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The magnetic core of a conventional tubular flux-switching permanent magnet machine (TFSPMM) is made of axially laminated silicon steel sheets. The laminated direction is consistent with the motion direction of the mover. According to the flux-switching principle, a large amount of eddy current loss will be generated when the alternating flux passes through the silicon steel sheets vertically. The soft magnetic composite (SMC) material is magneto-thermal isotropy and has low eddy current loss, which can be used in TFSPMM. However, the permeability of SMC is much lower than that of silicon steel sheets, and the hysteresis loss is large at low frequency. When the two materials are used as the hybrid material magnetic core in the machine, their respective advantages can be used to improve the thrust force of the machine and reduce core loss. In order to study the effect of SMC core and hybrid material magnetic core on the machine, the performances of TFSPMM with different kinds of cores are calculated and compared by finite element method (FEM), including magnetic density distribution, permanent magnet (PM) flux linkage, back-electromotive force (EMF), inductance, detent force, thrust force and core loss. Finally, a prototype machine with hybrid material magnetic core is made and experimental verification is carried out.

Nonlinear excitation controllers for power systems are considered as promising replacements for their linear counterparts, such as power system stabilizers (PSSs). However, Lyapunov based nonlinear controllers currently available in literature have been derived using simplified third order machine dynamics and have not been tested on realistic power system models, and the costs associated with these controllers have also not been studied. The paper aims to assess the performance of existing Lyapunov controllers for a detailed benchmark power system model. Two new Lyapunov based nonlinear controllers have been derived using IEEE recommended subtransient machine model, and their performance and costs have been compared with existing methods. The controllers have been evaluated using metrics for both small signal stability and transient stability. It has been proven theoretically (via stability analysis) and demonstrated via simulations that proposed Lyapunov controllers are more viable options for control of power system oscillatory dynamics. It has also been demonstrated that detailed subtransient model is required for accurate estimation of states via dynamic state estimation for use in the realization of Lyapunov controllers.

This paper presents improved iron loss analytical prediction models for interior permanent magnet synchronous motors (IPMSMs) used in electric vehicles. The effects of slotting harmonics, pulse-width modulation (PWM) carrier harmonics, temperature rise and mechanical stress are considered in the proposed models. Specifically, by investigating the stator flux density as piecewise linear with trapezoidal waveform, the iron losses in the teeth and yoke regions are calculated separately, considering the different magnetic field distributions and waveforms. To deliberate the PWM harmonic influence, a correction coefficient is added to the hysteresis loss models, while the eddy current loss models are updated by summing all the eddy current losses caused by the power supplying current harmonics. Moreover, the coupling interaction effects of magnetic, thermal, and stress fields on the empirical coefficients of hysteresis and eddy current losses are analyzed in detail and also implemented in the iron loss prediction process. The feasibility and superiority of the proposed models are verified by numerical and experimental case studies on an IPMSM prototype.

The popularization of solar generation enables residential households to supply their loads and trade the surplus energy through residential peer-to-peer (P2P) energy trading market. Facing the increasing complexity of the market structure and decision-making strategies, this article proposes a P2P energy trading model for residential households, and the objective is to help the centralized market coordinator optimize the benefit of participants under such a P2P market. To this end, a new mathematical model, including the rules for buying and selling energy, is presented. In this model, a supply function bidding mechanism is formulated to match the power supply imbalance and calculate the market-clearing price. An optimization problem is formulated to identify the optimal strategies for energy buying and selling, which consists of two parts: the first part is to maximize the social welfare; the second part is to minimize the unfair benefit distribution that participants can gain through P2P energy trading. The case study based on the real data for four different household categories has revealed that households can achieve 26.38% net cost reduction, and the proposed fair benefit distribution function also can fairly allocate the benefit by enforcing households' benefit variance indexes at a low level.

Lithium-ion (Li-ion) batteries are widely utilized as energy storage units owing to their high energy density and safety. However, when battery degradation occurs, Li-ion batteries deteriorate and become untrustworthy. Accurate diagnosis and identification of the degradation modes (DMs) constitute a critical task for systems employing Li-ion batteries. Current diagnosis methods are usually postanalysis and cannot be directly employed for diagnosing the batteries that are in operation. This study proposes a ResNet-50-based diagnosis model for DMs, which can quantify the contribution of three DMs for the synthetic datasets. Because the real and synthetic datasets are independent and identically distributed, it is difficult to apply this model to the real datasets. To bridge the gap, this article proposes a deep domain adaptation method to minimize the classification loss and domain adaptation loss between the source domain (synthetic) and the target domain (real), such that the degradation knowledge learned from the synthetic batteries can be transferred to the real batteries. The model's input, structure, and parameters are optimized through simulation tests to improve the diagnosis accuracy. A validation session is designed to verify the classification accuracy of unlabeled DMs of the lithium iron phosphate (LFP) battery. The results show that the proposed method can effectively transfer the knowledge of degradations from synthetic batteries to real-world LFP batteries to diagnose and identify DMs of LFP batteries with relatively high classification accuracy.

