bluuebunny/crossref_metadata_2025 huggingface dataset:
Sensor fault estimation of networked vehicle suspension system with deny‐of‐service attack
Authors: Ye Zehua , Ni Hongjie , Xu Zhenhua , Zhang Dan
Published: October 2019

This study is concerned with the sensor fault estimation problem for network‐based vehicle suspension system with deny‐of‐service attack, where a linear robust observer is designed. First of all, the attack behaviour switching is modelled as a Markovian jumping process, and then a sufficient condition based on the Markovian jumping system approach is proposed such that the sensor fault estimation error system is asymptotically stable in the mean‐square sense with a prescribed performance level. In this work, the occurring and transition probabilities of the attack are allowed to be partially unknown and uncertain. Finally, a simulation example is presented that validates the effectiveness of design method.
Traffic sign recognition by combining global and local features based on semi‐supervised classification
Authors: He Zhenli , Nan Fengtao , Li Xinfa , Lee Shin‐Jye , Yang Yun
Published: October 2019

The legibility of traffic signs has been considered from the beginning of design, and traffic signs are easy to identify for humans. For computer systems, however, identifying traffic signs still poses a challenging problem. Both image‐processing and machine‐learning algorithms are constantly improving, aimed at better solving this problem. However, with a dramatic increase in the number of traffic signs, labelling a large amount of training data means high cost. Therefore, how to use a small number of labelled traffic sign data reasonably to build an efficient and high‐quality traffic sign recognition (TSR) model in the Internet‐of‐things–based (IOT‐based) transport system has been an urgent research goal. Here, the authors propose a novel semi‐supervised learning approach combining global and local features for TSR in an IOT‐based transport system. In their approach, histograms of oriented gradient, colour histograms (CH), and edge features (EF) are used to build different feature spaces. Meanwhile, on the unlabelled samples, a fusion feature space is found to alleviate the differences between different feature spaces. Extensive evaluations on a collection of signs from the German Traffic Sign Recognition Benchmark (GTSRB) dataset shows that the proposed approach outperforms the others and provides a potential solution for practical applications.
Embedded 3D multi‐band antenna with ETS process technology covering LTE/WCDMA/ISM band operations in a smart wrist wearable wireless mobile communication device design
Authors: Chung Ming‐An
Published: October 2019

An internal etching for three‐dimensional structures (ETS) antenna capable of operating in the 810–960, 1370–1450 and 1710–2630 MHz bands, respectively, for long‐term evolution (LTE)/wideband code division multiple access (WCDMA)/2.4 GHz industrial, scientific and medical (ISM) band operations suitable for being embedded within a smart wrist wearable wireless mobile communication device is proposed. The proposed antenna radiation efficiency can achieve 37.8–55.3% from 810–960 MHz, 53.1–92.63% from 1370–1450 MHz and 52.9–93.1% from 1710–1630 MHz for LTE/WCDMA bands and 87.4% for ISM band without the hand phantom in the free space condition. The antenna radiation efficiency of the LTE/WCDMA and ISM bands is reduced by half because of the hand phantom with lossy material properties. Furthermore, the health risk related to exposure to electromagnetic radiation, the specific absorption rates of the device are evaluated and the results were far below the limits required by the Federal Communications Commission (FCC) organisation. The embedded three‐dimensional (3D) multi‐band antenna can be adapted for the smart wrist wearable wireless mobile communication device. This study breaks the pattern that wearable devices must work with mobile phones. The wearable device proposed herein can directly communicate with the carrier base station independently.
Cavity‐backed bow‐tie antenna with dielectric loading for ground‐penetrating radar application
Authors: Liu Shufang , Li Mengqian , Li Huimin , Yang Linna , Shi Xiaowei
Published: October 2019

Bow‐tie antennas are widely used in practical ground‐penetrating radars. In spite of simple structure and wideband characteristics, boresight gain of the antenna is small and the radiation pattern is omnidirectional. In this study, a cavity‐backed bow‐tie antenna with dielectric loading is presented. The bow‐tie antenna is resistively loaded to reduce ringing effects. The resistively loaded antenna is placed within a rectangle cavity, which acts as a reflector, for unidirectional radiation and high gain. A dielectric is loaded on the antenna aperture to enlarge the operational bandwidth and stabilise the radiation patterns. Parameter studies of the dielectric substrate are performed to optimise the antenna performance. The final design is fabricated and measured. It has been shown that the antenna exhibits wide impedance bandwidth of 120% for . The boresight gain is more than 5 dBi. The radiation patterns are stable and unidirectional over the whole band, and the late‐time ringing is small.
High diversity gain super‐wideband single band‐notch MIMO antenna for multiple wireless applications
Authors: Saxena Gaurav , Jain Priyanka , Awasthi Yogendra Kumar
Published: October 2019

