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Investigation of Biomimetic Adaptive Mechanisms for Hybrid Power Plant Control

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In this paper, biologically inspired adaptive control mechanisms are investigated for highly integrated, complex energy plants. The adaptive mechanisms are designed to augment the performance and robustness of baseline control laws under normal and abnormal operating conditions.  Novel artificial neural network-based and artificial immune system-based approaches are developed and investigated for an advanced power plant through linear model simulation. Abnormal conditions are simulated by altering the parameters of the transfer functions (gains, delays, and time constants). The performance metrics used to analyze the different control solutions include integral and mean of absolute value of tracking error and overshoot. Comparative results demonstrate the promising capability of the biomimetic adaptive mechanisms to increase robustness of baseline control laws under plant abnormalities. The proposed approach creates premises for the development of comprehensive technologies for complex power plant control with high performance within nominal and outside design boundaries.
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Power Plant Monitoring And Control; Artificial Intelligence Techniques; Artificial Immune System; Artificial Neural Networks

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