A Review on Different ANN Based Fault Detection Techniques for HVDC Systems

Nagar, Khushboo; Shah, Manish

doi:10.21276/ijirem.2016.3.6.5

Cited by 6 publications

(3 citation statements)

References 8 publications

(10 reference statements)

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“…Typically, in a power system, the values of voltage and current are very unstable and change under various types of faults. Hence, an artificial neural network would be a great choice for detecting, classifying, and locating the transmission line faults of HVDC systems, as a neural network is basically able to incorporate dynamic changes in power systems [28][29][30][31][32].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Artificial Neural Network-based Fault Location in Terminal-hybrid High Voltage Direct Current Transmission Line

Hadaeghi¹,

Iliyaeifar²,

Chirani

2023

IJE

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In this article, a fault location technique based on artificial neural networks (ANN) for Terminal-Hybrid LCC-VSC-HVDC has been assessed and scrutinized. As is known, in conventional HVDC systems (LCC-based and VSC-based HVDCs), the same type of filter is used on both sides due to the use of similar converters in both sender and receiver terminals. In this article, it is concluded that due to the use of two different types of converters at the both ends of the utilized Terminal-hybrid LCC-VSC-HVDC system, and the use of different DC filters on both sides, fault location using positive and negative pole currents of the rectifier side has much better results than the rest of input signals. Therefore, it will be finalized that by increasing and designing suitable DC filters on the transmission line of HVDC systems, fault localization matter will be remarkably and surprisingly facilitated. Nowadays, the fault location of HVDC transmission lines with a value of more than 1% is generally discussed in most articles. In this research, the fault location with a value of 0.0045%, i.e., a distance of 22.5 meters from the fault point in the most satisfactory case is obtained, which shows the absolute feasibility of the ANN along with the wavelet transform. To validate the proposed method, a ±100 KV, Terminal-hybrid LCC-VSC-HVDC system is simulated via MATLAB. The outcomes verify that the proposed technique works perfectly under various fault locations, resistances, and fault types.

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Section: Artificial Neural Networkmentioning

confidence: 99%

Artificial Neural Network-based Fault Location in Terminal-hybrid High Voltage Direct Current Transmission Line

Hadaeghi¹,

Iliyaeifar²,

Chirani

2023

IJE

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“…Other researchers demonstrated that the fault detection and classification algorithm is based on a modified Clarke's transformation, while a model based on artificial neural networks (ANNs) improves fault detection and classification [20]. The effectiveness of ANN applications for fault detection on HVDC transmission lines is compared, and neural network-based fault detection techniques are presented by Nagar and Shah [21].…”

Section: -Hvdc Transmission Systemmentioning

confidence: 99%

Artificial Intelligence-based Control Techniques for HVDC Systems

Abdulwahid

2023

Emerg Sci J

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The electrical energy industry depends, among other things, on the ability of networks to deal with uncertainties from several directions. Smart-grid systems in high-voltage direct current (HVDC) networks, being an application of artificial intelligence (AI), are a reliable way to achieve this goal as they solve complex problems in power system engineering using AI algorithms. Due to their distinctive characteristics, they are usually effective approaches for optimization problems. They have been successfully applied to HVDC systems. This paper presents a number of issues in HVDC transmission systems. It reviews AI applications such as HVDC transmission system controllers and power flow control within DC grids in multi-terminal HVDC systems. Advancements in HVDC systems enable better performance under varying conditions to obtain the optimal dynamic response in practical settings. However, they also pose difficulties in mathematical modeling as they are non-linear and complex. ANN-based controllers have replaced traditional PI controllers in the rectifier of the HVDC link. Moreover, the combination of ANN and fuzzy logic has proven to be a powerful strategy for controlling excessively non-linear loads. Future research can focus on developing AI algorithms for an advanced control scheme for UPFC devices. Also, there is a need for a comprehensive analysis of power fluctuations or steady-state errors that can be eliminated by the quick response of this control scheme. This survey was informed by the need to develop adaptive AI controllers to enhance the performance of HVDC systems based on their promising results in the control of power systems. Doi: 10.28991/ESJ-2023-07-02-024 Full Text: PDF

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“…Artificial neural networks (ANN) or connectionist systems are computing systems based on biological neural networks that make up animal brains [25]. Neural network has been viewed as framework rather than been called an algorithm, which enhances several differ machine learning algorithms not only to work together but also process complex inputs data [26], [27].…”

Section: Ann-based Fault Analysismentioning

confidence: 99%

Analysis of unsymmetrical faults based on artificial neural network using 11 kV distribution network of University of Lagos as case study

et al. 2021

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The occurrence of faults in any operational power system network is inevitable, and many of the causative factors such as lightning, thunderstorm among others is usually beyond human control. Consequently, there is the need to set up models capable of prompt identification and classification of these faults for immediate action. This paper, explored the use of artificial neural network (ANN) technique to identify and classify various faults on the 11 kV distribution network of University of Lagos. The ANN is applied because it offers high speed, higher efficiency and requires less human intervention. Datasets of the case study obtained were sectioned proportionately for training, testing, and validation. The mathematical formulations for the method are presented with python used as the programming tools for the analysis. The results obtained from this study, for both the voltage and current under different scenarios of faults, are displayed in graphical forms and discussed. The results showed the effectiveness of the ANN in fault identification and classification in a distribution network as the model yielded satisfactory results for the available limited datasets used. The information obtained from this study could be helpful to the system operators in faults identification and classification for making informed decisions regarding power system design and reliability.

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A Review on Different ANN Based Fault Detection Techniques for HVDC Systems

Cited by 6 publications

References 8 publications

Artificial Neural Network-based Fault Location in Terminal-hybrid High Voltage Direct Current Transmission Line

Artificial Neural Network-based Fault Location in Terminal-hybrid High Voltage Direct Current Transmission Line

Artificial Intelligence-based Control Techniques for HVDC Systems

Analysis of unsymmetrical faults based on artificial neural network using 11 kV distribution network of University of Lagos as case study

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