A two‐element super‐wideband (SWB) MIMO antenna is proposed in this study. A step impedance microstrip feed line structure is introduced to achieve an SWB impedance bandwidth of 185%. This antenna is designed to be circularly polarised at 1.575 GHz (L1‐band) and 26.0 GHz (K‐band) by using closed ring resonator on the radiating patch. The EBG structures are used to improve FBR in lower (1.5–5 GHz) and higher bands (25–35 GHz). The proposed antenna is designed with compact size 55.6 × 50.5 × 1.6 mm3 on FR‐4 substrate including exotic diversity performance. This MIMO antenna is suitable for defence and handheld devices covering GPS/DCS/PCS/UMTS/Wi‐BRO/ISM/IRNSS/LTE (M/HB)/BLUETOOTH/IoT/WiMAX/X/Ku/K/Ka‐band and it is also suitable for 5G (sub‐6 GHz/mm) applications with channel capacity of 11.34 bps/Hz. Simulated results of MIMO antenna like radiated power‐based ECC and diversity parameters are also verified experimentally, which are in the acceptable range. Finally, this antenna is tested in a realistic environment for SAR application.
Improved GMM‐based method for target detection
Authors: Zhao Juan , Xie Guihui , Li Dongming , Song Mailing
Published: October 2019

The Gaussian mixture model (GMM) is prone to large‐scale misdetection in the static case where the background and foreground have similar colours. This study presents an improved GMM method to solve this problem. First, the principal component analysis is used to transform the high‐dimensional space into the low‐dimensional one with three colour channels, which aims to reduce runtime. Then, the images are processed by GMM to obtain the foreground areas. At the same time, the mean and difference of pixel features in red, green and blue and hue, saturation and value (HSV) colour models are calculated to analyse the similar‐coloured components. The areas with similar colours are classified in this process, and thus the foreground is separated from similar‐coloured background. Then, all of the extracted areas are targets. The experimental results show that the presented method identified the target areas with high accuracy and high efficiency. Compared with conventional GMM, the presented method obtained the detection‐accuracy improvement by about 6% on average.
E2CA‐SM: an energy‐efficient channel allocation with sleep mode for base station in fifth‐generation‐based cellular network systems
Authors: Venkata Krishna Parimala , Saritha Vankadara , Obaidat Mohammad Salameh
Published: October 2019

In today's world, power is one of the resources that needs effective utilisation similar to the bandwidth utilisation. As the battery life of the nodes in the cellular networks is not a problem, there is not much extensive research toward energy consumption in the cellular networks. Nevertheless, currently the focus is toward conserving the power being utilised by various components in the cellular network. Hence, an energy‐efficient channel allocation (E2CA) procedure for cellular networks with fifth‐generation network characteristics is proposed in this study, where the energy consumption is reduced with the concept of sleep mode for base station (E2CA‐SM). The SM is more popular in sensor networks when compared with cellular networks. The proposed E2CA‐SM algorithm shows low‐energy consumption during peak and non‐peak hours of cellular traffic. It is shown that the energy consumption per packet is 35.5% less when compared with multi‐traffic, with queue, learning automata‐based reservation, 32% when compared with E2CA. The system is modelled using single‐dimension Markov's model. There will be a trade‐off between blocking, dropping probability, and energy consumption, which is tackled in this study.
Base station traffic prediction using XGBoost‐LSTM with feature enhancement
Authors: Du Qingbo , Yin Faming , Li Zongchen
Published: October 2019

With the development of information technology, base station traffic prediction is becoming more and more vital in allocating resource, and finally improving terminal users’ quality of experience. Temporal and periodic characteristics are important for handling the issue of efficient and accurate traffic prediction. Considering these characteristics, this study proposes base station traffic prediction using extreme gradient boosting‐long–short‐term memory (XGBoost‐LSTM) with feature enhancement. First, the collected dataset is preprocessed, especially realising missing values filling. Then, to mine the tidal property, feature engineering is performed, which contains feature creation and feature selection. More importantly, the variance contribution of the indicators is calculated based on the factor analysis. The variance contribution of the indicators is used to determine the weights of each selected features. Finally, the XGBoost‐LSTM model is adopted to predict the traffic of base stations. By observing the predicted values, the authors find that the simple combination of XGBoost and LSTM can achieve great improvement. Experimental results show that the proposed scheme can get much better performance when compared with competing algorithms.
Non‐linearities mitigation with fuzzy neural networks using a machine learning algorithm in a CO‐OFDM system
Authors: Kaur Gurpreet , Kaur Gurmeet
Published: October 2019

This study presents a fuzzy neural network based non‐linear equaliser to diminish the non‐linearities in a coherent optical orthogonal frequency division multiplexing (CO‐OFDM) system. The numerical results show that the proposed technique based CO‐OFDM system outperforms the CO‐OFDM system without non‐linear equaliser by 5 and 7.5% EVM performance, and 2.36 and 4.87 dB Q‐factor performance after 1000 km transmission and −3 dBm input launch power at a bit rate of 40 and 80 Gbps, respectively. Moreover, it has been generally accepted in statistics that the rank‐based Wilcoxon methodology provide more robust results in a contradiction of outliers. Therefore, the aim of this study is to analyse fuzzy neural network based non‐linear equaliser and compare the results with that of Wilcoxon approach fuzzy neural network based non‐linear equaliser.
Torque ripple and switching frequency reduction of interior permanent magnet brushless direct current motors using a novel control technique
Authors: Faiz Jawad , Heidari Mehdi , Sharafi Hussein
Published: October 2019

