Direct‐Current Triboelectric Generator

The first direct‐current triboelectric generator (DC‐TEG) based on sliding electrification for harvesting mechanical energy from rotational motion is reported. The DC‐TEG consists of two rotating wheels and one belt for connecting them, which are made of distinctly different triboelectric materials with a specific requirement. During the rotation, the contact‐induced electrification and the relative sliding between the two wheels and the belt can induce a continuous increase of the accumulated positive and negative triboelectric charges at the two rotating wheels, respectively, resulting in a Corona discharge and producing the observed current through an external load. The DC‐TEG can deliver an open‐circuit voltage of larger than 3200 V and a maximum power of 100 μW under an external load of 60 MΩ at a rotational speed of 1000 r min^–1. By designing a point metal discharge electrode near the accumulated positive charges on the metal wheel, the instantaneous short‐circuit current can be up to 0.37 mA. The DC‐TEG can be utilized as a direct power source to light up 1020 serially connected commercial light‐emitting diodes (LEDs) and the produced energy can also be stored in a capacitor for other uses. This work presents a DC‐TEG technology to harvest mechanical energy from rotational motion for self‐powered electronics.     

基于MATLAB的光伏离网系统仿真研究

介绍了单相光伏离网发电控制仿真系统,采用DC/DC和DC/AC两级拓扑结构对光伏离网系统进行了研究和设计。研究光伏组件、输入滤波电容和离网逆变器之间的耦合关系,通过双闭环控制策略,实现直流母线电压稳定以及逆变器稳定输出。在MATLAB仿真环境下,对所设计的光伏离网系统进行了仿真,仿真结果表明该方案和控制策略的正确性。     

Combined spatial-temporal energy harvesting and relay selection for cognitive wireless powered networks

In order to improve the Energy Efficiency (EE) and spectrum utilization of Cognitive Wireless Powered Networks (CWPNs), a combined spatial-temporal Energy Harvesting (EH) and relay selection scheme is proposed. In the proposed scheme, for protecting the Primary User (PU), a two-layer guard zone is set outside the PU based on the outage probability threshold of the PU. Moreover, to increase the energy of the CWPNs, the EH zone in the two-layer guard zone allows the Secondary Users (SUs) to spatially harvest energy from the Radio Frequency (RF) signals of temporally active PUs. To improve the utilization of the PU spectrum, the guard zone outside the EH zone allows for the constrained power transmission of SUs. Moreover, the relay selection transmission is designed in the transmission zone of the SU to improve the EE of the CWPNs. In addition to the EE of the CWPNs, the outage probabilities of the SU and PU are derived. The results reveal that the setting of a two-layer guard zone can effectively reduce the outage probability of the PU and improve the EE of CWPNs. Furthermore, the relay selection transmission decreases the outage probabilities of the SUs.     

含氢储能的多源联合发电系统调度研究

风电和光伏发电具有间歇性和随机性,为了降低在多源联合发电系统中的弃风弃光率,采用含氢储能系统和火电机组配合来平滑风电和光电机组出力。文中以系统运行成本最小和弃电惩罚成本最小为目标,以系统功率平衡、火电机组出力和爬坡、热备用、风电和光电出力及储能系统储氢罐容量、电解槽和燃料电池功率等为约束条件构建了多源联合发电系统日前调度模型。通过YALMIP工具箱对模型进行编程,并调用CPLEX对编写的程序进行求解。对含有风电、光电、火电机组以及储能系统的多源联合发电系统进行算例分析,通过对比有无储能系统的弃风弃光量和系统总运行成本,证明了含氢储能系统可以有效降低系统的弃风弃光率,并提高系统的经济性。     

WSN中基于多分辨率和压缩感知的数据融合方案

当前基于压缩感知的传感器网络数据融合方案中,不论数据字段有何特征,均假设网络具有固定而均匀的压缩阈值,从而导致数据通信量过高,能耗浪费较大。提出一种基于多分辨率和压缩感知的数据融合方案。首先,对传感器网络进行配置,以生成多个层次类型不同的簇结构,用于过渡式数据收集,在该结构上,最低层的叶节点只传输原始数据,其他层的数据收集簇进行压缩采样;然后将其测量值向上发送,当母数据收集簇收到测量值时,利用基于反向DCT和DCT模型的CoSaMP算法恢复原始数据;最后,在SIDnet-SWANS平台上部署了该方案,并在不同的二维随机部署传感器网络规模下进行了测试。实验结果表明,随着分层位置的变化,大部分节点的能耗均显著降低,与NCS方案相比,能耗下降50%~77%,与HCS方案相比,能耗下降37%~70%。     

Genetic algorithm-based partial charging schedule of rechargeable sensor networks

The use of rechargeable sensors is a promising solution for wireless sensor networks. On this type of network, mobile charging vehicles (MC) are used for charging sensors using wireless energy transfer (WET) technology. In on-demand charging, a sensor transmits a charging request to the service station, and the MC visits the sensor to transfer energy. The key disadvantages of utilizing MC-based WET are its high energy expenditure rate due to mobility, long service time, and slow charging rate. Because of these reasons, sensors deplete their energy and become dead before the MC reaches the requesting nodes to recharge. We have adapted a genetic algorithm-based partial charging scheme to serve the charging requests. Our objective is to improve the survival ratio of the network. Using comprehensive simulations, we analyze the performance of our proposed method and compare it to two other existing approaches. The simulation results demonstrate that our proposed algorithm improves the survival ratio by up to 20 % by developing a dynamic energy threshold function for transmitting charging requests from the sensors and a partial charging schedule using a genetic algorithm.     

