Digitalisation of hydropower plants improves power plant performance by optimising asset management and reducing operations and maintenance (O&M) costs. Digital solutions access and analyse plant performance data to derive actionable insights for making the right decision at the right time and to enhance efficiency. Moreover, the performance of turbines, plants and equipment is also improved through the use of new digital controls. Many advanced tools are used nowadays to improve performance, including artificial intelligence (AI), machine learning (ML), internet of things (IoT) and digital twins, among others.
Need for digital technologies
Digitalisation of hydropower plants can play a significant role in maximising the value of current assets and help improve productivity. It optimises O&M costs and improves power plant safety. Digital controls improve the performance of turbines, plants and equipment, which further reduces O&M cost, enhances operational efficiency, and improves asset management. Further, digital controllers provide a more accurate measurement of input and output parameters such as flow, pressure and power that improves project efficiency of hydropower plants. Digital solutions also provide improved reservoir management.
In a digital power plant, constant data collection helps to analyse and identify performance deviations and possible faults. With the use of smart software, faults can be detected before they occur and, thus significantly reduce project downtime. An advanced real-time monitoring system can be programmed to identify normal parameters of plant functions, and can thus raise an alert in case of an anomaly. With this, the asset management strategy for hydropower plants is evolving from corrective to preventive and finally to predictive maintenance. This has significantly reduced equipment breakdown and plant outages resulting in cost saving, increased generation and improved performance. Automation solutions also allow power plant operators to run the plant remotely. An analytical software can be used to provide operators with a clear end-to-end overview and management of maintenance activities. Such softwares are also useful for smoother scheduling, better planning, shorter maintenance lead time and greater accuracy on the required maintenance work.
Digitalisation also allows hydropower plants to operate more efficiently with other renewable energy sources by supporting the operations of the hydropower plant and improving decision-making. Apart from this, advanced technologies can also be used to upgrade operations of ageing hydroelectric plants (HEPs).
Key digital technologies
One of the key upcoming technologies for digital operations of the hydropower segment is digital twin. Digital twins create virtual HEPs through AI, ML and mathematical models that mimic the behaviour of a real plant. It allows operators to analyse plant’s performance in real time, through simulations, and predictive and preventive analytics. It integrates engineering, operations and information technologies that allows managing every individual asset in the hydropower plant from design to operation remotely. It allows end-to-end management of the hydropower plants and also provides multiple user interface services, including 3D visualisation and augmented views. Digital twins allow operators to make a more informed decision, with access to real-time analytical and design views of the plant. It also allows operators to run different simulation scenarios and make decisions based on parameters such as weather, sediment and demand.
IoT is the key technology solution to digitalise the operations of a HEP. The use of sensors across the plant provides real-time data on plant performance and ambient conditions. The sensors embedded in the assets are useful to measure key parameters such as wear and tear, and temperature. This facilitates real-time asset health monitoring, allowing for more granular insight into asset performance trends. IoT allows collection of higher frequency time-series data and allows performing meaning analysis, and higher accuracy historical data analysis. It is also useful to provide real-time visibility and automatic reporting and is also helpful for transitioning to predictive and preventive maintenance.
The supervisory control and data acquisition (SCADA) system is an integral part of hydropower plant automation. A SCADA system allows power plant operators to undertake several plant operations such as open/close valves/switches and monitor alarms remotely. It is used to control, monitor and analyse devices and processes. The system enables remote and onsite data collection, allowing developers to access and control turbine data without being onsite and allowing companies to manage their HEPs remotely.
Another tool widely used to automate the operations of HEPs is computational fluid dynamics that makes internal flow predictions with high accuracy. It detects expected flow problems and allows undertaking improvement in turbine components to prevent failure. Apart from this, distributed control system is a crucial component of a digital hydropower plant, involving a network of instrumentation comprising sensors, flow switches, transducers, etc. It provides real-time information to operators, allowing complete control of machines and auxiliaries from the control room, often located in a remote location. With this system, no manual intervention is required as numerical relays are able to record and store a large number of events and disturbance records, providing excellent fault diagnosis tools.
A key area of technology intervention in the hydropower segment is for remote monitoring purposes. With the help of drones and computer vision, asset inspection can be done. Since the majority of hydropower plants in the country are in remote terrains, the use of drones for inspection would reduce the need for personnel to assess the equipment condition. Human supervision can be further reduced when remote monitoring is coupled with AI- and ML-enabled technologies. Moreover, the use of new and emerging technologies such as AI/ML can enable predictive maintenance by learning failure modes and applying fault tree analysis, which will allow regularity of asset health checks and early identification of risks. AI/ML, coupled with simulation, can help optimise the management of capital and operations expenditure allocation. For instance, early initiation of remediation can extend asset life through accurate assessment of replace/repair decisions.
Data analytics
Big data is utilised to draw key insights and trends from data. It plays a major role in aggregating large volumes of data from multiple sources to facilitate the integration of other energy sources and market data into production planning. It also plays a vital role in managing the incoming data used in inflow forecasting tools. In contrast, advanced analytics leverages the higher granularity of data from IoT devices for early detection of underperformance and failure modes. Such insights can be utilised to boost productivity from individual machines, up to the entire power plant. Analytical capability is necessary to process the incoming data on inflow and production forecasts of other energy sources and market conditions to optimise production for maximum revenue. This can also be used for historical trend analyses to identify weather that can be exploited in the future for production planning.
Challenges and the way forward
Hydropower plants, like other industrial plants, produce large volumes of data and thus require an expansive IT infrastructure and framework for managing it. Skill gaps are another challenge. The fast pace of technological improvements requires an improved level of IT knowledge and expertise, not only for the design and implementation of digital technologies but also for the operation of assets and organisation; and continuous improvement to keep up with the pace of digitalisation. Additionally, cybersecurity is described as being one of the top digitalisation issues for the hydropower sector. The advent of digital solutions and more complex IT systems to support digitalisation can create the need for greater security measures.
Overall, data-driven asset condition monitoring can enable a more dynamic approach to maintenance planning by detecting faults early and only scheduling maintenance when required. This can alleviate logistics issues such as stocking more spare parts than necessary or not having spare parts when required. This can also lead to cost reduction through avoiding equipment failure and premature replacement/maintenance of equipment.
Net, net, digitalisation is bringing in a wave of new technologies for future operations, while optimising reservoir management and maximising revenues for hydropower producers.
