This paper addresses a method for voltage stability monitoring with hybrid artificial neural networks. A hybrid neural network is presented to estimate the index for voltage instability and capture the feature extraction of the power system transition. In this paper, a decentralized neural network scheme is proposed to handle a large scale power systems so that the curse of dimensionality is alleviated. The proposed method is demonstrated in a sample system.<>
{"title":"A decentralized scheme for voltage instability monitoring with hybrid artificial neural networks","authors":"H. Mori, Y. Tamaru","doi":"10.1109/ANN.1993.264318","DOIUrl":"https://doi.org/10.1109/ANN.1993.264318","url":null,"abstract":"This paper addresses a method for voltage stability monitoring with hybrid artificial neural networks. A hybrid neural network is presented to estimate the index for voltage instability and capture the feature extraction of the power system transition. In this paper, a decentralized neural network scheme is proposed to handle a large scale power systems so that the curse of dimensionality is alleviated. The proposed method is demonstrated in a sample system.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"49 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":"134371614","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 project described is aimed at automating the short-term load forecasting of the Belgian national power system control centre, usually done with a minimum lead time of 24 hours. It is hoped that the resulting system will improve the quality of forecasting methods, through a better modeling of the nonlinear relationship between load and climatic factors. In view of the various aspects of the problem, the authors intend to develop a hybrid neural network (ANN)-knowledge based system (KBS) application: the ANN will form the basis of the system and will make the forecast in normal situations; the KBS should manage exceptions and special phenomena as well as provide specific knowledge-based facilities. The authors focus on the development of a prototype for the ANN. The ANN is to be a model of the evolution of the load w.r.t. input parameters, therefore the ANN predicts the ratio between the load for one day and the day before, instead of the raw load value.<>
{"title":"Automation, with neural network based techniques, of short-term load forecasting at the Belgian national control centre","authors":"F. de Viron, J. Claus, F. Dongier, M. Monteyne","doi":"10.1109/ANN.1993.264350","DOIUrl":"https://doi.org/10.1109/ANN.1993.264350","url":null,"abstract":"The project described is aimed at automating the short-term load forecasting of the Belgian national power system control centre, usually done with a minimum lead time of 24 hours. It is hoped that the resulting system will improve the quality of forecasting methods, through a better modeling of the nonlinear relationship between load and climatic factors. In view of the various aspects of the problem, the authors intend to develop a hybrid neural network (ANN)-knowledge based system (KBS) application: the ANN will form the basis of the system and will make the forecast in normal situations; the KBS should manage exceptions and special phenomena as well as provide specific knowledge-based facilities. The authors focus on the development of a prototype for the ANN. The ANN is to be a model of the evolution of the load w.r.t. input parameters, therefore the ANN predicts the ratio between the load for one day and the day before, instead of the raw load value.<<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":"114299230","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 maximum electric load prediction method using a neural network. The proposed prediction system learns 2-past-weeks data, consisting of the temperature at peak load, its difference from the previous day, the weather, and peak load on each day. Then it forecasts the rate of change in peak load for the following day, inputting the temperature, its difference, the weather and so on. Simulation results show that the average prediction error of the method is about 3%. The prediction error can be further reduced by, for example, changing the number of hidden layers and neural network parameters, such as the system temperature.<>
{"title":"Maximum electric power demand prediction by neural network","authors":"Y. Mizukami, T. Nishimori","doi":"10.1109/ANN.1993.264331","DOIUrl":"https://doi.org/10.1109/ANN.1993.264331","url":null,"abstract":"This paper presents a maximum electric load prediction method using a neural network. The proposed prediction system learns 2-past-weeks data, consisting of the temperature at peak load, its difference from the previous day, the weather, and peak load on each day. Then it forecasts the rate of change in peak load for the following day, inputting the temperature, its difference, the weather and so on. Simulation results show that the average prediction error of the method is about 3%. The prediction error can be further reduced by, for example, changing the number of hidden layers and neural network parameters, such as the system temperature.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"48 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":"114804510","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}
