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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.

The permanent magnet synchronous motor (PMSM) has become a core component of electromechanical energy conversion in the modern industrial field. In order to expand the application of the PMSM in the field of high-power traction, a robust predictive torque control (R-PTC) strategy for the N -segment three-phase PMSM ( N *3-phase PMSM) is proposed in this article. First, the output characteristics of the N *3-phase PMSM are illustrated with the finite-element analysis method, and the mathematical model is established. Then, the six-segment three-phase PMSM predictive control system driven by six parallel inverters is designed to generate the required torque. Furthermore, the influence of the parameter mismatch on the predicted torque and stator flux is taken into consideration based on the conventional predictive torque control (PTC). Finally, a novel R-PTC method with the proportional controller is developed for the N *3-phase PMSM, which can effectively improve the accuracy and robustness of predictive control performance under the parameters mismatch. The simulation and experimental results verify that, compared with the conventional PTC, the proposed R-PTC method can make the predicted stator flux and torque value accurately track its reference values while achieving lower stator flux and torque ripple.

Robot intelligence includes motion intelligence and cognitive intelligence. Aiming at the motion intelligence, a hierarchical reinforcement learning architecture considering stochastic wind disturbance is proposed for the decision-making of the power line maintenance robot with autonomous operation. This architecture uses the prior information of the mechanism knowledge and empirical data to improve the safety and efficiency of the robot operation. In this architecture, the high-level policy selection and the low-level motion control at global and local levels are considered comprehensively under the condition of stochastic wind disturbance. Firstly, the operation task is decomposed into three sub-policies: global obstacle avoidance, local approach and local tightening, and each sub-policy is learned. Then, a master policy is learned to select the operation sub-policy in the current state. The dual deep Q network algorithm is used for the master policy, while the deep deterministic policy gradient algorithm is used for the operation policy. In order to improve the training efficiency, the global obstacle avoidance sub-policy takes the random forest composed of dynamic environmental decision tree as the expert algorithm for imitation learning. The architecture is applied to a power line maintenance scenario, the state function and reward function of each policy are designed, and all policies are trained in an asynchronous and parallel computing environment. It is proved that this architecture can realize stable and safe autonomous operating decision for the power line maintenance robot subjected to stochastic wind disturbance.

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.

Purpose In the extreme power environment of flexible transmission line, wind load, high voltage and strong electromagnetic interference, the motion performance of the robot manipulator is strongly affected by the extreme environment. Therefore, this study aims to improve the manipulator motion control performance of power cable maintenance robot and effectively reduce the influence of specific operation environment on the robot manipulator motion posture. Design/methodology/approach The mathematical model under three typical operation conditions, namely, flexible line, wind load and strong electromagnetic field have been established, correspondingly the mapping relationship between different environment parameters and robot operation conditions are also given. Based on the nonlinear approximation feature of neural network, a back propagation (BP) neural network is adopted to solve the posture control problems. The power cable line sag, robot tile angle caused by wind load and spatial field strength are the input signals of the BP network in the robot motion posture control method. Findings Through the training and learning of the BP network, the output control variables are used to compensate the actual robot operation posture. The simulation experiment verifies the effectiveness of the proposed algorithm, and compared with the conventional proportional integral differential (PID) control, the method has high real-time performance and sound stability. Finally, field operation experiments further validate the engineering feasibility of the control method, and at the same time, the proposed control method has the remarkable characteristics of sound universality, adaptability and easy expansion. Originality/value A multi-layer control architecture which is suitable for smart grid platform maintenance is proposed and a robot system platform for network operation and maintenance management is constructed. The human–machine–environment coordination and integration mode and intelligent power system management platform can be realized which greatly improves the intelligence of power system management. Mathematical models of the robot under three typical operation conditions of flexible wire wind load and strong electromagnetic field are established and the mapping relationship between different environmental parameters and the robot operation conditions is given. Through the non-linear approximation characteristics of BP network, the control variables of the robot joints can be obtained and the influence of extreme environment on the robot posture can be compensated. The simulation results of MATLAB show that the control algorithm can effectively restrain the influence of uncertain factors such as flexible environment, wind load and strong electromagnetic field on the robot posture. It satisfied the design requirements of fast response, high tracking accuracy and good stability of the control system. Field operation tests further verify the engineering practicability of the algorithm.

Purpose To improve the operational efficiency and intelligence of live operation robots in dynamic-unstructured operation environments, this paper aims to propose a fuzzy logic-based method for the autonomous search and visual localization control of a manipulator end effector applied to a drainage plate bolt on a high-voltage transmission line. The proposed approach is based on a four-way video image information output from a dual-operation manipulator. Design/methodology/approach First, based on the structural characteristics of the drainage line, an autonomous search method for the drainage plate bolt and a mapping relationship between the autonomous search control parameters and the relative posture of the operation manipulator-drainage line are proposed. The posture control parameters of the dual manipulators can then be obtained, and a two-dimensional fuzzy controller is designed with the posture offset distance and the posture offset angle as its input signals. This enables the localization control of the bolt and nut alignment to be realized through a visual process. Findings The proposed fuzzy control algorithm is used for bolt location control, and its performance is compared with that of the conventional approach. The simulation results indicate that the fuzzy control algorithm greatly improves the localization accuracy and operational efficiency of live operation robots. Originality/value Field operation experiments on actual transmission lines verify that the fuzzy control-based visual localization control of the robot manipulator has great engineering practicality. Therefore, the proposed method further improves operational intelligence compared with conventional algorithms.

