Pumps-as-Turbines (PATs) are increasingly used in micro-hydropower applications due to their cost competitiveness that is brought about by lower acquisition, design, operation, and maintenance costs. Despite these, limited research exists that investigates PAT failures. Notably, there is a literature gap concerning cavitation in PATs. As such, this study proposes an improvement to the deviation from the normal distribution (DND) technique to facilitate application in PAT cavitation detection. Probability density functions of vibration signals collected during operation at design speed and various cavitation states are developed and the DND computed using two approaches, i.e., the use of baseline data and the original method, for comparison purposes. Normal probability plots are presented to depict suitability of the two approaches in quantifying the DND. Results show higher deviation when using baseline data, hence, improved detection capabilities with amplification of the slope of the trend line under cavitating conditions when using the proposed DND approach. The proposed method also allows for establishing clear alarm limits for the condition monitoring of PATs in practice. Moreover, the proposed method is validated by application at various PAT operating speeds and cavitation states. The proposed method is found to be responsive, reliable, and independent from operating speed.

The recently adopted EU Nature Restoration law emphasises the urgent need to address the ecological impacts of river barriers, which fragment habitats and disrupt natural flows. However, efforts to remove barriers are often constrained by prohibitive costs, regulatory hurdles, and public opposition. In Ireland, barrier removal costs range between EUR 200,000 and EUR 500,000 per structure, representing a substantial financial burden given that more than 73,000 barriers are identified nationwide. Although removal would restore ecological function, it would also eliminate the potential to repurpose these structures for hydropower, thereby reducing opportunities to contribute to the national target of 80% renewable electricity generation by 2030. This study outlines the development of a river barrier modification system to serve the dual purposes of upstream and downstream fish lift over barriers and generation of electricity for local consumption using a fish-friendly pump-as-turbine unit. Under normal flows, the unit generates electricity while during low flows it operates in pumping mode to enable fish passage. A prototype was fabricated and tested at a fish farm using both artificial and live fish. An assessment of the regional potential was also extrapolated from preliminary results suggesting that the BMS offers a cost-effective alternative to full barrier removal, potentially offsetting costs by 50–85% while contributing to both EU restoration targets and national renewable energy goals.

Hydraulic turbines increasingly operate under variable hydraulic regimes, creating a need for condition monitoring (CM) systems that support diagnostics and predictive maintenance, rather than only protection or threshold-based alarms. However, sensor selection in hydropower monitoring studies is often reported as an experimental detail, with limited guidance on how monitoring objectives, degradation mechanisms, measurable signatures, sensor modalities, data acquisition (DAQ) requirements and field constraints should be linked. This paper presents a sensor specification and deployment framework for hydraulic turbine CM in practice. Its main contribution is an operational decision process that links monitoring objectives to mechanisms, signatures, sensor roles, DAQ requirements, deployment constraints and site-specific sensor-suite selection. The framework was verified on a laboratory pump-as-turbine (PAT) rig using flow, pressure, vibration and acoustic emission (AE) measurements under three quasi-steady regimes defined by Q/Q_max = 0.90, 0.63 and 0.33, where Q_max is the maximum measured PAT flow. Flow measurement separated the regimes with coefficients of variation of 2.47–7.61%; pressure measurements provided hydraulic-loading and fluctuation context; accelerometers showed direction-dependent vibration responses; and Hsu–Nielsen testing confirmed AE reproducibility within the ASTM E976 criterion. Field adaptation to five Pelton, Francis and Kaplan plants showed how the same specification logic produced site-specific sensor layouts.