This paper analyses the application of Kohonen's self-organizing feature map to short-term forecasting of daily electrical load. The aim of the paper is to study the feasibility of the Kohonen's self-organizing feature maps for the classification of electrical loads. The network not only 'learns' similarities of load patterns in a unsupervised manner, but it uses the information stored in the weight vectors of the Kohonen network to forecast the future load. The results are evaluated by using several months of hourly load data of a real system to train the network, and forecasting the daily loads for two periods of one month. The method is then improved by adding a second type of neural network for weather sensitive correction of the load previously calculated with the Kohonen network. This second type of network is a one-layered linear delta rule network.<>
{"title":"Application of the Kohonen network to short-term load forecasting","authors":"Timo Baumann, A. Germond","doi":"10.1109/ANN.1993.264313","DOIUrl":"https://doi.org/10.1109/ANN.1993.264313","url":null,"abstract":"This paper analyses the application of Kohonen's self-organizing feature map to short-term forecasting of daily electrical load. The aim of the paper is to study the feasibility of the Kohonen's self-organizing feature maps for the classification of electrical loads. The network not only 'learns' similarities of load patterns in a unsupervised manner, but it uses the information stored in the weight vectors of the Kohonen network to forecast the future load. The results are evaluated by using several months of hourly load data of a real system to train the network, and forecasting the daily loads for two periods of one month. The method is then improved by adding a second type of neural network for weather sensitive correction of the load previously calculated with the Kohonen network. This second type of network is a one-layered linear delta rule network.<<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":"133769886","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 query-based learning and inverted neural network methods are developed for static security assessment of power system. By the proposed method, the demand for huge amounts of data to evaluate the security of the power system can be considerably reduced. The inversion algorithm to generate input patterns at the boundaries of the security region is introduced. The query algorithm is used to enhance the accuracy of the boundaries in the areas where more training data are needed. The IEEE-30 bus system is used to test the proposed method.<>
{"title":"Application of query-based learning to power system static security assessment","authors":"M. El-Sharkawi, Shiyu Huang","doi":"10.1109/ANN.1993.264340","DOIUrl":"https://doi.org/10.1109/ANN.1993.264340","url":null,"abstract":"A query-based learning and inverted neural network methods are developed for static security assessment of power system. By the proposed method, the demand for huge amounts of data to evaluate the security of the power system can be considerably reduced. The inversion algorithm to generate input patterns at the boundaries of the security region is introduced. The query algorithm is used to enhance the accuracy of the boundaries in the areas where more training data are needed. The IEEE-30 bus system is used to test the proposed method.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"20 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":"133175335","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 recurrent neural network based approach to short-term load forecasting in power systems. Recurrent neural networks in multilayer perceptrons have an advantage that the context layer is able to cope with historical data. As a result, it is expected that recurrent neural networks give better solutions than the conventional feedforward multilayer perceptrons in term of accuracy. Also, the differential equation form of the time series is utilized to deal with the nonstationarity of the daily load time series. Furthermore, this paper proposes the diffusion learning method for determining weights between units in a recurrent network. The method is capable of escaping from local minima with stochastic noise. A comparison is made between conventional multilayer perceptrons and the proposed method for actual data.<>
{"title":"A recurrent neural network for short-term load forecasting","authors":"H. Mori, T. Ogasawara","doi":"10.1109/ANN.1993.264315","DOIUrl":"https://doi.org/10.1109/ANN.1993.264315","url":null,"abstract":"This paper proposes a recurrent neural network based approach to short-term load forecasting in power systems. Recurrent neural networks in multilayer perceptrons have an advantage that the context layer is able to cope with historical data. As a result, it is expected that recurrent neural networks give better solutions than the conventional feedforward multilayer perceptrons in term of accuracy. Also, the differential equation form of the time series is utilized to deal with the nonstationarity of the daily load time series. Furthermore, this paper proposes the diffusion learning method for determining weights between units in a recurrent network. The method is capable of escaping from local minima with stochastic noise. A comparison is made between conventional multilayer perceptrons and the proposed method for actual data.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"10 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":"131491669","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 GA method to minimize distribution system losses including power transformer iron loss. Since the transformer iron loss is approximately proportional to the square of a transformer's primary voltage, one can minimize the sum of transformer iron loss and line resistive loss by adjusting line voltages and line currents appropriately. Since the problem is formulated as a complex combinatorial optimization problem, it is solved by applying a genetic algorithm (GA) in this paper. Several numerical examples are shown to demonstrate the proposed method.<>