To monitor the thermal performance of the battery, the surface temperature (ST) of the battery is normally directly measured by temperature sensors. As the number of battery cells or strings increases, the number of temperature sensors increases proportionally. This increases the cost and reduces the reliability of the battery systems. To solve this problem, this article introduces a method to accurately estimate the ST of lithium-ion batteries using a recurrent neural network (RNN) with gated recurrent unit (GRU). First, this article analyzes the battery ST distribution theory and proves that it is a time series task since the present ST is conditioned on the previous state. Second, a GRU-RNN model is adopted to estimate the battery ST as this model has the ability to automatically encode dependencies in time and accurately estimate the battery ST without using any physical battery models or filters. Third, an improved data normalization method is proposed to enhance the estimation accuracy and robustness. Fourthly, the proposed data normalization method is incorporated into the stacked GRU-RNN to estimate the battery ST from compulsory online signals. The proposed method is verified with LiFePO4 using US06 and Federal Urban Driving Schedule (FUDS) profiles under four fixed ambient temperatures and with LiNiCoAIO2 using a mixed dynamic profiles under varying ambient temperature ranges (from 10 °C to 25 °C). The estimation error using mean absolute error (MAE) is less than 0.2 °C over all the fixed ambient temperature conditions and 0.42 °C over the varying ambient temperature conditions.

This paper shows how to exploit the degree of freedom represented by the common-mode voltage, which is inherent in three-phase Cascaded H-Bridge (CHB) converters, to minimize the total Root Mean Square (RMS) current value of the distributed dc sources. An optimal common-mode voltage injection law is derived, constituting a Maximum Power Per Ampere (MPPA) modulation strategy with respect to the currents of the dc sources. The potential benefits introduced by the proposed algorithm are analyzed in the context of battery-fed CHB converters and validated experimentally with a three-time-constant Randles model of a battery cell. The MPPA strategy is compared with traditional common-mode voltage injection methods, and battery loss reduction is demonstrated. The obtained battery energy saving is dependent on the converter modulation index and power factor. Experimental tests on a 36-cell full-bridge CHB converter validates the simulation and numerical derivation. To demonstrate the efficacy of the proposed method in practical applications, a 3 MW Energy Storage System (ESS) and a 110 kW Battery Electric Vehicle (BEV) undergoing standard drive cycles are presented as case studies. Compared to traditional modulation strategies, the MPPA strategy reduces the battery losses by up to 10.9% in the ESS and 27.5% in the BEV application.

Compared with large-scale physical batteries, aggregated and coordinated generic energy storage (GES) resources provide low-cost, but uncertain, flexibility for power grid operations. While GES can be characterized by different types of uncertainty, the literature mostly focuses on decision-independent uncertainties (DIUs), such as exogenous stochastic disturbances caused by weather conditions. Instead, this manuscript focuses on newly-introduced decision-dependent uncertainties (DDUs) and considers an optimal GES dispatch that accounts for uncertain available state-of-charge (SoC) bounds that are affected by incentive signals and discomfort levels. To incorporate DDUs, we present a novel chance-constrained optimization (CCO) approach for the day-ahead economic dispatch of GES units. Two tractable methods are presented to solve the proposed CCO problem with DDUs: (i) a robust reformulation for general but incomplete distributions of DDUs, and (ii) an iterative algorithm for specific and known distributions of DDUs. Furthermore, reliability indices are introduced to verify the applicability of the proposed approach with respect to the reliability of the response of GES units. Simulation-based analysis shows that the proposed methods yield conservative, but credible, GES dispatch strategies and reduced penalty cost by incorporating DDUs in the constraints and leveraging data-driven parameter identification. This results in improved availability and performance of coordinated GES units., Comment: Decision-dependent uncertainty; Generic Energy Storage

The voltage regulation of a grid-tied PV system is constantly perturbed for the maximum power point tracking (MPPT), the transients of which determine the dynamic MPPT efficiency. Meanwhile, the PV-link voltage ripple coupled from the DC to single-phase AC power conversion adversely affects the static MPPT efficiency. Thus, optimal dynamic and steady-state performance is necessary to maximize the solar energy harvest. This brief proposes a novel adaptive mechanism preserving the advantages of two observers: the classical extended state observer and reduced-order generalized proportional integral observer. Simulation and experimental results prove the enhanced performance regarding the overshoot-free fast transient response and steady state against disturbances, even with small input capacitance. Meanwhile, the observer switching happens seamlessly through the proposed mechanism, which is confirmed analytically and experimentally.

