Autonomous operation and control of photovoltaic/solid oxide fuel cell/battery energy storage based microgrid using fuzzy logic controller

Vigneysh, T.; Kumarappan, N.

doi:10.1016/j.ijhydene.2015.11.022

Cited by 127 publications

(67 citation statements)

References 32 publications

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“…The stability of micro‐grid in island mode is the most critical issue . One of the storage systems benefits is to increase the MG efficiency in island conditions; however, a management and control approach is proposed to maintain the voltage and frequency stability in micro‐grids …”

Section: Introductionmentioning

confidence: 99%

Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis

Mir

Dayyani

Sutikno

et al. 2019

Computational Intelligence

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There are mainly two most essential problems in power networks, load frequency control and power flow management, which are grown recently because of growth in dimension/complication of grids. Present work suggests a controller based on fuzzy systems in which controller design is performed in a supervisory manner over a multiagent system aiming to control the frequency variation as well as generation cost minimization in the entire grid. The designing processes for low‐frequency controller (LFC) and management are mostly performed separately, which results in the disruption of both outputs. This challenge is tackled in this paper by the integration of them in the designing process. Additionally, stability guarantee is in high importance in the power systems, which is neglected in most of the related works. The Gaussian particle swarm optimization (GPSO) algorithm is applied for determining the optimal values of the decision variables, which can also guarantee the stability of the system by adopting a chaotic map by Gaussian function to balance the seeking abilities of particles that promotes the computation effectiveness without affecting the efficiency of the fuzzy controller. Then, the stability situationof the fuzzy + GPSO method is derived that guarantees a suitable global exploration and rapid convergence, with no require to gradients.

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Section: Introductionmentioning

confidence: 99%

Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis

Mir

Dayyani

Sutikno

et al. 2019

Computational Intelligence

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“…It is proved that proton exchange membrane fuel cells have suitable performance in (standalone) microgrids application, as a support system, because they are compact, mobile, and have low weight and low operating temperature. 14,15 The state of art power system control strategies are mainly divided into two groups 16 : Besides, the difficulty of achieving both high energy density and high power density simultaneously through one energy storage technology brings the principle of hybridization of the energy type and power type storage technologies.…”

Section: Introductionmentioning

confidence: 99%

A comparative study of PI, fuzzy‐PI, and sliding mode control strategy for battery bank SOC control in a standalone hybrid renewable system

Allahvirdizadeh

Shayanfar

Moghaddam

2019

Int Trans Electr Energ Syst

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Summary Hybrid renewable systems (HRSs) by offering green energy play an important role in supporting increasing electricity demand with regard to shrinking natural resources, global warming, and rising electric charges. Generally, higher efficiency, better power quality, improved reliability, and stability can be achieved from hybrid renewable energy systems when compared with single renewable energy systems. As a result, in this study, a hybrid system consisting renewable energy sources (PV/WT) is chosen. Besides, a backup system comprising hydrogen‐based storage system and batteries is provided because of the intermittent and undispatchable PV/WT power generation. In this paper, the power management system (PMS) includes the application of three control strategies: proportional‐integral controller (PI), fuzzy‐PI, and sliding mode control to meet design and demand objectives. The target of this paper is to investigate and compare the performance of the aforementioned control strategies considering hydrogen consumption, fuel/fuel‐cell efficiency, shorter settling time, and fewer fluctuations around the reference value. Two scenarios of constant and random loads are taken into consideration. Taking into consideration the before said criteria, the sliding mode control has a better control response in comparison with the other strategies.

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“…Among them, AC microgrids are the most widely microgrid type. AC microgrids can operate either in the grid-connected or islanded mode; and several control techniques have been developed to perform active and reactive power sharing, frequency and voltage regulation, and power quality disturbance compensation [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Energy Management Strategy Based on Multiple Operating States for a Photovoltaic/Fuel Cell/Energy Storage DC Microgrid

Han

Chen

2017

Energies

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It is a great challenge for DC microgrids with stochastic renewable sources and volatility loads to achieve better operation performance. This study proposes an energy management strategy based on multiple operating states for a DC microgrid, which is comprised of a photovoltaic (PV) array, a proton exchange membrane fuel cell (PEMFC) system, and a battery bank. This proposed strategy can share the power properly and keep the bus voltage steady under different operating states (the state of charge (SOC) of the battery bank, loading conditions, and PV array output power). In addition, a microgrids test platform is established. In order to verify the effectiveness of the proposed energy management strategy, the strategy is implemented in a hardware system and experimentally tested under different operating states. The experimental results illustrate the good performance of the proposed control strategy for the DC microgrid under different scenarios of power generation and load demand.

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Autonomous operation and control of photovoltaic/solid oxide fuel cell/battery energy storage based microgrid using fuzzy logic controller

Cited by 127 publications

References 32 publications

Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis

Employing a Gaussian Particle Swarm Optimization method for tuning Multi Input Multi Output‐fuzzy system as an integrated controller of a micro‐grid with stability analysis

A comparative study of PI, fuzzy‐PI, and sliding mode control strategy for battery bank SOC control in a standalone hybrid renewable system

Energy Management Strategy Based on Multiple Operating States for a Photovoltaic/Fuel Cell/Energy Storage DC Microgrid

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