Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065953
Sonu Jha, C. L. Dewangan, N. Verma
The accuracy of load demand forecasting plays a vital role in economic operation and planning in the power sector. Therefore, many techniques and approaches have been proposed in the literature for forecasting. However, there is still an essential need to develop more accurate load forecast method. In this paper, three different strategies of Multi-Step-Ahead Load Forecasting (MSALF), i.e. Direct Strategy (DS), Recursive Strategy (RS) and DirRec Strategy (Direct-Recursive Strategy or DRS) have been used for electricity load demand forecasting by using the Artificial Neural Network (ANN) with Levenberg-Marquardt (LM) training algorithm. The performance evaluation for three different strategies of MSALF has been analysed on two different substations of NE-ISO data sets. Each data sets is analysed for four different cases. The performance of the DRS is better than DS and RS.
{"title":"Multi-Step Load Demand Forecasting Using Neural Network","authors":"Sonu Jha, C. L. Dewangan, N. Verma","doi":"10.1109/ISAP48318.2019.9065953","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065953","url":null,"abstract":"The accuracy of load demand forecasting plays a vital role in economic operation and planning in the power sector. Therefore, many techniques and approaches have been proposed in the literature for forecasting. However, there is still an essential need to develop more accurate load forecast method. In this paper, three different strategies of Multi-Step-Ahead Load Forecasting (MSALF), i.e. Direct Strategy (DS), Recursive Strategy (RS) and DirRec Strategy (Direct-Recursive Strategy or DRS) have been used for electricity load demand forecasting by using the Artificial Neural Network (ANN) with Levenberg-Marquardt (LM) training algorithm. The performance evaluation for three different strategies of MSALF has been analysed on two different substations of NE-ISO data sets. Each data sets is analysed for four different cases. The performance of the DRS is better than DS and RS.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"119 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132124382","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065941
Shota Ogawa, H. Mori
This paper proposes a new method for hierarchical Voltage and Reactive Power Control (VQC) with Predator-Prey Brain Storm Optimization (PPBSO) of high performance evolutionary computation. The objective of VQC is to maintain nodal voltage profiles within certain bounds. Recently, the penetration of renewables has been widely spread in power systems, which has brought about large fluctuations on nodal voltage and system frequency due to weather-dependable generation output. In this paper, an efficient VQC method is proposed with three strategies, i.e., hierarchical optimization, PPBSO and parallel computation of OpenMP. The proposed method is tested in the IEEE 57-node system.
{"title":"A Hierarchical Scheme for Voltage and Reactive Power Control with Predator-Prey Brain Storm Optimization","authors":"Shota Ogawa, H. Mori","doi":"10.1109/ISAP48318.2019.9065941","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065941","url":null,"abstract":"This paper proposes a new method for hierarchical Voltage and Reactive Power Control (VQC) with Predator-Prey Brain Storm Optimization (PPBSO) of high performance evolutionary computation. The objective of VQC is to maintain nodal voltage profiles within certain bounds. Recently, the penetration of renewables has been widely spread in power systems, which has brought about large fluctuations on nodal voltage and system frequency due to weather-dependable generation output. In this paper, an efficient VQC method is proposed with three strategies, i.e., hierarchical optimization, PPBSO and parallel computation of OpenMP. The proposed method is tested in the IEEE 57-node system.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116552309","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065988
R. Devarapalli, B. Bhattacharyya
This paper describes the damping nature offered by power system stabilizer (PSS) and static synchronous compensator (STATCOM) under system perturbations. The parameters of the damping devices are obtained from the novel natural inspired Harris hawks algorithm (HHA). Further, the modified version of Harris hawks algorithm is proposed with logarithmic function for escaping energy of prey for better damping characteristics for the system states. The damping natured offered to the system states under perturbations and eigenvalues of the system are analyzed with the proposed technique. The system study is conducted for different loading conditions, and the proposed algorithm is compared with state-of-the-art algorithms, namely, grey wolf optimization, and moth flame optimization. The control action offered by STATCOM and PSS also investigated for the coordinated operation during different system operating conditions.
