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An event-triggered control technique has been developed recently. This technique explicitly reduced the signal transmission by introducing a flexible design of threshold inequalities. It was later extended to event-triggered model-predictive control for power converter systems. In this letter, by incorporating this control technique into an extended state-observer-based finite-control-set model-predictive control framework, we have developed a new model-predictive control architecture for power converter systems with parametric uncertainties. Meanwhile, a novel cost function with respect to the angle minimization term is embedded into this proposal. The novelty of our development lies not only in integrating the event-triggered mechanism with the suggested finite-control-set model-predictive control architecture for facilitating the alleviation of performance deterioration caused by parameter variations and model uncertainties, but also in a multiobjective optimization design that allows the switching frequency in a low value. Finally, extensive simulative and experimental investigations for a modular multilevel converter confirm the interest and the viability of the proposed design methodology. [ABSTRACT FROM AUTHOR]

In this letter, a neural predictor-based dynamic surface predictive control framework, endowed with the merits of adaptive dynamic surface control and finite control-set model predictive control, is proposed where the estimation of neural predictor is incorporated to identify the system dynamics and lumped unknown uncertainties. The key features of the proposal are that, first, the issue of “explosion of complexity” inherent in the classical back-stepping control is avoided, second, the model uncertainties and disturbances are explicitly dealt with, and, third, the tedious determination procedure of weighting factors is removed. These features lead to a much simpler adaptive predictive control solution, which is convenient to implement in applications. Furthermore, a Lyapunov function is constructed, and the stability analysis is given. It demonstrates that all signals in the closed-loop system are uniformly ultimately bounded. Finally, this proposal is experimentally assessed, where the performance evaluation of steady-state and transient-state confirms the availability of the proposed solution for modular multilevel converter. [ABSTRACT FROM AUTHOR]

This letter proposes a data-driven iterative learning predictive control architecture for power converters. The main objectives of this letter are to enhance the robustness and remain the high performance of finite control-set model predictive control (FCS-MPC) under unmodeled dynamics and parameter mismatch conditions. More specifically, an iterative dynamic linearization technique is utilized to equivalently reformulate the nonlinear power converter system at each operating point. Based on this, a model-free adaptive control scheme is presented to iteratively determine the optimal control actions. Due to the incorporation of iterative learning control and data-driven concept into the FCS-MPC framework, the effect of parameter perturbations can be alleviated in the proposed method, while creating a positive effect on the tracking error. Finally, a convergence analysis is provided and experimental investigations on a three-level neutral-point-clamped (NPC) converter confirm the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This article proposes a novel data-driven neural predictors-based robust finite control-set model predictive control (FCS-MPC) methodology for power converters, which aims to enhance the robustness of the control system and the flexibility of multiple control objectives. To be specific, the data-driven predictors-based neural network solution, which has a good potential to estimate the unknown nonlinear system dynamics by deploying real-time and historical data, is incorporated into the proposed design with a cost function derived intuitively from Lyapunov’s control theory. The key feature of this work is that the undesired effects of the uncertainties and the complex multiobjective optimization procedure can be explicitly dealt with, without involving accurate modeling information and weighting factor combinations, while guaranteeing adaptability to unknown nonlinear system dynamics, model variations, and environment changes. Finally, the stability analysis is given, and the effectiveness of the proposed FCS-MPC methodology is verified analytically and confirmed by comprehensive results that prove the theoretical investigations for active front-end modular multilevel converter. [ABSTRACT FROM AUTHOR]

This article investigates the influences of end turn length on consequent-pole Vernier permanent-magnet (CPVPM) machines. Four structures are compared to discuss the tradeoff between the end turn length and the gear ratio, and nine structures are compared to discuss the tradeoff between the end turn length and the winding factor. It is found that, when the end turn length is taken into account, the CPVPM machines with concentrated winding have the potential to produce larger torque than those with the distributed winding. Motivated by the above observations, an integral slot concentrated winding CPVPM machine is proposed to be a promising candidate in VPM machine family, which has high torque capability, small end turn length, good fault tolerance, and desired power factor. The influences of key design parameters are investigated. The machine prototype is manufactured, and the electromagnetic performances are experimentally validated. [ABSTRACT FROM AUTHOR]

Traditional predictive control schemes dominated the research field of numerous power electronic applications over the past few years, since they usually lead to control solutions with good dynamic and steady results. However, these solutions strongly depend on the available knowledge of the control system (e.g., accurate modeling information), which often results in the lack of robustness in the presence of parametric uncertainties. Furthermore, unnecessary energy loss heavily correlates with high switching frequency, which directly affects efficiency. Aiming to cumbersome aforementioned scarcities, this letter is concerned with a novel approach to the controller design for a modular multilevel converter, which makes use of event-triggered neural-predictor-based finite-control-set model-predictive control methodology under low-switching-frequency operation. The salient feature of the proposed controller is that the uncertainties and unnecessary energy loss in practical systems can be explicitly dealt with, while keeping the switching frequency in a low value. Finally, steady-state and transient-state performance tests together with the analysis of the simulation and experimental results confirm the interest of the proposal, and the results found are promising and motivate further research in this field. [ABSTRACT FROM AUTHOR]

