Javier Munoz

Bio: Javier Munoz is an academic researcher from University of Talca. The author has contributed to research in topics: Model predictive control & AC power. The author has an hindex of 25, co-authored 217 publications receiving 2201 citations. Previous affiliations of Javier Munoz include Instituto Politécnico Nacional & University of Concepción.

Design of a Discrete-Time Linear Control Strategy for a Multicell UPQC

Abstract: A discrete-time linear control strategy for a multilevel three-phase unified power quality conditioner (UPQC) based on single-phase power cells is presented. The multi-variable, nonlinear, and coupled features of these topologies make the control strategy design a difficult task. Controlling this kind of system with single-variable linear controllers-as proposed in this work-presents significant advantages compared with other approaches as simplicity in the design steps due to the large amount of tools developed for this kind of schemes. Particularly, a classic design method based on the root locus approach is used to choose the controllers parameters in order to achieve a given dynamical behavior. Compensation of reactive power and fundamental frequency disturbances is presented in this paper as part of a general control strategy for multilevel active power filters. The proposed control strategy is implemented on the TMS320C6713 DSP-based system for a low-power laboratory prototype, and thus the controllers design is carried out on the discrete-time and -frequency domain. Also, due to the inherent asymmetries among the power cells in a modular topology, a dedicated local control strategy is proposed to ensure a symmetrical distribution of the power among the power cells. This feature allows the semiconductor devices of each module to operate under the same voltage and current ratings. Simulated and experimental results showing stationary and transient conditions demonstrate the feasibility of the control scheme.

Recent Advances and Industrial Applications of Multilevel Converters

Abstract: Multilevel converters have been under research and development for more than three decades and have found successful industrial application. However, this is still a technology under development, and many new contributions and new commercial topologies have been reported in the last few years. The aim of this paper is to group and review these recent contributions, in order to establish the current state of the art and trends of the technology, to provide readers with a comprehensive and insightful review of where multilevel converter technology stands and is heading. This paper first presents a brief overview of well-established multilevel converters strongly oriented to their current state in industrial applications to then center the discussion on the new converters that have made their way into the industry. In addition, new promising topologies are discussed. Recent advances made in modulation and control of multilevel converters are also addressed. A great part of this paper is devoted to show nontraditional applications powered by multilevel converters and how multilevel converters are becoming an enabling technology in many industrial sectors. Finally, some future trends and challenges in the further development of this technology are discussed to motivate future contributions that address open problems and explore new possibilities.

Model Predictive Control for Power Converters and Drives: Advances and Trends

Abstract: Model predictive control (MPC) is a very attractive solution for controlling power electronic converters. The aim of this paper is to present and discuss the latest developments in MPC for power converters and drives, describing the current state of this control strategy and analyzing the new trends and challenges it presents when applied to power electronic systems. The paper revisits the operating principle of MPC and identifies three key elements in the MPC strategies, namely the prediction model, the cost function, and the optimization algorithm. This paper summarizes the most recent research concerning these elements, providing details about the different solutions proposed by the academic and industrial communities.

Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq

Abstract: Single-cell expression profiles of melanoma Tumors harbor multiple cell types that are thought to play a role in the development of resistance to drug treatments. Tirosh et al. used single-cell sequencing to investigate the distribution of these differing genetic profiles within melanomas. Many cells harbored heterogeneous genetic programs that reflected two different states of genetic expression, one of which was linked to resistance development. Following drug treatment, the resistance-linked expression state was found at a much higher level. Furthermore, the environment of the melanoma cells affected their gene expression programs. Science, this issue p. 189 Melanoma cells show transcriptional heterogeneity. To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.