Machine learning-based interactive dynamic resilience assessment for complex hydropower systems

Complex hydropower systems are vital infrastructure in modern societies. Because of the risks associated with the operation of hydropower plants and dam structures, it is necessary to assess the dynamic resilience of these systems by considering the effects of faulty equipment and/or extreme environmental events. Common approach is to evaluate the performance of system components when exposed to a set of scenarios, combining various equipment faults in adverse conditions (e.g. flooding, earthquake). The evaluation is usually carried out with specific needs: (1) establishing design criteria; (2) providing real-time operation of the hydropower system. However, these specific needs may limit the application in complex circumstances, as they require repetition of evaluation system performance with a widespread of input datasets. The authors propose a novel methodology for interactive dynamic resilience assessment based on machine learning techniques and big data. The methodology is based on producing an extensive computational dataset for various scenario assessment and providing a machine learning-based tool for rapid search within the dataset previously simulated. These scenarios are then evaluated interactively, without subsequent simulations, against user-defined resilience metrics. This way, an operation manager may evaluate the dynamic resilience of the hydropower system for various operational constraints in timely manner. The Pirot hydropower system located in Serbia is analysed and findings are presented. The results indicate that the proposed search methodology enhances the traditional simulation model for dynamic resilience assessment by reducing the time required for series of simulation runs, thereby enabling faster improvements in system resilience.