North China Electric Power University

Large-scale high temperature superconducting (HTS) magnets usually have tens of double/single pancake coil units. The structural optimization and manufacture of the magnet assembly result in varying axial gaps between adjacent pancakes. This brings additional difficulties for inserting the superconducting joints and dealing with the welding factors such as the gap, welding angle and overlapped length. This article presents the simulation modeling and general joint design guideline of a spiral joint scheme for large-scale HTS magnets and other similar applications. An easy-to-implement model has been built to characterize the physical behaviors of joint resistance. This article explores feasible joint design guidance and performance evaluation solutions for largescale applications of HTS magnets having a series of HTS-HTS joints among pancake coils.

In the coal mining process, a large amount of Coal Mine-Associated energy (CMAE), such as coal mine methane and underground wastewater, is produced. Research on the modeling and optimization dispatching of a Coal Mine-Integrated Energy System (CMIES) with CMAE effectively saves energy and reduces carbon pollution. CMAE has great uncertainties owing to the affections of the hydrogeology conditions and mining schedules. In addition, thermal loads have high comfort requirements in mines, which brings great challenges to the optimization dispatching of CMIESs. Therefore, this paper studies the architecture and solution of CMIESs with a flexible thermal load and source-load uncertainty. First, to effectively improve the electric and thermal conversion efficiency, the architecture of CMIES, including a concentrating solar power station, is built. Second, for the scheduling model with bilateral uncertainty, the interval representation method with interval variables is proposed, and a multi-objective scheduling model based on the interval variables and flexible thermal load is constructed. Finally, we propose a solution method for the model with interval variables. A case study is conducted to demonstrate the performance of our model and method for lowering carbon emissions and cost.

The bin picking system integrates manipulators with cameras to automatically pick randomly piled objects. It has the commercial potential to help streamline production and is evaluated by its work efficiency and purchase cost. For universal object picking, the system requires the manipulator to cover six degrees of freedom (DoFs). If the picking objects have a uniform shape, this requirement may be relaxed to achieve higher efficiency with cheaper low-DoF manipulators. On the one hand, the pose of revolution-symmetry (RS) objects is 5-DoF for rotating around their revolution axes without changing their poses. On the other hand, the widely used selective compliance assembly robot arm (SCARA) can achieve 4-DoF kinematics due to four joint actuators. Inspired by the fact that the DoF of the RS pose is exactly the same as adding one DoF to SCARA, we develop a SCARA+ system of a SCARA with an additional revolute joint and explore the possibility of integrating it with a 3-D camera to achieve bin picking of RS objects. Toward this end, we first discuss the SCARA+ kinematics with the modified Denavit–Hartenberg (DH) parameters. Then, to calibrate the additional DH and the hand–eye parameters in the kinematics, we construct an axis-point model and provide an iterative solution without singularity. Finally, comprehensive experiments verify the superiority of the SCARA+ system. When compared with the state-of-the-art systems, our system achieves a significant efficiency improvement with relatively lower costs. It has also been successfully applied in the spinning industry for practical bobbin loading.

In this paper, the AC losses mainly about hysteresis loss in the preliminary design of Rutherford cable (Rfc) made from high-temperature superconducting (HTS) Quasi-isotropic Strands (Q-ISs) are evaluated by numerical simulations. A 2-D structure for Rfc including 10 Q-ISs is proposed. Then, transport AC loss in the cable made from Q-ISs under different frequencies and amplitudes of transport currents, and the magnetization loss under external AC magnetic fields of different magnitudes with different orientations are separately calculated using T-A formulation at 77 K. Additionally, the simulated results of the Rfc are compared with those of a directly stacked conductor (DSC). The results show that the transport AC loss of the Rfc made from Q-ISs is smaller than that composed of DSCs. The magnetization loss of Rfc made from Q-ISs is also smaller than that of DSCs in some certain range of external field orientation. The proposed Rfc is a potential and promising solution for superconducting cables for AC applications of feeder line and bus with high current capacity.

