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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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Artificial neural net approach for capacitor placement in power system 电力系统中电容器布置的人工神经网络方法
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI: 10.1109/ANN.1991.213469
P. Dash, S. Saha, P. Nanda
{"title":"Artificial neural net approach for capacitor placement in power system","authors":"P. Dash, S. Saha, P. Nanda","doi":"10.1109/ANN.1991.213469","DOIUrl":"https://doi.org/10.1109/ANN.1991.213469","url":null,"abstract":"The authors propose a new methodology for controlling multitap capacitors in a power system using a three layer feedforward neural network. The neural network, in the proposed scheme is separately trained with two algorithms namely backpropagation and a combined backpropagation-Cauchy's learning algorithm. Studies on 30 bus IEEE test system are carried out and quite satisfactory results are obtained. The inputs to the net are the real power, reactive power and voltage magnitude at a few selected buses and the network's outputs are the values of capacitive Var injection. Performance comparison is made between two algorithms and the combined backpropagation-Cauchy's algorithm is found to be better than the other.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"77 5 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":"126162925","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: 15
Power flow classification for static security assessment 静态安全评估的潮流分类
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI: 10.1109/ANN.1991.213502
D. Niebur, A. Germond
{"title":"Power flow classification for static security assessment","authors":"D. Niebur, A. Germond","doi":"10.1109/ANN.1991.213502","DOIUrl":"https://doi.org/10.1109/ANN.1991.213502","url":null,"abstract":"The authors investigate the classification of power system states using an artificial neural network model, Kohonen's self-organizing feature map. The ultimate goal of this classification is to assess power system static security in real-time. Kohonen's self-organizing feature map is an unsupervised neural network which maps N-dimensional input vectors to an array of M neurons. After learning, the synaptic weight vectors exhibit a topological organization which represents the relationship between the vectors of the training set. This learning is unsupervised, which means that the number and size of the classes are not specified beforehand. In the application developed, the input vectors used as the training set are generated by off-line load-flow simulations. The learning algorithm and the results of the organization are discussed.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"1 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":"131243937","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: 20
Artificial neural network & pattern recognition approach for narrowband signal extraction 窄带信号提取的人工神经网络与模式识别方法
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI: 10.1109/ANN.1991.213461
P. Dash, P. Nanda, S. Saha, R. Doraiswami
{"title":"Artificial neural network & pattern recognition approach for narrowband signal extraction","authors":"P. Dash, P. Nanda, S. Saha, R. Doraiswami","doi":"10.1109/ANN.1991.213461","DOIUrl":"https://doi.org/10.1109/ANN.1991.213461","url":null,"abstract":"Estimation of unknown frequency, extraction of narrowband signals buried under noise and periodic interference are accomplished by employing existing techniques. However, the authors propose an artificial neural net based scheme together with pattern classification algorithm for narrowband signal extraction. A three layer feedforward net is trained with three different algorithms namely backpropagation, Cauchy's algorithm with Boltzmann's probability distribution feature and the combined backpropagation-Cauchy's algorithm. A constrained tangent hyperbolic function is used to activate individual neurons. Computer simulation is carried out with inadequate data to reinforce the idea of the net's generalization capability. The robustness of the proposed scheme is claimed with the results obtained by making 25% links faulty between the layers. Performance comparison of the three algorithms is made and the superiority of the combined backpropagation-Cauchy's algorithm is established over the other two algorithms. Simulation results for a wide variety of cases are presented for better appraisal.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"32 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":"128480410","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
An adaptively trainable neural network algorithm and its application to electric load forecasting 一种自适应可训练神经网络算法及其在电力负荷预测中的应用
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI: 10.1109/ANN.1991.213488
D.C. Park, O. Mohammed, M. El-Sharkawi, R. Marks
{"title":"An adaptively trainable neural network algorithm and its application to electric load forecasting","authors":"D.C. Park, O. Mohammed, M. El-Sharkawi, R. Marks","doi":"10.1109/ANN.1991.213488","DOIUrl":"https://doi.org/10.1109/ANN.1991.213488","url":null,"abstract":"A training procedure that adapts the weights of a trained layered perceptron type artificial neural network to training data originating from a slowly varying nonstationary process is proposed. The resulting adaptively trained neural network (ATNN), based on nonlinear programming techniques, is shown to adapt to new training data that is in conflict with earlier training data with affecting the neural networks' response minimally to data elsewhere. The ATNN demonstrates improved accuracy over conventionally trained layered perceptron when applied to the problem of electric load forecasting.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"5 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":"126262006","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: 7
Temporal difference method for multi-step prediction: application to power load forecasting 多步预测的时间差分法在电力负荷预测中的应用
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI: 10.1109/ANN.1991.213495
Jenq-Neng Hwang, S. Moon
{"title":"Temporal difference method for multi-step prediction: application to power load forecasting","authors":"Jenq-Neng Hwang, S. Moon","doi":"10.1109/ANN.1991.213495","DOIUrl":"https://doi.org/10.1109/ANN.1991.213495","url":null,"abstract":"The authors discuss a power load forecasting system based on a temporal difference (TD) method. The temporal difference method is a class of statistical learning procedure specialized for future prediction. The conventional back propagation (BP) has been successfully applied to power load forecasting in a one-to-one single-step prediction manner. The authors adopt a method which can gradually improve its prediction accuracy through the time evolution. In addition, a modified temporal difference (MTD) method which uses a different error function is evaluated and compared with the original one.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"136 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":"116753774","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: 12
