1,204 research outputs found
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
On the Reduction of Biases in Big Data Sets for the Detection of Irregular Power Usage
In machine learning, a bias occurs whenever training sets are not
representative for the test data, which results in unreliable models. The most
common biases in data are arguably class imbalance and covariate shift. In this
work, we aim to shed light on this topic in order to increase the overall
attention to this issue in the field of machine learning. We propose a scalable
novel framework for reducing multiple biases in high-dimensional data sets in
order to train more reliable predictors. We apply our methodology to the
detection of irregular power usage from real, noisy industrial data. In
emerging markets, irregular power usage, and electricity theft in particular,
may range up to 40% of the total electricity distributed. Biased data sets are
of particular issue in this domain. We show that reducing these biases
increases the accuracy of the trained predictors. Our models have the potential
to generate significant economic value in a real world application, as they are
being deployed in a commercial software for the detection of irregular power
usage
Insights into tunnel FET-based charge pumps and rectifiers for energy harvesting applications
In this paper, the electrical characteristics of tunnel field-effect transistor (TFET) devices are explored for energy harvesting front-end circuits with ultralow power consumption. Compared with conventional thermionic technologies, the improved electrical characteristics of TFET devices are expected to increase the power conversion efficiency of front-end charge pumps and rectifiers powered at sub-µW power levels. However, under reverse bias conditions the TFET device presents particular electrical characteristics due to its different carrier injection mechanism. In this paper, it is shown that reverse losses in TFET-based circuits can be attenuated by changing the gate-to-source voltage of reverse-biased TFETs. Therefore, in order to take full advantage of the TFETs in front-end energy harvesting circuits, different circuit approaches are required. In this paper, we propose and discuss different topologies for TFET-based charge pumps and rectifiers for energy harvesting applications.Peer ReviewedPostprint (author's final draft
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
- …