基于无人机智能视觉的输电线路全息全景重建

针对当前重建方法不能精确展现输电线路实际情况的问题,提出基于无人机智能视觉的输电线路全息全景重建方法。通过分析输电线路全息全景重建原理,采用无人机智能视觉技术对输电线路进行拍照,对输电线路全息全景图像进行采集和特征提取等预处理,跟踪图像特征,引入立体匹配算法,实现输电线路全息全景的重构。实验结果表明,所提方法输电线路重建精度高,视觉效果与实际相符,更具实用性及可行性。     

基于遗传算法的变电站巡检机器人任务路径规划方法研究

近年来,随着变电站巡检机器人在变电站中的广泛使用,巡检机器人路径规划问题越来越成为亟待解决的问题。巡检机器人在已知的拓扑地图中标记了待执行巡检任务的停靠点,不同任务需要从初始点出发经过不同的一系列停靠点再返回初始点,如何规划路径是机器人面临的问题。首先分析了路径规划面临的问题,然后通过分析拓扑地图的特征,对地图进行等价简化,再对问题进行建模使用遗传算法求解巡检任务路径规划的近似最优解。通过仿真实验证明,提出的基于遗传算法的路径规划方法是可行有效的,为变电站巡检机器人任务路径规划提供了一种有效方法。     

利用三次样条改进蚁群算法的无人机航路规划

针对无人机在二维平面自动飞行中转弯角度过大、路径规划困难的问题,研究了蚁群算法在复杂环境下航路规划中的应用,利用链接图简洁的特点建立空间模型,对无人机的飞行环境和航迹代价进行了描述,并结合三次样条插值函数与蚁群算法,提出了改进蚁群算法,对无人机飞行路径进行优化,并给出算法软件流程。利用MATLAB进行了仿真实验,得出了最优的航路,算法具有较好的稳定性和鲁棒性,对轨迹中不可飞的尖角进行了平滑处理,使得航路为曲线轨迹,满足无人机工作的性能要求,减少无人机在飞行中的代价损耗,验证了该优化算法在无人机航路规划中的可行性。     

一种人-自动化系统协作的无人机航迹规划方法

为解决无人机航迹规划中考虑“人在闭环”控制需求、结合人机综合优势以进一步提高规划能力的问题,提出了一种人-自动化系统协作的航迹规划方法;该方法利用操控员选取人工引导路径点,以起始点、人工引导路径点和目标点为根节点形成多树扩展;引入Dubins曲线应用于随机树扩展和寻找最近子节点过程,改善航迹平滑性;仿真实验结果表明,该方法在自动化系统局部自主能力的基础上引入人的智能,规划效率和可靠性得到了提高,可用于无人机航迹规划实践。     

高压交流输电线路故障检修技术研究

对高压交流输电线路故障检修技术进行研究,可以提高电力质量,减少因线路故障产生的一系列人财有损情况的发生。当前方法将短期的输电线路检修当作单重不确定性的优化问题,对其进行建模以及求解。而在现场运行过程中,架空线路可靠性指标不能准确表达线路故障发生的可能性,其理论基础相对薄弱,仅考虑了单重不确定性的问题,无法对高压交流输电线路进行高精度地检修。为此,提出一种基于层次分析的高压交流输电线路故障检修方法。该方法先将高压交流输电线路故障划分为：单相接地故障、高压交流输电线路短路故障、高压交流输电线路导线断路故障。然后利用多个电流测量点,通过FFT程序对电流故障分量相位进行求解,根据比较相邻测点相位差判别故障区域,最后利用图论实现高压交流输电线路故障检修时间控制函数,以及线路故障检修经济控制目标函数的制定,与分类和定位结果结合完成高压交流输电线路故障的检修。实验结果证明,所提方法可以有效地对高压交流输电线路故障进行检修,具有一定的利用价值。     

