In this paper two different advanced control approaches for a pressurized SOFC hybrid system are investigated and compared against traditional proportional–integral–derivative (PID). Both advanced control methods use model predictive control (MPC): in the first case, the MPC has direct access to the plant manipulated variables, in the second case the MPC operates on the setpoints of PIDs which control the plant. In the second approach the idea is to use MPC at the highest level of the plant control system to optimize the performance of bottoming PIDs, retaining system stability and operator confidence. Two MIMO (multi-input multi-output) controllers were obtained: fuel cell power and cathode inlet temperature are the controlled variables; fuel cell by-pass flow, current and fuel mass flow rate (the utilization factor kept constant) are the manipulated variables. The two advanced control methods were tested and compared against the conventional PID approach using a SOFC hybrid system model. Then, the MPC controller was implemented in the hybrid system emulator test rig developed by the Thermochemical Power Group (TPG) at the University of Genoa. Experimental tests were carried out to compare MPC against classic PID method: load following tests were carried out. Ramping the fuel cell load from 100% to 80% and back, keeping constant the target of the cathode inlet temperature, the MPC controller was able to reduce the mismatch between the actual and the target values of the cathode inlet temperature from 7 K maximum of the PID controller to 3 K maximum, showing more stable behavior in general.

Monitoring aircraft performance in a fleet is fundamental to ensure optimal operation and promptly detect anomalies that can increase fuel consumption or compromise flight safety. Accurate failure detection and life prediction methods also result in reduced maintenance costs. The major challenges in fleet monitoring are the great amount of collected data that need to be processed and the variability between engines of the fleet, which requires adaptive models. In this paper, a framework for monitoring, diagnostics, and health management of a fleet of aircrafts is proposed. The framework consists of a multi-level approach: starting from thresholds exceedance monitoring, problematic engines are isolated, on which a fault detection system is then applied. Different methods for fault isolation, identification, and quantification are presented and compared, and the related challenges and opportunities are discussed. This conceptual strategy is tested on fleet data generated through a performance model of a turbofan engine, considering engine-to-engine and flight-to-flight variations and uncertainties in sensor measurements. Limitations of physics-based methods and machine learning techniques are investigated and the needs for fleet diagnostics are highlighted.

Despite the high efficiency and flexibility of fuel cells, which make them an attractive technology for the future energy generation, their economic competitiveness is still penalized by their short lifetime, due to multiple degradation phenomena. As a matter of fact, electrochemical performance of solid oxide fuel cells (SOFCs) is reduced because of different degradation mechanisms, which depend on operating conditions, fuel and air contaminants, impurities in materials, and others. In this work, a real-time, one dimensional (1D) model of a SOFC is used to simulate the effects of voltage degradation in the cell. Different mechanisms are summarized in a simple empirical expression that relates degradation rate to cell operating parameters (current density, fuel utilization and temperature), on a localized basis. Profile distributions of different variables during cell degradation are analyzed. In particular, the effect of degradation on current density, temperature, and total resistance of the cell are investigated. An analysis of localized degradation effects shows how different parts of the cell degrade at a different time rate, and how the various profiles are redistributed along the cell as consequence of different degradation rates.

The hybridization of solid oxide fuel cell (SOFC) and gas turbine technologies provides an increase in system efficiency and economic performance. The latter aspect is significantly affected by fuel cell degradation, due to several mechanisms. However, hybrid systems allow different control strategies to minimize degradation effects on system performance and their impact on economic feasibility. A real-time distributed model of a SOFC was used to simulate fuel cell degradation in the cases of a standalone stack and a hybrid configuration, in the latter of which the numerical model is normally coupled with the hybrid system hardware components of the National Energy Technology Laboratory (NETL) Hyper facility. The results showed how in a hybrid system it is possible, with an appropriate strategy, to maintain constant voltage even if the cell is degrading, reducing degradation rate during time. At constant power demand, fuel cell life could be significantly extended using the operating strategies allowed by coupling with a turbine (an order of magnitude longer than a standalone fuel cell), maintaining high system efficiency despite fuel cell degradation.

Fuel cell gas turbine hybrids present significant challenges in terms of system control because of the coupling of different timescale phenomena. Hence, the importance of studying the integrated system dynamics is critical. With the aim of safe operability and efficiency optimization, the cold air bypass valve was considered an important actuator since it affects several key parameters and can be very effective in controlling compressor surge. Two different tests were conducted using a cyber-physical approach. The Hybrid Performance (HyPer) facility couples gas turbine equipment with a cyber physical solid oxide fuel cell in which the hardware is driven by a numerical fuel cell model operating in real time. The tests were performed moving the cold air valve from the nominal position of 40% with a step of 15% up and down, while the system was in open loop, i.e. no control on turbine speed or inlet temperature. The effect of the valve change on the system was analyzed and transfer functions were developed for several important variables such as cathode mass flow, total pressure drop and surge margin. Transfer functions can show the response time of different system variables, and are used to characterize the dynamic response of the integrated system. Opening the valve resulted in an immediate positive impact on pressure drop and surge margin. A valve change also significantly affected fuel cell temperature, demonstrating that the cold air bypass can be used for thermal management of the cell.

The market for the small-scale micro gas turbine is expected to grow rapidly in the coming years. Especially, utilization of commercial off-the-shelf components is rapidly reducing the cost of ownership and maintenance, which is paving the way for vast adoption of such units. However, to meet the high-reliability requirements of power generators, there is an acute need of a real-time monitoring system that will be able to detect faults and performance degradation, and thus allow preventive maintenance of these units to decrease downtime. In this paper, a micro gas turbine based combined heat and power system is modelled and used for development of physics-based diagnostic approaches. Different diagnostic schemes for performance monitoring of micro gas turbines are investigated.

The coupling of a pressurized solid oxide fuel cell (SOFC) and a gas turbine has been proven to result in extremely high efficiency and reduced emissions. The presence of the gas turbine can improve system durability compared to a standalone SOFC, because the turbomachinery can supply additional power as the fuel cell degrades to meet the power request. Since performance degradation is an obstacles to SOFC systems commercialization, the optimization of the hybrid system to mitigate SOFC degradation effects is of great interest. In this work, an optimization approach was used to innovatively study the effect of gas turbine size on system durability for a 400 kW fuel cell stack. A larger turbine allowed a bigger reduction in SOFC power before replacing the stack, but increased the initial capital investment and decreased the initial turbine efficiency. Thus, the power ratio between SOFC and gas turbine significantly influenced system economic results.

Since aeronautic transportation is responsible for a rising share of polluting emissions, it is of primary importance to minimize the fuel consumption any time during operations. From this perspective, continuous monitoring of engine performance is essential to implement proper corrective actions and avoid excessive fuel consumption due to engine deterioration. This requires, however, automated systems for diagnostics and decision support, which should be able to handle large amounts of data and ensure reliability in all the multiple conditions the engines of a fleet can be found in. In particular, the proposed solution should be robust to engine-to-engine deviations and different sensors availability scenarios. In this paper, a probabilistic Bayesian network for fault detection and identification is applied to a fleet of engines, simulated by an adaptive performance model. The combination of the performance model and the Bayesian network is also studied and compared to the probabilistic model only. The benefit in the suggested hybrid approach is identified as up to 50% higher accuracy. Sensors unavailability due to manufacturing constraints or sensor faults reduce the accuracy of the physics-based method, whereas the Bayesian model is less affected.