9 research outputs found
Planning of Fast Charging Infrastructure for Electric Vehicles in a Distribution System and Prediction of Dynamic Price
The increasing number of electric vehicles (EVs) has led to the need for
installing public electric vehicle charging stations (EVCS) to facilitate ease
of use and to support users who do not have the option of residential charging.
The public electric vehicle charging infrastructures (EVCIs) must be equipped
with a good number of EVCSs, with fast charging capability, to accommodate the
EV traffic demand, which would otherwise lead to congestion at the charging
stations. The location of these fast-charging infrastructures significantly
impacts the distribution system (DS). We propose the optimal placement of
fast-charging EVCIs at different locations in the distribution system, using
multi-objective particle swarm optimization (MOPSO), so that the power loss and
voltage deviations are kept at a minimum. Time-series analysis of the DS and EV
load variations are performed using MATLAB and OpenDSS. We further analyze the
cost benefits of the EVCIs under real-time pricing conditions and employ an
autoregressive integrated moving average (ARIMA) model to predict the dynamic
price. The simulated test system without any EVCI has a power loss of 164.36 kW
and squared voltage deviations of 0.0235 p.u. Using the proposed method, the
results obtained validate the optimal location of 5 EVCIs (each having 20 EVCSs
with a 50kWh charger rating) resulting in a minimum power loss of 201.40 kW and
squared voltage deviations of 0.0182 p.u. in the system. Significant cost
benefits for the EVCIs are also achieved, and an R-squared value of dynamic
price predictions of 0.9999 is obtained. This would allow the charging station
operator to make promotional offers for maximizing utilization and increasing
profits
Identification of Forced Oscillation Sources in Wind Farms using E-SINDy
The rapid growth of wind power generation has led to increased interest in
understanding and mitigating the adverse effects of wind turbine wakes and
forced oscillations in wind farms. In this paper, we model a wind farm
consisting of three wind turbines connected to a distribution system. Forced
oscillations due to wind shear and tower shadow are injected into the system.
If these oscillations are unchecked, they could pose a severe threat to the
operation of the system and damage to the equipment. Identifying the source and
frequency of forced oscillations in wind farms from measurement data is
challenging. Thus, we propose a data-driven approach that discovers the
underlying equations governing a nonlinear dynamical system from measured data
using the Ensemble-Sparse Identification of Nonlinear Dynamics (E-SINDy)
method. The results suggest that E-SINDy is a valuable tool for identifying
sources of forced oscillations in wind farms and could facilitate the
development of suitable control strategies to mitigate their negative impacts
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
Machine Learning as an Accurate Predictor for Percolation Threshold of Diverse Networks
The percolation threshold is an important measure to determine the inherent
rigidity of large networks. Predictors of the percolation threshold for large
networks are computationally intense to run, hence it is a necessity to develop
predictors of the percolation threshold of networks, that do not rely on
numerical simulations. We demonstrate the efficacy of five machine
learning-based regression techniques for the accurate prediction of the
percolation threshold. The dataset generated to train the machine learning
models contains a total of 777 real and synthetic networks. It consists of 5
statistical and structural properties of networks as features and the
numerically computed percolation threshold as the output attribute. We
establish that the machine learning models outperform three existing empirical
estimators of bond percolation threshold, and extend this experiment to predict
site and explosive percolation. Further, we compared the performance of our
models in predicting the percolation threshold using RMSE values. The gradient
boosting regressor, multilayer perceptron and random forests regression models
achieve the least RMSE values among considered models
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
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
Local Interpretable Model Agnostic Shap Explanations for machine learning models
With the advancement of technology for artificial intelligence (AI) based
solutions and analytics compute engines, machine learning (ML) models are
getting more complex day by day. Most of these models are generally used as a
black box without user interpretability. Such complex ML models make it more
difficult for people to understand or trust their predictions. There are
variety of frameworks using explainable AI (XAI) methods to demonstrate
explainability and interpretability of ML models to make their predictions more
trustworthy. In this manuscript, we propose a methodology that we define as
Local Interpretable Model Agnostic Shap Explanations (LIMASE). This proposed ML
explanation technique uses Shapley values under the LIME paradigm to achieve
the following (a) explain prediction of any model by using a locally faithful
and interpretable decision tree model on which the Tree Explainer is used to
calculate the shapley values and give visually interpretable explanations. (b)
provide visually interpretable global explanations by plotting local
explanations of several data points. (c) demonstrate solution for the
submodular optimization problem. (d) also bring insight into regional
interpretation e) faster computation compared to use of kernel explainer.Comment: Under revie