12 research outputs found
Experimental Evaluation of Indoor Localization Methods for Industrial IoT Environment
294-307With the evolution of new technology, GPS brings a lot of revolution in the Localization system but it is not an effective
solution in Indoor Environment. This is because of the fact that the signals coming from the satellite are attenuated,
absorbed, scattered by the walls, roofs, and other objects. Due to this, lots of errors may arise in the system. To overcome
this problem sensor node based localization system is used for mobile IoT domain. Recently, in IoT based system, various
sensor nodes have been used in different kinds of localization based applications such as Location-Based Services (LBS)
and Proximity-Based Services (PBS). However, such sensor nodes may not be stable in every environment to provide an
accurate positioning. Although there are different localization techniques are available to find out the location of any object,
but there is a common challenge of finding best localization technique suitable for most of the environment. This work
investigates different existing indoor localization methods for its practical suitability in Lab and actual Industrial
environment for deployment on IoT nodes. A mobile application has also been developed to implement four state-of-the-art
localization techniques for getting the position and calculating its accuracy in different environments. During the
experiment, BLE Beacons are used as sensor nodes due to their ease of deployment, lower complexity, lower cost, and
higher power consumption. The error in the accuracy of estimated position got calculated in terms of Average Error.
According to the experimental result in real environments it has been revealed that the Weighted Centroid Localization
technique provides better accuracy in industrial as well as laboratory environment
Identification of Surface Defects on Solar PV Panels and Wind Turbine Blades using Attention based Deep Learning Model
According to Global Electricity Review 2022, electricity generation from
renewable energy sources has increased by 20% worldwide primarily due to more
installation of large green power plants. Monitoring the renewable energy
assets in those large power plants is still challenging as the assets are
highly impacted by several environmental factors, resulting in issues like less
power generation, malfunctioning, and degradation of asset life. Therefore,
detecting the surface defects on the renewable energy assets would facilitate
the process to maintain the safety and efficiency of the green power plants. An
innovative detection framework is proposed to achieve an economical renewable
energy asset surface monitoring system. First capture the asset's
high-resolution images on a regular basis and inspect them to detect the
damages. For inspection this paper presents a unified deep learning-based image
inspection model which analyzes the captured images to identify the surface or
structural damages on the various renewable energy assets in large power
plants. We use the Vision Transformer (ViT), the latest developed deep-learning
model in computer vision, to detect the damages on solar panels and wind
turbine blades and classify the type of defect to suggest the preventive
measures. With the ViT model, we have achieved above 97% accuracy for both the
assets, which outperforms the benchmark classification models for the input
images of varied modalities taken from publicly available sources
A Resilient Power Distribution System using P2P Energy Sharing
The adoption of distributed energy resources (DERs) such as solar panels and
wind turbines is transforming the traditional energy grid into a more
decentralized system, where microgrids are emerging as a key concept.
Peer-to-Peer (P2P) energy sharing in microgrids enhances the efficiency and
flexibility of the overall system by allowing the exchange of surplus energy
and better management of energy resources. This work analyzes the impact of P2P
energy sharing for three cases - within a microgrid, with neighboring
microgrids, and all microgrids combined together in a distribution system. A
standard IEEE 123 node test feeder integrated with renewable energy sources is
partitioned into microgrids. For P2P energy sharing between microgrids, the
results show significant benefits in cost, reduced energy dependence on the
grid, and a significant improvement in the system's resilience. We also
predicted the energy requirement for a microgrid to evaluate energy resilience
for the control and operation of the microgrid. Overall, the analysis provides
valuable insights into the performance and sustainability of microgrids with
P2P energy sharing.Comment: arXiv admin note: text overlap with arXiv:2212.0231
Advancements in Arc Fault Detection for Electrical Distribution Systems: A Comprehensive Review from Artificial Intelligence Perspective
This comprehensive review paper provides a thorough examination of current
advancements and research in the field of arc fault detection for electrical
distribution systems. The increasing demand for electricity, coupled with the
increasing utilization of renewable energy sources, has necessitated vigilance
in safeguarding electrical distribution systems against arc faults. Such faults
could lead to catastrophic accidents, including fires, equipment damage, loss
of human life, and other critical issues. To mitigate these risks, this review
article focuses on the identification and early detection of arc faults, with a
particular emphasis on the vital role of artificial intelligence (AI) in the
detection and prediction of arc faults. The paper explores a wide range of
methodologies for arc fault detection and highlights the superior performance
of AI-based methods in accurately identifying arc faults when compared to other
approaches. A thorough evaluation of existing methodologies is conducted by
categorizing them into distinct groups, which provides a structured framework
for understanding the current state of arc fault detection techniques. This
categorization serves as a foundation for identifying the existing constraints
and future research avenues in the domain of arc fault detection for electrical
distribution systems. This review paper provides the state of the art in arc
fault detection, aiming to enhance safety and reliability in electrical
distribution systems and guide future research efforts
Efficient Fault Detection and Categorization in Electrical Distribution Systems Using Hessian Locally Linear Embedding on Measurement Data
Faults on electrical power lines could severely compromise both the
reliability and safety of power systems, leading to unstable power delivery and
increased outage risks. They pose significant safety hazards, necessitating
swift detection and mitigation to maintain electrical infrastructure integrity
and ensure continuous power supply. Hence, accurate detection and
categorization of electrical faults are pivotal for optimized power system
