Abstract
In this paper, the problem of frequency regulation in the multi-area power systems with demand response, energy storage system (ESS) and renewable energy generators is studied. Dissimilarly to most studies in this field, the dynamics of all units in all areas are considered to be unknown. Furthermore time-varying solar radiation, wind speed dynamics, multiple load changes, demand response (DR), and ESS are considered. A novel dynamic fractional-order model based on restricted Boltzmann machine (RBM) and deep learning contrastive divergence (CD) algorithm is presented for online identification. The controller is designed by the dynamic estimated model, error feedback controller and interval type-3 fuzzy logic compensator (IT3-FLC). The gains of error feedback controller and tuning rules of the estimated dynamic model are extracted through the fractional-order stability analysis by the linear matrix inequality (LMI) approach. The superiority of a schemed controller in contrast to the type-1 and type-2 FLCs is demonstrated in various conditions, such as time-varying wind speed, solar radiation, multiple load changes, and perturbed dynamics.
Highlights
Accepted: 18 November 2021By developing the renewable energy systems, the problem of the load frequency control (LFC) in power systems has become one of the interesting topics
In the remain of this study, the problem is described in Section 3, the suggested dynamic fractional-order model is described in Section 4, the proposed interval type-3 fuzzy logic compensator (IT3-fuzzy logic controllers (FLCs)) is given in Section 5, the stability is analyzed Section 6, the simulations and conclusions are presented in Sections 7 and 8, respectively
It is understood that the regulation performance corresponded to the suggested method is significantly better than PI-T1-FLC and PI-T2-FLC
Summary
By developing the renewable energy systems, the problem of the load frequency control (LFC) in power systems has become one of the interesting topics. The fuzzy systems are one of the best approach to deal with the uncertainties [13,14,15] In this case, commonly the type-1 fuzzy logic systems (T1-FLSs) are employed to extract the gains of the PID control scheme and various optimization techniques specially evolutionary based algorithms are used for optimization of the parameters of FLSs. For example, in [16], a FPID is designed and the big bang–big crunch algorithm is proposed for optimization. In [18], the teaching–learning optimization algorithm is studied for designing the FPID controller for LFC and it is verified that the regulation performance is improved in contrast to the other optimized PID controllers. In [24], the effectiveness of grey wolf algorithm is studied in comparison with the bee colony method in the designing of FPID controller for LFC. In the remain of this study, the problem is described in Section 3, the suggested dynamic fractional-order model is described in Section 4, the proposed IT3-FLC is given in Section 5, the stability is analyzed Section 6, the simulations and conclusions are presented in Sections 7 and 8, respectively
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