EEE6008 Condition Monitoring in Power Engineering (Classroom & Synchronous e-Learning)

Training Provider: SINGAPORE INSTITUTE OF TECHNOLOGY
Course Reference: TGS-2021006643
S$993
Original: S$3,309
Save S$2,316

About This Course

Failure modes and reliability assessment
Instrumentation and signal processing
Integrated condition monitoring and health prognosis
Temperature-based monitoring techniques
Chemical-based monitoring techniques;
Vibration-based monitoring techniques;
Electrical measurement-based monitoring techniques
Partial discharge-based techniques
Computational intelligence-based techniques
Condition-based maintenance and asset management

What You'll Learn

To provide the module participants with in-depth knowledge of the deterioration and failure mechanisms of major power systems equipment and assets, and with the competency to monitor and assess the conditions and health status of these assets. With the number of aging assets in power generation and T&D growing, and increasing installation of new type of assets such as solar photovoltaic and power electronic-based systems, there is urgent need to improve the knowledge and competency of power engineering professionals in condition monitoring of these assets. The topics of this module shall be presented in three broad areas:

Condition Monitoring of Rotating Electrical Machines – Generators and motors are subjected to high mechanical and electrical stresses and have many possible failure mechanisms. Requirements of instrumentation, signal processing and monitoring through temperature, chemical, vibrational and electrical techniques are covered.
Condition Monitoring of High Voltage Equipment - Transmission lines, cables, transformer and switchgears are subjected to high voltage stresses and understanding of failure mechanisms in the different types of insulation employed in such equipment is important. Gas, oil and partial discharge analysis are covered.
Recent Advances in Condition Monitoring and Health Prognosis –Electrochemical-based energy storage systems, solar photovoltaic systems and power flow converters are increasingly integrated as assets in power grids and the common failure mechanisms are of electrochemical origins, semiconductor nature or metallic nature in the die-packaging interface. Big data, pervasive sensing and computational intelligence are transforming the nature of condition monitoring and health prognosis applications in smart grids.

Entry Requirements

Bachelor degree in Engineering
At least one year of relevant working experience

Course Details

Duration 39 hours
Language Not specified
Training Commitment Not specified
Total Enrolled 21 students
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