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This paper develops a novel model free-predictive flux vector control method (MF-PFVC) for high-performance stator flux and torque monitoring of the multi-module three-phase PMSM (N ∗ 3-Phase PMSM) drive with parameters mismatch. The proposed MF-PFVC method is based on a convenient combination of direct predictive torque control (PTC) and model free predictive control techniques. First, the voltage source inverter (VSI) vector control of N ∗ 3-Phase PMSM is determined by the three-segment stator windings structure. Then, the parameter mismatch sensitivity of the N ∗ 3-Phase PMSM drive system is analyzed based on the conventional PTC. Furthermore, a novel MF-PFVC method with multivariable unknown disturbance integral terminal sliding mode observer (SMO) is proposed for N ∗ 3-Phase PMSM drive, which can effectively enhance robustness against the parameters mismatch. Finally, compared with the conventional PTC, the simulation and experimental results of MF-PFVC method illustrated improved stator flux control performance concerning lower stator flux ripple as well as reduced torque ripple while it can eliminate the influence of parameter mismatch.

In this study, a novel robust deadbeat predictive current control algorithm using voltage coefficient matrix update (DPCC-VCMU) is proposed for permanent magnet synchronous motor (PMSM), which can eliminate the influence of model parameter mismatch. First, the parameter sensitivity of conventional deadbeat predictive current control (DPCC) is discussed, and the multiple parameter error online identifiers are designed based on an improved Adaline neural network (ANN) weight adjustment algorithm. The novel identification structure with parameter mismatch error as the weight of neural network is then proposed to enhance the current tracking robustness under model parameter mismatch. Moreover, the parameter error identification value is used to update the coefficient matrix of the control voltage and predict the current value of next moment simultaneously. Finally, compared with conventional DPCC method, the simulation and experimental results verify that the proposed DPCC-VCMU-algorithm can significantly improve the current response accuracy by using voltage coefficient matrix update, while achieving lower torque ripple and smaller current harmonics.

In order to reduce the influence of environmental noise and different operating conditions on the accuracy of motor fault diagnosis, this paper proposes a capsule network method combining multi-channel signals and the efficient channel attention (ECA) mechanism, sampling the data from multiple sensors and visualizing the one-dimensional time-frequency domain as a two-dimensional symmetric dot pattern (SDP) image, then fusing the multi-channel image data and extracting the image using a capsule network combining the ECA attention mechanism features to match eight different fault types for fault classification. In order to guarantee the universality of the suggested model, data from Case Western Reserve University (CWRU) is used for validation. The suggested multi-channel signal fusion ECA attention capsule network (MSF-ECA-CapsNet) model fault identification accuracy may reach 99.21%, according to the experimental findings, which is higher than the traditional method. Meanwhile, the method of multi-sensor data fusion and the use of the ECA attention mechanism make the diagnosis accuracy much higher.

Over the years, many ellipse detection algorithms have been studied broadly, while the critical problem of accurately and effectively detecting ellipses in the real-world using robots remains a challenge. In this paper, we proposed a valuable real-time robot-oriented detector and simple tracking algorithm for ellipses. This method uses low-cost RGB cameras for conversion into HSV space to obtain reddish regions of interest (RROIs) contours, effective arc selection and grouping strategies, and the candidate ellipses selection procedures that eliminate invalid edges and clustering functions. Extensive experiments are conducted to adjust and verify the method’s parameters for achieving the best performance. The method combined with a simple tracking algorithm executes only approximately 30 ms on a video frame in most cases. The results show that the proposed method had high-quality performance (precision, recall, F-Measure scores) and the least execution time compared with the existing nine most advanced methods on three public actual application datasets. Our method could detect elliptical markers in real-time in practical applications, detect ellipses adaptively under natural light, well detect severely blocked and specular reflection ellipses when the elliptical object was far from or close to the robot. The average detection frequency can meet the real-time requirements (>10 Hz).

Obstacle distance measurement is one of the key technologies for autonomous navigation of high-voltage transmission line inspection robots. To address the robustness of obstacle distance measurement under varying illumination conditions, this article develops a research method that fuses image enhancement with robot monocular vision so that the robot can adapt to various levels of illumination running along the transmission line. During the inspection of high-voltage transmission lines in such an overexposed (excessively bright) environment, a specular highlight suppression method is proposed to suppress the specular reflections in an image; when scene illumination is insufficient, a robust low-light image enhancement method based on a tone mapping algorithm with weighted guided filtering is presented. Based on the monocular vision measurement principle, the error generation mechanism is analyzed through experiments, and we introduce the parameter modification mechanism. The two proposed image enhancement methods outperform other state-of-the-art enhancement algorithms in qualitative and quantitative analyses. The experimental results show that the measurement error is less than 3% for static distance measurements and less than 5% for dynamic distance measurements within 6 m. The proposed method can meet the requirements of high-accuracy positioning, real-time performance and strong robustness. This method greatly contributes to the sustainable development of inspection robots in the power industry.

