Aalborg University
  • Aalborg, Denmark
Recent publications
With the shift of the offshore wind energy sector to deeper waters, demand for the development of more complex foundation solutions, particularly suction bucket–supported tripod/tetrapod and jacket foundations, has increased. This paper is divided into two main sections. The first part comprises a comprehensive review of the performance of circular surface and shallow foundations under combined loading (VHM), and how it can principally be understood in a theoretical framework of plasticity theory. Examination of the considered data suggested that the general assumption of over-estimated non-association degree with constant failure surface parameters and increasing vertical load may require further investigation. This may be attributed to the complex interplay of multiple properties such as stress level, soil strength profile and foundation geometry. The existing data in the literature were also used to provide practical guidance for a successful implementation of the elasto-plastic constitutive relationships in offshore foundation design. In second part of the paper, the suitability of the non-associated plasticity formulation for a baseline multi-pod system in H-M load space was investigated using three-dimensional finite element (FE) analyses and not verified. Furthermore, the failure envelopes and hardening law for caissons with different embedment ratios differed from those recommended in the literature were established. Parametric studies of multi-caisson foundations revealed that the failure mechanism of multi-bucket foundations under horizontal loading depended greatly on the bucket spacing. The horizontal bearing capacities increased with the bucket spacing until they reached a threshold. Meanwhile, analyses of the multi-bucket foundation under moment loading confirmed the occurrence of a push-pull failure mechanism.
In this research, a series of non-linear dynamic finite element (FE) effective stress analyses were conducted to analyze the influence of the suction caisson geometry, ground motion intensity and contact pressure caused by the offshore wind turbine (OWT) on the settlement pattern and seismic demand of the offshore wind turbine’s structure on saturated dense sand. The baseline model and the FE procedure were validated using a database of well-documented centrifuge test. However, particular attention was given to the calibration campaign based on the measured system response quantities, such as the settlement, acceleration and pore-pressure time histories. The FE results identified the contact pressure as an important state parameter caused by the OWT’s mass; the governing ground-shaking intensity measures that play a significant role in the derivation of an analytical framework for predicting liquefaction-induced offshore wind turbine settlements during major events are the shaking intensity rate (SIR), Arias Intensity (I_a) and spectral acceleration at a period equal to 1 s (T = 1 s). The results revealed that approximating expressions derived using the modified least-squares method (MLSM) reasonably capture the complex phenomenon of liquefaction-induced settlement, with some exceptions at lower SIR values. Finally, to obtain the approximating expressions, the database was combined with a machine learning (ML)-based group method of data handling that appropriately describes the interplay of multiple properties of the foundation soil, structure and seismic events while incorporating the effect of the interaction between the suction caisson, foundation soil, excess pore-pressure generation and cyclic shear stresses.
Quantum mechanics (QM) can be understood as a set of rules that forms the basis for developing all quantum theories. One of these theories is quantum computation (QC), i.e., computation based on QM logic. It is believed that QC provides paths to the problem solution that may not be possible for classical computers. Therefore, it has received attention to solve complex computational problems in different areas. Most of the research efforts, however, have concentrated on problems in theoretical physics and computer science, leaving little attention to solve practical problems in industrial applications. This is particularly true in power system applications where QC is mostly unknown. This paper mainly aims to attract the attention of power system researchers/engineers to QC as a potential solution to address emerging computational challenges of power systems. To this end, the historical development of QC and its fundamental concepts are first described. Then, recent contributions to solving computationally-demanding power system problems such as AC and DC power flow (PF), contingency analysis, state estimation, electromagnetic transients simulation (EMT), fault diagnosis, unit commitment (UC), and facility location–allocation (FLA) problems are discussed. Unfortunately, power system researchers have not yet been able to convincingly demonstrate a quantum advantage in solving large-scale power system problems mainly because we are in the noisy intermediate-scale quantum (NISQ) era, where quantum devices are noisy and have limited quantum resources. However, it may be demonstrated in the future with technological advances and increased research efforts in the area.
The high operating costs of Internet Data Centers (IDC) are a major challenge for their owners worldwide. Therefore, more attention has recently been paid to the energy and cost management of IDCs. This paper investigates the optimal operational strategy for minimizing the electricity costs of a group of globally distributed IDCs in different locations under various day-ahead electricity markets, and each is equipped with a high-performance energy storage system. For this goal, optimal workload dispatching and optimal energy management of the storage units of all IDCs are simultaneously perused by the proposed problem. The system is modeled regarding power balancing constraints, battery costs, and quality of service (QoS). For more practical results, a penalty function is also considered when QoS constraints are not perfectly met, and the impact of the batteries’ depth of discharge on the cost of energy storage is also modeled. Moreover, the cross-correlations between the traffic of IDCs are also considered by the multidimensional copula function. The proposed energy cost optimization is linearized for increasing the accuracy of convergence. The results show that not only the power consumption pattern of the IDCs is significantly improved, but also the cost of power consumption is reduced by 34%. The results also prove the positive effect of battery discharge on workload dispatch and represent a compromise between battery costs and electricity cost savings.
