Energy management based fuzzy logic controller of hybrid system wind/photovoltaic/diesel with storage battery

Roumila, Zoubir; Rekioua, Djamila; Rekioua, Toufik

doi:10.1016/j.ijhydene.2017.06.006

Cited by 125 publications

(56 citation statements)

References 26 publications

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“…where P ph (κ) is the solar power, V ph (κ) is the solar voltage, E(κ) is the error calculated by change in solar power with respect to change in solar voltage, CE(κ) is the change in error E(κ), κ represents the sample time. The equivalent circuit of the solar cell and its current, power derivation has been referred from [32]. The power from solar has been extracted maximum with the fuzzy based MPPT controller to obtain pulses for the DC-DC converter and the control circuit is shown in Figure 3.…”

Section: Solar Cellmentioning

confidence: 99%

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Coordinated Control of Aichi Microgrid for Efficient Power Management Using Novel Set Point Weighting Iterative Learning Controller

Angalaeswari

Kamaraj

2020

Energies

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A novel Set Point Weighting Iterative Learning Controller (SPW-ILC) has been proposed for voltage stabilization at AC/DC bus, coordinated control among the distributed sources in the modeled hybrid microgrid (HMG) and synchronization of HMG with utility grid. The Aichi Micro grid test system located at Aichi Institute of Technology, Japan has been considered for the simulation studies and modeled in MATLAB/Simulink environment. The Aichi microgrid can be operated in autonomous mode as AC system and DC system. When it is working as DC system, the dc bus voltage is maintained stable by incorporating dedicated fuzzy logic controllers (FLC) for DC-DC converters due to the variable distributed sources. Meanwhile, the bidirectional converter also called as Interlinking Converter (IC) located between ac bus and dc bus controlled by proposed SPW-ILC converts the DC voltage into AC voltage and meets AC loads. In AC system of autonomous mode, the inverters are controlled by proposed controller to meet the ac demands. The grid connected mode of Aichi microgrid system is performed by properly controlling the IC to meet ac and dc loads. The proposed SPW-ILC reduces the voltage deviation and maintains the power balance under variable source and load conditions. The results have been compared with the conventional proportional integral (PI) controller and FLC to validate the performance of the controller. The results show that the proposed SPW-ILC has efficiently control the voltage and maintain the power balance.

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Section: Solar Cellmentioning

confidence: 99%

“…Modeling of battery has been considered as in [32] as shown in Figure 5. The voltage output of the battery is formed as in Equation (9):…”

Section: Batterymentioning

confidence: 99%

Coordinated Control of Aichi Microgrid for Efficient Power Management Using Novel Set Point Weighting Iterative Learning Controller

Angalaeswari

Kamaraj

2020

Energies

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“…The rule-based EMSs are easy to implement and have been widely adopted [15][16][17]. The technique proposed in Reference [15] divides system states into three categories according to the battery's State of Change (SoC) and determines fuel-cell reference power by the rules according to load power and fuel-cell power constraints.…”

Section: Introductionmentioning

confidence: 99%

Traffic-Condition-Prediction-Based HMA-FIS Energy-Management Strategy for Fuel-Cell Electric Vehicles

Yao

et al. 2019

Energies

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In the field of Fuel Cell Electric Vehicles (FCEVs), a fuel-cell stack usually works together with a battery to improve powertrain performance. In this hybrid-power system, an Energy Management Strategy (EMS) is essential to configure the hybrid-power sources to provide sufficient energy for driving the FCEV in different traffic conditions. The EMS determines the overall performance of the power supply system; accordingly, EMS research has important theoretical significance and application values on the improvement of energy-utilization efficiency and the serviceability of vehicles' hybrid-power sources. To overcome the deficiency of apparent filtering lag and improve the adaptability of an EMS to different traffic conditions, this paper proposes a novel EMS based on traffic-condition predictions, frequency decoupling and a Fuzzy Inference System (FIS). An Artificial Neural Network (ANN) was designed to predict traffic conditions according to the vehicle's running parameters; then, a Hull Moving Average (HMA) algorithm, with filter-window width decided by the prediction result, is introduced to split the demanded power and keep low-frequency components in order to meet the load characteristics of the fuel cell; afterward, an FIS was applied to manage power flows of the FCEV's hybrid-power sources and maintain the State of Change (SoC) of the battery in a predefined range. Finally, an FCEV simulation platform was built with MATLAB/Simulink and comparison simulations were carried out with the standard test cycle of the Worldwide harmonized Light vehicle Test Procedures (WLTPs). Simulation results showed that the proposed EMS could efficiently coordinate the hybrid-power sources and support the FCEV in following the reference speed with negligible control errors and sufficient power supply; the SoC of the battery was also maintained with good adaptability in different driving conditions.

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“…A fuzzy logic controller can be used for the power flow control of solar and wind hybrid system. The difference between the power generated by the renewable energy sources and load demand and state of charge of battery are the inputs, and battery reference power is the output of the controller . A frequency‐based approach is also used for the energy management in distributed energy systems.…”

Section: Introductionmentioning

confidence: 99%

“…The difference between the power generated by the renewable energy sources and load demand and state of charge of battery are the inputs, and battery reference power is the output of the controller. 18,19 A frequency-based approach is also used for the energy management in distributed energy systems. It uses a hybrid combination of battery and UC as the energy storage device and compensates the wind/load power fluctuations.…”

Section: Introductionmentioning

confidence: 99%

Energy storage management of hybrid solar/wind standalone system using adaptive neuro‐fuzzy inference system

Varghese

Reji

2019

Int Trans Electr Energ Syst

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Summary This paper proposes a charge controller for ultracapacitor that acts as an energy storage device so that it can minimize the effect of irregular solar radiation and widely varying wind velocity. A suitable supervision for each generating unit and ultracapacitor are mandatory for a standalone system. The primary energy sources solar and wind systems are operating at maximum power point. Adaptive neuro‐fuzzy inference system (ANFIS) is utilized to foresee the voltage of panel at which extreme power is obtained for a solar system and also to ascertain the most extreme power delivered by the nonuniform and unpredictable characteristics of solar and wind energy sources. A common energy management technique is developed to manage the control flow between the distinctive power sources, loads, and ultracapacitor in the system. The results obtained by ANFIS are compared with artificial neural network. A Simulink model of the hybrid standalone system was developed and tested for various insolation, temperature, and wind velocity.

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Energy management based fuzzy logic controller of hybrid system wind/photovoltaic/diesel with storage battery

Cited by 125 publications

References 26 publications

Coordinated Control of Aichi Microgrid for Efficient Power Management Using Novel Set Point Weighting Iterative Learning Controller

Coordinated Control of Aichi Microgrid for Efficient Power Management Using Novel Set Point Weighting Iterative Learning Controller

Traffic-Condition-Prediction-Based HMA-FIS Energy-Management Strategy for Fuel-Cell Electric Vehicles

Energy storage management of hybrid solar/wind standalone system using adaptive neuro‐fuzzy inference system

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