arXiv subjects

Masoud H. Nazari

Publications and source records attributed to Masoud H. Nazari.

15 recordsLinked to original sources

arXiv2026

Inter-Area Oscillation Damping in Data-Center-Integrated Power Systems

This paper develops explicit dynamic models of a hyperscale data center, including its heating, ventilation, and air conditioning (HVAC) and uninterruptible power supply (UPS) subsystems, and integrates them into a small-signal stability framework to investigate the impact of data center demand response on power system inter-area oscillations. Through eigenvalue analysis and time-domain simulations, the results demonstrate that UPS-based demand response can enhance inter-area oscillation damping. In contrast, the HVAC subsystem is shown to be inherently incapable of providing effective oscillation damping due to its limited thermal response bandwidth. A gradient-based optimization algorithm is used to tune the UPS controller gain to maximize the damping ratio of the critical inter-area mode. The effectiveness of the proposed approach is validated using the IEEE 39-bus test system.

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arXiv2026

Census Tract-Level Power Outage Prediction and Sensitivity Analysis During Extreme Events

This paper develops a two-stage hurdle model for predicting power outage occurrence and severity at the census-tract level. The proposed framework is then used to assess the sensitivity of power outage to socioeconomic, demographic, and environmental factors during extreme events. Five heterogeneous data streams are integrated at the census tract level: 15-minute customer outage data, OpenMeteo hourly weather records, American Community Survey (ACS) socioeconomic indicators, Centers for Disease Control (CDC) social vulnerability indices (SVI), and Geographic Information System (GIS) derived vegetation coverage. The proposed framework is validated using a high-resolution power outage dataset covering 290 census tracts in the Detroit area over a period exceeding 14 months, with a temporal resolution of 15 minutes.

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arXiv2026

A Review of Community-Centric Power System Resilience: Strategies, Data-Driven Methods, and Techno-Legal Perspectives

This paper presents a comprehensive review of community-centric power system resilience, emphasizing the integration of community-level resilience considerations and techno-legal governance frameworks with engineering-based resilience enhancement strategies and data-driven approaches to address extreme events. Recent large-scale outages have demonstrated that power disruptions can cascade beyond electrical infrastructure and disproportionately affect vulnerable communities, critical services, and interconnected urban systems, highlighting the need for resilience approaches that integrate technical, social, and regulatory dimensions. Within this community-centric perspective, the review first summarizes state-of-the-art strategies for enhancing power system resilience, including network hardening, resource allocation, optimal scheduling, and system reconfiguration techniques, while highlighting the growing role of artificial intelligence (AI) and data-driven analytics in supporting resilience planning and operational decision-making. It then examines the interdependencies between power system resilience and community resilience, addressing socioeconomic and behavioral dimensions, cross-infrastructure interconnections, and the emerging role of resilience hubs. The paper further examines the techno-legal frameworks governing resilient energy systems by comparing the regulatory landscapes of the European Union (EU) and the United States, highlighting key similarities and distinctions that shape resilience planning and implementation. By analyzing state-of-the-art engineering-based, AI-driven, and techno-legal methods for assessing and mitigating the impacts of high-impact, low-probability (HILP) events, the review identifies critical research gaps and outlines promising directions for future investigation.

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arXiv2026

Predictive Modeling of Power Outages during Extreme Events: Integrating Weather and Socio-Economic Factors

This paper presents a novel learning based framework for predicting power outages caused by extreme events. The proposed approach targets low-probability high-consequence outage scenarios and leverages a comprehensive set of features derived from publicly available data sources. We integrate EAGLE-I outage records from 2014 to 2024 with weather, socioeconomic, infrastructure, and seasonal event data. Incorporating social and demographic indicators reveals patterns of community vulnerability and improves understanding of outage risk during extreme conditions. Four machine learning models are evaluated, including Random Forest (RF), Graph Neural Network (GNN), Adaptive Boosting (AdaBoost), and Long Short-Term Memory (LSTM). Experimental validation is performed on a large-scale dataset covering counties in the lower peninsula of Michigan. Among all models tested, the LSTM network achieves higher accuracy.

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arXiv2025

A Learning-Driven Stochastic Hybrid System Framework for Detecting Unobservable Contingencies in Power Systems

This paper presents a new learning based Stochastic Hybrid System (LSHS) framework designed for the detection and classification of contingencies in modern power systems. Unlike conventional monitoring schemes, the proposed approach is capable of identifying unobservable events that remain hidden from standard sensing infrastructures, such as undetected protection system malfunctions. The framework operates by analyzing deviations in system outputs and behaviors, which are then categorized into three groups: physical, control, and measurement contingencies based on their impact on the SHS model. The SHS model integrates both system dynamics and observer-driven state estimation error dynamics. Within this architecture, machine learning classifiers are employed to achieve rapid and accurate categorization of contingencies. The effectiveness of the method is demonstrated through simulations on the IEEE 5-bus and 30-bus systems, where results indicate substantial improvements in both detection speed and accuracy compared with existing approaches.