This paper presents a gourd-shape closed loop with double circular configurations at different diameters, which is arranged by slitting second-generation high-temperature superconducting (2G HTS) tape along its length direction. The flux density amplification and flux accumulation mechanisms of this architecture with closed-loop HTS coils using both experimental and numerical methods were performed. Flux density amplification and flux accumulation in small circular configuration was observed by using field-cooling (FC) method in LN2 temperature. The mechanisms of flux density amplification and flux accumulation can be understood by the flux conservation law in superconducting closed loop. Three types of arrangements were carried out to confirm the principles of flux density amplification and flux accumulation. Compared with existing HTS magnetic magnets by winding on former, this gourd-shape HTS coil is more compact and operates in persistent current mode (PCM). Besides, it can be particularly generalized to big dimensional size permanent HTS magnet by stacking gourd coils if 2G tape with large width can be prepared for industrial application. Our results show that the proposed gourd-shape HTS coil is very promising in full HTS magnet with higher magnetic field in low temperature and has the potential to provide much stronger magnetic fields relative to existing permanent magnets.

Printed Circuit Board (PCB) Rogowski coil array is capable of measuring the currents of the paralleled chips inside power devices, the operating status and failure behaviour of power devices may be thus monitored. The conventional continuous electrostatic shielding provides immunity to capacitive coupling interference for the PCB Rogowski coil array, but fails to suppress inductive coupling interference. Equivalent circuit models of the PCB Rogowski coil array with continuous electrostatic shielding are established and the influence mechanism of the inductive coupling interference on the Rogowski coil array is analyzed in this paper. The symmetry of the inductive coupling interference components is discovered in the waveforms from the PCB Rogowski coil array with continuous electrostatic shielding. This paper proposes a discrete electrostatic shielding structure for the PCB Rogowski coil array, which is validated through switching experiments to have the immunity against both capacitive and inductive coupling interference. The PCB Rogowski coil array with discrete electrostatic shielding proposed in this paper can be used for researches on paralleled chips of power devices, thus providing a foundation for condition monitoring and design optimization of power devices.

Gate oxide degradation under dynamic gate stress has been demonstrated as a reliability issue for SiC MOSFETs recently. Investigating the influence of dynamic drain-source voltage stress ( V_DS ) and load current ( I_L ) involved in switching operation on gate oxide degradation is very significant to identify the way for effectively assessing gate oxide reliability. In this article, a buck converter with continuous switching condition and constant high temperature is built and operated to evaluate gate oxide degradation. Moreover, the results from buck converter are compared to results regarding AC bias temperature instability (BTI) under the same conditions for devices with different gate structures. The degradation degree of different gate oxide locations under the two operations is analyzed combining I-V and split C-V characteristics. It is found that there is consistent degradation of the gate oxide above JFET region, but depending on the operation mode, the degradation is different above channel region, indicating that VDS and I_L have different effects on different gate oxide locations. Therefore, AC BTI test cannot sufficiently evaluate gate oxide degradation and may overestimate or underestimate its reliability, depending on the device structure and fabrication process. It is necessary to investigate the gate oxide reliability in typical switching operation.

It is a big challenge for current source converter (CSC) to ensure the desired tracking performance, robustness, and immunity simultaneously in the presence of resonance caused by the grid-side LC filter and slowly time-varying filter parameters or load disturbances. Aiming at this problem, combining the advantages of passivity-based control (PBC) and nonsingular terminal sliding mode control (NTSMC), a hybrid PBC-NTSMC method is proposed. Based on virtual damping injection and energy dissipation theory, the PBC is firstly designed for the inner loop followed by the construction of the Euler-Lagrange (EL) model. In this way, the resonance suppression is realized and the CSC system is proved to be passive. Furthermore, the NTSMC is combined with PBC to improve the dynamic response, reduce the chattering problem of traditional sliding mode control, and enhance the robustness of the system while maintaining passivity. In addition, the power references are modified to enable system to flexibly configure the control target depending on actual application requirements under nonideal grid. To further enhance the immunity, an ultra-local model predictive controller based on extended state observer disturbance estimation is designed for the outer loop. Finally, the simulation and experimental results verify the effectiveness of the proposed method.

