Improvement of Frequency Regulation of a Wind-Integrated Power System Based on a PD-PIDA Controlled STATCOM Tuned by the Artificial Rabbits Optimizer

Abstract

In this research, a novel optimized Proportional-Derivative with Proportional-Integral-Derivative-Acceleration (PD-PIDA) driven static synchronous compensator (STATCOM) is proposed with the goal of improving the frequency performance of power systems through the manipulation of reactive power. When there is a variation in load or when there is a failure of generation, the presented controller is able to maintain the frequency of the system within the allowable limits (±2% as per IEC 60034-1 standard). A recently developed metaheuristic optimization strategy known as the Artificial Rabbits Optimizer, or ARO, is used to do the fine-tuning the PD-PIDA controller's gain settings. In addition to this, the functionality of the ARO-based PD-PIDA-controlled STATCOM is evaluated in the context of two IEEE standard systems: the two-area four-machine system (Kundur system) and the New England IEEE 39-bus system. According to the outcomes, the ARO-based PD-PIDA controller can give a superior frequency response than the PIDA controller whose parameters are optimized by Marine Predator Algorithm (MPA) that was published in the prior literature. This was determined by comparing the two controllers. In addition, the proposed controller's resiliency is tested by integrating case studies with wind generation. This helps to ensure that the controller is fit for purpose. In every single one of the examples that were researched, the application of the ARO-based PD-PIDA-controlled STATCOM resulted in a remarkable boost in the frequency performance. Additionally, the outputs are introduced in the form of time domain simulations that were carried out with MATLAB/SIMULINK.

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“…[18]. Furthermore, numerous optimization approaches, such as genetic algorithms [19]- [21], chaotic map-based chameleon Swarm Algorithm [22], the Sunflower (SFO) algorithm [23], Artificial Rabbits Optimizer [24], the Cuttlefish optimization algorithm [25], the particle swarm optimization (PSO) [26]- [29], particle swarm optimizationgravitational search algorithm [30], Artificial Fish Swarm Algorithm [31], and improved Mutation particle swarm optimization [32]. Many of these approaches possess both advantages and challenges [33].…”

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“…However, they cannot fully-mitigate grid fluctuations, are sensitive to parameter changes, and have low design freedom. Two recent applications of artificial rabbits optimizer (ARO) algorithm for control design have been presented in [28,29]. In [28], ARO has been applied for designing the conventional PI and PID controllers in multi-sourced microgrid systems.…”

“…In [28], ARO has been applied for designing the conventional PI and PID controllers in multi-sourced microgrid systems. In [29], the PD-PID accelerated controller has been designed using ARO for static synchronouscompensators (STATCOM) in power systems. Another Fuzzy PIDD2 controller has been presented with the Gradient-Based Optimizer (GBO) algorithm in [30] for stabilizing combined voltage-frequency loops for two-area-based interconnected grids.…”

“…A comprehensive comparison of existing work in the literature compared with the proposed contribution is presented in Table 1. Although the ARO has been presented in [28,29] for control design, it has not yet been applied for FO control design and cascaded FO control systems with considering the characteristics of SMES, connected RESs, and loads. The main contributions of this paper are summarized as:…”

“…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…”