Evaluation of a Trapezoidal Predictive Controller for a Four-Wire Active Power Filter for Utility Equipment of Metro Railway, Power-Land Substations

The realization of an improved predictive current controller based on a trapezoidal model is described, and the impact of this technique is assessed on the performance of a 2 kW, 21.6 kHz, four-wire, Active Power Filter for utility equipment of Metro Railway, Power-Land Substations. The operation of the trapezoidal predictive current controller is contrasted with that of a typical predictive control technique, based on a single Euler approximation, which has demonstrated generation of high-quality line currents, each using a 400 V DC link to improve the power quality of an unbalanced nonlinear load of Metro Railway. The results show that the supply current waveforms become virtually sinusoidal waves, reducing the current ripple by 50% and improving its power factor from 0.8 to 0.989 when the active filter is operated with a 1.6 kW load. The principle of operation of the trapezoidal predictive controller is analysed together with a description of its practical development, showing experimental results obtained with a 2 kW prototype

Approach of Passive Filters using NSGA II in industrial installations: Part I

The optimization of passive filters in industrial systems has been presented by different computational methods. The objective of this paper is to develop a computational algorithm with NSGA II to select the configuration and design parameters of a set of passive filters for industrial installations. As a methodology, the optimization problem was addressed using three independent objective functions of innovative character for compensation of harmonics through passive filters as a multiobjective problem. The results were the computational solution to this problem that determines a set of Pareto optimal solutions (Frontier). In addition, the computational tool has several new features such as: calculates the parameters that characterize the filters, but also selects the type of configuration and the number of branches of the filter in each candidate bar according to a set of pre-established configurations according to PRODIST-M8 (Brazilian Standard) and IEEE 519-2014. Also determine solutions with good power quality indicators (THD, TDD and NPV) for several characteristic and non-characteristic scenarios of the system that allow to represent: daily variations of the load, and variations of system parameters and filters. It evaluates the cost of energy bills in an industrial power grid that has different operating conditions (characteristic scenarios) and evaluates the economic effect of harmonic filters as reactive power compensators

An Effective Model Predictive Control Method With Self-Balanced Capacitor Voltages for Single-Phase Three-Level Shunt Active Filters

This paper presents an effective model predictive control (MPC) method for single-phase three-level T-type inverter-based shunt active power filters (SAPFs). The SAPF using T-type inverter topology has not been reported in the literature yet. Contrary to most of the existing MPC methods, the proposed MPC method eliminates the need for using weighting factor and additional constraints required for balancing dc capacitor voltages in the cost function. The design of cost function is based on the energy function. Since the factor used in the formulation of the energy function does not have any adverse influence on the performance of the system, the cost function becomes weighting factor free. The weighting factor free based MPC brings simplicity in the practical implementation. The effectiveness of the proposed MPC method has been investigated in steady-state as well as dynamic transients caused by load changes. The theoretical considerations are verified through experimental studies performed on a 3 kVA system

A single-objective predictive control method for a multivariable single-phase three-level NPC converter-based active power filter

© 1982-2012 IEEE. A single-objective predictive control method that deals with four main control objectives applied to a multivariable single-phase three-level neutral-point-clamped converter operating as an active power filter is proposed in this paper. The four control objectives are to self-support the dc-bus voltage under load variations, to compensate the reactive power and the current harmonics, and to balance the dc capacitor voltages by using a predefined combination of the redundant switching states of the converter. The main contribution of the proposed method is that these objectives are accomplished without using weighting factors in the cost function, which eliminates problems such as multiobjective optimization or additional empirical procedures for determination of these factors. As a result, the method is easy to implement and rapidly selects the optimal voltage to improve the dynamic-state performance. Experimental results from a 2-kVA prototype are presented to prove that the method is valid for single-phase compensation. The well-known effect of model parameter errors' issue, which is inherent in predictive control methods, is also tested to confirm that the harmonic distortion in the grid current is below 5% even when the predictive model has a 25% error between actual and theoretically estimated grid impedance values

A Virtual Space Vectors based Model Predictive Control for Three-Level Converters

Three-phase three-level (3-L) voltage source converters (VSC), e.g., neutral-point clamped (NPC) converters, T-type converters, etc., have been deemed to be suitable for a wide range of medium- to high-power applications in microgrids (MGs) and bulk power systems. Compared to their two-level (2-L) counterparts, adopting 3-L VSCs in the MG applications not only reduces the voltage stress across the power semiconductor devices, which allows achieving higher voltage levels, but also improves the quality of the converter output waveforms, which further leads to considerably smaller output ac passive filters. Various control strategies have been proposed and implemented for 3-L VSCs. Among all the existing control methods, finite-control-set model predictive control (FCS-MPC) has been extensively investigated and applied due to its simple and intuitive design, fast-dynamic response and robustness against parameter uncertainties. However, to implement an FCS-MPC on a 3-L VSC, a multi-objective cost function, which consists of a term dedicated specifically to control the dc-link capacitor voltages such that the neutral-point voltage (NP-V) oscillations are minimized, must be designed. Nevertheless, selecting proper weighting factors for the multiple control objectives is difficult and time consuming. Additionally, adding the dc-link capacitor voltages balancing term to the cost function distributes the controller effort among different control targets, which severely impacts the primary goal of the FCS-MPC. Furthermore, to control the dc-link capacitor voltages, additional sensing circuitries are usually necessary to measure the dc-link capacitor voltages and currents, which consequently increases the system cost, volume and wiring complexity as well as reduces overall reliability. To address all the aforementioned challenges, in this dissertation research, a novel FCS-MPC method using virtual space vectors (VSVs), which do not affect the dc-link capacitor voltages of the 3-L VSCs, was proposed, implemented and validated. The proposed FCS-MPC strategy has the capability to achieve inherent balanced dc-link capacitor voltages. Additionally, the demonstrated control technique not only simplifies the controller design by allowing the use of a simplified cost function, but also improves the quality of the 3-L VSC output waveforms. Furthermore, the execution time of the proposed control algorithm was significantly reduced compared to that of the existing one. Lastly, the proposed FCS-MPC using the VSVs reduces the hardware cost and complexity as the additional dc-link capacitor voltages and current sensors are not required, which further enhances the overall system reliability

