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Proceedings of the First International Forum on Applications of Neural Networks to Power Systems最新文献

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Genetic algorithms approach to voltage optimization 遗传算法在电压优化中的应用
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI: 10.1109/ANN.1991.213512
T. Haida, Y. Akimoto
{"title":"Genetic algorithms approach to voltage optimization","authors":"T. Haida, Y. Akimoto","doi":"10.1109/ANN.1991.213512","DOIUrl":"https://doi.org/10.1109/ANN.1991.213512","url":null,"abstract":"The authors consider the use of genetic algorithms as a measure of voltage optimization of electric power system. Genetic algorithms are optimization and learning techniques based on natural selection and natural population genetics. A formation of a power system is encoded to a string of characters called an artificial chromosome the initial population of strings are generated at random, and then they are evolved by a genetic algorithm. The experiments with the prototype implementation are presented. These results verified the feasibility of genetic algorithms approach to power engineering.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"237 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122695330","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 28
A neural networks approach to voltage security monitoring and control 基于神经网络的电压安全监测与控制
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI: 10.1109/ANN.1991.213503
K. C. Hui, M. Short
{"title":"A neural networks approach to voltage security monitoring and control","authors":"K. C. Hui, M. Short","doi":"10.1109/ANN.1991.213503","DOIUrl":"https://doi.org/10.1109/ANN.1991.213503","url":null,"abstract":"Voltage collapse evaluation methods require elaborate computations to determine the existence of feasible load flow solutions in power systems. The time-consuming process of solving the stiff nonlinear system equations in these evaluation methods makes them inefficient for on-line monitoring of voltage collapse. The authors introduce an artificial neural network approach to voltage security monitoring and control. The neural network uses its association mechanism to approximate the complicated mathematical formulation of the voltage collapse phenomenon. The inherent parallel information processing nature of the neural network, which provides the capability of fast computation, enables the neural network approach to meet the rigorous demands of real-time monitoring and control. The IEEE 57 busbar system is used to demonstrate the applicability of the artificial neural network approach to the problem of voltage security monitoring and control in power systems.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"142 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122861850","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
Unsupervised learning strategies for the detection and classification of transient phenomena on electric power distribution systems 配电系统暂态现象检测与分类的无监督学习策略
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI: 10.1109/ANN.1991.213506
D.L. Lubkeman, C.D. Fallon, A. Girgis
{"title":"Unsupervised learning strategies for the detection and classification of transient phenomena on electric power distribution systems","authors":"D.L. Lubkeman, C.D. Fallon, A. Girgis","doi":"10.1109/ANN.1991.213506","DOIUrl":"https://doi.org/10.1109/ANN.1991.213506","url":null,"abstract":"A number of utilities are currently installing high-speed data acquisition equipment in their distribution substations. This equipment will make it possible to record the transient waveforms due to events such as low and high-impedance faults, capacitor switching, and load switching. The authors describe the potential of applying unsupervised learning strategies to the classification of the various events observed by a substation recorder. Several strategies are tested using simulation studies and the effectiveness of unsupervised learning is compared to current classification strategies as well as supervised learning.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122917158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 11
Neural network based preventive control support system for power system stability enhancement 基于神经网络的电力系统稳定性增强预防控制支持系统
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI: 10.1109/ANN.1991.213513
H. Saitoh, Y. Shimotori, J. Toyoda
{"title":"Neural network based preventive control support system for power system stability enhancement","authors":"H. Saitoh, Y. Shimotori, J. Toyoda","doi":"10.1109/ANN.1991.213513","DOIUrl":"https://doi.org/10.1109/ANN.1991.213513","url":null,"abstract":"The authors propose an application of a newly developed neural network to the preventive control of a power system. The purpose of the proposed control is to improve the damping effect of the system on electromechanical modes by reallocating load to generators. Since the neural network has flexible learning capability the authors apply it to identify the complex and nonlinear relation between the damping effect and the distribution of generating power. The trained neural network acts as the support system which aids an operator in performing the generating reallocation for enhancing the system stability. Furthermore, the authors develop a new type of neural network which can deal with the equal constraints about the output layer in the error-back-propagation type of neural network because it is important for the generating reallocation to satisfy the equal constraint about the energy balance between generation and load.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"428 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131881600","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
A hybrid neural network and expert system for monitoring fossil fuel power plants 化石燃料电厂监测的混合神经网络与专家系统
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI: 10.1109/ANN.1991.213475
T. Kraft, K. Okagaki, R. Ishii, P. Surko, A. Brandon, A. DeWeese, S. Peterson, R. Bjordal
{"title":"A hybrid neural network and expert system for monitoring fossil fuel power plants","authors":"T. Kraft, K. Okagaki, R. Ishii, P. Surko, A. Brandon, A. DeWeese, S. Peterson, R. Bjordal","doi":"10.1109/ANN.1991.213475","DOIUrl":"https://doi.org/10.1109/ANN.1991.213475","url":null,"abstract":"A fully recurrent neural network and a rule-based expert system are combined in a hybrid architecture to provide power plant operators with an intelligent on-line advisory system. Its purpose is to alert the operator to impending or occurring abnormal conditions related to the plant's boiler. The hybrid system is trained to provide a model of the boiler under normal operation, while the rules address a general set of diagnostic events. Deviation from normal conditions trigger rules to suggest corrective action. This system is intended to increase plant availability and efficiency by automatically deducing abnormal boiler conditions before they become critical.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125293482","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 6
Short term electric load forecasting using an adaptively trained layered perceptron 基于自适应训练分层感知器的短期电力负荷预测
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI: 10.1109/ANN.1991.213487
M. El-Sharkawi, S. Oh, R. Marks, M. Damborg, C.M. Brace
{"title":"Short term electric load forecasting using an adaptively trained layered perceptron","authors":"M. El-Sharkawi, S. Oh, R. Marks, M. Damborg, C.M. Brace","doi":"10.1109/ANN.1991.213487","DOIUrl":"https://doi.org/10.1109/ANN.1991.213487","url":null,"abstract":"The authors address electric load forecasting using artificial neural network (NN) technology. They summarize research for Puget Sound Power and Light Company. In this study, several structures for NNs are proposed and tested. Features extraction is implemented to capture strongly correlated variables to electric loads. The NN is compared to several forecasting models. Most of them are commercial codes. The NN performed as well as the best and most sophisticated commercial forecasting systems.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1991-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114965423","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 40
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