Christopher Hailstone brings a wealth of field-tested experience to the table, having spent decades navigating the complexities of energy management and grid reliability. As a leading voice in the utilities sector, he has seen firsthand how the shift toward renewable energy requires more than just building new assets—it demands a radical rethinking of how we maintain the aging giants of our power grid. With a background deeply rooted in the technical nuances of electricity delivery and the security of infrastructure, he is uniquely positioned to discuss the Di-Hydro research project. This initiative represents a critical leap forward, aiming to breathe new digital life into hydropower plants that have served communities for over half a century. By blending sophisticated sensor technology with artificial intelligence, the project addresses the urgent need for modernization in a world striving for a climate-neutral economy.
The conversation that follows delves into the high-stakes world of hydropower digitalization, exploring the intersection of mechanical integrity and environmental stewardship. We explore how cutting-edge structural health monitoring can detect the faintest tremors of material fatigue in massive turbines and how real-time water quality analysis can prevent the ecological “green soup” of algal blooms. From the deployment of digital twins to the use of portable holographic microscopes for pathogen detection, the discussion highlights a future where data-driven decision-making ensures that hydropower remains a stable, sustainable cornerstone of the global energy mix.
Hydropower plants across Europe and the United States are reaching a critical age, with many facilities operating for well over 40 to 60 years. In your view, how does this aging infrastructure impact our current grid reliability, and why is the Di-Hydro project’s focus on digitalization so essential for these legacy assets?
When you look at the statistics, the situation is quite stark: the average age of a hydropower plant in Europe is between 42 and 46 years, and in the United States, that number climbs to about 64 years. In contrast, China’s fleet is much younger, estimated at just 20 years, which gives them a different set of operational advantages. For those older plants in the West, we are dealing with infrastructure that was designed in a pre-digital era, yet we are asking these facilities to be the backbone of a modern, climate-neutral economy. As these components age, the risk of unplanned outages increases, and the lack of granular data makes it difficult to predict when a penstock might fail or a bearing might seize. Digitalization through projects like Di-Hydro is essentially a lifeline for these assets; it allows us to retrofit smart devices and data acquisition techniques onto machinery that has been humming along since the mid-20th century. By developing digital twins, we can create a virtual mirror of the plant that facilitates a sophisticated exchange of data, allowing operators to see “inside” the machines in real-time. This transition from reactive maintenance—fixing things when they break—to predictive and intelligent control is the only way we can bolster renewable energy production to meet future demand without compromising the stability of the grid.
The Di-Hydro project introduces non-invasive structural health monitoring using acoustic emission sensors and multi-sensor units. Could you explain the technical significance of these tools in detecting defects, and what have we learned from their pilot installation at the Ilarionas plant in Greece?
The beauty of the structural health monitoring node developed by the team at CERTH is its low-cost, low-power design, which can be retrofitted without ripping apart existing infrastructure. We use an acoustic emission system based on the Qawrums RAEM-2 architecture, which is incredibly sensitive; it picks up elastic transient waves generated by materials under stress, such as when a crack begins to form or a gear tooth starts to deform. We’ve paired this with a multisensor unit on a Sense HAT (B) board—connected to a Raspberry Pi—that tracks everything from triaxial acceleration and gyroscopic movement to temperature and humidity. At the Ilarionas hydropower plant in Greece, we specifically targeted the drainage pumps and the penstock, areas that previously lacked this level of scrutiny. By monitoring the evolution history of amplitude, RMS, and power values, we can catch the “silent” signs of faulty bearings or defective drive trains before they lead to a catastrophic burst or a total plant shutdown. Seeing those AE history plots during pilot testing was a revelation, as it turned abstract mechanical stress into actionable data that a plant manager can use to schedule repairs during low-demand periods rather than during an emergency.
Beyond the mechanical health of the turbines, hydropower reservoirs face significant environmental hurdles like water stagnation and the dreaded “green soup” of algal blooms. How is the project utilizing biosensors and holographic microscopy to protect both the local ecosystem and the operational efficiency of the plant?
Water stagnation in reservoirs is a silent killer of water quality because it leads to stratification, where layers of water end up with vastly different temperatures and oxygen levels. This lack of mixing triggers a chain reaction: nitrification intensifies in low-oxygen zones, nutrient cycles are thrown out of whack, and you suddenly have the perfect conditions for “green soup,” or massive algal blooms fueled by excess nitrogen and phosphorus. These blooms aren’t just an eyesore; they produce toxins that threaten drinking water and recreation, and they can physically clog the HPP piping systems, which can reduce or even completely halt power generation. To fight this, Di-Hydro has deployed a suite of sensors including a new electrochemical sensor for ammonia, fluorescence-based algae sensors, and an E. coli biosensor. One of the most impressive tools is the portable multiparametric platform—a rugged suitcase equipped with a tryptophan-like fluorescence sensor that can estimate pathogenic contamination in seconds. We even use a portable Digital Holographic Microscope that uses laser interferometry to produce 3D images of microorganisms like cyanobacteria and diatoms. This allows us to assess the biodiversity of the reservoir with incredible precision, giving operators the data they need to trigger controlled water releases or aeration before the water quality reaches a tipping point.
Integrating high-frequency sensor data with decades of historical reports seems like a massive data challenge. How do the AI and Machine Learning models within this project bridge these gaps to improve operational efficiency?
It is a significant hurdle because you are dealing with datasets that have wildly different temporal resolutions—you might have a sensor giving you minute-by-minute updates while your historical water quality reports only come in once or twice a year. The Di-Hydro project solves this by using two interconnected AI/ML models that work in tandem with the plant’s existing SCADA system. The first model is focused on the biology; it takes the sensor data and the automated 3D analysis from the holographic microscope to predict biological activity and potential blooms in the reservoir. The second model then takes those biological insights and correlates them with the actual performance data of the hydropower plant and its historical operational records. This allows the system to recognize patterns, such as how a specific change in water turbidity might precede a drop in turbine efficiency or an increase in wear on the penstock valve. By integrating weather data, water flow, and even societal needs into this intelligent decision-making tool, we move toward an era of “smart hydropower.” This isn’t just about generating electricity; it’s about optimizing every drop of water for power, environmental compliance, and the long-term integrity of the machinery, all while ensuring we adhere to the standards set by the European Union’s Horizon Europe programme.
What is your forecast for the future of the global hydropower sector?
My forecast is that the next decade will be defined by a “digital renaissance” for hydropower, where we see the global fleet move away from being viewed as static, mechanical relics and toward becoming dynamic, data-rich nodes in a smart grid. We will see the average age of plants in the US and Europe continue to climb, but the risks associated with that aging will be mitigated by the widespread adoption of digital twins and real-time structural health monitoring, much like what we have pioneered with the Di-Hydro project. I expect that environmental and biodiversity monitoring will no longer be an afterthought or a compliance burden but will be fully integrated into the plant’s operational DNA, with AI-driven systems automatically adjusting water flows to prevent algal blooms while maximizing peak-load generation. As we strive for a climate-neutral economy, hydropower will prove to be more than just a source of “baseload” power; it will become a highly flexible, intelligent asset capable of balancing the intermittency of wind and solar. We are moving toward a future where a 60-year-old plant can operate with the precision and foresight of a brand-new facility, ensuring that this reliable, renewable resource remains a cornerstone of our energy security for the next half-century.