Electricity theft behaviors have caused great harm to the economic benefits of power companies and the secure operation of power systems, thus electricity theft detection is paid much attention in the actual power supply management. In this work, a two-step electricity theft detection strategy is proposed to identify electricity theft users and predict potentially stolen electricity (PSE) for maximizing economic return. In the first step, a neural network model called convolutional autoencoder (CAE) is proposed for electricity theft identification, and the convolutional layer is adopted in CAE to extract and identify the abnormalities of electricity theft users against the uniformity and periodicity of normal power consumption features. In the second step, the PSE of each identified electricity theft user is predicted by the improved regression algorithm named Tr-XGBoost, which combines the extreme gradient boosting (XGBoost) algorithm and transfer adaptive boosting (TrAdaBoost) training strategy. The propsoed Tr-XGBoost could learn the relationship between the extracted electricity features and the PSE of each electricity theft user, and then the predicted PSE can be used to determine the list of electricity theft users to be inspected for maximizing economic return. Case studies on both the IEEE 33-bus test system and a low-voltage distribution system of a province in China show that the proposed two-step electricity theft detection strategy can improve the accuracy of electricity theft identification, and obtain a larger economic return because of a more accurate result of PSE prediction than other state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

This letter investigates the possibility of deploying a novel finite control-set model predictive control solution for solving the ongoing research challenges in predictive control regulated modular multilevel converter, i.e., model parameter sensitiveness and excessive computational burden as well as weighting factors selection. Specifically, it is realized by cascading a predictor-based neural network design, which enables a smooth and fast identification of system dynamics, and a computationally efficient finite-set predictive control, which is responsible for simplifying the rolling optimization and reducing the computational complexity. The main contribution of the proposed methodology relies on the fact that no knowledge of any model parameters and weighting factors in whole control process are required, which leads to a significant enhancement in the robustness and reliability of the control system in the presence of parametric uncertainties, while remaining computationally feasible. Finally, the stability analysis is given, and the proposed methodology is experimentally assessed for modular multilevel converter, where steady-state and transient-state performance tests confirm the interest of the proposal. [ABSTRACT FROM AUTHOR]

The dropper is one of the core components of the high speed railway catenary and dropper failure will lead to serious transportation accidents. It is very important to carry out dropper failure and defect detection. There are a large number of droppers installed in the catenary. The dropper images are collected by the high-definition camera installed on the top of the moving catenary inspection vehicles. The image quality and image consistency are poor, and it is very difficult to identify dropper defects automatically. The railway department and companies lack efficient and intelligent detection methods. This article innovatively proposes a multialgorithm fusion image processing technology, and builds a dropper recognition and failure–defect detection model based on a deep learning algorithm and subpixel level dropper defect detection model, and achieves high accuracy dropper failure and defect detection. The detection model based on the Faster R-CNN algorithm is studied to realize the positioning and recognition of the dropper and the failure detection of bending slack and broken dropper. The subpixel level dropper defect detection algorithm is based on the fusion of the image preprocessing, dropper fine positioning algorithm, edge fitting and bending zoom algorithms, the Hough transform algorithm, and so on. These can be used to realize detection of defects, such as microdeformation, dropper-strands loosened, dropper-strands broken, and foreign body adhesions. The test is verified by the catenary images taken from a practical high speed railway. The detection accuracy, real-time performance, and stability of the algorithm meet the needs of inspection and maintenance for a high speed railway. [ABSTRACT FROM AUTHOR]

This article compares the electromagnetic performances of doubly salient permanent magnet (DSPM) machines with different stator iron core segments. Since the permanent magnets (PMs) of the DSPM machines studied in this article exist in pairs, the feasible numbers of PMs in the 12/8-pole DSPM machine are 12, 6, 4, and 2, and thus, the corresponding stator tooth number between two adjacent PMs is 1, 2, 3, and 6, respectively. Therefore, the stator iron core segments of the four DSPM machines have different shapes. Furthermore, to improve the average torque, PMs are inserted into the slots, and flux focusing teeth are adopted. All four machines are designed to achieve larger average torque under the fixed copper loss and envelope size. The comparison shows that the DSPM machine with two stator teeth between adjacent PMs exhibits the largest average torque and the smallest torque ripple. Moreover, the DSPM machine with six stator teeth between two adjacent PMs has the optimal PM utilization. To verify the predicted results, a 12/8-pole DSPM prototype with two stator teeth between adjacent PMs is manufactured and tested. [ABSTRACT FROM AUTHOR]

