Browsing by Author "Poblete Durruty, Pablo Martín"

Now showing 1 - 6 of 6
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    Consensus-Based Distributed Control of a Multilevel Battery Energy Storage System
    (IEEE, 2020) Neira Castillo, Sebastian Felipe; Poblete Durruty, Pablo Martín; Pereda Torres, Javier Eduardo; Nuñez Retamal, Felipe Eduardo
    Battery Energy Storage Systems (BESS) based on modular multilevel cascaded topologies allow splitting the battery array into the converter sub-modules, improving efficiency and reliability of the solution. Modular converters can perform active balance of the battery packs and regulation of the output power through the use of multi-objective controllers commanding the power of each sub-module accordingly. However, despite the hardware modularity of multilevel BESS, the controller is usually based on centralized designs, which present issues in terms of computational load and scalability. This paper proposes a two-layer distributed control scheme, based on a high-level consensus algorithm to perform the State of Charge (SoC) balance and low-level individual multi-variable controllers to regulate the operation of the converter sub-modules. The controller was implemented in a cascaded topology, where each sub-module determines and regulates its output power considering the information of its neighbours. The proposed solution allows to fully exploit the modularity of the converter, distributing the control units in the power modules to improve the overall flexibility and scalability of the system. Simulations results validate the operation of the proposed system, showing how the converter sub-modules distribute its power outputs to equalize the SoC levels. Furthermore, the distribution of the multi-variable controllers reduces the computational burden, as each unit just regulates the operation of the respective sub-module.
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    Denoising and Voltage Estimation in Modular Multilevel Converters Using Deep Neural-Networks
    (2020) Langarica Chavira, Saúl Alberto; Pizarro Lorca, Germán Eduardo; Poblete Durruty, Pablo Martín; Radrigán Sepúlveda, Felipe Ignacio; Pereda Torres, Javier Eduardo; Rodriguez, Jose; Núñez Retamal, Felipe Eduardo
    Modular Multilevel Converters (MMCs) have become one of the most popular power converters for medium/high power applications, from transmission systems to motor drives. However, to operate properly, MMCs require a considerable number of sensors and communication of sensitive data to a central controller, all under relevant electromagnetic interference produced by the high frequency switching of power semiconductors. This work explores the use of neural networks (NNs) to support the operation of MMCs by: i) denoising measurements, such as stack currents, using a blind autoencoder NN; and ii) estimating the sub-module capacitor voltages, using an encoder-decoder NN. Experimental results obtained with data from a three-phase MMC show that NNs can effectively clean sensor measurements and estimate internal states of the converter accurately, even during transients, drastically reducing sensing and communication requirements.
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    Distributed Current Control of Cascaded Multilevel Inverters
    (IEEE, 2019) Poblete Durruty, Pablo Martín; Pereda Torres, Javier Eduardo; Nuñez Retamal, Felipe Eduardo; Aguilera, Ricardo P.
    Multilevel inverters are widely used in the medium voltage energy market. Among them, the Cascaded H-Bridge (CHB) and Modular Multilevel Inverter (MMC) stand out for their modular hardware, which allows the easy replacement of their modules in the case of failures. Normally, these converters are operated with centralized control schemes, which require a large processing capacity, multiple digital outputs and wires for the switching signals of the transistors. The use of a centralized controller reduces the modularity of the system and generates difficulties as the number of inverters connected in series (cells) increases. In this work, a distributed control strategy for CHB converters is proposed, allowing each cell to have an independent local controller, which increases the modularity of the multilevel converter without deteriorating the performance of the system. Extensive simulations are carried out, showing the advantages and disadvantages of the proposed distributed control strategy offers when compared to a PI current controller on dq axes and Shifted PWM. The proposed control scheme is also tested under fault conditions and is finally applied in a Field Oriented Control (FOC) of a permanent magnet synchronous machine (PMSM), in order to evaluate its performance in electrical drives applications.
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    Predictive Optimal Variable-Angle PS-PWM Strategy for Cascaded H-Bridge Converters
    (2024) Poblete Durruty, Pablo Martín; Gajardo Rojas, José Ignacio; Cuzmar Leiva, Rodrigo Hernán; Aguilera, Ricardo P.; Pereda Torres, Javier Eduardo; Lu, Dylan; Márquez, Abraham M.
