Search query: intelligent power module

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abstract: Due to practical limitations in the manufacturing of power semiconductor dies, high power modules are composed of several dies in parallel in order to meet the desired load current requirements. With careful attention to the design, modern insulated gate bipolar transistor (IGBT) based power modules feature relatively balanced current distribution amongst the parallel dies. However, owing to the increased switching speeds of wide bandgap devices, i.e. silicon carbide (SiC) it is challenging to design the package to achieve both low loss and balanced operation. In this article, a technique is proposed where the individual dies in a multidie power module can be selectively driven by a closely integrated gate buffer. Amongst the benefits achieved by selectively driving the die gates, a profiling of the power loss within the intelligent power module is enabled. Furthermore, a practical technique to estimate the individual die temperatures is presented, and using the same method, the on state voltage of the power module during load current conduction can be estimated. Finally, it is experimentally demonstrated that the combination of individual junction temperature estimation and the selective gate driving can be used to increase the power density of the power module by better utilizing the component dies.
author_names: Julio Brandelero

Jeffrey Ewanchuk

Stefan V Mollov
title: Selective Gate Driving in Intelligent Power Modules
venue: IEEE Transactions on Power Electronics
year: 2021

abstract: Nonintrusive load monitoring (NILM) is the de facto technique for extracting device level power consumption fingerprints at (almost) no cost from only aggregated mains readings. Specifically, there is no need to install an individual meter for each appliance. However, a robust NILM system should incorporate a precise appliance identification module that can effectively discriminate between various devices. In this context, this paper proposes a powerful method to extract accurate power fingerprints for electrical appliance identification. Rather than relying solely on time domain (TD) analysis, this framework abstracts the phase encoding of the TD description of power signals using a two dimensional (2D) representation. This allows mapping power trajectories to a novel 2D binary representation space, and then performing a histogramming process after converting binary codes to new decimal representations. This yields the final histogram of 2D phase encoding of power signals, namely, 2D PEP. An empirical performance evaluation conducted with three realistic power consumption databases collected at distinct resolutions indicates that the proposed 2D PEP descriptor achieves outperformance for appliance identification in comparison with other recent techniques. Accordingly, high identification accuracies are attained on the GREEND, UK DALE, and WHITED data sets, where 99.54% 98.78% and 100% rates have been achieved, respectively, using the proposed 2D PEP descriptor.
author_names: Yassine Himeur

Abdullah Alsalemi

Faycal Bensaali

Abbes Amira
title: An intelligent nonintrusive load monitoring scheme based on 2D phase encoding of power signals
venue: Int. J. Intell. Syst.
year: 2021

abstract: This paper investigates an intelligent reflecting surface (IRS) aided cooperative communication network, where the IRS exploits large reflecting elements to proactively steer the incident radio frequency wave towards destination terminals (DTs) As the number of reflecting elements increases, the reflection resource allocation (RRA) will become urgently needed in this context, which is due to the non ignorable energy consumption. The goal of this paper, therefore, is to realize the RRA besides the active passive beamforming design, where RRA is based on the introduced modular IRS architecture. The modular IRS consists with multiple modules, each of which has multiple reflecting elements and is equipped with a smart controller, all the controllers can communicate with each other in a point to point fashion via fiber links. Consequently, an optimization problem is formulated to maximize the minimum SINR at DTs, subject to the module size constraint and both individual source terminal (ST) transmit power and the reflecting coefficients constraints. Whereas this problem is NP hard due to the module size constraint, we develop an approximate solution by introducing the mixed row block \ell_{1,F} norm to transform it into a suitable semidefinite relaxation. Finally, numerical results demonstrate the meaningfulness of the introduced modular IRS architecture.
author_names: Yulan Gao

Chao Yong

Zehui Xiong

Dusit Tao Niyato

Yue Xiao

Jun Zhao
title: Resource Allocation for Intelligent Reflecting Surface Aided Cooperative Communications
venue: GLOBECOM 2020 2020 IEEE Global Communications Conference
year: 2020