Neural networks as a tool for unit commitment 神经网络作为单元承诺的工具
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI: 10.1109/ANN.1991.213465
M.Sc.E.E. Peter Renne-Hansen, Ph.D. Jan Renne-Hansen, DK-2800 Lyngby
{"title":"Neural networks as a tool for unit commitment","authors":"M.Sc.E.E. Peter Renne-Hansen, Ph.D. Jan Renne-Hansen, DK-2800 Lyngby","doi":"10.1109/ANN.1991.213465","DOIUrl":"https://doi.org/10.1109/ANN.1991.213465","url":null,"abstract":"Some of the fundamental problems when solving the power system unit commitment problem by means of neural networks have been attacked. It has been demonstrated for a small example that neural networks might be a viable alternative. Some of the major problems solved in this initiating phase form a basis for the analysis of real life sized problems. These will be investigated in the near future.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"1 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":"125700042","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: 7
Fault detection and diagnosis of power systems using artificial neural networks 基于人工神经网络的电力系统故障检测与诊断
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI: 10.1109/ANN.1991.213505
K. Swarup, H. Chandrasekharaiah
{"title":"Fault detection and diagnosis of power systems using artificial neural networks","authors":"K. Swarup, H. Chandrasekharaiah","doi":"10.1109/ANN.1991.213505","DOIUrl":"https://doi.org/10.1109/ANN.1991.213505","url":null,"abstract":"Real time fault detection and diagnosis (FDD) is an important area of research interest in knowledge based expert systems. Neurocomputing is one of fastest growing areas of research in the fields of artificial intelligence and pattern recognition. The authors explore the suitability of pattern classification approach of neural networks for fault detection and diagnosis. The suitability of using neural networks as pattern classifiers for power system fault diagnosis is described in detail. A neural network design and simulation environment for real-time FDD is presented. An analysis of the learning, recall and generalization characteristic of the neural network diagnostic system is presented and discussed in detail.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"28 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":"132428701","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: 38
Security assessment of a turbine generator using H/sup infinity / control based on artificial neural networks and expert systems 基于人工神经网络和专家系统的H/sup∞/控制汽轮发电机安全评估
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI: 10.1109/ANN.1991.213496
E. Nascimento, P. Goswami, E. Kasenally, B. Cory, D. Macdonald
{"title":"Security assessment of a turbine generator using H/sup infinity / control based on artificial neural networks and expert systems","authors":"E. Nascimento, P. Goswami, E. Kasenally, B. Cory, D. Macdonald","doi":"10.1109/ANN.1991.213496","DOIUrl":"https://doi.org/10.1109/ANN.1991.213496","url":null,"abstract":"The authors describe a preliminary framework for real time security assessment of turbine generators that integrates artificial neural networks (ANN) and knowledge-based expert systems (KBES). The authors also present the transient stability assessment of a turbine generator using a back propagation artificial neural network. Additional signals have been added to the AVR and governor loops of the turbine generator using H/sup infinity / control. The ANN's ability to learn, interpolate and reproduce behaviour is presented, showing how the stability of a high order nonlinear system can be obtained without the prior solution of the state equations.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"9 2-4 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":"134052202","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: 1
Neural network application to state estimation computation 神经网络在状态估计计算中的应用
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI: 10.1109/ANN.1991.213480
T. Nakagawa, Y. Hayashi, S. Iwamoto
{"title":"Neural network application to state estimation computation","authors":"T. Nakagawa, Y. Hayashi, S. Iwamoto","doi":"10.1109/ANN.1991.213480","DOIUrl":"https://doi.org/10.1109/ANN.1991.213480","url":null,"abstract":"In power systems state estimation computation takes an important role in security controls, and the weighted least squares method and the fast decoupled method are widely-used at present. State estimation computation using the existing Von-Neumann type computer is reaching a limit as far as the solution techniques are concerned, and it is very difficult to expect much faster methods. In order to solve the problem, the authors employ a neural network theory, the Hopfield network theory, which has an ultra parallel algorithm and is different from the existing calculating algorithms, for state estimation computation. A feasibility study using a 6 bus system is shown.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"53 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":"114897680","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: 37
Neural networks for topology determination of power systems 电力系统拓扑确定的神经网络
Proceedings of the First International Forum on Applications of Neural Networks to Power Systems Pub Date : 1991-07-23 DOI: 10.1109/ANN.1991.213459
A. P. Alves da Silva, V. Quintana, G. Pang
{"title":"Neural networks for topology determination of power systems","authors":"A. P. Alves da Silva, V. Quintana, G. Pang","doi":"10.1109/ANN.1991.213459","DOIUrl":"https://doi.org/10.1109/ANN.1991.213459","url":null,"abstract":"The authors describe a parallel distributed topology classifier. The idea is to determine the system configuration in a very fast way, even in the presence of incorrect or unavailable switch/breaker status and analog measurements. A new supervised learning algorithm suitable for very large training sets is introduced.<<ETX>>","PeriodicalId":119713,"journal":{"name":"Proceedings of the First International Forum on Applications of Neural Networks to Power Systems","volume":"95 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":"115178549","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: 8
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