A novel pulsewidth modulation (PWM) technique has been presented in this article, which enables the converter to operate in the conventional (boost) mode as well as in the buck mode (proposed). The proposed buck PWM method significantly reduces the current stresses of the components compared to the boost mode. Using this combined modulation technique, a dual-mode operation of such a class of inverters is demonstrated to accommodate a wide input voltage variation while maintaining the regulation of the output ac voltage. The proposed modulation technique is simple to implement and has been implemented in some of the conventional Type-I and Type-II inverters to demonstrate a very wide input voltage range (200-400 V dc), while keeping a fixed output voltage of 110 V ac rms. The theoretical analysis, key operating circuit, and waveforms are presented along with the thermal profile of various switches. Experimental results show that Type-I and Type-II inverters rated at 500 VA have around 95.5 ± 1% efficiency for a wide load range with a peak efficiency of 97.3% in buck + invert mode for Type-I and 95.5% for Type-II inverters.

This papers uses stochastic geometry and queuing theory to study he scalability of long-range (LoRa) networks, accounting for duty cycling restrictions and imperfect spreading factor (SFs) orthogonality. The scalability is characterised by the joint boundaries of device density and traffic intensity per device. Novel cross-correlation factors are used to quantify imperfect SForthogonality. Our results show that a proper characterisation of LoRa orthogonality extends the scalability of the network. They also highlight that for low/medium densities decreasing the SF extends the spanned spectrum of sensing applications characterised by their traffic requirements (i.e. sensing rate). However, for high density (> 104 nodes/Km2 ), the Pareto frontiers converge to a stability limit governed by the SF allocation scheme and the predefined capture thresholds. The results further evince the importance of capturing threshold distribution among the SFs to mitigate the unfair latency.

In this work, a parametric up-converter amplifier is introduced as a wideband impedance-matching network. Chu’s limit restricts the minimum Q-factor of unloaded small antennas. However, the practical bandwidth (BW) of small antennas is defined by their loaded Q-factor. By connecting a small antenna to an amplifier with a real input impedance that is several times greater than the radiation resistance of the antenna, we propose increasing the return loss, which leads to a reduction in the loaded Q-factor and an increase in the BW. In addition, a parametric amplifier is used because, in comparison with transistor amplifiers, it offers low-noise characteristics. The gain of the low-noise parametric amplifier compensates for the loss due to the imposed mismatch. Our simulation result shows BW improvements up to 32 times can be accomplished by trading 2 dB of noise figure (NF), compared to the 15 dB suggested by Chu’s limit for a lossy antenna. Accepted version

Due to their unique capabilities, hollow-core waveguides are playing an increasingly important role, especially in meeting the growing demand for integrated and low-cost photonic devices and sensors. Here, we present the antiresonant hollow-core microgap waveguide as a platform for the on-chip investigation of light-gas interaction over centimeter-long distances. The design consists of hollow-core segments separated by gaps that allow external access to the core region, while samples with lengths up to 5 cm were realized on silicon chips through 3D-nanoprinting using two-photon absorption based direct laser writing. The agreement of mathematical models, numerical simulations and experiments illustrates the importance of the antiresonance effect in that context. Our study shows the modal loss, the effect of gap size and the spectral tuning potential, with highlights including extremely broadband transmission windows (>200 nm), very high contrast resonance (>60 dB), exceptionally high structural openness factor (18%) and spectral control by nanoprinting (control over dimensions with step sizes (i.e., increments) of 60 nm). The application potential was demonstrated in the context of laser scanning absorption spectroscopy of ammonia, showing diffusion speeds comparable to bulk diffusion and a low detection limit. Due to these unique properties, application of this platform can be anticipated in a variety of spectroscopy-related fields, including bioanalytics, environmental sciences, and life sciences.

