Pub Date : 2022-12-13DOI: 10.1109/MEPCON55441.2022.10021698
A. Elmitwally, M. Kotb, E. Gouda
―Distributed generators (DG) have serious impacts on Directional overcurrent relays (DORs)-based protection system. Miscoordinated operation of DORs and escalated electromagnetic stresses on equipment are major concerns. Topology variation of the power network adds complexity to the problem. This paper proposes a simultaneous resetting-fault current limiters (FCLs) approach to sustain DORs' coordination in a reconfigurable network at minimum cost. A multi-objective constrained optimization problem is formulated. It is solved by the particle swarm optimization technique to obtain the optimal FCLs sizes and the updated setting parameters of few selected DORs. The approach is applied to IEEE 30-bus system. It efficiently restores DORs coordination and eliminates extra stresses on components.
{"title":"A Strategy for Protection System Recovery in a Topology-Changing Network with DGs","authors":"A. Elmitwally, M. Kotb, E. Gouda","doi":"10.1109/MEPCON55441.2022.10021698","DOIUrl":"https://doi.org/10.1109/MEPCON55441.2022.10021698","url":null,"abstract":"―Distributed generators (DG) have serious impacts on Directional overcurrent relays (DORs)-based protection system. Miscoordinated operation of DORs and escalated electromagnetic stresses on equipment are major concerns. Topology variation of the power network adds complexity to the problem. This paper proposes a simultaneous resetting-fault current limiters (FCLs) approach to sustain DORs' coordination in a reconfigurable network at minimum cost. A multi-objective constrained optimization problem is formulated. It is solved by the particle swarm optimization technique to obtain the optimal FCLs sizes and the updated setting parameters of few selected DORs. The approach is applied to IEEE 30-bus system. It efficiently restores DORs coordination and eliminates extra stresses on components.","PeriodicalId":174878,"journal":{"name":"2022 23rd International Middle East Power Systems Conference (MEPCON)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133008576","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 : 2022-12-13DOI: 10.1109/MEPCON55441.2022.10021700
Mohammed Ebeed, A. Hossam-Eldin
The presence of the DG units influences the accuracy of the conventional fault detection and fault location methods. In this paper, a new approach to classify and accurately identify the fault location was presented. contrary to the traditional artificial intelligent methods which handle each signal separately, all the measured voltages and currents are converted to digital images. Accordingly, all the signals measured are processed simultaneously using a convolution neural network (CNN) to get more precise results. The Matlab Simulink, python programming, and google colab have been used to design a trained and validated CNN. Both of the faulty line and the type of fault have been precisely identified with insignificant errors not exciting 0.001 km. the CNN contains 1051885 trainable neurons.
{"title":"Convolution Neural Network Fault Identifier in Distribution Network in the Presence of Distribution Generation Units","authors":"Mohammed Ebeed, A. Hossam-Eldin","doi":"10.1109/MEPCON55441.2022.10021700","DOIUrl":"https://doi.org/10.1109/MEPCON55441.2022.10021700","url":null,"abstract":"The presence of the DG units influences the accuracy of the conventional fault detection and fault location methods. In this paper, a new approach to classify and accurately identify the fault location was presented. contrary to the traditional artificial intelligent methods which handle each signal separately, all the measured voltages and currents are converted to digital images. Accordingly, all the signals measured are processed simultaneously using a convolution neural network (CNN) to get more precise results. The Matlab Simulink, python programming, and google colab have been used to design a trained and validated CNN. Both of the faulty line and the type of fault have been precisely identified with insignificant errors not exciting 0.001 km. the CNN contains 1051885 trainable neurons.","PeriodicalId":174878,"journal":{"name":"2022 23rd International Middle East Power Systems Conference (MEPCON)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127916571","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 : 2022-12-13DOI: 10.1109/MEPCON55441.2022.10021765
M. Farhat, S. Kamel, A. Atallah, J. Domínguez-García
this research deals with the optimal power flow (OPF) problem from the uncertainty perspective which arises due to the high penetration levels of renewable energy sources (RESs) in recent years. In this work, RESs are represented by wind and solar PV generators and their uncertain outputs are modeled by weibull and lognormal probability density functions (PDFs), respectively. From economic point of view, the uncertain output of wind and solar power is translated into the total power cost in form of reserve or penalty cost based on the situation of their output. The IEEE-30 bus and 57 bus power systems are adjusted to involve wind and solar PV generators. Gradient based optimization (GBO) algorithm is employed for solving the OPF problem in these circumstances. The obtained results have been compared with the results of other optimization algorithms presented in literature. GBO has achieved the minimum total power cost for both modified IEEE-30 and 57 bus power systems, 781.5504 $/h, and 20233.5012 $/h, respectively with low computation time and fast convergence of solution.
