Alessandro Palmieri

The primary regulation of photovoltaic (PV) systems is a current matter of research in the scientific community. In Grid-Feeding operating mode, the regulation aims to track the maximum power point in order to fully exploit the renewable energy sources and produce the amount of reactive power ordered by a hierarchically superior control level or by the local Distribution System Operator (DSO). Actually, this task is performed by Proportional–Integral–Derivative (PID)-based regulators, which are, however, affected by major drawbacks. This paper proposes a novel control architecture involving advanced control theories, like Model Predictive Control (MPC) and Sliding Mode (SM), in order to improve the overall system performance. A comparison with the conventional PID-based approach is presented and the control theories that display a better performance are highlighted.

The new electric power generation scenario, characterized by growing variability due to the greater presence of renewable energy sources (RES), requires more restrictive dynamic requirements for conventional power generators. Among traditional power generators, gas turbines (GTs) can regulate the output electric power faster than any other type of plant; therefore, they are of considerable interest in this context. In particular, the dynamic performance of a GT, being a highly nonlinear and complex system, strongly depends on the applied control system. Proportional–integral–derivative (PID) controllers are the current standard for GT control. However, since such controllers have limitations for various reasons, a model predictive control (MPC) was designed in this study to enhance GT performance in terms of dynamic behavior and robustness to model uncertainties. A comparison with traditional PID-based controllers and alternative model-based control approaches (feedback linearization control) found in the literature demonstrated the effectiveness of the proposed approach.

Model-based control techniques have been gaining more and more interest these days. These complex control systems are mostly based on theories, such as feedback linearization, model predictive control, adaptive and robust control. In this paper the latter approach is investigated, in particular, sliding mode (SM) control is analyzed. While several works on the description and application of SM control on single-input single-output systems can easily be found, its application on multi-input multi-output systems is not examined in depth at the same level. Hence, this work aims at formalizing some theoretical complements about the necessary conditions for the feasibility of the SM control for multi-input-multi-output systems. Furthermore, in order to obtain the desired performance from the control system, a method for parameter tuning is proposed in the particular case in which the relative degree of the controlled channels is equal to one. Finally, a simple control problem example is shown with the aim of stressing the benefits derived from the application of the theoretical complements described here.

Gas Turbines are raising nowadays growing interest in the power generation industry. Being able to regulate their power production faster than any other form of conventional source, they are extremely valuable in compensating stochastic power fluctuations deriving from renewable energy sources in large scale electrical grids, preserving grid stability, etc. In this context, gas turbines dynamical performance plays a key role as it needs to be enhanced compatibly with preserving the machine lifetime. Nonlinear model based control systems can effectively deal with this problem, exploiting the system structure to obtain optimal dynamical performance. Therefore, this paper aims at developing a Sliding Mode model based control for gas turbines and at comparing the results obtained by its application on a heavy duty Ansaldo Gas Turbine model (AE94.3A) with the ones obtained thanks to the traditional controls employed in these applications.

In this paper are presented FPGA-based models of IGBT power converters for hardware in the loop systems. The analytical model of each single power device is straightforwardly obtained from technical specifications. The proposed approach can be considered a viable solution featuring minimal modeling complexity and thus reduced computational efforts, still maintaining high accuracy. The main target is to provide an easy model plant for testing electric drive configurations and control strategies, fault tolerant drives, and so on. The Field-Programmable Gate Arrays (FPGA)-based real-time models have been used to simulate a step down dc-dc converter and a 10kW three-phase inverter for automotive applications. The results carried out using the hardware in the loop system are in good agreement with that obtained from off-line numerical simulations and experimental tests.