maintenance and operation. In this work, we propose a novel approach for
detecting and categorizing electrical faults using the Hessian locally linear
embedding (HLLE) technique and subsequent clustering with t-SNE (t-distributed
stochastic neighbor embedding) and Gaussian mixture model (GMM). First, we
employ HLLE to transform high-dimensional (HD) electrical data into
low-dimensional (LD) embedding coordinates. This technique effectively captures
the inherent variations and patterns in the data, enabling robust feature
extraction. Next, we perform the Mann-Whitney U test based on the feature space
of the embedding coordinates for fault detection. This statistical approach
allows us to detect electrical faults providing an efficient means of system
monitoring and control. Furthermore, to enhance fault categorization, we employ
t-SNE with GMM to cluster the detected faults into various categories. To
evaluate the performance of the proposed method, we conduct extensive
simulations on an electrical system integrated with solar farm. Our results
demonstrate that the proposed approach exhibits effective fault detection and
clustering across a range of fault types with different variations of the same
fault. Overall, this research presents an effective methodology for robust
fault detection and categorization in electrical systems, contributing to the
advancement of fault management practices and the prevention of system
failures
Fault Classification in Electrical Distribution Systems using Grassmann Manifold
Electrical fault classification is vital for ensuring the reliability and
safety of power systems. Accurate and efficient fault classification methods
are essential for timely and effective maintenance. In this paper, we propose a
novel approach for effective fault classification through Grassmann manifolds,
which is a non-Euclidean space that captures the intrinsic structure of
high-dimensional data and offers a robust framework for feature extraction. We
use simulated data for electrical distribution systems with various types of
electrical faults. The proposed method involves transforming the measurement
fault data into Grassmann manifold space using techniques from differential
geometry. This transformation aids in uncovering the underlying fault patterns
and reducing the computational complexity of subsequent classification steps.
To achieve fault classification, we employ a machine learning technique
optimized for the Grassmann manifold. The support vector machine classifier is
adapted to operate within the Grassmann manifold space, enabling effective
discrimination between different fault classes. The results illustrate the
efficacy of the proposed Grassmann manifold-based approach for electrical fault
classification which showcases its ability to accurately differentiate between
various fault types
Modelling of the Electric Vehicle Charging Infrastructure as Cyber Physical Power Systems: A Review on Components, Standards, Vulnerabilities and Attacks
The increasing number of electric vehicles (EVs) has led to the growing need
to establish EV charging infrastructures (EVCIs) with fast charging
capabilities to reduce congestion at the EV charging stations (EVCS) and also
provide alternative solutions for EV owners without residential charging
facilities. The EV charging stations are broadly classified based on i) where
the charging equipment is located - on-board and off-board charging stations,
and ii) the type of current and power levels - AC and DC charging stations. The
DC charging stations are further classified into fast and extreme fast charging
stations. This article focuses mainly on several components that model the EVCI
as a cyberphysical system (CPS)
Advancements in Enhancing Resilience of Electrical Distribution Systems: A Review on Frameworks, Metrics, and Technological Innovations
This comprehensive review paper explores power system resilience, emphasizing
its evolution, comparison with reliability, and conducting a thorough analysis
of the definition and characteristics of resilience. The paper presents the
resilience frameworks and the application of quantitative power system
resilience metrics to assess and quantify resilience. Additionally, it
investigates the relevance of complex network theory in the context of power
system resilience. An integral part of this review involves examining the
incorporation of data-driven techniques in enhancing power system resilience.
This includes the role of data-driven methods in enhancing power system
resilience and predictive analytics. Further, the paper explores the recent
techniques employed for resilience enhancement, which includes planning and
operational techniques. Also, a detailed explanation of microgrid (MG)
deployment, renewable energy integration, and peer-to-peer (P2P) energy trading
in fortifying power systems against disruptions is provided. An analysis of
existing research gaps and challenges is discussed for future directions toward
improvements in power system resilience. Thus, a comprehensive understanding of
power system resilience is provided, which helps in improving the ability of
distribution systems to withstand and recover from extreme events and
disruptions
Experimental Evaluation of Indoor Localization Methods for Industrial IoT Environment
With the evolution of new technology, GPS brings a lot of revolution in the Localization system but it is not an effective solution in Indoor Environment. This is because of the fact that the signals coming from the satellite are attenuated, absorbed, scattered by the walls, roofs, and other objects. Due to this, lots of errors may arise in the system. To overcome this problem sensor node based localization system is used for mobile IoT domain. Recently, in IoT based system, various sensor nodes have been used in different kinds of localization based applications such as Location-Based Services (LBS) and Proximity-Based Services (PBS). However, such sensor nodes may not be stable in every environment to provide an accurate positioning. Although there are different localization techniques are available to find out the location of any object, but there is a common challenge of finding best localization technique suitable for most of the environment. This work investigates different existing indoor localization methods for its practical suitability in Lab and actual Industrial environment for deployment on IoT nodes. A mobile application has also been developed to implement four state-of-the-art localization techniques for getting the position and calculating its accuracy in different environments. During the experiment, BLE Beacons are used as sensor nodes due to their ease of deployment, lower complexity, lower cost, and higher power consumption. The error in the accuracy of estimated position got calculated in terms of Average Error. According to the experimental result in real environments it has been revealed that the Weighted Centroid Localization technique provides better accuracy in industrial as well as laboratory environment