In this proposal, systematic learning-based approximation of higher-order (HO) system is performed utilising rank-exponent (RE) assisted weight determination method incorporating grey-wolf-optimisation (GWO) algorithm...
详细信息
In this proposal, systematic learning-based approximation of higher-order (HO) system is performed utilising rank-exponent (RE) assisted weight determination method incorporating grey-wolf-optimisation (GWO) algorithm. The time-moments (TMs) along with Markov-parameters (MPs) of HO system and desired approximant are ascertained in order to utilise in the approximation process. The errors between TMs as well as MPs of HO system and desired approximant are used to frame weighted objective-function. The weights associated with objective-function are derived systematically using RE method. Once, weights are derived using RE method, GWO algorithm is used for minimising the resultant objective-function. The sixth-order system of hydro-power-station is utilised as real-test scenario in this study to demonstrate efficacy and effectiveness of proposed RE-based methodology. The considered sixth-order fixed-coefficient HO system is approximated to reduced-order-model of order three. For better examination and analysis of proposed methodology, responses along with quantitative analysis in tabular form for time-domain-specifications and performance-error-indices are presented.
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