Design Optimization of Improved Fractional-Order Cascaded Frequency Controllers for Electric Vehicles and Electrical Power Grids Utilizing Renewable Energy Sources

In this study, a nonlinear Archimedes wave swing (AWS) energy conversion system was employed to enable the use of irregular sea waves to provide useful electricity. Instead of the conventional PI controllers used in prior research, this study employed fractional-order PID (FOPID) controllers to control the back-to-back configuration of AWS. The aim was to maximize the energy yield from waves and maintain the grid voltage and the capacitor DC link voltage at predetermined values. In this study, six FOPID controllers were used to accomplish the control goals, leading to an array of thirty parameters required to be fine-tuned. In this regard, a hybrid jellyfish search optimizer and particle swarm optimization (HJSPSO) algorithm was adopted to select the optimal control gains. Verification of the performance of the proposed FOPID control system was achieved by comparing the system results to two conventional PID controllers and one FOPID controller. The conventional PID controllers were tuned using a recently presented metaheuristic algorithm called the Coot optimization algorithm (COOT) and the classical particle swarm optimization algorithm (PSO). Moreover, the FOPID was also tuned using the well-known genetic algorithm (GA). The system investigated in this study was subjected to various unsymmetrical and symmetrical fault disturbances. When compared with the standard COOT-PID, PSO-PID, and GA-FOPID controllers, the HJSPSO-FOPID results show a significant improvement in terms of performance and preserving control goals during system instability

Section: Objectives and Contributionsmentioning

confidence: 99%

Optimal Design of Fractional-Order PID Controllers for a Nonlinear AWS Wave Energy Converter Using Hybrid Jellyfish Search and Particle Swarm Optimization

Ali,

Ahmed,

Hasanien

et al. 2023

“…This approach introduces more adaptable weighting functions with a variety of adjustable parameters for mixed sensitivity problems, thereby offering increased flexibility [15]. Fractional-order Laplace operators are a term of fractional calculus which has gained substantial popularity within the control community [16][17][18][19][20][21]. In this context, this operator has been used in H ∞ controllers, either in design step or in controller structure.…”

Section: Introduction 1the Contex Of Researchmentioning

confidence: 99%

Fractional Order Weighted Mixed Sensitivity-Based Robust Controller Design and Application for a Nonlinear System

Ilten

2023

This paper focuses on fractional-order modeling and the design of a robust speed controller for a nonlinear system. An induction motor (IM), widely used in Electrical Vehicles (EVs), is preferred in this study as a well-known nonlinear system. The major challenge in designing a robust speed controller for IM is the insufficiency of the machine model due to inherent machine dynamics. Fractional calculus is employed to model the IM using the small-signal method, accounting for model uncertainties. In this context, experimental data is approximated using a fractional-order small-signal transfer function. Consequently, a mixed sensitivity problem is formulated with fractional-order weighting functions. The primary advantage of these weighting functions is their greater flexibility in solving the mixed sensitivity problem by involving more coefficients. Hereby, three robust speed controllers are designed using the PID toolkit of the Matlab program and solving the H∞ mixed sensitivity problem, respectively. The novelty and contribution of the proposed method lie in maintaining the closed-loop response within a secure margin determined by fractional weighting functions while addressing the controller design. After evaluating the robust speed controllers with Bode diagrams, it is proven that all the designed controllers meet the desired nominal performance and robustness criteria. Subsequently, real-time implementations of the designed controllers are performed using the dsPIC microcontroller unit. Experimental results confirm that the designed H∞-based fractional-order proportional-integral-derivative (FOPID) controller performs well in terms of tracking dynamics, exhibits robustness against load disturbances, and effectively suppresses sensor noise compared to the robust PID and fixed-structured H∞ controller.

“…A cascaded FOPI-IDDF LFC scheme optimized with the crow search algorithm (CSA) has been presented in [47] with solarthermal generation-based grids. Another cascased 1+PD/FOPID LFC method has been proposed in [48] for EV-based power grids, whereas the MRFO has been applied for optimizing the various parameters of the presented controller. The 3DOF cascaded LFC using FOTPID-TIDF control has been presented in [49].…”

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confidence: 99%

“…The paper is concluded in Section 6. I JBO [25] PID - [28] PI, PID ARO [26] Nonlinear PI DO [27] I, PI PSO [30] Fuzzy PIDD2 GBO [40] iFOI GWO [35] FOPID SCA Have more parameters to tune [36] FOPID MDWA FO-based Possess higher flexibility compared to IO ones [37] FOPIDF ICA LFC with Have improved disturbance mitigation compared to IO [38] TFOID AEO single input Have reduced rejection of existing disturbances [50] TID MRFO [39] FOPIDA GWO [41] TFOID SMA [42] TFOID CMRFO, WCM-RFO [43] TFOID with PI SMA [31] PD-PI ESMOA Possess high number of parameters to tune [32] 2DOF PID TLBO Cascaded LFC Have more flexibilities (degree of freedom) in design [33] PD-PID BA with multiple Provide better disturbance mitigation due to using cascaded loops [34] PI-PDF DTBO inputs can mitigate high as well as low frequency disturbances [44] 3DOF TID SSA Have better performance than single input LFC methods [45] FO-IDF ICA [46] PI-TDF SSA [29] PD-PIDA (STATCOM) ARO [47] FOPI-IDDF CSA [48] 1+PD/FOPID MRFO [49] 3DOF…”

mentioning

confidence: 99%

New Cascaded 1+PII2D/FOPID Load Frequency Controller for Modern Power Grids including Superconducting Magnetic Energy Storage and Renewable Energy

El-Sousy,

Aly,

Alqahtani

et al. 2023

Self Cite

Having continuous decrease in inertia and being sensitive to load/generation variation are considered crucial challenging problems for modern power grids. The main cause of these problems is the increased penetration capacities of renewables. An unbalanced load with generation power largely affects grids’ frequency and voltage profiles. Load frequency control (LFC) mechanisms are extensively presented to solve these problems. In the literature, LFC methods are still lacking in dealing with system uncertainty, parameter variation, structure changes, and/or disturbance rejection. Therefore, this paper proposes an improved LFC methodology using the hybrid one plus proportional integral double-integral derivative (1+PII2D) cascaded with fractional order proportional-integral-derivative (FOPID), namely, the proposed 1+PII2D/FOPID controller. The contribution of superconducting magnetic energy storage devices (SMES) is considered in the proposed design, also considering hybrid high-voltage DC and AC transmission lines (hybrid HVDC/HVAC). An optimized design of proposed 1+PII2D/FOPID controller is proposed using a new application of the recently presented powerful artificial rabbits optimizers (ARO) algorithm. Various performance comparisons, system changes, parameter uncertainties, and load/generation profiles and changes are considered in the proposed case study. The results proved superior regulation of frequency using proposed 1+PII2D/FOPID control and the ARO optimum parameters.