Moth Flame Optimization Based Fuzzy-PID Controller for Power–Frequency Balance of an Islanded Microgrid

Sanki, Prasun; Mazumder, Surjakanta; Basu, Mousumi; Pal, Partha Sarathi; Das, Debapriya

doi:10.1007/s40031-021-00607-4

Cited by 17 publications

(11 citation statements)

References 25 publications

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“…In Sanki et al [ 126 ], the author developed a novel fuzzy proportional integral derivative (PID) controller based MFO and implemented it to improve stability and proper power management in the islanded micro-grid (MG) systems. Further to examine the proposed controller's resilience and sensitivity (RLP), the authors used fluctuations in solar-wind power output under random load perturbation.…”

Section: Applications On Mfo Algorithmmentioning

confidence: 99%

Moth Flame Optimization: Theory, Modifications, Hybridizations, and Applications

Sahoo

Saha

Ezugwu

et al. 2022

Arch Computat Methods Eng

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The Moth flame optimization (MFO) algorithm belongs to the swarm intelligence family and is applied to solve complex real-world optimization problems in numerous domains. MFO and its variants are easy to understand and simple to operate. However, these algorithms have successfully solved optimization problems in different areas such as power and energy systems, engineering design, economic dispatch, image processing, and medical applications. A comprehensive review of MFO variants is presented in this context, including the classic version, binary types, modified versions, hybrid versions, multi-objective versions, and application part of the MFO algorithm in various sectors. Finally, the evaluation of the MFO algorithm is presented to measure its performance compared to other algorithms. The main focus of this literature is to present a survey and review the MFO and its applications. Also, the concluding remark section discusses some possible future research directions of the MFO algorithm and its variants.

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Section: Applications On Mfo Algorithmmentioning

confidence: 99%

Moth Flame Optimization: Theory, Modifications, Hybridizations, and Applications

Sahoo

Saha

Ezugwu

et al. 2022

Arch Computat Methods Eng

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“…A class of objects with a range of membership degrees is referred to as a fuzzy set [25]. This type of set defines a membership (characteristic) function that assigns each object a membership degree ranging between [0, 1] [26].…”

Section: Fuzzy Optimized Feature Selectionmentioning

confidence: 99%

A Two-Tier Fuzzy Meta-Heuristic Hybrid Optimization for Dynamic Android Malware Detection

Jhansi¹,

Chakravarty²,

Varma³

2022

Journal of Cyber Security

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Application Programming Interface (API) call feature analysis is the prominent method for dynamic android malware detection. Standard benchmark android malware API dataset includes features with high dimensionality. Not all features of the data are relevant, filtering unwanted features improves efficiency. This paper proposes fuzzy and meta-heuristic optimization hybrid to eliminate insignificant features and improve the performance. In the first phase fuzzy benchmarking is used to select the top best features, and in the second phase meta-heuristic optimization algorithms viz., Moth Flame Optimization (MFO), Multi-Verse Optimization (MVO) & Whale Optimization (WO) are run with Machine Learning (ML) wrappers to select the best from the rest. Five ML methods viz., Decision Tree (DT), Random Forest (RF), K-Nearest Neighbors (KNN), Naïve Bayes (NB) & Nearest Centroid (NC) are compared as wrappers. Several experiments are conducted and among them, the best post reduction accuracy of 98.34% is recorded with 95% elimination of features. The proposed novel method outperformed among the existing works on the same dataset.

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“…Thus, the authors suggested a multi-objective-based tuning strategy to maximize controllers' significance with no high-power density sources. In [29]-[31], a method for LFC for both single and multi-area IMGs is proposed using conventional PI, PID, and fuzzy-based PID controllers. The optimum traditional controller tuning for the LFC is presented based on numerous single objective error criteria that various optimization techniques have optimized.…”

Section: Introductionmentioning

confidence: 99%

“…The optimum traditional controller tuning for the LFC is presented based on numerous single objective error criteria that various optimization techniques have optimized. In [29], the moth flame optimizer (MFO) tunes controller parameters without considering sources' GRC of reheat-thermal units of two-area IMG. In [30] and [31], the grey wolf optimization algorithm (GWO) and whale optimization algorithm (WOA) took the sources' GRC into account.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Conventional Controllers for Load Frequency Control of Isolated Microgrids Using Proposed Multi-Objective Formulation via Artificial Rabbits Optimization Algorithm

et al. 2023

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Isolated microgrids (IMGs) power remote areas. However, IMG may lower the frequency stability and increase frequency excursions with low system inertia. Load frequency management ensures system stability. Thus, the paper proposes a novel multi-objective tuning strategy to improve IMG's load frequency control (LFC) and take the microgrid controller's control signals into account. Diesel engine generator, fuel cell, battery energy storage system, and renewable energy sources (RESs) like photovoltaic and wind systems make up the IMG. Conventional controllers such as proportional-integral (PI) and proportional integral derivative (PID) are classically tuned based on the standard error criteria as a traditional single-objective tuning approach. Due to the low inertia of the system and the stochastic nature of RES, they cannot act as required under different operating scenarios. Therefore, the PI and PID controllers are tuned using the proposed multi-objective-based tuning approach to reduce the frequency deviations. In addition, anti-windup is applied to the enhanced classic controllers to keep them distant from the nonlinear zone and beyond the source's physical constraints. The proposed tuning process also considers the maximum practical generation rates for different sources. The recent Artificial Rabbits Optimization (ARO) algorithm is applied to simultaneously adjust the controller parameters for several controlled sources in IMG. Extensive simulations in MATLAB and Simulink confirm the effectiveness of the proposed approach to keep the system stable even when facing high levels of disturbances. In addition, accomplishing sensitivity analysis, severe ±25% changes to the system's parameters guarantee that the proposed tuning strategy keeps the system stable.

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Moth Flame Optimization Based Fuzzy-PID Controller for Power–Frequency Balance of an Islanded Microgrid

Cited by 17 publications

References 25 publications

Moth Flame Optimization: Theory, Modifications, Hybridizations, and Applications

Moth Flame Optimization: Theory, Modifications, Hybridizations, and Applications

A Two-Tier Fuzzy Meta-Heuristic Hybrid Optimization for Dynamic Android Malware Detection

Enhancing the Conventional Controllers for Load Frequency Control of Isolated Microgrids Using Proposed Multi-Objective Formulation via Artificial Rabbits Optimization Algorithm

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