In inverters based on a single proportional-integral (PI) or deadbeat (DB) controller, an inherent resonance peak may emerge near their current loop cut-off frequency, which results in harmonic amplification or even resonance. Additionally, inappropriate filter circuits implemented in sampling circuits may result in the expansion of the resonance peak. Thus, this paper further investigates the influence of the sampling circuits on a PI- or DB-based control loop. Then, the RC filter in the sampling circuit is designed to reduce the inherent resonance peak. Moreover, a compound control strategy based on an improved repetitive controller (IRC) plus a PI controller is adopted for the grid-side converter of a direct-drive wind system. This strategy enhances the harmonic and reactive compensation performance by reconstructing the internal model of the classic repetitive controller (CRC) and limiting the bandwidth of the PI-based loop to a low level. The parameters of the presented IRC-plus-PI control are designed for the purpose of resonance peak elimination and system stability. Furthermore, the non-integer delay problem is solved with an inserted fraction compensator (FC), which plays the role of a low-pass filter in the IRC. Finally, the feasibility and effectiveness of the presented control method is verified by the experimental results.

In this study, a robust predictive power control (R-PPC) method for an N*3-phase permanent magnet synchronous motor (PMSM) is developed in the field of flywheel energy storage systems application, which can effectively improve robustness against inductance parameter mismatch and compensate for the one-beat delay. Firstly, the mathematical model of the N*3-phase PMSM is illustrated, and the topological structure of the N*3-phase PMSM is established. The R-PPC method of the N*3-phase PMSM is then proposed by using the d–q axis current robust predictive control theory. Robustness factors are adopted to modify the current response values in the proposed robust predictive power controller, which can obtain excellent current control performance under the inductance parameter mismatch. Moreover, the next current predicted value is used to replace the current sampled value in the proposed R-PPC method to eliminate the one-beat delay. Finally, comparative simulation and experimental results verify that the proposed R-PPC method can achieve excellent current track performance and smaller torque ripple under both the charge state and discharge state.

The ESS is considered as an effective tool for enhancing the flexibility and controllability of a wind farm, and the optimal control scheme of a wind farm with distributed ESSs is vital to the stable operation of wind power generation. In this paper, a coordinated active and reactive power control strategy based on model predictive control (MPC) is proposed for doubly fed induction generator (DFIG)-based wind farm (WF) with distributed energy storage systems (ESSs). The proposed control scheme coordinates the active and reactive power output among DFIG wind turbines (WTs), grid-side converters (GSCs), and distributed ESSs inside the WF, and the aim is to decrease fatigue loads of WTs, make the WT terminal voltage inside the extent practicable, and take the WF economic operation into consideration. Moreover, the best reactive power references of DFIG stator and GSC are produced independently based on their dynamics. At last, the control scheme generates optimal power references for all ESS to make the SOC of each ESS converge to their average state. With the distributed ESSs, the WF controller regulates the WTs inside WF more flexibly. A WF composed of 10 DFIG WTs was utilized to verify the control performance of the proposed coordinated active and reactive power control strategy.

In this study, a novel modular three-phase permanent magnet synchronous motor (PMSM) is proposed for low-voltage high power applications. The proposed modular three-phase PMSM has an independent segregated three-phase winding configuration, facilitating the implementation of the control algorithm. Firstly, on the basis of the electromagnetic properties, the mathematical model of the modular three-phase PMSM is established, considering the asymmetrical mutual inductances investigated by finite element analysis (FEA). Then, the predictive torque control (PTC) method combining the inductance characteristics of modular three-phase PMSM is developed, and excellent performance is obtained by adjusting the stator flux and torque. Finally, simulation and experiment are performed, and the results show that the proposed novel modular three-phase PMSM with the PTC method exhibits excellent control performance, and small stator current total harmonic distortion (THD).

With the rapid development of the social economy, high-voltage transmission lines as power supply infrastructure are increasing, subsequently presenting a new challenge to the effective monitoring of transmission lines. The dynamic sensor network integrated with robots can effectively solve the elastic monitoring of transmission lines, but the problems of real-time performance, energy consumption and economy of the network need to be solved. To solve this problem, a dynamic network deployment method based on the hybrid hierarchical network (HHN) is proposed to realize a low-cost, energy-saving and real-time dynamic sensing system for overhead high-voltage transmission lines. Through case analysis and simulation, combined with the vague set multi-attribute decision-making method (MADM) with scheme preference, the network deployment and optimization results under multi-parameter constraints are obtained.

