This paper presents the genetic algorithm approach to adaptive optimal economic dispatch of electrical power systems. Genetic algorithms, also termed as the machine learning approach to artificial intelligence, are powerful stochastic optimization techniques with potential features of random search, hill climbing, statistical sampling and competition. Genetic algorithmic approach to power system optimization, as reported here for a case of economic power dispatch, consists essentially of minimizing the objective function while gradually satisfying the constraint relations. The unique problem solving strategy of the genetic algorithm and their suitability for power system optimization is described. The advantages of the genetic algorithmic approach in terms of problem reduction, flexibility and solution methodology are also discussed. The suitability of the proposed approach is described for the case of a 15 generator power system.<>
{"title":"Optimal economic power dispatch using genetic algorithms","authors":"M. Yoshimi, K. Swarup, Y. Izui","doi":"10.1109/ANN.1993.264297","DOIUrl":"https://doi.org/10.1109/ANN.1993.264297","url":null,"abstract":"This paper presents the genetic algorithm approach to adaptive optimal economic dispatch of electrical power systems. Genetic algorithms, also termed as the machine learning approach to artificial intelligence, are powerful stochastic optimization techniques with potential features of random search, hill climbing, statistical sampling and competition. Genetic algorithmic approach to power system optimization, as reported here for a case of economic power dispatch, consists essentially of minimizing the objective function while gradually satisfying the constraint relations. The unique problem solving strategy of the genetic algorithm and their suitability for power system optimization is described. The advantages of the genetic algorithmic approach in terms of problem reduction, flexibility and solution methodology are also discussed. The suitability of the proposed approach is described for the case of a 15 generator power system.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"33 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":"115870573","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 fuzzy logic power system stabilizer is proposed, and a neural network is utilized for its real time tuning to keep its performance optimal under wider ranges of operating conditions. Simulation results show the efficiency of the proposed real time tuning of the fuzzy logic power system stabilizer by the neural network. The proposed fuzzy logic power system stabilizer can be configured by using a microcomputer and an A/D and a D/A conversion boards, and easily implemented in power systems.<>
{"title":"Application of neural network to real time tuning of fuzzy logic PSS","authors":"T. Hiyama","doi":"10.1109/ANN.1993.264311","DOIUrl":"https://doi.org/10.1109/ANN.1993.264311","url":null,"abstract":"A fuzzy logic power system stabilizer is proposed, and a neural network is utilized for its real time tuning to keep its performance optimal under wider ranges of operating conditions. Simulation results show the efficiency of the proposed real time tuning of the fuzzy logic power system stabilizer by the neural network. The proposed fuzzy logic power system stabilizer can be configured by using a microcomputer and an A/D and a D/A conversion boards, and easily implemented in power systems.<<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":"122027086","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 the methodology of a nonlinear version of factor analysis by four layer feedforward neural networks and, as an example of its application, the result of modeling the structure of partial discharge data measured on a power cable. Here, the authors introduce the four layer auto associative memory with a reduced size of its second layer that learns identity mapping (the same pattern is used for both of the input data and the supervisory data for the network) and is used for data compression of the multivariate data, then they show that it is valid as a tool for so-called 'piecewise linear factor analysis'. They demonstrate the advantages of the piecewise linear factor analysis method over the current linear scheme regarding the modeling of the unknown structure of multivariate data such as electric pulse distribution data generated by simulated partial discharge.<>
{"title":"Piecewise linear factor analysis by four layer neural networks and its application for modeling the partial discharge data","authors":"T. Yonekura, Y. Tsutsumi, S. Sigiyama, T. Kikuchi","doi":"10.1109/ANN.1993.264303","DOIUrl":"https://doi.org/10.1109/ANN.1993.264303","url":null,"abstract":"This paper presents the methodology of a nonlinear version of factor analysis by four layer feedforward neural networks and, as an example of its application, the result of modeling the structure of partial discharge data measured on a power cable. Here, the authors introduce the four layer auto associative memory with a reduced size of its second layer that learns identity mapping (the same pattern is used for both of the input data and the supervisory data for the network) and is used for data compression of the multivariate data, then they show that it is valid as a tool for so-called 'piecewise linear factor analysis'. They demonstrate the advantages of the piecewise linear factor analysis method over the current linear scheme regarding the modeling of the unknown structure of multivariate data such as electric pulse distribution data generated by simulated partial discharge.<<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":"130230905","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 a new approach to the design of a supplementary stabilizing controller for a HVDC transmission link using fuzzy logic. The fuzzy controller relates significant and observable variables like speed and its rate of the generator speed and its rate of change of the generator to a control signal for the rectifier current regulator loop using fuzzy membership functions. These variables evaluate the control rules using the compositional rules of inference. The fuzzy controller is equivalent to a nonlinear PI controller, whose gains are adapted depending on the error and its rate of change. The effectiveness of the proposed controller is demonstrated by simulation studies on a DC transmission link connected to a weak AC power system and subjected to transient disturbances.<>
