A 5-day 5-credit Course at Aalto on Partial Discharge Detection in High Voltage Equipment by Dr. Murtaza Hashmi

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The IEEE Finland Section and IEEE Finland joint chapter IE13/PE31/IA34/PEL35, together with Aalto University, is offering an intensive 5-day 5-credit course on the Partial Discharge Detection in High Voltage Equipment taught by Dr Murtaza Hashmi, a recognized Expert at Power Systems. The assessment of the course will be based on attendance, active participation in the discussions, and performance in Group Work and homework activities. It is worth mentioning that the course is free for our IEEE Members. Please note that this is an in-person course, and we only have a few places available, so registration is binding. Participants are responsible for organizing and paying their own travel costs, accommodation, and food.

Please find the additional information as follows.

Course General Information:
This course will address advanced Partial Discharge (PD) detection techniques used for insulation diagnostics and predictive maintenance of High Voltage (HV) equipment. A brief overview of understanding PD phenomenon, its types and characteristic, detection principles and methods, comparison of offline and online PD testing, PD signals propagation and attenuation, de-noising of PD signals due to interferences and calibration of PD testing and monitoring systems will be discussed. The offline and online PD detection techniques in power transformers, Air Insulated Switchgears (AIS), Gas Insulated Switchgears (GIS), power cables and accessories, overhead distribution lines, electric machines and switchyard equipment including insulators, overhead transmission lines and other hardware infrastructure will be elaborated in depth. The international standards including IEEE, IEC and CIGRE and best prevailing practices and guidelines will be explained to perform PD testing, data analysis and interpretation for effective insulation diagnostics in HV equipment. Several successful case studies and lessons learnt will be presented based on real-life PD testing for improved fault detection and localization. An overview of smart sensing infrastructure for online real-time continuous PD monitoring and its application for digital transformation will be carried out which builds the basis of Industry 4.0 for advanced condition monitoring, diagnostics and predictive maintenance of HV equipment.

Learning Outcomes:
Upon successful completion of this course, students will be able to:

• Explain the complex phenomenon of PD, its types and distinguished characteristics.
• Explain PD detection principles and methods, PD signals propagation and attenuation, de-noising of PD signals and measurement calibration for effective and reliable insulation diagnostics and predictive maintenance.
• Demonstrate offline and online PD testing techniques in different HV equipment including power transformers, switchgears, power cables and accessories, overhead distribution lines, electric machines and switchyard equipment.
• Describe international standards and best prevailing practices and guidelines to help perform PD testing, data analysis and interpretation for effective insulation diagnostics in HV equipment.
• Highlight successful case studies and lessons learnt based on real-life PD testing for improved fault detection and localization in HV equipment.
• Develop plan to implement online real-time PD monitoring systems for the diagnostics and predictive maintenance of HV equipment under Industry 4.0 initiative

 

Learning Resources:
A set of course presentation slides, and supporting materials based on publications, white papers, case studies, and technology brochures will be available.

Learning and Teaching Activities:
This course relies on lectures and interactive discussions as the primary delivery mechanism. A Group Work will be assigned to the students in the classroom to reinforce the theoretical concepts encountered in lectures and deliver brief presentation by each Group. In addition, a Homework will be assigned to the students in the form of writing an essay to summaries the details of advanced condition monitoring systems for power assets to gauge their progress and understanding.

Assessment:
The assessment of the course will be based on the class attendance, active participation in the discussions, and performance in Group Work and Homework activities.

Language:
The working language of the course is English.

 

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Agenda: all days 09:00-17:00

Day 1, Mon. Aug 10

• Understanding PD phenomenon, its types and characteristic, detection principles and methods.
• PD signals propagation and attenuation, different types of noises or interferences and their elimination.
• Calibration of PD testing and monitoring systems

Day 2, Aug 11

• Online Dissolved Gas Analysis (DGA) monitoring system
• Bushing monitoring system for bushing and winding health condition assessment
• Other monitoring systems based on PD measurements using ultrasonic and high frequency sensors
• International standards, best practices and guidelines for PD detection in power transformers
• Case studies based on real-life PD detection in power transformers

Day 3, Aug 12 

• Online PD monitoring system for Air Insulated Switchgears (AIS)
• Online PD monitoring system for Gas Insulated Switchgears (GIS)
• International standards, best practices and guidelines for PD detection in switchgears
• Case studies based on real-life PD detection in switchgears

Day 4, Aug 13

• Offline and online PD testing in power cables and accessories
• Online PD monitoring in covered-conductor overhead distribution lines
• International standards, best practices and guidelines for PD detection in distribution networks
• Case studies based on real-life PD detection in distribution networks

Day 5, Aug 14

• Online PD monitoring in electric motors and generators
• Corona discharge testing for insulators, bushing and surge arresters, overhead transmission lines and other hardware infrastructure.
• International standards, best practices and guidelines for PD detection in other electrical equipment
• Case studies based on real-life PD detection in other electrical equipment

Group Work: Important elements of insulation diagnostic techniques- PD sensors, data collection, data analysis and interpretation, decision-making.

Explanation of Homework: An essay on implementing online real-time continuous PD monitoring system for the diagnostics and predictive maintenance of HV equipment – Technology, implications and challenges!

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