bluuebunny/crossref_metadata_2025 huggingface dataset:
Lytic polysaccharide monooxygenases and other histidine-brace copper proteins: structure, oxygen activation and biotechnological applications
Authors: Ipsen Johan Ø. , Hallas-Møller Magnus , Brander Søren , Lo Leggio Leila , Johansen Katja S.
Published: January 2021

Lytic polysaccharide monooxygenases (LPMOs) are mononuclear copper enzymes that catalyse the oxidative cleavage of glycosidic bonds. They are characterised by two histidine residues that coordinate copper in a configuration termed the Cu-histidine brace. Although first identified in bacteria and fungi, LPMOs have since been found in all biological kingdoms. LPMOs are now included in commercial enzyme cocktails used in industrial biorefineries. This has led to increased process yield due to the synergistic action of LPMOs with glycoside hydrolases. However, the introduction of LPMOs makes control of the enzymatic step in industrial stirred-tank reactors more challenging, and the operational stability of the enzymes is reduced. It is clear that much is still to be learned about the interaction between LPMOs and their complex natural and industrial environments, and fundamental scientific studies are required towards this end. Several atomic-resolution structures have been solved providing detailed information on the Cu-coordination sphere and the interaction with the polysaccharide substrate. However, the molecular mechanisms of LPMOs are still the subject of intense investigation; the key question being how the proteinaceous environment controls the copper cofactor towards the activation of the O-O bond in O2 and cleavage of the glycosidic bonds in polysaccharides. The need for biochemical characterisation of each putative LPMO is discussed based on recent reports showing that not all proteins with a Cu-histidine brace are enzymes.
Stabilization for switched linear systems: Hybrid observer‐based method
Authors: Li Zhihui , Wang Yue‐E , Wu Di
Published: January 2021

This paper investigates interval observer‐based controller design for switched linear systems involving additional disturbance and measurement noise, whose modes need not to be cooperative. First, by giving the upper bound and the lower bound of the disturbance and the measurement noise, we construct hybrid interval observer for the considered switched linear system by means of a switched coordinate transformation, which can transform the observer error systems into cooperative ones. The interval observer can supply certain state information at any instant. Compared with the interval observer design for switched systems with arbitrary switching sequences or dwell time switching based on common Lyapunov function, the difficulty consists in characterizing the jump of the multiple Lyapunov functions. Then, by using the multiple Lyapunov functions method and average dwell time scheme, some sufficient conditions are derived and applied to build the interval observer‐based state feedback controller. Finally, we provide an example to illustrate the validity of the derived results.
Three‐dimensional shape reconstruction of objects from a single depth view using deep U‐Net convolutional neural network with bottle‐neck skip connections
Authors: Valarezo Añazco Edwin , Rivera Lopez Patricio , Kim Tae‐Seong
Published: January 2021

Three‐dimensional (3D) shape reconstruction of objects requires multiple scans and complex reconstruction algorithms. An alternative approach is to infer the 3D shape of an object from a single depth image (i.e. single depth view). This study presents such a 3D shape reconstructor based on U‐Net 3D‐convolutional neural network (3D‐CNN) with bottle‐neck skipped connection blocks (U‐Net BNSC 3D‐CNN) to infer the 3D shapes of objects from only a single depth view. The BNSC block is a fully convolutional block that uses skip connections to improve the performance of the sequential 3D‐convolutional layers of U‐Net. The primary advantage of U‐Net BNSC 3D‐CNN is improving the accuracy of shape reconstruction while reducing the computational load. The evaluation of the proposed U‐Net BNSC 3D‐CNN uses unseen views from trained and untrained objects with two public databases, ShapeNet and Grasp database. Our reconstructor achieves 72.17% and 69.97% accuracy in terms of the Jaccard similarity index for trained and untrained objects, respectively, with the ShapeNet database, whereas previous reconstructor based on 3D‐CNN achieves 66.43% and 58.35%. With Grasp database, our reconstructor achieves 87.03% and 85.35%, whereas 3D‐CNN 76.52% and 76.02%. Also, our U‐Net BNSC 3D‐CNN reduces the computational load of the standard 3D‐CNN reconstructor by 6.67% in the computation time and by 98.69% in the number of trainable parameters.
A 2D WLP‐FDTD method with low numerical dispersion using artificial anisotropy parameters
Authors: Ma Ping , Chen Wei‐Jun , Tian Jing , Shi An‐Hua , Zhang Ning
Published: January 2021

A finite‐difference time‐domain method is proposed to reduce the numerical dispersion error induced by the non‐uniform meshing of multiscale configurations utilizing weighted Laguerre polynomials. The update functions of two‐dimensional TEz waves are obtained with the introduction of the artificial anisotropic parameters in the derivation procedure. The theoretical analysis of utilizing artificial anisotropic weighted Laguerre polynomial finite‐difference time‐domain for the suppression of numerical dispersion error is also presented. To verify the validation of the proposed method, the propagation of plane waves in a two‐dimensional cavity filled with the dielectric material is modelled. Compared with the existing approaches, the proposed method achieves low numerical dispersion error and improved accuracy without increasing computational cost.
A fast learning approach for autonomous navigation using a deep reinforcement learning method
Authors: Ejaz Muhammad Mudassir , Tang Tong Boon , Lu Cheng‐Kai
Published: January 2021

