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Yasuyuki Okamura - IEEE Xplore Author Profile

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Author details

Yasuyuki Okamura

Also published under: Y. Okamura

Publications

79

Citations

205

Publications by Year

19982016

Co-Authors:

Makoto AnjikiHironori AyaYoshiyuki AzumaTakatomo EnokiAkira Enokihara

Show All Co-Authors (60)

Yasuyuki Okamura

Also published under: Y. Okamura

Affiliation

Graduate School of Engineering Science

Osaka University

Toyonaka, Japan

Publication Topics

Electro-optic Modulator,
Rectangular Waveguide,
Optical Modes,
Light Wave,
Optical Fiber,
Radio Waves,
Patch Antenna,
Antenna Array,
Modulation Index,
Millimeter-wave Bands,
Operating Frequency,
Strong Electric Field

Biography

Yasuyuki Okamura received the B.Eng., M.Eng., and D.Eng. degrees in electrical engineering from Osaka University, Osaka, Japan, in 1973, 1975, and 1978, respectively, for studies on the anisotropic optical waveguides. From 1978 to 1981, he worked for Kokusai Denshin Denwa Corporation, where he was involved in developing ultralow loss optical fibers. From July 1981 to March 1996, he was with the Department of Electrical Engineering, Faculty of Engineering Science, Osaka University. From April 1996 to March 2003, he was a Professor of the Department of Optomechatronics, Faculty of Systems Engineering, Wakayama University, Wakayama, Japan. Since April 2003, he has been a Professor of the Division of Advanced Electronics and Optical Science, Gr...

Publications

79

Citations

205

Publications by Year

19982016

Co-Authors:

Makoto Anjiki
Hironori Aya
Yoshiyuki Azuma
Takatomo Enoki
Akira Enokihara

Show All Co-Authors (60)

Author's Published Works

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Showing 1-25 of 101 results

Journals (51)

Conferences (48)

Early Access Articles (2)

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Yang Weng(101)
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Ram Rajagopal(13)
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School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA(17)
Department of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA(15)
Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA, USA(9)
Key Laboratory of Control of Power Transmission and Conversion (Ministry of Education), Shanghai Jiao Tong University, Shanghai, China(7)
Department of Electrical and Computer Engineering, Arizona State University, Tempe, AZ, USA(5)
IEEE Transactions on Power Systems(22)
IEEE Transactions on Smart Grid(17)
IEEE Transactions on Power Delivery(5)
2023 IEEE Power & Energy Society General Meeting (PESGM)(4)
IEEE Open Access Journal of Power and Energy(4)
IEEE(101)
Beijing, China(4)
College Station, TX, USA(4)
Denver, CO, USA(4)
Orlando, FL, USA(4)
Seattle, WA, USA(3)
Power System(52)
Distribution Grid(34)
Distributed Energy Resources(31)
Smart Meters(27)
Power Grid(26)

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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 |

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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

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 |

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 |

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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

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 |

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 |

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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

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 |

Guaranteed Conversion From Static Measurements Into Dynamic Ones Based on Manifold Feature Interpolation

Lihao Mai;Haoran Li;Yang Weng;Erik Blasch;Xiaodong Zheng

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

Guaranteed Conversion From Static Measurements Into Dynamic Ones Based on Manifold Feature Interpolation

Lihao Mai;Haoran Li;Yang Weng;Erik Blasch;Xiaodong Zheng

IEEE Transactions on Power Systems

Year: 2025 | Early Access Article |

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

IEEE Transactions on Industry Applications

Year: 2025 | Volume: 61, Issue: 1 | Journal Article |

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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

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

IEEE Transactions on Industry Applications

Year: 2025 | Volume: 61, Issue: 1 | Journal Article |

Low-Dimensional ODE Embedding to Convert Low-Resolution Meters Into “Virtual” PMUs

Haoran Li;Zhihao Ma;Yang Weng;Haiwang Zhong;Xiaodong Zheng

IEEE Transactions on Power Systems

Year: 2025 | Volume: 40, Issue: 2 | Journal Article |

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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

Low-Dimensional ODE Embedding to Convert Low-Resolution Meters Into “Virtual” PMUs

Haoran Li;Zhihao Ma;Yang Weng;Haiwang Zhong;Xiaodong Zheng

IEEE Transactions on Power Systems

Year: 2025 | Volume: 40, Issue: 2 | Journal Article |

A Safe Energy Policy Optimization Method for Multienergy Microgrid Control

Yigong Zhang;Qiushi Cui;Lixian Shi;Yang Weng;Jian Li

IEEE Transactions on Power Systems

Year: 2025 | Volume: 40, Issue: 2 | Journal Article |

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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