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arXiv2025

Secure and Decentralized Peer-to-Peer Energy Transactions using Blockchain Technology

This paper presents an optimal peer-to-peer (P2P) energy transaction mechanism leveraging decentralized blockchain technology to enable a secure and scalable retail electricity market for the increasing penetration of distributed energy resources (DERs). A decentralized bidding strategy is proposed to maximize individual profits while collectively enhancing social welfare. The market design and transaction processes are simulated using the Ethereum testnet, demonstrating the blockchain network's capability to ensure secure, transparent, and sustainable P2P energy trading among DER participants.

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arXiv2025

A Learning-based Hybrid System Approach for Detecting Contingencies in Distribution Grids with Inverter-Based Resources

This paper presents a machine-learning based Stochastic Hybrid System (SHS) modeling framework to detect contingencies in active distribution networks populated with inverter-based resources (IBRs). In particular, this framework allows detecting unobservable contingencies, which cannot be identified by normal sensing systems. First, a state-space SHS model combining conventional and IRB-based resources is introduced to formulate the dynamic interaction between continuous states of distribution networks and discrete contingency events. This model forms a randomly switching system, where parameters or network topology can change due to contingencies. We consider two contingency classes: (i) physical events, such as line outages, and (ii) measurement anomalies caused by sensor faults. Leveraging multivariate time series data derived from high-frequency sampling of system states and network outputs, a time series-based learning model is trained for real-time contingency detection and classification. Simulation studies, carried out on the IEEE 33-bus distribution system, demonstrate a 96% overall detection accuracy.

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arXiv2025

Detecting Unobservable Contingencies in Active Distribution Systems Using a Stochastic Hybrid Systems Approach

This paper introduces a distributed contingency detection algorithm for detecting unobservable contingencies in power distribution systems using stochastic hybrid system (SHS) models. We aim to tackle the challenge of limited measurement capabilities in distribution networks that restrict the ability to detect contingencies promptly. We incorporate the dynamics of distribution network connections, load feeders, PV, and battery energy storage system (BESS) hybrid resources into a fully correlated SHS model representing the distribution system as a randomly switching system between different structures during contingency occurrence. We show that jumps in the SHS model correspond to contingencies in the physical power grid. We propose a probing approach based on magnitude-modulation inputs (MaMI) to make contingencies detectable. The effectiveness of the proposed approach is validated through simulations on a sample distribution system.

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arXiv2025

Early Detection and Classification of Hidden Contingencies in Modern Power Systems: A Learning-based Stochastic Hybrid System Approach

This paper introduces a novel learning-based Stochastic Hybrid System (LSHS) approach for detecting and classifying various contingencies in modern power systems. Specifically, the proposed method is capable of identifying hidden contingencies that cannot be captured by existing sensing and monitoring systems, such as failures in protection systems or line outages in distribution networks. The LSHS approach detects contingencies by analyzing system outputs and behaviors. It then categorizes them based on their impact on the SHS model into physical, control network, and measurement contingencies. The stochastic hybrid system (SHS) model is further extended into an advanced closed-loop framework incorporating both system dynamics and observer-based state estimation error dynamics. Machine learning methods within the LSHS framework are employed for contingency classification and rapid detection. The practicality and effectiveness of the proposed methodology are validated through simulations on an enhanced IEEE-33 bus system. The results demonstrate that the LSHS framework significantly improves the accuracy and speed of contingency detection compared to state-of-the-art methods, offering a promising solution for enhancing power system contingency detection.

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arXiv2024

Deep Learning-Based Weather-Related Power Outage Prediction with Socio-Economic and Power Infrastructure Data

This paper presents a deep learning-based approach for hourly power outage probability prediction within census tracts encompassing a utility company's service territory. Two distinct deep learning models, conditional Multi-Layer Perceptron (MLP) and unconditional MLP, were developed to forecast power outage probabilities, leveraging a rich array of input features gathered from publicly available sources including weather data, weather station locations, power infrastructure maps, socio-economic and demographic statistics, and power outage records. Given a one-hour-ahead weather forecast, the models predict the power outage probability for each census tract, taking into account both the weather prediction and the location's characteristics. The deep learning models employed different loss functions to optimize prediction performance. Our experimental results underscore the significance of socio-economic factors in enhancing the accuracy of power outage predictions at the census tract level.

