State shaping model predictive control for harmonic compensation
- Autores: Carlos Cateriano Yáñez
- Directores de la Tesis: Javier Sanchis Sáez (dir. tes.), Gerwald Lichtenberg (dir. tes.)
- Lectura: En la Universitat Politècnica de València ( España ) en 2024
- Idioma: español
- Tribunal Calificador de la Tesis: Eduardo Prieto Araujo (presid.), Carlos Vargas Salgado (secret.), Adrian Gambier (voc.)
- Programa de doctorado: Programa de Doctorado en Automática, Robótica e Informática Industrial por la Universitat Politècnica de València
- Materias:
- Enlaces
- Tesis en acceso abierto en: RiuNet
- Resumen
This thesis is dedicated to developing model-based predictive control concepts for harmonic compensation in power systems with renewable energy sources.
Specifically, these concepts provide a reference compensation current for an active power filter connected at the point of common coupling, thereby enhancing the system's power quality.
Nevertheless, results could be generically applied to control problems where the task is to follow a certain shape of a signal.
The thesis proposes two main control approaches based on model predicitve control (MPC) theory.
The first controller, i.e., the linear state signal shaping model predictive control (LS3MPC), relies on standard quadratic MPC theory.
However, contrary to standard fixed reference control practice, the LS3MPC embeds the desired system dynamics directly into its cost function, using the so-called linear shape class residuals.
This approach allows the LS3MPC' cost function to be more adaptive, providing more dynamic trade-offs, especially when constrained.
By using shape class residuals, the MPC problem ensures that the controlled plant follows the desired dynamics given by the shape class.
In this case, the target dynamics are given by the proposed linear harmonic shape class, i.e, the dynamics of a fundamental harmonic signal of fixed frequency.
From an application perspective, an explicit MPC formulation for the LS3MPC is proposed to enhance its real-time applicability.
The proposed explicit LS3MPC uses an equidistant mesh grid approach in tensor format to approximate the piecewise affine explicit MPC solution.
Using tensor decomposition, the explicitLS3MPC can break the curse of dimensionality, significantly reducing memory burden and trivializing the online point localization problem.
The second controller, i.e., the limit cycle model predictive control (LCMPC), focuses on addressing the shortcomings of the LS3MPC.
Namely, the LCMPC addresses the lack of direct amplitude control by reaching into nonlinear MPC theory.
The LCMPC introduces a nonlinear harmonic shape class based on a supercritical Neimark-Sacker bifurcation normal form.
Similarly to theLS3MPC, the LCMPC also embeds its nonlinear harmonic shape class residual directly in its cost function, providing the same benefits mentioned before.
Regarding system stability, sufficient conditions are developed for a given initial state to ensure that the closed-loop system remains inside the normal form region of attraction for a sufficiently small disturbance.
Both controllers are tested with simulation studies in multiple scenarios, providing consistently satisfactory compensation results.