Based on gas dynamics and computational fluid dynamics, the numerical simulation of the steady-state flow field of igniter working in the two conditions was conducted with FLUENT. The numerical simulation results were validated by experiments. Influences of ignition gas flow pipe on the igniter's working performance were analyzed on the basis of test validation. The analysis results show that the combustion chamber pressure has atmospheric pressure and that the igniter works with ignition gas flow pipe. The velocity of throat is lower than the speed of sound and the sonic position moves to the position of ignition gas flow pipe; the velocity, pressure and temperature of throat increase; but the flux, pressure and temperature of outlet decrease. Under the different conditions of combustion chamber pressure, the outlet velocity, pressure, temperature and flux have a stable section; but the flux decreases.

In this article, a novel predictive torque and stator flux control (PTSF) method is proposed for N -segment three-phase permanent magnet synchronous motor ( N *3-phase PMSM), which can effectively eliminate the influence of parameter mismatch on stator flux and torque. The proposed PTSF method has two loops, stator flux loop and torque loop, both implemented with robust predictive control algorithm. First, the stator flux predictive controller with parameter robustness is designed, and the sensitivity of its parameters is analyzed. In the dq stator flux space, the robust stator flux predictive control with one-step delay compensation is then proposed, which can effectively enhance robustness against parameters mismatch. Moreover, the robust torque predictive control based on unknown torque disturbance observer is developed, which can effectively strengthen the robustness against the load torque disturbance and parameter mismatch. Finally, the validity and feasibility of the proposed PTSF method are verified by simulation and experiment. Excellent simulation and experimental results were achieved with respect to the stator flux tracking error, torque /flux ripple reduction, and stator current distortion over conventional predictive torque control.

An adaptive droop-based hierarchical optimal voltage control (DHOVC) scheme is proposed for voltage-source converter high-voltage-direct-current (VSC-HVDC) connected offshore wind farms (WFs). The wind turbines (WTs) and WF side VSC (WFVSC) are coordinated to minimize the voltage deviations of buses inside the WF from the nominal voltage and mitigate reactive power (Var) fluctuations of WTs. The model predictive control (MPC) is used to improve the performance of the DHOVC scheme during a certain predictive horizon. A hierarchical solution method based on the alternating direction method of multipliers (ADMM) is developed to reduce the calculation burden of the central controller while improving the information privacy protection. During the predictive horizon, the WTs and WFVSC are coordinated to achieve the near global optimal performance without global information. A WF with 32 × 5MW WTs was used in the matlab/simulink to test the proposed DHOVC scheme.

Purpose In response to the poor reliability of live maintenance robots in semi-structured environments and the difficulty of monitoring their operation status, this paper aims to propose an online method for evaluating the operation status of high-voltage live maintenance robots based on fuzzy control. Design/methodology/approach The robot bolt tightening operation is taken as an example. During the whole operation process, the key technologies of bolt tightening are analyzed theoretically, a two-dimensional fuzzy control model of bolt tightening process control is established and the control parameters, which characterize the operation status, are obtained. Through dynamic adjustment of the fuzzy controller, real-time online monitoring of the robot operation status can be achieved. Findings The results of simulation experiments and 220 kV live operation experiments show that the reliability of robot bolt tightening is greatly enhanced by the proposed control method. Originality/value The results not only verify the engineering practicability of the fuzzy control-based method but also indicate that it can improve efficiency, safety and operability.

In this paper, a distributed optimal active and reactive power control (DARPC) strategy based on the alternating direction method of multipliers (ADMM) is proposed for wind farms (WFs). The WFs operate in a distributed manner to minimize the network power loss, voltage deviations of buses from the rated voltage, and active power output deviations of WTs from their proportional distribution (PD)-based power reference. An optimization problem is formulated as a quadratic programming (QP) problem by using the linearized DistFlow model. The ADMM-based solution is used to decompose the centralized optimization problem to several subproblems which are solved in individual local controllers with exchanged information from their practical neighbor controllers. Compared with existing distributed/hierarchical optimal control, the impacts of the active power injection from the WTs are taken into consideration and optimized while meeting the dispatch command from the transmission system operator (TSO). The ADMM-based distributed solution eliminates the requirement of a central unit. Compared with conventional centralized optimal control, the scalability of the WFs is improved. A WF consisting of 20 WTs is simulated in MATLAB/Simulink to test the control effectiveness of the proposed DARPC strategy.

