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Electricity price forecasting using neural networks trained with entropic criterion

7th International Symposium on Neural Networks, ISNN 2010

作者： Zhang, Jianhua Wang, Jingyue Wang, Rui Hou, Guolian Beijing Key Laboratory of Industrial Process Measurement and Control New Technology and System North China Electric Power University Beijing 102206 China

ISBN: (纸本)9783642129896

Accurate electricity price forecasting is critical to market participants in wholesale electricity markets. The problem becomes more complex because the acquired data series are non-linear and non-Gaussian. In this paper, Multi Layer Perceptrons (MLP) trained with minimizing error entropy (MEE) algorithm is utilized to forecast electricity price. Compared with the conventional MLP with mean square error (MSE) criterion, the proposed approach can achieve better performance in simulated examples. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Entropy

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Design and realization of a remote monitoring and diagnosis and prediction system for large rotating machinery

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中国高等学校学术文摘·机械工程 2010年第2期5卷 165-170页

作者： Shaohong WANG Tao CHEN Jianghong SUN School of Mechanical Vehicular Engineering Beijing Institute of Technology Beijing 100081 China Beijing Key Laboratory on Measurement and Control of Mechanical and Electrical System Beijing Information Science & Technology University Beijing 100192 China

Traditional on-site fault diagnosis means cannot meet the needs of large rotating machinery for its performance and complexity. Remote monitoring and diagnosis technology is a new fault diagnosis mode combining computer technology, communication technology, and fault diagnosis technology. The designed remote monitoring and diagnosis and prediction system for large rotating machinery integrates the distributed resources in different places and breaks through shortcomings as the offline and decentralized information. The system can make further implementation of equipment prediction technology research based on condition monitoring and fault diagnosis, provide on-site analysis results, and carry out online actual verification of the results. The system monitors real-time condition of the equipment and achieves early fault prediction with great significance to guarantee safe operation, saves maintenance costs, and improves utilization and management of the equipment.

关键词： large rotating machinery remote monitoring fault diagnosis prediction system

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PID neural network control of a membrane structure inflation system

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机械工程前沿 2010年第4期5卷 418-422页

作者： Qiushuang LIU Xiaoli XU School of Mechanical & Vehicular Engineering Beijing Institute of Technology Beijing 10081China Beijing Key Laboratory (Measurement and Control of Mechanical and Electrical System) Beijing Information Science & Technology University Beijing 100081China

Because it is difficult for the traditional PID algorithm for nonlinear time-variant control objects to obtain satisfactory control results, this paper studies a neuron PID controller. The neuron PID controller makes use of neuron self-learning ability, complies with certain optimum indicators, and automatically adjusts the parameters of the PID controller and makes them adapt to changes in the controlled object and the input reference signals. The PID controller is used to control a nonlinear time-variant membrane structure inflation system. Results show that the neural network PID controller can adapt to the changes in system structure parameters and fast track the changes in the input signal with high control precision.

关键词： PID neural network membrane structure

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An information theoretic approach for design MIMO networked control systems

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7th International Symposium on Neural Networks, ISNN 2010

作者： Zhang, Jianhua Wang, Hong Beijing Key Laboratory of Industrial Process Measurement and Control New Technology and System North China Electric Power University Beijing 102206 China University of Manchester PO Box 88 Manchester M60 1QD United Kingdom

ISBN: (纸本)9783642129896

This paper presents a control system design strategy for multi-input and multi-output (MIMO) networked control systems with random delays. The performance index of the control systems is constructed by entropies of tracking error which can be estimated by Modha's density estimator via multi-layer perceptron (MLP). Finally, a simulation example is given to illustrate the efficiency and feasibility of the proposed approach. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Networked control systems

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Research on data-based nonlinear fault prediction theory and methods in multi-transform domains for electromechanical equipment

Research on data-based nonlinear fault prediction theory and...

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23rd International Congress on Condition Monitoring and Diagnostic Engineering Management, COMADEM 2010

作者： Wang, Shaohong Chen, Tao Xu, Xiaoli School of Mechanical and Vehicular Engineering Beijing Institute of Technology Beijing China Beijing Key Laboratory on Measurement and Control of Mechanical and Electrical System Beijing Information Science and Technology University Beijing China

ISBN: (纸本)9784883254194

Most faults of large-scale electromechanical equipment are trendy ones, often have long course characteristics. As the fault information is usually lost in non-fault information of condition changes, the traditional methods are difficult to predict it effectively. To solve this problem, field data-based fault prediction theory and methods are studied primary for large rotating electromechanical equipment, and new nonlinear prediction way in multi-transform domains is proposed to achieve long course prediction for the equipment under variable operating conditions. In the new way fault feature extraction method of nonlinear dimensionality reduction is investigated to extract fault and potential fault sensitive information and to separate faulty development changes from non-fault energy changes. The research is important for large electromechanical equipment to achieve early fault prediction, guarantee safe operation, save maintenance costs, improve utilization and implement scientific maintenance.

