Limitations of Modern Diagnostic and Prognostic Systems for a Hydraulic Unit’s Health

Evgeniia Georgievskaia

doi:10.3390/eng2010003

Abstract

Modern diagnostic systems for the hydraulic unit’s health play an important role in ensuring the reliability and safety of the hydroelectric power plant (HPP). However, they cannot provide timely detection of such dangerous operational defects as fatigue cracks. This article reflects two main reasons for this problem. The first one is a high level of the individuality of hydraulic units, which does not allow the effective use of statistical methods of information processing, including BIG DATA and MACHINE LEARNING technologies. The second is the fundamental impossibility to identify cracks in some key components of hydraulic units only on the basis of data analysis from a standard diagnostic system usually used at the HPP. Developed computational studies on the example of Francis turbines confirmed this. It is proposed to supplement the functionality of standard diagnostic systems with a prognostic block for an individual analytical forecast of the unit’s residual lifetime based on the calculated assessment of fatigue strength. This article presents the developed conceptual diagram and the demonstration version of the proposed analytical predictive system. The comparison of the standard vibration diagnostic system and the proposed solution as a tool for the early detection of cracks in a Francis turbine runner shows some advantages of the proposed approach.

Highlights

The modern systems for monitoring and diagnostics of the equipment technical state installed at hydroelectric power plants (HPP) should help to improve the reliability and safety of hydraulic unit’s operation due to the timely detection of damages and emerging defects in the operating equipment
It should be noted that the systems for monitoring and diagnostics of HPP’s equipment are constantly evolving, taking into account the tightening requirements for the conditions of the unit’s usage [3,4,5,6] and the changing regulatory framework [7,8,9]
Predictive self-learning systems using artificial intelligence and based on BIG DATA and MACHINE LEARNING (ML) technologies [10,11,12] have been actively introduced at various energy facilities

Summary

Introduction

The modern systems for monitoring and diagnostics of the equipment technical state installed at hydroelectric power plants (HPP) should help to improve the reliability and safety of hydraulic unit’s operation due to the timely detection of damages and emerging defects in the operating equipment. Cost minimization is possible on the basis of effective technical condition monitoring for the units under operation and reasonable forecasting of the timing of necessary repairs These functions should be performed by the modern diagnostic and/or prognostic systems installed in the hydraulic units. The problem of “dirty data” has become so urgent that it was included in the traditional collection of 20 technology trends in 2020 published by the Norwegian company Telenor [33] Another significant reason for the ineffectiveness of diagnostic systems in the detection of fatigue cracks is the high structural rigidity of the unit and its individual components. The possibility of identifying cracks on a runner using existing diagnostic systems and the effect of a developing crack on the vibration parameters recorded by the diagnostic system have been studied with the example of a powerful Francis hydraulic unit by numerical simulation methods with the ANSYS software

Design Model

Calculation Results

Prospects for Development of Diagnostic and Prognostic Systems

Andritz Hydro