arXiv subjects

Arslan Ahmad

Publications and source records attributed to Arslan Ahmad.

16 recordsLinked to original sources

arXiv2026

Measuring outage resilience in a distribution system with the number of outages in large events

We develop LENORI, a Large Event Number of Outages Resilience Index measuring distribution system resilience with the number of forced line outages observed in large extreme events. LENORI is calculated from standard utility outage data. The statistical accuracy of LENORI is ensured by taking the logarithm of the outage data. A related Average Large Event Number of Outages metric ALENO is also developed, and both metrics are applied to a distribution system to quantify the power grid strength relative to the extreme events stressing the grid. The metrics can be used to track resilience and quantify the contributions of various types of hazards to the overall resilience.

eess.SY↗

arXiv2026

Quantifying resilience for distribution system customers with SALEDI

The impact of routine smaller outages on distribution system customers in terms of customer minutes interrupted can be tracked using conventional reliability indices. However, the customer minutes interrupted in large blackout events are extremely variable, and this makes it difficult to quantify the customer impact of these extreme events with resilience metrics. We solve this problem with the System Average Large Event Duration Index SALEDI that logarithmically transforms the customer minutes interrupted. We explain how this new resilience metric works, compare it with alternatives, quantify its statistical accuracy, and illustrate its practical use with standard outage data from five utilities.

eess.SY↗

arXiv2026

Typical models of the distribution system restoration process

Accurate probabilistic modeling of the power system restoration process is essential for resilience planning, operational decision-making, and realistic simulation of resilience events. In this work, we develop data-driven probabilistic models of the restoration process using outage data from four distribution utilities. We decompose restoration into three components: normalized restore time progression, total restoration duration, and the time to first restore. The Beta distribution provides the best fit for restore time progression, and the Uniform distribution is a defensible, parsimonious approximation for many events. Total duration is modeled as a heteroskedastic Lognormal process that scales superlinearly with event size. The time to first restore is well described by a Gamma model for moderate and large events. Together, these models provide an end-to-end stochastic model for Monte Carlo simulation, probabilistic duration forecasting, and resilience planning that moves beyond summary statistics, enabling uncertainty-aware decision support grounded in utility data.

eess.SY↗

arXiv2026

Extracting resilience events from utility outage data based on overlapping times and locations

To study power system resilience with real data, it is necessary to group individual power outages recorded by utilities into events in which outages cluster and overlap due to extreme weather. We show how to automatically group utility outage data into resilience events based on their time and location. Each outage is represented as a cylinder in three-dimensional space, with a disk centered at the outage location in the geographic plane and a vertical extent corresponding to a limited outage duration, so that two outages overlap in time and space if their cylinders intersect. The grouping algorithm can be implemented as a graph whose nodes are the outages and whose edges represent the overlaps of outages in time and space, so that events are the connected components of the graph. Extending time-based grouping to both time and location is particularly useful when extracting events from outage data collected across a wide area, as it prevents unrelated outages from being incorrectly merged into anomalous events solely due to temporal overlap. We propose a metric to tune the parameters of the grouping algorithm to minimize anomalous events. The grouping of outages into events works with both detailed utility outage data and web-scraped EAGLE-I outage data. Results are validated against NOAA storm event records and DOE-417 reports. The automatically extracted events from utility data closely match documented major weather events.

physics.soc-ph↗

arXiv2026

Quantifying the resilience benefits of undergrounding a circuit with utility data

We leverage historical outage data to quantify the resilience benefits of undergrounding a circuit. The historical performance of the overhead circuit is compared to the performance if the circuit had been undergrounded in the past. The number of outages, customers affected, outage duration, and customer hours lost are used as metrics to quantify the benefits of undergrounding. Results show 75% and 78% reductions in customer hours lost per year for two selected circuits, as well as a significant reduction in the average number of outages and customers affected per year, highlighting the advantages of undergrounding. The benefits of investments that result in 10% faster outage restoration are also calculated by rerunning history with the faster restoration included.

eess.SY↗

arXiv2025

Quantifying Power Systems Resilience Using Statistical Analysis and Bayesian Learning

The increasing frequency and intensity of extreme weather events is significantly affecting the power grid, causing large-scale outages and impacting power system resilience. Yet limited work has been done on systematically modeling the impacts of weather parameters to quantify resilience. This study presents a framework using statistical and Bayesian learning approaches to quantitatively model the relationship between weather parameters and power system resilience metrics. By leveraging real-world publicly available outage and weather data, we identify key weather variables of wind speed, temperature, and precipitation influencing a particular region's resilience metrics. A case study of Cook County, Illinois, and Miami-Dade County, Florida, reveals that these weather parameters are critical factors in resiliency analysis and risk assessment. Additionally, we find that these weather variables have combined effects when studied jointly compared to their effects in isolation. This framework provides valuable insights for understanding how weather events affect power distribution system performance, supporting decision-makers in developing more effective strategies for risk mitigation, resource allocation, and adaptation to changing climatic conditions.