Off-site research institutes serve as crucial platforms for universities to facilitate the translation and application of their scientific research outcomes. Additionally, these institutes act as vital intermediaries for local governments to harmonize scientific inputs with economic development and play a key role in the industrial transformation and elevation of regional science and innovation levels. Drawing on the triple helix theory, this paper outlines strategic planning for off-site research institutes and develops a multi-objective linear programming model aimed at optimizing resource allocation. This model focuses on enhancing both the efficiency of resource utilization and the efficiency of resource allocation at these institutes. To address the issue of local minima commonly encountered in optimization algorithms, this study employs a simulated annealing algorithm to refine the performance of the particle swarm optimization algorithm. The resulting hybrid algorithm termed the simulated annealing particle swarm algorithm, is applied to solve the proposed model and investigate the determinants of optimal resource allocation. The findings indicate a significant improvement in resource allocation efficiency, with the coefficient for heterogeneous research institutes decreasing from an average of 0.84 in 2020 to 0.68. This optimization has led to a more effective and rational distribution of resources, better meeting the needs of the institutes. Furthermore, the analysis reveals that financial support and talent introduction and development account for approximately 69.7% of the variance in the optimized development of resource allocation at these institutes. The study provides actionable insights that could guide the optimal development of off-site research institutes, offering valuable references for future applications.

Fast charging of electric vehicles (EVs) significantly impacts both power distribution networks (PDN) and transportation networks (TN). However, developing a systematic EV fast charging strategy faces challenges in modeling the coupled power-transportation networks (PTN), devising charging price mechanisms, and optimizing path planning. This paper addresses the EV fast charging navigation problem using a coupled network weighted pricing approach. The charging navigation problem for each EV is formulated as an optimization problem that aims to minimize comprehensive costs, including time consumption cost and charging cost. Specifically, a novel PTN model based on the multilayer network theory is first proposed, which captures the dynamic characteristics of TN and PDN using extended graphic models and also considers the influence of coupled nodes on the PTN through a coupled weighting matrix. Then, a coupled network weighted charging pricing is developed, considering the dynamic characteristics and interdependence of PTN. To handle the large-scale dynamic complexity of coupled networks, a distributed biased min-consensus algorithm is employed to solve the optimization problem. Finally, simulation results verify the effectiveness and practicality of the proposed navigation strategy, facilitating the optimal operation of “Vehicle-Station-Networks”.

Transitioning towards a low-carbon future necessitates massive efforts from both the transport and power sectors. Electric vehicles (EVs) have emerged as a promising approach to realize this objective, leveraging their smart routing strategies and vehicle-to-grid (V2G) techniques. Previous studies have addressed various challenges in EV routing and scheduling through model-based optimization methods while ignoring the system uncertainties and dynamics. This paper focuses on studying the carbon-aware EV joint routing and scheduling problem within a coupled power-transport network that can enable EV recharging behaviors within the transport network while concurrently delivering carbon-intensity services within the power network. Specifically, a carbon emission flow model is introduced as a mechanism for tracing and calculating the nodal carbon intensity signals tailored for EVs to provide their carbon services. To solve this problem, we propose a model-free multi-agent reinforcement learning method that harnesses graph convolutional networks to capture essential network features and employs a parameter-sharing framework to learn large-scale control policies. The efficacy and scalability of the proposed method in achieving cost-effective and low-carbon transitions are verified through case studies involving two power-transport networks with 100 and 1,000 EVs, respectively.

This article considers the model predictive control (MPC) problem for a class of time-varying systems subject to both disturbances and constraints of states as well as input. Instead of directly negating disturbance by its estimation in feedback control, we exploit the disturbance estimation in the MPC optimization problem for seeking the optimal control input. In particular, the extended state observer (ESO) is constructed to obtain the disturbance estimation to be incorporated into the prediction model. Furthermore, less conservative tightened constraints and terminal constraints with consideration of disturbance estimation are constructed to guarantee robust constraint satisfaction and recursive feasibility. Also, the input-to-state stability (ISS) of the closed-loop system is rigorously proven. Finally, the proposed method is applied to the liquid-level control system. The experimental results demonstrate the effectiveness of our MPC algorithm.