{"title":"GBO Algorithm Application for Solving OPF Problem Considering Renewable Energy Uncertainty","authors":"M. Farhat, S. Kamel, A. Atallah, J. Domínguez-García","doi":"10.1109/MEPCON55441.2022.10021765","DOIUrl":"https://doi.org/10.1109/MEPCON55441.2022.10021765","url":null,"abstract":"this research deals with the optimal power flow (OPF) problem from the uncertainty perspective which arises due to the high penetration levels of renewable energy sources (RESs) in recent years. In this work, RESs are represented by wind and solar PV generators and their uncertain outputs are modeled by weibull and lognormal probability density functions (PDFs), respectively. From economic point of view, the uncertain output of wind and solar power is translated into the total power cost in form of reserve or penalty cost based on the situation of their output. The IEEE-30 bus and 57 bus power systems are adjusted to involve wind and solar PV generators. Gradient based optimization (GBO) algorithm is employed for solving the OPF problem in these circumstances. The obtained results have been compared with the results of other optimization algorithms presented in literature. GBO has achieved the minimum total power cost for both modified IEEE-30 and 57 bus power systems, 781.5504 $/h, and 20233.5012 $/h, respectively with low computation time and fast convergence of solution.","PeriodicalId":174878,"journal":{"name":"2022 23rd International Middle East Power Systems Conference (MEPCON)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126659216","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 : 2022-12-13DOI: 10.1109/MEPCON55441.2022.10021759
Mostafa Al‐Gabalawy, H. Ramadan, M. Mostafa, S. Hussien
Theoretically, the output of the wind turbine might be estimated based the most known power equation that depends mainly in the wind speed. There are many issues appeared in the phase of the estimating and control while applying this equation due to ignoring many weather conditions. This paper introduces a multivariate estimation for the power curve of the wind turbine considering the weather conditions such as wind speed, air density, wind turbulence, and wind share. There variables are termed features, and a lot of measurement has been occurred to collect all possible data for these features, where measurements (system data) exceed 47,000 points. this data is proceeded mainly by three steps of the data sciences; exploratory data analysis (EDA), data processing, and building the model, using Python programming language, where it gives more flexibility more than the other languages. The power curve estimation is executed using different machine learning tools such as linear regression, polynomial regression, random forest regression, gradient boost (G Boost), and extreme gradient regression (XGBoost). A comparative study is introduced considering the R-square and the root mean square error. From the results, XGBoost learning tool provides the best performance in terms of root mean square error (RMSE). The RMSE value decreases to 6.404 while using the proposed algorithm compared to (6.631, 6.6721, and 9.072) attained through the alternative G Boost, forest random, and 4th-degree polynomial respectively.
{"title":"Power Curve Estimation of a Wind Turbine Considering Different Weather Conditions using Machine Learning Algorithms","authors":"Mostafa Al‐Gabalawy, H. Ramadan, M. Mostafa, S. Hussien","doi":"10.1109/MEPCON55441.2022.10021759","DOIUrl":"https://doi.org/10.1109/MEPCON55441.2022.10021759","url":null,"abstract":"Theoretically, the output of the wind turbine might be estimated based the most known power equation that depends mainly in the wind speed. There are many issues appeared in the phase of the estimating and control while applying this equation due to ignoring many weather conditions. This paper introduces a multivariate estimation for the power curve of the wind turbine considering the weather conditions such as wind speed, air density, wind turbulence, and wind share. There variables are termed features, and a lot of measurement has been occurred to collect all possible data for these features, where measurements (system data) exceed 47,000 points. this data is proceeded mainly by three steps of the data sciences; exploratory data analysis (EDA), data processing, and building the model, using Python programming language, where it gives more flexibility more than the other languages. The power curve estimation is executed using different machine learning tools such as linear regression, polynomial regression, random forest regression, gradient boost (G Boost), and extreme gradient regression (XGBoost). A comparative study is introduced considering the R-square and the root mean square error. From the results, XGBoost learning tool provides the best performance in terms of root mean square error (RMSE). The RMSE value decreases to 6.404 while using the proposed algorithm compared to (6.631, 6.6721, and 9.072) attained through the alternative G Boost, forest random, and 4th-degree polynomial respectively.","PeriodicalId":174878,"journal":{"name":"2022 23rd International Middle East Power Systems Conference (MEPCON)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124898979","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 : 2022-12-13DOI: 10.1109/MEPCON55441.2022.10021686
Reem Y. Abdelghany, S. Kamel, Hamdy M. Sultan, Mohamed H. Hassan, L. Nasrat
One of the most important issues in improving of the efficiency of the photovoltaic system (PV) is finding the correct PV model. Determination of optimum parameters for PV models is vital to optimize and simulate PV systems based on the I-V characteristic, which is a nonlinear relationship Therefore, reaching the best PV model requires effective optimizers. This paper applies a new bio-inspired algorithm called Sooty Tern Optimization Algorithm (STOA) to obtain values of unknown parameters of various types of solar cells. The results of the experiment showed that, compared to other optimization algorithms, the used algorithm can obtain more accurate values of the estimated parameters.