    Cascaded H-bridge (CHB) converters are an attractive candidate for numerous applications, including static synchronous compensators and next-generation photovoltaic and battery energy storage inverters. Due to its simplicity, scalability, and excellent harmonic performance, phase-shifted pulsewidth modulation (PS-PWM) is one of the preferred modulation strategies for CHB converters. However, the latter advantage might be drastically affected when an unbalanced operation in the H-bridge cells is required, e.g., setting different dc-voltage levels and/or ac-voltage references among cells. This work proposes a predictive optimal variable angle PS-PWM (OVA-PS-PWM) strategy for CHB converters. The proposed OVA-PS-PWM introduces a bilinear dynamic model that describes the impact of the phase-shift angles (PS-angles) over the CHB output voltage harmonics. This model is then employed to formulate an optimal control problem that minimizes the output voltage harmonic distortion. An analytical optimal solution for a PS-angle update rule that applies to CHB converters of any number of cells is derived. As a result, the proposed OVA-PS-PWM updates each PS-angle at every sampling instant, significantly improving the harmonic content of the CHB output voltage even under severely unbalanced operation scenarios. Experimental results are provided with a CHB converter with nine cells to verify the effectiveness of the proposed optimal modulation strategy.
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    Sequential Phase-Shifted Model Predictive Control for a Multilevel Converter with Integrated Battery Energy Storage
    (IEEE, 2020) Neira Castillo, Sebastian Felipe; Poblete Durruty, Pablo Martín; Cuzmar Leiva, Rodrigo Hernán; Pereda Torres, Javier Eduardo; Aguiler, R. P.
    Cascaded converters have risen as a suitable solution for the connection of Utility-scale Battery Energy Storage Systems (BESS) to the grid. These converters allow to split the battery array into the power modules, reducing the total series-connected battery cells and improving the reliability of the system. Different types of modules have been proposed to integrate the batteries in the converter. The three-port full-bridge module connects the batteries through a second deport decoupled from the harmful low-frequency oscillations and current peaks. However, the multi-variable controller required to manage the power interaction between the battery and the grid presents a challenge in terms of computational burden and scalability. This work proposes the use of the Sequential Phase-Shifted Model Predictive Control (PS-MPC) in a multilevel BESS implementation using three-port full-bridge modules. The proposed controller outperforms a standard FCS-MPC, as it obtains the optimal duty cycles for the operation of the converter with the same fast dynamic response, but also with the fixed spectrum of the PS-PWM and low computational burden, which facilitates its scalability to multilevel BESS with a large number of cells. Simulation results show the ability of the system to exchange different amounts of power with the grid, ensuring the best battery operational conditions.
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    Sequential Phase-Shifted Model Predictive Control for Modular Multilevel Converters
    (2021) Poblete Durruty, Pablo Martín; Neira Castillo, Sebastian Felipe; Aguilera, Ricardo P.; Pereda Torres, Javier Eduardo; Pou, Josep
    Model predictive control has emerged as a promising approach to govern modular multilevel converters (MMCs), due to its flexibility to include multiple control objectives and simple design process. However, this control scheme presents relevant issues, such as high computational complexity and variable switching frequency. This work proposes a sequential phase-shifted model predictive control (PS-MPC) for MMCs. The key novelty of this proposal lies in the way the predictive control strategy is formulated to fully exploit a phase-shifted pulsewidth modulation technique, by means of an appropriate choice of synchronized average models for each carrier. In this way, the proposed predictive controller obtains independent optimal modulating signals for each carrier in a sequential manner, by solving an optimization problem with reduced computational effort independent of the number of submodules. This allows one to formulate the optimal control problem to achieve multiple control objectives, similarly to the finite-control-set MPC (FCS-MPC). Nevertheless, the MMC governed with the proposed PS-MPC generates an output voltage with fix-spectrum and operates with an even power loss distribution among semiconductors in steady-state, outperforming the standard FCS-MPC strategy. Experimental results are provided to verify the proposed PS-MPC effectiveness when governing a three-phase MMC with four half-bridges per stack.