Integrated demand response (IDR) is acknowledged as a cost-effective and low-carbon tool that helps alleviate imbalances between energy supply and demand in energy systems. However, the imperfect rationality of consumers can lead to a series of new issues, such as cognitive characteristics of bounded rationality as well as irrationality, subjective uncertainty, and correlated risks. This paper proposes an improved consumer model by using a dynamic subjective weight function with a rationality degree indicator to cope with cognitive characteristics, coupled with a deduced skew distribution modelling the subjective uncertainty. Besides, MESP model is also improved to deal with the correlated risks by managing the diversification level of risks that is defined based on Shannon entropy. The mathematical formulation of the proposed model is expressed as a bi-level stochastic optimization problem, which is equivalently converted into a single-objective optimization problem to be solved efficiently. Simulation results validate advantages of our model in enhancing the accuracy of consumer behavior prediction, effectiveness of incentive strategies, diversification level of response risks, which contributes to achieve a win-win situation between consumers and MESP by decreasing not only total response power deviation as well as cumulative response risks of MESP but also consumer’s discomfort level.

Integrating heating and electricity networks offers extra flexibility to the energy system operation while improving energy utilization efficiency. This paper proposes a data-driven joint distributionally robust chance-constrained (DRCC) operation model for multiple integrated electricity and heating systems (IEHSs). Flexible reserve resources in IEHS are exploited to mitigate the uncertainty of renewable energy. A distributed and parallel joint DRCC operation framework is developed to preserve the decision-making independence of multiple IEHSs, where the optimized CVaR approximation (OCA) approach is developed to transform the local joint DRCC model into a tractable model. An alternating minimization algorithm is presented to improve the tightness of OCA for joint chance constraints by iteratively tuning the OCA. Case studies on the IEEE 33-bus system with four IEHSs and the IEEE 141-bus system with eight IEHSs demonstrate the effectiveness of the proposed approach.

The cutting-edge applications of cyber-physical power systems (CPPS) must transmit large volumes of data packets collected by massive remote terminal units (RTUs) to the control center. To develop high-reliability and self-sustainable communication networks for the RTUs deployed in hard-to-reach areas, we propose an RTU satellite-terrestrial multi-hop network with energy cooperation for remote CPPS. Specifically, data packets generated by RTUs are either transmitted to faraway base station (BS) in a multi-hop manner or uploaded to satellite network, and each RTU harvests ambient renewable power with the capacity to transfer harvested energy to the relay RTU. We then develop a multi-agent learning-based packet routing and energy cooperation approach (MAQMIX-PREC) to maximize the network throughput by jointly optimizing relay selection, sub-slot partition, and channel allocation. This approach effectively decouples the decision-making and coordinates the training among RTUs in the RTU multi-hop network. Experimental evaluations illustrate that the proposed approach achieves congestion-awareness and energy cooperation, and outperforms benchmark methods in terms of training convergence, network throughput, and traffic intensity.

Within a press-pack insulated gate bipolar transistor (PP IGBT) submodule, the components are packaged by external clamping force. This external clamping force significantly affects the chip's dynamic avalanche and further degrades its turn-off capability, which needs to be focused. In this paper, it is found for the first time that the pressure distribution is the key factor for the IGBT chip's dynamic avalanche during a single turn-off period. Simulation results indicate that the maximum pressure difference on the chip surface rises by 1.5 times when the clamping force increases from 1 kN to 2.5 kN. During turn-off, that pressure distribution leads to a 21.21% increase in the maximum current density inside the single chip, which strengthens the dynamic avalanche. To verify this mechanism, the turn-off waveforms of a single chip under different forces are then measured and compared in detail. Meanwhile, these conclusions regarding the pressure distribution effect are applicable to the single chip inside the multi-chip device in practical applications. Moreover, suggestions for the packaging design and practical operation are put forward to improve the PP IGBT device's turn-off capability effectively.

