Digital twin is the new buzzword in the power generation sector. A digital twin is exactly what it sounds like – a copy of the physical power plant, processes, systems and devices on the virtual screen. There are several benefits of creating a digital twin, from minimising downtime through remote monitoring to decreasing the cost of production and increasing profit margins. In the wind power segment, it is important to understand the health of the generating asset and plan the O&M activities in advance.
The digital twin is an interesting concept as it enables proactive processes. The virtual twin can practically eliminate the need for personnel to be present at the generation site and can allow real-time monitoring of the asset from remote locations. This can help the operator predict equipment failures, increase operational efficiency and initiate product enhancement. The advantage of asset visualisation is that it provides insights that are otherwise hard to achieve, making the O&M process highly reliable. It also intimates the operator about the remaining life of components to plan the replacement schedules. The primary aim of a digital twin is to assist in O&M activities to the point where any unplanned event can be predicted, and then eliminated or converted into a planned activity. As a result, the downtime reduces considerably.
Digital twins are also bridging the gap between pre-sales generation claims and after-sales operational outcomes. The digital twin can provide insights into the turbine’s post-installation performance even before it is put to use. It is also used to predict the remaining life of the asset and how the turbine will behave over its lifetime. Design engineers, operators and manufacturers can benefit from this technology by improving the operational parameters to eliminate unplanned downtime and elevate the performance of the asset. Moreover, the digital twin concept allows operators to simulate generation scenarios with multiple variables. These virtual experiments require changing the generation parameters or wind speeds or any other variable to understand its effects on the wind power plant. It can also be used to simulate greater power generation to earn higher revenues by adjusting the power rating during peak hours. The virtual experiments can provide an insight into how the plant would function or how its operations would be impacted if one of these events occurred. This can be particularly useful in disaster-prone areas or places with damaging wind speeds.
O&M has not been high on the priority list of wind power generators even though it accounts for about 20 per cent of the cost of generation. Most wind farms in the country have been traditionally inclined towards using manual labour for O&M activities. The increasing size of assets dispersed over several kilometres and over different geographical regions has rendered traditional O&M models obsolete. Digitalisation brings together all remote assets to a centralised location, which helps in efficient monitoring and cost reduction.
The automation of O&M processes through technologies such as predictive maintenance, big data analytics, digital twin, drones and robotics has become essential, especially for developers and operators with multiple assets. The capital cost of digitalising O&M activities may seem high, but long-term gains in generation, downtime and revenues compensate for the costs. Meanwhile, several O&M platforms exist and continue to evolve with artificial intelligence, machine learning and other such advanced technologies to save O&M costs. With rapid technological advancements, the cost of ownership is likely to reduce. As a result, more developers and operators will adopt digitalised and automated O&M processes.