A Safe Energy Policy Optimization Method for Multienergy Microgrid Control

Yigong Zhang;Qiushi Cui;Lixian Shi;Yang Weng;Jian Li

IEEE Transactions on Power Systems

Year: 2025 | Volume: 40, Issue: 2 | Journal Article |

Privacy-Preserving Line Outage Detection in Distribution Grids: An Efficient Approach With Uncompromised Performance

Chenhan Xiao;Yizheng Liao;Yang Weng

IEEE Transactions on Power Systems

Year: 2025 | Volume: 40, Issue: 1 | Journal Article |

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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

Privacy-Preserving Line Outage Detection in Distribution Grids: An Efficient Approach With Uncompromised Performance

Chenhan Xiao;Yizheng Liao;Yang Weng

IEEE Transactions on Power Systems

Year: 2025 | Volume: 40, Issue: 1 | Journal Article |

Constrained Branching Search for Topology Identification Stream Computing with Lightweight Implementation

Zhuoheng Wang;Jie Gao;Qiushi Cui;Yang Weng

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

Constrained Branching Search for Topology Identification Stream Computing with Lightweight Implementation

Zhuoheng Wang;Jie Gao;Qiushi Cui;Yang Weng

IEEE Transactions on Power Systems

Year: 2024 | Early Access Article |

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

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 |

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

IEEE Open Access Journal of Power and Energy

Year: 2024 | Volume: 11 | Journal Article |

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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

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

IEEE Open Access Journal of Power and Energy

Year: 2024 | Volume: 11 | Journal Article |

Determining Feasibility of DC Power Flow Using Graph Neural Networks

Hamad Alduaij;Baosen Zhang;Yang Weng

2024 IEEE Power & Energy Society General Meeting (PESGM)

Year: 2024 | Conference Paper |

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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

Determining Feasibility of DC Power Flow Using Graph Neural Networks

Hamad Alduaij;Baosen Zhang;Yang Weng

2024 IEEE Power & Energy Society General Meeting (PESGM)

Year: 2024 | Conference Paper |

Data Imputation with Uncertainty Using Stochastic Physics-Informed Learning

Lihao Mai;Haoran Li;Yang Weng

2024 IEEE Power & Energy Society General Meeting (PESGM)

Year: 2024 | Conference Paper |

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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

Data Imputation with Uncertainty Using Stochastic Physics-Informed Learning

Lihao Mai;Haoran Li;Yang Weng

2024 IEEE Power & Energy Society General Meeting (PESGM)

Year: 2024 | Conference Paper |

Online Voltage Regulation With Minimum Disturbance for Distribution Grid Without System Knowledge

Hamad Alduaij;Yang Weng

IEEE Open Access Journal of Power and Energy

Year: 2024 | Volume: 11 | Journal Article |

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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

Online Voltage Regulation With Minimum Disturbance for Distribution Grid Without System Knowledge

Hamad Alduaij;Yang Weng

IEEE Open Access Journal of Power and Energy

Year: 2024 | Volume: 11 | Journal Article |

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

Year: 2024 | Volume: 15, Issue: 4 | Journal Article |

Cited by: Papers (3)

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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

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

Year: 2024 | Volume: 15, Issue: 4 | Journal Article |

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

IEEE Transactions on Power Delivery

Year: 2024 | Volume: 39, Issue: 3 | Journal Article |

Cited by: Papers (3)

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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

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

IEEE Transactions on Power Delivery

Year: 2024 | Volume: 39, Issue: 3 | Journal Article |

Distribution Grid Line Outage Identification With Unknown Pattern and Performance Guarantee

Chenhan Xiao;Yizheng Liao;Yang Weng

IEEE Transactions on Power Systems

Year: 2024 | Volume: 39, Issue: 2 | Journal Article |

Cited by: Papers (1)

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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

Distribution Grid Line Outage Identification With Unknown Pattern and Performance Guarantee

Chenhan Xiao;Yizheng Liao;Yang Weng

IEEE Transactions on Power Systems

Year: 2024 | Volume: 39, Issue: 2 | Journal Article |

Hd-Deep-EM: Deep Expectation Maximization for Dynamic Hidden State Recovery Using Heterogeneous Data