This letter provides the design rule of the droop coefficient for the power synchronization loop (PSL) based grid-forming wind turbine (WT) considering the transient rotor speed constraints. The dynamic model for PSL-based WT is established first, and it is shown that the larger the droop coefficient, the larger the steady-state rotor speed deviation is. Accordingly, the maximum droop coefficient for ensuring the existence of equilibrium is derived. Finally, with the proper scaling of the transient rotor dynamic equation, the conservative evaluation of the maximum droop coefficient is obtained to make sure the WT does not touch the lower limit of the rotor speed during the large frequency disturbance.

Deep graph convolutional networks can mine the deeper information of non-structured data, e.g., capturing complex interactions within sensor topology. However, the over-smoothing problem severely limits the depth of the graph convolutional network (GCN). The initial residual can ensure the nodes retain some initial information during the propagation process, which largely alleviates the over-smoothing problem in deep graph convolutional networks. However, current works only use the grid search method to determine a fixed initial residual ratio, which can not assign the most appropriate initial information for the nodes with different over-smoothnesses. This paper proposes a novel method named Node-Smoothness Based Adaptive Initial Residual Deep Graph Convolutional Network (NSAIR-GCN). Specifically, it can be divided into two processes: (1) considering the over-smoothing from another perspective, i.e., determining whether nodes are over-smoothed based on the difference in node representations before and after updating and identifying those severely over-smoothed nodes; (2) assigning appropriate initial residual ratios to these nodes based on their smoothness. Extensive semi-supervised node classification experiments on several standard datasets have shown that the adaptive initial residual ratio determined by node smoothness performs better than the previous fixed initial residual ratio and achieves the state-of-the-art.

Sliding-mode observer (SMO) is widely used to achieve high performance sensorless control of permanent magnet synchronous motor (PMSM) drives. However, the conventional SMO presents increased position estimation error as the sampling ratio decreases even without parameter errors. It is shown in this paper that the position error resulted from SMO itself can be decomposed into three components, which are caused by model discretization, non-ideal sliding-mode motion, and phase-locked loop (PLL), respectively. The first two error components are opposite in sign but the sum of them may be not zero which causes steady-state estimation error, while the last one only exists during transient processes. To address the above issues, steady-state error components are theoretically analyzed and quantified. Then, calculation of back-electromotive force is redesigned with an improved SMO to eliminate the position estimation error under low sampling ratio. After that, a flux-compensation-based PLL is proposed to suppress position error during fast speed variations. Finally, simulation and experimental tests were carried out to validate the effectiveness of the proposed method. The obtained results, along with a video demonstration, justify theoretical analysis and satisfactory performance of the proposed method.

The stray flux (SF) at the back of the stator core of pumped storage unit (PSU) can provide an effective response to the operating condition of the rotor winding. This study investigates the SF characteristics of the rotor winding before and after an inter-turn short circuit (ITSC), with a view to achieving online monitoring of the inter-turn insulation health level. Firstly, the expressions for the SF in the healthy state and ITSC state of the rotor winding are derived, obtaining the characteristic harmonics of SF corresponding to the different states. Secondly, the sensing mechanism of reflective magneto-optical crystal (RMOC) is analysed, on the basis of which an optical fiber weak magnetic (OFWM) detection system is built and a conversion expression between the Faraday magneto-optical deflection angle and the flux density is obtained. Thirdly, a two-dimensional finite element simulation model is built based on the actual dimensions of the operating PSU, according to which the evolution of the time/frequency domain characteristics of the radial SF before and after the ITSC fault is analyzed, obtaining the peak radial SFes of no-load/load conditions are 111 μT and 115 μT respectively, which can provide a reference for the design of OFWM sensor. Finally, the OFWM probe is calibrated with a sensitivity of 0.244 mrad/μT and a lower detection limit of 0.1 μT _rms . The probe is fixed vertically to the stator housing of the synchronous generator for real-time monitoring, and the radial SF information is collected at different levels of ITSC, with the peak values are 2.57 μT and 2.81 μT for no-load/load conditions respectively. The monitoring results demonstrate that the changes in the time domain characteristic waveform and spectral characteristic harmonics of the SF can effectively characterise the rotor winding inter-turn insulation health level, which expands a new technical path for rotor winding ITSC condition monitoring.