This paper investigates the dynamic voltage support (DVS) of inverter-based resources (IBRs) under voltage sags to enhance the low-voltage ride-through performance. The DVS problem is formulated as a nonconvex optimization program that maximizes the positive-sequence voltage magnitude at the point of common coupling (PCC) subject to the current, active power, and synchronization stability constraints. Then, we perform the optimality analysis to explore the global optimum analytically. It is found that the unique global optimum has three scenarios/stages (S1S3), which depends on the specific relationship among grid voltage, grid strength, and physical limits of IBRs. The explicit closed-form solutions in S1 and S3 are derived, and the optimality conditions (an implicit solution) of S2 are provided, which guarantees the global optimality and enables the compatibility with fast real-time control. Finally, we implement the optimum with a grid-connected single-stage photovoltaic (PV) power plant by integrating a DVS controller. Dynamic simulations are carried out under different scenarios to test our proposal. The robustness against model errors is also discussed. Dynamic simulations are carried out under different scenarios to test our proposal and compare it with other existing methods.

This article proposes a hybrid method to find the optimal place of dc power flow controllers (DC-PFC)s in HVdc grids. This hybrid method consists of max flow-min cut theory with Ford–Fulkerson augmenting paths and combining sensitivity analysis to find the most important HVdc lines and the best placement. In this article, series DC-PFC is modeled and get placed. A significant advantage of this method is, reducing the computational burden of calculations by finding the most important and susceptible HVdc lines to get overloaded without focusing on other unnecessary HVdc lines. The static security of the power system strongly depends on the placement of DC-PFCs, which has become an important issue because of rapid-developing of HVdc grids. Static security studies have been done on an eight-terminal HVdc grid incorporating double scenarios.

Harmonic current estimation is required in active power filters for compensation purposes. The most efficient way of calculating the total harmonic current up to the infinite order is to subtract the fundamental component from the distorted current. Direct determination of the fundamental component of a distorted current of mains frequency was realized recently with a third-order modulation integral observer. This article shows that using an exponential modulation function (Exp-MF) for the integral observer will: 1) significantly enhance the robustness of the observer against noise and 2) automatically remove the low-frequency envelope arising from the D/A and A/C sampling processes compared to previous polynomial modulation function. These new and advantageous features are supported with detailed analysis and experimental verification. The robust observer can be implemented in grid-connected power electronics circuits that require the instantaneous information of the fundamental and/or harmonic currents. Practical comparative tests with the adaptive notch filter and recursive discrete Fourier transform (DFT) methods in an active power filter have confirmed the good performance under both the steady and dynamic states of the proposed Exp-MF integral observer.

This work presents the modelling fundamentals to study the dynamics of inverter-based resources (IBRs) in weak distribution grids and derive disturbance performance characteristics describing how they should behave under different conditions. More specifically, with respect to small-signal disturbances we study the possible voltage violations following frequency response from IBRs due to low system strength and also propose specific design requirements for IBR d-q current control to guarantee a stable response. In the context of large-signal disturbances, we highlight how active power-voltage control may not be effective due to a delay imposed by the physical features of the distribution network. Further, we mathematically discuss how IBR reactive power control could enhance its phase-locked loop stability. The proposed disturbance performance characteristics are then integrated into the IBR converter control via a novel voltage-priority reference generation strategy. Simulation results on a real Australian network show the efficacy of the proposed operational and control design requirements, and highlight possible unexpected active/reactive power interactions in weak distribution grids.

With their unique optoelectronic properties, perovskite nanocrystals are highly advantageous semiconductor materials for tailored light applications including an interaction with circularly polarized light. Although chiral perovskite nanocrystals have been obtained by the adsorption of chiral molecules, their chiroptical response is still intrinsically weak. Alternatively, perovskites have been combined with artificial chiral surfaces demonstrating enhanced chiroptical responses. However, bulk perovskite films of considerable thickness were required, mitigating the perovskite’s photoluminescence efficiency and processability. Here we developed a hybrid system of a dielectric chiral nanoantenna array that was coated with a monolayer of cubic all-inorganic lead halide perovskite nanocrystals. By tuning the thickness of the perovskite film down to one monolayer of nanocrystals, we restricted the interactions exclusively to the near-field regime. The chiral surface built of z-shaped Si nanoantennas features pronounced chiral resonances in the visible to IR region. We demonstrate that the two-photon excited photoluminescence emission of the nanocrystals can be enhanced by up to one order of magnitude in this configuration. This emission increase is controllable by the choice of the excitation wavelength and polarization with an asymmetry in emission of up to 25% upon left and right circularly polarized illumination. Altogether, our findings demonstrate a pathway to an all-optical control and modulation of perovskite light emission via strong polarization sensitive light–matter interactions in the near-field, rendering this hybrid system interesting for sensing and display technologies.

