A new direct method for transient security assessment of multimachine power systems is presented. A local approximation of the stability boundary is made by tangent hypersurfaces which are developed from Taylor series expansion of the transient energy function in the state space nearby a certain class of unstable equilibrium points (UEP). Two approaches for an estimation of the stability region are proposed by taking into account the second order coefficients or alternatively, the second and third order coefficients of the hypersurfaces. Results for two representative power systems are described and a comparison is made with the hyperplane method, demonstrating the superiority of the proposed approach and its potential in real power system applications. Artificial neural networks are used to determine the unknown coefficients of the hypersurfaces independently of operating conditions.<>
{"title":"Learning tangent hypersurfaces for fast assessment of transient stability","authors":"M. Djukanovic, D. Sobajic, Y. Pao","doi":"10.1109/ANN.1993.264302","DOIUrl":"https://doi.org/10.1109/ANN.1993.264302","url":null,"abstract":"A new direct method for transient security assessment of multimachine power systems is presented. A local approximation of the stability boundary is made by tangent hypersurfaces which are developed from Taylor series expansion of the transient energy function in the state space nearby a certain class of unstable equilibrium points (UEP). Two approaches for an estimation of the stability region are proposed by taking into account the second order coefficients or alternatively, the second and third order coefficients of the hypersurfaces. Results for two representative power systems are described and a comparison is made with the hyperplane method, demonstrating the superiority of the proposed approach and its potential in real power system applications. Artificial neural networks are used to determine the unknown coefficients of the hypersurfaces independently of operating conditions.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127545866","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}
This paper develops a hydro-scheduling algorithm based on the simulated annealing technique for a two-interval schedule horizon. In the algorithm, the load balance constraint, the total water discharge constraint and the constraint on the operation limits of the equivalent thermal generator are fully accounted for. The performance of the algorithm is demonstrated through its application to a test system. The results are presented and are compared to a conventional method.<>
{"title":"A simulated annealing approach to short-term hydro scheduling","authors":"K. Wong, Y. W. Wong, Yunbei Yu","doi":"10.1109/ANN.1993.264327","DOIUrl":"https://doi.org/10.1109/ANN.1993.264327","url":null,"abstract":"This paper develops a hydro-scheduling algorithm based on the simulated annealing technique for a two-interval schedule horizon. In the algorithm, the load balance constraint, the total water discharge constraint and the constraint on the operation limits of the equivalent thermal generator are fully accounted for. The performance of the algorithm is demonstrated through its application to a test system. The results are presented and are compared to a conventional method.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122661439","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}
A novel methodology is presented for the purpose of modeling complex systems through the utilization of artificial neural networks (ANNs) as linguistic value generators. Complexity is considered as a function of the distinct ways one may interact with a system and the number of separate modes required to describe these interactions. In the present approach ANN's are employed in the framework of the anticipatory paradigm. In an anticipatory system a decision is taken based not only on the current condition of the system; but also on an estimate of what the system may be doing in the near future. The prediction agency is a model of the system and/or its environment which is internal to the system. A library of ANNs is used to provide the predictive models required for computing fuzzy values. The fuzzy values describe the system behavior in a manner suitable for decision making purposes in a fuzzy environment. The methodology is demonstrated utilizing actual data obtained during a start-up period of an experimental nuclear reactor.<>
{"title":"Modeling complex systems with neural network generated fuzzy reasoning","authors":"A. Ikonomopoulos, R. Uhrig, L. Tsoukalas","doi":"10.1109/ANN.1993.264312","DOIUrl":"https://doi.org/10.1109/ANN.1993.264312","url":null,"abstract":"A novel methodology is presented for the purpose of modeling complex systems through the utilization of artificial neural networks (ANNs) as linguistic value generators. Complexity is considered as a function of the distinct ways one may interact with a system and the number of separate modes required to describe these interactions. In the present approach ANN's are employed in the framework of the anticipatory paradigm. In an anticipatory system a decision is taken based not only on the current condition of the system; but also on an estimate of what the system may be doing in the near future. The prediction agency is a model of the system and/or its environment which is internal to the system. A library of ANNs is used to provide the predictive models required for computing fuzzy values. The fuzzy values describe the system behavior in a manner suitable for decision making purposes in a fuzzy environment. The methodology is demonstrated utilizing actual data obtained during a start-up period of an experimental nuclear reactor.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129617096","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}
