Accurate Inverter Error Compensation and Related Self-Commissioning Scheme in Sensorless Induction Motor Drives

This paper presents a technique for accurately identifying and compensating the inverter nonlinear voltage errors that deteriorate the performance of sensorless field-oriented controlled drives at low speed. The inverter model is more accurate than the standard signum-based models that are common in the literature, and the self-identification method is based on the feedback signal of the closed-loop flux observer in dc current steady-state conditions. The inverter model can be identified directly by the digital controller at the drive startup with no extra measures other than the motor phase currents and dc-link voltage. After the commissioning session, the compensation does not require to be tuned furthermore and is robust against temperature detuning. The experimental results, presented here for a rotor-flux-oriented SFOC IM drive for home appliances, demonstrate the feasibility of the proposed solution

Characterization and Compensation of Network-Level Anomalies in Mixed-Signal Neuromorphic Modeling Platforms

Advancing the size and complexity of neural network models leads to an ever increasing demand for computational resources for their simulation. Neuromorphic devices offer a number of advantages over conventional computing architectures, such as high emulation speed or low power consumption, but this usually comes at the price of reduced configurability and precision. In this article, we investigate the consequences of several such factors that are common to neuromorphic devices, more specifically limited hardware resources, limited parameter configurability and parameter variations. Our final aim is to provide an array of methods for coping with such inevitable distortion mechanisms. As a platform for testing our proposed strategies, we use an executable system specification (ESS) of the BrainScaleS neuromorphic system, which has been designed as a universal emulation back-end for neuroscientific modeling. We address the most essential limitations of this device in detail and study their effects on three prototypical benchmark network models within a well-defined, systematic workflow. For each network model, we start by defining quantifiable functionality measures by which we then assess the effects of typical hardware-specific distortion mechanisms, both in idealized software simulations and on the ESS. For those effects that cause unacceptable deviations from the original network dynamics, we suggest generic compensation mechanisms and demonstrate their effectiveness. Both the suggested workflow and the investigated compensation mechanisms are largely back-end independent and do not require additional hardware configurability beyond the one required to emulate the benchmark networks in the first place. We hereby provide a generic methodological environment for configurable neuromorphic devices that are targeted at emulating large-scale, functional neural networks

Implementing UPQC based Intelligent Islanding for the Microgrid System

Increased penetration of small scale renewable energy sources in the electrical distribution network, improvement of power quality has become more critical than where the current harmonics or disturbances and level of voltage can vary widely. For this reason, Custom Power Devices (CPDs) such as the Unified Power Quality Conditioner (UPQC) can be the most appropriate solution used for improving the dynamic performance of the distribution network, where accurate prior knowledge may not be available. Therefore, the main objectives are (i) placement (ii) integration (iii) capacity enhancement and (iv) real time control of the Unified Power Quality Conditioner (UPQC) to improve the power quality of a distributed generation (DG) network connected to the grid or microgrid. A new integration method of the UPQC has been developed: helps to the DGs to deliver quality of power in the case of islanding and help to reintegrate with the grid seamlessly post fault. It perform both control operation such as Detection of Islanding and reconnection techniques, hence, it is termed UPQC?G. The DG Inverter with storage supplies the active fundamental power only and the shunt part of the UPQC compensates the reactive and harmonic power of the load during both interconnected and islanding mode

Implementation of ANN Controller Based UPQC Integrated with Microgrid

This study discusses how to increase power quality by integrating a unified power quality conditioner (UPQC) with a grid-connected microgrid for clean and efficient power generation. An Artificial Neural Network (ANN) controller for a voltage source converter-based UPQC is proposed to minimize the system’s cost and complexity by eliminating mathematical operations such as a-b-c to d-q-0 translation and the need for costly controllers such as DSPs and FPGAs. In this study, nonlinear unbalanced loads and harmonic supply voltage are used to assess the performance of PV-battery-UPQC using an ANN-based controller. Problems with voltage, such as sag and swell, are also considered. This work uses an ANN control system trained with the Levenberg-Marquardt backpropagation technique to provide effective reference signals and maintain the required dc-link capacitor voltage. In MATLAB/Simulink software, simulations of PV-battery-UPQC employing SRF-based control and ANN-control approaches are performed. The findings revealed that the proposed approach performed better, as presented in this paper. Furthermore, the influence of synchronous reference frame (SRF) and ANN controller-based UPQC on supply currents and the dc-link capacitor voltage response is studied. To demonstrate the superiority of the suggested controller, a comparison of percent THD in load voltage and supply current utilizing SRF-based control and ANN control methods is shown

