Audio bird repelling strategy for transmission line based on improved Q-learning algorithm
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摘要:
日益频繁的鸟类活动给输电线路的安全运行带来了极大威胁,而现有拟声驱鸟装置由于缺乏智能性,无法长期有效驱鸟.为了解决上述问题,本文提出基于改进Q-learning算法的拟声驱鸟策略.首先,为了评价各音频的驱鸟效果,结合模糊理论,将鸟类听到音频后的动作行为量化为不同鸟类反应类型.然后,设计单一音频驱鸟实验,统计各音频驱鸟效果数据,得到各音频的初始权重值,为拟声驱鸟装置的音频选择提供实验依据.为了使计算所得的音频权重值更符合实际实验情况,对CRITIC (Criteria Importance Though Intercrieria Correlation)方法的权重计算公式进行了优化.最后,使用实验所得音频权重值对Q-learning算法进行改进,并设计与其他拟声驱鸟策略的对比实验,实验数据显示改进Q-learning算法的拟声驱鸟策略驱鸟效果优于其他三种驱鸟策略,收敛速度快,驱鸟效果稳定,能够降低鸟类的适应性.
Abstract:
Increasingly frequent bird activities have brought serious threat on the safe operation of transmission lines,and the existing audio bird-repelling device cannot perennially effectively drive birds due to the lack of intellectuality.In order to solve the above problems,this paper presents an audio bird-repelling strategy based on improved Q-learning algorithm.First of all,in order to evaluate the effect of each audio,the behavior of birds after hearing the audio is quantified into different bird response types by combining with the fuzzy theory.Then,an audio bird-repelling experiment is designed,the data of each audio bird-repelling effect is counted,and the initial weight of each audio is obtained,which provides experimental basis for the audio selection of audio bird-repelling device.In order to make the audio weight more consistent with the actual experimental situation,the weight calculation formula of CRITIC (Criteria Importance Though Intercrieria Correlation) is optimized.Finally,the Q-learning algorithm is improved via the audio weights obtained from the above experiment,and a contrast experiment with other audio bird-repelling strategies is designed.Experimental results show that the improved Q-learning algorithm outperforms other audio bird-repelling strategies,characterized by fast convergence,stable bird-repelling performance,and reducing the adaptability of birds.