In contemporary power transmission systems, substation monitoring stands as a vital but challenging task. While robotics offers promise in this regard, its potential is still nascent, struggling to replicate human intelligence. This article's core aim was to optimize robot path planning (RPP). Employing the enhanced red deer algorithm (ERDA), we sought to bolster RPP for more efficient substation inspections. The key methods used seem to be modeling, experimentation, comparative analysis, and some elements of data benchmarking to systematically evaluate and validate their proposed technique and models both in simulation and the real world. Research aims to enhance substation inspection effectiveness and bolster the safety of power usage in society. Proposed hybrid approach, combining proportional–integral–derivative (PID) with ERDA (PID–ERDA), underpins an Intelligent Intelligent RPP framework tailored to substation inspections. Examining the PID–ERDA model's performance, it significantly improved path length by 18%–29% and reduced response times by 14%–26% compared with PID or ERDA alone. PID–ERDA consistently achieved optimal solutions in 40–60 trials out of 85, while PID and ERDA managed 20–40 trials with inconsistent optimization. Additionally, it reduced average response times to 17–20 s from 21 to 27 s observed when using PID and ERDA separately. PID–ERDA also demonstrated superior path accuracy, surpassing methods like improved adaptive control algorithm‐feedforward neural network, enhanced unified algorithm‐susceptible‐infected‐removed, and bounded behavior‐particle swarm optimization by 7%–13%. The study affirms that the PID–ERDA model significantly enhances path planning for substation inspections, representing a milestone in RPP for power station inspections within modern power transmission systems. The primary contribution of this research is the significant improvement it brings to RPP for power station inspections, especially in substation monitoring within modern power transmission systems.

中文翻译：

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PID控制器和增强型红鹿算法在变电站巡检机器人最优路径规划中的实现

在当代输电系统中，变电站监控是一项至关重要但具有挑战性的任务。尽管机器人技术在这方面提供了希望，但其潜力仍处于萌芽状态，难以复制人类智能。本文的核心目标是优化机器人路径规划（RPP）。我们采用增强型红鹿算法 (ERDA)，寻求支持 RPP，以实现更高效的变电站检查。使用的关键方法似乎是建模、实验、比较分析和数据基准测试的一些要素，以在模拟和现实世界中系统地评估和验证他们提出的技术和模型。研究旨在提高变电站检查效率，保障社会用电安全。所提出的混合方法将比例积分微分 (PID) 与 ERDA (PID-ERDA) 相结合，支撑了专为变电站检查量身定制的智能 RPP 框架。检查 PID-ERDA 模型的性能，与单独的 PID 或 ERDA 相比，它显着将路径长度提高了 18%-29%，并将响应时间缩短了 14%-26%。 PID-ERDA 在 85 次试验中，在 40-60 次试验中始终获得最佳解决方案，而 PID 和 ERDA 在 20-40 次试验中实现了不一致的优化。此外，将单独使用 PID 和 ERDA 时观察到的平均响应时间从 21 秒减少到 27 秒，减少到 17 秒到 20 秒。 PID-ERDA还表现出优越的路径精度，超越了改进的自适应控制算法-前馈神经网络、增强的统一算法-易感感染-去除和有界行为-粒子群优化等方法7%–13%。该研究证实，PID-ERDA 模型显着增强了变电站检查的路径规划，代表了现代输电系统中电站检查 RPP 的一个里程碑。这项研究的主要贡献是它为电站检查的 RPP 带来了显着改进，特别是在现代输电系统中的变电站监测方面。