{"title":"Distribution systems copper and iron loss minimization by genetic algorithm","authors":"K. Nara, M. Kitagawa","doi":"10.1109/ANN.1993.264298","DOIUrl":"https://doi.org/10.1109/ANN.1993.264298","url":null,"abstract":"This paper proposes a new GA method to minimize distribution system losses including power transformer iron loss. Since the transformer iron loss is approximately proportional to the square of a transformer's primary voltage, one can minimize the sum of transformer iron loss and line resistive loss by adjusting line voltages and line currents appropriately. Since the problem is formulated as a complex combinatorial optimization problem, it is solved by applying a genetic algorithm (GA) in this paper. Several numerical examples are shown to demonstrate the proposed method.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"70 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":"121589490","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 describes a power system transient stability evaluation method using an artificial neural network (ANN). To improve the accuracy of the evaluation, the authors propose a new type of training signal which is a reciprocal of the action time of a step-out relay (SOR) after the fault occurrence. In simulation results of a 16-bus system, the evaluation accuracy of the ANN trained using the proposed training signal is about 20 percent more accurate than that of an ANN trained using the conventional 0/1 digital signal.<>
{"title":"Transient stability evaluation using an artificial neural network (power systems)","authors":"K. Omata, K. Tanomura","doi":"10.1109/ANN.1993.264301","DOIUrl":"https://doi.org/10.1109/ANN.1993.264301","url":null,"abstract":"This paper describes a power system transient stability evaluation method using an artificial neural network (ANN). To improve the accuracy of the evaluation, the authors propose a new type of training signal which is a reciprocal of the action time of a step-out relay (SOR) after the fault occurrence. In simulation results of a 16-bus system, the evaluation accuracy of the ANN trained using the proposed training signal is about 20 percent more accurate than that of an ANN trained using the conventional 0/1 digital signal.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"7 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":"116843648","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 reports about the application of artificial neural networks (ANN) as nonlinear filters. The ANNs are used to restore current waveforms distorted by saturation of current transducers. The paper presents the progress in this application of ANN.<>
{"title":"Restoring current signals in real time using feedforward neural nets","authors":"U. Braun, K. Feser","doi":"10.1109/ANN.1993.264308","DOIUrl":"https://doi.org/10.1109/ANN.1993.264308","url":null,"abstract":"The paper reports about the application of artificial neural networks (ANN) as nonlinear filters. The ANNs are used to restore current waveforms distorted by saturation of current transducers. The paper presents the progress in this application of ANN.<<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":"114979925","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}
C. Rodríguez, J.I. Martin, C. Ruiz, A. Lafuente, S. Rementeria, J. Perez, J. Muguerza
Neural network approaches to the design of diagnosis systems for electrical networks have to cope with serious problems derived from the large size of such systems, which makes modularity the obvious solution. A modular approach which is based on functional criteria and provides scalability and adaptability to topological changes is presented. The hypotheses generated by the neural system are justified by a competitive system which detects simple or simultaneous disturbances. This approach allows for a parallel, distributed implementation.<>
{"title":"Fast and reliable fault analysis in complex power systems","authors":"C. Rodríguez, J.I. Martin, C. Ruiz, A. Lafuente, S. Rementeria, J. Perez, J. Muguerza","doi":"10.1109/ANN.1993.264356","DOIUrl":"https://doi.org/10.1109/ANN.1993.264356","url":null,"abstract":"Neural network approaches to the design of diagnosis systems for electrical networks have to cope with serious problems derived from the large size of such systems, which makes modularity the obvious solution. A modular approach which is based on functional criteria and provides scalability and adaptability to topological changes is presented. The hypotheses generated by the neural system are justified by a competitive system which detects simple or simultaneous disturbances. This approach allows for a parallel, distributed implementation.<<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":"134144309","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. Iwata, K. Wakayama, T. Sasaki, K. Nakamura, T. Tsuneizumi, F. Ogasawara