Zhihao Ma;Haoran Li;Yang Weng;Erik Blasch;Xiaodong Zheng

IEEE Transactions on Power Systems

Year: 2024 | Volume: 39, Issue: 2 | Journal Article |

Cited by: Papers (7)

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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

Hd-Deep-EM: Deep Expectation Maximization for Dynamic Hidden State Recovery Using Heterogeneous Data

Zhihao Ma;Haoran Li;Yang Weng;Erik Blasch;Xiaodong Zheng

IEEE Transactions on Power Systems

Year: 2024 | Volume: 39, Issue: 2 | Journal Article |

Reduced Voltage-Dependency by Categorical Location Information and Distance Along Street Metric for Meter-Transformer Mapping in Distribution Systems

Bilal Saleem;Yang Weng;Vijay Vittal

IEEE Transactions on Power Systems

Year: 2024 | Volume: 39, Issue: 2 | Journal Article |

Cited by: Papers (1)

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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

Reduced Voltage-Dependency by Categorical Location Information and Distance Along Street Metric for Meter-Transformer Mapping in Distribution Systems

Bilal Saleem;Yang Weng;Vijay Vittal

IEEE Transactions on Power Systems

Year: 2024 | Volume: 39, Issue: 2 | Journal Article |

Distribution Grid Topology and Parameter Estimation Using Deep-Shallow Neural Network With Physical Consistency

Haoran Li;Yang Weng;Vijay Vittal;Erik Blasch

IEEE Transactions on Smart Grid

Year: 2024 | Volume: 15, Issue: 1 | Journal Article |

Cited by: Papers (13)

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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

Distribution Grid Topology and Parameter Estimation Using Deep-Shallow Neural Network With Physical Consistency

Haoran Li;Yang Weng;Vijay Vittal;Erik Blasch

IEEE Transactions on Smart Grid

Year: 2024 | Volume: 15, Issue: 1 | Journal Article |

Enhance Unobservable Solar Generation Estimation via Constructive Generative Adversarial Networks

Jingyi Yuan;Yang Weng

IEEE Transactions on Power Systems

Year: 2024 | Volume: 39, Issue: 1 | Journal Article |

Cited by: Papers (2)

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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

Enhance Unobservable Solar Generation Estimation via Constructive Generative Adversarial Networks

Jingyi Yuan;Yang Weng

IEEE Transactions on Power Systems

Year: 2024 | Volume: 39, Issue: 1 | Journal Article |

PIX-GAN: Enhance Physics-Informed Estimation via Generative Adversarial Network

Haoran Li;Yang Weng

2023 IEEE International Conference on Data Mining (ICDM)

Year: 2023 | Conference Paper |

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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

PIX-GAN: Enhance Physics-Informed Estimation via Generative Adversarial Network

Haoran Li;Yang Weng

2023 IEEE International Conference on Data Mining (ICDM)

Year: 2023 | Conference Paper |

Learn Dynamic Hosting Capacity Based on Voltage Sensitivity Analysis

Jiaqi Wu;Jingyi Yuan;Yang Weng;Raja Ayyanar

2023 IEEE Power & Energy Society General Meeting (PESGM)

Year: 2023 | Conference Paper |

Cited by: Papers (1)

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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

Learn Dynamic Hosting Capacity Based on Voltage Sensitivity Analysis

Jiaqi Wu;Jingyi Yuan;Yang Weng;Raja Ayyanar

2023 IEEE Power & Energy Society General Meeting (PESGM)

Year: 2023 | Conference Paper |

Invertible Neural Network for Consistent State Estimation in Distribution Grid with Unobservability

Jingyi Yuan;Yang Weng

2023 IEEE Power & Energy Society General Meeting (PESGM)

Year: 2023 | Conference Paper |

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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

Invertible Neural Network for Consistent State Estimation in Distribution Grid with Unobservability

Jingyi Yuan;Yang Weng

2023 IEEE Power & Energy Society General Meeting (PESGM)

Year: 2023 | Conference Paper |

Sig2Vec: Dictionary Design for Incipient Faults in Distribution Systems

Qiushi Cui;Yang Weng;Muhao Guo

2023 IEEE Power & Energy Society General Meeting (PESGM)

Year: 2023 | Conference Paper |

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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

Sig2Vec: Dictionary Design for Incipient Faults in Distribution Systems

Qiushi Cui;Yang Weng;Muhao Guo

2023 IEEE Power & Energy Society General Meeting (PESGM)

Year: 2023 | Conference Paper |

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