The dc-link voltage dynamics can be directly used to synchronize grid-forming (GFM) converters with ac grids. However, such control scheme is prone to unstable low-frequency oscillations (LFO), owing to the constant-power dynamics at the dc link. This paper analyzes first the small-signal stability of dc-link voltage-synchronized GFM converters, and proposes a control method for enhancing the system stability under different operating scenarios. In the approach, a q -axis voltage-feedforward control is introduced in parallel with the dc-link voltage control for the grid-synchronization purpose. The LFO problem with the conventional dc-link voltage-synchronization control is mitigated. Compared to conventional cascaded/paralleled dc-link voltage- and active power-based synchronization, the method mitigates the synchronous oscillation problem. Experimental tests are performed for the GFM converter operating in both rectifier and inverter modes and with different grid strengths. The results corroborate theoretical analyses and validate the effectiveness of the approach.
DC power supply system has been widely applied in metro traction power system. This article presents a novel framework for future metro traction power system (MTPS) with renewable energy, named renewable electricity-hydrogen-integrated metro traction power system (RMTPS), to reduce overall energy consumption and carbon emission of the MTPS. The framework and operation modes of RMTPS are first developed, where triple active bridge (TAB) converter-based multi-port metro energy router (MMER) is innovatively presented to integrate electricity subsystem, hydrogen subsystem and MTPS. Then, an impedance modelling method of MMER is developed based on the extra element theorem (EET). It can adaptively obtain terminal impedance characteristics of MMER considering the effect of multi-directional power flow and source/load characteristic variation on other terminals. Further, the stability of RMTPS under different operation modes is investigated based on the developed impedance modelling method. Simulation and experimental results are given to validate the effectiveness of the proposed impedance model and stability analysis for RMTPS. The proposed method can further benefit stability design of multi-port metro energy router in practical application.
A flux switching permanent magnet (FSPM) motor is designed and optimized in this paper to achieve high torque density and expanded speed regulation range from a design viewpoint of pole-changing (PC). Based on the field modulation theory, the PM field in the FSPM motor is modulated by the stator and rotor teeth, generating rich harmonics with different pole-pair numbers in the air-gap flux density. By adopting an appropriate slot pole combination, working harmonics with different slot pitch angles are obtained to establish a basis of the PC-FSPM motor. Then, different working harmonics can be used to achieve different energy conversions with different winding configurations, so the PC operation can be performed to realize the high torque and wide speed regulation range by switching the working modes. Based on the field modulation theory, the operation principle of conventional and PC-FSPM motors are analyzed and compared. Then, according to the design principle of the PC-FSPM motor, a 24/22-pole PC-FSPM motor with the E-core is designed and optimized by a multi-level optimization method. Finally, the electromagnetic performances are analyzed by finite element analysis and the test results of the prototype have verified the feasibility of the motor.
Speed-sensorless control (SSC) techniques enhance the overall reliability while reducing the cost, and thus become attractive in induction motors (IMs). Simplicity and flexibility are the main features that make phase-locked loop (PLL)- and frequency-locked loop (FLL)-based speed estimation schemes stand out among considerable estimation methods. However, many PLL and FLL schemes exhibit a poor estimation accuracy in the cases of acceleration and deceleration (AaD). Accordingly, an adaptive second-order generalized integrator-FLL (SOGI-FLL)-based scheme is introduced. While, the issue of the estimated speed feedback (ESF) in the adaptive SOGI-FLL scheme remains an obstacle. With the above, a speed estimation scheme that combines the super-twisting algorithm with an FLL (STA-FLL) is proposed for the SSC of IM drives in this paper. In the proposed STA-FLL scheme, an STA-based quadrature signal generator (STA-QSG) is properly designed, in which the ESF is cancelled to further ensure speed estimation. Moreover, a closed-loop flux observer is adopted in the implementation to enhance the proposed STA-FLL scheme in terms of disturbance mitigation. Experimental tests are performed to validate the proposed STA-FLL scheme.
In this paper, the detailed steps for the derivation of linearized minimum current stress (LMCS) modulation scheme for single-phase bidirectional and isolated dual active bridge (DAB) ac-dc converters is presented. In order to reduce the current stress of the converter, a Lagrange multiplier method (LMM) scheme is designed to obtain the control coordinates constraint on the minimum value of the current stress. Compared with the conventional minimum current stress (MCS) control scheme, the proposed scheme realizes the linearization of power control, which significantly reduces the complexity of the controller algorithm. Moreover, the synchronous inverter control applied to the back-stage full-bridge is designed. Thus, the single stage power conversion is realized and the converter topology is electrolytic capacitorless, which promote the improvement of system reliability and power density. The impact of the ZVS scheme is analyzed in detail, and the adaptive-ZVS scheme for less system loss is selected. Finally, experimental results confirm the validity and feasibility of the proposed control scheme.
