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IEEE Region 10 International Conference TENCON

作者： Norasage Pattanadech Phethai Nimsanong Electrical Engineering Dept King Mongkut's Institute of Technology Ladkrabang Bangkok Thailand Power System Operation and Control Section 2 Metropolitan Electricity Authority Bangkok Thailand

The aim of this paper is to describe the effect of training methods on the accuracy of PCA-KNN partial discharge (PD) classification model. This model used principal component analysis (PCA) combined with k-nearest neighbor (KNN) model, so called, PCA-KNN PD classification model for PD pattern classification. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air (CLV), surface discharge (SF), and internal discharge (IN) were experimented in the shielding room. Electromagnetic wave due to PD phenomena was detected using a log-periodic antenna and recorded employing a spectrum analyzer. 80 PD experiments in total were performed. The original independent variables for the classification model, skewness and kurtosis of each period of the captured signals, were calculated. To study the effect of training methods: two patterns for data training, odd/even and block training methods were investigated. In case of the block training method, the effect of training data number can be examined as well. Besides, noise signals were generated with the computer program and trained into the PD classification models. The peak of noise signal was set up at 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generated a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PCA-KNN PD classification model. It was found that the block data training method provided the higher accuracy PD classification compared with the odd/event data training method. The block training method with 80% training data/20% testing data gave the highest accuracy (95% correction) for PD classification without noise signal. However, this training technique provided the lowest accuracy (56.25% correction) for PD classification with the mixed noise-PD signals.

关键词： Partial discharges Training Noise Accuracy Discharges (electric) Training data Testing

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Effect of noise signals on partial discharge classification models

Effect of noise signals on partial discharge classification ...

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IEEE Region 10 International Conference TENCON

This document proposes the comparison of four statistical classification models for partial discharge (PD) classification as follows: k-nearest neighbors (KNN) model, probabilistic neural network (PNN) model, and other two statistical models using principal component analysis (PCA) for a data reduction approach combined with KNN and PNN models, so called, PCA-KNN model and PCA-PNN model. PD phenomena, corona at high voltage side in air (CHV), corona at low voltage side in air (CLV), surface discharge (SF), and internal discharge (IN) were simulated and measured in the shielding room. Electromagnetic wave due to PD phenomena was detected using a log-periodic antenna and recorded employing a spectrum analyzer. 80 experiments in total were performed for CHV, CLV, SF, and IN. The original independent variables for each classification model, skewness and kurtosis of each period of the captured signals, were calculated. Then, 60% of experimented data was used as a training data for the PD classification model. Another 40% experimented data was used to evaluate the performance of the designed PD classification models. Besides, noise signals were generated with computer program and trained into the PD classification model as well. The peak of noise signal was set up at 10%, 20% and 30% of the peak value of the PD signal. These noise signals were added with the PD signals to generate a mixed noise - PD signal. Then, the mixed noise - PD signals were used to evaluate the performance of the PD classification models. It was found that the designed KNN, PNN, PCA-KNN and PCA - PNN model can predict PD patterns without noise signal with the accuracy 100%. Noise signals with amplitude of 20% or more of peak value of PD signal have obviously influence the accuracy of PD pattern classification. The combination of PCA with KNN model can improve the ability of PD classification compared with KNN PD classification model. However, it seems that PCA provided negative effect when PCA was c

关键词： Partial discharges Noise Principal component analysis Discharges (electric) Atmospheric modeling Data models Training

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Ultra-supercritical power Unit Steam Temperature control Based on Model Predictive control

Ultra-supercritical Power Unit Steam Temperature Control Bas...

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International Conference on Sustainable Energy and Environmental Engineering (ICSEEE 2012)

作者： Wang, Guo-Liang Yan, Wei-Wu Chen, Shi-He Zhang, Xi Tian, Heng-feng Hu, Yong Shanghai Jiao Tong Univ Dept Automat Shanghai 200240 Peoples R China Minist Educ China Key Lab System Control & Informat Shanghai 200240 Peoples R China Guangdong Elect Power Res Inst Guangzhou 510600 Peoples R China

ISBN: (纸本)9783037856345

The steam temperature control is important to Ultra-supercritical (USC) unit for safe and efficient operation under load tracking. In this paper, a model predictive control (MPC) method is introduced for reheated steam temperature control of USC unit. Two inputs (i.e. damper, spray attemperator) are employed to control two outputs (i.e. primary reheater outlet temperature and finish reheater outlet temperature). Step response models of the reheater temperature are achieved using the two inputs by two outputs model. In simulation, the reheated steam temperature can be controlled around the setpoint closely in load tracking. The simulation results show the effectiveness of the proposed methods.