CoFEE: Context-aware futuristic energy estimation model for sensor nodes using Markov model and autoregression

Nowadays, the emerging internet of things (IoT) technology offers the connectivity and communication between all things (various objects/things, devices, actuators, sensors, and mobile devices) at anywhere and anytime. These devices have embedded environment monitoring capabilities (sensors) and significant computational responsibilities. Most of the devices are working by utilizing their limited resources such as energy, memory, and bandwidth. Obviously, battery power is a crucial factor in any network. It makes tedious overheads to the network operations. Prediction of the future energy of the devices could be more helpful for managing resources, connectivity, and communication between the devices in IoT and wireless sensor networks (WSNs). It also facilitates the reliable internet and network connection establishment to the nodes. Hence, this paper presents an energy estimation model to predict the future energy of devices using the Markov and autoregression model. The proposed model facilitates smarter energy management among internet-connected devices. Performance results show that the proposed method gives significant improvement compared with the neural network and other existing predictions. Further, the proposed model has very lower error performance metrics such as mean square error and computation overhead. The proposed model yields more perfect energy predictions for a node with 64% to 97% and 16% to 43% of higher prediction accuracy throughout the time series.     

DEICA: A differential evolution-based improved clustering algorithm for IoT-based heterogeneous wireless sensor networks

With the evolution of technology, many modern applications like habitat monitoring, environmental monitoring, disaster prediction and management, and telehealth care have been proposed on wireless sensor networks (WSNs) with Internet of Things (IoT) integration. However, the performance of these networks is restricted because of the various constraints imposed due to the participating sensor nodes, such as nonreplaceable limited power units, constrained computation, and limited storage. Power limitation is the most severe among these restrictions. Hence, the researchers have sought schemes enabling energy-efficient network operations as the most crucial issue. A metaheuristic clustering scheme is proposed here to address this problem, which employs the differential evolution (DE) technique as a tool. The proposed scheme achieves improved network performance via the formulation of load-balanced clusters, resulting in a more scalable and adaptable network. The proposed scheme considers multiple parameters such as nodes' energy level, degree, proximity, and population for suitable network partitioning. Through various simulation results and experimentation, it establishes its efficacy over state-of-the-art schemes in respect of load-balanced cluster formation, improved network lifetime, network resource utilization, and network throughput. The proposed scheme ensures up to 57.69%, 33.16%, and 57.74% gains in network lifetime, energy utilization, and data packet delivery under varying network configurations. Besides providing the quantitative analysis, a detailed statistical analysis has also been performed that describes the acceptability of the proposed scheme under different network configurations.     

Encapsulation of lead-free Sn/Zn/Bi solder alloy particles by coating with wax powder for improving oxidation resistance

An encapsulation treatment of lead-free Sn/Zn/Bi solder powder was investigated for improving the oxidation resistance. Sn-8mass%Zn-3mass%Bi alloy particles were coated with a wax (12-hydroxystearic acid) powder by means of a dry mechanical treatment method using a ball mill. In order to determine the optimum operating conditions of the ball mill in the wax-coating treatments, the compressive energy required for deforming a single Sn/Zn/Bi alloy particle was measured with an unconfined compression tester and the mechanical energy applied to the alloy particles in the ball mill was estimated using the results of the compression test. The optimum operating conditions were determined based on both the applied energy and the flowability of solder pastes, and the wax-coated alloy particles maintaining the spherical shape were obtained under the conditions. The wettability test and the solder balling test for the solder pastes containing the wax-coated alloy particles stored at room temperature in air were carried out to evaluate the oxidation resistance performance. The wax-coated alloy particles had an excellent wettability compared with the original alloy particles, and the oxidation resistance of the Sn/Zn/Bi solder powder was improved by the encapsulation treatment.     

一种应用于能量攫取系统的低功耗的工作于亚阈值区域的基准电压源的设计

We propose a bandgap reference, which works in sub-threshold regions to the reduce power consumption in applications such as those in energy harvesting systems that stimulate the development of power management for low power consumption applications.Measurements shows that the supply current of the proposed bandgap reference is only 6.87 μA, including a voltage buffer consuming 3.6 μA of supply current, when the supply voltage is 5 V.The supply voltage can vary from 3 to 11 V and the line regulation of the proposed bandgap reference output voltage is 0.875 mV/V at room temperature.The temperature coefficiency is 88.9 ppm from 10 to 100° C when the supply voltage is 5 V.