{"title":"Optimal Parameter Tuning of Power Oscillation Damper by MHHO Algorithm","authors":"R. Devarapalli, B. Bhattacharyya","doi":"10.1109/ISAP48318.2019.9065988","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065988","url":null,"abstract":"This paper describes the damping nature offered by power system stabilizer (PSS) and static synchronous compensator (STATCOM) under system perturbations. The parameters of the damping devices are obtained from the novel natural inspired Harris hawks algorithm (HHA). Further, the modified version of Harris hawks algorithm is proposed with logarithmic function for escaping energy of prey for better damping characteristics for the system states. The damping natured offered to the system states under perturbations and eigenvalues of the system are analyzed with the proposed technique. The system study is conducted for different loading conditions, and the proposed algorithm is compared with state-of-the-art algorithms, namely, grey wolf optimization, and moth flame optimization. The control action offered by STATCOM and PSS also investigated for the coordinated operation during different system operating conditions.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126138728","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065929
Abhineet Prakash, Kundan Kumar, S. Parida
This paper deals with the LFC (load frequency control) of two area power system. The two areas comprise of thermal-gas and thermal-hydro system. Later the standalone wind turbine system (WTS) is incorporated in area-1 and its variability impact on power system dynamics is studied. As HVDC tie-lines are considered as DC capacitors, this paper utilizes stored energy in HVDC tie-line to surpass the system dynamic performances. For that purpose, a virtual inertia based capacitive energy storage (CES) is incorporated in the system. The overall system is investigated in the presence of proportional (P)-integral (I)-derivative (D) controller with filter (F) i.e. PIDF controller and new I-PDF controller. To optimize the controller gains a metaheuristic Sine Cosine Algorithm (SCA) technique is adopted. Further sensitivity analysis is performed by variation in system parameters like thermal turbine and reheat turbine time constant to analyze the robustness of SCA based I-PDF controller.
{"title":"A novel I-PDF controller for LFC with AC/DC Tie-line","authors":"Abhineet Prakash, Kundan Kumar, S. Parida","doi":"10.1109/ISAP48318.2019.9065929","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065929","url":null,"abstract":"This paper deals with the LFC (load frequency control) of two area power system. The two areas comprise of thermal-gas and thermal-hydro system. Later the standalone wind turbine system (WTS) is incorporated in area-1 and its variability impact on power system dynamics is studied. As HVDC tie-lines are considered as DC capacitors, this paper utilizes stored energy in HVDC tie-line to surpass the system dynamic performances. For that purpose, a virtual inertia based capacitive energy storage (CES) is incorporated in the system. The overall system is investigated in the presence of proportional (P)-integral (I)-derivative (D) controller with filter (F) i.e. PIDF controller and new I-PDF controller. To optimize the controller gains a metaheuristic Sine Cosine Algorithm (SCA) technique is adopted. Further sensitivity analysis is performed by variation in system parameters like thermal turbine and reheat turbine time constant to analyze the robustness of SCA based I-PDF controller.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"133 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124203146","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065940
Kyu han Kim, Heung-seok Lee, Juneho Park
The fault detection system using K-means algorithm based on Auto-Associative Neural Network (AANN) is proposed for boiler tube leakage in a thermal power plant. The normal operation state of the power plant is modeled using the AANN proposed by Kramer among various neural network techniques. The difference between the normal operation state estimation value which is the output of the model and the actual value of the main variables related to the fault is called residual. Using the residuals and residual variation of each variable, the fault detection system of boiler tube leakage is implemented. Finally, the actual fault cases of the boiler tube leakage are applied to verify the possibility of fault detection.
{"title":"Detection of Boiler Tube Leakage Fault in a Thermal Power Plant Using K-means Algorithm based on Auto-Associative Neural Network","authors":"Kyu han Kim, Heung-seok Lee, Juneho Park","doi":"10.1109/ISAP48318.2019.9065940","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065940","url":null,"abstract":"The fault detection system using K-means algorithm based on Auto-Associative Neural Network (AANN) is proposed for boiler tube leakage in a thermal power plant. The normal operation state of the power plant is modeled using the AANN proposed by Kramer among various neural network techniques. The difference between the normal operation state estimation value which is the output of the model and the actual value of the main variables related to the fault is called residual. Using the residuals and residual variation of each variable, the fault detection system of boiler tube leakage is implemented. Finally, the actual fault cases of the boiler tube leakage are applied to verify the possibility of fault detection.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127150741","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065944
Mahsa Khorram, P. Faria, Omid Abrishambaf, Z. Vale, J. Soares
Uncertainty is the state of all operation, components, and objective environment that makes impossible to describe the existing state. Forecasting techniques are essential in the field of knowledge development to overcome the uncertainty to increase the efficiency of all systems. In this paper, artificial neural network algorithm is applied to forecast the CO2 concentration in an office building. The algorithm is implemented in Rstudio software using neural net package. The case study of the paper presents two scenarios with different input data to propose the impacts of train data on forecasting algorithms results. The used dataset in the case study is real data that have been monitored for 2 years. The obtained results of algorithms show the predicted values of CO2 concentration in one office for 600 minutes of a working day. The mean percentage error means absolute percentage error, and standard deviation of predicted data for both scenarios are presented in results section.
