Conductive nanocomposites have been explored extensively for structural health monitoring (SHM) due to their self-sensing nature via the piezoresistive effect. Combined with a non-invasive conductivity imaging modality such as electrical impedance tomography (EIT), piezoresistivity is a powerful tool for SHM. To date, however, the combination of the piezoresistive effect and EIT has been limited to just damage detection. From a SHM perspective, it may be more beneficial to pre-emptively predict failure before it occurs. To that end, we propose a novel methodology for failure prediction in nanocomposites using piezoresistive inversion. Our approach makes use of a genetic algorithm (GA) to determine the mechanical state of the structure using conductivity changes observed via EIT. First, a rectangular nanocomposite specimen with a central hole is manufactured. Second, the specimen is loaded in tension to induce stress concentrations near the hole. Third, EIT is used to image the resulting stress concentration-induced conductivity changes near the hole. Fourth, GA-enabled piezoresistive inversion is implemented to determine the underlying displacements from the observed conductivity changes. The strains are then determined from kinematic relations and the stresses from constitutive relations. Lastly, a failure criterion is used to predict failure. By validating our results with finite element analysis and digital image correlation, we demonstrate that the proposed approach can accurately predict the onset of failure and therefore enable unprecedented SHM capabilities in piezoresistive nanocomposites.

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This work addresses the protection of a high voltage DC (HVDC) transmission system, utilizing the modular multilevel converter (MMC) topology in addition to incorporating a hybrid transmission corridor (transmission line including overhead line and cable sections). A solution is proposed for identifying the section within which a DC fault is located for the purpose of maintaining power delivery. A detailed model of the MMC-HVDC system is simulated using PSCAD, and an in depth fault analysis is performed. A characteristic signal is discovered and then implemented into a novel solution. The end result is a fault section identification protectionalgorithm, implementing protective relay coordination to protect the system from false circuit breaker reclose as well as enabling fast system restart for nonpermanent faults. This restart protection algorithm is implemented without the use of a communications channel between converter stations, introducing novelty and quick restart response.

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