Although interior permanent magnet brushless direct current (IPMBLDC) motors have higher torque and power density compared to surface‐mounted PMBLDC motors, the reluctance torque of IPMBLDC motors generates higher torque ripples. This study introduces a new current waveform as a reference to control the IPM motors while keeping the total developed torque constant. It also helps to considerably reduce the torque ripple of the motors. The experimental results show that this method significantly reduces the damaging impact of the reluctance torque ripple without complicating computations or changing the stator and rotor structures of the motor. Additionally, the switching frequency and consequently switching losses are reduced using the proposed technique.
Passivity‐based control of the hybrid rectifier for medium and high‐power application
Authors: Li Jianguo , Wang Mian , Zhao Yuming , Wang Jiuhe , Yang Daokuan , Lv Xiuping
Published: October 2019

This study proposes a practical passivity‐based control (PBC) strategy of the hybrid rectifier based on Vienna and single switch three‐phase boost (SSTPB) rectifiers. For medium and high application, the SSTPB rectifier operates at higher output power state with low frequency, the Vienna rectifier operates at power quality improvement state with high frequency, and the hybrid rectifier has the advantages of perfect sinusoidal input currents, unity power factor, high reliability, high efficiency and low cost. PBC is stable globally and has strong robustness to internal and external interference with less parameters and PBC of the hybrid rectifier has the advantages of less parameters, good dynamic characteristic, high current control precision and strong robustness. The detailed mathematical model based on Euler–Lagrange is built, the sixth harmonic current injection strategy is proposed, and the PBC controller is designed by damping injecting method in the study, and simulation results in SIMULINK are also given. At last, a down‐sized hybrid rectifier prototype is built and the experimental results verify correctness and effectiveness of the proposed solution and analysis.
Fast and simple space vector modulation method for multilevel converters
Authors: Zhang Haitao , Meng Yongqing , Ning Lianhui , Zou Yichao , Wang Xiuli , Wang Xifan
Published: October 2019

This study proposes a simplified space vector pulse‐width modulation (SVPWM) method for multilevel converters. To avoid unnecessary coordination transformations, firstly, a further derivation is conducted on the basis of the 60° coordinate transformation. Then, all the nearest three vectors (NTVs), duty cycles and optimal switching sequence within one switching period can be identified through a simple comparison of three‐phase modulation waves. To further reduce the switching frequency, detailed steps to achieve the minimum number of transitions among different NTVs are constructed. Combined with the optimal switching sequence within one switching period, the minimum number of transitions within a fundamental period can be achieved easily. Finally, a thorough discussion is conducted to simplify the procedure further and the flowchart for the proposed SVPWM is also presented. Compared with the previous methods, the proposed method does not require any iterative calculation or coordinate transformation. To the best of the authors’ knowledge, the calculation amount is reduced by almost seven times compared with the methods proposed. In addition, the switching frequency and total harmonic distortions remain at the same level without any deterioration. Finally, the simulation and experiment for modular multilevel converter are performed to verify the correctness and efficiency of the proposed method.
Comparative analysis of CB‐PWM techniques in modified multilevel DC link inverter for PV applications
Authors: Pilli Naresh K. , Singh Santosh K.
Published: October 2019

The performance analysis of modified multilevel DC link inverter (MMLDCLI) operating with various carrier‐based pulse width modulation (CB‐PWM) techniques is studied. In MMLDCLI, seven‐level is achieved with six controllable switches only, making it an attractive option as a reduced device count multilevel (MLI, multilevel inverter) topology. MMLDCLI operates with two different levels of voltage source, and the absence of transformer makes this topology suitable for photovoltaic (PV) applications. Two different levels of sources can be achieved with two different combinations of PV panel arrangements. The CB‐PWM is reputed modulation approach for MLIs. In this study, various CB‐PWM techniques are implemented for MMLDCLI to evaluate performance parameters such as harmonic analysis, device losses and device stress. All the simulations are performed in SIMULINK, and hardware prototype is developed to validate the results.
Model, design and implementation of a low‐cost HIL for power converter and microgrid emulation using DSP
Authors: Bastos Renan F. , Fuzato Guilherme H. , Aguiar Cassius R. , Neves Rodolpho V.A. , Machado Ricardo Q.
Published: October 2019

In this study, the authors propose a method to implement a low‐cost hardware‐in‐the‐loop (HIL) system for power converters and microgrids design, test and analysis. This approach uses a digital signal processor (DSP) Texas Instruments as the HIL core. All the differential equations of the power converters are solved in real‐time by the DSP and displayed in the digital‐to‐analogue outputs. Three different converters are modelled in this study: boost converter, single‐phase inverter connected to the grid and three‐phase inverter connected to the grid. Experimental results are obtained and compared to the HIL response. These results were made triggering the real converter and the HIL with the same open‐loop pulse width modulation signal, showing high fidelity between the digital models over the real systems. In a second moment, a microgrid is modelled in the proposed HIL and tested with a closed‐loop controller. The experiments show that the proposed hardware supports time steps as low as 1 μs or 1 MHz update rate, depending on the model. The proposed technique has the potential to reduce testing time and cost, once commercial HIL devices such as Typhoon, dSPACE and RTDS have a significant cost, not affordable or available to all the research community
Hybrid 3.3 kV/450 A half‐bridge IGBT power module with SiC Schottky barrier diodes
Authors: Li Daohui , Luo Haoze , Huang Yue , Li Xiang , Qi Fang , Packwood Matthew , Luo Haihui , Chen Ximing , Li Chengzhan , Wang Yangang , Dai Xiaoping , Liu Guoyou
Published: October 2019