Deep reinforcement learning‐based methods employ an ample amount of computational power that affects the learning process. This paper proposes a novel approach to speed up the training process and improve the performance of autonomous navigation for a tracked robot. The proposed model named “layer normalization dueling double deep Q‐network” has been trained in a virtual environment and then implemented it to a tracked robot for testing in a real‐world scenario. Depth images have been used instead of RGB images to preserve the temporal information. Features are extracted using convolutional neural networks, and actions are derived using the dueling double deep Q‐network. The input data has been normalized before each convolutional layer, which reduces the covariate shift by 69%. This end‐to‐end network architecture of the proposed model provides stability to the network, relieves the burden of computational cost, and converges in much less number of episodes. Compared with three Q‐variant models, the proposed model demonstrates outstanding performance in terms of episodic reward and convergence rate. The proposed model took 12.8% fewer episodes for training compared to other models.
Modelling and control of district heating networks with reduced pump utilisation
Authors: Bastida Hector , Ugalde‐Loo Carlos E. , Abyesekera Muditha , Qadrdan Meysam
Published: January 2021

District heating systems (DHS) provide thermal energy to a range of consumers. Hence, an adequate sizing of the key elements involved in the energy supply system and their management are critical. Pumps and valves are essential components of a DHS as they ensure hydraulic operating conditions are met for the energy distribution process. To achieve this, a hydraulic system is typically controlled by defining a differential pressure set‐point at a critical location in the network. However, a good understanding of the dynamic behaviour of the hydraulic system during the diverse operating conditions is required for its efficient control and to maximise its performance. This paper presents a control strategy based on suitable dynamic models of the hydraulic system. These non‐linear models enable the simulation of the behaviour of mass flow rate, pressure drops in pipes, power consumption of the pump and the heat delivery to meet the thermal loads. Control system design is carried out in MATLAB, and the designed controller is verified with Apros—a commercial process simulation software. It is shown that the hydraulic behaviour of a DHS is well described by the dynamic models presented. In addition, the designed control scheme reduces the electricity consumption of pumps compared with a conventional mass flow rate controller based on a look‐up table and a differential pressure valve.
Two‐dimensional analytical model for double field excitation synchronous machines
Authors: Hosseinpour Alireza , Mardaneh Mohammad , Rahideh Akbar
Published: January 2021

This paper examines a two‐dimensional analytical magnetic model presented for double excitation synchronous machines (DESMs). DESMs employ both permanent magnets (PMs) and excitation coils (ECs), and therefore they offer the advantages of permanent magnet synchronous machines and electrically excited synchronous machines. Due to the linearity assumptions, the problem is divided in three sub‐problems: magnetic field of the PMs, magnetic field of the ECs, and magnetic field of the armature reaction. ECs, which have non‐overlapping structure, are in the rotor slots and carrying DC currents. The armature reaction field prediction is presented for both overlapping and non‐overlapping windings. The magnetic flux density of a brushless PM motor with six stator slots and four rotor slots has been calculated by the proposed analytical method. The results of the analytical method are compared with those of the finite element method to evaluate the effectiveness of proposed model. The presented model can be used for machines with any radius independent magnetization pattern PM and here radial, parallel and multi‐segment Halbach magnetization patterns are used as examples. Moreover, results are validated by numerical method.
UDA‐Net: Densely attention network for underwater image enhancement
Authors: Li Yang , Chen Rong
Published: January 2021

Underwater imaging usually suffers from negative impacts due to the absorption and scattering effects in water. Underwater images thus have unfavourable visual quality to support the work in such environment. This paper addresses the problem of image improvement for single underwater image. The core idea lies in a new enhancement model based on deep learning architecture, in which a feature‐level attention model is developed. This model is a multi‐scale grid convolutional neural network that can facilitate fusing different types of information during representation learning. According to this information combination, a synergistic pooling mechanism is proposed to extract the channel‐wise attention maps to derive the locally weighted features. Therefore, this model can adaptively focus on the feature regions corresponding to degraded patches in one underwater image and improve these patches consistently. Comprehensive experiments are conducted on benchmark and natural underwater images, and it can be demonstrated that this model is effective.
Effect of atomic layer deposited ultra‐thin SiO2 layer on vapour‐liquid‐solid (VLS) grown high dielectric TiO2 film for Si‐based MOS device applications
Authors: Lodh Soham , Chakraborty Rajib , Das Anindita
Published: January 2021