{"title":"Optimal Solar Cell Parameter Estimation Based on Sooty Tern Optimization Algorithm","authors":"Reem Y. Abdelghany, S. Kamel, Hamdy M. Sultan, Mohamed H. Hassan, L. Nasrat","doi":"10.1109/MEPCON55441.2022.10021686","DOIUrl":"https://doi.org/10.1109/MEPCON55441.2022.10021686","url":null,"abstract":"One of the most important issues in improving of the efficiency of the photovoltaic system (PV) is finding the correct PV model. Determination of optimum parameters for PV models is vital to optimize and simulate PV systems based on the I-V characteristic, which is a nonlinear relationship Therefore, reaching the best PV model requires effective optimizers. This paper applies a new bio-inspired algorithm called Sooty Tern Optimization Algorithm (STOA) to obtain values of unknown parameters of various types of solar cells. The results of the experiment showed that, compared to other optimization algorithms, the used algorithm can obtain more accurate values of the estimated parameters.","PeriodicalId":174878,"journal":{"name":"2022 23rd International Middle East Power Systems Conference (MEPCON)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131152766","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 : 2022-12-13DOI: 10.1109/MEPCON55441.2022.10021781
Ghazi A. Ghazi, E. Al-Ammar, H. Hasanien, Rania A. Turky
In standalone or grid-connected PV systems, maximum power point tracking (MPPT) methods are essential since they control DC boost converter's duty cycles, thereby extracts the maximum power possible from such systems. Moreover, the power supplied by the PV array is a nonlinear function of its terminal voltage and current, which require a successful MPPT to pursue the maximum power point (MPP) regardless of operating conditions. This paper suggests a Transient Search Optimization (TSO) based on self-tuning fuzzy-Proportional Integral (PI) controller for MPP pursuing in the standalone PV system. The PI regulator is utilized to regulate to the PV voltage in relation to a reference voltage generated by TSO method in which its gain parameters are tuned using a fuzzy expert system. The simulation was done using MATLAB/SIMULINK for standard and different test conditions (STC and DTC). The obtained results reveal that the maximum power delivered to the load has been achieved with an efficiency of 100% at STC, while it was achieved concerning variable irradiation at DTC
{"title":"Transient Search Optimization Based Fuzzy-PI Controller for MPPT of Standalone PV System","authors":"Ghazi A. Ghazi, E. Al-Ammar, H. Hasanien, Rania A. Turky","doi":"10.1109/MEPCON55441.2022.10021781","DOIUrl":"https://doi.org/10.1109/MEPCON55441.2022.10021781","url":null,"abstract":"In standalone or grid-connected PV systems, maximum power point tracking (MPPT) methods are essential since they control DC boost converter's duty cycles, thereby extracts the maximum power possible from such systems. Moreover, the power supplied by the PV array is a nonlinear function of its terminal voltage and current, which require a successful MPPT to pursue the maximum power point (MPP) regardless of operating conditions. This paper suggests a Transient Search Optimization (TSO) based on self-tuning fuzzy-Proportional Integral (PI) controller for MPP pursuing in the standalone PV system. The PI regulator is utilized to regulate to the PV voltage in relation to a reference voltage generated by TSO method in which its gain parameters are tuned using a fuzzy expert system. The simulation was done using MATLAB/SIMULINK for standard and different test conditions (STC and DTC). The obtained results reveal that the maximum power delivered to the load has been achieved with an efficiency of 100% at STC, while it was achieved concerning variable irradiation at DTC","PeriodicalId":174878,"journal":{"name":"2022 23rd International Middle East Power Systems Conference (MEPCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131282351","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 : 2022-12-13DOI: 10.1109/MEPCON55441.2022.10021769
M. Ali, A. Abdelsalam, Eyad S. Oda, A. Abdelaziz
In the electrical power system, the detection of power quality disturbances (PQDs) is a critical mission. In this paper, two-step methodology is used to solve PQDs detection; features extraction and classification. The features extraction step uses wavelet time scattering and the classification step uses the long short-term memory (LSTM) techniques. To assess the efficacy of the proposed approach, various simple PQ disturbances such as sag, swell, harmonics, and interruption, as well as complicated power quality events such as sag with harmonics and swell with harmonics, are produced using the MATLAB programming framework. A comparison using several methodologies is provided. The results demonstrate that wavelet scattering with LSTM can decrease classification computation complexity. Furthermore, it may significantly shorten classification time while assuring classification accuracy better than different approaches.