This work presents a hybrid 3.3kV/450 A insulated‐gate bipolar transistor (IGBT) power module, utilising the half‐bridge topology. In contrast to the traditional fashion, each IGBT chip in this module is allocated with two anti‐parallel silicon carbide (SiC) Schottky barrier diodes (SBDs). Each SBD describes smaller footprint than the conventionally used silicone (Si) based fast recovery diode at this voltage and current level. By adopting smaller SiC SBDs in this hybrid‐style packaging structure, the SBD chip yield can be greatly improved, without any additional fabrication cost. To benchmark the advantages of this hybrid power module over the Si‐based counterpart, both power modules are designed, fabricated as well as tested statically and dynamically. It is shown that while both types show similar thermal behaviour, the hybrid power module describes much lower IGBT turn‐on current overshooting, diode reverse recovery loss and IGBT turn‐on loss. Based on the measured results, the power losses of both modules under a three‐phase inverter operation condition are calculated, showing that the hybrid module has considerably lower power losses and junction temperature rise.
Compact pulse position control‐based inverter for high efficiency inductive power transfer to electric vehicle
Authors: Varikkottil Sooraj , J.L. Febin Daya
Published: October 2019

A compact high efficient inductive power transfer (IPT) topology based on pulse position control is proposed for wireless charging of electric vehicle. Near‐field wireless charging is efficiently achieved by the resonance‐enhanced IPT technique. The system sustains high power transfer efficiency under contingency in misalignment of pickup coil and load variations. The performance evaluation of the proposed system under the occurrence of anticipated perturbation in load and mutual inductance is studied numerically. Experimental results from the developed prototype corroborate the theoretical and simulated results discussed in the proposed study.
Robust single‐phase inverter based on the buck–boost converter through an efficient hybrid control
Authors: Yalcin Faruk , Arifoglu Ugur , Yazici Irfan , Erin Kenan
Published: October 2019

This study presents a new and robust single‐phase inverter based on the buck–boost converter. The proposed inverter topology has minimised numbers of active and passive elements that provide less complexity and cost. Unlike similar studies in the literature, an efficient hybrid control technique is used for the control of the inverter operation. The hybrid control technique is comprised of the traditional feedback proportional–integral–derivative (PID) controller and the new proposed open‐loop control technique called ‘control law’. This hybrid control technique provides stable and high response performance operation for the proposed inverter while the system parameters change. The proposed inverter can operate with a wide range of output frequency and output voltage value on different load conditions while satisfying <5% voltage total harmonic distortion (THD) value of the output voltage. An experimental laboratory setup with the values of 0.5 kW, 0–100 Vp and 0–50 Hz is built for the proposed inverter. The experimental results demonstrate that the proposed inverter can produce high‐quality output voltage with <5% THD on different input and output system parameters.
Stochastic PV model for power system planning applications
Authors: Al‐Sumaiti Ameena Saad , Ahmed Mohammed Hassan , Rivera Sergio , El Moursi Mohammed Shawky , Salama Mohamed M.A. , Alsumaiti Tareefa
Published: October 2019

Planning photovoltaic (PV) power systems integration into the grid necessitates accurate modelling of renewable power generation. Global solar irradiance, weather temperature and PV power losses due to overheating specifically in hot regimes are major factors contributing to PV power generation uncertainty. This study targets demonstrating the effectiveness of deploying advanced five parameter probabilistic distribution ‘Wakeby’ for modelling PV uncertain power generation, measured as a function of such factors, in power system planning applications. The impact of different approaches for incorporating weather temperature on PV energy estimation is studied. Wakeby‐Monte Carlo Simulation for PV power data training with an emphasis on MCS stopping criteria for such advanced distribution is presented. The model is tested and verified in 31‐bus distribution system to demonstrate its effectiveness over other literature uncertainty modelling approaches when planning integration of PV systems' integration into the grid to minimise the grid losses cost. Real PV power measurements are utilised as benchmark verifying the accuracy and suitability of the presented uncertainty modelling approach. Simulation results demonstrate a small error of $4.7 in the expected annual cost of grid losses when deploying Wakeby model compared to the benchmark case and that error can vary significantly when deploying other PV models.
DC voltage deviation‐dependent voltage droop control method for VSC‐MTDC systems under large disturbances
Authors: Wang Yizhen , Li Bin , Zhou Zexin , Chen Zhengguang , Wen Weijie , Li Xialin , Wang Chengshan
Published: October 2019

