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Author

Pei Zhang

Other affiliations: Accenture, Iowa State University, Imperial College London  ...read more
Bio: Pei Zhang is an academic researcher from Beijing Jiaotong University. The author has contributed to research in topics: Electric power system & Probabilistic logic. The author has an hindex of 30, co-authored 139 publications receiving 4034 citations. Previous affiliations of Pei Zhang include Accenture & Iowa State University.


Papers
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Journal ArticleDOI
TL;DR: A unique vision for the future of smart transmission grids is presented in which their major features are identified and each smart transmission grid is regarded as an integrated system that functionally consists of three interactive, smart components.
Abstract: A modern power grid needs to become smarter in order to provide an affordable, reliable, and sustainable supply of electricity. For these reasons, considerable activity has been carried out in the United States and Europe to formulate and promote a vision for the development of future smart power grids. However, the majority of these activities emphasized only the distribution grid and demand side leaving the big picture of the transmission grid in the context of smart grids unclear. This paper presents a unique vision for the future of smart transmission grids in which their major features are identified. In this vision, each smart transmission grid is regarded as an integrated system that functionally consists of three interactive, smart components, i.e., smart control centers, smart transmission networks, and smart substations. The features and functions of each of the three functional components, as well as the enabling technologies to achieve these features and functions, are discussed in detail in the paper.

894 citations

Journal ArticleDOI
TL;DR: The Task Force on Understanding, Prediction, Mitigation, and Restoration of Cascading Failures, under the IEEE PES Computer Analytical Methods Subcommittee (CAMS), seeks to consolidate and review the progress of the field towards methods and tools of assessing the risk of cascading failure as mentioned in this paper.
Abstract: Cascading outages can cause large blackouts, and a variety of methods are emerging to study this challenging topic. The Task Force on Understanding, Prediction, Mitigation, and Restoration of Cascading Failures, under the IEEE PES Computer Analytical Methods Subcommittee (CAMS), seeks to consolidate and review the progress of the field towards methods and tools of assessing the risk of cascading failure. This paper discusses the challenges of cascading failure and summarizes a variety of state-of-the-art analysis and simulation methods, including analyzing observed data, and simulations relying on various probabilistic, deterministic, approximate, and heuristic approaches. Limitations to the interpretation and application of analytical results are highlighted, and directions and challenges for future developments are discussed.

403 citations

Proceedings ArticleDOI
20 Jul 2008
TL;DR: In this article, the authors define cascading failure for blackouts and give an initial review of the current understanding, industrial tools, and the challenges and emerging methods of analysis and simulation.
Abstract: Large blackouts are typically caused by cascading failure propagating through a power system by means of a variety of processes. Because of the wide range of time scales, multiple interacting processes, and the huge number of possible interactions, the simulation and analysis of cascading blackouts is extremely complicated. This paper defines cascading failure for blackouts and gives an initial review of the current understanding, industrial tools, and the challenges and emerging methods of analysis and simulation.

341 citations

Journal ArticleDOI
TL;DR: This paper proposes a vision of next-generation monitoring, analysis, and control functions for tomorrow's smart power system control centers and identifies the technology and infrastructure gaps that must be filled, and develops a roadmap to realize the proposed vision.
Abstract: This paper proposes a vision of next-generation monitoring, analysis, and control functions for tomorrow's smart power system control centers. The paper first reviews the present control center technology and then presents the vision of the next-generation monitoring, analysis, and control functions. The paper also identifies the technology and infrastructure gaps that must be filled, and develops a roadmap to realize the proposed vision. This smart control center vision is expected to be a critical part of the future smart transmission grid.

329 citations

Journal ArticleDOI
TL;DR: It is observed that the IS could provide 100% classification accuracy and very low prediction error on its decided instances and makes the IS promising for practical application since the potential unreliable results can be eliminated for use.
Abstract: A new intelligent system (IS) is developed for real-time dynamic security assessment (DSA) of power systems. Taking an ensemble learning scheme, the IS structures a series of extreme learning machines (ELMs) and generalizes the randomness of single ELMs during the training. Benefiting from the unique properties of ELM and the strategically designed decision-making rules, the IS learns and works very fast and can estimate the credibility of its DSA results, allowing an accurate and reliable pre-fault DSA mechanism: credible results can be directly adopted while incredible results are decided by alternative tools such as time-domain simulation. This makes the IS promising for practical application since the potential unreliable results can be eliminated for use. Case studies considering classification and prediction are, respectively, conducted on an IEEE 50-machine system and a dynamic equivalent system of a real-world large power grid. The efficiency, robustness, accuracy, and reliability of the IS are demonstrated. In particular, it is observed that the IS could provide 100% classification accuracy and very low prediction error on its decided instances.

192 citations


Cited by
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Journal ArticleDOI
TL;DR: In this paper, the authors survey the literature till 2011 on the enabling technologies for the Smart Grid and explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system.
Abstract: The Smart Grid, regarded as the next generation power grid, uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. In this article, we survey the literature till 2011 on the enabling technologies for the Smart Grid. We explore three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system. We also propose possible future directions in each system. colorred{Specifically, for the smart infrastructure system, we explore the smart energy subsystem, the smart information subsystem, and the smart communication subsystem.} For the smart management system, we explore various management objectives, such as improving energy efficiency, profiling demand, maximizing utility, reducing cost, and controlling emission. We also explore various management methods to achieve these objectives. For the smart protection system, we explore various failure protection mechanisms which improve the reliability of the Smart Grid, and explore the security and privacy issues in the Smart Grid.

2,433 citations

01 Jan 2012
TL;DR: This article surveys the literature till 2011 on the enabling technologies for the Smart Grid, and explores three major systems, namely the smart infrastructure system, the smart management system, and the smart protection system.

2,337 citations

Journal ArticleDOI
TL;DR: The proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households and is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting.
Abstract: As the power system is facing a transition toward a more intelligent, flexible, and interactive system with higher penetration of renewable energy generation, load forecasting, especially short-term load forecasting for individual electric customers plays an increasingly essential role in the future grid planning and operation. Other than aggregated residential load in a large scale, forecasting an electric load of a single energy user is fairly challenging due to the high volatility and uncertainty involved. In this paper, we propose a long short-term memory (LSTM) recurrent neural network-based framework, which is the latest and one of the most popular techniques of deep learning, to tackle this tricky issue. The proposed framework is tested on a publicly available set of real residential smart meter data, of which the performance is comprehensively compared to various benchmarks including the state-of-the-arts in the field of load forecasting. As a result, the proposed LSTM approach outperforms the other listed rival algorithms in the task of short-term load forecasting for individual residential households.

1,415 citations

Journal ArticleDOI
TL;DR: In this paper, the authors report the current state of the theoretical research and practical advances on this subject and provide a comprehensive view of these advances in ELM together with its future perspectives.

1,289 citations