High power density energy storage permanent magnet (PM) motor is an important energy storage module in flywheel energy storage system for urban rail transit. To expand the application of the PM motor in the field of urban rail transit, a predictive power control (PPC) strategy for the N*3-phase PM energy storage motor is proposed in this paper. Firstly, the output characteristics of the N*3-phase PM energy storage motor are analyzed by using the finite element method, and the mathematical model of the N*3-phase PM energy storage motor is established. Then, the topological structure and operation principle of N*3-phase PM energy storage motor system is illustrated. Furthermore, the N*3-phase PM energy storage motor system driven by six parallel voltage source inverters (VSIs) is proposed to generate the required power. Finally, a novel predictive direct power control method is developed for the N*3-phase PM energy storage motor. The feasibility and effectively of the proposed PPC method are verified by experiment and simulation. Comprehensive simulation and experimental results both show that the proposed PPC method can obtain the lower torque/stator flux ripple, smaller values of THD of stator winding currents, and zero error tracking of stator winding flux.

The permanent magnet synchronous generator (PMSG) is widely used in high voltage DC transmission technology of wind power systems due to its high-power density, high reliability and simple maintenance. To meet the increasing rated power of the generator (≥10 MW), the multi-converter parallel connection is usually used, which significantly increases the complexity and cost of the system. Therefore, a new modular N* three-phase PMSG is proposed in this paper. Based on the structure and working principle of the generator, the finite element model is established, and the electromagnetic properties are obtained by finite element analysis. Aiming at the unique winding structure and characteristics of modular N* three-phase PMSG, a generator side current converter harmonic control algorithm, combining a resonant controller with the proportional-integral regulator, is proposed. The suppression of stator current harmonics at different speeds could be achieved by the proposed algorithm, and the efficiency of the wind power generation system can be improved. Finally, the feasibility of the novel generator and the effectiveness of the proposed algorithm are verified by simulation and experiment.

In a complex rigid–flexible coupling environment of overhead high-voltage transmission line, the manipulator robust trajectory tracking motion control of live maintenance robot is the premise of fulfilling the entire maintenance operations. In the process of manipulator’s different function reconfigurable (drainage board bolt tightening and insulator strings replacement), in response to the shortage of excessive pursuit of robust stability for robot manipulator when using H ∞ control, as it ignores the influence of structure changes caused by terminal reconfigurable on the system stability to a certain extent, the structured singular value theory–based robot robust μ synthesis control method was proposed in this article. The intrinsic relationship between structured singular value control ( μ method) and H ∞ control was elaborated, a general control model of the multi-arms and multi-actions robot system was established, the multifunction terminal reconfigurable live maintenance robot with its two kinds of operations was set as a research object, and the corresponding robot μ controller was designed. In the MATLAB development environment, simulation results have shown that on the premise of maintaining the robust stability, compared to the H ∞ control method, μ control can obtain better performance robustness for the perturbation of its own structural changes in the process of different operation functions switch. Finally, the validity and engineering practicability of this method is verified by actual 220 kV live operation experiments.

Gait planning is an effective approach aiming at the difficulty in locomotion control of a multi-degree-of-freedom snake-like robot. In this article, a gait-generating method is presented with regard to the issue of its locomotion control in the process of winding–crossing variable-diameter cylindrical obstacles involving a P-R-joint scale-driven snake-like robot. The proposed method solves the problem of trajectory discontinuity of variable-diameter helix lines through linear fitting in the process of the obstacle crossing of the robot. The collision/interference between the robot and the obstacle is avoided by adding an extended helix segment to the front and rear ends of the obstacle. For direct linear fitting of the locomotion trajectory curve that will lead to velocity discontinuity, B-spline curves are used for smooth transition on generating the trajectory curve. A simulation experiment analysis is performed to demonstrate that the proposed gait-generating method can enable the snake-like robot to cross variable-diameter cylindrical obstacles.

This study proposes a fault-tolerant control method for permanent magnet synchronous motors (PMSMs) based on the active flux linkage concept, which addresses permanent magnet (PM) demagnetisation faults in PMSMs. First, a mathematical model for a PMSM is established based on active flux linkage, and then the effect of PM demagnetisation on the PMSM is analysed. Second, the stator current in the static coordinate is set as the state variable, an observer is designed based on a sliding-mode variable structure, and an equation for active flux linkage is established for dynamic estimation based on the equivalent control principle of sliding-mode variable structure. Finally, the active flux linkage for the next moment is predicted according to the operating conditions of the motor and the observed values of the current active flux linkage. The deadbeat control strategy is applied to eliminate errors in the active flux linkage and realise the objective of fault-tolerant control. A timely and effective control for demagnetisation faults is achieved using the proposed method, which validity and feasibility are verified by the simulation and experiment results.