{"title":"An adaptive fuzzy logic controller for AC-DC power systems","authors":"P. Dash, A. Routray, S. Rahman","doi":"10.1109/ANN.1993.264287","DOIUrl":"https://doi.org/10.1109/ANN.1993.264287","url":null,"abstract":"The paper presents a new approach to the design of a supplementary stabilizing controller for a HVDC transmission link using fuzzy logic. The fuzzy controller relates significant and observable variables like speed and its rate of the generator speed and its rate of change of the generator to a control signal for the rectifier current regulator loop using fuzzy membership functions. These variables evaluate the control rules using the compositional rules of inference. The fuzzy controller is equivalent to a nonlinear PI controller, whose gains are adapted depending on the error and its rate of change. The effectiveness of the proposed controller is demonstrated by simulation studies on a DC transmission link connected to a weak AC power system and subjected to transient disturbances.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"121 50","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1993-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"113944847","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 coarse-grained parallel simulated annealing algorithm for short-term hydro scheduling. The design of the algorithm takes into consideration load balancing, processor synchronization reduction, communication overhead reduction and memory contention elimination. The algorithm is implemented on an i860 processor in a simulated environment and is applied to a test system. The scheduling results are presented and are compared with those found by the systolic, clustered and sequential algorithms.<>
{"title":"A parallel simulated annealing algorithm for short-term hydro scheduling","authors":"K. Wong, Y. W. Wong","doi":"10.1109/ANN.1993.264325","DOIUrl":"https://doi.org/10.1109/ANN.1993.264325","url":null,"abstract":"This paper develops a coarse-grained parallel simulated annealing algorithm for short-term hydro scheduling. The design of the algorithm takes into consideration load balancing, processor synchronization reduction, communication overhead reduction and memory contention elimination. The algorithm is implemented on an i860 processor in a simulated environment and is applied to a test system. The scheduling results are presented and are compared with those found by the systolic, clustered and sequential algorithms.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"84 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":"122699914","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 new method for the optimal design of the electromagnetic devices is presented. The method utilizes artificial neural networks (ANNs) in a design environment which encompasses numerical computations and expert's input for generating a variety of ANN training data. Results of two implementation examples are provided. The optimal design is obtained quickly (in a matter of milliseconds) once the ANNs are trained with a variety of geometrical topologies. The procedure explained in this paper can be used to provide good initial designs for use with iterative search techniques (currently used) to reduce searching time. This aspect is highly desirable to increase the effectiveness of the optimal design procedure.<>
{"title":"Design optimization of electromagnetic devices using artificial neural networks","authors":"Osama A. Mohammed, D. C. Park, F. G. Uler","doi":"10.1109/ANN.1993.264321","DOIUrl":"https://doi.org/10.1109/ANN.1993.264321","url":null,"abstract":"A new method for the optimal design of the electromagnetic devices is presented. The method utilizes artificial neural networks (ANNs) in a design environment which encompasses numerical computations and expert's input for generating a variety of ANN training data. Results of two implementation examples are provided. The optimal design is obtained quickly (in a matter of milliseconds) once the ANNs are trained with a variety of geometrical topologies. The procedure explained in this paper can be used to provide good initial designs for use with iterative search techniques (currently used) to reduce searching time. This aspect is highly desirable to increase the effectiveness of the optimal design procedure.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"2017 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":"128037920","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 author proposes an adaptive learning pulse width modulation (PWM) for a current controller which adaptively minimizes a current ripple with a constant switching frequency. This employs neuro- and/or fuzzy computing philosophy as well as adaptive learning pattern recognition principles to overcome the problems concerning variations of the system parameters. The proposed system is applied to an electrical drive system with an induction motor(IM) and is studied by various simulations. As opposed to the known classical methods, the proposed system shows the better performance.<>
