As a promising choice of alternative energy, wind power will account for a major part of energy generation in future Energy Internet. With the exploitation of wind power, multiple wind turbines (WTs) are deployed at remote and harsh areas, in which the adverse working environment may lead to enormous WT operating and maintenance costs. Deploying unmanned aerial vehicles (UAVs) for WT detection and sensory data processing in wind farms has been considered as a promising technology to reduce the costs and improve inspection efficiency. In this paper, a mobile edge computing (MEC) driven UAV routine inspection scheme is proposed, in which the UAV not only detects WTs in multiple sorties, but also provides computing and offloading services. To provide seamless communication service, UAV can offload the sensory data to the ground station or satellite optimally. In order to minimize the total completion time, we jointly optimize the UAV trajectory and computation operations, while guaranteeing the data processing accuracy. In the proposed scheme, in order to overcome the influence of wind on UAV trajectory planning, a low complexity WT routine inspection trajectory and UAV scheduling approach is designed firstly. Then, we present an iterative optimization solution to minimize the energy consumption of computation processing, via finding the optimal offloading trajectory and computation offloading parameters. Finally, simulation results show that the proposed scheme can effectively improve the efficiency of UAV routine inspection system performance.INDEX TERMS Energy Internet, mobile edge computing, wind turbine, unmanned aerial vehicle, task offloading.

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Abstract-In view of the problem of low energy collection efficiency and low efficiency of photovoltaic power supply modules in wireless sensor networks, a maximum power point tracking algorithm suitable for photovoltaic power supply of wireless sensor nodes is proposed. Firstly, the photovoltaic cell model is analyzed. Based on this, the traditional maximum power point tracking algorithm is analyzed. Combining with the advantages of disturbance observing method and load current maximization method, the problem of low working efficiency and low energy collection efficiency of functional modules is solved. Current observation method maximum power point tracking algorithm, and complete the relevant hardware circuit design. Experimental results show that the power consumption of MPPT circuit is low, and the efficiency caused by environmental factors is very small. The efficiency is kept above 90% and the overall system efficiency is about 87%, which provides a stable and reliable photovoltaic power supply for WSN nodes.Keywords-WSN Node, MPPT, Photovoltaic Power Supply, Current Observation IntroductionAs a new information acquisition and processing technology, wireless sensor network(WSN) can monitor, perceive and collect the relevant information of various monitored objects in the network distribution area in real time and has a bright future. However, WSN nodes are generally powered by common chemical batteries, and their limited service life directly affects the life of wireless sensor networks [1][2]. Taking the solar energy with higher energy density as the energy source of the wireless sensor node can greatly increase the life span of the wireless sensor node. Due to the low photoelectric conversion efficiency of photovoltaic cells, and the output power is greatly affected by external parameters such as light intensity and temperature, if it is necessary to ensure that the node obtains more energy to facilitate the storage of the energy storage element and improve the service quality of the node, To ensure that iJOE

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A photovoltaic power supply with a simple structure and high tracking efficiency is needed in self-powered, wireless sensor networks. First, a maximum power point tracking (MPPT) algorithm, including the load current maximization-perturbation and observation (LCM-P&O) methods, with a fixed step size, is proposed by integrating the traditional load current maximization (LCM) method and perturbation and observation (P&O) method. By sampling the changes of load current and photovoltaic cell input current once the disturbance is applied, the pulse width modulation (PWM) regulation mode, i.e., increasing or reducing, can be determined in the next process. Then, the above algorithm is improved by using the variable step size strategy. By comparing the difference between the absolute value of the observed current value and the theoretical current value at the maximum power point of the photovoltaic cell with the set threshold value, the variable step size for perturbation is determined. MATLAB simulation results show that the LCM-P&O method, with a variable step size, has faster convergence speed and higher tracking accuracy. Finally, the two MPPT algorithms are tested and analyzed under constant voltage source input and indoor fluorescent lamp illumination through an actual circuit, respectively. The experimental results show that the LCM-P&O method with variable step size has a higher tracking efficiency, about 90%–92%, and has higher stability and lower power consumption.