This paper presents the real-time monitoring of the degradation parameters of the DC-DC boost converter by utilizing the Digital Twin (DT) method. The DT is a real-time high-fidelity mathematical (digital) model that runs in parallel to the physical DC-DC boost converter, providing information for its monitoring and control using the measurement data coming from the sensors already existing in the asset. The digital model is obtained by means of the Backward Euler numerical methodology for discrete-time representation of the mathematical switching model of the physical system. The estimation of degradation parameters of the physical system is based on the data analysis carried out by comparing the data measured by the sensors existing in the physical system and the data coming from the digital model. The data analysis is done by applying the heuristic algorithm like Particle Swarm Optimization (PSO) method. The proposed method is validated through the digital-switching model realized by means of the FPGA and it runs on a suitable control board. Experimental tests on the converter prototype are illustrated. For further validation, a sensitivity analysis is carried out.

In the field of energy generation from renewables, such as wind power, it is essential to avoid service interruption, as well as to ensure scheduled maintenance. For this reason, it is necessary to monitor critical components, such as the power electronic converters (PECs). This paper proposes an online real-time monitoring method based on Digital Twin (DT) concept applied to PECs generally used for energy conversion in wind offshore systems, focusing on the parameters estimation of an AC-DC 6-phase converter. The DT is realized as a real-time digital model (RTDM) that runs in parallel to the physical 6-phase boost rectifier for its entire lifecycle. The Typhoon solver is used to implement the RTDM of the 6-phase boost rectifier to reach a solving time within 1μs. By applying the heuristic optimization algorithm like the Particle Swarm Optimization (PSO) method, the actual value of the monitored on-state resistances of the switches, the stator impedance of the permanent magnet synchronous machine (PMSM), and the DC-link capacitance can be found. The proposed monitoring method is validated through the RTDM implemented on Typhoon HIL 606, which is equipped with a control board that runs the same physical asset control algorithm. Experimental tests are illustrated.

In this paper, the analysis and the design of a high-efficiency power electronic conversion system for offshore wind applications are presented. This system is composed of a 6-phase AC–DC converter based on the SiC power semiconductors, to be used to control the achievable power from the wind turbine electrical generator. Thanks to the phase redundancy, the proposed boost rectifier is suitable for applications where reliability and fault tolerance capability are the main targets. To select the appropriate power semiconductor devices, voltage and current ratings have to be determined. After that, the power loss equations are derived in order to evaluate the conversion efficiency. To design the appropriate DC-bus capacitor configuration, an analytical investigation is carried out by estimating the DC-bus RMS current and the voltage ripple. Finally, the thermal sizing of the system is calculated to identify a suitable heatsink. To validate the proposed analysis, the analytical results are compared to simulation ones using the Plexim/PLECS tool in the MATLAB/Simulink environment. For further validation, a prototype of the converter is built and the experimental results are carried out. The results demonstrate that the peak efficiency of the 6-phase boost rectifier can reach 98% at 100 kHz switching frequency.

The Digital Twin (DT) technology is transforming the energy conversion industry by replicating in real-time the behavior of physical systems by means of high-fidelity digital models (DMs). Specifically, close monitoring of parameters influencing the health status of components prone to failure is essential for the application of predictive maintenance strategies. This capability is made achievable through the use of the DT concept and the advanced optimization algorithms introduced in this work. This paper presents the parameters’ estimation approach based on the DT method applied to 3-phase AC-DC switching converters. First, a Particle Swarm Optimization (PSO) algorithm is employed to estimate the characteristics of the L-type AC filter and the DC capacitance. The same approach has been repeated using the Genetic Algorithm (GA) and the Simulated Annealing (SA) as optimization algorithms. Balanced and unbalanced situations have been tested to demonstrate the robustness and feasibility of the proposal and the comparison between the three proposed optimization algorithms has been carried out. The results show the potential of the procedure for application in system identification and condition monitoring.

The paper deals with the control structure and implementation of a fully integrated Two-Phase Cooling system for power converters. A suitable testbed has been properly designed and built to perform the experimental campaign for the performance evaluation of the Neural Network based control structure applied to an advanced multivariable Two-Phase Cooling system. With respect to traditional cooling approaches, the proposed arrangement allows a greater extraction of the heat at a very low flow rate of the cooling fluid, even with standard industrial-grade heat-sinks. The technology could be implemented for the next generation of power electronics converters. The controllability of such a cooling system is still an open issue in many cases, and the proposed approach tries to suggest a feasible approach, which could be implemented on an industrial grade control board.