A neural network approach for electric load forecasting using CombNET-II has been investigated. The records on hourly electric load values from June 1986 to May 1990 (four years) as well as the corresponding maximum temperatures, average temperatures in a day and temperatures in every three hours at Nagoya were used. The networks have been trained to make up the mapping functions between these temperature trends and the electric load trends. The performance of the networks are evaluated by forecasting the records in the years from June 1989 to May 1990. The average errors for all days in a week were 3.18% to 3.01%. Considering that the network utilizes the weather parameters only, these results are quite acceptable. The performance of the load forecasting by CombNET-II is superior to that of the BP network, the average which was 4.72%.<>
{"title":"Electric load forecasting using a structured self-growing neural network model 'CombNET-II'","authors":"A. Iwata, K. Wakayama, T. Sasaki, K. Nakamura, T. Tsuneizumi, F. Ogasawara","doi":"10.1109/ANN.1993.264347","DOIUrl":"https://doi.org/10.1109/ANN.1993.264347","url":null,"abstract":"A neural network approach for electric load forecasting using CombNET-II has been investigated. The records on hourly electric load values from June 1986 to May 1990 (four years) as well as the corresponding maximum temperatures, average temperatures in a day and temperatures in every three hours at Nagoya were used. The networks have been trained to make up the mapping functions between these temperature trends and the electric load trends. The performance of the networks are evaluated by forecasting the records in the years from June 1989 to May 1990. The average errors for all days in a week were 3.18% to 3.01%. Considering that the network utilizes the weather parameters only, these results are quite acceptable. The performance of the load forecasting by CombNET-II is superior to that of the BP network, the average which was 4.72%.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"22 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":"131873792","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}
Constant extinction angle control of an inverter in a MTDC-AC system is of utmost importance for proper operation under all contingencies. In this paper, the process of control is treated as a pattern recognition problem. A neuro-fuzzy controller is implemented and used for online operation of a MTDC-AC system to enhance the performance of extinction angle control. The proposed controller has significantly improved the system performance for cases studied.<>
{"title":"Neuro-fuzzy controller for enhancing the performance of extinction angle control of inverters in a MTDC-AC system","authors":"R. Jayakrishna, H. Chandrasekharaiah, K. Narendra","doi":"10.1109/ANN.1993.264289","DOIUrl":"https://doi.org/10.1109/ANN.1993.264289","url":null,"abstract":"Constant extinction angle control of an inverter in a MTDC-AC system is of utmost importance for proper operation under all contingencies. In this paper, the process of control is treated as a pattern recognition problem. A neuro-fuzzy controller is implemented and used for online operation of a MTDC-AC system to enhance the performance of extinction angle control. The proposed controller has significantly improved the system performance for cases studied.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"25 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":"134240689","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}
Understanding the cause of a fault in an electric power system in the system operation is essential for quick and adequate recovery actions such as the determination of the propriety of carrying out forced line charging and the necessity of network switching, and efficient patrolling. In this paper, the authors discuss a technique using an artificial neural network and knowledge-base for reasoning causes of power network faults and present the results obtained from a verification in which this technique was applied to a prototype system.<>
{"title":"An artificial neural network and knowledge-based method for reasoning causes of power network faults","authors":"Y. Shimakura, J. Inagaki, S. Fukui, S. Hori","doi":"10.1109/ANN.1993.264336","DOIUrl":"https://doi.org/10.1109/ANN.1993.264336","url":null,"abstract":"Understanding the cause of a fault in an electric power system in the system operation is essential for quick and adequate recovery actions such as the determination of the propriety of carrying out forced line charging and the necessity of network switching, and efficient patrolling. In this paper, the authors discuss a technique using an artificial neural network and knowledge-base for reasoning causes of power network faults and present the results obtained from a verification in which this technique was applied to a prototype system.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"50 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":"123149787","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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