Along with the increasing demand of large power transfer and integration of renewable energy sources, voltage source converter based multi‐terminal high‐voltage DC (VSC‐MTDC) technology has represented both challenges and opportunities. However, the DC voltage of the droop‐controlled VSC station may exceed the DC voltage limits when facing large disturbances, thereby losing the DC voltage control and power sharing capabilities. To tackle this problem, a DC voltage deviation‐dependent voltage droop control method (VDM) is proposed to change the droop coefficients adaptively when large disturbances occur, not only retaining the advantages of the fixed VDM in the steady state, but also preventing frequent adjustments of the droop coefficients in the transient state. The basic analytical model of droop‐controlled VSC is analysed first, and then the generalised linear model of VSC‐MTDC is derived based on the restrict requirements of DC voltage deviation. The DC voltage deviation‐dependent VDM is derived from the generalised linear model of the VDC‐MTDC. Case studies and simulations of a ±320 kV VSC‐MTDC system are conducted in PSCAD/EMTDC software to validate the effectiveness of the proposed DC voltage deviation‐dependent VDM.
Wind turbine power curve estimation based on earth mover distance and artificial neural networks
Authors: Bai Li , Crisostomi Emanuele , Raugi Marco , Tucci Mauro
Published: October 2019

A data‐based estimation of the wind–power curve in wind turbines may be a challenging task due to the presence of anomalous data, possibly due to wrong sensor reads, operation halts, malfunctions or other. In this study, the authors describe a data‐based procedure to build a robust and accurate estimate of the wind–power curve. In particular, they combine a joint clustering procedure, where both the wind speeds and the power data are clustered, with an Earth Mover Distance‐based Extreme Learning Machine algorithm to filter out data that poorly contribute to explain the unknown curve. After estimating the cut‐in and the rated speed, they use a radial basis function neural network to fit the filtered data and obtain the curve estimate. They extensively compared the proposed procedure against other conventional methodologies over measured data of nine turbines, to assess and discuss its performance.
Clutter suppression algorithm for non‐side looking airborne radar with high pulse repetition frequency based on elevation–compensation–prefiltering
Authors: Li Xinzhe , Xie Wenchong , Wang Yongliang
Published: October 2019

Clutter suppression for non‐side‐looking airborne radar operating at high pulse repetition frequency is an intractable problem because the clutter statistical properties vary with the range, especially in the near range. In addition, the near‐range clutter is always coupled with the far‐range clutter. Elevation prefiltering is recognised as an effective method to suppress the range‐dependent near‐range clutter, but the elevation training samples required must be independent and identically distributed (IID) and not contaminated by the far‐range clutter. Here, an elevation–compensation–prefiltering space–time adaptive processing (STAP) algorithm is developed. In the proposed algorithm, the elevation training samples are selected from the elevation snapshots corresponding to the first pulse, which are not contaminated by the far‐range clutter. An elevation compensation method is presented to alleviate the range dependence, and then there are adequate IID training samples to estimate the elevation covariance matrix. The performance of the near‐range clutter suppression is improved. As for the residual range‐independent far‐range clutter, it can be suppressed by the cascaded azimuth–Doppler STAP. Numerical examples demonstrate the effectiveness of the proposed algorithm.
Quantum two‐mode squeezing radar and noise radar: covariance matrices for signal processing
Authors: Luong David , Balaji Bhashyam
Published: October 2019

Recently, the authors have built and evaluated a prototype quantum radar in the laboratory which operates at microwave frequencies. This radar, which they call a quantum two‐mode squeezing radar (QTMS radar), generates a pair of entangled microwave signals and transmits one of them through free space, using the other signal as a reference to perform matched filtering. The specific type of entanglement is called a two‐mode squeezed vacuum, a type of continuous‐variable entanglement between two frequencies. Motivated by the success of these experiments, they try to better understand the entangled QTMS radar signals in this study. They do so by comparing it to a simpler, more conventional radar system, which they call a two‐mode noise radar (TMN radar). They also show how both types of radars are related to standard noise radars as described in the literature. They find that the signals for QTMS radar signals and TMN radar signals have the same mathematical form and that they are related to noise radar by a simple mathematical transformation. This shows that QTMS radar signals can be emulated by a fictional, idealised TMN radar and that it is possible to apply results from the noise radar literature to QTMS radar.
Constant false alarm rate detection based on estimating statistical distribution of non‐homogeneous sea clutter in sky‐wave over‐the‐horizon radar
Authors: Li Yang , Wu Longshan , Zhang Ning , Wang Xinyang
Published: October 2019

In sky‐wave over‐the‐horizon radar (OTHR), there are some difficulties to efficiently detect targets in the non‐homogeneous sea clutter by using conventional constant false alarm rate detectors. The amplitude of the clutter is supposed to follow the Weibull distribution in OTHR. Moreover, the characteristic of the clutter with a continuous region distribution in the range‐Doppler (RD) map suggests that there exist the spatial relationship between neighbouring cells. The authors propose a rigorous algorithm to incorporate these relationships and the regularisation of interferences into the estimation of the shape parameter and scale parameter for every cell under test. This algorithm is mainly based on the consideration that the improvement of detection performance could be carried out by properly utilising the spatial information between neighbouring cells in the non‐homogeneous Weibull clutter. Then, the adaptive detection threshold is calculated by the estimated parameters. The validity and effectiveness of the proposed algorithm are verified by simulations and the experiments in the real RD maps collected from an experimental sky‐wave OTHR system.
Enhanced Loran skywave delay estimation based on artificial neural network in low SNR environment
Authors: Zhang Kai , Wan Guobin , Xi Xiaoli
Published: October 2019

