Performance Optimization Analysis of Partial Discharge Detection Manipulator Based on STPSO-BP and CM-SA Algorithms

• Published in: Sensors (Basel, Switzerland) Vol. 25; no. 16; p. 5214
• Main Authors: Luo, Lisha, Huang, Junjie, Chen, Yuyuan, Zhao, Yujing, Hu, Jufang, Xiong, Chunru
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 21.08.2025; MDPI
• Subjects: Accuracy; Algorithms; Analysis; Back propagation; Efficiency; end-positioning error compensation; Energy consumption; energy consumption optimization; Kinematics; manipulator inverse kinematics; Mathematical optimization; Methods; Neural networks; Optimization algorithms; partial discharge detection; STPSO-BP neural network; United States; manipulator inverse kinematics; STPSO-BP neural network; end-positioning error compensation; energy consumption optimization; partial discharge detection
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s25165214

In high-voltage switchgear, partial discharge (PD) detection using six-degree-of-freedom (6-DOF) manipulators presents challenges. However, these involve inverse kinematics (IK) solution redundancy and the lack of synergistic optimization between end-effector positioning accuracy and energy consumption. To address these issues, a dual-layer adaptive optimization model integrating multiple algorithms is proposed. In the first layer, a spatio-temporal correlation particle memory-based particle swarm optimization BP neural network (STPSO-BP) is employed. It replaces traditional IK, while long short-term memory (LSTM) predicts particle movement trends, and trajectory similarity penalties constrain search trajectories. Thereby, positioning accuracy and adaptability are enhanced. In the second layer, a chaotic mapping-based simulated annealing (CM-SA) algorithm is utilized. Chaotic joint angle constraints, dynamic weight adjustment, and dynamic temperature regulation are incorporated. This approach achieves collaborative optimization of energy consumption and positioning error, utilizing cubic spline interpolation to smooth the joint trajectory. Specifically, the positioning error decreases by 68.9% compared with the traditional BP neural network algorithm. Energy consumption is reduced by 60.18% in contrast to the pre-optimization state. Overall, the model achieves significant optimization. An innovative solution for synergistic accuracy–energy control in 6-DOF manipulators for PD detection is offered.