Fault Location in Power Distribution Systems via Deep Graph Convolutional Networks

Gong

et al. 2021

Sensors

This paper proposes a new Image-to-Image Translation (Pix2Pix) enabled deep learning method for traveling wave-based fault location. Unlike the previous methods that require a high sampling frequency of the PMU, the proposed method can translate the scale 1 detail component image provided by the low frequency PMU data to higher frequency ones via the Pix2Pix. This allows us to significantly improve the fault location accuracy. Test results via the YOLO v3 object recognition algorithm show that the images generated by pix2pix can be accurately identified. This enables to improve the estimation accuracy of the arrival time of the traveling wave head, leading to better fault location outcomes.

Section: Accuracy Improvement Effect Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Novel Pix2Pix Enabled Traveling Wave-Based Fault Location Method

Gong

et al. 2021

Sensors

“…The model can detect the fault and determine the possible fault type. And in Chen et al (2019) developed a novel graph convolutional network (GCN) framework for fault location in power distribution networks. The proposed approach coordinates multiple measurements at different buses while taking system topology into account.…”

Section: Introductionmentioning

confidence: 99%

Toward fault tolerant modelling for SCADA based electricity distribution networks, machine learning approach

Masri

Al-Jabi

2021

PeerJ Computer Science

Maintaining electrical energy is a crucial issue, especially in developing countries with very limited possibilities and recourses. However, the increasing reliance on electrical appliances generates many challenges for operators to fix any fault optimally within minimum time. Even with numerous researches conducted in this area, very few were interested in minimizing the fault duration, especially in the developing countries with very limited resources. Since decision-making requires enough information within minimum time, the integration of information technology with the existing electrical grids is the most appropriate. In this paper, we propose precise and accurate load redistribution estimation models. While several modeling techniques exist, the proposed modeling techniques in this work are based on machine learning models: multiple linear regression, nonlinear regression, and classifier neural network models. The novelty of this work is it introduces a fault-tolerant approach that relies on machine learning and supervisory control and data acquisition system (SCADA). The purpose of this approach is to help electricity distribution companies to maintain power for the customers and to shorten the fault duration from many hours to the minimum possible time. The work was performed based on real data of smart grids split into zones of about 20 transformers. The models’ input data collected from the sensors allocated in the power grid, make the grid becomes able to redistribute the loads by sufficient strategies. To test and validate the models, two powerful modeling tools were used: MATLAB and Anaconda–Python. The results showed an accuracy of about 97% with a standard deviation of 2.3%. The load redistribution was also presented in details. With such eager results, they approve the validity of our model in minimizing the fault duration, by helping the system in taking ideal actions within the optimal time.

“…The basic idea behind GCN is to distill the highdimensional information about a node's graph neighborhood into a vector representation with dimension reduction. With this in mind, GCNs are also employed in the field of power system recently, to deal with fault location and load shedding [29], [30]. Specially, under the context of TSA, James J Q et al [31] designs a GCN model for recovery of the missing PMU data and indicate lower errors than existing implementation [14].…”

Section: Introductionmentioning

confidence: 99%

Recurrent Graph Convolutional Network-Based Multi-Task Transient Stability Assessment Framework in Power System

Huang

Guan

et al. 2020

IEEE Access

Reliable online transient stability assessment (TSA) is fundamentally required for power system operation security. Compared with time-costly classical digital simulation methods, data-driven deep learning (DL) methods provide a promising technique to build a TSA model. However, general DL models show poor adaptability to the variation of power system topology. In this paper, we propose a new graphbased framework, which is termed as recurrent graph convolutional network based multi-task TSA (RGCN-MT-TSA). Both the graph convolutional network (GCN) and the long short-term memory (LSTM) unit are aggregated to form the recurrent graph convolutional network (RGCN), where the GCN explicitly integrate the bus (node) states with the topological characteristics while the LSTM subsequently captures the temporal features. We also propose a multi-task learning (MTL) scheme, which provides joint training of stability classification (Task-1) as well as critical generator identification (Task-2) in the framework, and accelerate the process with parallel computing. Test results on IEEE 39 Bus system and IEEE 300 Bus system indicate the superiority of the proposed scheme over existing models, as well as its robustness under various scenarios. INDEX TERMS Deep graph-based learning, transient stability assessment (TSA), graph convolutional network (GCN), recurrent graph convolutional network (RGCN), multi-task learning (MTL).