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The structure of a submersible permanent magnet tubular linear motor (SPPMTLM) can significantly affect its load spectrum. However, the existing models do not account for this effect. This study proposes an advanced SPPMTLM model that incorporates these considerations, offering enhanced accuracy and practicality. First, a dynamic sub-model of the SPPMTLM was developed to elucidate the dynamic load spectrum during one working cycle. Subsequently, a one-dimensional analytical sub-model and a lumped-parameter thermal network sub-model of the SPPMTLM were established to calculate the electromagnetic and thermal performance. The proposed model utilizes the Newton-Raphson method to address the nonlinear equations derived from the integration of the three constituent sub-models. This approach enables a detailed analysis of the coupling relationships among the stress, fluid, thermal, and electromagnetic fields. Based on the proposed model, the impacts of the motor structural parameters, materials, and operating conditions on the electricity consumption per ton of fluid were analyzed. Furthermore, the particle swarm optimization algorithm was applied to the proposed model to optimize the SPPMTLM performance under different well fluid supply capacities. Finally, prototype and field experimental validations are conducted to verify the accuracy and practicality of the proposed method.

In this article, an improved space vector pulsewidth modulation strategy for matrix converter is proposed under unbalanced input voltages. First, to obtain the higher output voltage, two novel space vector pulsewidth modulation strategies based on the linear superimposition principle are presented by modifying the magnitude of the reference output voltage and the reference input current vectors. Second, in order to evaluate the performance of the proposed strategy, the harmonic characteristics of the input currents and output voltages are quantitatively analyzed by employing the double Fourier integral transform method. The novelty of this article lies not only in the modification of a conventional space vector pulsewidth modulation strategy for a higher output voltage under unbalanced input voltages, but also in a quantitative analysis that accurately evaluates the overmodulation performance with input unbalance using the double Fourier integral transform approach. Finally, the performance of the proposed strategy and the correctness of the harmonic analysis method are verified by the comprehensive simulation and experimental results for matrix converters.

This paper focuses on a fast finite-set model predictive control (FFS-MPC) for three-phase four-arm active front end modular multilevel converters (AFE-MMCs) under unbalanced and distorted network conditions. The main aim of this paper is to enhance the steady-state performance of the whole system while remaining computationally feasible. Firstly, a novel topology, which has a good potential to improve the fault tolerance ability of MMCs, is presented in this literature. Secondly, in order to enhance the steady-state control performance, a new FFS-MPC methodology is proposed to serve this purpose. Specifically, the philosophy behind the proposed solution is to formulate a user-predefined cost function formula by embedding a power compensation term and an integral error term at the same time, which improves the power quality under normal and under abnormal conditions. However, it is important to notice that the computational complexity will be increased while applying the proposed solution to the control of three-phase four-arm AFE-MMCs. To solve this issue, a fast MPC is introduced into the proposed methodology to improve the computational efficiency, making it suitable for multilevel converters control. Finally, the effectiveness and feasibility of the proposed FFS-MPC methodology can be validated by the comprehensive results for regulated three-phase four-arm AFE-MMCs.

Information Extraction (IE) is the task of distilling structured information from unstructured texts by identifying references to named entities as well as relationships between such entities. Existing IE solutions, including Relation Extraction and Open IE, can hardly take cross-sentence information like coreferences into account and are severely restricted by limited relation types as well as informal relation specifications (e.g., free-text based relation triples). In order to overcome the weaknesses, we propose a novel IE framework named QA4IE, which leverages the flexible question answering approaches to produce high-quality relation triples across sentences. Based on this framework, we develop a real-time IE system, which can perform general IE throughout the entire document. For training and evaluating our system, we build a large-scale IE benchmark using distant supervision under human evaluation. We deploy both component analyses and pipeline experiments to evaluate our system. The results show that our system can generalize on unseen entities and relations, as well as achieve significant improvements over existing IE systems.

Doubly-fed flux reversal permanent magnet (DF-FRPM) machine with two independent armature windings accommodating in stator and rotor slots separately exhibits larger torque density than conventional FRPM due to higher utilization rate of rotor space. In this paper, the investigation is focused on the airgap field modulation and torque contribution of dominating harmonics in DF-FRPM machine. Besides the open-circuit airgap flux density, both stator and rotor armature reaction are qualitatively analyzed by magneto-motive force (MMF)-permeance model considering stator and rotor saliency since the two independently supplied armature windings both make contributions to the torque production. The pole pairs and rotating speed of field harmonics are addressed. Dominating field harmonics that contribute to electromagnetic torque are identified and validated by finite element (FE) analysis.

A fault tolerant control strategy for dual three-phase permanent magnet synchronous motor (DTP-PMSM) with 0° phase shifting between two windings is proposed in this paper. When a DTP-PMSM suffers from single- or two-phase open-circuit fault, to maintain the torque performance, current in faulty phase must be compensated by other healthy phases, which causes asymmetric self- and mutual inductances. Therefore, the 2nd harmonic component can be observed in the dq-axis currents and torque ripple also increases. To analyse this harmonic component in two types of faults, post-fault model of no-phase-shifting DTP-PMSM based on vector space decomposition (VSD) method is built in this paper. Then, proportion-integral-resonant (PIR) controller which can be used to suppress the specific periodic disturbance is applied to compensate the 2nd harmonic components. As the resonant controller is rarely dependent on motor parameters, phase and amplitude of current harmonic, the PIR controller can be applied in no-phase-shifting DTP-PMSM to suppress the harmonic caused by single- and two-phase open-circuit faults. The experimental results validate the effectiveness of the proposed fault tolerant control under single- and two-phase open-circuit faulty conditions.

This paper focuses on a novel passivity-based cascade-free finite-control-set model predictive control scheme (CFFCS-MPC) for nested neutral point-clamped converters (NNPCs). It can be realized by combining an FCS-MPC and a passivity-based dynamic reference design point of view, which is responsible for controlling the multi-objective optimization control problem based on a cascade-free structure without weighting factors. However, it is of importance to highlight that, due to the existence of a large number of switching combinations and redundancies, the online computational effort will be increased by employing the proposed CFFCS-MPC approach, especially for higher-level power converter topologies. To solve this issue, a simplified computational scheme is presented by introducing the passivity-based dynamic reference concept into the design of sector distribution method. Alternatively, a simplified cost function based on the reference output voltage-vector tracking error is embedded into the proposed design. The merits of the proposed solution summarized as follows: 1) Enhanced system robustness; 2) no weighting factors; 3) high dynamics; and 4) low computational complexity. Finally, the proposed methodology is verified analytically and confirmed by comprehensive results that prove the theoretical investigations.

Massive open online courses (MOOCs) have flourished in recent years, which is conducive to the redistribution of high-quality educational resources globally. However, the high dropout rate in the course of operation has seriously affected its development. Therefore, in order to improve the degree of completion, it is an effective way to study how to effectively predict the dropout in MOOCs and intervene in advance. Traditional methods rely on manually extracted features, which is difficult to guarantee the final prediction effect. In order to solve this problem, this paper proposes an integrated framework with feature selection (FSPred) to predict the dropout in MOOCs, which includes feature generation, feature selection, and dropout prediction. Specifically, FSPred applies a fine-grained feature generation method in days to generate features and then uses an ensemble feature selection method to select valid features and feed them into a logistic regression model for prediction. Extensive experiments on a public data set have shown that FSPred can achieve the comparable results with other dropout prediction methods in terms of precision, recall, F1 score, and AUC score. Finally, through the analysis of the features of the final selection, the suggestions for the construction of the MOOCs are put forward.