输电线巡检机器人弱光条件下的障碍物识别研究

在室外光照条件变化下进行有效的障碍物识别是高压输电线路巡检机器人所面临的技术难点之一。针对弱光条件下障碍物识别的稳健性问题,提出了一种基于机器人视觉的障碍物识别智能方法,以使巡检机器人适应各种不同程度的弱光变化。通过对采集的障碍物图像进行自适应同态滤波处理,以减少部分光照的影响;将障碍物图像分成均匀大小的子区域,运用改进的局部方向模式提取各个子区域图像的特征直方图向量,并把子块特征直方图逐个串联为总的直方图;再选用卡方距离法进行统计识别。实验结果表明:该方法使巡检机器人对输电线上的防震锤、悬垂线夹和绝缘子串能够进行有效的识别。相比于其他算法,其具有更好的抗光照干扰效果和更高的准确识别率;提升了机器人巡检过程中图像识别的稳健性、适应性和准确性,极大地提高了巡检机器人在电力行业的可持续发展性。     

无人机多阶段航迹预测协同任务规划

针对多无人机(Unmanned Aerial Vehicles, UAVs)协同控制问题,提出了一种UAVs多阶段航迹预测分布式任务规划方法。定义从一次任务分配开始到其中一项任务完成为一个任务周期。在每个规划周期,首先,各UAV使用A*算法快速预测到所有任务目标的路径,提供至任务分配;然后,采用聚类算法修改目标价值向量,协商分配结果,并实时计算探测范围内的最短路径;最后,采用三次B样条曲线平滑所分配的最短路径,在线规划出满足飞行约束的飞行航迹。通过仿真实验对算法的有效性进行了验证,结果表明,提出的算法能够实时获得近似最优的任务分配结果并规划出可飞行航迹,并有效处理突发任务。     

220kV SISFCL对距离保护的影响分析

为解决在输电线路中接入饱和铁心型超导限流器(Saturated Iron Core Supercondutive Fault Current Limiter,SISFCL)后对原距离保护选择性、灵敏性产生影响的问题,在PSCAD中建立了220kV SISFCL、输电线路距离保护Ⅰ段、Ⅱ段的模型,分析了饱和铁心型超导限流器对输电线路距离保护产生影响的机理,由公式推导得到两种理论上可消除此影响的方法,即实时改变整定阻抗和改变接线方法,最后提出一种距离保护的新型整定方法。仿真结果表明所提出的整定方法有效地提高了距离保护的选择性和灵敏度,为SISFCL应用于输电线路提供理论依据。     

特斯拉线圈输电的新型探究实验

针对传统特斯拉线圈输电实验中原理深邃、线路复杂问题,设计了新型简化探究实验。在单线传输系统基础上,通过大量实验,探究了互电容在单线输电和无线输电中的等效方式与同频耦合对输电效率的影响,得出与实验结果较为吻合的简化输电模型,有利于在大学物理实验中开展教学,并对该输电技术在准无线输电方面的应用做了展望。     

浅谈特高压输变电

 电能是表示电流做多少功的物理量.电能的一个重要特性是不能简单储存,所以就要求从发电厂发出的电能,通过输电线路立刻送到各个用电地区.根据输送电能距离的远近,采用不同的电压.从我国现在的电力情况来看,送电距离在15～20千米时通常采用10千伏输电;送电距离在50千米左右采用35千伏输电;送电距离在100千米左右时采用110千伏输电;送电距离在200～300千米时采用220千伏的电压输电;输送距离300千米以上需要采用更高的电压.输电电压在110千伏、220千伏的线路,称为高压输电线路,输电电压在330～750千伏的线路,称为超高压输电线路,而输电电压在1000千伏左右的线路,则称为特高压输电线路.特高压输电是目前电力生产的先进技术,对我国国民经济的发展起着重要的作用.     

