Resonant power converters in contactless energy transfer: electric vehicle and renewable energy processing

Резонансные преобразователи стали популярны в начале 80-х а позже ими пренебрегали. Их достижения вспомнены сейчас, когда оказалось, что они незаменимы для беспроводной передачи энергии. Резонанс широко употребляется в радиосообщениях, но его новейшая цель, быть инструментом силовой электроники высокого КПД. В этой статье показаны базисные принципы, помогающие приложить идей зарядки батареи электрических/гибридных автомобилей, как в стационарном, так и в динамическом беспроводном режиме. Представлены и другие идеи, напр., замена постоянных магнитов (синхронных) ветреных генераторов. Идеализированный Резонансный Преобразователь Мощности использован для определения режимов с высшим КПД, также для трансформатора с слабой магнитной связью. Предлагается незамедленное управление резонансного преобразователя (с прогнозированием).The resonant converters became popular in the early 1980s, and were quite overlooked later on. Their achievements are remembered recently, when they turned to be irreplaceable for the wireless transfer of energy. The resonance is widely used in the radio-communications but its recent target is to be a high efficiency instrument for the power electronics. In this article, some basic principles are shown that help to implement the ideas of electric/hybrid cars battery charging at distance, both in static and in dynamic (on-line) mode. Other ideas are presented too, e.g. permanent magnets substitution for the (synchronous) wind generators. The idealized Resonant Power Converter is used to define the most efficient modes of operation, also for loosely coupled transformer. Аn instantaneous (predictive) control of the converter, is suggested

Impact of Biases in Big Data

The underlying paradigm of big data-driven machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. Is having simply more data always helpful? In 1936, The Literary Digest collected 2.3M filled in questionnaires to predict the outcome of that year's US presidential election. The outcome of this big data prediction proved to be entirely wrong, whereas George Gallup only needed 3K handpicked people to make an accurate prediction. Generally, biases occur in machine learning whenever the distributions of training set and test set are different. In this work, we provide a review of different sorts of biases in (big) data sets in machine learning. We provide definitions and discussions of the most commonly appearing biases in machine learning: class imbalance and covariate shift. We also show how these biases can be quantified and corrected. This work is an introductory text for both researchers and practitioners to become more aware of this topic and thus to derive more reliable models for their learning problems

The Challenge of Non-Technical Loss Detection using Artificial Intelligence: A Survey

Detection of non-technical losses (NTL) which include electricity theft, faulty meters or billing errors has attracted increasing attention from researchers in electrical engineering and computer science. NTLs cause significant harm to the economy, as in some countries they may range up to 40% of the total electricity distributed. The predominant research direction is employing artificial intelligence to predict whether a customer causes NTL. This paper first provides an overview of how NTLs are defined and their impact on economies, which include loss of revenue and profit of electricity providers and decrease of the stability and reliability of electrical power grids. It then surveys the state-of-the-art research efforts in a up-to-date and comprehensive review of algorithms, features and data sets used. It finally identifies the key scientific and engineering challenges in NTL detection and suggests how they could be addressed in the future

A battery-less, self-sustaining RF energy harvesting circuit with TFETs for µW power applications

This paper proposes a Tunnel FET (TFET) power management circuit for RF energy harvesting applications. In contrast with conventional MOSFET technologies, the improved electrical characteristics of TFETs promise a better behavior in the process of rectification and conversion at ultra-low power (µW) and voltage (sub-0.25 V) levels. RF powered systems can not only benefit from TFETs in front-end rectifiers by harvesting the surrounding energy at levels where conventional technologies cannot operate but also in the minimization of energy required by the power management circuit. In this work we present an energy harvesting circuit for RF sources designed with TFETs. The TFET controller emulates an adequate impedance at the output of the rectifier in order to allow maximum transfer of power from the RF source to the input of the boost converter. The output load is activated once the output capacitor reaches a voltage value of 0.5 V. The results show an efficiency boost of 89 % for an output load consuming 1 µW with an available RF power of -25 dBm.Postprint (published version

Using FCA to Suggest Refactorings to Correct Design Defects

Design defects are poor design choices resulting in a hard-to- maintain software, hence their detection and correction are key steps of a\ud disciplined software process aimed at yielding high-quality software\ud artifacts. While modern structure- and metric-based techniques enable\ud precise detection of design defects, the correction of the discovered\ud defects, e.g., by means of refactorings, remains a manual, hence\ud error-prone, activity. As many of the refactorings amount to re-distributing\ud class members over a (possibly extended) set of classes, formal concept\ud analysis (FCA) has been successfully applied in the past as a formal\ud framework for refactoring exploration. Here we propose a novel approach\ud for defect removal in object-oriented programs that combines the\ud effectiveness of metrics with the theoretical strength of FCA. A\ud case study of a specific defect, the Blob, drawn from the\ud Azureus project illustrates our approach

Is Big Data Sufficient for a Reliable Detection of Non-Technical Losses?

Non-technical losses (NTL) occur during the distribution of electricity in power grids and include, but are not limited to, electricity theft and faulty meters. In emerging countries, they may range up to 40% of the total electricity distributed. In order to detect NTLs, machine learning methods are used that learn irregular consumption patterns from customer data and inspection results. The Big Data paradigm followed in modern machine learning reflects the desire of deriving better conclusions from simply analyzing more data, without the necessity of looking at theory and models. However, the sample of inspected customers may be biased, i.e. it does not represent the population of all customers. As a consequence, machine learning models trained on these inspection results are biased as well and therefore lead to unreliable predictions of whether customers cause NTL or not. In machine learning, this issue is called covariate shift and has not been addressed in the literature on NTL detection yet. In this work, we present a novel framework for quantifying and visualizing covariate shift. We apply it to a commercial data set from Brazil that consists of 3.6M customers and 820K inspection results. We show that some features have a stronger covariate shift than others, making predictions less reliable. In particular, previous inspections were focused on certain neighborhoods or customer classes and that they were not sufficiently spread among the population of customers. This framework is about to be deployed in a commercial product for NTL detection.Comment: Proceedings of the 19th International Conference on Intelligent System Applications to Power Systems (ISAP 2017

Wireless Energy Transfer with Three-Phase Magnetic Field System: Experimental Results

In this paper a three-phase magnetic field system is applied to the wireless power transfer system. The research is directed not only to the distribution of the magnetic field but to optimize the energy transfer efficiency, and to reduce the electromagnetic field influence to the surroundings. The development of the future intelligent transportation system depends on the electric mobility, namely, the individual or the public electric vehicles. It is crucial to achieve progress in the batteries and the battery charging, especially through a wireless power transfer technology. The study of the magnetic field is important in this technology. The energy transfer efficiency depends of the alignment, the size of the coils, the spatial orientation of the magnetic field, the detachment and the tilt between the windings