针对以往算法在直流配电网故障定位方面呈现的收敛速度慢、易陷入局部最小值、收敛精度低等问题,采用改进人工蜂群黏菌算法进行改善和解决。在黏菌算法的基础上引入自适应可调节的反馈因子和改进的交叉算子以提高收敛速度,引入人工蜂群算法提高搜索能力以跳出局部最小值,组成人工蜂群黏菌算法。首先基于六端直流配电网拓扑结构,以G-VSC与W-VSC之间发生故障为例,建立双极短路故障以及单极接地短路故障的数学模型。然后详细介绍改进人工蜂群黏菌算法的原理,建立合适的适应度函数作为直流配电网故障定位的衡量标准。最后进行实验仿真,将寻优得到故障点与实际值进行对比,验证算法的精准度。此外,通过对比其他算法进一步验证人工蜂群黏菌算法的高效性和鲁棒性。
Abstract
In order to overcome the problems of previous algorithms in DC distribution network fault location, including slow convergence speed, easy to fall into local minimum and low convergence accuracy, an improved artificial bee colony slime mould algorithm was adopted. On the basis of this algorithm, adaptive adjustable feedback factor and optimized crossover operator were introduced to improve the convergence speed, and artificial bee colony algorithm was combined to enhance the search ability. An improved adaptive artificial bee colony slime mould algorithm（ISMA） was formed, thus effectively solving the problem of fault location.. Firstly, based on the six-terminal DC distribution network topology, a mathematical model of bipolar short-circuit fault as well as single-pole grounded short-circuit fault is established taking a fault occurring between G-VSC and W-VSC as an example. Then the principle of the improved artificial bee colony slime algorithm is introduced in detail, and a suitable fitness function is established as the measure of fault location in DC distribution network. Finally, experimental simulations are conducted to obtain fault points from the optimization search and compare them with the actual values to verify the accuracy of the algorithm. In addition, the efficiency and robustness of the artificial bee colony slime mould algorithm are further verified by comparing with other algorithms.
关键词
配电网 /
故障定位 /
参数识别 /
直流配电系统 /
人工蜂群黏菌算法
Key words
distribution networks /
fault location /
parameter identification /
DC distribution system /
artificial bee colony slime mould algorithm
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基金
河北省重点研发计划（20314301D）; 国家电网有限公司科技项目（kj2021-003）
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