Load-frequency control in an islanded microgrid PV/WT/FC/ESS using an optimal self-tuning fractional-order fuzzy controller

Naderipour, Amirreza; Abdul‐Malek, Zulkurnain; Davoodkhani, Iraj Faraji; Kamyab, Hesam; Ali, Rusmidah

doi:10.1007/s11356-021-14799-1

Cited by 56 publications

(15 citation statements)

References 27 publications

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“…A self-tuning controller is designed in [76] using fuzzy logic for the control of microgrid systems. A fractional-order controller is developed in combination with a fuzzy logic algorithm for load-frequency control of the off-grid microgrid.…”

Section: Applications and Self-tuned Fo-pidsmentioning

confidence: 99%

A Review of Recent Developments in Autotuning Methods for Fractional-Order Controllers

et al. 2022

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The scientific community has recently seen a fast-growing number of publications tackling the topic of fractional-order controllers in general, with a focus on the fractional order PID. Several versions of this controller have been proposed, including different tuning methods and implementation possibilities. Quite a few recent papers discuss the practical use of such controllers. However, the industrial acceptance of these controllers is still far from being reached. Autotuning methods for such fractional order PIDs could possibly make them more appealing to industrial applications, as well. In this paper, the current autotuning methods for fractional order PIDs are reviewed. The focus is on the most recent findings. A comparison between several autotuning approaches is considered for various types of processes. Numerical examples are given to highlight the practicality of the methods that could be extended to simple industrial processes.

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Section: Applications and Self-tuned Fo-pidsmentioning

confidence: 99%

A Review of Recent Developments in Autotuning Methods for Fractional-Order Controllers

et al. 2022

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“…The paper [43] implements a multi-objective cuckoo search algorithm to evaluate the effects of utilizing BES on optimal performance of power system. Authors in [44] proposed a self-tuning controller based on fuzzy logic to address traditional controller parameter uncertainties, such as operating conditions, microgrid operational purpose modifications, and microgrid modeling uncertainty. The paper [45] proposed a technique for regulating active and reactive power flow in a renewable generating system operating in the islanded mode for point of common connection (PCC).…”

Section: Energy Optimization Microgrid Centric Techniques For Islande...mentioning

confidence: 99%

Real-Time Scheduling for Optimal Energy Optimization in Smart Grid Integrated With Renewable Energy Sources

et al. 2022

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Load scheduling, battery energy storage control, and improving user comfort are critical energy optimization problems in smart grid. However, system inputs like renewable energy generation process, conventional grid generation process, battery charging/discharging process, dynamic price signals, and load arrival process comprise controller performance to accurately optimize real-time battery energy storage scheduling, load scheduling, energy generation, and user comfort. Thus, in this work, the virtual queue stability based Lyapunov optimization technique (LOT) is adopted to investigate real-time energy optimization in a grid-connected sustainable smart home with a heating, ventilation, and air conditioning (HVAC) load considering unknown system inputs dynamics. The main goal is to minimize overall time average energy cost and thermal discomfort cost in a long time horizon for sustainable smart home accounting for changes in home occupancy state, the most comfortable temperature setting, electrical consumption, renewable generation output, outdoor temperature, and the electricity costs. The employed algorithm creates and regulates four queues for indoor temperature, electric vehicle (EV) charging, and energy storage system (ESS). Extensive simulations are conducted to validate the employed algorithm. Simulation results illustrate that the proposed algorithm performs real-time energy optimization and reduces the time average energy cost of 20.15% while meeting the user's energy and comfort requirement. INDEX TERMSReal-time, optimization, convex optimization, dynamic pricing, renewable energy generation, scheduling, energy storage, user comfort. NOMENCLATURE Abbreviations Explanation AI Artificial intelligence. ACO Ant colony optimization. ABC Artificial bee colony. AEP Annual energy production. BES Battery energy storage. DR Demand response.

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“…The biological artificial neural network (ANN) for humans/animals 94-109 is derived by NN, a popular multilayer method. [110][111][112][113][114][115] By means of the layer mechanism, ANN can handle appropriate complexities in multidimensional issue space to estimate the objectives. [116][117][118][119][120][121][122][123][124][125][126][127] Multilayer perceptron (MLP) is a basic practical type of feed ANN.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Compressive strength of efficient self‐compacting concrete with rice husk ash, fly ash, and calcium carbide waste additives using multiple artificial intelligence methods

Mohamed

Bashir

Dirar

et al. 2021

Structural Concrete

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The principal benefit of mixed concrete is to minimize the disadvantages of certain complementary cement ingredients by combining them with other better materials so that overall costs are managed and efficiencies of concrete production are increased. The structural and economic characteristics of concrete can also be improved. In this work, the compressive strength (CS) of selfcompacting concrete (SCC) was assessed and their interrelations were addressed using binary and ternary cementitious combinations of rice husk, fly ash (FA), and calcium carbide. Accordingly, various admixtures were prepared with diverse amounts of wastes by substituting ordinary Portland cement (5%). Thus, due to the raising of rice husk and FA, the CS of SCC was considerably improved. To raise the accuracy of the analysis, three soft computing models of particle swarm optimization (PSO), adaptive neuro-fuzzy inference system (ANFIS), and artificial neural network (ANN) were used. Six input parameters comprising FA replacement ratio, rice husk, total cementitious material, water cement ratio, high ratio water reducing agent and age of samples (AS), and one output parameter as the CS of SCC have been investigated. Subsequently, following the root mean square error and R 2 results, three methods were shown as reliable tools for assessing the influence of cementitious material on CS of self-compact concrete; however, ANFIS remarkably was better than ANN and PSO. As a result, rice husk showed less contribution to the strength of concrete at short times, but much at longer time than FA and calcium carbide. The enhanced influence of low amount of calcium carbide on CS was not significant. Also, it was found that the specimen incorporating of cement with 15% calcium carbide showed better workability than that of normal SCC specimen without calcium. Therefore, rice husk ash showed higher potential to be applied as partial replacements in SCC production.Discussion on this paper must be submitted within two months of the print publication. The discussion will then be published in print, along with the authors' closure, if any, approximately nine months after the print publication.

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Load-frequency control in an islanded microgrid PV/WT/FC/ESS using an optimal self-tuning fractional-order fuzzy controller

Cited by 56 publications

References 27 publications

A Review of Recent Developments in Autotuning Methods for Fractional-Order Controllers

A Review of Recent Developments in Autotuning Methods for Fractional-Order Controllers

Real-Time Scheduling for Optimal Energy Optimization in Smart Grid Integrated With Renewable Energy Sources

Compressive strength of efficient self‐compacting concrete with rice husk ash, fly ash, and calcium carbide waste additives using multiple artificial intelligence methods

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