Optimal Non-Integer Sliding Mode Control for Frequency Regulation in Stand-Alone Modern Power Grids

Abstract

In this paper, the concept of fractional calculus (FC) is introduced into the sliding mode control (SMC), named fractional order SMC (FOSMC), for the load frequency control (LFC) of an islanded microgrid (MG). The studied MG is constructed from different autonomous generation components such as diesel engines, renewable sources, and storage devices, which are optimally planned to benefit customers. The coefficients embedded in the FOSMC structure play a vital role in the quality of controller commands, so there is a need for a powerful heuristic methodology in the LFC study to adjust the design coefficients in such a way that better transient output may be achieved for resistance to renewable sources fluctuations. Accordingly, the Sine Cosine algorithm (SCA) is effectively combined with the harmony search (HS) for the optimal setting of the controller coefficients. The Lyapunov function based on the FOSMC is formulated to guarantee the stability of the LFC mechanism for the test MG. Finally, the hardware-in-the-loop (HIL) experiments are carried out to ensure that the suggested controller can suppress the frequency fluctuations effectively, and that it provides more robust MG responses in comparison with the prior art techniques.

Special Issue on Microgrids

“…Last, but not least, [5] focuses on the load frequency control of islanded microgrids consisting of diesel engines, renewable sources, and storage devices. For developing the proposed control, the concept of fractional calculus is combined with sliding mode control.…”

Effect of electric vehicles on load frequency control in interconnected thermal and hydrothermal power systems utilising CF‐FOIDF controller

“…Via suitable control arrangements, LFC reinstate the system stability and preserve the frequency/power at anticipated values. Various optimal, robust and intelligent control methodologies as stated few above are utilised as potential solutions to get a robust performance and stability of real PSs [12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. These include hybrid human brain emotional learning PI [12], sine-cosine algorithm based on wavelet mutation (SCAWM) based model-free non-linear sliding mode controller (MFNSMC) [13], hybrid SCA-HS algorithm based FO-SMC [14], firefly algorithm-pattern search (hFA-PS) tuned PI/PID [15], hybrid invasive weed optimisation-PS (hIWO-PS) tuned PI/2-DOF-PID [16], multi-objective genetic algorithm (MOGA)/GA tuned PI/PID [17,18], modified evolutionary particle swarm optimisation-time varying acceleration coefficient (MEPSO-TVAC) tuned PID [19], dragonfly algorithm (DA) tuned PID/2DOF-PID [20], blended biogeography based optimisation (BBBO) tuned PID [21], grey wolf optimisation (GWO)/ensemble of mutation and crossover strategies and parameters in differential evolution (EPSDE) tuned PI/PID [22], hybrid gravitational search algorithm-PS (hGSA-PS) tuned PI/PID with filter (PIDF) [23], salp swarm algorithm (SSA) tuned PIDF/ tilt IDF (TIDF)/cascade control-TIDF (CC-TIDF) [24], differential evolution (DE) tuned PID/TIDF [25] and lozi map-based chaotic optimisation algorithm (LCOA) tuned PID [26] controllers applied on different PS configurations.…”

A diffusive representation approach toward H∞ sliding mode control design for fractional-order Markovian jump systems

“…MJSs have been well investigated formerly, and the results on their stabilization, filtering and controller design are available. 1–7 Particularly, one popular control strategy is sliding mode control (SMC) which is a powerful robust method with many applications in various systems, 8–11 and has been extensively employed in the literature for MJSs. 12–14 For instance, in Xu et al, 15 an adaptive SMC strategy based on an integral type sliding surface has been proposed to investigate the stability of a Markovian jump model with parameter uncertainties, nonlinearities and Lévy noises.…”