Mapping Quality Tags in OSIsoft PI AF: A Guide for T&D Implementations

Mapping Quality Tags in OSIsoft PI AF: A Guide for T&D Implementations

When it comes to implementing robust data structures within the OSIsoft PI Asset Framework (AF), a common challenge faced by Transmission and Distribution (T&D) teams is mapping quality tags to their corresponding measurement tags. This process is especially pertinent when integrating SCADA data, where each measurement often comes with a corresponding quality indicator. Let's explore effective strategies to tackle this challenge, considering typical industry practices.

Understanding Quality Tags

In T&D environments, quality tags serve as indicators of the data reliability fetched from SCADA systems. Whether they are strings of binary values, such as "0001001000000001," or represented through digital state sets with descriptors like "Good," "Substituted," or "Bad Input," quality tags play a crucial role in ensuring data integrity.

Approaches to Mapping Quality Tags

When designing an AF structure to incorporate these quality indicators, several methodologies can be adopted:

1. Separate Attribute Naming

One intuitive method is maintaining separate attributes for quality tags by appending "_Q" or a similar identifier to the corresponding measurement attribute's name. This approach is straightforward but can lead to a cluttered attribute list, especially with large data volumes.

2. Using Child Attributes

Another practice is employing child attributes to house quality indicators under each measurement attribute. This organizational method keeps the main attribute list cleaner and logically associates quality data with its measurement counterpart. This structure is particularly useful when you need to capture complex quality information like full bit strings or digital states.

Additional Strategies

Beyond initial mapping, several advanced techniques can enhance the value extracted from quality data:

AF Formulae and Analytics

Utilize AF formulae or custom analyses to create derived quality indicators or "qualified values" that adjust based on quality readings. For example, one might change a measurement to zero or a specific digital state if the quality is deemed poor, thus automating the validation process.

Event Frame Generation

Implement PI AF Analytics to trigger Event Frames (EFs) based on specific quality conditions. These frames can help identify and manage periods when data integrity is compromised, though practitioners should be cautious of potential false positives due to systemic issues as opposed to genuine anomalies.

Considerations for Large-Scale Systems

Managing extensive datasets (such as 750k tags each for measurements and quality) requires efficient processing strategies to avoid performance bottlenecks. Streamline the attribute structure and leverage scalable analytics to ensure consistent performance across the PI System.

Conclusion

Choosing the right strategy to implement quality tags in your PI AF environment can significantly impact data quality management. Whether you prefer separate naming conventions or integrated child attributes, both serve crucial roles depending on your operational scale and complexity. By leveraging AF's analytical capabilities, utilities can enhance their system's robustness and operational reliability in the often dynamic and expansive landscape of T&D systems.

Utilize these insights to improve your T&D data strategies, fostering more effective data-driven decisions with PI AF.