Adaptive intelligent techniques for microgrid control systems: A survey

Mahmoud, Magdi S.; Alyazidi, Nezar M.; Abouheaf, Mohamed I.

doi:10.1016/j.ijepes.2017.02.008

Cited by 129 publications

(54 citation statements)

References 137 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Fuzzy logic controller is a powerful tool and has become popular in the few recent years. It allows the control of the complex processes whose parameters are unknown, imprecise or have high degree of non‐linearity . Figure represents the schematic block of a FLC.…”

Section: Control Strategies For the Wecsmentioning

confidence: 99%

See 1 more Smart Citation

Power control of variable speed wind turbine based on doubly fed induction generator using indirect field‐oriented control with fuzzy logic controllers for performance optimization

Zamzoum

Mourabit

Errouha

et al. 2018

Energy Science & Engineering

View full text Add to dashboard Cite

The main objective of this study is to improve the energy quality extracted by variable speed wind turbine based on doubly fed induction generator. First, the dynamic modeling of different parts of the system is presented. The electrical machine is modeled in PARK reference frame. Thereafter, the control technique is detailed. Indirect field‐oriented control with fuzzy logic controllers (FLC) is used to control the rotor currents and consequently the real and reactive powers of the stator generator. Then, the design of FLC is detailed by tuning its most significant parameters, which are scaling factor rules base and membership functions. Finally, a simulation using the MATLAB/SIMULINK environment is carried out to prove the validation of the control strategy using a 2 MW wind turbine. The results show a satisfactory performance in terms of rapidity, stability, and precision under variable wind speed conditions.

show abstract

Section: Control Strategies For the Wecsmentioning

confidence: 99%

“…It allows the control of the complex processes whose parameters are unknown, imprecise or have high degree of non-linearity. 13,14,41 Figure 6 represents the schematic block of a FLC. It consists of three blocks 18,42 :…”

Section: Fuzzy Logic Controller Designmentioning

confidence: 99%

Power control of variable speed wind turbine based on doubly fed induction generator using indirect field‐oriented control with fuzzy logic controllers for performance optimization

Zamzoum

Mourabit

Errouha

et al. 2018

Energy Science & Engineering

View full text Add to dashboard Cite

show abstract

“…These control methods are divided into noncommunication-based control methods [6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] and communication control methods. [25][26][27][28][29] The first one includes droop control, peer-to-peer control, hierarchical control, PI/PID control, and sliding mode control. The second one includes, master-slave control and distributed control method.…”

Section: Introductionmentioning

confidence: 99%

“…24 Master/slave control method is advised in Mortezaei 25 to adjust the system frequency, 25 control the system power flow, 26 and to achieve an appropriate load sharing among DGs. 27 The distributed control method is applied to control the frequency and voltage 28 and to control the MG in the presence of parametric uncertainties using adaptive dynamic programming method and neural network. 29 Among these control methods, droop controller is the simplest one as it is a decentralized control method where each input is paired with one output and it depends only on local information of the DGs.…”

Section: Introductionmentioning

confidence: 99%

A multistage H‐infinity–based controller for adjusting voltage and frequency and improving power quality in islanded microgrids

Sedhom

El‐Saadawi

Hatata

et al. 2019

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

Summary This paper proposes a novel methodology for controlling voltage and frequency of islanded microgrids and meanwhile improving the system power quality. The proposed methodology is based on applying a multi‐stage H‐Infinity (H∞) controller with different control loops to restore the nominal values of voltage and frequency after any load variation. These control loops include power control, current control, and voltage control loops. The H∞ is also applied with an LCL filter to enhance the system power quality. The proposed controller is applied to a test system under three different loading conditions and the obtained results are compared with that obtained by applying the model predictive control (MPC) method. The results are compared with that obtained by applying a proposed traditional control method in the same test system. A comparison between the droop controller and the proposed controller, MPC is performed based on fitness value, controller response time, and maximum frequency deviation. In addition, the system stability is analysed using root locus, system step response, singular value decomposition, and bode diagram. The results prove the effectiveness and the efficiency of the proposed method in restoring voltage and frequency and improving the system power quality.

show abstract

“…In grid-connected mode, voltage and frequency references are provided by the main grid and each distributed generation unit (DGu) achieves active power and reactive power regulation via current control. In islanding mode, voltage and frequency reference are maintained by the main DGu of a microgrid [5].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Higher-Order Sliding Mode Control for Islanding and Grid-Connected Operation of a Microgrid

Han

Cui

2018

Energies

View full text Add to dashboard Cite

Grid-connected and islanding operations of a microgrid are often influenced by system uncertainties, such as load parameter variations and unmodeled dynamics. This paper proposes a novel adaptive higher-order sliding mode (AHOSM) control strategy to enhance system robustness and handle an unknown uncertainty upper bounds problem. Firstly, microgrid models with uncertainties are established under islanding and grid-connected modes. Then, adaptive third-order sliding mode and adaptive second-order sliding mode control schemes are respectively designed for the two modes. Microgrid models' descriptions are divided into nominal part and uncertain part, and higher-order sliding mode (HOSM) control problems are transformed into finite time stability problems. Again, a scheduled law is proposed to increase or decrease sliding mode control gain adaptively. Real higher-order sliding modes are established, and finite time stability is proven based on the Lyapunov method. In order to achieve smooth mode transformation, an islanding mode detection algorithm is also adopted. The proposed control strategy accomplishes voltage control and current control of islanding mode and grid-connected mode. Control voltages are continuous, and uncertainty upper bounds are not required. Furthermore, adjustable control gain can further whittle control chattering. Simulation experiments verify the validity and robustness of the proposed control scheme.

show abstract

Adaptive intelligent techniques for microgrid control systems: A survey

Cited by 129 publications

References 137 publications

Power control of variable speed wind turbine based on doubly fed induction generator using indirect field‐oriented control with fuzzy logic controllers for performance optimization

Power control of variable speed wind turbine based on doubly fed induction generator using indirect field‐oriented control with fuzzy logic controllers for performance optimization

A multistage H‐infinity–based controller for adjusting voltage and frequency and improving power quality in islanded microgrids

Adaptive Higher-Order Sliding Mode Control for Islanding and Grid-Connected Operation of a Microgrid

Contact Info

Product

Resources

About