1.Guangdong University of Technology;2.The Jiangmen Power Supply Bureau of Guangdong Power Grid Corporation
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方法(Criteria Importance Though Intercrieria Correlation)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。
Increasingly frequent bird activities have brought serious threat on the safe operation of transmission lines, and the existing audio bird-repelling device can not 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 was quantified into different bird response types by combining with the fuzzy theory. Then, an audio bird-repelling experiment was designed, the data of each audio bird-repelling effect is counted, and the initial weight of each audio is obtained, which provided 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 by using the audio weight which is obtained from the experiment, and the contrast experiment with other audio bird-repelling strategies is designed. Experimental data show that the effect of the improved Q-learning algorithm for audio bird-repelling strategy is better than the other audio bird-repelling strategies, which has fast convergence, stable bird-repelling effect, and can reduce the adaptability of birds.
南京信息工程大学学报 ® 2022 版权所有 技术支持：北京勤云科技发展有限公司