Functional muscle network is a critical concept in describing functional synergistic muscle synchronization and functional connectivity needed for the execution of complex motor tasks. Muscle network is typically derived from decomposition of intermuscular coherence (IMC) at different frequency bands of multichannel electromyography (EMG) measurements, which potentially limits out-of-clinic applications. In this investigation, we introduce muscle network analysis to assess the functional coordination and functional connectivity of muscles based on mechanomyography (MMG). We focus on a targeted group of muscles vital for activities of daily living (ADLs) in the upper-limb. Functional muscle networks are evaluated for ten able-bodied participants and three upper-limb amputees. Muscle activity was acquired from a custom-made wearable armband of MMG sensors placed over four superficial muscles around the forearm (flexor carpi radialis (FCR), brachioradialis (BR), extensor digitorum communis (EDC), and flexor carpi ulnaris (FCU)) while participants performed four different hand gestures. Muscle connectivity analysis at multiple frequency bands shows significant topographical differences across gestures for low (i.e., 5 Hz) and high (i.e., 12 Hz) activation frequencies as well as observable network differences between amputee and non-amputee subjects. Results demonstrate MMG can be used for the analysis of functional muscle connectivity and mapping of synergistic functional synchronization of upper-limb muscles in complex movement tasks. The new physiological modality provides key insights into neural circuitry of motor coordination. Findings further offer the concomitant outcomes of demonstrating feasibility of MMG to map muscle coherence from a neurophysiological perspective and providing a mechanistic basis for its translation in human-robot interface.

Recently, inverters with a common ac and dc ground are gaining significant interest due to their zero common-mode voltage that made them particularly attractive for the solar photovoltaic application. However, the dc source current of the existing topologies is discontinuous, and their voltage gains are limited. This article proposes a novel common-ground-type boost inverter that achieves continuous dc source current with significantly enhanced dynamic voltage gain. It consists of one boost inductor, four capacitors, nine power MOSFETs, and two power diodes. Voltage boosting and generation of nine-level ac voltage are achieved concurrently within a single-stage operation. The operation of the proposed CGT-9L-BI inverter is analyzed. Simulation and experimental results are presented for validation.

Streaming data provides substantial challenges for data analysis. From a computational standpoint, these challenges arise from constraints related to computer memory and processing speed. Statistically, the challenges relate to constructing procedures that can handle the so-called concept drift--the tendency of future data to have different underlying properties to current and historic data. The issue of handling structure, such as trend and periodicity, remains a difficult problem for streaming estimation. We propose the real-time adaptive component (RAC), a penalized-regression modeling framework that satisfies the computational constraints of streaming data, and provides the capability for dealing with concept drift. At the core of the estimation process are techniques from adaptive filtering. The RAC procedure adopts a specified basis to handle local structure, along with a least absolute shrinkage operator-like penalty procedure to handle over fitting. We enhance the RAC estimation procedure with a streaming anomaly detection capability. The experiments with simulated data suggest the procedure can be considered as a competitive tool for a variety of scenarios, and an illustration with real cyber-security data further demonstrates the promise of the method.

Future mobile networks are envisioned to become multi-service systems, enabling the dynamic deployment of services with vastly different performance requirements, accommodating the needs of diverse service providers. Virtualizing the mobile network infrastructure is of fundamental importance for realizing this vision in a cost-effective manner. While there have been extensive research efforts in virtualization for the mobile core network, virtualization in the radio access network (RAN) is still at an early stage. In this paper, we present AIRTIME, a new RAN slicing system that enables the dynamic on-the-fly virtualization of RANs, with the programmability required by service providers to customize any aspect of their virtual RAN to meet their service needs. We present a prototype implementation of AIRTIME and evaluate the: (i) capacity to create virtual RANs on-the-fly, (ii) performance experienced by slice owners, (iii) isolation among multiple virtual RANs sharing the same physical infrastructure, and (iv) scalability to accommodate a large number of virtual RANs. Accepted version

An ability to extract detailed spirometry-like breath-ing waveforms from wearable sensors promises to greatly improve respiratory health monitoring. Photoplethysmography (PPG) has been researched in depth for estimation of respiration rate, given that it varies with respiration through overall intensity, pulse amplitude and pulse interval. We compare and contrast the extraction of these three respiratory modes from both the ear canal and finger and show a marked improvement in the respiratory power for respiration induced intensity variations and pulse amplitude variations when recording from the ear canal. We next employ a data driven multi-scale method, noise assisted multivariate empirical mode decomposition (NA-MEMD), which allows for simultaneous analysis of all three respiratory modes to extract detailed respiratory waveforms from in-ear PPG. For rigour, we considered in-ear PPG recordings from healthy subjects, both older and young, patients with chronic obstructive pulmonary disease (COPD) and idiopathic pulmonary fibrosis (IPF) and healthy subjects with artificially obstructed breathing. Specific in-ear PPG waveform changes are observed for COPD, such as a decreased inspiratory duty cycle and an increased inspiratory magnitude, when compared with expiratory magnitude. These differences are used to classify COPD from healthy and IPF waveforms with a sensitivity of 87% and an overall accuracy of 92%. Our findings indicate the promise of in-ear PPG for COPD screening and unobtrusive respiratory monitoring in ambulatory scenarios and in consumer wearables.

