Control and estimation techniques applied to smart microgrids: A review

Mbungu, Nsilulu T.; Adam, Ali Ahmed; Al-Shabi, Mohammad; Bansal, Ramesh C.; Elnady, A.; Hamid, A-K.

doi:10.1016/j.rser.2023.113251

Cited by 65 publications

(14 citation statements)

References 178 publications

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“…where the W out dimension is P × N. Using y ′ to denote the predicted value, when u(t) is input at time t, x(t + 1) can be obtained from Equation (6), and the predicted value y ′ (t + 1) at time t +1 is obtained from the W out obtained by training.…”

Section: Echo State Networkmentioning

confidence: 99%

“…In addition, wind speed prediction helps to develop wind power scheduling schemes and provides favourable support for reducing grid operation costs [5]. At the same time, in the face of large‐scale distributed renewable energy integration into a microgrid, a wind turbine model predictive control strategy supported by high‐precision ultra‐short‐term wind speed prediction methods can also provide technical support for the formulation of coordinated control strategies for the microgrid [6]. Wind speed prediction methods are mainly divided into four categories: physical models, statistical models, artificial intelligence models, and combined models.…”

Section: Introductionmentioning

confidence: 99%

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A double‐layer neural network wind speed prediction framework based on training set segmentation and error correction

Liu,

Zhang

et al. 2024

IET Renewable Power Gen

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Highly accurate wind speed prediction is one of the effective ways to improve wind energy utilization. Therefore, this study proposes a novel double‐layer neural network (D‐NN) framework based on feedforward neural networks. First, a feedforward neural network with an extremely short training time is used as the basic learner to ensure that the D‐NN framework has a short training and optimization time. Then, the double‐layer neural network automatically segments and adjusts the length of the training set using an improved error correction method, thus improving the model's ability to capture sequence features and enhancing the model's generalization performance in a non‐directly‐connected superposition manner. Finally, a genetic algorithm is used to automatically optimize the hyperparameters of each sub‐neural network in the D‐NN and the optimal training set length of each sub‐neural network, resulting in the final optimized prediction model. The accuracy and reliability of the prediction models designed based on the D‐NN framework are verified by comparing several different models. The results of the five different prediction evaluation metrics show that the D‐NN framework can greatly improve the accuracy of wind speed prediction.

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Section: Echo State Networkmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A double‐layer neural network wind speed prediction framework based on training set segmentation and error correction

Liu,

Zhang

et al. 2024

IET Renewable Power Gen

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“…However, a more in-depth analysis of the results obtained from the case study would have provided deeper insights into the practical implications of the SASM. Nsilulu T. Mbungu et al [8] delve in their study into various control and estimation techniques applied to smart microgrids. They discuss a plethora of control techniques, including linear, non-linear, robust, predictive, intelligent, and adaptive control techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Storage management optimization based on electrical consumption and production forecast in a photovoltaic system

Aouad,

Almaksour,

Abbes

2024

Mathematics and Computers in Simulation

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“…Some studies have been steered on the control and management of uncertainty in DCMGs. For instance, some researchers have demonstrated the DGS' reconnections as uncertainty for grid and load turbulence as a computable parameter [9]. The DCMG parameters uncertainties have been considered in [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…Some researchers also propose the robust voltage regulation for the independent MGs to layout a multi-loop status feedback [9,34,35]. Some researchers also propose the robust voltage regulation for a solitary loop output feedback controller [36,37].…”

Section: Introductionmentioning

confidence: 99%

Bi‐level multi‐agent system (MAS) to protect DC microgrids (DCMGs) in free renewable sources and uncertainties penetration

Bamshad,

Ghaffarzadeh

2023

IET Generation Trans & Dist

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DC microgrids (DCMGs) have significant advantages, but their protection can be a challenge for DCMGs expansion expressly in distributed generation sources’ (DGS) tendance. This paper proposes a quick, and precise strategy to perceive and trip any fault in DCMGs in the free tendance of DGS. A bi‐level multi‐agent system (MAS) is proposed to protect the DCMG and DGs. The MAS first level perceives the fault in DCMG via a discrete wavelet transform (DWT). The multiply of two harvest levels’ summation of DWT is exerted to detect and distinguish DC lines faults. The MAS second level detects the fault in DGS and their switchers to the point of common connection (PCC) via a differential pole (DP) controller. A four bus DCMG containing solar PV and wind sources is implemented, and the results demonstrate that the proposed plan can recognize and trip the fault by 100% of reliability.

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Control and estimation techniques applied to smart microgrids: A review

Cited by 65 publications

References 178 publications

A double‐layer neural network wind speed prediction framework based on training set segmentation and error correction

A double‐layer neural network wind speed prediction framework based on training set segmentation and error correction

Storage management optimization based on electrical consumption and production forecast in a photovoltaic system

Bi‐level multi‐agent system (MAS) to protect DC microgrids (DCMGs) in free renewable sources and uncertainties penetration

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