Titanium dioxide (TiO2) ultra‐thin films (≈13 nm) are grown on p‐type Si substrate <400> with and without atomic layer deposited SiO2 (5 nm) passivation layer by employing vapour‐liquid‐solid technique. Scanning electron microscope images reveal high quality film for both the samples. The formation of a crystalline TiO2 film is confirmed by X‐ray diffraction pattern, the orthorhombic (brookite) TiO2 films with [130], [160] and [212] planes are seen from peaks at 2θ= 33.5°, 61.7° and 75.7° on SiO2/Si and Si substrates, respectively. Current–voltage (I–V) characteristics reveal leakage current of ≈8.61 × 10‐8 and ≈4.05 × 10‐6 A/cm2 at +2 V for film with and without SiO2 passivation layer, respectively. Leakage current of TiO2/SiO2/Si device follows Fowler–Nordheim (FN) tunnelling at low field and Poole–Frenkel (PF) tunnelling at high field, whereas TiO2/Si device follows Schottky emission at low field and PF tunnelling at high field. Capacitance–voltage (C–V) characteristics reveal superior quality TiO2 films with high dielectric constant of 44.5 and 35.8 for TiO2/SiO2/Si and TiO2/Si samples, respectively. Significant improvement in frequency dispersion and hysteresis voltage is observed for the sample with SiO2 passivation layer. Introduction of SiO2 passivation layer also improves interface traps and reliability of the device. Hence, a low cost novel alternative technique to grow high quality TiO2 film on p‐type Si substrate with SiO2 as passivation layer is suggested for development of high performance MOS devices.
Photonic scheme for implementing quantum square root controlled Z gate using phase and intensity encoding of light
Authors: Mandal Minakshi , Mukhopadhyay Sourangshu
Published: January 2021

Quantum logic gate operates on a number of qubits, where a controlled Z gate operates on two qubit data. Similarly, the square root of controlled Z (SRCZ) operates also on two qubit data. The SRCZ gate is very much advantageous in quantum computing. Different quantum gates are implemented by using the physical properties of light such as phase, polarization, intensity, and frequency. Here, the authors propose a photonic scheme for implementing quantum SRCZ logic gate using phase as well as intensity encoding. The non‐linear property of Pockels material with proper external biasing voltage is used to develop the scheme.
All‐optical half adder based on non‐linear triangular lattice photonic crystals with improved contrast ratio
Authors: Saadi Keivan , Kashaninia Alireza , Sabbaghi‐Nadooshan Reza
Published: January 2021

An all‐optical half adder based on two‐dimensional photonic crystals is proposed. The proposed structure is composed of a triangular array of dielectric rods made of non‐linear optical material in air. The structure consists of four waveguides and one ring resonator which are formed by removing a number of dielectric rods. The structure has been simulated and analysed by the plane wave expansion method and the finite‐difference time‐domain method. The size of the proposed structure is small with dimensions of 17 μm × 14.8 μm, and the material of the rods and lattice constant are uniform across the structure. The proposed half adder has a minimum contrast ratio of 13.51 dB in the steady state, with a maximum time delay of 2 ps.
Stability of sliding mode controlled buck converters with unmodelled dynamics of circuit elements and hall sensor
Authors: Wang Yanmin , Niu Ziming , Yang Mingyang , Sun Lanxin , Zhang Weiqi
Published: January 2021

This paper investigates the stability problem of sliding mode controlled buck converters affected by unmodelled dynamics of circuit elements and Hall sensor. The parasitic resistors of all elements are contained in the modelling of buck converters. Different from the traditional sliding mode approach based on the nominal model, the circuit parasitic parameters are directly included in the controller design and stability analysis, which divide the regulation region located in the right half axis into four sub‐ranges. It is more accurate and has no need of extra compensator. For Hall sensor, singular perturbation theory is adopted for modelling and analysing, giving a stable condition concerning its dynamic and static parameters. Finally, the influence of the two types of unmodelled dynamics on the whole closed‐loop system is investigated by constructing an equivalent model, giving a more strict stability condition. Simulations and experiments are presented to illustrate the non‐negligible influence of these unmodelled dynamics.
Optimal switching sequence model predictive control for three‐level NPC grid‐connected inverters
Authors: Lyu Jianguo , Yan Han , Ding Jinyong , Wu Qiuwei , Lyu Xun , Sun Zhuang
Published: January 2021

In order to concentrate the frequency spectrum of the output voltage and improve the quality of grid currents for the three‐level neutral point clamped inverter with the model predictive control, this paper proposes an optimal switching sequence model predictive control algorithm. Based on the increments of grid currents and the neutral‐point voltage, the predictive model of the inverter is established in αβ frame. Moreover, including grid currents and the neutral point voltage tracking, the cost function is designed and simplified by arranging the voltage vector sequence in a sampling cycle appropriately. Meanwhile, the calculation of the optimal dwell time for each voltage vector sequence is derived by Lagrange multiplier method, and its solving process is simplified to reduce online computations. Furthermore, according to different voltage vector sectors, a voltage vector sequence preselection principle is introduced in this paper, besides, considering redundant small vectors in a pair have opposite affections on the neutral point voltage, the amount of voltage vector sequence that needs to be verified is further cut down, for obtaining the optimal voltage vector sequence. Finally, the experimental results show that the proposed method can concentrate the output frequency spectrums, which maintains a good quality of grid currents with a small neutral‐point voltage fluctuation.
Discrete‐time sliding mode control based on disturbance observer applied to current control of permanent magnet synchronous motor
Authors: Smidt Gabbi Thieli , Gründling Hilton Abílio , Padilha Vieira Rodrigo
Published: January 2021