Comparative Analysis of Multilevel Converters for Medium-Voltage Applications

The electric energy demand has been steadily growing during the last century, and all forecasts indicate that it will keep growing in the following years. Within this frame, and due to all the problems that this demand increase generate in the environment, it is necessary improving the current techniques of electric energy conversion and transmission in order to increase the whole system efficiency. On the other hand, it is also necessary increasing the renewable energy resources exploitation through more efficient generation systems. According to these lines, the power electronics systems that have been installed in the last decades allowed to obtain better efficiency from the renewable natural resources like the wind or the solar power. These systems have also notably improved the quality of the power supplied, reducing the losses through what are known as power quality applications. Power converters are currently essential in any power electronics system. Within them, the multilevel converters specially suppose a breakthrough compared with the classical two level converters, as they allow obtaining voltage and current signals with lower harmonic content, what means fewer losses in high power medium voltage applications. In this Thesis a comparative study of some multilevel converter topologies normally used in high power medium voltage applications is done. The objective is analyzing in detail each topology and comparing it with the rest following different criteria, with the aim to know the advantages and drawbacks of each one and to realize which one is more suitable for each application

Estrategias de Optimización del control predictivo de un convertidor Multinivel-NPC y su implementación en FPGA

Los convertidores de potencia multinivel representan una alternativa ampliamente utilizada en la gestión del flujo de energía eléctrica en sistemas de alta potencia y alta o media tensión, como puedan ser sistemas de generación de energía eólica o, en general, sistemas de generación, transmisión y distribución de energía para aplicaciones industriales. El control de este tipo de convertidores puede abordarse desde distintas estrategias, como las técnicas de Modulación de Espacios Vectoriales, la Eliminación Selectiva de Armónicos, por nombrar algunas. Por otro lado, las técnicas de Control Predictivo basado en el Modelo (MPC) han adquirido una gran relevancia en el ámbito del control de convertidores y máquinas eléctricas, debido a su precisión, rápida respuesta dinámica y la posibilidad de establecer varios objetivos de control simultáneos. La idea básica consiste en predecir el comportamiento del convertidor, utilizando un modelo discreto o descripción matemática del mismo. Por contra, estas técnicas implican una gran carga computacional, debido a que, en cada periodo de muestreo, debe optimizarse una función de coste, mediante la exploración de cada uno de los posibles estados de conmutación del convertidor. El Control Predictivo sobre un Conjunto Finito de Estados (FCS-MPC) supone un caso particular de MPC, donde el algoritmo solo se aplica a un número finito de estados de conmutación del convertidor. Posteriormente, el convertidor es configurado en aquel estado de conmutación óptimo que minimice cierta función de coste y ello, durante cada intervalo de muestreo. Para establecer la importancia de una variable respecto a otras, se incluye un factor de peso para cada uno de los términos de la función de coste. Ajustar los valores óptimos de estos factores de peso, es una etapa crucial en la especificación del algoritmo FCS-MPC, y resulta una difícil tarea, habitualmente resuelta mediante la técnica de ensayo-error. Por ello, en la mayoría de trabajos publicados en este ámbito de investigación, una vez se determinan los factores de peso, estos permanecen siempre constantes. En esta tesis, se propone un proceso general automático de ajuste de estos factores de peso. Para demostrar la viabilidad del método propuesto, se ha aplicado al caso del control FCS-MPC de un convertidor multinivel de voltaje de Punto Neutro Fijo de Tres Niveles (3L-NPC). Se han estudiado diferentes estrategias para realizar este proceso, y finalmente, se ha considerado una solución basada en Redes Neuronales Artificiales (ANN), como la mejor opción para implementar una novedosa estrategia que se ha venido en denominar: Modelo de Control Predictivo Adaptativo (A-MPC) como una versión optimizada del MPC tradicional. La función de coste incluye el error de seguimiento de las corrientes de red, la frecuencia de conmutación de los transistores IGBT, y el desequilibrio de las tensiones del Bus-DC. Consideradas como figuras de mérito, el error de las corrientes generadas, la distorsión armónica total (THD), la frecuencia media de conmutación y el desequilibrio del Bus-DC, junto con las referencias de potencia activa y reactiva, son las entradas de la red neuronal, cuyas salidas serán los factores de peso óptimos. Para reducir la carga computacional, el algoritmo trabaja usando una transformación a componentes alfa-beta de las corrientes y tensiones. Además, en la tesis se propone una metodología de implementación sobre FPGA del algoritmo de control propuesto. Por último, para evaluar las prestaciones del método de ajuste propuesto, se han realizado numerosas simulaciones y se han llevado a cabo experimentos sobre una plataforma de procesamiento conectada a un convertidor multinivel ubicado en el laboratorio de investigación del grupo GEISER