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.

Web-based tutoring in power engineering

Abstract: This paper deals with the application of web-based technologies to power engineering education in an interactive student/mentor environment. The modern teaching/learning concepts and new technologies to support these concepts have been developed. This project incorporates web-based tools, including Internet, videoconferencing, and educational intelligent system modules for power engineering education. Different student learning styles are adapted for power engineering applications. The proposed interactive learning environment allows the mentor and students to best utilize the facilities for effective teaching and learning.

An online operational expert system with data validation capabilities (for power systems)

Abstract: The implementation and operation of an expert system in a control center environment is discussed. CRAFT (customer restoration and fault testing) is a rule-based expert system which is capable of locating the faulted section of a transmission line equipped with automatic switches and circuit breakers. In this study, the capabilities of CRAFT are extended by adding two new knowledge bases with rules to determine the status of unsupervised automatic switches and to detect the presence and location of a manual switch with an incorrect status, following a fault. An approach to appending a separate expert system computer to the SCADA system is developed. Software and hardware interfaces are designed and implemented. The completed implementation is operating online. The implementation represents a step toward practical application of expert systems for real time operation of power systems. >

Coupling market clearing with congestion management to prevent strategic bidding

Abstract: The potential for strategic bidding in deregulated electricity markets is well known. Earlier work has highlighted the role of congestion in such strategies. The authors examine a model in which a supplier can create congestion problems in a noncongestive system even when he is the not the low cost supplier of the system. If that supplier has several units located at different buses in the grid, it can profit from creating congestion under some auction mechanisms actually in use or under consideration. An integrated auction prevents profitable gaming, but requires the simultaneous handling of market clearing and system dispatch, raising concerns about the neutrality of the system operator. This paper provides a practical demonstration of the integrated auction, and compares it to other mechanisms.

Impact of LVRT capability of wind turbines on distance protection of AC grids

Abstract: When a transmission line close to a wind farm (WF) with doubly fed induction generators (DFIGs) experiences a short circuit (SC) fault, the resulting voltage dip on the WF terminal bus may trigger actions from the crowbar circuits of DFIGs These actions prevent high rotor currents that can be damaging to power electronic converters Under this condition, DFIGs have to absorb reactive power from the external power grid to provide generator excitation As a result, it will further depress the voltage at the WF terminal bus and affect the protective relay operation of transmission lines in severe situations This paper is concerned with the performance of distance relays on a power grid with WFs that are equipped with crowbar circuits Numerical simulations are conducted on IEEE 39 bus system with WF connections It is found that the fault on the terminal line of a WF is likely to induce mis-coordination of distance relays, leading to system security problems

Optimal Capacity and Placement of Microgrids for Resiliency Enhancement of Distribution Networks Under Extreme Weather Events

Abstract: When a fault or a series of faults occur in a distribution network, it is of considerable significance to feeding loads, most importantly critical loads. Although network reconfiguration by switching operations has been usually considered as a relatively low-cost method for load restoration, it alone may not able to restore critical loads under extreme weather events such as hurricanes where multiple faults can happen within the network. Under such severe circumstances, one of the complementary methods for service restoration is benefiting from existing installed microgrids. In this paper, the idea of planning future microgrids -in terms of optimal location and capacity- in combination with switching operations to restore critical loads, for the first time, is considered. To this planning-operation concept end, a graph-theoretic method is developed to find optimal switching operations coupled with a heuristic optimization method developed to determine future microgrids' location and capacity to maximize the resiliency of the network while keeping the associated cost with distributed generations (DGs) in microgrids as low as possible. Simulations results on the modified IEEE 37-node distribution network show the effectiveness of the proposed idea. Moreover, using appropriate reduction techniques, the computational efficacy of the method has also been greatly improved.

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.