Distributed Secondary Voltage Control of Islanded Microgrids Based on RBF-Neural-Network Sliding-Mode Technique

Shen, Xin; Wang, Haiqing; Li, Jian; Su, Qingyu; Lan, Guanghui

doi:10.1109/access.2019.2915509

Cited by 41 publications

(30 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Improvement of voltage oscillation, compensation for unwanted harmonics, tracking of command voltages and quality control of power profile of MG particularly for islanded mode operation using modern control techniques have been the area of continuous research which can be seen in the references [32]- [34] and so forth. The work presented in [35], [36] and the works presented in the paper are completely different in the spectrum of control designs.…”

Section: A Literature Reviewmentioning

confidence: 99%

Robust Extended H_∞ Control Strategy Using Linear Matrix Inequality Approach for Islanded Microgrid

Armin

Rahman

et al. 2020

IEEE Access

View full text Add to dashboard Cite

This paper presents the design of an extended parameterisations of H ∞ controller for off grid operation of a microgrid. The microgrid consists of distributed generation units, filters and local loads. The filters are used to achieve accurate sinusoidal output voltage. However, loads which are connected to the microgrid are parametrically uncertain. Hence, it undergoes with unknown loads uncertainties. These unknown loads may create unknown loads harmonics, non-linearities which may reduce the voltage and current profile of the microgrid. As a result, the sudden rise and fall of voltage current profile damages the domestic and commercial loads. The proposed controller provides robust stability against various unknown loads and uncertainties. The design of the controller is presented using linear matrix inequality approach and satisfies the Lyapunov stability criterion. Moreover, it provides lower closed-loop H ∞ norm and has better tracking accuracy than other. For justification, several load conditions have been tested in MATLAB/SimPowerSystem Toolbox to ensure the robust stability of the proposed controller. All the results presented in the paper indicate high performance of the controller.

show abstract

Section: A Literature Reviewmentioning

confidence: 99%

Robust Extended H_∞ Control Strategy Using Linear Matrix Inequality Approach for Islanded Microgrid

Armin

Rahman

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The RBF neural network sliding mode distributed controller in a microgrid is suggested in [71]. The RBF neural network sliding mode distributed controller incorporates the advantages of the sliding mode and neural network into a distributed controller.…”

Section: (Radial Basis Function) Rbf Neural Network Sliding Mode Contmentioning

confidence: 99%

Distributed Control Methods and Impact of Communication Failure in AC Microgrids: A Comparative Review

et al. 2019

View full text Add to dashboard Cite

The objectives of this paper are to review and compare the distributed control methods in AC microgrids and also to identify the impact of communication failure on this type of the controller. The current AC microgrids are distinguished from the traditional power system topologies because of the high penetration of advanced control methods, measurements, sensors, power electronic devices, and communication links. Also, because of the increasing integration of renewable energy sources, control strategy for congestion management, frequency control, and optimal dispatch of microgrids has become more complicated. This paper explains the characteristics and features of distributed control systems and discusses the challenges of these approaches. In addition, a comprehensive review of the advantages and disadvantages of these techniques are explained in detail. On the other hand, the possible challenges, related to communication failure, noise, delay, and packet dropout on the operation of the distributed controller are presented, and several techniques, which reduce the impact of communication failure of the distributed controller, are compared. This comprehensive study on distributed control systems reveals the challenges in and future possible studies on this issue.

show abstract

“…For instance, NNs are used to approximate and compensate the uncertainties caused by the unknown dynamics of the distributed generators (DGs) in [17]. A Radial Basis Function (RBF) NN-based distributed secondary voltage control of islanded MGs is presented in [18]. In [19], an NN-based adaptive voltage and frequency control of islanded MGs is investigated.…”

Section: Introductionmentioning

confidence: 99%

“…[17]), requires the grid to be inductive (e.g. [18]), or uses the hierarchical control structure thus suffer from the same problems, such as bad transient response, slow frequency/voltage recovery (e.g. [18], [19]).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data-driven distributed frequency/voltage and power sharing control for islanded microgrids

Zheng

Karimi

2020

2020 IEEE Conference on Control Technology and Applications (CCTA)

View full text Add to dashboard Cite

In this paper, a new data-driven distributed control structure for frequency/voltage regulation and active/reactive power sharing of islanded microgrids is proposed. By using a droop-free concept, the proposed method avoids the inherent timescale separation between primary and secondary control, and improves the transient response for microgrids with inductive, resistive or mixed lines. Besides, the proposed control algorithm is fully data-driven and does not rely on the accurate model or physical parameters of the microgrid. Instead, a classical neural network is adopted to learn the unknown system dynamics online, and an adaptive controller is designed based on the learning results. Because no system model is needed, and the control algorithm can be adjusted in real-time, a remarkable plug-and-play capability is also achieved. The effectiveness of the proposed method is demonstrated via simulations.

show abstract

Distributed Secondary Voltage Control of Islanded Microgrids Based on RBF-Neural-Network Sliding-Mode Technique

Cited by 41 publications

References 27 publications

Robust Extended H_∞ Control Strategy Using Linear Matrix Inequality Approach for Islanded Microgrid

Robust Extended H_∞ Control Strategy Using Linear Matrix Inequality Approach for Islanded Microgrid

Distributed Control Methods and Impact of Communication Failure in AC Microgrids: A Comparative Review

Data-driven distributed frequency/voltage and power sharing control for islanded microgrids

Contact Info

Product

Resources

About

Distributed Secondary Voltage Control of Islanded Microgrids Based on RBF-Neural-Network Sliding-Mode Technique

Cited by 41 publications

References 27 publications

Robust Extended H∞ Control Strategy Using Linear Matrix Inequality Approach for Islanded Microgrid

Robust Extended H∞ Control Strategy Using Linear Matrix Inequality Approach for Islanded Microgrid

Distributed Control Methods and Impact of Communication Failure in AC Microgrids: A Comparative Review

Data-driven distributed frequency/voltage and power sharing control for islanded microgrids

Contact Info

Product

Resources

About

Robust Extended H_∞ Control Strategy Using Linear Matrix Inequality Approach for Islanded Microgrid

Robust Extended H_∞ Control Strategy Using Linear Matrix Inequality Approach for Islanded Microgrid