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Explainable AI-Enhanced Detection of Cyber Attacks on AGC Systems with Varying RES Penetration
Atakan Ozturk;Mostafa Mohammadpourfard;Yang Weng;Istemihan Genc
2025 IEEE Texas Power and Energy Conference (TPEC)
Year: 2025 | Conference Paper |
This study addresses the limitations of traditional methods in detecting False Data Injection Attacks (FDIA) on Automatic Generation Control (AGC) systems, particularly due to State Estimation (SE) algorithms' tolerance for measurement errors. To overcome these limitations, six machine learning algorithms are compared across different levels of Renewable Energy Source (RES) penetration. As RES lev...Show More
Hybrid Deep Learning Model for Accurate Short-Term Electricity Price Forecasting
Arash Moradzadeh;Mostafa Mouhammadpourfard;Yang Weng;Suhas Pol;S. M. Muyeen
2025 IEEE Texas Power and Energy Conference (TPEC)
Year: 2025 | Conference Paper |
Accurate short-term electricity price forecasting (STEPF) is critical for efficient energy market operations, guiding investment strategies, resource allocation, and consumer behavior. This study introduces a hybrid deep learning approach specifically designed to improve STEPF accuracy by leveraging historical Hourly Ontario Energy Price (HOEP) data from 2017 to 2019. The model integrates advanced...Show More
Normal State and Transitions IDS (NSTIDS): Cyber-Attack Detection for Industrial Processes
Abdullah Khalili;Mostafa Mohammadpourfard;Yang Weng;Manohar Chamana;Suhas Pol
2025 IEEE Texas Power and Energy Conference (TPEC)
Year: 2025 | Conference Paper |
With the increasing reliance on modern information technologies for real-time and remote management of critical infrastructures, the risk of cyber-attacks has escalated. Industrial Intrusion Detection Systems (IIDSs) play a crucial role in identifying such attacks by monitoring the system for abnormal behavior. However, existing IIDSs have limitations in detecting anomalous states and fail to capt...Show More
Guaranteed Conversion From Static Measurements Into Dynamic Ones Based on Manifold Feature Interpolation
IEEE Transactions on Power Systems
Year: 2025 | Early Access Article |
The increasing penetration of renewable energy sources, coupled with the variability of loads such as Electric Vehicles (EVs), is leading to stability issues in power systems. Addressing this problem requires dynamic measurements. However, there may be a limited number of High-Resolution (HR) meters, such as Phasor Measurement Units (PMUs), especially in distribution grids. In contrast, there are ...Show More
Intrinsic and Extrinsic Learning Framework for Multi-Equipment Incipient Fault Detection and Classification
Lixian Shi;Yang Weng;Qiushi Cui;Xiaodong Zheng;Wenyuan Li;Jian Li
Incipient faults (IFs) are the precursors of power equipment failures. Due to the low occurrence frequency, IF data are scarce. The scarcity of IFs leads to the difficulty of identifying IFs. Traditional methods lack the ability to learn rich and meaningful representations of IF data, especially under the circumstance of limited IF data. Besides, some methods that involve transforming waveforms in...Show More
Low-Dimensional ODE Embedding to Convert Low-Resolution Meters Into “Virtual” PMUs
Power systems are integrating uncertain generations, demanding transient analyses using dynamic measurements. However, High-Resolution (HR) Phasor Measurement Units are few. The aim is to interpolate dynamic data for extensive but Low-Resolution (LR) meters. Existing interpolation methods capture data correlations but ignore the governing equations, i.e., Ordinary Differential Equations (ODEs) or ...Show More
A Safe Energy Policy Optimization Method for Multienergy Microgrid Control
Multi-energy microgrid (MEMG) control plays a crucial role in meeting diverse energy demands on the user side. Traditional methods face challenges in handling renewable energy sources (RES) uncertainty, as it is difficult to accurately model uncertainty in the numerical solution process. Recent researches address uncertainty by employing Markov decision process theory to develop deep reinforcement...Show More
Privacy-Preserving Line Outage Detection in Distribution Grids: An Efficient Approach With Uncompromised Performance
Recent advancements in research have shown the efficacy of employing sensor measurements, such as voltage and power data, in identifying line outages within distribution grids. However, these measurements inadvertently pose privacy risks to electricity customers by potentially revealing their sensitive information, such as household occupancy and economic status, to adversaries. To safeguard raw d...Show More
Constrained Branching Search for Topology Identification Stream Computing with Lightweight Implementation
IEEE Transactions on Power Systems
Year: 2024 | Early Access Article |