This study proposes a high precision algorithm to estimate the Enhanced Loran skywave delay. It is based on the artificial neural network. The algorithm establishes a neural network model between the receiving Enhanced Loran signal and the skywave propagation delay. By training a large number of data, the neural network model can more accurately reflect the relationship between the receiving signal and the skywave delay. This is an innovative application of the skywave delay estimation algorithm in the Enhanced Loran receiving system, especially in low signal‐to‐noise ratio (SNR) environments. The experimental results show that the accuracy of this algorithm is about hundreds of nanoseconds in the condition of normal receiving SNR. The accuracy of the algorithm is about us in the condition of low SNR, while the previous algorithm cannot be used in this case. It has also been proved by the off‐air data.
Information theoretic waveform design for OFDM radar‐communication coexistence in Gaussian mixture interference
Authors: Du Zhen , Zhang Zenghui , Yu Wenxian
Published: October 2019

In this study, mutual information (MI)‐based waveform design methods for the coexistence between orthogonal frequency division multiplexing (OFDM) radar system and communication system corrupted by Gaussian mixture interference are investigated for the first time. Firstly, the received signal is formulated as a Bayesian linear model. Subsequently, from a statistical perspective, the approximate differential entropy of received signal is derived, followed by three different approximate MIs and corresponding theoretic upper bounds. After that, two adaptive waveform design methods are proposed by maximising MI and minimising radar transmitted power, respectively. The numerical results show that exploiting the reflected communication signals helps to obtain higher MI value and reduce radar transmitted power. Besides, by maximising the MIs, the optimised radar waveforms remarkably outperform the fixed ones, which benefits the estimation performance.
Dielectric properties of mineral oil‐based nanofluids under temperature variations
Authors: Muangpratoom Pichai , Pattanadech Norasage
Published: October 2019

The aim of this research is to improve the electrical properties of dielectric liquids. An examination of the dielectric properties of mineral oil‐based nanofluids was carried out, using three different nanoparticle types: titanium dioxide, zinc oxide, and barium titanate, each with a mean diameter not exceeding 100 nm. The test samples were divided into two groups, including mineral oil mixed with nanoparticles at a concentration of 0.01 and 0.03% volume fraction, respectively. Determination of the AC breakdown voltage of the unmodified mineral oil and the nanofluid samples was performed through testing at temperatures varying within the range of 35–90°C in accordance with the standards of IEC 60156. Furthermore, testing was also performed to determine the dielectric dissipation factor (tanδ) for each of the liquid samples according to IEC 60247. From the study, it was shown that the performance of nanofluid was significantly affected by increasing the temperature. It could be observed from the findings that the use of nanoparticles in creating mineral oil‐based nanofluids had potential to bring a number of advantages in terms of dielectric applications.
Effective analysis of time‐domain dielectric response for reliable diagnosis of power transformer insulation using statistical parameter evaluated from time‐varying model
Authors: Banerjee Chandra Madhab , Baral Arijit , Chakravorti Sivaji
Published: October 2019

Various types of insulation models with time‐invariant parameters are available in the literature. Depending on the aging sensitive performance parameters to be evaluated, different models need to be employed (e.g. XY model for oil and paper‐conductivity, conventional Debye model (CDM) for paper‐moisture and tanδ). While the XY model cannot be used for estimating paper‐moisture directly, analysis based on CDM parameter becomes dependent on its branch parameters, which are non‐unique. These factors lead to either incomplete or ambiguous insulation diagnosis. These problems are resolved using the proposed new insulation model containing unique time‐varying branch parameters. Another major advantage of the proposed model is that it can be used to evaluate a host of performance parameters (like paper‐conductivity, oil and paper‐moisture, dielectric loss) thus giving a complete picture of the insulation concerned. The application of the proposed model is also tested on data collected from several real‐life power transformers.
Decision tree‐based method for optimum decomposition level determination in wavelet transform for noise reduction of partial discharge signals
Authors: Soltani Amir Abbas , Shahrtash Seyyed Mohammad
Published: October 2019

Partial discharge (PD) monitoring in high‐voltage equipment is one of the effective methods for assessment of its insulation strength. To do this, noise reduction is one of the essential processes on measured PD signals. One of the most popular tools employed for PD de‐noising is wavelet transform. To exploit this transformation, three main parameters, including ‘mother wavelet’, ‘number of decomposition level’, and ‘thresholding procedure’, should be assigned. In this study, a novel and also more accurate method for the determination of required decomposition level is suggested. The proposed method employs a decision tree which takes the pattern of energy spectral density of PD signals in the frequency domain and delivers the optimum decomposition level for de‐noising by wavelet transform. The results, as compared with others, show the superiority of the proposed method in noise reduction of PD signals, both for simulations and field measured signals.
Stacking battery energy storage revenues with enhanced service provision
Authors: Brogan Paul Vincent , Best Robert , Morrow John , Duncan Robin , Kubik Marek
Published: October 2019