This article proposes a novel axially magnetized Vernier permanent-magnet (AMVPM) machine which has permanent magnets (PMs) clamped on both ends of the rotor with alternately magnetized directions. The topology improves the mechanical strength of the structure without weakening the electromagnetic performances. The detailed operating principles are described and the design parameters are optimized. The open-circuit back electromotive force (EMF) is calculated analytically by using the harmonic series expressions. Then, a comparison between the AMVPM machine and regular Vernier permanent-magnet (VPM) machine is analyzed by finite-element analysis (FEA) method. Finally, the machine prototype is manufactured and the electromagnetic performances are experimentally validated. [ABSTRACT FROM AUTHOR]

The prediction of iron losses in soft magnetic materials is difficult and has not yet been entirely solved. Although much work has been performed for loss prediction under dc bias conditions, little work evaluates the behavior of iron losses with saturation involved. In this paper, measurements of iron losses have been performed to gain information on how losses are affected by a dc bias when saturation occurs. The Steinmetz parameters are calculated together with the Steinmetz premagnetization graph (SPG) method. The SPG is improved by expressing the parameter \beta as a function of both H_{\textrm {dc}} and \Delta B$ to take the saturation effect into consideration. The calculated losses are compared with the measured ones. The accuracy has always been ≤±10%. [ABSTRACT FROM PUBLISHER]

Current-controlled variable inductors adjust the inductance of an alternating current (ac) coil by applying a controlled dc current to saturate the iron cores of the ac coil. The controlled dc current has to be maintained during operation, which results in increased power losses. This paper presents a new stress-based variable inductor to control inductance using the inverse magnetostrictive effect of a magnetostrictive material. The stress can be applied by a piezoelectrical material, and thus a voltage-controlled variable inductor can be realized with zero-power consumption. The new stress-based variable inductor concept is validated using a 3-D finite-element analysis. A prototype was manufactured, and the experimental results are presented. A linear relationship between inductance and applied stress can be achieved. [ABSTRACT FROM AUTHOR]

A typical giant magnetostrictive actuator (GMA) expands a magnetostrictive rod to generate strain by varying the current in the coil that surrounds the magnetostrictive rod. The heat generated by the current deteriorates the GMA performance. In particular, a constant current in the coil is required to produce the desired magnetic field when an output strain should be maintained. The GMA does not produce any mechanical power in this condition, but constant power is being consumed by the excitation coil. This paper presents a new type of motor-driven GMA (MDGMA), which works in a coil-free driven manner. The magnetic field in the iron–gallium alloy (Galfenol), which is a type of magnetostrictive material, is periodically altered by rotating the permanent magnets instead of varying the coil current in the traditional GMA. The proposed MDGMA not only achieves continuous adjustment of the output strain, but can also maintain a constant output strain without consuming any power. In addition, the coil-free design releases the new MDGMA from the heat generated by the excitation coil, which allows the MDGMA to work more stably. [ABSTRACT FROM AUTHOR]

This paper proposes a novel rotor structure for high-speed interior permanent magnet motors to overcome huge centrifugal forces under high-speed operation. Instead of the conventional axial stacking of silicon-steel laminations, the retaining shield rotor is inter-stacked by high-strength stainless-steel plates to enhance the rotor strength against the huge centrifugal force. Both mechanical characteristics and electromagnetic behaviors of the retaining shield rotor are analyzed using finite-element method in this paper. Prototypes and experimental results are demonstrated to evaluate the performance. The analysis and test results show that the proposed retaining shield rotor could effectively enhance the rotor strength without a significant impact on the electromagnetic performance, while some design constraints should be compromised. [ABSTRACT FROM PUBLISHER]

Photovoltaic (PV) solar power generation is proven to be effective and sustainable but is currently hampered by relatively high costs and low conversion efficiency. This paper addresses both issues by presenting a low-cost and efficient temperature distribution analysis for identifying PV module mismatch faults by thermography. Mismatch faults reduce the power output and cause potential damage to PV cells. This paper first defines three fault categories in terms of fault levels, which lead to different terminal characteristics of the PV modules. The investigation of three faults is also conducted analytically and experimentally, and maintenance suggestions are also provided for different fault types. The proposed methodology is developed to combine the electrical and thermal characteristics of PV cells subjected to different fault mechanisms through simulation and experimental tests. Furthermore, the fault diagnosis method can be incorporated into the maximum power point tracking schemes to shift the operating point of the PV string. The developed technology has improved over the existing ones in locating the faulty cell by a thermal camera, providing a remedial measure, and maximizing the power output under faulty conditions. [ABSTRACT FROM PUBLISHER]