Generation of Power Plant Artificial Immune System Using the Partition of the Universe Approach
The process of generating an artificial immune system for a complex power plant using an alternative method is presented in this paper. As compared to the clustering approach, the proposed partition of the universe approach is less computationally intensive and facilitates the use of full-dimensional self for system abnormal condition detection. In conjunction with a positive selection-type detection algorithm, it does not require explicit non-self generation. Using full-dimensional self prevents the hiding of non-self regions within self projections. These advantages are illustrated using the self for an acid gas removal unit built with the partition of the universe approach to detect the occurrence of several abnormal conditions. Excellent detection performance is obtained in terms of detection time, false alarms, and detection rate. Therefore, the proposed approach can potentially represent an effective and efficient tool for complex system abnormal condition monitoring.
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