{"title":"CO2 Concentration Forecasting in an Office Using Artificial Neural Network","authors":"Mahsa Khorram, P. Faria, Omid Abrishambaf, Z. Vale, J. Soares","doi":"10.1109/ISAP48318.2019.9065944","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065944","url":null,"abstract":"Uncertainty is the state of all operation, components, and objective environment that makes impossible to describe the existing state. Forecasting techniques are essential in the field of knowledge development to overcome the uncertainty to increase the efficiency of all systems. In this paper, artificial neural network algorithm is applied to forecast the CO2 concentration in an office building. The algorithm is implemented in Rstudio software using neural net package. The case study of the paper presents two scenarios with different input data to propose the impacts of train data on forecasting algorithms results. The used dataset in the case study is real data that have been monitored for 2 years. The obtained results of algorithms show the predicted values of CO2 concentration in one office for 600 minutes of a working day. The mean percentage error means absolute percentage error, and standard deviation of predicted data for both scenarios are presented in results section.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"42 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123580847","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065950
B. Dey, B. Bhattacharyya
Optimal scheduling of the distributed generation (DG) sources for a microgrid is very essential for the economical and a balanced load sharing operation of the same. Various classical and evolutionary optimization techniques are being used to solve this scheduling problem. This paper deals in performing energy management of a rural microgrid test system using hybrids of Grey wolf optimizer (GWO). GWO is first modified (MGWO) as mentioned in literature. Further MGWO is amalgamated with sine cosine algorithm (SCA), particle swarm optimization (PSO) and crow search algorithm (CSA) to perform the optimization. All of numerical results, pictorial and statistical data point towards the superiority of the proposed MGWOPSO among the four other optimizers used.
{"title":"Hybrid Intelligence Techniques for Unit Commitment of Microgrids","authors":"B. Dey, B. Bhattacharyya","doi":"10.1109/ISAP48318.2019.9065950","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065950","url":null,"abstract":"Optimal scheduling of the distributed generation (DG) sources for a microgrid is very essential for the economical and a balanced load sharing operation of the same. Various classical and evolutionary optimization techniques are being used to solve this scheduling problem. This paper deals in performing energy management of a rural microgrid test system using hybrids of Grey wolf optimizer (GWO). GWO is first modified (MGWO) as mentioned in literature. Further MGWO is amalgamated with sine cosine algorithm (SCA), particle swarm optimization (PSO) and crow search algorithm (CSA) to perform the optimization. All of numerical results, pictorial and statistical data point towards the superiority of the proposed MGWOPSO among the four other optimizers used.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123617927","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065976
F. Lezama, J. Soares, Z. Vale
Increased adoption of distributed resources and renewables in distribution networks has led to a significant interest in local energy transactions at lower levels of the energy supply chain. Local energy markets (LM) are expected to play a crucial part in guaranteeing the balance between generation and consumption and contribute to the reduction of carbon emissions. Besides, LMs aim at increasing the participation of small end-users in energy transactions, setting the stage for transactive energy systems. In this work, we explore the use of evolutionary algorithms (EAs) to solve a bi-level optimization problem that arises when trading energy in an LM. We compare the performance of different EAs under a realistic case study with nine agents trading energy in the day-ahead LM. Results suggest that EAs can provide solutions in which all agents can improve their profits. It is shown the advantages in terms of profits that an LM can bring to market participants, thereby increasing the tolerable penetration of renewable resources and facilitating the energy transition.