In this paper, we investigate the control of inverterbased resources (IBRs) for optimal voltage unbalance attenuation (OVUA). This problem is formulated as an optimization program under a tailored dq-frame, which minimizes the negativesequence voltage at the point of common coupling (PCC) subject to the current, active power, synchronization stability, and feasibility constraints. The program is inherently nonconvex and intractable. So, to guarantee the optimality, a rigorous optimality analysis is performed by leveraging analytical optimization. The analysis is divided into two cases: full mitigation of VU and partial attenuation of VU. For the former case, we directly solve the original program since the resultant VU is immediately deducible. For the latter one, directly solving the problem becomes very hard. Thus, we reformulate the program into an equivalent but more tractable form under certain conditions, by which the analytical optimum can be derived. It is found that the optimum trajectory has three stages (O1–O3), depending on two critical boundary conditions (C1 and C2). We implement the optimum with a photovoltaic (PV)-storage system by developing an OVUA controller. The proposed approach is demonstrated by dynamic simulations under different VU conditions and is compared with several existing practices. Finally, we discuss the extension of the proposed solution in a multi-IBR system.

We consider optimal control problems for continuous-time systems with time-dependent dynamics, in which the time-dependence arises from the presence of a known exogenous signal. The problem has been elegantly solved in the case of linear input-affine systems, for which it has been shown that the solution has a remarkable structure: it is given by the sum of two contributions; a state feedback, which coincides with the unperturbed optimal control law, and a purely feedforward term in charge of compensating the effect of the exogenous signal. The objective of this note is to extend the above result to nonlinear input-affine systems. It is shown that, while some of the relevant features of the linear case indeed rely heavily on linearity and are not preserved in the nonlinear setting, several structural claims can be proved also in the nonlinear case.

Delivering low-carbon heat will require the substitution of natural gas with low-carbon alternatives such as electricity and hydrogen. The objective of this paper is to develop a method to soft-link two advanced, investment-optimising energy system models, RTN (Resource-Technology Network) and WeSIM (Whole-electricity System Investment Model), in order to assess cost-efficient heat decarbonisation pathways for the UK while utilising the respective strengths of the two models. The linking procedure included passing on hourly electricity prices from WeSIM as input to RTN, and returning capacities and locations of hydrogen generation and shares of electricity and hydrogen in heat supply from RTN to WeSIM. The outputs demonstrate that soft-linking can improve the quality of the solution, while providing useful insights into the cost-efficient pathways for zero-carbon heating. Quantitative results point to the cost-effectiveness of using a mix of electricity and hydrogen technologies for delivering zero-carbon heat, also demonstrating a high level of interaction between electricity and hydrogen infrastructure in a zero-carbon system. Hydrogen from gas reforming with carbon capture and storage can play a significant role in the medium term, while remaining a cost-efficient option for supplying peak heat demand in the longer term, with the bulk of heat demand being supplied by electric heat pumps.

The article reviews significant advances in networked signal and information processing, which have enabled in the last 25 years extending decision making and inference, optimization, control, and learning to the increasingly ubiquitous environments of distributed agents. As these interacting agents cooperate, new collective behaviors emerge from local decisions and actions. Moreover, and significantly, theory and applications show that networked agents, through cooperation and sharing, are able to match the performance of cloud or federated solutions, while offering the potential for improved privacy, increasing resilience, and saving resources.

This paper presents a new sensorless control strategy with the fault-tolerant ability for switched reluctance motors (SRMs). Nowadays, many rotor position detection methods are utilized, but most of them increase the system cost due to the additional hardware or complex calculations. To address this problem, various methods of using phase inductance to estimate the rotor position and speed have been proposed. However, the traditional inductance detection method will cause signal distortion due to the presence of interference signals and the unbalanced phase inductance. In particular, the turn-ON angle is advanced. To further improve the stability of the control system, the interference signals are eliminated via establishing the reference time of the inductance characteristic point and the error band in this work. When the position signal disappears, the inductance characteristic points of adjacent phases will be adopted for signal compensation to realize fault-tolerant control. Finally, an SRM drive system is used to verify the effectiveness of the proposed control strategy.

