Perturbation Observer Based Fractional-Order Control for SMES Systems Based on Jellyfish Search Algorithm

Abstract

The electric energy storage system (EESS) is considered as an efficient and promising tool to alleviate the power imbalance of grid-connected microgrid with distributed generation (DG). This work develops a perturbation observer-based fractional-order control (POFOC) strategy for superconducting magnetic energy storage (SMES) system. Initially, a high-gain state and perturbation observer (HGSPO) is designed for reliable estimation of the combined impact of the nonlinearities, parameter uncertainties, unmodeled dynamics, and external disturbances of SMES. Then the storage function of an SMES system is designed, which takes favorable terms into serious consideration to sufficiently utilize the physical properties of the SMES system. Moreover, a fractional-order control framework is applied for complete compensation for the estimated perturbation and adopted as the attached input to boost its dynamical responses. Furthermore, a newly proposed jellyfish search algorithm (JSA) is utilized to realize optimization and tuning of control gains of the developed strategy, upon which high-quality global optimum can be obtained to ensure prominent controlling performance. Case studies, e.g., active power and reactive power supply and system restoration capability under power grid fault effectively validate the effectiveness and reliability of the POFOC strategy compared with traditional PID control and interconnection and damping assignment passivity-based controller (IDA-PBC). In particular, the overshoot of PID is 115.264% of the rated value, while POFOC has no overshoot.

Fractional-Order Boosted Jellyfish Search Optimizer with Gaussian Mutation for Income Forecast of Rural Resident

“…Fractional calculus extends the order of calculus from integer to fraction and recursively deduces the solution limit through the difference approximation of integer calculus, that is, the differentiation and integration with the order of fraction. There are many definitions of derivatives, such as Grünwald–Letnikov, Caputo fractional derivatives [ 37 ], Riesz potential [ 38 ], and so on. The most commonly used definitions is Grünwald–Letnikov (G-L) [ 39 ] definition: where α is the fractional coefficient of a public signal z ( t ), Γ is a gamma function, and T is the truncation term.…”

Optimal Operational Reliability and Reconfiguration of Electrical Distribution Network Based on Jellyfish Search Algorithm

“…Fur-thermore, the numeric fitness function reveals the volume of food. The JFS was recently applied in effective manner for several engineering problems such as selective harmonic elimination in multilevel inverters [27], combined heat and power dispatching [28], maximum power point tracking (MPPT) of solar PV systems [29], design of a solar-powered thermoelectric air-conditioning system [30], control of superconducting magnetic energy storage system [31], PV parameter estimation [32], integration of renewable energy sources in power systems [33].…”

DE optimized IPIDF controller for management frequency in a networked power system with SMES and HVDC link

“…Superconducting Magnetic Energy Storage (SMES), an active power source with a quick response that can absorb frequency fluctuations, is extremely successful at enhancing the power system's dynamic performance. The governor system's slow response prevents it from doing so (Luo et al, 2021). The frequency regulation of a networked system with SMES has been documented in the literature (Banerjee et al, 1990;Sudha and Vijaya, 2012;Luo et al, 2021) for enhancing system performance.…”

“…The governor system's slow response prevents it from doing so (Luo et al, 2021). The frequency regulation of a networked system with SMES has been documented in the literature (Banerjee et al, 1990;Sudha and Vijaya, 2012;Luo et al, 2021) for enhancing system performance. There have been reports in the literature (Dekaraja, Chandra Saikia;Wang et al, 2021) about the effects of various FACTS controllers for AGC when used in conjunction with SMES (Luo et al, 2021).…”

“…Compares the performance of a number of classical controller structures used in the AGC for multi-area interconnected thermal systems, including the integral (I), proportional integral (PI), integral derivative (ID), PID, and integral double derivative (IDD) (Luo et al, 2021). Over the past few decades, numerous scholars have suggested various control strategies for LFC of power systems (Sahu et al, 2016;Ahmed et al, 2022;El-Ela et al, 2022).…”