Multivariable control of modular multilevel converters with convergence and safety guarantees

Well-designed current control is a key factor in ensuring the efficient and safe operation of modular multilevel converters (MMCs). Even though this control problem involves multiple control objectives, conventional current control schemes are comprised of independently designed decoupled controllers, e.g., proportional-integral (PI) or proportional-resonant (PR). Due to the bilinearity of the MMC dynamics, tuning PI and PR controllers so that good performance and constraint satisfaction are guaranteed is quite challenging. This challenge becomes more relevant in an AC/AC MMC configuration due to the complexity of tracking the single-phase sinusoidal components of the MMC output. In this paper, we propose a method to design a multivariable controller, i.e., a static feedback gain, to regulate the MMC currents. We use a physics-informed transformation to model the MMC dynamics linearly and synthesise the proposed controller. We use this linear model to formulate a linear matrix inequality that computes a feedback gain that guarantees safe and effective operation, including (i) limited tracking error, (ii) stability, and (iii) meeting all constraints. To test the efficacy of our method, we examine its performance in a direct AC/AC MMC simulated in Simulink/PLECS and in a scaled-down AC/AC MMC prototype to investigate the ultra-fast charging of electric vehicles.Comment: Submitted to IEEE Open Journal of the Industrial Electronic

Long-Horizon Nonlinear Model Predictive Control of Modular Multilevel Converters

Modular Multilevel Converters (MMCs) are a topology that can scale several voltage levels to obtain higher efficiency and lower harmonics than most voltage-source converters. MMCs are very attractive for renewable energy applications and fast charging stations for electric vehicles, where they can improve performance and reduce costs. However, due to the complex architecture and the large number of submodules, the current control of modular multilevel converters is a challenging task. The standard solution in practice relies on hierarchical decoupling and single-input-single-output control loops, which are limited in performance. Linearization-based model predictive control was already proposed for current control in MMCs, as it can optimize transient response and better handle constraints. In this paper, we show that the validity of linear MMC models significantly limits the prediction horizon length, and we propose a nonlinear MPC (NMPC) solution for current control in MMCs to solve this issue. With NMPC, we can employ long prediction horizons up to 100 compared to a horizon of 10, which is the limit for the prediction range of a linear MMC model. Additionally, we propose an alternative MMC prediction model and corresponding cost function, which enables directly controlling the circulating current and improves the capacitor voltages’ behavior. Using the state-of-the-art in sequential quadratic programming for NMPC, we show that the developed NMPC algorithm can meet the real-time constraints of MMCs. A performance comparison with a time-varying linearization-based MPC for an MMC topology used in ultra-fast charging stations for electric vehicles illustrates the benefits of the developed approach

Model validation tool for model predictive control applications based on artificial intelligence.

Quantificar a qualidade de um modelo é um problema existente na área de Identificação de Sistemas. Esta atividade, também conhecida como validação, é fundamental nas aplicações onde se utilizam Controladores Preditivos baseados em Modelos, porque estes precisam de um modelo adequado para seu bom funcionamento. Baseado nisto, nesta dissertação são implementados três algoritmos de Inteligência Artificial capazes de predizer, de forma autônoma, quão adequado pode ser um modelo para este tipo de aplicação. Os algoritmos são Árvores de Decisão, Máquina de Suporte de Vetores e Redes Neurais Artificiais. Eles predizem a qualidade do modelo a partir de resultados de outras métricas de validação não existentes. As plantas para a implementação destes algoritmos são: (i) Planta de Clarke (simulada) e (ii) Planta Piloto de Neutralização de pH (real) do Laboratório de Controle de Processos Industriais da Escola Politécnica da Universidade de São Paulo. Em ambos os casos se usa um algoritmo Dynamic Matrix Control - DMC ou sua variante Quadratic Dynamic Matrix Control - QDMC (em caso de se ter restrições) para executar o controle. Como resultado deste trabalho obtiveram-se algoritmos capazes de predizer a qualidade do modelo com uma acurácia de 84,1%, 91,5% e 91,0% para a malha de controle da Planta de Clarke, e de Nível e de pH para a Planta Piloto de Neutralização de pH, respectivamente.Quantifying the quality of a model is an existing problem in the Systems Identification area. This task, also known as validation, is fundamental in applications where Model Predictive Control is used, because they need an adequate model for their proper operation. Based on this need, in this dissertation, the author implements three Artificial Intelligence algorithms that are capable of autonomously predicting how suitable a model can be for this type of application. The algorithms are Decision Trees, Support Vector Machine and Artificial Neural Networks. They predict the quality of the model from the results of other non-existent validation metrics. The plants for the implementation of these algorithms are the Clarke Plant (simulated) and the pH Neutralization Pilot Plant (real) of the Industrial Process Control Laboratory of the Polytechnic School of the University of São Paulo. In both cases, a Dynamic Matrix Control - DMC algorithm or its Quadratic Dynamic Matrix Control - QDMC variant (in case of constrained problems) is used to perform the control. This work results are algorithms capable of predicting the model quality with an accuracy of 84.1%, 91.5%, and 91.0% for the Clarke Plant, and the Level and pH control loops pH of the Neutralization Pilot Plant, respectively

A consensus approach to distributed balancing control of modular multilevel converters

Modular multilevel converters (MMCs) are predominant in novel high and medium power applications, such as renewable energy generation and high-voltage direct-current transmission. This hardware offers better performance than other voltage-source-converters due to its higher efficiency, modular design and lower harmonic content. To achieve the expected results, it is essential to coordinate the actions of all the MMC modules using balancing control methods. Different from the previous solutions, this paper proposes a consensus control approach using a leader-follower topology to solve the balancing control problem for MMCs. The proposed control method guarantees asymptotic convergence of all modules to a consensus state, allows a distributed implementation and increases the robustness against module failures. Simulation results using Matlab show the advantages of the developed method.</p

Practical deadbeat MPC design via controller matching with applications in power electronics

Several applications, including power electronics and electrical machines, require a fast system response, which is typically achieved via deadbeat control. Hence, there has been an interest in developing model predictive control (MPC) algorithms with deadbeat control properties, i.e., finite-time convergence to a set-point, for power electronics applications. In this paper, we design a practical deadbeat MPC via controller matching. We make use of an existing result for tuning the weight matrices of the MPC cost function such that the corresponding unconstrained MPC solution matches a desired deadbeat controller. This approach allows for a positive definite input weight matrix and provides stability and recursive feasibility guarantees for the resulting MPC controller. We additionally propose a vertex relaxation of the matching problem, which reduces conservatism, and a method for enlarging the terminal set of the deadbeat MPC controller. Three benchmark examples from the power electronics field are used to show the effectiveness of the proposed MPC design.</p