This paper proposes a new position sensorless method for switched reluctance motor (SRM) drive control based on a sliding mode observer using nonlinear Fourier flux linkage model. First, to obtain an accurate mathematical model of SRM for establishment of sliding mode position observer (SMPO), an analytical method based on Fourier polynomial function is introduced to regress the nonlinear relationship between flux linkage, position and current. Then, a new SMPO is designed in detail to estimate the rotor position, including the establishment of SRM model of sliding mode observer, and analysis of estimation error and convergence conditions of SMPO. Finally, position sensorless control of SRM drive based on SMPO is carried out in terms of accuracy and robustness in simulation. Experiments are also given on a dynamic testing platform to verify the effectiveness of the proposed position sensorless scheme.

In this paper, afull-car prototype of the recently proposed mechatronic suspension, Series Active Variable Geometry Suspension (SAVGS), is developed for on-road driving experimental proof of concept, aiming to be adopted by suspension OEMs (original equipment manufacturers) as an alternative solution to fully active suspensions. Particularly, mechanical modifications are performed to both corners of the front double-wishbone suspensionof a production car, with active single-links attached to the upper-ends of the spring-damper units, while both corners of the rear suspension remain inthe original (passive) configurations.The mechanical modifications involve innovatively designed parts to enable the desired suspension performance improvements, while maintaining ride harshness at conventional levels.Areal-time embedded system is further developed to primarily implement:1) power supply, data acquisition and measurementsof the vehicle dynamics related variables, and 2) robust control application for the ride comfort and road holding enhancement, which is based on a derived linearized model of the full-car dynamics and a newly synthesizedH-infinity control scheme. Results obtained from on-road driving experiments are inessential agreement with numerical simulation results also produced. Overall, the full-car prototypeof SAVGS demonstrates promising suspension performance,with anaverage 3 dB attenuation (or equivalently 30% reduction) of the chassis vertical acceleration at aroundthe human-sensitive frequencies (2-5Hz),as compared to the original vehicle with the passive suspension system. More importantly, the prototype also indicatesthe practicality of the solution, as the SAVGS retrofit to a real car is achieved by simple mechanical modifications, compact actuator packaging, smallmass increment(21.5kg increase with respect to the original vehicle), limited power usage (an average value of 134W in DC batteries with a Class D random road) and acceptable economic cost.

This paper presents an investigation of a 3D-magnetic flux permanent magnet synchronous motor (3D-MF PMSM) used for electric vehicle applications. The investigated 3D-MF PMSM consists of an integrated radial-flux and axial-flux structure. It has two radial-flux air-gaps and two axial-flux air-gaps, as well as a toroidal winding wound stator. The integrated structure helps to concentrate all the flux within the motor to maximize torque production. Moreover, there are no end-windings in this motor and all the stator windings effectively are used in torque production. A comprehensive performance evaluation, in terms of the back-electromotive force, average output torque, cogging torque, torque ripple, flux-weakening capability, etc., of the investigated 3D-MF PMSM is conducted. An interior PMSM is purposely included as a benchmark for comparison. The results show that compared to the benchmark interior PMSM, the original 3D-MF PMSM exhibits significantly improved torque density, higher power factor, and higher efficiency, but suffers from serious cogging torque and torque ripple. Accordingly, an unaligned arrangement is introduced to the 3D-MF PMSM. As a result, the cogging torque and torque ripple are significantly reduced. National Research Foundation (NRF) Submitted/Accepted version This work was supported by National Research Foundation (NRF) Singapore under Grant NRF-NRFF12-2020-0003.

Motivated by the challenges arising in the field of multi-agent systems (MAS) control, we consider linear heterogenous MAS subject to local communication and investigate the problem of designing distributed controllers for such systems. We provide a game theoretic framework for systematically designing distributed controllers, taking into account individual objectives of the agents and their possibly incomplete knowledge of the MAS. Linear state-feedback control laws are obtained via the introduction of a distributed differential game, namely the combination of local non-cooperative differential games, which are solved in a decentralised fashion. Conditions for stability of the MAS are provided for the special cases of acyclic and strongly connected communication graph topologies. These results are then exploited to provide stability conditions for general graph topologies. The proposed framework is demonstrated on a tracking synchronisation problem associated with the design of a distributed secondary voltage controller for microgrids and on a numerical example.