eess.SY↗

arXiv2025

Logarithmic resilience risk metrics that address the huge variations in blackout cost

Resilience risk metrics must address the customer cost of the largest blackouts of greatest impact. However, there are huge variations in blackout cost in observed distribution utility data that make it impractical to properly estimate the mean large blackout cost and the corresponding risk. These problems are caused by the heavy tail observed in the distribution of customer costs. To solve these problems, we propose resilience metrics that describe large blackout risk using the mean of the logarithm of the cost of large-cost blackouts, the slope index of the heavy tail, and the frequency of large-cost blackouts.

q-fin.RM↗

arXiv2025

Resiliency metrics quantifying emergency response in a distribution system

The electric distribution system is a cornerstone of modern life, playing a critical role in the daily activities and well-being of individuals. As the world transitions toward a decarbonized future, where even mobility relies on electricity, ensuring the resilience of the grid becomes paramount. This paper introduces novel resilience metrics designed to equip utilities and stakeholders with actionable tools to assess performance during storm events. The metrics focus on emergency storm response and the resources required to improve customer service. The practical calculation of the metrics from historical utility data is demonstrated for multiple storm events. Additionally, the metrics' improvement with added crews is estimated by "rerunning history" with faster restoration. By applying this resilience framework, utilities can enhance their restoration strategies and unlock potential cost savings, benefiting both providers and customers in an era of heightened energy dependency.

eess.SY↗

arXiv2025

The improvement in transmission resilience metrics from reduced outages or faster restoration can be calculated by rerunning historical outage data

Transmission utilities routinely collect detailed outage data, including resilience events in which outages bunch up due to weather. The resilience events and their resilience metrics can readily be extracted from this historical outage data. Improvements such as grid hardening or investments in restoration lead to reduced outages or faster restoration. We show how to rerun this history with the effects of the reduced outages or faster restoration included to find the resulting improvement in resilience metrics, thus quantifying the benefits of these investments. This is demonstrated with case studies for specific events (a derecho and a hurricane), and all large events or large thunderstorms in the Midwest USA. Instead of predicting future extreme events with models, which is very challenging, the historical rerun readily quantifies the benefits that a resilience investment would have had if it had been made in the past. The historical rerun is particularly vivid in making the case for resilience investments to stakeholders because it quantifies the benefits for events actually experienced by those stakeholders, rather than for future events predicted with uncertainty.

physics.soc-ph↗

arXiv2024

Quantifying distribution system resilience from utility data: large event risk and benefits of investments

We focus on large blackouts in electric distribution systems caused by extreme winds. Such events have a large cost and impact on customers. To quantify resilience to these events, we formulate large event risk and show how to calculate it from the historical outage data routinely collected by utilities' outage management systems. Risk is defined using an event cost exceedance curve. The tail of this curve and the large event risk is described by the probability of a large cost event and the slope magnitude of the tail on a log-log plot. Resilience can be improved by planned investments to upgrade system components or speed up restoration. The benefits that these investments would have had if they had been made in the past can be quantified by "rerunning history" with the effects of the investment included, and then recalculating the large event risk to find the improvement in resilience. An example using utility data shows a 12% and 22% reduction in the probability of a large cost event due to 10% wind hardening and 10% faster restoration respectively. This new data-driven approach to quantify resilience and resilience investments is realistic and much easier to apply than complicated approaches based on modeling all the phases of resilience. Moreover, an appeal to improvements to past lived experience may well be persuasive to customers and regulators in making the case for resilience investments.

eess.SY↗

arXiv2023

Towards using utility data to quantify how investments would have increased the wind resilience of distribution systems

We quantify resilience with metrics extracted from the historical outage data that is routinely recorded by many distribution utilities. The outage data is coordinated with wind data to relate average outage rates in an area to wind speed measured at a nearby weather station. A past investment in wind hardening would have reduced the outage rates, and the effect of this on metrics can be calculated by sampling a reduced number of the historical outages and recomputing the metrics. This quantifies the impact that the hardening would have had on customers. This is a tangible way to relate an investment in wind resilience to the benefits it would have had on the lived experience of customers that could help make the case for the investment to the public and regulators. We also quantify the impact of earlier or faster restoration on customer metrics and compare this to the impact of investment in hardening. Overall this is a new and straightforward approach to quantify resilience and justify resilience investments to stakeholders that is directly driven by utility data. The approach driven by data avoids complicated models or modeling assumptions.

eess.SP↗

arXiv2022

Supervised Learning based QoE Prediction of Video Streaming in Future Networks: A Tutorial with Comparative Study