Array design for mm-wave UAV-assisted cooperative communications with simultaneous wireless information and power transmission

High performance UAV-assisted communications system using simultaneous wireless information and power transmission (SWIPT) in mm-wave band is presented. UAV is a moving relay powered from a ground source through a power-splitting mechanism. In mm-wave band we utilize antenna array to increase the antenna gain while keeping array size small and practical. The radiation pattern of the UAV antenna is continuously adjusted to peak towards the source and destination. Two array geometries, a line and a cross, are designed for UAV antenna. We achieve near optimal pattern, utilizing innovative low power switches instead of phase shifters which are high power consuming components and using them here defies the purpose. We maximize the end-to-end cooperative throughput by optimizing the UAV power profile, power-splitting ratio profile, antenna weights (0, 1), and UAV trajectory for amplify-and-forward (AF) protocol. We consider two cases. Case1: UAV transmits and receives data simultaneously along two predefined trajectories. The antenna weights for line and cross arrays are optimized utilizing genetic algorithm. The power profile and, power-splitting ratio profile are also optimized using the penalty method. Case2: UAV accumulates the data and power along an optimal trajectory until it reaches the vicinity of target, when it transmits data at high bit rate. Here we define the optimization of parameters mentioned in Case1, while at an optimal point along the trajectory, as sub-problem1, and finding the next optimal point as sub-problem2. Sub-problem1 is solved using the genetic algorithm and dual decomposition method. Sub-problem2 is then solved using successive concave optimization. The overall problem, i.e. cooperative throughput, is solved by reciprocal iteration over the two sub problems. The simulation results show the proposed mm-wave band cross array antenna and switches can overcome the high frequency propagation losses, hence, achieving higher power harvest and data rates. The achieved higher data throughput outperforms the conventional single antenna low frequency systems.     

激光供能无人机的一种优化跟踪算法

航天事业的发展以及新能源技术的开发，使得小型电动无人机在现代战争、科学研究等方面具有较高的应用价值。激光无线能量传输技术能有效解决小型电动无人机续航时间短的问题，极大提高了无人机的工作能效。以无人机激光供能系统结构原理为基础，针对小型电动无人机激光无线供能的特点，提出了一种最大功率点优化跟踪方法:即采用恒定电压法（CV法）和萤火虫算法（FA法）相结合的优化控制算法，在激光投射到无人机上的光伏电池板上后，通过对无人机激光无线充电过程中最大功率点的跟踪，提高激光利用率及充电稳定性。并且通过数值仿真，验证了所提算法的准确性和适用性。     

UAV path design with connectivity constraint based on deep reinforcement learning

Cellular networks are expected to communicate effectively with unmanned aerial vehicles (UAVs) and support various applications. However, existing cellular networks are primarily designed to cover users on the ground; thus, coverage holes in the sky will exist. In this paper, we investigate the problem of path design for cellular-connected UAVs, taking into account the interruption performance throughout the UAV mission to minimize the completion time. Two types of connectivity constraints requirements are assumed to be available. The first is defined as the maximum continuous time interval that the UAV loses connection with base stations (BSs) below a predefined threshold. For the second, we consider the sum outage of UAV is limited during the entire UAV mission. The UAV is tasked with flying from a starting location to a final destination while minimization the mission time, satisfying the two constraints, separately. The formulated path design problem which involves continues variables and a dynamic radio environment, is not convex and thus is extremely difficult to solve directly. To tackle this challenge, a deep reinforcement learning (DRL) based trajectory design algorithm is proposed, where the Dueling Double Deep Q Network(Dueling DDQN) with multi-steps learning method is applied. Simulation results demonstrate the effectiveness of the proposed DRL algorithm and achieve a trade-off between the trajectory length of the UAV and connection quality.     

Time allocation improvement method for UAV-based wireless energy transfer cooperative mobile edge