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.

With the development of engineering technology, the distributed design-based Branch-Trimming Robot (BTR) has been used to ensure the power supply security of transmission lines. However, it remains difficult to combine distributed BTRs with a wireless sensor network to build an efficient multi-robot system. To achieve this combination, a dynamic network topology control method was proposed, combining the motion characteristics of robots with the structure of a distributed wireless sensor network. In addition, a topology-updating mechanism based on node signal strength was adopted as well. To achieve efficient data transmission for distributed multi-robot systems, the present study focused on the design of a distributed network model and a dynamic network topology control strategy. Several simulation and test scenarios were implemented, and the changes of network performance under different parameters were studied. Furthermore, the real scene-based dynamic topology control method considers the relationship between network performance and antenna layout.

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.

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 (&ge, 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.

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.

The rapid development of the smart grid has led to higher maintenance cost and greater scalability of transmission lines. An effective and secure monitoring system for power lines has become a bottleneck restricting the intellectualization of power grids. To address this problem, a novel method is proposed for the intelligent monitoring of power grids (Robot Delay-Tolerant Sensor Network, RDTSN) based on an inspection robot, Wireless Sensor Network (WSN) and Delay-Tolerant Sensor Network (DTSN) to achieve low-cost, energy-efficient, elastic and remote monitoring of power grids. With RDTSN, a smart grid can detect the fault of transmission lines and evaluate the operational state of power grids. To build an effective monitoring system for a smart grid, this research focuses on designing a methodology that achieves efficient and secure delivery of the data inspected on transmission lines. Multiple RDTSN scenarios are performed, in which different routing algorithms are explored to determine the optimal parameters, with a balance in network performance and financial cost. Furthermore, a data delivery strategy is introduced to ensure communication security.

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.

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.

Based on relative theories of gas dynamics and computational fluid dynamics, the flow field computation software ANSYS Fluent was used to simulate the steady flow field of the solid type ignition device of liquid-propellant rocket engine in two working conditions (condition I: without ignition channel, condition II: with ignition channel). On this basis, the influence of ignition channel on the working characteristics of the solid type ignition device of the liquid-propellant rocket engine was analyzed and experimentally tested. The results showed that when the pressure in the combustion chamber was atmospheric pressure, under condition II, the gas velocity at the throat of the ignition device did not reach the sonic velocity, and the position of sonic velocity moved to the downstream section of the ignition channel. Compared to condition I, the gas velocity and energy at the ignition outlet increased, which would be beneficial for initial ignition, and the gas pressure and temperature at the throat increased as well, indicating that the structural strength at the throat should be evaluated. The gas flow, gas pressure, and gas temperature at the ignition outlet decreased compared to working condition I, yet the changes were small and would have minimal effect on the ignition performance. During the pressure increase process in the combustion chamber, the gas pressure, velocity, temperature, flow, and energy at the ignition outlet experienced a steady stage in both working conditions before coming to an inflection point. The inflection point under condition II is smaller than that under condition I. To improve the ignition reliability, the working pressure of the ignition device should be further increased.

In this paper, a predictive stator flux control (PSFC)algorithm for permanent magnet synchronous motor (PMSM) drives is proposed, which can eliminate the influence of flux linkage parameter perturbation and rotor position error. First, the sensitivity of conventional predictive current control (PCC) to the flux linkage parameter and rotor position is analyzed. Then, the novel composite discrete time sliding mode observer (SMO) based on stator flux state is designed, which can estimate theflux linkage parameter perturbation, rotor position error and load torque simultaneously. Finally, a novel PSFC method is developed, which can enhance the robustness of PCC against flux linkage parameter perturbation and rotor position error by using composite discrete time SMO. Simulation and experimental results indicate that the proposed PSFC can achieve low stator current harmonics, low torque ripple and excellent steady-state performance under the flux linkage parameter perturbation and rotor position error.

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.

When the inspection robot runs on the overhead transmission line, self-adaptive climbing ability is an important index to estimate the robot application to real complex conditions, especially in the mountainous areas. The slipping of driving wheel usually happens when the robot climbs the lines with slopes, which would increase the abrasion of the driving wheel, reduce the efficiency of operation and waste the energy. So it is important to develop the wheels slipping identification and control method to improve the climbing ability of the inspection robot. This paper proposed a self-adaptive slipping identification and control method aiming to solve the slipping problem of the driving wheels. Firstly, the mechanism and force situation of the robot has been analyzed when the wheel slipping happens. Then we discussed the major factors to control the robot slipping and developed a dynamic model to identify the wheel slipping. Finally, we proposed a slipping identification strategy and a fuzzy control-based slipping control method. This slipping identification and control method have been tested by the experimental and practical field experiments to make the robot run in the experimental lines and the high voltage transmission lines (HVTL) in real, respectively. The performance results indicate that the proposed method is effective to improve the robot self-adaptive capacity to climb on the lines with slope and enhance the autonomous climbing and running ability of the robot.