Accurate topological awareness is critical to the stability of low-voltage distribution networks (LVDNs). However, traditional impedance-based topology restoration assumes accuracy that is often unattainable due to impedance data inaccuracy. Given LVDN sensor quality, robustness against data quality issue is crucial. Additionally, the integration of distributed energy resources (DERs) is expanding...Show More
Fast Iterative Multi-Site Hosting Capacity Analysis for Distribution Systems with Search Space Pruning
Kishan Prudhvi Guddanti;Shiva Poudel;Monish Mukherjee;Alok Kumar Bharati;Xiaoyuan Fan;Yang Weng
2024 IEEE Power & Energy Society General Meeting (PESGM)
Year: 2024 | Conference Paper |
Interconnection studies for distributed energy re-sources (DERs) are a time-intensive process, primarily due to the necessity of solving a large number of powerflow scenarios. Hosting capacity analysis (HCA) is a time-consuming aspect of interconnection studies that is divided into single-site HCA (SHCA) and multi-site HCA (MHCA). For computation effi-ciency, big data nature, and lucidity, the ind...Show More
Learning Power Systems Waveform Incipient Patterns Through Few-Shot Meta-Learning
Lixian Shi;Qiushi Cui;Yang Weng;Yigong Zhang;Shilong Chen;Jian Li;Wenyuan Li
Incipient faults (IFs) are abnormal states before the permanent failure of power equipment. IFs are typically transient and generally do not trigger the operation of relay protection devices. This leads the difficulty in capturing IF data from waveform monitoring or recording devices. However, traditional detection methods cannot achieve satisfactory performance when faced with limited data. Besid...Show More
Determining Feasibility of DC Power Flow Using Graph Neural Networks
2024 IEEE Power & Energy Society General Meeting (PESGM)
Year: 2024 | Conference Paper |
With increasing renewable installation, the operating conditions of the transmission network exhibit high uncertainty. When taking a recourse action under such uncertainty, operators must solve a large number of power flow problems in a short period of time, which is time-consuming for large networks. However, many of these recourse actions may lead to infeasible power flow without solutions. This...Show More
Data Imputation with Uncertainty Using Stochastic Physics-Informed Learning
2024 IEEE Power & Energy Society General Meeting (PESGM)
Year: 2024 | Conference Paper |
The influx of uncertain generations and loads like Electric Vehicle (EV)s challenges the grid stability, demanding dynamic data for analysis, e.g., data from a few Measurement Units (PMUs). To handle data scarcity with uncertainty, we propose to interpolate the Low-Resolution (LR) meters. Existing methods are mainly deterministic and based on correlations. Hence, we propose to model the process as...Show More
Online Voltage Regulation With Minimum Disturbance for Distribution Grid Without System Knowledge
Distribution systems have limited observability, as they were a passive grid to consume power. Nowadays, increasing distributed energy resources turns individual customers into “generators,” and two-way power flow between customers makes the grid prone to power outages. This calls for new control methods with performance guarantees in the presence of limited system information. However, limited sy...Show More
Collaborative Solution of Distance Protection and Dual Current Control for Outgoing Lines of Inverter-Based Resources During Line-to-Line Faults
Chenxu Chao;Xiaodong Zheng;Yang Weng;Zhongping Liu;Hai Ye;Hulin Liu;Huaiyu Zhang;Yu Liu;Yulong Wang;Nengling Tai
IEEE Transactions on Smart Grid
Cited by: Papers (3)
Unlike synchronous generators (SGs), inverter-based resources (IBRs) exhibit unique fault behaviors. These fault behaviors include limited fault current amplitude, controlled fault current phase, and unsteady sequence impedance. The unique fault behaviors of IBRs, combined with the absence of zero-sequence current, can lead to the malfunction of IBR-side distance protection on outgoing lines of IB...Show More
High-Sensitivity Differential Protection for Offshore Wind Farms Collection Line With MMC-HVDC Transmission
Chenxu Chao;Xiaodong Zheng;Yang Weng;Hai Ye;Zhongping Liu;Hulin Liu;Yu Liu;Nengling Tai
When single-phase-to-ground (SPG) or phase-to-phase (PP) faults occur on collection lines of an offshore wind farm with modular multilevel converter-based high voltage direct current (MMC-HVDC) transmission, the fault current level may be low or even approach zero due to the controlled current at both ends of the collection line. As a result, the traditional current differential protection may mal...Show More
Distribution Grid Line Outage Identification With Unknown Pattern and Performance Guarantee
IEEE Transactions on Power Systems
Cited by: Papers (1)
