Electrical Drive Radiated Emissions Estimation in Terms of Input Control Using Extreme Learning Machines

Wefky, Ahmed; Espinosa, Felipe; Santiago, Luis de; Revenga, P.; Lázaro, José; Martínez, María Victoria Luna

doi:10.1155/2012/790526

Cited by 6 publications

(4 citation statements)

References 25 publications

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“…EMI signals have been sampled at 125 MHz, the sweep duration, or the capture time, was 100 µs, and time between sweeps, i.e., sweeping time, was 1 second. The relationship between sweeping, sampling and capture times has been explained in [17]. It's noteworthy that authors are not interested in absolute values of radiated emissions.…”

Section: Methodsmentioning

confidence: 99%

“…In order to complete the proposed solution in [17,18], the urban trip illustrated in Figure 9 has been realized with a Think City in Alcala de Henares (Madrid) recording only the on-road car speed by means of an onboard Differential Global Positioning System (DGPS) device. Then the instantaneous speed and calculated acceleration values during the trip have been applied to the proposed model in this paper to predict the corresponding magnetic radiated emissions as shown in Figure 10.…”

Section: Ann Model Developmentmentioning

confidence: 99%

“…Furthermore, such antenna would receive a lot of radiated waves from various interfering sources like: other vehicles, WiFi, GPS, Bluetooth, television broadcast, radio broadcast, mobile networks, satellite networks, and power lines. In previous articles [17,18], authors have already proposed a procedure of three steps to solve this problem as shown in Figure 1. In this paper, the same methodology has been applied on a Renault Twizy and a Think City.…”

Section: Introductionmentioning

confidence: 99%

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On-Road Magnetic Emissions Prediction of Electric Cars in Terms of Driving Dynamics Using Neural Networks

Wefky¹,

Espinosa²,

Leferink³

et al. 2013

PIER

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Abstract-This paper presents a novel artificial neural network (ANN) model estimating vehicle-level radiated magnetic emissions of an electric car as a function of the corresponding driving pattern. Real world electromagnetic interference (EMI) experiments have been realized in a semi-anechoic chamber using Renault Twizy. Time-domain electromagnetic interference (TDEMI) measurement techniques have been employed to record the radiated disturbances in the 150 kHz-30 MHz range. Interesting emissions have been found in the range 150 kHz-3.8 MHz approximately. The instantaneous vehicle speed and acceleration have been chosen to represent the vehicle operational modes. A comparative study of the prediction performance between different static and dynamic neural networks has been done. Results showed that a Multilayer Perceptron (MLP) model trained with extreme learning machines (ELM) has achieved the best prediction results. The proposed model has been used to estimate the radiated magnetic field levels of an urban trip carried out with a Think City electric car.

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Section: Methodsmentioning

confidence: 99%

Section: Ann Model Developmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On-Road Magnetic Emissions Prediction of Electric Cars in Terms of Driving Dynamics Using Neural Networks

Wefky¹,

Espinosa²,

Leferink³

et al. 2013

PIER

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show abstract

“…The above three common methods consider the TEP allocation problem. There are also many other methods from different perspectives studying the related problems, such as [20][21][22]. Therefore, the TEP allocation problem is a hot research issue in many subjects.…”

Section: Introductionmentioning

confidence: 99%

Allocating Tradable Emissions Permits Based on the Proportional Allocation Concept to Achieve a Low-Carbon Economy

Dai

Xie

et al. 2014

Mathematical Problems in Engineering

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A key issue within the emissions trading system is how tradable emissions permits (TEPs) are initially allocated among a set of entities. This study proposes an approach based on the proportional allocation concept to allocate TEPs among a set of decision making units (DMUs). We firstly deduce a TEP allocation set based on the rule that the TEPs allocated to DMUs should be proportional to their environmental contribution. We then obtain the allocation intervals of DMUs from the set, expressing the allocation as the convex combination between the upper and the lower bound. Finally, we define the satisfaction degree as the coefficient of the convex combination, and propose an algorithm based on the max-min fairness of satisfaction degrees to obtain a unique TEP allocation plan. To illustrate our approach, we provide the example of how TEPs are allocated among 30 provincial administrative regions in China. Our findings indicate that our allocation method can be helpful for achieving a saving in energy consumption and reducing emissions. In addition, from the data envelopment analysis perspective, the TEP allocation set can ensure that both each individual DMU and the organization as a whole become efficient under a common set of variable weights.

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Power Quality Analysis Based on Time Series Similarity and Extreme Learning Machine

Jia

Zhang

et al. 2018

Communications in Computer and Information Science

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Electrical Drive Radiated Emissions Estimation in Terms of Input Control Using Extreme Learning Machines

Cited by 6 publications

References 25 publications

On-Road Magnetic Emissions Prediction of Electric Cars in Terms of Driving Dynamics Using Neural Networks

On-Road Magnetic Emissions Prediction of Electric Cars in Terms of Driving Dynamics Using Neural Networks

Allocating Tradable Emissions Permits Based on the Proportional Allocation Concept to Achieve a Low-Carbon Economy

Power Quality Analysis Based on Time Series Similarity and Extreme Learning Machine

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