Battery energy storage systems (BESSs) offer many desirable services from peak demand lopping/valley filling too fast power response services. These services can be scheduled so they enhance each other; in this manner, the inverter size is effectively leveraged with battery capacity. A variable cost for under‐frequency service provision will be required to incentivise this type of operation, and various options are explored in this study. It is demonstrated that BESSs may struggle for profitability under certain service payments; however, returns can be maximised through revenue stacking. In this study, enhanced service provision results in increased power system service provision and returns from energy arbitrage. A 10 and 2 years datasets, containing information on the Irish power system, are used to investigate potential per MW revenue from a BESS with a 1.5 and 2.5 h batteries. Three statistically derived, engineering‐based, power system service scalar multipliers are investigated and compared with the scalar being introduced on the Irish system. It is demonstrated that flexible service payments can be increased by more than 10% while receiving arbitrage payments. The seasonal variation in BESS revenue is investigated and observed to be mitigated or exacerbated by service scalars.
Realisation of RPS from electrical home appliances in a smart home energy management system
Authors: Gautam Samir , Dah‐Chuan Lu Dylan , Xiao Weidong , Lu Yuezhu
Published: October 2019

With the increasing integration of photovoltaic power generation in the low‐voltage distribution network, the grid voltage regulation becomes critical, which demands support from different resources. This study presents the feasibility study of home appliance to be applied for appliance to grid mode of operation. The analysis includes the amendments in topology and control to support the concept of supportive platform provided by smart home and smart grid. Home appliances are then proposed as new distributed reactive sources, which are utilised to resolve the issue of voltage regulation, as well as produce reactive power locally for voltage stability. This study discusses the technical transitions in current home appliance to accommodate auxiliary functionality of grid reactive power support (RPS) and how it can fit in the home energy management system architecture to provide the required RPS.
Quantification and visualisation of extreme wind effects on transmission network outage probability and wind generation output
Authors: Jamieson Magnus R. , Strbac Goran , Bell Keith R.W.
Published: October 2019

An approach is demonstrated to visualise overhead line failure rates and estimated wind power output during extreme wind events on transmission networks. Reanalysis data is combined with network data and line failure models to illustrate spatially resolved line failure probability with data corrected for asset altitude and exposure. Wind output is estimated using a corrected power curve to account for high speed shutdown with wind speed corrected for altitude. Case studies demonstrate these methods' application on representations of real networks of different scales. The proposed methods allow users to determine at‐risk regions of overhead line networks and to estimate the impact on wind power output. Such techniques could equally be applied to forecasted weather conditions to aid in resilience planning. The methods are shown to be particularly sensitive to the weather data used, especially when modelling risk on overhead lines, but are still shown to be useful as an indicative representation of system risk. The techniques also provide a more robust method of representing weather‐related failure rates on lines considerate of the altitude, voltage level, and their varying exposure to weather conditions than current techniques typically provide, which can be used to usefully represent failure probability of lines during storms.
Reinforcement learning for control of flexibility providers in a residential microgrid
Authors: Mbuwir Brida V. , Geysen Davy , Spiessens Fred , Deconinck Geert
Published: October 2019

The smart grid paradigm and the development of smart meters have led to the availability of large volumes of data. This data is expected to assist in power system planning/operation and the transition from passive to active electricity users. With recent advances in machine learning, this data can be used to learn system dynamics. This study explores two model‐free reinforcement learning (RL) techniques – policy iteration (PI) and fitted Q‐iteration (FQI) for scheduling the operation of flexibility providers – battery and heat pump in a residential microgrid. The proposed algorithms are data‐driven and can be easily generalised to fit the control of any flexibility provider without requiring expert knowledge to build a detailed model of the flexibility provider and/or microgrid. The algorithms are tested in multi‐agent collaborative and single‐agent stochastic microgrid settings – with the uncertainty due to lack of knowledge on future electricity consumption patterns and photovoltaic production. Simulation results show that PI outperforms FQI with a 7.2% increase in photovoltaic self‐consumption in the multi‐agent setting and a 3.7% increase in the single‐agent setting. Both RL algorithms perform better than a rule‐based controller, and compete with a model‐based optimal controller, and are thus, a valuable alternative to model‐ and rule‐based controllers.
LEACH‐TLC: a strategy of reducing and uniform energy consumption based on target location constraint
Authors: Zhou Lin , Fang Yongjun , Wei Qian , Jin Yong , Hu Zhentao
Published: October 2019

