System Configuration, Fault Detection, Location, Isolation and Restoration: A Review on LVDC Microgrid Protections

Javed, Waqas; Chen, Dong; Farrag, Mohamed Emad; Xu, Yan

doi:10.3390/en12061001

Cited by 54 publications

(28 citation statements)

References 118 publications

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“…In comparison to traditional model-based methods, ANN's are data-driven, self-adaptive methods learning from examples whilst picking-up subtle and hidden functional relationships that are otherwise unknown or hard to describe. In addition, ANNs are suitable for solving problems where explicit knowledge is difficult to identify, but a vast amount of data is available [17][18][19]. A. Lapedes et al [20], demonstrates how backpropagation neural networks surpass by order of magnitude any of the conventional linear and polynomial methods dealing with chaotic time series of data.…”

Section: Research Backgroundmentioning

confidence: 99%

Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters

et al. 2020

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In this paper, a fault detection algorithm for photovoltaic systems based on artificial neural networks (ANN) is proposed. Numerous literatures can be found on the topic of PV fault detection through the implementation of artificial intelligence. The novel part of this research is the successful development, deployment and validation of a fault detection PV system using radial basis function (RBF), requiring only two parameters as the input to the ANN (solar irradiance and output power). The results obtained through the testing of the developed ANN on a PV installation of 2.2 kW capacity, provided an accuracy of 97.9%. To endorse the accuracy of the newly developed algorithm, the ANN was tested on another PV system, installed at a remote location.The total capacity of the new system was significantly higher, 4.16 kW. A vital part of the test was to see how the proposed ANN would perform with 'scaled-up' input data, during normal operation as well as partial shading scenarios. The validation process provided an overall fault detection accuracy of above 97%. The decrease in accuracy was due to the varying nature of the two systems in terms of total capacity, number of samples and type of faults.

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Section: Research Backgroundmentioning

confidence: 99%

Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters

et al. 2020

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“…DC circuit breaker technology can be divided several ways, of which the zero-crossing breaking method safely manages fault current by generating zero current in the main transmission line and turning off the switch [11,12]. The performance of a DC circuit breaker is an indicator of the breaking time of the main transmission line and the amount of leakage current to the outside [13][14][15]. Therefore, it is very important to manage electric current on the transmission line elements during high-power transmission [16].…”

Section: Introductionmentioning

confidence: 99%

A Zero Crossing Hybrid Bidirectional DC Circuit Breaker for HVDC Transmission Systems

et al. 2021

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With the increasing demand for renewable energy power generation systems, high-power DC transmission technology is drawing considerable attention. As a result, stability issues associated with high power DC transmission have been highlighted. One of these problems is the fault current that appears when a fault occurs in the transmission line. If the fault current flows in the transmission line, it has a serious adverse effect on the rectifier stage, inverter stage and transmission line load. This makes the transmission technology less reliable and can lead to secondary problems such as fire. Therefore, fault current must be managed safely. DC circuit breaker technology has been proposed to solve this problem. However, conventional technologies generally do not take into account the effects of fault current on the transmission line, and their efficiency is relatively low. The purpose of this study is to introduce an improved DC circuit breaker that uses a blocking inductor to minimize the effect of fault current on the transmission line. It also uses a ground inductor to efficiently manage the LC resonant current and dissipate residual current. DC circuit breakers minimize adverse effects on external elements and transmission lines because the use of elements placed on each is distinct. All of these processes are precisely verified by conducting simulation under 200 MVA (±100 kV) conditions based on the VSC-based HVDC transmission link. In addition, the mechanism was explained by analyzing the simulation results to increase the reliability of the circuit in this paper.

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“…ULNERABILITY of short-circuit faults and lack of mature protection scheme are the major constraints in the practical implementation of voltage source converter (VSC) based low-voltage direct current (LVDC) distribution system [1]. However, LVDC distribution system (or DC Microgrid) is becoming an attractive option due to increased penetration and development of renewable energy sources (RES), energy storage systems (EES), and electric vehicles (EV) [2].…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Fault Localization of a DC Microgrid with Refined Data Input

Javed

Dong

2020

2020 IEEE 29th International Symposium on Industrial Electronics (ISIE)

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This paper proposes an online fault localization method for low voltage DC microgrids. This method is based on Artificial Neural Network (ANN) and only requires real-time measurements of a local power converter to locate a fault. During a DC fault, the current component fed by the AC grid can contribute to time-variant non-linearity, which is undesirable to the development of the data-driven method. A novel real-time scheme is thus proposed to exclude such components from DC fault current. The principle of the scheme is introduced and illustrated with time-domain analysis. The effectiveness is verified by case studies of locating a DC fault in a radial DC network fed by a 3-phase voltage source converter.

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System Configuration, Fault Detection, Location, Isolation and Restoration: A Review on LVDC Microgrid Protections

Cited by 54 publications

References 118 publications

Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters

Artificial neural network based photovoltaic fault detection algorithm integrating two bi-directional input parameters

A Zero Crossing Hybrid Bidirectional DC Circuit Breaker for HVDC Transmission Systems

Data-Driven Fault Localization of a DC Microgrid with Refined Data Input

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