By Kevin Dobson, Shahrokh Ahmadi, Mona Zaghloul (auth.), Haeng Kon Kim, Sio-Iong Ao, Mahyar A. Amouzegar, Burghard B. Rieger (eds.)
IAENG Transactions on Engineering Technologies comprises forty-nine revised and prolonged learn articles, written by way of trendy researchers engaging within the convention. issues coated comprise circuits, engineering arithmetic, keep watch over conception, communications platforms, structures engineering, manufacture engineering, computational biology, chemical engineering, and business purposes. This ebook deals the country of paintings of great advances in engineering applied sciences and actual technological know-how and purposes, and likewise serves as a great resource of reference for researchers and graduate scholars operating with/on engineering applied sciences and actual technology and applications.
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Unlike the approach proposed in  where the learning rule is realized by neural networks, this chapter proposed an adaptive scheme based on the input-output linearization control where the learning rule is implemented by a fuzzy logic technique. To show the advantage of the adaptive method over the nonlinear feedback linearization method, the standalone WECS model is chosen as the same as in  and the performance of the two controllers are compared via simulations. It should be emphasized that this chapter is an extended and revised version of the work presented in .
Nowadays most WECS are connected to utility grids. However, there is still demand for standalone WECS which provide electrical power to remote areas where utility grids are not available. , resulting in Hybrid Wind Energy Systems (HWES). Due to the presence of other energy sources, there are two primary control objectives in the HWES. One is to maximize the power conversion of the standalone WECS under stochastic wind changes, and the other is to ensure constant power flow to local loads. This chapter is focused on the first control objective of maximum power conversion.
Nguyen and D. S. Naidu ðrÀ1Þ "es ¼ e_ s À eðrÞ þ ::: þ krÀ1 e_ o ; o ¼ k1 eo ð5:28Þ eo ¼ ym À y; ð5:29Þ where es is the tracking error, eo is the output error. 24) is approximated by a fuzzy system as ^ uðxÞ ¼ hTu nu ðxÞ; ð5:31Þ where hu is a parameter vector including values of singleton membership function at consequent propositions of the fuzzy system rule base, nu ðxÞ is a fuzzy regressive vector. hu is updated online such that ^uðxÞ approaches uÃ ðxÞ. The optimal parameter vector is & ' T Ã Ã hu ¼ arg min sup hu nu ðxÞ À u : ð5:32Þ h2Dh x2Dx Because uÃ ðxÞ is approximated by a fuzzy system possessing a finite number of rules, there exists an unavoidable structural error du ðxÞ.