This paper proposes a robust current control technique based on a discrete‐time sliding mode controller and a disturbance observer for high‐performance permanent magnet synchronous motor (PMSM) drives. This scheme is applied in the PMSM current control loops to enable the decoupling between the dq current axes, rejection of disturbances caused by mechanical load changes and robustness under parametric uncertainties. In order to ensure the discrete‐time sliding mode properties, which make the system cross the sliding surface at each sampling period, the PMSM model is extended, including the digital implementation delay resulting from the discrete‐time algorithm execution. The development of this method allows direct implementation in microcontrollers and digital signal processors. Stability and convergence analysis are developed in the discrete‐time domain. Simulation and experimental results demonstrate the effectiveness and good performance of the proposed current control approach.
Harmonic mitigation and power quality improvement in utility grid with solar energy penetration using distribution static compensator
Authors: Mahela Om Prakash , Khan Baseem , Alhelou H. H. , Tanwar Sudeep , Padmanaban Sanjeevikumar
Published: January 2021

Distribution static compensator is based on power electronic devices technology which is utilized to supply rapid changes in active power as well as reactive power of utility grids. This is useful to achieve corrections in power factor, balancing of load, compensation of current and filtering of harmonics. Therefore, proposed work investigates the improvement of the power quality by utilizing the distribution static compensator, which is equipped by battery energy storage system and interfaced to distribution network with solar photo voltaic (PV) energy integration. In the present study, distribution static compensator is controlled using a control strategy based on the synchronous reference frame theory. Customised IEEE‐13 nodes test system incorporating solar PV generation and distribution static compensator, is utilized to perform the harmonic mitigation and power quality analysis. Disturbances of power quality and harmonics have been investigated due to abrupt changes in the insolation of solar radiation, outage of PV plant from grid and synchronization of PV plant to grid. MATLAB/Simulink environment is utilized to perform the study. Effectiveness of a developed approach is validated by comparing results of simulation with results extracted in real time using real time digital simulator. Results indicate that the developed method is more effective for harmonic mitigation and improving power quality of electrical power in distribution network integrated with solar PV generation. Performance of the approach is compared with the performance of methods reported in the literature to establish the suitability of the method for harmonics mitigation and power quality improvement in grid with solar energy.
Design and implementation of an improved power‐electronic system for feeding loads of smart homes in remote areas using renewable energy sources
Authors: Zamanzad Ghavidel Behnam , Maalandish Mohammad , Hosseini Seyed Hossein , Sabahi Mehran , Mohammadi‐Ivatloo Behnam
Published: January 2021

This paper suggests an improved step‐up step‐down DC‐DC system along with three‐input and four‐output for smart home applications. In this configuration, two unidirectional power ports have been identified as an Input power supply and one bidirectional power port for the power‐saving element, which can be used as a bidirectional converter for the hybrid vehicle to discharge in a dependent structure. This system can be used to combine renewable energy sources like photovoltaic (PV), fuel cell, battery and hybrid vehicle (HV) to prepare power for remote smart homes. By using this system, serving different loads with different voltage range from high voltage to ultra‐low voltage is possible, also battery charge and discharge with the energy‐saving method can be achieved. In this system, the condition of all possible low‐voltage load and high‐voltage load conditions has been assumed. In this structure, nine power switches have been used, in which all of these switches are in control with independent and dependent duty cycles. By using these cycles, maximum power can be earned from PV sources, bus‐bar voltage regulation, and battery power control is possible too. In this topology, depending on environmental conditions, five scenarios have been identified. To prove the capability of the system before the build, some valid simulations are needed. In this study, the suggested system has been simulated with power system computer aided design/electromagnetic transients including DC (PSCAD/EMTDC).
Natural gas unavailability, price uncertainty, and emission reduction policy in stochastic programming‐based optimal bidding of compressed air energy storage and wind units
Authors: Akbari Ebrahim , Hooshmand Rahmat‐Allah , Gholipour Mehdi , Parastegari Moein
Published: January 2021