This paper presents a new, artificial neural network (ANN) based model for the calculation of next day's load forecasts. The model's most significant aspects fall into the following two areas: training process and selection of the input variables. Insights gained during the development of the model regarding the choice of the input variables, and their transformations, the design of the ANN structure, the selection of the training cases and the training process itself are described in the paper. The new model has been tested under a wide variety of conditions and it is shown in this paper to produce excellent results. Comparison results between an existing regression-based model that is currently in production use and the ANN model are very encouraging. The ANN model consistently outperforms the existing model in terms of both average errors over a long period of time and number of 'large' errors. Conclusions reached from this development are sufficiently general to be used by other electric power utilities.<>
{"title":"Short-term system load forecasting using an artificial neural network","authors":"A. Papalexopoulos, S. Hao, T. Peng","doi":"10.1109/ANN.1993.264284","DOIUrl":"https://doi.org/10.1109/ANN.1993.264284","url":null,"abstract":"This paper presents a new, artificial neural network (ANN) based model for the calculation of next day's load forecasts. The model's most significant aspects fall into the following two areas: training process and selection of the input variables. Insights gained during the development of the model regarding the choice of the input variables, and their transformations, the design of the ANN structure, the selection of the training cases and the training process itself are described in the paper. The new model has been tested under a wide variety of conditions and it is shown in this paper to produce excellent results. Comparison results between an existing regression-based model that is currently in production use and the ANN model are very encouraging. The ANN model consistently outperforms the existing model in terms of both average errors over a long period of time and number of 'large' errors. Conclusions reached from this development are sufficiently general to be used by other electric power utilities.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128669216","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}
A microcomputer based fuzzy logic power system stabilizer is implemented to an actual hydroelectric generator with the rating of 5.25 MVA to investigate its efficiency in real time control. The stabilizing signal is determined by using sampled real power signals to damp the system oscillations. The results show the proposed stabilizer improves the system damping effectively subject to various types of disturbances.<>
{"title":"Experimental studies on micro-computer based fuzzy logic power system stabilizer","authors":"T. Hiyama, S. Oniki, H. Nagashima","doi":"10.1109/ANN.1993.264288","DOIUrl":"https://doi.org/10.1109/ANN.1993.264288","url":null,"abstract":"A microcomputer based fuzzy logic power system stabilizer is implemented to an actual hydroelectric generator with the rating of 5.25 MVA to investigate its efficiency in real time control. The stabilizing signal is determined by using sampled real power signals to damp the system oscillations. The results show the proposed stabilizer improves the system damping effectively subject to various types of disturbances.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124120046","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}
The paper presents an artificial neural network (ANN) approach using ART2 (Adaptive Resonance Theory 2) to a diagnostic system for gas insulated switchgear (GIS). To begin with, the authors show the background of abnormality diagnosis of GISs from the view point of predictive maintenance of them. Then, they discuss the necessity of ART-type ANNs, as an unsupervised learning method, in which neuron(s) are self-organized and self-created when detecting unexpected signals even if untrained by ANNs through a sensor. Finally, they present brief simulation results and their evaluation.<>
{"title":"Abnormality diagnosis of GIS using adaptive resonance theory","authors":"H. Ogi, H. Tanaka, Y. Akimoto, Y. Izui","doi":"10.1109/ANN.1993.264293","DOIUrl":"https://doi.org/10.1109/ANN.1993.264293","url":null,"abstract":"The paper presents an artificial neural network (ANN) approach using ART2 (Adaptive Resonance Theory 2) to a diagnostic system for gas insulated switchgear (GIS). To begin with, the authors show the background of abnormality diagnosis of GISs from the view point of predictive maintenance of them. Then, they discuss the necessity of ART-type ANNs, as an unsupervised learning method, in which neuron(s) are self-organized and self-created when detecting unexpected signals even if untrained by ANNs through a sensor. Finally, they present brief simulation results and their evaluation.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124005676","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}
For the efficient operation of power transmission facilities, the authors have developed a new fault location (FL) system and put it to practical use. This system uses neural networks to analyze the distribution pattern of the current induced in overhead ground wires along the poser line. Improved reliability results from the introduction of fuzzy operation of input data, a fault-type decision method and an index expressing the reliability of the fault location result. These FL systems are installed in eight commercial lines, and run normally, one of which experienced a fault due to lightning and successfully located the fault point.<>
