Frequency Control of Low Inertia Microgids in Presence of Wind and Solar Units Using Fuzzy-neural Controllers

Zadeh, Mahmoud Mollayousefi; Afshar, Zakaria; Harandi, Mahdi Jafari; Bathaee, S.M.T.

doi:10.1109/epdc56235.2022.9817192

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…The droop controller [20] controls the inverter so as to make it operate with stabilized AC voltage and frequency injecting the renewable power to the load. The conventional droop control module with voltage and frequency control can be observed in Figure 3.…”

Section: Issn: 2302-9285 mentioning

confidence: 99%

Comparative analysis and validation of advanced control modules for standalone renewable micro grid with droop controller

Swathi,

Kumar,

Upendar

2024

Bulletin EEI

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A micro grid system with renewable source operation control is a complex part as each source operates at different parameters. This renewable micro grid with multiple sources like solar plants, wind farm, fuel cell, battery backup has to be operated in both grid connected and standalone condition. During grid connection the micro grid, inverter has to inject power to the grid and compensate load in synchronization to the grid voltages. And during standalone condition the inverter is controlled with droop control module which stabilizes the voltage and frequency of the system even during grid disconnection. The droop control module is further updated with new advanced controllers like fuzzy inference system (FIS) and adaptive neuro-fuzzy inference system (ANFIS) replacing the traditional proportional integral derivative (PID) and proportional integral (PI) controllers improving the response rate and for achieving better stabilization. This paper has comparative analysis of the micro grid system with different droop controllers under various operating conditions. Parameters like voltage magnitude (Vmag), frequency (F), load and inverter powers (Pload and Pinv) of the test system are compared with different controllers. A numeric comparison table is given to determine the optimal controller for the inverter operation. The analysis is carried out in MATLAB/Simulink software with graphical and parametric validations.

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Section: Issn: 2302-9285 mentioning

confidence: 99%

Comparative analysis and validation of advanced control modules for standalone renewable micro grid with droop controller

Swathi,

Kumar,

Upendar

2024

Bulletin EEI

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“…In [202], another soft computing approach comprising fuzzy logic controllers and PSO is used to improve the frequency response to its maximum extent by the PV output adjustment. A hybrid fuzzy and neural network is presented in [203,204] to control the solar PV system frequency.…”

Section: Other Soft Computing Approachesmentioning

confidence: 99%

Solar and Wind Energy Integrated System Frequency Control: A Critical Review on Recent Developments

et al. 2023

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A paradigm shift in power systems is observed due to the massive integration of renewable energy sources (RESs) as distributed generators. Mainly, solar photovoltaic (PV) panels and wind generators are extensively integrated with the modern power system to facilitate green efforts in the electrical energy sector. However, integrating these RESs destabilizes the frequency of the modern power system. Hitherto, the frequency control has not drawn sufficient attention due to the reduced inertia and complex control of power electronic converters associated with renewable energy conversion systems. Thus, this article provides a critical summary on the frequency control of solar PV and wind-integrated systems. The frequency control issues with advanced techniques, including inertia emulation, de-loading, and grid-forming, are summarized. Moreover, several cutting-edge devices in frequency control are outlined. The advantages and disadvantages of different approaches to control the frequency of high-level RESs integrated systems are well documented. The possible improvements of existing approaches are outlined. The key research areas are identified, and future research directions are mentioned so that cutting-edge technologies can be adopted, making the review article unique compared to the existing reviews. The article could be an excellent foundation and guidance for industry personnel, researchers, and academicians.

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“…A controller was proposed by using the concept of perturbation observer to obtain a high amount of power from a wind power generation system with entirely unclear parameters [40]. Zadeh et al [41] utilized fuzzy-neural controllers to develop a frequency control of low inertia MGs to obtain equivalency for the values of production and demand of electricity power. Various electricity power sources including wind turbine, PV panel, energy storage system, and diesel generators were used to design the MG network.…”

Section: Introductionmentioning

confidence: 99%

Smart Frequency Control of a Multicarrier Microgrid in the Presence of V2G Electric Vehicles

Fini

Shariatmadar

Amir

2023

International Transactions on Electrical Energy Systems

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In recent years, renewable resources have widely been used to provide necessary energy due to the increasing fossil fuel prices, environmental pollution concerns, and the necessity to meet the growth in energy demands. The output of renewable resources especially solar and wind energies is associated with meteorological parameters, so their reliability creates many challenges for the energy sector. The consumption peak of the gas network is taken into account to adjust the frequency of the microgrid (MG). Both gas network load and electric load distributions are adjusted at the same time. In a multicarrier network, the frequency is regulated in a nonlinear manner. Meanwhile, new necessary loads for production and electric vehicles have imposed new loads on the power network; if proper management is not performed to respond to these new loads, the increase of network frequency deviations may lead the network to fail and even break down. In this paper, a network of various sources including the wind turbine, solar panel, storage (battery and flywheel), electric vehicle (EV), diesel generator (DG) electric power generation, and multicarrier energy hub (MCEH) with combined heat and power (CHP) was designed to examine vehicle-to-grid (V2G) electric vehicles. The ANFIS adaptive fuzzy control method was used to provide a fine-tuning frequency of the network. A comparison between the suggested approach and a fuzzy controller system was carried out to examine the superiority of the introduced approach to the frequency control. The simulations were obtained using MATLAB/SIMULINK software. The simulation outcomes indicated that the SMART controller can achieve good efficiency in frequency regulation and reliable output power in the examined microgrid. Further comparison in terms of effective (RMS) values and maximum frequency deviation indicates the superior performance of the proposed method over the fuzzy method.

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Frequency Control of Low Inertia Microgids in Presence of Wind and Solar Units Using Fuzzy-neural Controllers

Cited by 4 publications

References 15 publications

Comparative analysis and validation of advanced control modules for standalone renewable micro grid with droop controller

Comparative analysis and validation of advanced control modules for standalone renewable micro grid with droop controller

Solar and Wind Energy Integrated System Frequency Control: A Critical Review on Recent Developments

Smart Frequency Control of a Multicarrier Microgrid in the Presence of V2G Electric Vehicles

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