PM $_{2.5}$ concentration forecasting is important yet challenging. First, complicated local fluctuations in PM $_{2.5}$ concentrations disturb modeling global trends. Second, forecasting errors are often accumulated through an autoregressive process. To contend with the two challenges, we propose a C ategory G uidance based PM ${_{2.5}}$ sequence F orecasting training framework (CGF) to enhance the performance of existing PM ${_{2.5}}$ concentration forecasting models. CGF contains a Category based Representation Learning (CRL) module and a Category based Self-paced Learning (CSL) module, both of which utilize PM ${_{2.5}}$ category information that is easily obtained and publicly available. First, CRL employs category information to guide forecasting models to produce more robust hidden representations that are insensitive to local fluctuations, thus alleviating the negative impact of local fluctuations. Second, CSL adaptively selects real PM ${_{2.5}}$ concentration values vs. autoregressive PM ${_{2.5}}$ forecast values when training forecasting models, helping alleviate error accumulations. The CGF framework is applied to existing PM ${_{2.5}}$ forecasting models, and the experimental results on two real-world datasets demonstrate that CGF is able to consistently improve the accuracy of existing forecasting models. Furthermore, to validate the generality of CGF, we conduct extensional experiments in two other time-series prediction tasks, including exchange rate forecasting and electricity forecasting. The experimental results also verify the effectiveness of CGF.
With the diffusion of online mobile devices with geo-location capabilities, the infrastructure necessary for real-world deployment of Spatial Crowdsourcing (SC), where so-called mobile workers are assigned location-sensitive tasks, is in place. Some SC tasks cannot be completed by a single worker due to their complexity, but rather must be assigned to and completed by a group of users. Achieving such group assignments that satisfy all group members evenly is an open challenge. To address this challenge, we propose a novel preference-aware group task assignment framework encompassing two components: Mutual Information-based Preference Modeling (MIPM) and Preference-aware Group Task Assignment (PGTA). The MIPM component learns the preferences of groups contrastively by maximizing the mutual information between workers and worker groups based on worker-task and group-task interaction data and by using an attention mechanism to weight group members adaptively. In addition, curriculum negative sampling is adopted to generate a small number of negative workers for each worker group, following the principles of curriculum learning. Next, the PGTA component offers an optimal task assignment algorithm that employs tree decomposition to assign tasks to appropriate worker groups, with the aim of maximizing the number of task assignments while prioritizing more interested groups when assigning tasks. The task assignment framework also features preference-constrained pruning of unpromising worker groups to speed up the assignment process. Finally, we report extensive experiments that offer evidence of the effectiveness and practicality of the paper's proposal.
Unbalanced loads are general in uninterruptible power supply (UPS), standalone power generation applications and the failure mode of three-phase inverters. Three-phase four-leg inverters are a well-known solution to handle neutral currents caused by unbalanced loads. In four-leg inverters, three-dimensional space vector modulation (3DSVM) is widely used. However, the 3DSVM of four-leg current source inverter (CSI) has not been systematically studied so far. To fill this gap, this paper proposes a 3DSVM for CSI. The definition of the current vector, the identification of the position of reference vector and the sequence of switches are introduced. Under unbalance loads, the symmetrical load voltage can be realized, which complies with IEEE Std. 1159-2019. The output current can comply with IEEE Std. 519-2014. The proposed 3DSVM has the potential to extend the advanced modulation strategies that have been implemented/ proposed on three-leg CSI.
Modular multilevel converters (MMCs) have attracted extensive research interests in various ac and dc conversion applications due to their modular structure and excellent harmonic performance. However, the large number of power switches increases the potential risk of submodule (SM) failure, which greatly challenges the safe and reliable operation of the MMC. This paper presents a detailed review of fault diagnosis and fault-tolerant control methods of the MMC under SM failures. On this basis, comprehensive comparisons are conducted among different fault diagnosis methods, and verification results are provided to analyze the advantages and disadvantages of the popular fault-tolerant control methods. Finally, the review is concluded, and future trends and research opportunities are discussed.