Wireless power transfer (WPT) and mobile edge computing (MEC) are two advanced technologies that could improve the computing power and range of mobile devices. However, by integrating the unmanned aerial vehicle (UAV) into wireless powered MEC systems, wireless energy transfer will be susceptible to the “double near–far” effect. Therefore, in order to further overcome the influence of the “double near–far” effect, this paper considers the optimization of time slot allocation for UAV-assisted wireless powered cooperative MEC system, which includes an access point (UAV) and two mobile devices. The purpose of the study is to minimize the total transmission energy of the UAV while satisfying the delay and size of the computational tasks, so this paper proposed a 2:1:1 time-slot optimization allocation method. The method exploits the synergy of users so that the mobile device which is closer to the UAV acts as an offloading relay, by combining power and time slot optimization to minimize the total energy consumption of the UAV. Compared with the equal time slot scheme before the improvement, this method can not only utilize the wireless transmission energy to charge the mobile device for more time in the first period, but also can save the time of data transmission of the closer device in the third period, and it can enhance the rate of data transmission of the mobile devices at the same time. The results show that the task capacity of the system computed will be increased compared to the original scheme; the total transmission rate of the whole system is also improved by the same order of magnitude. The simulation results verify the effectiveness and reliability of the algorithm of the paper, and the comprehensive performance of the system can be maximized by the flexible offloading algorithm.     

基于改进Fleury算法的激光扫描投影路径规划方法

为改善激光扫描投影系统在扫描复杂图形时由于扫描频率远小于20 Hz而引起的严重闪烁问题，基于图论理论将Fleury算法应用于激光扫描投影路径规划，并针对该算法无法优化非欧拉路径的局限性，提出了改进的Fleury算法。应用MATLAB对上述算法的扫描路径优化效果进行仿真分析，并应用到已有的激光扫描投影系统中进行实验，实验结果表明:基于改进的Fleury算法的激光扫描投影路径优化技术可以使激光扫描投影频率提升至20 Hz左右，有效解决频闪问题，提高绘制图像的质量，从而提升自主研发激光扫描投影仪器的实际应用价值。     

Region coverage-aware path planning for unmanned aerial vehicles: A systematic review

The use of unmanned aerial vehicles (UAVs) to carry out remote aerial surveys has become prominent in recent years. The UAV-based survey faces several operational issues, such as complicated terrain, limited UAV resources, obstacles, sensor limitations, and other environmental factors. In addition, the coverage plan includes numerous objectives, such as lowering path length, maximizing coverage, and limiting survey time, necessitating multi-objective optimization. UAVs require effective coverage path planning (CPP) to generate the ideal route. It involves determining the path which encompasses every point inside the required region under different constraints. The process automates the process of route determination for autonomous operation by considering various environmental constraints. In this paper, we explore and analyze the existing research on the various techniques used in coverage route planning for UAVs. It provides an overview of the current state-of-the-art CPP methods for UAVs. The study discusses the key challenges and requirements of CPP for UAVs and presents various approaches proposed in the literature to tackle these challenges. We explore a variety of geometric flight patterns for the area of interest having UAV deployment. It also features multi-UAV and multi-region coverage strategies, providing a new dimension to UAV-based operations. The energy consumption of UAVs during CPP is an essential factor, as it influences their flight length and mission duration. The design of the CPP algorithm is determined by the unique requirements of the UAV application, such as the size and form of the region to be mapped, the existence of obstacles, and the desired coverage resolution. Path planning strategies in a three-dimensional environment and dynamic coverage are also included in the study. Moreover, we compare the existing strategies using different performance metrics to evaluate the success of covering missions. Finally, the problems and unresolved concerns related to UAV coverage path planning are examined to provide valuable insights to the readers.     

Joint uplink and downlink scheduling and UAV trajectory design in the presence of multiple unfriendly jammers and eavesdroppers

In this paper, we investigate secure uplink and downlink communications between an unmanned aerial vehicle (UAV) and multiple user equipments (UEs) in the presence of multiple ground-based eavesdroppers (EVs) and unfriendly jammers. In order to guarantee the secure uplink and downlink transmissions, we consider a novel secure transmission scheme, which involves a power splitting based downlink transmission and scheduling of uplink and downlink transmission. Explicitly, we aim to maximize the average secrecy rate (ASR) by optimizing the UAV trajectory, the transmit power of the UAV and UEs, and scheduling of uplink and downlink transmission. Although the formulated problem is nonconvex, we propose an efficient solution by jointly applying the techniques of block coordinate descent and successive convex approximation. Numerical results show that the proposed scheme achieves a better ASR than the benchmark schemes.