Active Power Filter Shape Class Model Predictive Controller tuning by Multiobjective Optimization

Author:

Cateriano Yáñez Carlos1ORCID,Richter Jörg2,Pangalos Georg3ORCID,Lichtenberg Gerwald2ORCID,Sanchís Saez Javier4ORCID

Affiliation:

1. Department of Systems Engineering and Automation, Universitat Politècnica de València, Spain, Faculty Life Sciences, Hamburg University of Applied Sciences, Germany, Application Center Power Electronics for Renewable Energy Systems, Fraunhofer Institute

2. Faculty Life Sciences, Hamburg University of Applied Sciences, Germany.

3. Application Center Power Electronics for Renewable Energy Systems, Fraunhofer Institute for Silicon Technology, Germany

4. Department of Systems Engineering and Automation, Universitat Politècnica de València, Spain.

Abstract

As the share of renewable energy sources (RES) in distribution grids increases, several power quality challenges arise. Due to its intermittent nature, RES lead to voltage and frequency fluctuations in the grid that affect power quality. Moreover, as RES are connected via power converters, there is also a higher harmonic distortion pollution introduced by the switching power electronics involved, (Liang, 2017). A proven solution is the implementation of Active Power Filters (APF), which are able to compensate the unbalanced, harmonic, and reactive components of a load under different supply conditions. In order to achieve the desired compensation characteristics, the selection of an appropriate control strategy is critical, (Kumar & Mishra, 2016). Classic APF control strategies achieve said goals, although with struggles under changing load scenarios with limitations on their operational modes, (Weihe, Cateriano Yáñez, Pangalos, & Lichtenberg, 2018).This paper proposes the use of an advanced model-based control method, i.e. Model Predictive Control (MPC), to improve the performance of APF devices. Model-based control methods allow for better performance when the model of the plant is known before hand or through measurements, the MPC extends this further by introducing a cost function that ensures optimal operation even under constraints, (Maciejowski, 2002). References Kumar, P., & Mishra, M. K. (2016). A comparative study of control theories for realizing APFs in distribution power systems. 2016 National Power Systems Conference (NPSC), 1–6. https://doi.org/10.1109/NPSC.2016.7858905 Liang, X. (2017). Emerging Power Quality Challenges Due to Integration of Renewable Energy Sources. IEEE Transactions on Industry Applications, 53(2), 855–866. https://doi.org/10.1109/TIA.2016.2626253 Maciejowski, J. M. (2002). Predictive Control with Constraints. Pearson education. Weihe, K., Cateriano Yáñez, C., Pangalos, G., & Lichtenberg, G. (2018, July). Comparison of Linear State Signal Shaping Model Predictive Control with Classical Concepts for Active Power Filter Design. 167–174. Retrieved from http://www.scitepress.org/PublicationsDetail.aspx?ID=QatbWGUbqSE=&t=1

Publisher

Universitat Politècnica València

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