{"title":"Optimal Bidding in Local Energy Markets using Evolutionary Computation","authors":"F. Lezama, J. Soares, Z. Vale","doi":"10.1109/ISAP48318.2019.9065976","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065976","url":null,"abstract":"Increased adoption of distributed resources and renewables in distribution networks has led to a significant interest in local energy transactions at lower levels of the energy supply chain. Local energy markets (LM) are expected to play a crucial part in guaranteeing the balance between generation and consumption and contribute to the reduction of carbon emissions. Besides, LMs aim at increasing the participation of small end-users in energy transactions, setting the stage for transactive energy systems. In this work, we explore the use of evolutionary algorithms (EAs) to solve a bi-level optimization problem that arises when trading energy in an LM. We compare the performance of different EAs under a realistic case study with nine agents trading energy in the day-ahead LM. Results suggest that EAs can provide solutions in which all agents can improve their profits. It is shown the advantages in terms of profits that an LM can bring to market participants, thereby increasing the tolerable penetration of renewable resources and facilitating the energy transition.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"236 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132230983","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065994
Adhishree Srivastava, S. Parida
This work describes a preliminary research investigation to access the feasibility of using advanced machine learning techniques for predicting and diagnosing fault type and fault location in a power distribution network consisting distributed generation. The proposed approach uses three phase voltage and current measurements data, assumed to be available at all the source bus. To understand the potential of the machine learning methodology, practical scenarios in a distribution grid such as all types of faults i.e. SLG, LLG, LL, and LLL with different fault locations are addressed in this work. Initially, the fault data is generated which is used to train a fault locator module. Further same data is used to design a fault type detector model in offline mode. The online real time data when fed to these models are able to give exact location and type of fault. The results are obtained from seven techniques of machine learning and their comparison is also done. The approach is proved to be a feasible tool for fault analysis.
{"title":"Recognition of Fault Location and Type in a Medium Voltage System with Distributed Generation using Machine Learning Approach","authors":"Adhishree Srivastava, S. Parida","doi":"10.1109/ISAP48318.2019.9065994","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065994","url":null,"abstract":"This work describes a preliminary research investigation to access the feasibility of using advanced machine learning techniques for predicting and diagnosing fault type and fault location in a power distribution network consisting distributed generation. The proposed approach uses three phase voltage and current measurements data, assumed to be available at all the source bus. To understand the potential of the machine learning methodology, practical scenarios in a distribution grid such as all types of faults i.e. SLG, LLG, LL, and LLL with different fault locations are addressed in this work. Initially, the fault data is generated which is used to train a fault locator module. Further same data is used to design a fault type detector model in offline mode. The online real time data when fed to these models are able to give exact location and type of fault. The results are obtained from seven techniques of machine learning and their comparison is also done. The approach is proved to be a feasible tool for fault analysis.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133809139","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}
Pub Date : 2019-12-01DOI: 10.1109/ISAP48318.2019.9065955
Ricardo Faia, F. Lezama, P. Faria, Z. Vale
In the smart grid era, when the power system is under stress, demand response (DR) is considered a viable and practical solution for smoothing the demand curve. DR is a procedure that is applied to provide changes in consumers power consumption. These changes can be obtained by optimization techniques producing solutions for the management of power profiles of consumers. In general, optimization techniques can be divided into two groups: the exact methods and the approximate methods. In this paper, an optimization DR problem is formulated and solved using an approximate method based on evolutionary computation. The differential evolution (DE) and one variant called hybrid-adaptive DE (HyDE), as well as the Particle swarm optimization (PSO) algorithms are used and their performance is compared. The results show that DE algorithms are superior to PSO for this application and their performance is close to that obtained with an exact method.
{"title":"Differential Evolution Optimization for a Residential Demand Response Application","authors":"Ricardo Faia, F. Lezama, P. Faria, Z. Vale","doi":"10.1109/ISAP48318.2019.9065955","DOIUrl":"https://doi.org/10.1109/ISAP48318.2019.9065955","url":null,"abstract":"In the smart grid era, when the power system is under stress, demand response (DR) is considered a viable and practical solution for smoothing the demand curve. DR is a procedure that is applied to provide changes in consumers power consumption. These changes can be obtained by optimization techniques producing solutions for the management of power profiles of consumers. In general, optimization techniques can be divided into two groups: the exact methods and the approximate methods. In this paper, an optimization DR problem is formulated and solved using an approximate method based on evolutionary computation. The differential evolution (DE) and one variant called hybrid-adaptive DE (HyDE), as well as the Particle swarm optimization (PSO) algorithms are used and their performance is compared. The results show that DE algorithms are superior to PSO for this application and their performance is close to that obtained with an exact method.","PeriodicalId":316020,"journal":{"name":"2019 20th International Conference on Intelligent System Application to Power Systems (ISAP)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133113224","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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