A Real‐Time Reliable Condition Assessment System for 500kV Transmission Towers Based on Stress Measurement

Abstract

Transmission power towers play an important role in power delivery systems. In recent years, some important results on reliability of transmission towers have been obtained based on theoretical analysis, but there are very few practical application systems of real-time condition monitoring. This paper proposes a new real-time reliable condition assessment system for 500kV transmission power towers based on stress measurement. The necessity of such systems and the architecture of the online monitoring system will first be presented. Through calculating the stress distribution condition of different components of the transmission tower under typical working conditions, those positions with relatively high failure probability in the transmission tower can be identified monitored for installing the stress sensors on them. A new method is presented for calculating the reliability index of the transmission tower structure is also developed based on the mechanical structure of the tower. In particular, the tower structure is simplified to a series system, and the Ditlevsen’s bounds is used to estimate the reliability of the tower system. Finally, a designed example for the online reliable condition assessment procedure is given using a 500kV oxytropis tower as an illustration.

Application of BPNN Optimized by Fireworks Algorithm on Structural Bearing Capacity Analysis of Transmission Tower under Icing Conditions

“…The relevant research methods of transmission tower structure bearing capacity analysis have been constantly explored, and some research results have been achieved [15][16][17]. The integration of artificial neural network (ANN) with other soft computing methods, such as backpropagation (BP), imperialist competitive algorithm (ICA), support vector regression (SVR), backpropagation neural network (BPNN), genetic algorithms (GA), and multilayer feed forward (MLFF) has been deeply reviewed [18][19][20][21]. As a classical neural network algorithm, BPNN has strong adaptability and fault-tolerant performance.…”

Vibration sensor for monitoring systems of construction elements of high-voltage power lines

“…Поэтому, именно надежные опоры ЛЭП гарантируют безаварийную подачу электроэнергии потребителям, исключая перебои и возникновение внештатных, аварийных ситуаций. Следует отметить, что, несмотря на то, что отказы ЛЭП из-за разрушения опор составляют всего 13%, само разрушение опор имеет наиболее тяжелые последствия и приводит к большим затратам, связанным с восстановлением ЛЭП, и перерыву в электроснабжении [4,5].…”