Today, with growing expansion of renewable energy resources, electricity production is accompanied by uncertainties. The usage and optimal management of energy storage is one of the effective ways to compensate for these uncertainties. Compressed air energy storage (CAES) is one of the two bulk electricity storage methods for power systems, burning natural gas (NG) to extract the stored energy. Therefore, the NG price uncertainty and gas availability along with carbon emission resulting from burning NG can affect optimal bidding result of this unit. Hence, this study addresses the optimal bidding problem of CAES and wind units, considering the aforementioned issues, while taking into account uncertainties of day‐ahead (DA) and balancing market prices, wind speeds, and NG prices and availability. Furthermore, the dynamics of natural gas flow in the pipeline is modelled. The stochastic programming (SP) method is proposed for solving this problem while taking risk into consideration. The scheduling has been presented for participation of generating company in DA and carbon emission markets. Simulation results indicate the capability of the proposed method in optimal bidding of CAES units while taking gas‐burning related constraints into consideration.
Axial flux generator with novel flat wire for direct‐drive wind turbines
Authors: Cakal Gokhan , Keysan Ozan
Published: January 2021

With the increased demand for renewable energy resources, the direct‐drive wind turbine generators have been studied intensely in the last few decades. One megawatt direct‐drive axial flux permanent magnet generator is presented. The novelty in this design is that instead of conventional stranded wires, the windings of the generator are manufactured from a conductor sheet by cutting and bending processes. This creates critical advantages of superior current ratings, ease of manufacturing, and reduced structural mass. An optimization study is conducted with the presented fully analytical model, and the optimum design is compared with the other wind power generators presented in the literature. At the end, a 1 MW, 480 kNm, and 16 tons direct‐driven generator is achieved.
Optimal operation scheduling of a pump hydro storage system coupled with a wind farm
Authors: Graça Gomes João , Telhada João , Xu Huijin , Sá da Costa António , Zhao Changying
Published: January 2021

The variability in non‐dispatchable power generation makes essential the improvement of production management. This study focuses on the development of an optimisation model for a renewable power unit, composed of a wind farm and a hydro‐pump storage power plant, to maximise its revenue. The combination of the two technologies allows the mitigation of risks associated with wind production and electricity price variability. The problem is formulated using linear programming and encompasses the selling of electricity in the Iberian day‐ahead market and through a bilateral contract. The model is tested and scrutinised with sets of real historical generation and price data. In the day‐ahead market scenario, the proposed methodology leads to an average yearly increase of net revenue ranging between 5% and 20%. In the bilateral contract scenario, the coupling of the wind farm and hydropower plant can reduce the imbalances costs substantially. Furthermore, the work identifies a positive correlation between reservoir dimension, turbine capacity and revenue value. The study also detects a revenue reduction tendency related to the decrease in the volatility of electricity prices.
Deep‐learning–based method for faults classification of PV system
Authors: Zaki Sayed A. , Zhu Honglu , Fakih Mohammed Al , Sayed Ahmed Rabee , Yao Jianxi
Published: January 2021

The installation of photovoltaic (PV) system, as a renewable energy source, has significantly increased. Therefore, fast and efficient fault detection and diagnosis technique is highly needed to prevent unpredicted power interruptions. This is obtained in this study in the following steps. First, an efficient meta‐heuristic algorithm is proposed for extracting the optimal five parameters of the PV model in order to assist the MATLAB simulation model. It is used due to its simplicity and high efficiency in building the PV array simulation. Second, a new PV system deep‐learning convolutional neural network (CNN) fault classification method is presented for the advantage of automatic feature extraction, which reduces the computational burden and increases the high classification capability. Finally, for the practical and theoretical validation of the employed CNN model, normal and six fault cases are selected based on different atmospheric conditions. At same time, three electrical indicators are analysed and accordingly chosen as inputs to the proposed classification model. Moreover, the proposed model is compared with other machine‐learning models.
Optimised yaw model for adaptive wind direction characteristic based on a data‐driven approach
Authors: Dong Xinghui , Wang Yizhou , Wang Hui , Kong Lingxing
Published: January 2021

The wind direction characteristics of different wind turbines (WTs) in different wind farms are diverse and some may have obvious differences. The WTs are prone to ‘over‐yaw’ and ‘under‐yaw’ conditions adopting constant yaw threshold and the same control strategy, which reduces the utilisation efficiency of wind energy. This study makes statistics on the variation characteristics of the WT historical wind direction record data, and obtains the maximum probability angle of wind direction change that is used as the control error angle of yaw start (CEAYS). Then, the wind direction value is monitored in real‐time, and the average wind direction change angle in the sliding window is calculated. When the deviation angle is larger than CEAYS, the characteristics of current wind direction are classified based on the clustering of historical wind direction, and a back‐propagation neural network is used to predict the deviation angle of future short‐term wind direction. The adaptive yaw strategy is constructed by CEAYS and deviation angle. The yaw calculation model is analysed and verified with the increase of power extraction and power consumption as the optimisation indexes. The calculation results show that each WT achieved an average 1% increase in power extraction.
Analysis and design of gradient descent based pre‐synchronization control for synchronverter
Authors: R Hariharan , Mishra Mahesh K.
Published: January 2021