Considering the important applications of wireless sensor networks in target monitoring, it has received attention in the civil and military fields. For the problem that the working nodes die early, the network energy consumption is large, and the node energy distribution is uneven, especially the impact of the monitored target on the network performance, this study is based on the traditional low‐energy adaptive clustering hierarchy (LEACH) and target location constraint, an improved LEACH target location constraint (LEACH‐TLC) algorithm is proposed. First, under the premise of monitoring accuracy, this study constructs a network monitoring performance parameter model. Second, the wake‐up/dormant threshold of the network node is obtained based on the network performance parameter model, and the network node dormant strategy is constructed utilising the threshold, and then the working node set is constructed. Finally, based on the proposed network energy uniform mechanism, combined with the distance factor between the node and target/base station, a new cluster head selection rule is proposed. The simulation results show that the LEACH‐TLC algorithm can decrease the number of working nodes, reduce network energy consumption, uniform node energy consumption distribution, and prolong network lifetime.
E‐VAR: enhanced void avoidance routing algorithm for underwater acoustic sensor networks
Authors: Nazareth Pradeep , Chandavarkar Beerappa R.
Published: October 2019

Underwater acoustic sensor networks (UASNs) have gained attention among researchers due to its various aquatic applications. On the other hand, UASNs encounter many research challenges due to its inherent characteristics such as high propagation delay, limited bandwidth, high bit‐error‐rate, limited energy, and communication void during routing. These limitations severely affect the performance of delay‐sensitive and reliable applications of UASNs. The primary objective of this study is to address the communication void during routing. Various methods, such as backward–forwarding, passive participation, flooding, heuristic, and transmission power adjustments, are proposed to address the communication void during routing. The major drawbacks of these methods are void as a part of routing, loops, unreachable data to the sink, and more number of transmission of duplicate packets. This study proposes a void avoidance routing algorithm referred to as enhanced‐void avoidance routing (E‐VAR) using an idea of void awareness among the nodes. The E‐VAR inhibits the participation of void in routing, thereby resulting in better performance in comparison with the state‐of‐the‐art. Through MATLAB simulations, E‐VAR is compared with interference‐aware routing and state‐of‐the‐art backward–forwarding, in terms of the number of nodes reachable and unreachable due to looping to the sink, average hop‐count, and distance.
Map of Faraday rotation angle in Advanced Land Observing Satellite‐2 full polarimetric data
Authors: Ji Yifei , Zhang Qilei , Zhang Yongsheng , Li Jinhui , Dong Zhen , Yao Baidong
Published: October 2019

Spaceborne synthetic aperture radar (SAR) systems operating at low frequency, such as L‐ and P‐bands, are significantly influenced by ionospheric effects. One of the remarkable effects is Faraday rotation (FR), which is known as a serious error source for the polarimetric SAR (PolSAR) application. The FR effect mainly results from the background ionosphere so that the FR angles (FRAs) within a SAR image tend to take on a potential spatial‐variable distribution. The typical estimator is used to accurately estimate FRA from the PolSAR data. Moreover, a step‐to‐step procedure is proposed to derive FRA map. On the basis of the real data processing, the experimental results validate the effectiveness of the FRA map, which indicates the immense capability of the PolSAR data to retrieve a spatial distribution of the FRA.
Extracting animal migration pattern from weather radar observation based on deep convolutional neural networks
Authors: Hu Cheng , Li Siwei , Wang Rui , Cui Kai , Wu Dongli , Ma Shuqing
Published: October 2019

The weather radar can operate in all weathers and all time, and has a large coverage area. Besides monitoring the weather, the weather radar can receive other echoes including biological echoes. In order to utilise weather radar biological monitoring capability, recognising and classifying local insect and bird echoes is one of the biggest obstacles for analysing their migration, foraging, and reproduction activities. Here, a pixel‐wise classification method based on the fully convolutional network (FCN) is proposed which is trained by the radar reflectivity and the spectral width images. Moreover, to increase the biometric detection accuracy, the region growing method is combined for achieving the region edge alignment. Finally, the proposed method is validated based on the real weather radar datasets in Yantai. The FCN training results have a high pixel accuracy of 92.96%, and the region growing method performs well in the edge alignment.
Hybrid optimisation algorithm and its application for pattern synthesis of planar arrays
Authors: Qi Zhengdong , Lu Yangyi , Kong Yue , Cong Youji
Published: October 2019

A hybrid approach to the synthesis of sparse arrays radiating shaped or focused beampattern is presented in this paper. Specifically, an iterative scheme is used where the prescribed pattern response in the mainlobe peak direction is cast as a multi‐convex problem at each step that the non‐convex lower bound constraint is relaxed while including a reweighted l 1 ‐norm minimisation based on the magnitudes of the elements. Numerical tests, referred to benchmark problems, show that the proposed method achieves improved radiation performance by using fewer number of radiating elements.
Intelligent ECCM technology via cognition and agility for the airborne radar
Authors: Meifang Luo , Zhimei Hao , Yimin Zhu
Published: October 2019

The electronic counter countermeasures (ECCM) technology is crucial to the modern airborne radar. In this study, opposed to the intelligent electronic countermeasure system, an intelligent anti‐jamming technology is developed, which adjusts the working parameters and ECCM steps in real time and optimises the radar performance in various jamming scenarios. The framework of the proposed intelligent anti‐jamming technology includes the cognitive jamming detection and suppression, adaptive frequency agility, and waveform diversity. The application of the proposed technology in the airborne radar is analysed via numerical simulations.