Jian Xie

University of Central Florida

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Wei Sun

University of Central Florida

Xiaoru Wang

Wuhan National Laboratory for Optoelectronics

Zixiao Ma

Iowa State University

I. Fernandez

University of Central Florida

Zhaoyu Wang

Iowa State University

Airin Rahman

University of Central Florida

Yuan Yue

Lanzhou University

Weili Bai

Fifth Hospital of Shijiazhuang

Di Shi

New Mexico State University

Hongyu Wu

Kansas State University

Cooperative Institutions

Southwest Jiaotong University

University of Central Florida

National Renewable Energy Laboratory

Amazon (United States)

Tsinghua University

Iowa State University

Xi'an Jiaotong University

University of Tennessee at Knoxville

University of Illinois Urbana-Champaign

Global Energy Interconnection Research Institute North America

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Data-Driven Based Method for Power System Time-Varying Composite Load Modeling

arXiv (Cornell University) (2019)

Jian Xie Zixiao Ma Zhaoyu Wang Fankun Bu

Fast and accurate load parameters identification has great impact on the power systems operation and stability analysis. This paper proposes a novel transfer reinforcement learning based method to identify composite ZIP and induction motor (IM) load models. An imitation learning process is firstly introduced to improve the exploitation and exploration process. The transfer learning process is then employed to overcome the challenge of time consuming optimization when dealing with new tasks. An Associative memory is designed to realize demension reduction and knowledge learning and transfer between different optimization tasks. Agents can exploit the optimal knowledge from source tasks to accelerate search rate and improve solution accuracy. The greedy rule is adopted to balance global search and local search. Convergency analysis shows that the proposed method can converge to the global optimal solution with probability 1. The performance of the proposed ITQ appraoch have been validated on 68-bus system. Simulation results in multi-test cases verify that the proposed method has superior convergence rate and stability.

Identification

10.48550/arxiv.1905.02688

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SimTDE: Simple Transformer Distillation for Sentence Embeddings

Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval (2023)

Jian Xie Xin He Jiyang Wang Zimeng Qiu Ali Kebarighotbi

In this paper we introduce SimTDE, a simple knowledge distillation framework to compress sentence embeddings transformer models with minimal performance loss and significant size and latency reduction. SimTDE effectively distills large and small transformers via a compact token embedding block and a shallow encoding block, connected with a projection layer, relaxing dimension match requirement. SimTDE simplifies distillation loss to focus only on token embedding and sentence embedding. We evaluate on standard semantic textual similarity (STS) tasks and entity resolution (ER) tasks. It achieves 99.94% of the state-of-the-art (SOTA) SimCSE-Bert-Base performance with 3 times size reduction and 96.99% SOTA performance with 12 times size reduction on STS tasks. It also achieves 99.57% of teacher's performance on multi-lingual ER data with a tiny transformer student model of 1.4M parameters and 5.7MB size. Moreover, compared to other distilled transformers SimTDE is 2 times faster at inference given similar size and still 1.17 times faster than a model 33% smaller (e.g. MiniLM). The easy-to-adopt framework, strong accuracy and low latency of SimTDE can widely enable runtime deployment of SOTA sentence embeddings.

10.1145/3539618.3592063

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Citations (1)

Stability analysis of 3D square trapdoors in cohesive soil

Taylor & Francis eBooks (2007)

Abdul Rahim Michael Gunn Jian Xie

Square (algebra)

10.1201/noe0415440271.ch59

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A Transfer and Deep Learning-Based Method for Online Frequency Stability Assessment and Control

IEEE Access (2021)

Jian Xie Wei Sun

Fast and accurate prediction and control of power system dynamic frequency after disturbance is essential to enhance power system stability. Machine learning methods have great potential in harnessing data for online application with accurate predictions. This paper proposes a two-stage novel transfer and deep learning-based method to predict power system dynamic frequency after disturbance and provide optimal event-based load shedding strategy to maintain system frequency. The proposed deep learning model combines convolutional neural network (CNN) and long short-term memory (LSTM) network to harness both spatial and temporal measurements in the input data, through a four-dimensional (4-D) tensor input construction process including, 1) capture system network topology information and critical measurements from different time intervals; 2) compute a multi-dimensional electric distance matrix and reduce to a 2-D plane which can describe the system nodal distribution; 3) construct 3-D tensors based on state variables at different sample times; and 4) integrate into 4-D tensor inputs. Moreover, a transfer learning process is employed to overcome the challenge of insufficient data and operating condition changes in real power systems for new prediction tasks. Simulation results in IEEE 118-bus system verify that the CNN-LSTM method not only greatly improves the timeliness of online frequency control, but also presents good accuracy and effectiveness. Test cases in the New England 39-bus system and the South Carolina 500-bus system validate that the transfer learning process can provide accurate results even with insufficient training data.

Transfer of learning

10.1109/access.2021.3082001

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Citations (39)

Simplified Federated Filtering Algorithm With Different States in Local Filters

Journal of Dynamic Systems Measurement and Control (2010)

Guoliang Liu Jian Xie Shizuo Yan Wenyi Qiang

In this paper, to reduce the computation load of federated Kalman filters, a simplified federated filtering algorithm for integrated navigation systems is presented. It has been known that the per-cycle computation load grows roughly in proportion to the number of states and measurements for a single centralized Kalman filter. Hence, the states that have poor estimation accuracies are removed from local filters, so that the per-cycle computation load is reduced accordingly. Local filters and master filter of the federated Kalman filter may have different states, so the transition matrices are required to combine the outputs from the local filters and the master filter properly and to reset the global solution into the local filters and the master filter correctly. An experiment demonstrates that the proposed algorithm effectively reduces the computation load, compared with the standard federated Kalman filtering algorithm.

