A fault detection and reconfiguration approach for MPC-based energy management in an experimental microgrid

Márquez, Juan J.; Zafra-Cabeza, Ascensión; Bordons, Carlos; Ridao, Miguel A.

doi:10.1016/j.conengprac.2020.104695

Cited by 26 publications

(8 citation statements)

References 27 publications

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“…A microgrid's energy management system was studied by Marquez et al [19], who used a fault detection and reconfiguration process. A reconfiguration block received fault information through acquiring residuals.…”

Section: Related Workmentioning

confidence: 99%

Fault Diagnosis Using Data Fusion with Ensemble Deep Learning Technique in IIoT

Venkatasubramanian¹,

Raja

et al. 2022

Mathematical Problems in Engineering

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Detecting the breakdown of industrial IoT devices is a major challenge. Despite these challenges, real-time sensor data from the industrial internet of things (IIoT) present several advantages, such as the ability to monitor and respond to events in real time. Sensor statistics from the IIoT can be processed, fused with other data sources, and used for rapid decision-making. The study also discusses how to manage denoising, missing data imputation, and outlier discovery using preprocessing. After that, data fusion techniques like the direct fusion technique are used to combine the cleaned sensor data. Fault detection in the IIoT can be accomplished by using a variety of deep learning models such as PropensityNet, deep neural network (DNN), and convolution neural networks-long short term memory network (CNS-LSTM). According to various outcomes, the suggested model is tested with Case Western Reserve University (CWRU) data. The results suggest that the method is viable and has a good level of accuracy and efficiency.

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Section: Related Workmentioning

confidence: 99%

Fault Diagnosis Using Data Fusion with Ensemble Deep Learning Technique in IIoT

Venkatasubramanian¹,

Raja

et al. 2022

Mathematical Problems in Engineering

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“…Such methods, however, do not ensure power delivery to critical loads for persistent faults. Controller reconfiguration strategies are proposed to deal with faults in microgrid systems [26] and industrial plants [27], in which there are many backup options to generate the required amount of power; hence, storing an amount of backup energy may not be necessary. On the other hand, many papers propose proactive scheduling methods to deal with faults.…”

Section: Introductionmentioning

confidence: 99%

Resilient Microgrid Energy Management Algorithm Based on Distributed Optimization

Casagrande

Prodan

Spurgeon

et al. 2023

IEEE Systems Journal

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This article proposes a fully distributed energy management algorithm for dc microgrids, resilient to different faults. Specifically, we employ distributed model-predictive control to deal with the uncertainty that characterizes the microgrid operation. The optimization problem is solved at each time step through a distributed optimization algorithm, which has three main advantages: 1) agents of the network require a small computational power; 2) local information is not shared among the network nodes, hence preserving a certain level of privacy; and 3) it is suitable for implementation in large-scale systems. The resilience property of the algorithm stems from additional constraints that are enforced in order to store in the system enough energy to sustain the microgrid in the case of utility grid or line fault. Simulation results show that the algorithm is suitable to schedule the operation of agents that are always connected to the microgrid (e.g., loads) as well as agents that may be connected and disconnected (e.g., electric vehicles).

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“…At present, some scholars had turned their attention to the load recovery of MG after natural disasters (Wang et al, 2019;Nazemi and Dehghanian, 2020), but MG operation inevitably encountered other faults. The energy management system (EMS) could deal with the fault problems in the dispatching process (Marquez et al, 2021). Handling such problems can be divided into island division, load curtailment, economic dispatching of MG, etc.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Optimal Scheduling of Multi-Microgrids Considering Renewable Energy Intermittency

Wang

2022

Front. Energy Res.

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Accelerating the penetration of renewable energy (RE) in energy consumption is an important method to realize the promotion of CO2 emission peaking and carbon neutrality. The energy transaction between two microgrids (MGs) makes up for the limitations that a single MG cannot deal well with the intermittence and fluctuation of RE in the real-time scheduling of the system. Multi-microgrids (MMGs) composed of multiple MGs have become an effective supplement to China’s power system. However, extreme weather and natural disasters can easily cause fault shutdown of wind turbines (WTs) and photovoltaics (PVs) in the microgrid (MG). To better balance the flexible load curtailment and satisfaction of MMGs, this paper proposes a coordinated scheduling model for MMGs. This model covers the WT, the PV, the fuel cell (FC), the energy storage system (ESS), and flexible load curtailment. First, the energy management system (EMS) of MMGs collects information on all the distributed generators’ output and three types of loads. The contribution bargaining game is applied to realize the energy transaction between each two MGs. Second, balancing multi-microgrid satisfaction and the profit of each MG is taken as the objective function, and the scheduling strategy of each MG is formulated. Also, an improved optimization method is applied to solve the amount of flexible load curtailment of each MG and realize the reasonable scheduling of MMG in the fault state. In the case study, the superiority of the model and the proposed method has been verified.

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A fault detection and reconfiguration approach for MPC-based energy management in an experimental microgrid

Cited by 26 publications

References 27 publications

Fault Diagnosis Using Data Fusion with Ensemble Deep Learning Technique in IIoT

Fault Diagnosis Using Data Fusion with Ensemble Deep Learning Technique in IIoT

Resilient Microgrid Energy Management Algorithm Based on Distributed Optimization

Real-Time Optimal Scheduling of Multi-Microgrids Considering Renewable Energy Intermittency

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