abstract: In this paper, a silicon carbide chip based dual side liquid cooling power module with integrated temperature sensors is designed. The designed power module has footprint of 40mm x 40mm. The major components of the designed power module include the silicon carbide dies and thin film type thermistor chips. To develop this new generation power module, the approach combining the thermal performance analysis, packaging material selection, cooling plate optimization and test sample realization is implemented. At first, a thermal model is constructed and the thermal simulation is performed to simulate the thermal characteristic of the proposed IPM. Effect of different packaging materials on the power module thermal resistance is investigated and analyzed. A group of materials which produce the lowest thermal resistance is recommended for the proposed SiC chip based IPM. Then the IPM sample is manufactured with the recommended materials. Furthermore, the liquid cooling cold plates with jet impingement technology and optimized parameters are designed and fabricated. Finally, the test samples for thermal performance characterization is assembled. The simulation results indicate that the thermal interface material (TIM) imposes the most remarkable effects to the thermal resistance of the proposed IPM, while the effect of the other packaging materials, such as die attach (DA) epoxy molding compound (MC) and interconnect joint material (JM) on the thermal resistance of the proposed IPM are insignificant. Meanwhile, nozzle length, jet to wall distance and flow channel width are critical parameters to be optimized for the cooling performance of the liquid cooling plate.
author_names: Gongyue Tang

Leong Ching Wai

Siak Boon Lim

Boon Long Lau

Yamamoto Kazunori

Xiao Wu Zhang
title: Thermal Analysis, Characterization and and Material Selection for SiC Device Based Intelligent Power Module (IPM)
venue: 2020 IEEE 70th Electronic Components and Technology Conference (ECTC)
year: 2020

abstract: This paper presents a high scalability real time intelligent traffic monitoring system, based on Radio Frequency Identification (RFID) The main features of this system are low cost, low power consumption, traffic monitoring, and connectivity. The system's architecture includes an RFID reader, a passive tag, and a Raspberry Pi. Our solution collects vehicle information from the labels and stores the data into a database by employing only one antenna. The main challenge is that the RFID module is not robust enough to recognize the information in the vehicle's RFID tag on each information query, while the label is in the reading zone. This instability does not allow us to know precisely when and where a vehicle enters or leaves the sensing zone. What is more, the high random error in the power signal and the complexity of its characteristic curve pattern add difficulty to the speed calculation, when we reduce the number of antennas to one. For this reason, an innovative approach has been designed, using customized modular neural network (MNN) This method fits the collected data (power signal vs time) affected by acute random noise, to the characteristic correspondence function among the signal power and the position of the terminal, which domains are dimensionally different. As a result, we can estimate the vehicle speeds and obtain the whole vehicle information. Under this novel method, we are able to reduce the hardware, in comparison with previous approaches, making it cheaper and decreasing power consumption.
author_names: Jose Luis Calderon Choy

Jinhua Wu

Chengnian Long

Yi-Bing Lin
title: Ubiquitous and Low Power Vehicles Speed Monitoring for Intelligent Transport Systems
venue: IEEE Sensors Journal
year: 2020

abstract: The increasing deployments of 5G mobile communication system is expected to bring more processing power and storage supplements to Internet of Things (IoT) and mobile devices. It is foreseeable the billions of devices will be connected and it is extremely likely that these devices receive compute supplements from Clouds and upload data to the back end datacentres for execution. Increasing number of workloads at the Cloud datacentres demand better and efficient strategies of resource management in such a way to boost the socio economic benefits of the service providers. To this end, this paper proposes an intelligent prediction framework named IGRU SD (Improved Gated Recurrent Unit with Stragglers Detection) based on state of art data analytics and Artificial Intelligence (AI) techniques, aimed at predicting the anticipated level of resource requests over a period of time into the future. Our proposed prediction framework exploits an improved GRU neural network integrated with a resource straggler detection module to classify tasks based on their resource intensity, and further predicts the expected level of resource requests. Performance evaluations conducted on real world Cloud trace logs demonstrate that the proposed IGRU SD prediction framework outperforms the existing predicting models based on ARIMA, RNN and LSTM in terms of the achieved prediction accuracy.
author_names: Yao Lu

Lu Liu

John Panneerselvam

Bo Yuan

Jiayan Gu

Nick Antonopoulos
title: A GRU Based Prediction Framework for Intelligent Resource Management at Cloud Data Centres in the Age of 5G
venue: IEEE Transactions on Cognitive Communications and Networking
year: 2020

abstract: Abstract Combining the current development status of intelligent robots, obstacle avoidance and automatic tracking are the focus of robot travel problems.Based on the development status of domestic intelligent control systems, current technology, etc, this paper uses arduino as the core control system, combined with infrared tracking module. Four modules, such as ultrasonic obstacle avoidance module, motor drive module and power module, have designed a good control scheme, thus realizing the intelligent tracking and obstacle avoidance function of the wheeled robot.
author_names: Zhen Li