This paper proposes a robust nonlinear predictive current control (RNPCC) method for permanent magnet synchronous motor (PMSM) drives, which can optimize the current control loop performance of the PMSM system with model parameter perturbation. First, the disturbance caused by parameter perturbation was considered in the modeling of PMSM. Based on this model, the influence of parameter perturbation on the conventional predictive current control (PCC) was analyzed. The composite integral terminal sliding mode observer (SMO) was then designed to estimate the disturbance caused by the parameter perturbation in real time. Finally, a RNPCC method is developed without relying on the mathematical model of PMSM, which can effectively eliminate the influence of parameter perturbation by injecting the estimated disturbance value. Simulations and experiments verified that the proposed RNPCC method was able to remove the current error caused by the parameter perturbation during steady state operation.

The development of engineering technology such as inspection robots (IR) for transmission lines and wireless sensor networks (WSN) are widely used in the field of smart grid monitoring. However, how to integrate inspection robots into wireless sensor networks is still a great challenge to form an efficient dynamic monitoring network for transmission lines. To address this problem, a dynamic barrier coverage (DBC) method combining inspection robot and wireless sensor network (WSN) is proposed to realize a low-cost, energy-saving and dynamic smart grid-oriented sensing system based on mobile wireless sensor network. To establish an effective smart grid monitoring system, this research focuses on the design of an effective and safe dynamic network coverage and network nodes deployment method. Multiple simulation scenarios are implemented to explore the variation of network performance with different parameters. In addition, the dynamic barrier coverage method for the actual scene of smart grid monitoring considers the balance between network performance and financial costs.

The dc-link voltage balance and reactive power equilibrium of the cascaded H-bridge rectifier (CHBR) are the prerequisites for the safe and stable operation of the system. However, the conventional PI (Proportional-Integral) control strategy only puts emphasis on the CHBR dc-link voltage balance without taking into account its reactive power equilibrium under capacitive and inductive working conditions. For this reason, this paper has proposed a novel control strategy for the CHBR that can not only balance dc-link voltage, but also achieve reactive power equilibrium and eliminate the coupling effect between the voltage-balancing controller (VBC) and original system controller (OSC). The control strategy can achieve dc-link voltage balance and the reactive power equilibrium of the CHBR through modifying the active duty cycle by closed loop control, and adjusting the reactive duty cycle relatively according to the modifiable amount of the active duty cycle. Moreover, the strategy can eliminate the coupling effect between the VBC and OSC by the open loop control modification of the active and reactive duty cycle of any H-bridge module in CHBR. Simulations and experiments have shown that the proposed control strategy is feasible and effective in performing the CHBR dc-link voltage balance and reactive power equilibrium under all working conditions and load variations.

Inspection images of power transmission line provide vision interaction for the operator and the environmental perception for the cable inspection robot (CIR). However, inspection images are always contaminated by severe outdoor working conditions such as uneven illumination, low contrast, and speckle noise. Therefore, this paper proposes a novel method based on Retinex and fuzzy enhancement to improve the image quality of the inspection images. A modified multi-scale Retinex (MSR) is proposed to compensate the uneven illumination by processing the low frequency components after wavelet decomposition. Besides, a fuzzy enhancement method is proposed to perfect the edge information and improve contrast by processing the high frequency components. A noise reduction procedure based on soft threshold is used to avoid the noise amplification. Experiments on the self-built standard test dataset show that the algorithm can improve the image quality by 3⁻4 times. Compared with several other methods, the experimental results demonstrate that the proposed method can obtain better enhancement performance with more homogeneous illumination and higher contrast. Further research will focus on improving the real-time performance and parameter adaptation of the algorithm.

This paper presents a cascaded robust fault-tolerant predictive control (CRFTPC) strategy with integral terminal sliding mode observer (IT-SMO) to achieve high performance speed loop and current loop for permanent magnet synchronous motor (PMSM) drives. The modeling of PMSM considers the disturbance caused by parameter perturbation and permanent magnet demagnetization. With this model, we can derive the optimal control law of the proposed scheme, which avoids the tuning work of the weight factor effectively. This new CRFTPC strategy has a cascaded structure, external loop and internal loop, both implemented with robust fault-tolerant predictive control. In addition, a new integral terminal sliding mode observer is designed to estimate the disturbances, and thus the robustness of the proposed method can be increased significantly. Comparative simulations and experimentations verify that the proposed CRFTPC provides fast dynamic response, static-errorless speed, and current tracking, even with the system disturbance.

