Abstract
Within the power industry, there has been a wealth of research into the use of intelligent system techniques for alarm processing. Since each utility has different needs and requirements based on their infrastructure for network monitoring, over the years a variety of approaches have been investigated.
This history of research means that the strengths and weaknesses of each technique are well-understood. For example, the “knowledge bottleneck”, or intensity of time and effort required for knowledge-capture or building of models means that rule-based and model-based systems have limited penetration in the control room beyond some key installations. On the other hand, data-driven techniques such as neural networks require re-training whenever the network topology changes, and cannot present engineers with an explanation of their results, meaning that engineers are wary of the solutions they offer.
This history of research means that the strengths and weaknesses of each technique are well-understood. For example, the “knowledge bottleneck”, or intensity of time and effort required for knowledge-capture or building of models means that rule-based and model-based systems have limited penetration in the control room beyond some key installations. On the other hand, data-driven techniques such as neural networks require re-training whenever the network topology changes, and cannot present engineers with an explanation of their results, meaning that engineers are wary of the solutions they offer.
| Original language | English |
|---|---|
| Volume | 2012 |
| Specialist publication | PAC World |
| Publication status | Published - Mar 2012 |
Keywords
- power
- intelligent systems
- intelligent monitoring
- distribution monitoring