本文選題：線傳感器網絡 + 配電網故障檢測��； 參考：《傳感技術學報》2017年01期

【摘要】：針對電網故障檢測中使用的無線傳感器網絡節(jié)點定位精度較低,分簇不均問題,提出了一種基于DV-Hop算法改進均值粒子群算法(PSO),首先DV-Hop算法改進均值粒子群算法中粒子的速度與位移,使動態(tài)無線傳感器網絡重新定位簇頭節(jié)點坐標更加接近真實值;然后遞歸神經網絡學習算法迭代值逼近最合適的慣性權重值,優(yōu)化均值PSO粒子群算法使其達到最優(yōu)搜索能力。最后由Sink節(jié)點對每一次動態(tài)分簇后網絡節(jié)點進行數據采集后對電能耗盡的節(jié)點進行無線充電。仿真結果表明,改進后的PSO算法比PSO算法聚類分簇誤差更小,節(jié)點定位配電網故障的精確度提高12.8%,有效地延長了網絡生命周期。
[Abstract]:Aiming at the problem of low location accuracy and uneven clustering of wireless sensor network nodes used in power network fault detection, An improved mean particle swarm optimization (PSO) algorithm based on DV-Hop algorithm is proposed. Firstly, the DV-Hop algorithm improves the velocity and displacement of particles in the mean PSO algorithm, so that the coordinate of cluster head nodes can be relocated in dynamic wireless sensor networks (WSNs). Then the recursive neural network learning algorithm approximates the most appropriate inertial weight value and optimizes the mean PSO particle swarm optimization algorithm to achieve the optimal search ability. Finally, the Sink node collects the data of the network nodes after each dynamic clustering. The simulation results show that the improved PSO algorithm has less clustering error than the PSO algorithm, and the accuracy of node location and distribution network fault is increased by 12.8. the network life cycle is effectively prolonged.
【作者單位】： 安徽工程大學電氣工程學院;國網蕪湖供電公司;
【基金】：2016年安徽高校自然科學研究項目(KJ2016A794)
【分類號】：TP18;TP212.9;TN929.5
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本文編號：1935237