Line outage identification in distribution grids is essential for sustainable grid operation. In this work, we propose a practical yet robust detection approach that utilizes only readily available voltage magnitudes, eliminating the need for costly phase angles or power flow data. Given the sensor data, many existing detection methods based on change-point detection require prior knowledge of out...Show More
Hd-Deep-EM: Deep Expectation Maximization for Dynamic Hidden State Recovery Using Heterogeneous Data
IEEE Transactions on Power Systems
Cited by: Papers (7)
Uncertain power generations and loads are continually integrated into the power system, causing high risks of dynamic events. To better monitor systems, advanced meters like Phasor Measurement Units (PMUs) can record high-resolution system dynamic states in real time. However, due to the high cost, the placement of PMUs is limited in a system. Thus, it's hard to obtain all the dynamic system state...Show More
Reduced Voltage-Dependency by Categorical Location Information and Distance Along Street Metric for Meter-Transformer Mapping in Distribution Systems
IEEE Transactions on Power Systems
Cited by: Papers (1)
Deep penetration of distributed energy resources (DERs) and electric vehicles (EVs) introduce benefits but may cause the overloading of service transformers in distribution networks. Such situations require real-time transformer loading information, where an accurate mapping between smart meters and distribution transformers is a prerequisite, e.g., summing the downstream smart meter consumptions....Show More
Distribution Grid Topology and Parameter Estimation Using Deep-Shallow Neural Network With Physical Consistency
IEEE Transactions on Smart Grid
Cited by: Papers (13)
To better monitor and control distribution grids, the exact knowledge of system topology and parameters is a fundamental requirement. However, topology information is usually incomplete due to limited sensors in the grid. Therefore, estimating the system parameters using partial data is a critical topic for distribution systems. Due to the high nonlinearity of unobservable system quantities and no...Show More
Enhance Unobservable Solar Generation Estimation via Constructive Generative Adversarial Networks
IEEE Transactions on Power Systems
Cited by: Papers (2)
Power distribution grids experiences proliferation of solar photovoltaics (PV) at the system edge. However, its counterpart of sparse meter deployment provides insufficient monitoring of PVs, for which the potential violations challenge the operators for energy management and stable operation. Some previous works use satellite imagery to detect distributed PVs for the easy access of data. However,...Show More
PIX-GAN: Enhance Physics-Informed Estimation via Generative Adversarial Network
2023 IEEE International Conference on Data Mining (ICDM)
Year: 2023 | Conference Paper |
Worldwide urbanization requires control systems to accommodate uncertain sources, e.g., wind and solar generations in the energy sector. This uncertainty poses significant challenges to optimal system operations. One solution is the data-driven approach, requiring great data qualities. To produce high-fidelity data, recent studies combine Generative Adversarial Networks (GANs) with physics to expl...Show More
Learn Dynamic Hosting Capacity Based on Voltage Sensitivity Analysis
2023 IEEE Power & Energy Society General Meeting (PESGM)
Year: 2023 | Conference Paper |
Cited by: Papers (1)
The extensive use of distributed energy sources (DERs) presents the substantial design, planning, and operational issues for distribution systems, thus prompting the broad adaption of methodologies for photovoltaics (PV) hosting capacity analysis (HCA). Traditional HCA methods require running power flow analysis iteratively, typically in the time-series scenario, to consider the dynamic pattern. H...Show More
Invertible Neural Network for Consistent State Estimation in Distribution Grid with Unobservability
2023 IEEE Power & Energy Society General Meeting (PESGM)
Year: 2023 | Conference Paper |
State estimation (SE) serves as the basis for monitoring and control, but the performance is challenged when both prior knowledge and observability are limited due to increasing distribution system extension and renewable penetration, typically on system edges. To solve the problem, machine learning approaches have been recently applied to approximate the mapping from measurements to system states...Show More
Sig2Vec: Dictionary Design for Incipient Faults in Distribution Systems
2023 IEEE Power & Energy Society General Meeting (PESGM)
Year: 2023 | Conference Paper |
There are extremely high demands on power equipment fault detection and diagnosis at the equipment level. At the system level, the proportion of renewable energy in the grid is increasing year by year. The morphological structure of distribution grids is also very different from the past. Meanwhile, the new power electronics-based generation equipment and loads have a great impact on the fault cha...Show More
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