Measurement and Modeling of DTCR Software Parameters Based on Intranet Wide Area Measurement System for Smart Grid Applications

Hasan, Mohammad Kamrul; Musse, Mohamud Ahmed; Musa, Sherfriz Sherry

doi:10.1007/978-981-15-5148-2_96

Cited by 7 publications

(6 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The decoder then uses these node embeddings as input to reconstruct the attribute data and also the network structure. Anomalies are then termed as inconsistencies between input and the reconstructed network [58], [59].…”

Section: B Autoencoders (Ae)mentioning

confidence: 99%

DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks

et al. 2022

Self Cite

View full text Add to dashboard Cite

A significant aspect of today's digital information is attributed networks, which combine multiple node attributes with the basic network topology in order to extract knowledge. Anomaly Detection on attributed networks has recently drawn significant attention from researchers and is widely used in a number of high-impact areas. The majority of current approaches focus on shallow learning methods such as community analysis, ego network or selection of subspace method. These approaches have network sparsity and data nonlinearity problems, and they do not even capture the intricate relationships between various information sources. Deep learning approaches like graph autoencoders are utilized to perform anomaly detection through obtaining node embeddings while dealing with the network nonlinearity and sparsity issues. However, they suffer from the problem of ignoring the latent codes' embedding distribution, which results in poor representation in many instances. In this paper, we propose a new framework called DVAEGMM to detect anomalies on attributed networks. First, our framework utilizes a dual variational autoencoder for capturing the complex cross-modality relationships between node attributes and network structure, similar to vanilla autoencoders, but it also considers the potential data distribution and makes use of a generative adversarial network (GAN) for an adversarial regularization approach. An adversarial mechanism makes the encoder make more accurate estimates of how potential features might be distributed. As a result, decoders can make graphs that are more like the original graph. Each input data point is represented by a low-dimensional representation and a probability of reconstruction by the algorithm. Lastly, the Gaussian Mixture Model, a distinct estimation network, is used to approximate the latent vector density, resulting in the detection of anomalies from measuring sample energy. They are trained jointly as an end-to-end framework. DVAEGMM helps in the simultaneous optimization of the mixture model, generative adversarial network, and variational autoencoder parameters. The joint optimization balances the reconstruction probability, the latent representation density approximation, and regularization. Extensive experiments on attributed networks prove that DVAEGMM significantly beats the existing methods, proving the efficiency of the presented approach. The AUC scores of our proposed framework for the BlogCatalog, Flickr, Enron, and Amazon datasets are 0.89380, 0.87130, 0.72480, and 0.75102, respectively.

show abstract

Section: B Autoencoders (Ae)mentioning

confidence: 99%

DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks

et al. 2022

Self Cite

View full text Add to dashboard Cite

show abstract

“…(iii) Managing the Overall Operation of RE Integrated Smart Grid. Smart grid (SG) is a new generation of power plants that optimizes all sectors of the grid from generation to distribution, and storing energy [79,80]. With the speedy expansion of the powergrid and continuous advancement of making it intelligent, more effective, and efficient operation are expected by stakeholders to manage the grid.…”

Section: Machine Learning In Hresmentioning

confidence: 99%

Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

et al. 2021

Self Cite

View full text Add to dashboard Cite

This paper presents a comprehensive review of machine learning (ML) based approaches, especially artificial neural networks (ANNs) in time series data prediction problems. According to literature, around 80% of the world’s total energy demand is supplied either through fuel-based sources such as oil, gas, and coal or through nuclear-based sources. Literature also shows that a shortage of fossil fuels is inevitable and the world will face this problem sooner or later. Moreover, the remote and rural areas that suffer from not being able to reach traditional grid power electricity need alternative sources of energy. A “hybrid-renewable-energy system” (HRES) involving different renewable resources can be used to supply sustainable power in these areas. The uncertain nature of renewable energy resources and the intelligent ability of the neural network approach to process complex time series inputs have inspired the use of ANN methods in renewable energy forecasting. Thus, this study aims to study the different data driven models of ANN approaches that can provide accurate predictions of renewable energy, like solar, wind, or hydro-power generation. Various refinement architectures of neural networks, such as “multi-layer perception” (MLP), “recurrent-neural network” (RNN), and “convolutional-neural network” (CNN), as well as “long-short-term memory” (LSTM) models, have been offered in the applications of renewable energy forecasting. These models are able to perform short-term time-series prediction in renewable energy sources and to use prior information that influences its value in future prediction.

show abstract

“…Therefore, conductor temperature should be estimated accurately by the electricity dispatchers as it has significant impacts on the TL [31]. For conductor temperature estimation, Hadoop MapReduce has been used to estimate large data set analysis as reported in [32], [37]- [39]. This programming model contains the Hadoop Distributed File System (HDFS) file that is designed to consistently store and stream large data sets within a large bandwidth.…”

Section: Conductor Temperaturementioning

confidence: 99%

An Improved Dynamic Thermal Current Rating Model for PMU-Based Wide Area Measurement Framework for Reliability Analysis Utilizing Sensor Cloud System

et al. 2021

Self Cite

View full text Add to dashboard Cite

Information technology expressively improves remote electricity measurement and monitoring. Integrating Dynamic Thermal Current Rating (DTCR) software packs with the exclusive phasor measurement-based Wide Area Measurement (WAM) framework, the remote Transmission Lines (TLs) current rating can be measured. WAM is used for data acquisition from different sensors, and also allows data transmissions and processing for which sensor cloud system (SCS) plays a vital role. DTCR with phasor-measurement based WAM framework is mainly used to analyze and determine the current ratings of overhead TLs using weather condition estimation or prediction methods. However, the recent study suggests that the accuracy of the DTCR has become an issue in the smart grid of Sarawak Energy Berhad (SEB). Hence, this article studies and discusses the relevant models and systems, and then proposes an improved thermal pi (π) model for the transmission line thermal model of DTCR software in WAM Framework. The performance of the improved π model will be distinguished from the existing thermal model. The weather factors that bring a substantial impact on the current rating is also considered, where the relevant data is monitored via different weather sensors. Besides, this study also focuses on calibrating the DTCR through phasor measurement in the WAM system, as well as the field measured data. All the data is collected from relevant sensors, and a detailed comparative analysis is provided based on the proposed model for the sake of improving the reliability of the system. The performance analysis of the thermal models is evaluated using Matlab software-based numerical analysis.INDEX TERMS Dynamic thermal current ratings, data acquisition system, information technology, phasor measurement unit, reliability, sensors, sensor cloud system, wide area measurement. NOMENCLATURE ABBREVIATIONS ACSR Aluminum Conductor Steel Rectifier CTE Conductor Temperature EvaluationThe associate editor coordinating the review of this manuscript and approving it for publication was Eyuphan Bulut .

show abstract

Measurement and Modeling of DTCR Software Parameters Based on Intranet Wide Area Measurement System for Smart Grid Applications

Cited by 7 publications

References 15 publications

DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks

DVAEGMM: Dual Variational Autoencoder With Gaussian Mixture Model for Anomaly Detection on Attributed Networks

Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks

An Improved Dynamic Thermal Current Rating Model for PMU-Based Wide Area Measurement Framework for Reliability Analysis Utilizing Sensor Cloud System

Contact Info

Product

Resources

About