The Quality of Experience (QoE) based service management remains key for successful provisioning of multimedia services in next-generation networks such as 5G/6G, which requires proper tools for quality monitoring, prediction and resource management where machine learning (ML) can play a crucial role. In this paper, we provide a tutorial on the development and deployment of the QoE measurement and prediction solutions for video streaming services based on supervised learning ML models. Firstly, we provide a detailed pipeline for developing and deploying supervised learning-based video streaming QoE prediction models which covers several stages including data collection, feature engineering, model optimization and training, testing and prediction and evaluation. Secondly, we discuss the deployment of the ML model for the QoE prediction/measurement in the next generation networks (5G/6G) using network enabling technologies such as Software-Defined Networking (SDN), Network Function Virtualization (NFV) and Mobile Edge Computing (MEC) by proposing reference architecture. Thirdly, we present a comparative study of the state-of-the-art supervised learning ML models for QoE prediction of video streaming applications based on multiple performance metrics.

cs.NI↗

arXiv2019

QoE Management of Multimedia Streaming Services in Future Networks: A Tutorial and Survey

We provide in this paper a tutorial and a comprehensive survey of QoE management solutions in current and future networks. We start with a high level description of QoE management for multimedia services, which integrates QoE modelling, monitoring, and optimization. This followed by a discussion of HTTP Adaptive Streaming (HAS) solutions as the dominant technique for streaming videos over the best-effort Internet. We then summarize the key elements in SDN/NFV along with an overview of ongoing research projects, standardization activities and use cases related to SDN, NFV, and other emerging applications. We provide a survey of the state-of-the-art of QoE management techniques categorized into three different groups: a) QoE-aware/driven strategies using SDN and/or NFV; b) QoE-aware/driven approaches for adaptive streaming over emerging architectures such as multi-access edge computing, cloud/fog computing, and information-centric networking; and c) extended QoE management approaches in new domains such as immersive augmented and virtual reality, mulsemedia and video gaming applications. Based on the review, we present a list of identified future QoE management challenges regarding emerging multimedia applications, network management and orchestration, network slicing and collaborative service management in softwarized networks. Finally, we provide a discussion on future research directions with a focus on emerging research areas in QoE management, such as QoE-oriented business models, QoE-based big data strategies, and scalability issues in QoE optimization.

cs.NI↗

arXiv2019

5G network slicing using SDN and NFV- A survey of taxonomy, architectures and future challenges

In this paper, we provide a comprehensive review and updated solutions related to 5G network slicing using SDN and NFV. Firstly, we present 5G service quality and business requirements followed by a description of 5G network softwarization and slicing paradigms including essential concepts, history and different use cases. Secondly, we provide a tutorial of 5G network slicing technology enablers including SDN, NFV, MEC, cloud/Fog computing, network hypervisors, virtual machines & containers. Thidly, we comprehensively survey different industrial initiatives and projects that are pushing forward the adoption of SDN and NFV in accelerating 5G network slicing. A comparison of various 5G architectural approaches in terms of practical implementations, technology adoptions and deployment strategies is presented. Moreover, we provide a discussion on various open source orchestrators and proof of concepts representing industrial contribution. The work also investigates the standardization efforts in 5G networks regarding network slicing and softwarization. Additionally, the article presents the management and orchestration of network slices in a single domain followed by a comprehensive survey of management and orchestration approaches in 5G network slicing across multiple domains while supporting multiple tenants. Furthermore, we highlight the future challenges and research directions regarding network softwarization and slicing using SDN and NFV in 5G networks.

cs.NI↗

arXiv2017

A Survey of Active Attacks on Wireless Sensor Networks and their Countermeasures

Lately, Wireless Sensor Networks (WSNs) have become an emerging technology and can be utilized in some crucial circumstances like battlegrounds, commercial applications, habitat observing, buildings, smart homes, traffic surveillance and other different places. One of the foremost difficulties that WSN faces nowadays is protection from serious attacks. While organizing the sensor nodes in an abandoned environment makes network systems helpless against an assortment of strong assaults, intrinsic memory and power restrictions of sensor nodes make the traditional security arrangements impractical. The sensing knowledge combined with the wireless communication and processing power makes it lucrative for being abused. The wireless sensor network technology also obtains a big variety of security intimidations. This paper describes four basic security threats and many active attacks on WSN with their possible countermeasures proposed by different research scholars.

cs.NI↗

arXiv2017

Analysis of challenges faced by WebRTC videoconferencing and a remedial architecture

Lately, World Wide Web came up with an evolution in the niche of videoconference applications. Latest technologies give browsers a capacity to initiate real-time communications. WebRTC is one of the free and open source projects that aim at providing the users freedom to enjoy real-time communications, and it does so by following and redefining the standards. However, WebRTC is still a new project and it lacks some high-end videoconferencing features such as media mixing, recording of a session and different network conditions adaptation. This paper is an attempt at analyzing the shortcomings and challenges faced by WebRTC and proposing a Multipoint Control Unit or traditional communications entity based architecture as a solution.

cs.MM↗