Keeping up with the times and computer technology, many researchers have applied new mathematical approaches extensively to solve various problems in power systems. AI technology, fuzzy theory and artificial neural networks are recent trends. This paper presents a new optimization method for reactive power planning using genetic algorithms. The genetic algorithm (GA) is a kind of search algorithm based on the mechanics of natural selection and genetics. This algorithm can search for a global solution using a multiple path and have a structure fit to integer problems. The proposed method was applied to practical 51-bus and 224-bus systems to show its feasibility and capabilities.<>
{"title":"Optimal VAr allocation by genetic algorithm","authors":"K. Iba","doi":"10.1109/ANN.1993.264296","DOIUrl":"https://doi.org/10.1109/ANN.1993.264296","url":null,"abstract":"Keeping up with the times and computer technology, many researchers have applied new mathematical approaches extensively to solve various problems in power systems. AI technology, fuzzy theory and artificial neural networks are recent trends. This paper presents a new optimization method for reactive power planning using genetic algorithms. The genetic algorithm (GA) is a kind of search algorithm based on the mechanics of natural selection and genetics. This algorithm can search for a global solution using a multiple path and have a structure fit to integer problems. The proposed method was applied to practical 51-bus and 224-bus systems to show its feasibility and capabilities.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"9 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":"115789504","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}
D. C. Park, O. Mohammed, A. Azeem, R. Merchant, T. Dinh, C. Tong, J. Farah, C. Drake
The authors describe how an artificial neural network can be utilized for improving the shape of an electrical power load forecast. It is shown that the application of this method to make the shape of the forecast load curve conform to the shape of the typical seasonal load curve results in improvement in the overall accuracy of the electrical power load forecast.<>
{"title":"Load curve shaping using neural networks","authors":"D. C. Park, O. Mohammed, A. Azeem, R. Merchant, T. Dinh, C. Tong, J. Farah, C. Drake","doi":"10.1109/ANN.1993.264332","DOIUrl":"https://doi.org/10.1109/ANN.1993.264332","url":null,"abstract":"The authors describe how an artificial neural network can be utilized for improving the shape of an electrical power load forecast. It is shown that the application of this method to make the shape of the forecast load curve conform to the shape of the typical seasonal load curve results in improvement in the overall accuracy of the electrical power load forecast.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"98 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":"122814395","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 proposes a new method for nodal voltage diagnosis in power systems using a self-organization artificial neural network. ART2 is utilized to classify power system conditions. A probability voltage security index is evaluated by the resulting classification. The proposed method is used for tracking the voltage profile continuously.<>
{"title":"Probabilistic diagnosis of power system nodal voltages with ART2","authors":"H. Mori, N. Kanda","doi":"10.1109/ANN.1993.264294","DOIUrl":"https://doi.org/10.1109/ANN.1993.264294","url":null,"abstract":"This paper proposes a new method for nodal voltage diagnosis in power systems using a self-organization artificial neural network. ART2 is utilized to classify power system conditions. A probability voltage security index is evaluated by the resulting classification. The proposed method is used for tracking the voltage profile continuously.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"65 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":"122091141","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}
Transient stability assessment plays an important role in power systems. The transient stability deals with the electromechanical oscillation of synchronous generators, created by a disturbance in the power system. For example, in the case of a transmission line fault, assume that faulted line section is first isolated and then reclosed (reclosure); there then exists a threshold parameter known as the stable critical clearing time (CCT). This paper describes a neural network based adaptive pattern recognition approach for estimation of the critical clearing time. Numerical examples are presented to illustrate this approach. In the neural network considered in this research work, a multi DSP-PC system (digital signal processor-personal computer system) is used for realizing faster backpropagation by applying pipeline operation and parallel operation.<>
{"title":"Neural network based power system transient stability criterion using DSP-PC system","authors":"S. Wei, K. Nakamura, M. Sone, H. Fujita","doi":"10.1109/ANN.1993.264300","DOIUrl":"https://doi.org/10.1109/ANN.1993.264300","url":null,"abstract":"Transient stability assessment plays an important role in power systems. The transient stability deals with the electromechanical oscillation of synchronous generators, created by a disturbance in the power system. For example, in the case of a transmission line fault, assume that faulted line section is first isolated and then reclosed (reclosure); there then exists a threshold parameter known as the stable critical clearing time (CCT). This paper describes a neural network based adaptive pattern recognition approach for estimation of the critical clearing time. Numerical examples are presented to illustrate this approach. In the neural network considered in this research work, a multi DSP-PC system (digital signal processor-personal computer system) is used for realizing faster backpropagation by applying pipeline operation and parallel operation.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"40 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":"128944681","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}