关键词： Ultra-supercritical unit Reheat steam temperature control Model predictive control

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The Analysis of power Swing at Boryung Generator Sites

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Journal of International Council on Electrical Engineering 2012年第3期2卷 340-343页

作者： Sung-Moo Lee Jong-Man Cho System Operation & Control Department Korea Power Exchange

The Central dispatch center tried to close 70–71–0 section CB(Circuit Breaker) to send the power produced by #3,4,5,6 generators through 345kV Asan-Boryung#1 and Boryung-Boryung CC(Combined Cycle) transmission lines at 10/16/2010. When 70–71–0 section CB was closed, the power outputs of #3,4,5,6 generators start to oscillate and the maximum power output reached up to 600MW at each generator. In this paper, we will analyze the power oscillation of #3,4,5,6 Boryung generators when 70–71–0 section CB is closed. We will simulate this phenomenon using PSS/E developed by SIEMENS. In addition, this paper will provide how to prevent power oscillation and connect 345kV Boryung 70–71 bus and 75–76 bus safely.

关键词： power oscillation The connection of section CB

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Adaptive Variable Selection Using Nonnegative Garrote with Application to Soft Sensor and Fault Detection

Adaptive Variable Selection Using Nonnegative Garrote with A...

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第23届过程控制会议

作者： Jian-guo Wang Chang-Chun Pan Shi-Shang Jang David Shan-Hill Wong Shyan-Shu Shieh Chan-Wei Wu School of Mechatronical Engineering and Automation Shanghai University and Shanghai Key Labof Power Station Automation Technology Shanghai 200072 China Department of Automation and Key Laboratory of System Control and Information Processing Ministry ofEducation of China Shanghai Jiao Tong University Shanghai 200240 China Department of Chemical Engineering National Tsing-Hua University Hsin-Chu 30013 Taiwan Department of Occupational Safety and Hygiene Chang Jung University Tainan 71101 Taiwan Energy & Air Pollution Control Section New Materials R&D Dept. China Steel Corporation Kaohsiung 81233 Taiwan

ring normal operations, response or quality variables of a process will follow a input-output relation that depend on certain key sensor variables. A soft-sensor with limited model size can be developed. When the process enters into a “faulty” state, the structure, of this model may change. Moreover, disturbance that is not reflected by sensor outputs may also be present. In this paper, we introduce a recursive soft-sensor modeling algorithm which employs a nonnegative garrote (NNG) variable selection procedure. This model can be used for both prediction, and detection of structural model change and the emergence of disturbance. The advantages of the proposed method were demonstrated by a simulation example and an industrial application to temperature prediction of a blast furnace hearth.

关键词： NNG(nonnegative garrote) Soft sensor Fault detection

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Application of back propagation neural network to fault diagnosis of direct-drive wind turbine

Application of back propagation neural network to fault diag...

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2010 2nd World Non-Grid-Connected Wind power and Energy Conference, WNWEC 2010

作者： An, Xueli Jiang, Dongxiang Li, Shaohua State Key Laboratory of Control and Simulation of Power System and Generation Equipments Dept. of Thermal Engineering Tsinghua University Beijing 100084 China

ISBN: (纸本)9781424489213

The vibration signals of wind turbines are highly nonlinear and non-stationary due to wind turbine operation conditions that are very complicated. The signals will be more complex when a fault occurs. Aiming at these problems, a fault diagnosis method for direct-drive wind turbine is presented based on back propagation neural network (BPNN). The time-domain feature parameters of vibration signals in the horizontal and vertical direction are considered in the method. Five experiments of direct-drive wind turbine with normal, wind wheel mass imbalance, wind wheel aerodynamic imbalance, yaw and blade break are carried out in laboratory scale. Through analyzing the features of five conditions, the time-domain feature parameters in horizontal and vertical direction of the vibration signal are selected as the input samples of BPNN. By training, the BPNN model can be constructed between feature parameters and fault types. The validity of the BPNN model is verified using test samples. The results indicate that the proposed method has higher diagnostic accuracy. It can used in on-line fault diagnosis of direct-drive wind turbines. © 2010 IEEE.