The normal operation of a power grid largely depends on the effective monitoring and maintenance of transmission lines, which is a process that has many challenges. The traditional method of the manual or remote inspection of transmission lines is time-consuming, laborious, and inefficient. To address this problem, a novel method has been proposed for the Multi-Robot Cyber Physical System (MRCPS) of a power grid based on inspection robots, a wireless sensor network (WSN), and multi-agent theory to achieve a low-cost, efficient, fault-tolerant, and remote monitoring of power grids. For the sake of an effective monitoring system for smart grids, the very research is conducted focusing on designing a methodology that will realize the efficient, fault-tolerant, and financial balance of a multi-robot team for monitoring transmission lines. Multiple testing scenarios are performed, in which various aspects are explored so as to determine the optimal parameters balancing team performance and financial cost. Furthermore, multi-robot team communication and navigation control in smart grid environments are introduced.

Power lines are extending to complex environments (e.g., lakes and forests), and the distribution of power lines in a tower is becoming complicated (e.g., multi-loop and multi-bundle). Additionally, power line inspection is becoming heavier and more difficult. Advanced LiDAR technology is increasingly being used to solve these difficulties. Based on precise cable inspection robot (CIR) LiDAR data and the distinctive position and orientation system (POS) data, we propose a novel methodology to detect inspection objects surrounding power lines. The proposed method mainly includes four steps: firstly, the original point cloud is divided into single-span data as a processing unit; secondly, the optimal elevation threshold is constructed to remove ground points without the existing filtering algorithm, improving data processing efficiency and extraction accuracy; thirdly, a single power line and its surrounding data can be respectively extracted by a structured partition based on a POS data (SPPD) algorithm from “layer” to “block” according to power line distribution; finally, a partition recognition method is proposed based on the distribution characteristics of inspection objects, highlighting the feature information and improving the recognition effect. The local neighborhood statistics and the 3D region growing method are used to recognize different inspection objects surrounding power lines in a partition. Three datasets were collected by two CIR LIDAR systems in our study. The experimental results demonstrate that an average 90.6% accuracy and average 98.2% precision at the point cloud level can be achieved. The successful extraction indicates that the proposed method is feasible and promising. Our study can be used to obtain precise dimensions of fittings for modeling, as well as automatic detection and location of security risks, so as to improve the intelligence level of power line inspection.

With the growth of the national economy, there is increasing demand for electricity, which forces transmission line corridors to become structurally complicated and extend to complex environments (e.g., mountains, forests). It is a great challenge to inspect transmission line in these regions. To address these difficulties, a novel method of autonomous inspection for transmission line is proposed based on cable inspection robot (CIR) LiDAR data, which mainly includes two steps: preliminary inspection and autonomous inspection. In preliminary inspection, the position and orientation system (POS) data is used for original point cloud dividing, ground point filtering, and structured partition. A hierarchical classification strategy is established to identify the classes and positions of the abnormal points. In autonomous inspection, CIR can autonomously reach the specified points through inspection planning. These inspection targets are imaged with PTZ (pan, tilt, zoom) cameras by coordinate transformation. The feasibility and effectiveness of the proposed method are verified by test site experiments and actual line experiments, respectively. The proposed method greatly reduces manpower and improves inspection accuracy, providing a theoretical basis for intelligent inspection of transmission lines in the future.

Overhead high-voltage power lines are key components of power transmission and their monitoring has a very significant influence on security and reliability of power system. Advanced laser scanning techniques have been widely used to capture three-dimensional (3D) point clouds of power system scenes. Nevertheless, power line corridors are found in increasingly complex environments (e.g., mountains and forests), and the multi-loop structure on the same power line tower raises great challenges to process light detection and ranging (LiDAR) data. This paper addresses these challenges by constructing a new collection mode of LiDAR data for power lines using cable inspection robot (CIR). A novel method is proposed to extract and reconstruct power line using CIR LiDAR data, which has two advantages: (1) rapidly extracts power line point by position and orientation system (POS) extraction model; and (2) better solves pseudo-line during reconstruction of power line by structured partition. The proposed method mainly includes four steps: CIR LiDAR data generation, POS-based crude extraction, voxel-based accurate extraction and power line reconstruction. The feasibility and validity of the proposed method are verified by test site experiment and actual line experiment, demonstrating a fast and reliable solution to accurately reconstruct power line.