Discrete modeling and control of a versatile power electronic test bench with special focus on central photovoltaic inverter testing
Biskoping, Matthias; de Doncker, Rik W. (Thesis advisor); Monti, Antonello (Thesis advisor)
2019 : ISEA (2018, 2019)
Book, Dissertation / PhD Thesis
In: Aachener Beiträge des ISEA 117
Page(s)/Article-Nr.: 1 Online-Ressource (xvi, 236 Seiten) : Illustrationen, Diagramme
Dissertation, Rheinisch-Westfälische Technische Hochschule Aachen, 2018
The thesis proposes a new methodology for predicting the next plant state, which is an enhancement of the classical predictor to design rigid control schemes. The new model based prediction method takes the full pulse width modulation cycle into account and thus prevents the subsequent control scheme from compensating the prediction error. This leads to a considerable smoother output of the manipulated variable (duty cycle) and hence, a lower total harmonic distortion of the associated controlled output variable. The thesis shows the solutions for the single phase as well as the three-phase LC and LCL filters and highlights several symmetries. These new methods lead to lower filter requirements in conjunction with fast control loops and still fulfilling the grid requirements. Furthermore, it is shown for the first time, how the discretized system equation can be used to design a general discrete cascaded control scheme based exclusively on the discrete matrix entries. The design procedure is presented for the LC as well as for the LCL filter, but can easily be applied to similar problems with the same input and output description. Additionally, it is shown, how the discrete description of the dynamic behavior can be arranged in a way, that it shows the same properties as the continuous system. Similar approaches have been presented before for the L and LC filter, but not in the most general way, as presented in this thesis. Further, a solution for the third order system (LCL filter) is presented for the first time. Finally, the discrete control systems are developed using the advanced predictor and their performance capability with regard to different time events and a sensitivity analysis is presented.