Synchronverter (SV) control has emerged as a popular method for distributed energy resources (DERs), to emulate response of a synchronous generator. In this work, a simple gradient descent based pre‐synchronization control for SV scheme is proposed that varies the reference frequency in SV control alone. Thus, local load connection to DER can remain intact during synchronization with proposed pre‐synchronization method, unlike virtual current based methods. Normally, phase‐locked loop used for synchronization purpose, uses a first order loop filter such as a PI controller. In the proposed pre‐synchronization control, the inherent low pass filter of SV scheme itself is used as a loop filter. Transient response analysis is presented in this work, based on small signal transfer functions derived from the proposed method. From the theoretical analysis of proposed pre‐synchronisation control, design of the parameters is presented. Thus, there is no trial and error basis for parameters tuning in the proposed method, as compared to virtual current based methods. Validation of proposed pre‐synchronisation control through experiments are presented for all initial conditions. The transient response analysis, effectiveness of proposed method during local load changes and grid integration are verified by experimental results.
Parameter identification and uncertainty quantification of a non‐linear pump‐turbine governing system based on the differential evolution adaptive Metropolis algorithm
Authors: Zhang Chu , Peng Tian , Zhou Jianzhong , Nazir Muhammad Shahzad
Published: January 2021

Pumped storage units (PSUs) are now widely used for energy storage. However, the uncertainty of the identification results of the pump‐turbine governing system (PTGS) caused by the random observation noises and the lack of prior knowledge remains an unaddressed issue. In recent years, the differential evolution adaptive Metropolis algorithm (DREAM) based on the Bayesian theory has been extensively used for parameter estimation and uncertainty analysis, but its application to the uncertainty analysis of PTGS has been rare. This study systematically evaluates the applicability of DREAM in the parameter identification and uncertainty quantification of PTGS. A real PSU in China has been employed as a case study for numerical experiments. Four groups of control experiments with different proportions of observation noises and different prior search spaces have been constructed in this study. It can be concluded from this study that: (a) accurate point identification results and effective uncertainty quantification can be obtained simultaneously using DREAM, (b) a lower proportion of observation noises can enhance the efficiency and effectiveness of the DREAM algorithm when applying to PTGS simulation. The DREAM method can narrow the prior search space of PTGS parameters effectively and thus help the hydropower engineers to make decisions.
Hybrid transformerless PV converters with low leakage currents: Analysis and configuration
Authors: Tang Zhongting , Yang Yongheng , Wan Jianghu , Blaabjerg Frede
Published: January 2021

This paper proposes a hybrid transformerless photovoltaic (PV) converter with simultaneous AC and DC outputs. It is specifically suitable for residential PV systems due to its high efficiency, versatility and flexibility, while maintaining lower leakage currents. The proposed converter is configured by replacing the control switch of the boost converter with a transformerless voltage‐source inverter (VSI), enabling multiple outputs. In addition, a symmetrical boost inductor is adopted to clamp the common‐mode voltage as a constant, resulting in low leakage currents. To illustrate the configuration principle, a hybrid converter with a highly efficient and reliable inverter concept (HERIC) as the VSI is exemplified. Besides, the dedicated modulation scheme for the proposed converter is detailed to achieve low leakage currents, reactive power injection and high efficiency. Furthermore, as the shoot‐through state of inverter legs is allowable for the proposed converter, i.e. no need to add dead time, the reliability and power quality of the proposed converter can be improved. Simulations and experimental tests are performed on an example hybrid converter (with an HERIC as the VSI) to validate the analysis.
A comprehensive review on time‐delay compensation techniques for grid‐connected inverters
Authors: Elhassan Garba , Zulkifli Shamsul Aizam , Pathan Erum , Khan Mubashir H. , Jackson Ronald
Published: January 2021

The control of grid‐connected inverters is recently executed with digital microprocessors due to the advances in digital signal processing technology. However, the digital realisation has a drawback of the phase lag induced by the time‐delay. This phase lag challenges the stability and robustness of the controller of the inverters. In view of the challenge, this paper presents a comprehensive review of time‐delay compensation techniques employed in both model‐free (MF), and model‐based (MB) controls of an inverter in grid connection. MF techniques mainly use proportional‐integral, and proportional resonance controllers with some techniques to reduce time‐delay. Meanwhile, for MB, this paper discusses the commonly used control techniques, which are the Smith predictor, modified Smith predictor, deadbeat controller and model predictive controller. Several related techniques from the literature that have been adopted to mitigate the delays are tabulated comprehensively, and critical issues regarding the MF and MB techniques are also discussed. Finally, this paper presents a hypothesis on which technique is superior at present and suggests a hybrid technique from the MF and MB techniques to give readers a direction for further research.
Strict Lyapunov super twisting observer design for state of charge prediction of lithium‐ion batteries
Authors: Sethia Gautam , Majhi Somanath , Nayak Sisir Kumar , Mitra Sagar
Published: January 2021

