FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3035472033> ?p ?o ?g. }

• W3035472033 endingPage "106476" @default.
• W3035472033 startingPage "106476" @default.
• W3035472033 abstract "In this paper, an innovative hybrid multi-variable generator’s actual-output-power predicting model is proposed based on ant colony optimization algorithm and extreme learning machine network, and a data-driven performance evaluation model is presented based on the two indices, K-means clustering algorithm and Markov chain for the performance evaluation of the wind turbines. Ant colony optimization algorithm is used to optimize the initial weights and thresholds of the extreme learning machine network, then the optimized combinations of weights and thresholds are provided into the extreme learning machine models to overcome the sensitivity problem of initialization setting and the disadvantage of easily falling into local optimum. Through the actual-output-power prediction of the WTs in a wind farm, the results show that the proposed model has more higher prediction accuracy than other methods mentioned in this paper. The optimization process also shows that the prediction accuracy is sensitive to the number of hidden-layer nodes and is relatively insensitive to other model parameters. Then, the data-driven performance evaluation models are proposed based on the error sequences obtained above. The case study is conducted and the results show that the method can evaluate the operating performance of the wind turbines correctly. The effectiveness of the evaluation results is also verified by the actual operation results." @default.
• W3035472033 created "2020-06-19" @default.
• W3035472033 creator A5023607243 @default.
• W3035472033 date "2020-09-01" @default.
• W3035472033 modified "2023-10-05" @default.
• W3035472033 title "Modeling and performance evaluation of wind turbine based on ant colony optimization-extreme learning machine" @default.
• W3035472033 cites W1969729100 @default.
• W3035472033 cites W1978701159 @default.
• W3035472033 cites W1982904001 @default.
• W3035472033 cites W1993139783 @default.
• W3035472033 cites W2039442724 @default.
• W3035472033 cites W2049626702 @default.
• W3035472033 cites W2060913486 @default.
• W3035472033 cites W2066890570 @default.
• W3035472033 cites W2102836090 @default.
• W3035472033 cites W2135747958 @default.
• W3035472033 cites W2153263933 @default.
• W3035472033 cites W2186726360 @default.
• W3035472033 cites W2255738116 @default.
• W3035472033 cites W2441053277 @default.
• W3035472033 cites W2481168336 @default.
• W3035472033 cites W2514847968 @default.
• W3035472033 cites W2530363364 @default.
• W3035472033 cites W2560437273 @default.
• W3035472033 cites W2592036976 @default.
• W3035472033 cites W2599470722 @default.
• W3035472033 cites W2605149570 @default.
• W3035472033 cites W2626515140 @default.
• W3035472033 cites W2738507483 @default.
• W3035472033 cites W2745751986 @default.
• W3035472033 cites W2754015034 @default.
• W3035472033 cites W2754844703 @default.
• W3035472033 cites W2755841959 @default.
• W3035472033 cites W2788786189 @default.
• W3035472033 cites W2793641192 @default.
• W3035472033 cites W2795468861 @default.
• W3035472033 cites W2802184482 @default.
• W3035472033 cites W2844967669 @default.
• W3035472033 cites W2883904934 @default.
• W3035472033 cites W2884984074 @default.
• W3035472033 cites W2886292843 @default.
• W3035472033 cites W2886564381 @default.
• W3035472033 cites W2896864326 @default.
• W3035472033 cites W2896920734 @default.
• W3035472033 cites W2904016210 @default.
• W3035472033 cites W2908011629 @default.
• W3035472033 cites W2909474405 @default.
• W3035472033 cites W2909862330 @default.
• W3035472033 cites W2911265761 @default.
• W3035472033 cites W2912157889 @default.
• W3035472033 cites W2913276831 @default.
• W3035472033 cites W2913968966 @default.
• W3035472033 cites W2914856364 @default.
• W3035472033 cites W2914988706 @default.
• W3035472033 cites W2915629347 @default.
• W3035472033 cites W2921731747 @default.
• W3035472033 cites W2921887367 @default.
• W3035472033 cites W2922403949 @default.
• W3035472033 cites W2925057617 @default.
• W3035472033 cites W2926946531 @default.
• W3035472033 cites W2934360535 @default.
• W3035472033 cites W2936236242 @default.
• W3035472033 cites W2939128179 @default.
• W3035472033 cites W2939569436 @default.
• W3035472033 cites W2939993181 @default.
• W3035472033 cites W2942406345 @default.
• W3035472033 cites W2942736011 @default.
• W3035472033 cites W2944245994 @default.
• W3035472033 cites W2945883082 @default.
• W3035472033 cites W2946155684 @default.
• W3035472033 cites W2950895399 @default.
• W3035472033 cites W2952743544 @default.
• W3035472033 cites W2955529518 @default.
• W3035472033 cites W2963335750 @default.
• W3035472033 cites W2963599060 @default.
• W3035472033 cites W2964052759 @default.
• W3035472033 cites W2965133718 @default.
• W3035472033 cites W2971011823 @default.
• W3035472033 cites W2973960501 @default.
• W3035472033 cites W2979695010 @default.
• W3035472033 cites W2981840557 @default.
• W3035472033 cites W2986268142 @default.
• W3035472033 cites W2986463394 @default.
• W3035472033 cites W2988149727 @default.
• W3035472033 cites W2988272326 @default.
• W3035472033 cites W2990255447 @default.
• W3035472033 cites W2993003029 @default.
• W3035472033 cites W2993128518 @default.
• W3035472033 cites W2993207032 @default.
• W3035472033 cites W2997464701 @default.
• W3035472033 cites W2999628933 @default.
• W3035472033 cites W2999868636 @default.
• W3035472033 cites W3000694574 @default.
• W3035472033 cites W3001566518 @default.
• W3035472033 doi "https://doi.org/10.1016/j.asoc.2020.106476" @default.
• W3035472033 hasPublicationYear "2020" @default.
• W3035472033 type Work @default.
• W3035472033 sameAs 3035472033 @default.

• next