Hybrid AC/DC microgrid clusters are key building blocks of smart grid to support sustainable and resilient urban power systems. In microgrid clusters, the subgrid load-priorities and power quality requirements for different areas vary significantly. To realize optimal power exchanges among microgrid clusters, this paper proposes a decentralized self-optimizing power control scheme for interlinking converters (ILC) of hybrid microgrid clusters. A priority-driven optimal power exchange model of ILCs is built considering the priorities and capacities in subgrids. The optimization objective is to minimize the total DC-voltage/AC-frequency state deviations of subgrids. To realize the decentralized power flow control, an optimal-oriented quasi-droop control strategy of ILCs is introduced to not only achieve a flexible self-optimizing power flow management, but also provide an ancillary function of voltage support. Consequently, as each of ILCs only monitors the local AC-side frequency and DC-side voltage signals, the whole optimal power control of the wide-area microgrid clusters is achieved in a decentralized manner without any communication link. Thus, the proposed control algorithm has the features of decreased cost, increased scalability, reduced geographic restrictions and high resilience in terms of communication faults. Finally, the proposed method is validated by case studies with four interconnected microgrids through hardware-in-loop tests.

The interaction between wind turbine wakes and atmospheric turbulence is characterised by complex dynamics. In this study, two major components of the atmospheric boundary layer dynamics have been isolated, namely, the mean velocity profile shear and the thermal stratification, to examine their impact on the near-wake development by undertaking a series of highly resolved large-eddy simulations. Subsequently, instantaneous flow fields are extracted from the simulations and used to conduct Fourier analysis and proper orthogonal decomposition (POD) and compute the mean kinetic energy fluxes by different POD modes to better understand the tip-vortex instability mechanisms. Our findings indicate that shear can significantly affect the breakup of the wind turbine tip-vortices as well as the shape and stable length of the wake, whereas thermal stratification seems to only have limited contribution to the spatial development of the near-wake field. Finally, our analysis shows that the applied perturbation frequency determines the tip-vortex breakup location as it controls the onset of the mutual inductance instability.

As a special case of the Volterra system, the second-order Volterra (SOV) filter is very efficient for nonlinear system identification. The improved correntorpy based on the generalized Gaussian density function has been proven robust against impulsive noise. In this brief, we propose several SOV filters based on a recursive maximum correntropy (RMC) algorithm for nonlinear system identification. We first introduce a basic RMC algorithm, which faces a trade-off between filtering accuracy and tracking capability due to the use of a fixed forgetting factor (FFF). Two RMCs with variable FF (VFF) are further proposed to enhance the tracking ability. Simulation results demonstrate that our proposed algorithms outperform existing ones in impulsive noise environments and/or in time-varying systems.

This paper presents a model predictive sliding control (MPSC) strategy with the dynamic reference tracking for cascaded H-bridge multilevel converters. The nonlinear predicted sliding mode control (PSMC) and linear model predictive control (MPC) are combined as a two-level cascaded control structure. The proposed approach aims to get rid of time-consuming tuning to reach the good performance in the presence of uncertainties and disturbances, such as proportional-integral (PI). The MPC scheme is designed to predict the future optimal current and voltage vectors with a designed cost function. The high level PSMC algorithm is proposed for the dynamic current reference with less chattering problems. The stability of the proposed strategy is analyzed with the Lyapunov direct method. The design principle of the system parameters is introduced with a step-by-step tuning procedure. With the proposed method, the response time can be reduced from 60 to 30 ms during the start-up conditions in the simulation tests. The overshoot of the cell voltages can be eliminated with low harmonics current and dc-link voltage targets tracking. The validity and effectiveness of the proposed method are implemented by the experimental tests on a laboratory two-cell CHB converter. Compared with the PI-based MPC approach, the current total harmonic distortion can be reduced and the DC side performance, including overshoot/undershoot and response speed, can be improved.

Several software tools are available in the literature for the design and embedded implementation of linear model predictive control (MPC), both in its implicit and explicit (either exact or approximate) forms. Most of them generate C code for easy implementation on a microcontroller, and the others can convert the C code into hardware description language code for implementation on a field programmable gate array (FPGA). However, a unified tool allowing one to generate efficient embedded MPC for an FPGA, starting from the definition of the plant and its constraints, was still missing. The MOBY-DIC2 toolbox described in this brief bridges this gap. To illustrate its functionalities, the tool is exploited to embed the controller and observer for a real buck power converter in an FPGA. This implementation achieves a latency of about 30 µs with the implicit controller and 240 μs with the approximate explicit controller.