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Chen-Ching Liu

Bio: Chen-Ching Liu is an academic researcher from Virginia Tech. The author has contributed to research in topics: Electric power system & Electricity market. The author has an hindex of 57, co-authored 269 publications receiving 12126 citations. Previous affiliations of Chen-Ching Liu include Washington State University & Purdue University.


Papers
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Journal ArticleDOI
TL;DR: A systematic method to calibrate the model of a synchronous-generator-interfaced DG, namely, steady-state parameters and time constants, which are estimated in two successive stages using multiple event data is presented.

7 citations

Proceedings ArticleDOI
01 Oct 2014
TL;DR: In this paper, the authors focused on the physical security of power substations and proposed a methodology consisting of an intrusion classification method, dynamic and static threat assessment methods, and decision support for the system operator's response in a contingency scenario.
Abstract: Power grid facilities can be vulnerable with respect to malicious physical attacks. Preventive and/or remedial actions are essential to maintain the system integrity. This paper is focused on the physical security of power substations. The proposed methodology consists of an intrusion classification method, dynamic and static threat assessment methods, and decision support for the system operator's response in a contingency scenario. An important feature of the proposed method is to build the linkage between substation physical security monitoring and power system analysis. In addition, industry experience from two Transmission System Operators (TSOs) is incorporated in the development of the methodology.

7 citations

Posted Content
TL;DR: In this paper, a two-layer, four-level distributed control method for networked microgrid (NMG) systems, taking into account the proprietary nature of microgrid owners, is presented.
Abstract: This paper presents a two-layer, four-level distributed control method for networked microgrid (NMG) systems, taking into account the proprietary nature of microgrid (MG) owners. The proposed control architecture consists of a MG-control layer and a NMG-control layer. In the MG layer, the primary and distrib-uted secondary control realize accurate power sharing among distributed generators (DGs) and the frequency/voltage reference following within each MG. In the NMG layer, the tertiary control enables regulation of the power flowing through the point of common coupling (PCC) of each MG in a decentralized manner. Furthermore, the distributed quaternary control restores system frequency and critical bus voltage to their nominal values and ensures accurate power sharing among MGs. A small-signal dynamic model is developed to evaluate dynamic performance of NMG systems with the proposed control method. Time-domain simulations as well as experiments on NMG test systems are performed to validate the effectiveness of the proposed method.

7 citations

OtherDOI
30 Sep 2016
TL;DR: Smart grid is an important application area for artificial intelligence (AI) and computational intelligence (CI), as solutions to complex problems in power system engineering and electric energy markets depend on logic reasoning, heuristic search, perception, and the abilities to handle uncertainties.
Abstract: Smart grid is an important application area for artificial intelligence (AI) and computational intelligence (CI), as solutions to complex problems in power system engineering and electric energy markets depend on logic reasoning, heuristic search, perception, and the abilities to handle uncertainties. AI is concerned with decision‐making capabilities such as knowledge representation, search methods, inference techniques, heuristic reasoning, and machine learning. CI techniques include expert systems, fuzzy logic, genetic algorithms (GAs), and artificial neural networks (ANNs). CI can further involve adaptive mechanisms for intelligent behaviors in complex environments, such as the ability to adapt, generalize, abstract, discover, and associate. AI and CI have been applied to address the challenges that arise from the increasing complexity and highly nonlinear nature of electric power systems. AI and CI techniques provide effective solutions to the design of nonlinear, adaptive, and optimal controllers for generator excitation systems, High‐Voltage Direct Current (HVDC), and Flexible Alternating Current Transmission System (FACTS) devices.

7 citations

Journal ArticleDOI
TL;DR: In this article, the authors define the energy margin as the minimum distance in potential energy space between the first and second-kick trajectories, where the latter is chosen to be marginally stable.
Abstract: This paper introduces a new definition and computation method for the energy margin as a means to quantify the degree of stability of a dynamic power system model. The method is based on detailed device modeling that spans both transient and midterm time scales and includes effects of under-load tap-changer (ULTC) actions. The energy margin is defined as the minimum distance in potential energy space between the first- and second-kick trajectories, where the latter is chosen to be marginally stable. A generalized second-kick design is proposed. This consists of a combination of a load-step first kick and a three-phase fault second kick, applied at a time instant when the system is "closest" to the boundary of the stability region. The value of the energy margin is tracked through various tap-changer configurations. Thus, situations where ULTC actions are detrimental to stability can be uncovered, and "optimal" tap positions can be found. The concept is first illustrated on a single-machine infinite bus (SMIB); then, results are shown for a ten-bus voltage stability test system and for a modified version of the standard IEEJ 60-Hz test system, where some loads are fed through step-down ULTCs.

6 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Journal ArticleDOI
TL;DR: The Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC) at CERN as mentioned in this paper was designed to study proton-proton (and lead-lead) collisions at a centre-of-mass energy of 14 TeV (5.5 TeV nucleon-nucleon) and at luminosities up to 10(34)cm(-2)s(-1)
Abstract: The Compact Muon Solenoid (CMS) detector is described. The detector operates at the Large Hadron Collider (LHC) at CERN. It was conceived to study proton-proton (and lead-lead) collisions at a centre-of-mass energy of 14 TeV (5.5 TeV nucleon-nucleon) and at luminosities up to 10(34)cm(-2)s(-1) (10(27)cm(-2)s(-1)). At the core of the CMS detector sits a high-magnetic-field and large-bore superconducting solenoid surrounding an all-silicon pixel and strip tracker, a lead-tungstate scintillating-crystals electromagnetic calorimeter, and a brass-scintillator sampling hadron calorimeter. The iron yoke of the flux-return is instrumented with four stations of muon detectors covering most of the 4 pi solid angle. Forward sampling calorimeters extend the pseudo-rapidity coverage to high values (vertical bar eta vertical bar <= 5) assuring very good hermeticity. The overall dimensions of the CMS detector are a length of 21.6 m, a diameter of 14.6 m and a total weight of 12500 t.

5,193 citations

01 Jan 2003

3,093 citations

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