关键词： Failure analysis

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Trend of EMC standards for protective relays

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IEEJ Transactions on power and Energy 2010年第1期130卷 67-74页

作者： Matsumoto, Toshio Suga, Noriyoshi Seki, Masaya Energy Management and Control Dept. Fuji Electric Systems Co. Ltd 1 Fuji-machi Hino 191-8502 Japan Power Systems Protection and Control Dept. Fuchu Complex Toshiba Corporation 1 Toshiba-cho Fuchu 183-8511 Japan Power System Operation Dept Tokyo Electric Power Co. Ltd 1-1-3 Uchisaiwai-cho Chiyoda-ku Tokyo 100-8560 Japan

JEC-2501, a future JEC standard for protective relays' EMC environment is now under creation in conformance with related IECs EMC standards and existing Japanese EMC standards. This paper describes history of EMC related standards for protective relays in Japan, Structure and meanings of IECs EMC standards referred by JEC-2501, relation between EMC standards of IEC and those of Japan, and finally desirable way of creating future EMC standard in Japan. © 2010 The Institute of Electrical Engineers of Japan.

关键词： Relay protection

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Development of sequential calculating type autonomous stabilizing controller for maintaining transient stability in case of backup relay operation in load system

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IEEJ Transactions on power and Energy 2010年第2期130卷 165-174页

作者： Kobayashi, Ryoji Maekawa, Kazuo Shimomura, Kimihiko Sasaki, Takashi Kowada, Yasuyuki Maeda, Toru Power System Operations Dept. Chubu Electric Power Company Inc. 1Toshin-cho Higashi-kuNagoya 461-8680 Japan Protection and Control Dept. Mitsubishi Electric Corporation 1-1-2Wadasaki-cho Hyogo-kuKobe 652-8555 Japan

A new Autonomous Stabilizing controller (ASC) has been developed, which is based on the concept of using only locall information measured at the substation where ASC is installed. ASC prevents the power system from wide-area blackout by shedding generators optimally when a serious fault occurs in 154 kV-or-below-voltage power system and is cleared by backup relay. ASC consists of three functions, a function of detecting faults and judging of starting stability calculations (The first function), a function of calculating power system stability (The second function), and a function of selecting optimal generators to be shed (The third function). The first function composes a signal for starting stability calculations when a fault cleared by backup relay is detected. According to the starting signal composed by the first function, the second function executes stability calculations based on equal area criterion. If the result of stability calculations is instable, the third function selects a generator to be shed by considering not only the stabilizing effect but also the quickly restoration after shedding. This paper presents total algorithms applied in ASC. © 2010 The Institute of Electrical Engineers of Japan.

关键词： controllers

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Transient stability ranking technique based on simplified transient stability calculation

Transient stability ranking technique based on simplified tr...

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作者： Takeuchi, Akira Sato, Takashi Takafuji, Kouya Kokai, Yutaka Yatsu, Masahiro Takeishi, Masaru Power System Operations Dept. Chubu Electric Power Co. Inc. 1 Higashi-Shinmachi Higashi-ku Nagoya 461-8680 Japan Information and Control Systems Division Hitachi. Ltd. 5-2-1 Omika-cho Hitachi 319-1293 Japan

Over ten years have passed since the start of operation of on-line TSC (Transient Stability control) system, which is the world's first online-type stabilization system for electric power systems. This paper discusses the challenges this on-line TSC system has so far faced in its actual operations and our development of new technologies and functions to overcome these challenges. Using transient stability ranking technique based on simplified transient stability calculation, new TSC system expect two advantages;(1) Accurate response to changes in power system conditions. (2) Using differentiation of severity of stability for each contingency contribute cost reduction. © 2008 The Institute of Electrical Engineers of Japan.

关键词： Cost reduction

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PEEC method for modeling current distribution in grounding plane

PEEC method for modeling current distribution in grounding p...

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ICEMS 2005: 8th International Conference on Electrical Machines nd systems

作者： Hau, Beibei Sun, Xudong Jiang, Jianguo State Key Laboratory of Control and Simulation of Power System and Generation Equipment Dept. of Electrical Engineering Tsinghua University Beijing 100084 China

ISBN: (纸本)7506274078

In view of the impossibility of current measurement in actual grounding plane in power electronic equipment, an equivalent circuit model is set up in this paper using the partial element equivalent circuit (PEEC) method to simulate and calculate current distribution in grounding plane. The inductance parameters calculated are fairly coincident with experiment results, and some conclusions of current distribution are summarized. It is shown that the equivalent circuit model using PEEC method presented in this paper is a feasible and promising way to model and calculate current distribution in grounding plane.

关键词： Electric load distribution

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