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Efficient control of integrated power system using self-tuned fractional-order fuzzy PID controller

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Abstract

The integrated power system (IPS) uses various autonomous generation and energy storage systems like aqua electrolyzer, battery, diesel engine, flywheel, fuel cell, solar photovoltaic, ultracapacitor, wind turbine, etc. These may be switched on/off and may run at higher/lower power outputs, at different times. Additionally, IPS is also subjected to parameter variations of its components and the load. As a result, the frequency of an IPS fluctuates from the nominal desired value and therefore it requires a robust controller to accomplish the above-mentioned task. In this work, a self-tuned fractional-order fuzzy PID (STFOFPID) controller, tuned using cuckoo search algorithm, is investigated for efficient control of IPS. STFOFPID is essentially a Takagi–Sugeno model-based fuzzy adaptive controller comprising of non-integer-order differ-integral operators. To assess the relative performance of STFOFPID controller, it is compared with its integer-order counterpart on the basis of their respective objective function value defined as the sum of integral of squared error and integral of squared deviation of controller output. Intensive LabVIEW-based simulation studies have indicated the robustness and hence superiority of STFOFPID controller over its integral counterpart.

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Authors and Affiliations

  1. Division of Instrumentation and Control Engineering, Netaji Subhas Institute of Technology, Azad Hind Fauj Marg, Dwarka, New Delhi, 110078, India

    K. Nithilasaravanan, Nitisha Thakwani, Vineet Kumar & K. P. S. Rana

  2. Department of Electronics and Communication Engineering, GLA University, Mathura, U.P., 281406, India

    Puneet Mishra

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  1. K. Nithilasaravanan
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  2. Nitisha Thakwani
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  3. Puneet Mishra
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  4. Vineet Kumar
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  5. K. P. S. Rana
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Correspondence to Vineet Kumar.

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Nithilasaravanan, K., Thakwani, N., Mishra, P. et al. Efficient control of integrated power system using self-tuned fractional-order fuzzy PID controller. Neural Comput & Applic 31, 4137–4155 (2019). https://doi.org/10.1007/s00521-017-3309-9

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  • DOI: https://doi.org/10.1007/s00521-017-3309-9

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