A smart power electronic multiconverter for the residential sector

El futuro de la red incluye la generación distribuida y las tecnologías de red inteligente. Los sistemas de gestión del lado de la demanda (DSM) también serán esenciales para lograr un alto nivel de confiabilidad y robustez en los sistemas de energía. Para hacer eso, es necesario expandir la Infraestructura de medición avanzada (AMI) y los Sistemas de gestión de energía (EMS). La dirección de la tendencia es hacia la creación de centros de recursos energéticos, como el concepto de comunidad inteligente. Este documento presenta un sistema multiconvertidor inteligente para el sector residencial / vivienda con un Sistema de Almacenamiento de Energía Híbrido (HESS) que consta de supercapacitador y batería, y con integración de fuente de energía fotovoltaica (PV) local. El dispositivo funciona como una unidad de energía distribuida ubicada en cada casa de la comunidad, recibiendo puntos de ajuste de energía activos proporcionados por una comunidad inteligente EMS. Este SGA central es responsable de administrar los flujos de energía activa entre la red eléctrica, las fuentes de energía renovables, los equipos de almacenamiento y las cargas existentes en la comunidad. El multiconvertidor propuesto es responsable de cumplir con los puntos de referencia de potencia activa de referencia con la calidad de potencia adecuada; garantizando que los módulos fotovoltaicos locales funcionen con un algoritmo de seguimiento del punto de máxima potencia (MPPT); y prolongando la vida útil de la batería gracias a un funcionamiento cooperativo del HESS. Se ha desarrollado un modelo de simulación para mostrar el funcionamiento detallado del sistema. Finalmente, se implementó un prototipo de la plataforma de multiconversores y se realizaron algunas pruebas experimentales para validarlo.The future of the grid includes distributed generation and smart grid technologies. Demand Side Management (DSM) systems will also be essential to achieve a high level of reliability and robustness in power systems. To do that, expanding the Advanced Metering Infrastructure (AMI) and Energy Management Systems (EMS) are necessary. The trend direction is towards the creation of energy resource hubs, such as the smart community concept. This paper presents a smart multiconverter system for residential/housing sector with a Hybrid Energy Storage System (HESS) consisting of supercapacitor and battery, and with local photovoltaic (PV) energy source integration. The device works as a distributed energy unit located in each house of the community, receiving active power set-points provided by a smart community EMS. This central EMS is responsible for managing the active energy flows between the electricity grid, renewable energy sources, storage equipment and loads existing in the community. The proposed multiconverter is responsible for complying with the reference active power set-points with proper power quality; guaranteeing that the local PV modules operate with a Maximum Power Point Tracking (MPPT) algorithm; and extending the lifetime of the battery thanks to a cooperative operation of the HESS. A simulation model has been developed in order to show the detailed operation of the system. Finally, a prototype of the multiconverter platform has been implemented and some experimental tests have been carried out to validate it.Ministerio de Economía y Competitividad (España) y Fondos FEDER: Proyecto TEC2013-47316-C3-3-PpeerReviewe

Power Quality Improvement and Low Voltage Ride through Capability in Hybrid Wind-PV Farms Grid-Connected Using Dynamic Voltage Restorer

© 2018 IEEE. Translations and content mining are permitted for academic research only. Personal use is also permitted, but republication/redistribution requires IEEE permission.This paper proposes the application of a dynamic voltage restorer (DVR) to enhance the power quality and improve the low voltage ride through (LVRT) capability of a three-phase medium-voltage network connected to a hybrid distribution generation system. In this system, the photovoltaic (PV) plant and the wind turbine generator (WTG) are connected to the same point of common coupling (PCC) with a sensitive load. The WTG consists of a DFIG generator connected to the network via a step-up transformer. The PV system is connected to the PCC via a two-stage energy conversion (dc-dc converter and dc-ac inverter). This topology allows, first, the extraction of maximum power based on the incremental inductance technique. Second, it allows the connection of the PV system to the public grid through a step-up transformer. In addition, the DVR based on fuzzy logic controller is connected to the same PCC. Different fault condition scenarios are tested for improving the efficiency and the quality of the power supply and compliance with the requirements of the LVRT grid code. The results of the LVRT capability, voltage stability, active power, reactive power, injected current, and dc link voltage, speed of turbine, and power factor at the PCC are presented with and without the contribution of the DVR system.Peer reviewe

New techniques to improve power quality and evaluate stability in modern all-electric naval ship power systems

This dissertation focuses on two crucial issues in the design and analysis of the power electronic systems on modern all-electric naval ships, i.e., power quality control and stability evaluation. It includes three papers that deal with active power filter topology, active rectifier control, and impedance measurement techniques, respectively. To mitigate harmonic currents generated by high-power high-voltage shipboard loads such as propulsion motor drives, the first paper proposes a novel seven-level shunt active power filter topology, which utilizes tapped reactors for parallel operations of switching devices. The multi-level system has been implemented in both regular digital simulation and real-time digital simulator for validation. In the second paper, a harmonic compensation algorithm for three-phase active rectifiers is proposed. Based on the theory of multiple reference frames, it provides fast and accurate regulation of selected harmonic currents so that the rectifier draws balanced and sinusoidal currents from the source, even when the input voltages are unbalanced and contain harmonics. Extensive laboratory tests on a 2 kW prototype system verifies the effectiveness of the proposed control scheme. The last paper presents a new technique for impedance identification of dc and ac power electronic systems, which significantly simplifies the procedure for stability analysis. Recurrent neural networks are used to build dynamic models of the system based on a few signal injections, then the impedance information can be extracted using off-line training and identification algorithms. Both digital simulation and hardware tests were used to validate the technique --Abstract, page iv