Jun Yu Li

Xiao Fan Li

Y J Yang

Jie Xiao

Bowen Xu
title: Intelligent Tracking Obstacle Avoidance Wheel Robot Based on Arduino
year: 2020

abstract: Abstract In this paper, an application of intelligent non linear discrete proportional integral derivative (N DPID) controller for the maximum power point tracking (MPPT) of a photovoltaic (PV) system is presented. The proposed N DPID MPPT method retains the conventional properties of the basic PID controller where the integral and derivative terms are discretized using Forward Euler formula and, its integral gain is varied over the simulation time according to the error. To obtain the value of controller gains is a challenge for N DPID controllers. An intelligent technique using Particle swarm optimization (PSO) and genetic algorithm (GA) may help in finding out the gains in a dynamic situation. These optimization techniques are analyzed for proposed N DPID MPPT to track the MPP. Performance analysis and comparison of the GA and PSO tuned N DPID MPPT method is presented for a commercially available PV module SunPower SPR 305E WHT D. Furthermore, the performance is also analyzed for the smooth operation of a wound field DC (WFDC) motor fed by a PV array. Finally, results for MPPT and WFDC motor operation are compared for P&O, IC, GA PID, GA N DPID, PSO PID, and PSO N DPID MPPT methods.
author_names: Diwaker Pathak

Gautam Sagar

Prerna Gaur
title: An Application of Intelligent Non linear Discrete PID Controller for MPPT of PV System
year: 2020

abstract: Abstract In parallel to sustainable growth in solar fraction, continuous reductions in Photovoltaic (PV) module and installation costs fuelled a profound adoption of residential Rooftop Mounted PV (RMPV) installations already reaching grid parity. RMPVs are promoted for economic, social, and environmental factors, energy performance, reduced greenhouse effects and bill savings. RMPV modules and energy conversion units are subject to anomalies which compromise power quality and promote fire risk and safety hazards for which reliable protection is crucial. This article analyses historical data and presents a novel design that easily integrates with data storage units of RMPV systems to automatically process real time data streams for reliable supervision. Dominant Transformed Components (TCs) are online extracted through multiblock Principal Component Analysis (PCA) most sensitive components are selected and their time varying characteristics are recursively estimated in a moving window using smooth Kernel Density Estimation (KDE) Novel monitoring indices are developed as preventive alarms using Kullback Leibler Divergence (KLD) This work exploits data records during 2015 2017 from thin film, monocrystalline, and polycrystalline RMPV energy conversion systems. Fourteen test scenarios include array faults (line to line, line to ground, transient arc faults) DC side mismatches (shadings, open circuits) grid side anomalies (voltage sags, frequency variations) in addition to inverter anomalies and sensor faults.
author_names: Azzeddine Bakdi

Wahiba Bounoua

Saad Mekhilef

Laith M Halabi
title: Nonparametric Kullback divergence PCA for intelligent mismatch detection and power quality monitoring in grid connected rooftop PV
year: 2019

abstract: Abstract To increase the power output of a PV module or a field of PV modules, an electronic controller is incorporated between the PV generator and the load, whose role and main objective is the continuous monitoring of the maximum power point of the PV generator commonly known as MPPT (Maximum Power Point Tracking) and this in general per action on a DC DC conversion device. The regulation and control techniques provide the impedance matching function, transferring to the load the maximum electrical power output from the PV generator in any the temperature and sunshine conditions. The development of a revolutionary method based on neural algorithms for the prediction of an instantaneous command is the main objective in our work. Indeed, the paper presents a new control strategy for the photovoltaic PV, it is a command based on Neuronal Network technique. It is the first time that this technique has been introduced, and proposed by the authors in synthesizing control laws for the converters of electronic power. The new technical algorithm based on Neural Networks, is designed to be more robust in performance with respect to tracking speed and precision. Moreover, this new successful technical research, provides a robust neural structure compared to the noisy empirical data used for the prediction of the command. Consequently a smooth control signal without oscillation, targeting exactly the expected optimal control with an independent control of the sampling frequency of the system. This study, which is followed by a simulation, has enabled us to consolidate the idea that the new Neural Network controller when compared to their classical counterparts, and obtains the best performances concerning the speed of tracking and precision. The robustness of the networks of neurons opposite the noise of measurements, like, the smoothness of the power signal of PV system generated during the application of the neuronal order, will qualify this command as a practical alternative to the disadvantages recorded on the levels of the classical methods.
author_names: Salim Issaadi

Wassila Issaadi

Abdelkrim Khireddine
title: New intelligent control strategy by robust neural network algorithm for real time detection of an optimized maximum power tracking control in photovoltaic systems
venue: Energy
year: 2019