Health Condition Assessment for Pumped Storage Units Using Multihead Self-Attentive Mechanism and Improved Radar Chart

Abstract

A novel health assessment method for pumped storage units (PSUs) is presented in this article. First, multihead self-attentive mechanism (MSM) combined with quantile regression neural network (QRNN) are proposed to establish a health benchmark model for PSUs to reveal the intricate relationship between the vibration and its multiple influencing factors. Especially, MSM automatically learns the complex interaction features among multiple influencing factors, while QRNN explores the upper bounds of health vibration under specific operational parameters. Then, a fuzzy dimensionless function is constructed to map the deviation of the currently measured vibration from the predicted health vibration to the performance degradation indexes. Finally, an improved radar chart method is proposed to visually illustrate the health condition of multiple measurement locations and give comprehensive health assessment for PSUs. The proposed method is applied in a PSU in Zhejiang province of China. The results of comparative experiments illustrate its effectiveness and feasibility.