Summary of the DGA Course
The Dissolved Gas Analysis (DGA) course provided by Transformers Academy covers a comprehensive array of techniques and best practices for monitoring the health of power transformers. It delves deeply into the principles, methodologies, and advancements surrounding DGA, providing valuable insights for professionals in the field.
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Original price: $350
ALL levels discounted price: $280
Course author: Marius Grisaru
E-lesson 1: The Most Efficient Transformer Maintenance Method
The course begins by emphasizing the significance of DGA in transformer health assessments. It explains how transformers function as chemical reactors, with oil undergoing various reactions that result in dissolved gases. The lesson outlines the DGA process, covering sampling, gas extraction, separation, and measurement. The complexity of interpreting DGA results is highlighted, alongside advancements in online DGA devices. The session stresses the importance of selecting the right laboratories and understanding the limitations of existing data to ensure accurate life assessments.
E-lesson 2: Sampling Oil for DGA – Frequency and Safety Considerations
This lesson focuses on the critical aspects of oil sampling for DGA, including the role of experienced technicians and appropriate sampling equipment. Key considerations include maintaining accurate results by avoiding contamination and ensuring consistent sampling locations. The importance of sampling frequency is addressed, along with the necessity for aligning DGA sampling with transformer maintenance schedules.
E-lesson 3: Sampling Oil for DGA – Part II
The lesson continues by examining the detailed procedures for oil sampling, emphasizing the importance of skilled personnel and robust safety measures. It explores the best practices for using different types of sampling containers and techniques to minimize inaccuracies. The focus is on maintaining sample integrity by avoiding common pitfalls like air bubbles. The session reinforces that accurate DGA results depend heavily on proper sampling techniques.
E-lesson 4: Modern Techniques for Gas Separation and Measurement in Offline DGA
In this session, attendees learn about the challenges in gas extraction for DGA and the evolution of extraction techniques. The lesson covers both mercury-based and modern non-mercury systems, comparing their advantages and limitations. It also introduces online gas extraction methods and evaluates their potential vulnerabilities. The importance of calibration for accurate partition coefficients is discussed, along with the need for laboratories to conduct studies to ensure reliable measurements.
E-lesson 5: Theories and Practices of Modern DGA Diagnosis
This lesson provides an in-depth review of various DGA diagnostic methods, including predictive, graphical, physical/chemical, and combined approaches. It outlines the historical evolution of these methods, discussing challenges like gas concentration limits and measurement accuracy. Graphical diagnosis techniques and health index (HI) models are introduced as tools for assessing transformer conditions.
E-lesson 6: Improving DGA and Oil Test Requirements
This session explores ways to improve the speed and quality of DGA and oil testing processes. It compares in-house versus outsourced testing laboratories, emphasizing the importance of quality monitoring and adapting to new technologies. The speaker discusses the significance of metrology, calibration, and blind testing to maintain high standards for DGA results.
E-lesson 7: Critical Analysis of Offline and Online DGA Techniques
The session analyzes various methods for offline and online DGA measurements, highlighting their respective chemical principles and techniques. It evaluates advancements in measurement technologies, such as gas chromatography, spectrometry, and photoacoustic spectroscopy, and discusses their limitations and future applications in DGA. The lesson provides insights into the ongoing evolution of DGA measurement and its potential impact on transformer health monitoring.
E-lesson 8: Selecting and Using the Proper DGA Online Device
Focusing on online DGA devices, this lesson explores their development history and the factors influencing their performance, including cost, maintenance, and calibration. It provides a critical analysis of different online devices, sharing case studies to highlight successes and failures. The session stresses the need for users to select devices based on transformer importance and operational conditions, as well as the necessity of expertise in interpreting online data.
E-lesson 9: Preparing In-House Gas in Oil Standards
The lesson delves into the importance of calibrating DGA tests, focusing on the preparation of in-house gas-in-oil standards. The limitations of commercial standards are addressed, with recommendations for creating reliable in-house standards to improve calibration accuracy. The session also covers the challenges of achieving low detection limits and ensuring repeatability and reproducibility in DGA results.
E-lesson 10: When to Take a Transformer Out of Service
This session examines the process of determining when to take a transformer out of service based on DGA results. It discusses the advantages and limitations of DGA diagnosis, using real-world case studies to highlight both successful and unsuccessful diagnoses. The role of experienced DGA experts is emphasized, along with the importance of continuous research to improve DGA reliability for decision-making.
E-lesson 11: The Future of DGA – Advanced Approaches Based on AI
The penultimate lesson explores the future of DGA diagnosis, focusing on artificial intelligence (AI) and predictive techniques. It covers the evolution of transformer technology and how AI could enhance transformer health monitoring. The session discusses the challenges associated with integrating AI into DGA diagnosis, such as the need for large, unbiased data sets and continuous improvement in diagnostic models.
E-lesson 12: Special Lesson – Dr. Michel Duval’s Contributions
This lesson is dedicated to the work of Dr. Michel Duval, a pioneer in the field of DGA for transformers. Duval’s standardized methods and terminology, including the introduction of the concept of triangles and pentagons for interpreting DGA data, are reviewed. The lesson highlights his contributions to gas-in-oil standards, round-robin testing, and online DGA technologies, cementing his legacy in transformer condition monitoring.
E-lesson 13: Special DGA Diagnoses
This session offers a critical analysis of various special DGA diagnoses, examining challenges such as abnormal gas presence and the shift from in-house to outsourced DGA testing. It compares the costs and benefits of online versus offline DGA methods and explores the role of AI and machine learning in enhancing DGA diagnosis. The session concludes with an announcement of a new course on other transformer oil tests beyond DGA.
E-lesson 14: The Future of DGA and Alternative Options
The final lesson explores alternative techniques for gas analysis in transformer maintenance, including synthetic gas indicators and chemical tagging. It discusses the challenges of applying AI to transformer health evaluation and the importance of combining human expertise with machine intelligence. The session also reviews the overall course content and emphasizes the need for reliable service providers and a deep understanding of gas-in-oil standards.
Conclusion
This DGA course offers comprehensive knowledge on diagnosing and maintaining transformer health using Dissolved Gas Analysis. The course covers critical aspects of oil sampling, gas extraction, measurement techniques, and the application of DGA results for transformer condition monitoring. Attendees gain a deep understanding of modern DGA practices, including both offline and online methods, while exploring future advancements in artificial intelligence and alternative diagnostic approaches. Through case studies and expert insights, participants are equipped to make informed decisions regarding transformer maintenance, improving operational reliability and efficiency.