The effective implementation of battery management system (BMS) in various applications such as electric vehicles (EVs), renewable energy sources (RES) integrated smart‐grids and micro‐grids, necessitates accurate estimation of the battery parameters and states. This paper primarily focuses on offering an improved solution to the state of charge (SOC) estimation problem of lithium‐ion (Li‐ion) batteries. After extensive analysis of the current state‐of‐the‐art methods, a new strict Lyapunov super twisting algorithm (SLSTA) based approach is proposed for precise estimation of SOC under a comprehensive range of uncertainties. The error convergence and robustness of the proposed state observer are demonstrated using Lyapunov stability theory. Since the modelling parameters of the battery equivalent circuit utilised in this paper vary with various operational and external factors, a standard online method is employed for their real‐time identification. The presented method is executed on a lithium‐polymer (LiPo) battery with the help of a dynamic stress test (DST). The experimental results demonstrate that the proposed approach outperforms the well‐known approaches in terms of accuracy, computational complexity, and convergence time.
A regression model for extreme events and the presence of bimodality with application to energy generation data
Authors: Vasconcelos Julio Cezar Souza , Cordeiro Gauss Moutinho , Ortega Edwin Moises Marcos , Ribeiro João Gabriel
Published: January 2021

The application of the theory of extreme values has been growing due to increasing interest in extreme natural events. Many articles on extreme values in data modelling consider unimodal data. This work introduces an appropriate regression for extreme values to detect the presence of bimodality by means of systematic components of two parameters of the odd log‐logistic log‐normal distribution. The global influence is addressed to verify the model robustness and to find possible influential points. Quantile residuals are proposed to detect distribution deficiencies and outliers in the new regression. A real dataset from the electricity generation area is analysed, namely the Santo Antônio Hydroelectric Plant in the state of Rondônia (Brazil), to illustrate the potential of the new regression. The main results indicate that the proposed regression can identify changes in the means and variability of the power generation between extreme events, that is, between the months of June and December.
Bi‐level power management strategy in harmonic‐polluted active distribution network including virtual power plants
Authors: Rohani Arash , Abasi Mahyar , Beigzadeh Arsalan , Joorabian Mahmood , B. Gharehpetian Gevork
Published: January 2021

In this paper, a bi‐level power management strategy is presented for an active distribution network (ADN) in the presence of virtual power plant (VPP). In the proposed strategy, VPP contains renewable energy source (RES), energy storage system (ESS) and electric vehicles (EVs) parking lot coordinated with VPP operator (VPPO), and there is a coordinated framework between VPPO and distribution system operator. The proposed power management strategy (PMS) is considered in the context of the bi‐level optimization problem. In the first level, the optimal harmonic power flow formulation of the ADN is considered by minimizing the summation of energy cost, voltage deviation function and voltage THD% as a normalized objective function. In the second level, the VPP is modeled according to the framework of day‐ahead energy and ancillary services markets. In the next step, the linear format of the proposed non‐linear original problem is obtained with an optimal solution and low calculation burden. Also, the Karush‐Kuhn‐Tucker condition is used to convert the proposed bi‐level PMS approach to a single‐level model, and the scenariobased stochastic programming models the uncertainty of load, energy price, RES power, and EVs demand. Finally, the proposed strategy is applied on the 33‐bus distribution network using GAMS software.
Cascaded diode clamped inverter based grid‐connected photovoltaic energy conversion system with enhanced power handling capability and efficiency
Authors: Rao Pandry Narendra , Saini Lalit Mohan , Nakka Jayaram
Published: January 2021

This paper proposes a high power factor converter based symmetrical topology entitled cascaded diode clamped half‐bridge multi‐level inverter for high voltage photovoltaic (PV) applications along with a hybrid multi‐level pulse width modulation technique. Due to the restriction of peak inverse voltage (PIV) on switching devices, the PV array voltage is restricted. Also, operating at high current causes switching losses and degrades the efficiency, hence limiting its power enhancement. Unbalance in the capacitor voltages leads to high PIV across the devices, which is undesirable. The proposed topology allows connecting double times PV array voltage keeping the PIV and current alike cascaded H‐bridge multi‐level inverter, hence increasing the efficiency, and thereby reducing cost and space. It ensures a capacity enhancement in renewable energy systems linked to on/off‐grid applications. The projected scheme allows even power distribution among the sources along with maximum and efficient energy conversion. The control scheme adopted has the ability to balance dc‐link capacitor voltage in each half‐bridge results in equal PIV and switching losses across all the devices. Simulations of PV connected modified topology is carried out and validated with experimental results.
Optimization of the reverse electrodialysis power output through the ratio of the feed solutions salinity
Authors: Zoungrana Ali , Çakmakci Mehmet
Published: January 2021