Reset (finance)

10.1115/1.4002071

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A Hybrid Energy and Mode Decomposition-Based Method for Evaluating Generators Damping in Multi-Machine Power Systems

IEEE Access (2021)

Jian Xie Wei Sun Kai Sun Xiaoru Wang

The increasing number of low-frequency oscillation events has posed significant challenges to power system stability. It is imperative to accurately identify and properly control generators with poor or negative damping contribution for better oscillation damping. This paper proposes a hybrid energy and mode decomposition-based method for evaluating generators damping in multi-machine power systems. It is derived that the aggregate electric torque can be modeled as the superposition of electric torques in different modes. In each oscillation mode, the electric torque can be decomposed into the damping torque and the synchronizing torque. Moreover, a novel oscillation energy function is developed to evaluate generators' damping effect in each oscillation mode. The result proves that in each oscillation mode, generators with attenuated oscillation energies have negative damping effects. Simulation results in single and multinegative damping generators cases, as well as the comparison with other methods, demonstrate the effectiveness and robustness of the proposed method for damping evaluation.

Damping torque

Oscillation (cell signaling)

Synchronizing

Robustness

Magnetic damping

Damping ratio

Low-frequency oscillation

10.1109/access.2021.3063610

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A Review of Machine Learning Applications in Power System Resilience

2021 IEEE Power & Energy Society General Meeting (PESGM) (2020)

Jian Xie I. Fernandez Wei Sun

The integration of power electronics enabled devices and the high penetration of renewable energy drastically increase the complexity of power system operation and control. Power systems are still vulnerable to large-scale blackouts caused by extreme natural events or man-made attacks. With the recent development in artificial intelligence technique, machine learning has shown a processing ability in computational, perceptual and cognitive intelligence. It is an urgent challenge to integrate the advanced machine learning technology and large amount of real-time data from wide area measurement systems and intelligent electronic devices, in order to effectively enhance power system resilience and ensure the reliable and secure operation of power systems. Therefore, this paper aims to systematically review the existing application of machine learning methods on power system resilience enhancement, to expand the interest of researchers and scholars in this topic, and to jointly promote the application of artificial intelligence in the field of power systems.

Resilience

10.1109/pesgm41954.2020.9282137

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Citations (60)

Challenges and Opportunities in Cyber-Physical Security of Highly DER-Penetrated Power Systems

2021 IEEE Power & Energy Society General Meeting (PESGM) (2022)

Airin Rahman Xue Gao Jian Xie I. Fernandez Hamed Haggi

With the high penetration of distributed energy resources (DERs), active distribution systems requires advanced information technology (IT) and operational technology (OT) systems to provide data acquisition and communication, real-time monitoring and control, and distributed operation of grid-edge devices. The new development and advancement from grid modernization efforts has introduced new vulnerabilities and risks to the integrated cyber-physical system (CPS). The main focus of this paper is to review the advanced modeling of CPS and distributed operation and control, reveal the new vulnerabilities of distributed architecture in IT and OT systems, summarize the sophisticated and coordinated cyber-physical attacks, investigate new risks and attacks, and introduce the co-simulation platform for vulnerability assessment and impact analysis. The outcome of this paper is to raise the awareness of both challenges and opportunities in the secure and resilient operation of the modern cyber-physical power systems with high DER penetration.

Cyber-physical system

Vulnerability

Physical security

10.1109/pesgm48719.2022.9917144

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Citations (8)

A Comparison of Machine Learning Methods for Frequency Nadir Estimation in Power Systems

Xuebo Liu Jian Xie Xin Fang Haoyu Yuan Bin Wang

An increasing penetration level of inverter-based renewable energy resources changes the inertia of power systems, posing challenges for maintaining the desired system frequency stability. An accurate frequency nadir estimation is crucial for power system operators to prepare preventive actions against large frequency excursions. In this paper, five machine learning methods—linear regression, gradient boosting, support vector regression, an artificial neural network, and XGBoost—are applied to two different datasets, i.e., 1) the unit generation dataset and 2) the system total inertia and headroom dataset, for the prediction of the frequency nadir. The training and testing datasets are generated through extensive generation scheduling simulations using Multi-timescale Integrated Dynamic and Scheduling (MI-DAS) toolbox on the Western Electricity Coordinating Council 240-bus system with high renewable penetration levels. Numerical results show that all five machine learning methods perform well in predicting the nadir frequency of the system. Among them, the gradient boosting and the XGBoost are clear winners yielding the best prediction accuracy in terms of four evaluation metrics.

Boosting

Nadir

Gradient boosting

10.1109/kpec54747.2022.9814724

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Citations (8)

WITHDRAWN: Bifurcations of symmetric fixed points in a three-degree-of-freedom vibro-impact system

Communications in Nonlinear Science and Numerical Simulation (2007)

Yuan Yue Jian Xie

Degree (music)

10.1016/j.cnsns.2007.06.005

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