关键词： Feature extraction

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Bidding Strategy Based on Adaptive Particle Swarm Optimization for Electricity Market

Bidding Strategy Based on Adaptive Particle Swarm Optimizati...

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The 8th World Congress on Intelligent control and Automation(第八届智能控制与自动化世界大会 WCICA 2010)

作者： Jianhuan Zhang Yingxin Wang Rui Wang Guolian Hou Beijing Key Laboratory of Industrial Process Measurement & Control New Technology and System North China Electric Power University Beijing China

In an open electricity market, generation companies (GENCO) have to optimally bid to gain more profits with incomplete information of other competing generators. In this structure, market participants must develop their bids in order to maximize their profits. Building optimal bidding strategies for GENCO could need to evaluate some market parameters such as forecasting market-clearing price (MCP), non-convex production cost function and forecasting load. A new framework to build bidding strategies for GENCO in an electricity market is presented in this paper. A normal probability distribution function (PDF) is used to describe the bidding behaviors of other competing generators. Bidding strategy of a generator for each trading period in a day-ahead market is solved by a new adaptive particle swarm optimization APSO). APSO can dynamically follow the frequently changing market demand and supply in each trading interval. A numerical example serves to illustrate the essential features of the approach and the results are compared with the solutions by other PSO algorithms.

关键词： Optimization Particle swarm optimization Generators Power generation Electricity supply industry Companies Markov processes

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Forecasting next-day electricity prices with Hidden Markov Models

Forecasting next-day electricity prices with Hidden Markov M...

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IEEE Conference on industrial Electronics and Applications (ICIEA)

作者： Jianhua Zhang Jingyue Wang Rui Wang Guolian Hou Beijing Key Laboratory of Industrial Process Measurement & Control New Technology and System North China Electric Power University Beijing China

Next-day electricity prices forecasting is essential to consumers and producers. Due to the stochastic characteristics of the electricity price time series, a novel model of electricity price forecasting is presented based on the Hidden Markov Model (HMM). The factors impacting the electricity price forecasting are discussed. The proposed approach is utilized in an electricity market, the results show the effectiveness.

关键词： Hidden Markov models Economic forecasting Stochastic processes Predictive models technology forecasting Energy consumption Power markets Support vector machines Probability distribution Laboratories

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Generalized predictive control for wind turbine systems

Generalized predictive control for wind turbine systems

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IEEE Conference on industrial Electronics and Applications (ICIEA)

作者： Jianhua Zhang Hua Wang Guolian Hou Jinfang Zhang Beijing Key Laboratory of Industrial Process Measurement & Control New Technology and System North China Electric Power University Beijing China

ISBN: (纸本)9781424450459;9781424450466

It is difficult to deal with the variable speed constant frequency (VSCF) wind turbine systems due to the stochastic characteristics and the pneumatic effects of wind. In this paper, a new pitch controller based on generalized predictive control theory is designed to improve the power-output quality of variable speed constant frequency wind turbines. An application to a 300MW wind turbines is given, and simulation results show that the proposed method is effective in wind speed interference suppression and constant out power.

关键词： Predictive control Wind turbines Mathematical model Wind speed Power system modeling Rotors Shafts Wind power generation Frequency Wind energy generation

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Network-based reliable H∞ filter designing for the systems with sensor failures

Network-based reliable H∞ filter designing for the systems ...

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作者： Lilong, Du Zhou, Gu Jinliang, Liu Beijing Key Laboratory of Industrial Process Measurement and Control New Technology and System North China Electric Power University Beijing China School of Power Engineering Nanjing Normal University Nanjing Jiangsu China College of Information Science and Technology Donghua University Shanghai China

ISBN: (纸本)9780955529337

This paper is concerned with the reliable filtering problem for network-based linear continuous-time system with sensor failures, The purpose of the addressed filtering problem is to design a filter such that the error dynamics of the filtering process is stable. By using the linear matrix inequality (LMI) method, sufficient conditions are established that ensure the filter parameters are characterized by the solution to a set of LMIs. Simulation results are provided to illustrate effectiveness of the proposed method.

关键词： Continuous time systems

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Single sensor information fusion for local fault prediction of large rotating machinery

Single sensor information fusion for local fault prediction ...

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International Conference on Electrical and control Engineering, ICECE 2010

作者： Jianghong, Sun Yunbo, Zuo Xu, Xiaoli School of ElectroMechanical Engineering Beijing Information Science and Technology University Beijing China School of Mechanical and Vehicular Engineering Beijing Institute of Technology Beijing China Beijing Key Laboratory - Measurement and Control of Mechanical and Electrical System Beijing China

ISBN: (纸本)9780769540313

Data process of large rotating machinery is in line with basic features of information fusion. A framework of fault diagnosis and prediction based on sensor information fusion is built. An improved extracting method of features is used to deal with the information fusion of single sensor, which raises the calculation efficiency and precision. The local fault prediction process is presented, and the fault deterioration trend is judged on the basis of dynamic weighted method. Actual example of beijing Yanshan Petrochemical Co. shows the correction of conclusion. © 2010 Crown.

关键词： Information fusion

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