D. C. Park, O. Mohammed, R. Merchant, T. Dinh, C. Tong, A. Azeem, J. Farah, C. Drake
The authors present a new approach to power load forecasting under abnormal weather conditions using artificial neural networks (ANN). Accurate forecasting for cold fronts and warm fronts is of special importance to utility companies for monetary reasons and planning reasons. Temperatures below 50 degrees F are treated as cold fronts and temperatures above 90 degrees F are treated as warm fronts in the area of interest. The architectures take into account some inherent characteristics of these days. The results obtained by using ANN have been found to give better results than other conventional techniques.<>
{"title":"Forecasting abnormal load conditions with neural networks","authors":"D. C. Park, O. Mohammed, R. Merchant, T. Dinh, C. Tong, A. Azeem, J. Farah, C. Drake","doi":"10.1109/ANN.1993.264346","DOIUrl":"https://doi.org/10.1109/ANN.1993.264346","url":null,"abstract":"The authors present a new approach to power load forecasting under abnormal weather conditions using artificial neural networks (ANN). Accurate forecasting for cold fronts and warm fronts is of special importance to utility companies for monetary reasons and planning reasons. Temperatures below 50 degrees F are treated as cold fronts and temperatures above 90 degrees F are treated as warm fronts in the area of interest. The architectures take into account some inherent characteristics of these days. The results obtained by using ANN have been found to give better results than other conventional techniques.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"99 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":"129163920","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}
An artificial neural network is applied to the operation control of the photovoltaic/diesel hybrid power generation system. The optimal operation patterns of the diesel generator are calculated by dynamic programming (DP) under the known insolation and load demand, which minimize the fuel consumption of the diesel generator. These optimal patterns are learned by the three layer neural network, and it is tested for the different insolation and demand data from those used in the learning. Two kinds of neural networks are examined, and the results are compared with each other.<>
{"title":"Optimal operation of photovoltaic/diesel power generation system by neural network","authors":"Y. Ohsawa, S. Emura, K. Arai","doi":"10.1109/ANN.1993.264342","DOIUrl":"https://doi.org/10.1109/ANN.1993.264342","url":null,"abstract":"An artificial neural network is applied to the operation control of the photovoltaic/diesel hybrid power generation system. The optimal operation patterns of the diesel generator are calculated by dynamic programming (DP) under the known insolation and load demand, which minimize the fuel consumption of the diesel generator. These optimal patterns are learned by the three layer neural network, and it is tested for the different insolation and demand data from those used in the learning. Two kinds of neural networks are examined, and the results are compared with each other.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"13 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":"129393441","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 addresses short-term load forecasting using machine learning and neural network techniques. Neural networks, though accurate in weekday load forecasting, are poor at forecasting maximum daily load, weekend and holiday loads. This necessitates development of a robust forecasting technique to complement the neural networks for enhanced reliability of forecast and improved overall accuracy. The statistical decision tree method produces robust forecasts and human intelligible rules. These rules provide understanding of factors driving load demand. Decision trees when combined with neural network forecasts, produce robust and accurate forecasts. Simulations are performed on a service area susceptible to large and sudden changes in weather and load. Forecasts obtained by the proposed method are accurate under diverse conditions.<>
{"title":"Knowledge enhanced connectionist models for short-term electric load forecasting","authors":"S. Rahman, I. Drezga, J. Rajagopalan","doi":"10.1109/ANN.1993.264314","DOIUrl":"https://doi.org/10.1109/ANN.1993.264314","url":null,"abstract":"This paper addresses short-term load forecasting using machine learning and neural network techniques. Neural networks, though accurate in weekday load forecasting, are poor at forecasting maximum daily load, weekend and holiday loads. This necessitates development of a robust forecasting technique to complement the neural networks for enhanced reliability of forecast and improved overall accuracy. The statistical decision tree method produces robust forecasts and human intelligible rules. These rules provide understanding of factors driving load demand. Decision trees when combined with neural network forecasts, produce robust and accurate forecasts. Simulations are performed on a service area susceptible to large and sudden changes in weather and load. Forecasts obtained by the proposed method are accurate under diverse conditions.<<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":"130430890","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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