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.

Optimal transmission line switching incorporating dynamic line ratings

Abstract: Real-time ampacities of overhead transmission lines vary in time with the actual operation conditions. Therefore, the security margin of power system operation can be reduced when the dynamic line rating (DLR) is considered. In this paper, the model of homogeneous dispatch with transmission switching is proposed to take into account dynamic line ratings. The realistic power flow limits of transmission lines are decided by dynamic line ratings that depend on both real-time ambient conditions and conductor properties. Optimal transmission switching (OTS) based on N-1 DC Optimal Power Flow (DCOPF) model is formulated as a mixed-integer nonlinear programming (MINLP) problem. Then the homogeneity of power flow distribution is improved by transmission switching while maintaining N-1 security. The IEEE 118-bus test system is utilized to validate the effectiveness of the proposed model.

Intelligent physical security monitoring system for power substations

Abstract: Power substations are vulnerable with respect to malicious physical attacks. Various types of sensors and technologies are developed for enhancing the physical security of critical infrastructures. In this paper, the proposed intelligent substation physical security monitoring (ISPSM) system uses a multi-agent system (MAS) with the distributed Kalman filtering (DKF) algorithm. The system architecture and computational methods are presented. In addition, industry experience is considered. Data collected from a drill and computer simulations is used for validation of the proposed ISPSM system.

Effects of HVDC connection for offshore wind turbines on AC grid protection

Abstract: When a transmission line close to points of common coupling (PCCs) encounters a short circuit (SC), the resulting PCC voltage dip triggers fast reactive power control of the corresponding grid side voltage source converter (GSVSC) to boost the PCC voltage. The control action can cause the fault distance to be overestimated by its backup relay located on the adjacent line. It is possible for a Zone 2 fault to be viewed as a Zone 3 event, resulting in mis-coordination between protective relays. Numerical simulations demonstrate the effect of HVDC offshore wind network on distance protection of an ac grid. On the other hand, HVDC reactive power adjustment can increase the stability margin of onshore ac grids, as shown by contingency simulations. With the addition of HVDC-connected offshore wind turbines, the voltage source converter based HVDC (VSC-HVDC) control can function as an element of the overall power system defense plan to prevent system instability, reducing or avoiding the implementation of the last resort remedial option - load shedding.

Machine learning

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.).

The CMS experiment at the CERN LHC

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.

Smart Grid — The New and Improved Power Grid: A Survey

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.