{"title":"A neuro fuzzy controller for inverter fed variable speed induction motor drive on the power system","authors":"Kyu-Bock Cho","doi":"10.1109/ANN.1993.264351","DOIUrl":"https://doi.org/10.1109/ANN.1993.264351","url":null,"abstract":"The author proposes an adaptive learning pulse width modulation (PWM) for a current controller which adaptively minimizes a current ripple with a constant switching frequency. This employs neuro- and/or fuzzy computing philosophy as well as adaptive learning pattern recognition principles to overcome the problems concerning variations of the system parameters. The proposed system is applied to an electrical drive system with an induction motor(IM) and is studied by various simulations. As opposed to the known classical methods, the proposed system shows the better performance.<<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":"115579283","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 thermographic application system for the electrical power industry. The infrared imager has a range from 40 degrees C to 950 degrees C and maximum resolution down to 0.01 degrees C. A new algorithm for image matching has been devised to match slightly different infrared images of the same object by adaptively adjusting the five parameters, namely x- and y- translation, rotation, x- and y- scaling respectively. The diagnosis is automatically executed by a fuzzy logic-based expert system which extracts the major features within the thermograms and recommends appropriate actions for maintenance.<>
{"title":"Fuzzy logic based automatic diagnosis of power apparatus by infrared imaging","authors":"A. So, W. Chan, C. Tse, K.K. Lee","doi":"10.1109/ANN.1993.264292","DOIUrl":"https://doi.org/10.1109/ANN.1993.264292","url":null,"abstract":"This paper describes a thermographic application system for the electrical power industry. The infrared imager has a range from 40 degrees C to 950 degrees C and maximum resolution down to 0.01 degrees C. A new algorithm for image matching has been devised to match slightly different infrared images of the same object by adaptively adjusting the five parameters, namely x- and y- translation, rotation, x- and y- scaling respectively. The diagnosis is automatically executed by a fuzzy logic-based expert system which extracts the major features within the thermograms and recommends appropriate actions for maintenance.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"205 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":"114980078","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 two-phase genetic algorithm for economic load dispatching of generators in power systems. The problem of ELD is expressed as a Lagrange function. The conventional GA has a drawback that the algorithm is not so effective as the number of variables increases. To improve the GA characteristic, a two-phase GA is proposed to obtain better solutions. The proposed genetic algorithm may be applied to minimize the Lagrange function with respect to the generator unit output. The effectiveness of the proposed method is demonstrated in a 20-unit system.<>
{"title":"A genetic algorithm based approach to economic load dispatching","authors":"H. Mori, T. Horiguchi","doi":"10.1109/ANN.1993.264299","DOIUrl":"https://doi.org/10.1109/ANN.1993.264299","url":null,"abstract":"This paper presents a two-phase genetic algorithm for economic load dispatching of generators in power systems. The problem of ELD is expressed as a Lagrange function. The conventional GA has a drawback that the algorithm is not so effective as the number of variables increases. To improve the GA characteristic, a two-phase GA is proposed to obtain better solutions. The proposed genetic algorithm may be applied to minimize the Lagrange function with respect to the generator unit output. The effectiveness of the proposed method is demonstrated in a 20-unit system.<<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":"128277188","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 multilayer neural network with additional learning is applied to next day peak load forecasting. First, the performance of the neural network is studied by using time series data of a sinusoidal curve added on top of an increasing time function. The authors discuss what kind of additional learning method is effective when new time series data are obtained every day. Based on the above results, simulations of the next day peak load forecasting by the neural network are conducted using actual load data.<>
{"title":"Next day peak load forecasting using a multilayer neural network with an additional learning","authors":"Y. Morioka, K. Sakurai, A. Yokoyama, Y. Sekine","doi":"10.1109/ANN.1993.264349","DOIUrl":"https://doi.org/10.1109/ANN.1993.264349","url":null,"abstract":"A multilayer neural network with additional learning is applied to next day peak load forecasting. First, the performance of the neural network is studied by using time series data of a sinusoidal curve added on top of an increasing time function. The authors discuss what kind of additional learning method is effective when new time series data are obtained every day. Based on the above results, simulations of the next day peak load forecasting by the neural network are conducted using actual load data.<<ETX>>","PeriodicalId":121897,"journal":{"name":"[1993] Proceedings of the Second International Forum on Applications of Neural Networks to Power Systems","volume":"51 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":"116010205","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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