The utilization of hydrogen fuel in gas turbines brings significant changes to the thermophysical properties of flue gas, including higher specific heat capacities and an enhanced steam content. Therefore, hydrogen-fueled gas turbines are susceptible to health degradation in the form of steam-induced corrosion and erosion in the hot gas path. In this context, the fault diagnosis of hydrogen-fueled gas turbines becomes indispensable. To the authors’ knowledge, there is a scarcity of fault diagnosis studies for retrofitted gas turbines considering hydrogen as a potential fuel. The present study, however, develops an artificial neural network (ANN)-based fault diagnosis model using the MATLAB environment. Prior to the fault detection, isolation, and identification modules, physics-based performance data of a 100 kW micro gas turbine (MGT) were synthesized using the GasTurb tool. An ANN-based classification algorithm showed a 96.2% classification accuracy for the fault detection and isolation. Moreover, the feedforward neural network-based regression algorithm showed quite good training, testing, and validation accuracies in terms of the root mean square error (RMSE). The study revealed that the presence of hydrogen-induced corrosion faults (both as a single corrosion fault or as simultaneous fouling and corrosion) led to false alarms, thereby prompting other incorrect faults during the fault detection and isolation modules. Additionally, the performance of the fault identification module for the hydrogen fuel scenario was found to be marginally lower than that of the natural gas case due to assumption of small magnitudes of faults arising from hydrogen-induced corrosion.

The deployment of post-combustion CO2 capture on large-scale gas-fired power plants is currently progressing, hence the integration of the power and capture plants requires a good understanding of operational requirements and limitations to support this effort. This article aims to assist research in this area, by studying a micro gas turbine (MGT) integrated with an amine-based post-combustion CO2 capture unit. Both processes were simulated using two different software tools –IPSEpro and Aspen Hysys, and validated against experimental tests. The two MGT models were benchmarked at the nominal condition, and then extended to part-loads (50 and 80 kWe), prior to their integration with the capture plant at flue gas CO2 concentrations between 5 and 10 mol%. Further, the performance of the MGT and capture plant when gas turbine exhaust gases were recirculated was assessed. Exhaust gas recirculation increases the CO2 concentration, and reduces the exhaust gas flowrate and specific reboiler duty. The benchmarking of the two models revealed that the IPSEpro model can be easily adapted to new MGT cycle modifications since turbine temperatures and rotational speeds respond to reaching temperature limits; whilst a detailed rate-based approach for the capture plant in Hysys resulted in closely aligned simulation results with experimental data

Distribution networks (DNs) now operate under tighter conditions due to rising penetration of renewables, active prosumers, and exposure to transmission-level contingencies. Distribution Network Reconfiguration (DNR) has proven effective for reducing losses, improving voltage profiles, and enhancing the resiliency of the grid. This paper introduces a three-stage optimization strategy for DNR, combining topological reconfiguration with reactive power support. The first stage, Reconfiguration of Tie-Line Switches (RTLS), utilizes a Particle Swarm Optimization (PSO) algorithm augmented with a Depth-First Search (DFS) mechanism to identify optimal radial structures that minimize active power losses. Once a viable configuration is established, the process proceeds to the second stage, Shunt Capacitor Sizing (SCS), wherein PSO is again employed to determine optimal capacitor sizing across predefined bus locations. The third stage reexecutes the RTLS process using the updated reactive power profile to assess whether further improvements in loss reduction can be achieved. If a superior topology is discovered, it is adopted as the final configuration; otherwise, the SCS solution is retained. This iterative and feedback-based architecture ensures an effective balance between network efficiency and voltage stability using a heuristic approach. The proposed methodology is validated on the IEEE 33-bus and IEEE 123-bus benchmark systems, as well as a custom 7-bus test case. Comprehensive scenario-based analysis, including normal, heavily, and lightly loaded conditions and varying power factor (PF) cases (good and poor PF), confirms the robustness and effectiveness of the approach in achieving considerable loss minimization and voltage profile improvement. For instance, in heavy-load conditions, active-power losses dropped by 39% and 70% for 33-bus and 123-bus cases, respectively.