{"title":"A study on practical fault location system for power transmission lines using neural networks","authors":"H. Kanoh, K. Kanemaru, M. Kaneta, M. Nishiura","doi":"10.1109/ANN.1993.264357","DOIUrl":"https://doi.org/10.1109/ANN.1993.264357","url":null,"abstract":"For the efficient operation of power transmission facilities, the authors have developed a new fault location (FL) system and put it to practical use. This system uses neural networks to analyze the distribution pattern of the current induced in overhead ground wires along the poser line. Improved reliability results from the introduction of fuzzy operation of input data, a fault-type decision method and an index expressing the reliability of the fault location result. These FL systems are installed in eight commercial lines, and run normally, one of which experienced a fault due to lightning and successfully located the fault point.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128161798","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}
The authors propose a hybrid fault analysis system using an expert system (ES), neural networks (NNs), and a conventional fault analysis package (CFAP). The system detects fault type and approximate fault points using information from operated relays, circuit breakers (CBs), and fault voltage/current waveforms. Faulted sections are estimated by ES and the fault voltage/current waveform is analyzed by NNs. Since power systems require high reliability, the system uses a verification procedure based on CFAP for the result of NN waveform recognition. Four different types of NNs are compared and an appropriate NN is selected for waveform recognition. With NNs, ES and CFAP used together, the system can obtain the convenient features of these methods.<>
{"title":"Fault analysis system using neural networks and artificial intelligence","authors":"Y. Fukuyama, Y. Ueki","doi":"10.1109/ANN.1993.264355","DOIUrl":"https://doi.org/10.1109/ANN.1993.264355","url":null,"abstract":"The authors propose a hybrid fault analysis system using an expert system (ES), neural networks (NNs), and a conventional fault analysis package (CFAP). The system detects fault type and approximate fault points using information from operated relays, circuit breakers (CBs), and fault voltage/current waveforms. Faulted sections are estimated by ES and the fault voltage/current waveform is analyzed by NNs. Since power systems require high reliability, the system uses a verification procedure based on CFAP for the result of NN waveform recognition. Four different types of NNs are compared and an appropriate NN is selected for waveform recognition. With NNs, ES and CFAP used together, the system can obtain the convenient features of these methods.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133141541","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}
The artificial neural network (ANN) technique is utilized for power electric load forecasting using the backpropagation algorithm developed by the authors. The major contribution of this work is the ability to forecast the power electric load for weekends and holidays as well as weekdays with a relatively small training set. In addition the effect of seasonal change in load pattern can be tracked down. Their approach is to introduce three different sets of inputs to the ANN in order to follow the load pattern, weather pattern, seasonal factors and to consider special events like weekends and holidays.<>
{"title":"An artificial neural network based short term load forecasting with special tuning for weekends and seasonal changes","authors":"N. Moharari, A. Debs","doi":"10.1109/ANN.1993.264334","DOIUrl":"https://doi.org/10.1109/ANN.1993.264334","url":null,"abstract":"The artificial neural network (ANN) technique is utilized for power electric load forecasting using the backpropagation algorithm developed by the authors. The major contribution of this work is the ability to forecast the power electric load for weekends and holidays as well as weekdays with a relatively small training set. In addition the effect of seasonal change in load pattern can be tracked down. Their approach is to introduce three different sets of inputs to the ANN in order to follow the load pattern, weather pattern, seasonal factors and to consider special events like weekends and holidays.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"74 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127712050","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}
This paper compares the ability of six artificial neural networks to predict hourly system load for the Puget Sound Power and Light Company, a major North American electric utility. The neural nets, along with four other types of models, were used to forecast hourly system load for the next day on an hour by hour basis. This was done for the period November 1, 1991 to March 31, 1992.<>
{"title":"Another look at forecast accuracy of neural networks","authors":"M.C. Brace, V. Bui-Nguyen, J. Schmidt","doi":"10.1109/ANN.1993.264316","DOIUrl":"https://doi.org/10.1109/ANN.1993.264316","url":null,"abstract":"This paper compares the ability of six artificial neural networks to predict hourly system load for the Puget Sound Power and Light Company, a major North American electric utility. The neural nets, along with four other types of models, were used to forecast hourly system load for the next day on an hour by hour basis. This was done for the period November 1, 1991 to March 31, 1992.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115393314","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}
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