Modular multilevel converters are well known in the energy sector. Generally, their stable operation is at the expense of numerous sensors, communication burden, and computationally expensive balancing strategies that challenge their expansion to cost-driven applications. Hence, introducing a sensorless voltage-balancing strategy with a simple controller is an attractive objective. Diode-clamped MMCs offer a simple and effective solution by providing a unidirectional balancing path between two modules through a diode. Ideally, the modulation technique should compensate for the lack of bidirectional energy transfer; hence open-loop operation is possible. Although the sensorless operation is desirable to reduce costs, good knowledge of the modules' voltages for system monitoring and protection functions still improves operation in some applications or is mandatory in others. However, information should not be at the cost of additional sensors and communication bandwidth. This paper develops a compensated state-space model for diode-clamped MMCs to estimate module voltages using an optimal estimator without any direct measurement at module levels. The model considers the effect of the diode-clamped branches and their balancing effect, resulting in 30% to 50% reduction in estimation error compared to the conventional models using similar estimators. Simulations and experiments further confirm the provided analysis, where the estimator achieves >97.5% accuracy.
Due to its outstanding merits like quick response, multi-objective optimization and simple principle, Model predictive control (MPC) has been widely used in power converters and motor drive system. However, MPC highly rely on the precise circuit parameters and control models, and cannot be used in unknown circuit relationships. To solve this issue, this paper presents a model-free predictive control (MFPC) with multi-objective optimization (MOO) for two parallel three-level T-type rectifiers (3LT2Rs). First, the main control objectives of 3LT2Rs are analyzed, and the overall control scheme of the double closed-loop control is established. Second, based on the mathematical model of the parallel system, a MOO-MFPC for neutral-point (NP) voltage balance, current tracking and zero-sequence circulating current (ZSCC) elimination is proposed, which does not require any prior knowledge of the circuit parameters and circuit models, and it can achieve MOO control without weighting factors and its priority is not fixed. To solve the current difference updating stagnation problem in MOO-MFPC, a synchronous updating method is designed, which is faster than that of a single rectifier. Finally, the proposed method is tested on a hardware prototype of a 10-kW and a 5-kW parallel rectifier. Numerous experimental results demonstrate the merits of this method over existing methods under several typical scenarios.
The purpose of this letter is to present a simple technique for monitoring the health status of the output capacitor of a boost converter. The majority of previous studies have measured an electrolytic capacitor ESR or capacitance to track its health condition. However, this paper suggests a new approach based on the accurate measurement of the dissipation factor. The effectiveness of the proposed method is verified through experimental tests. According to the results, the dissipation factor is measured with a maximum error of 4%.
Grid-forming inverters (GFMI) are prone to have small-signal stability problems when connected to a stiff ac grid. Such stability problems originate significantly from the absence of GFMI control adaptivity to the varying short-circuit ratio (SCR). To confront this challenge, this letter proposes an intelligent synchronous power control (SPC) scheme that is robust against a wide range of SCR of the ac grid. The letter focuses on the design and digital implementation of brain emotional learning to provide adaptive tuning of the SPC control parameters, enabling the system to quickly adapt to changes in SCR. The approach passes the dependency of the active power control loop with the SPC on the operating point conditions, which enhances the system's robustness. Both theoretical and experimental validations confirmed the effectiveness of the proposed approach.
Stabilizer-based quantum secret sharing has two methods to reconstruct a quantum secret: The erasure correcting procedure and the unitary procedure. It is known that the unitary procedure has a smaller circuit width. On the other hand, it is unknown which method has smaller depth and fewer circuit gates. In this letter, it is shown that the unitary procedure has smaller depth and fewer circuit gates than the erasure correcting procedure which follows a standard framework performing measurements and unitary operators according to the measurements outcomes, when the circuits are designed for quantum secret sharing using the [[5, 1, 3]] binary stabilizer code. The evaluation can be reversed if one discovers a better circuit for the erasure correcting procedure which does not follow the standard framework.
In this study, low-profile probe-excited crossed antenna with sandwiched metasurface is proposed for very small and ultra-small spacecrafts at X-band. The designed metasurface antenna is lightweight, low cost, and occupies physical size suitable for very small/ultra-small spacecrafts. The main targets of this study are the use of an optimized sandwiched metasurface for increasing return loss and peak gain of proposed antenna at X-band. Moreover, the sandwiched metasurface is used for minimizing levels of generated back lobes and so interferences with electronic components inside the spacecraft box. The constructed sandwiched metasurface antenna, therefore, achieves a bandwidth of about 350 MHz and increases the antenna total gain by about 1.10 dBi at X-band despite its very small size. These results are in general very suitable for very small and ultra-small spacecrafts communications.
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13,509 members
Pooya Davari
  • Department of Energy Technology
Meg Duroux
  • Department of Health Science and Technology
Hiva Alipour
  • Department of Health Science and Technology
Cristiano Varrone
  • Department of Chemistry and Bioscience
Fredrik Bajers Vej 5, P.O. Box 159, 9100, Aalborg, Denmark
Head of institution
Per Michael Johansen
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