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arXiv2024

Decentralized P2P Trading based on Blockchain for Retail Electricity Markets

This paper introduces peer to peer (P2P) trading mechanisms based on decentralized Blockchain to facilitate retail electricity market for ever-increasing distributed energy resources (DERs). The Blockchain network supports fast and secure retail trading among DERs and facilitates a sustainable local P2P trading platform. In this decentralized Blockchain architecture no single entity or organization has control over the entire system rather all users collectively maintain control. The effectiveness of the proposed automated market design and optimization is simulated using different use case scenarios in an open source Blockchain Simulator and MATLAB. The results show the efficacy of the trading mechanism in achieving demand response through strategies such as peak load shaving, load shifting, and integration of DERs.

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arXiv2024

Distributed Anomaly Detection in Modern Power Systems: A Penalty-based Mitigation Approach

The evolving landscape of electric power networks, influenced by the integration of distributed energy resources require the development of novel power system monitoring and control architectures. This paper develops algorithm to monitor and detect anomalies of different parts of a power system that cannot be measured directly, by applying neighboring measurements and a dynamic probing technique in a distributed fashion. Additionally, the proposed method accurately assesses the severity of the anomaly. A decision-making algorithm is introduced to effectively penalize anomalous agents, ensuring vigilant oversight of the entire power system's functioning. Simulation results show the efficacy of algorithms in distributed anomaly detection and mitigation.

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arXiv2024

Contingency Detection in Modern Power Systems: A Stochastic Hybrid System Method

This paper introduces a new stochastic hybrid system (SHS) framework for contingency detection in modern power systems (MPS). The framework uses stochastic hybrid system representations in state space models to expand and facilitate capability of contingency detection. In typical microgrids (MGs), buses may contain various synchronous generators, renewable generators, controllable loads, battery systems, regular loads, etc. For development of SHS models in power systems, this paper introduces the concept of dynamic and non-dynamic buses. By converting a physical power grid into a virtual linearized state space model and representing contingencies as random switching of system structures and parameters, this paper formulates the contingency detection problem as a joint estimation problem of discrete event and continuous states in stochastic hybrid systems. This method offers unique advantages, including using common measurement signals on voltage and current synchrophasors to detect different types and locations of contingencies, avoiding expensive local direct fault measurements and detecting certain contingencies that cannot be directly measured. The method employs a small and suitably-designed probing signal to sustain the ability of persistent contingency detection. Joint estimation algorithms are presented with their proven convergence and reliability properties. Examples that use an IEEE 5-bus system demonstrate the main ideas and derivation steps. Simulation case studies on an IEEE 33-bus system are used for detecting transmission line faults and sensor interruptions.

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arXiv2024

Stochastic Hybrid System Modeling and State Estimation of Modern Power Systems under Contingency

This paper introduces a stochastic hybrid system (SHS) framework in state space model to capture sensor, communication, and system contingencies in modern power systems (MPS). Within this new framework, the paper concentrates on the development of state estimation methods and algorithms to provide reliable state estimation under randomly intermittent and noisy sensor data. MPSs employ diversified measurement devices for monitoring system operations that are subject to random measurement errors and rely on communication networks to transmit data whose channels encounter random packet loss and interruptions. The contingency and noise form two distinct and interacting stochastic processes that have a significant impact on state estimation accuracy and reliability. This paper formulates stochastic hybrid system models for MPSs, introduces coordinated observer design algorithms for state estimation, and establishes their convergence and reliability properties. A further study reveals a fundamental design tradeoff between convergence rates and steady-state error variances. Simulation studies on the IEEE 5-bus system and IEEE 33-bus system are used to illustrate the modeling methods, observer design algorithms, convergence properties, performance evaluations, and impact sensor system selections.

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arXiv2021

Revenue Analysis of Stationary and Transportable Battery Storage for Power Systems with High Penetration of Renewable Sources: A Market Participant Perspective

The power system needs more transmission capacity to deal with the increasing integration of renewable energy resources. The battery energy storage systems (BESSs) are effective to enhance the grid capacity and relieve the transmission congestion. A comprehensive revenue analysis of BESSs is critical for market participants to install such systems in a market-based power system. Taking PJM as an example, this paper carries out a thorough revenue analysis for the entire system. Highly profitable nodes in the system are revealed and characterized for BESS participants. A comparison study of stationary and transportable BESSs shows the transportable energy storage can produce higher potential revenue in the energy and regulation markets. Based on the results of the revenue analysis and characterization of commercial pricing nodes, an optimal placement algorithm is proposed for finding the profitable sites for market participants to install BESSs in the system and the algorithm is validated with real PJM market data.

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