A lab‐scale reverse electrodialysis module was constructed to highlight the optimal feed solutions concentration, the flow rate and their importance in improving the power density in reverse electrodialysis. The results showed that the increase of the feed solutions flow rate is beneficial by augmenting the power output, but too high flow rate harms the process performance. Even though the highest power density (0.54 W/m2) was obtained at 60 mL/min with 4 and 428 mm feed solutions, the optimal flow rate determined by this study was 30–45 mL/min. It has been discovered that the variation of NaCl concentration in the low compartment was more sensible in affecting the reverse electrodialysis performance compared to the high compartment solution. The NaCl concentration of 6 and 684 mm appeared to be the optimal values for low compartment and high compartment solutions, respectively, for high performance and efficient power generation with reverse electrodialysis using seawater and river. When 1/3 of the minimum flow rate of the Ceyhan River running into the Mediterranean Sea is operated in the reverse electrodialysis system, at least 17.18 × 106 KWh electrical energy was estimated to be generated per year, with a power density of 0.705 m2/W.
Demand response integration in microgrid planning as a strategy for energy transition in power systems
Authors: Mina‐Casaran Juan D. , Echeverry Diego F. , Lozano Carlos A.
Published: January 2021

For a long time, the planning of power systems has considered technical and economic aspects related to Supply Side Management. Given that this traditional planning approach is not very flexible, and due to new technological trends like the adoption of smart grids, it has become necessary for demand to play an active role in the operation of power systems. This raises the need to implement a new process for power system planning, focusing on technologies such as microgrids with Demand Side Management, which contribute to the sustainable development of communities. In this paper, a comprehensive review of microgrid planning, considering energy end‐user participation through Demand Response, is carried out. In addition, the main features and implications of Demand Response programs in microgrid planning are presented. As a contribution, in order to trigger a paradigm shift on how power systems are planned, the benefits of bottom‐up microgrid planning are presented.
GNSS data demodulation over fading environments: Antipodal and M‐ary CSK modulations
Authors: Ortega Lorenzo , Vilà‐Valls Jordi , Poulliat Charly , Closas Pau
Published: January 2021

This article investigates new strategies to compute accurate low‐complexity log‐likelihood ratio (LLR) values based on the Bayesian formulation under uncorrelated fading channels for both antipodal and code shift keying modulations when no channel state information (CSI) is available at the receiver. These LLR values are then used as input to modern error‐correcting schemes used in the data decoding process of last‐generation Global Navigation Satellite System signals. Theoretical analysis based on the maximum achievable rate is presented for the different methods in order to evaluate the performance degradation with respect to the optimal CSI channel. Finally, the frame error rate simulation results are shown, validating the appropriate performance of the proposed LLR approximation methods.
Magnetic properties of an ACSR conductor steel core at temperatures up to 230 ∘C and their impact on the transformer effect
Authors: Meyberg Ruyguara A. , Absi Salas Farith M. , Domingues Luis Adriano M. C. , Sens Márcio A. , Correia de Barros Maria Teresa , Lima Antonio C. S.
Published: January 2021

The current density distribution between layers of steel‐reinforced conductors having an odd number of aluminum layers is modified by the transformer effect, as a result of the steel core magnetisation. This redistribution of current affects the conductor alternating current resistance, the transitory temperature radial distribution, and depends on the total current intensity, and the core permeability, which varies with temperature. Although some steel‐cored conductors are designed to operate at high temperatures to maximise their current capacity, the core magnetic properties in this scenario need to be further investigated. This paper presents the results of experimental work on an ACSR conductor, and on its steel core to investigate the steel core magnetic properties over an extended temperature range, and how they affect the transformer effect. Results show an unexpected hump‐shaped variation of the permeability with temperature in a specific range of magnetic field strength, with maximum permeability values occurring at a temperature ranging from 150 to 170 C , which were not covered in previous studies. A similar behaviour is seen in the variation of the axial magnetic flux with increasing conductor temperature, causing the flux to decrease at high temperatures, and thus weakening the transformer effect during the conductor operation.
The challenge of controlling microgrids in the presence of rare events with deep reinforcement learning
Authors: Levent Tanguy , Preux Philippe , Henri Gonzague , Alami Réda , Cordier Philippe , Bonnassieux Yvan
Published: January 2021

The increased penetration of renewable energies and the need to decarbonise the grid come with a lot of challenges. Microgrids, power grids that can operate independently from the main system, are seen as a promising solution. They range from a small building to a neighbourhood or a village. As they co‐locate generation, storage and consumption, microgrids are often built with renewable energies. At the same time, because they can be disconnected from the main grid, they can be more resilient and less dependent on central generation. Due to their diversity and distributed nature, advanced metering and control will be necessary to maximise their potential. This paper presents a reinforcement learning algorithm to tackle the energy management of an off‐grid microgrid, represented as a Markov Decision Process. The main objective function of the proposed algorithm is to minimise the global operating cost. By nature, rare events occur in physical systems. One of the main contribution of this paper is to demonstrate how to train agents in the presence of rare events. Merging the combined experience replay method with novel methods called ‘Memory Counter’ unstucks the agent during its learning phase. Compared to baselines, an extended version of double deep Q‐network with a priority list of actions into the decision making strategy process lowers significantly the operating cost. Experiments are conducted using 2 years of real‐world data from Ecole Polytechnique in France.