{"title":"Detection of PQ Short Duration Variations using Wavelet Time Scattering with LSTM","authors":"M. Ali, A. Abdelsalam, Eyad S. Oda, A. Abdelaziz","doi":"10.1109/MEPCON55441.2022.10021769","DOIUrl":"https://doi.org/10.1109/MEPCON55441.2022.10021769","url":null,"abstract":"In the electrical power system, the detection of power quality disturbances (PQDs) is a critical mission. In this paper, two-step methodology is used to solve PQDs detection; features extraction and classification. The features extraction step uses wavelet time scattering and the classification step uses the long short-term memory (LSTM) techniques. To assess the efficacy of the proposed approach, various simple PQ disturbances such as sag, swell, harmonics, and interruption, as well as complicated power quality events such as sag with harmonics and swell with harmonics, are produced using the MATLAB programming framework. A comparison using several methodologies is provided. The results demonstrate that wavelet scattering with LSTM can decrease classification computation complexity. Furthermore, it may significantly shorten classification time while assuring classification accuracy better than different approaches.","PeriodicalId":174878,"journal":{"name":"2022 23rd International Middle East Power Systems Conference (MEPCON)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132586970","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 : 2022-12-13DOI: 10.1109/MEPCON55441.2022.10021748
S. Dabour, A. Aboushady, I. A. Gowaid, M. Elgenedy, M. Farrag
A simplified dual-output boosting inverter topology (DOBI) is proposed in this study. It can be used to supply two independent single-phase loads from a low voltage DC-power supply with fewer active and passive components. The output voltages can be at common frequency (CF) mode or different frequencies (DF) mode of operations. The DOBI topology uses a single inductor and capacitor to control boosting and inversion operations. Compared to the traditional dual output inverter, the proposed topology in this paper (DOBI) reduces the active switches by 25%, while compared to the basic Z-source-based dual output inverter, it reduces the passive elements by 50%. The main drawback of this topology is the high-frequency current commutation problem of the forward diodes. A novel carrier-based pulse width modulation scheme to obtain sinusoidal output voltage with low input current ripples of the proposed topology is presented in this paper. Moreover, the presented topology's operating principles and mathematical analysis are introduced. Finally, a simulation study is presented to confirm the theoretical study.
{"title":"Analysis and Control of Simplified Dual-Output Single-Phase Split-Source Boost Inverters","authors":"S. Dabour, A. Aboushady, I. A. Gowaid, M. Elgenedy, M. Farrag","doi":"10.1109/MEPCON55441.2022.10021748","DOIUrl":"https://doi.org/10.1109/MEPCON55441.2022.10021748","url":null,"abstract":"A simplified dual-output boosting inverter topology (DOBI) is proposed in this study. It can be used to supply two independent single-phase loads from a low voltage DC-power supply with fewer active and passive components. The output voltages can be at common frequency (CF) mode or different frequencies (DF) mode of operations. The DOBI topology uses a single inductor and capacitor to control boosting and inversion operations. Compared to the traditional dual output inverter, the proposed topology in this paper (DOBI) reduces the active switches by 25%, while compared to the basic Z-source-based dual output inverter, it reduces the passive elements by 50%. The main drawback of this topology is the high-frequency current commutation problem of the forward diodes. A novel carrier-based pulse width modulation scheme to obtain sinusoidal output voltage with low input current ripples of the proposed topology is presented in this paper. Moreover, the presented topology's operating principles and mathematical analysis are introduced. Finally, a simulation study is presented to confirm the theoretical study.","PeriodicalId":174878,"journal":{"name":"2022 23rd International Middle East Power Systems Conference (MEPCON)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123977817","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 : 2022-12-13DOI: 10.1109/MEPCON55441.2022.10021785
Shaimaa Zalat, H. A. Abdelsalam, N. Abbasy
Distribution systems are exposed to many events, such as natural disasters which may lead to a major failure in feeding many loads as a result of the destruction of its poles, lines or other parts. The power supply may be affected and cannot operate. This paper proposes an approach to supply the critical loads from distributed energy resources (DERs) using mixed-integer linear programming (MILP) load restoration method combined with the reconfiguration dragonfly algorithm (DA) to restore the maximum number of critical loads. The first stage of the proposed approach is using the DERs to form microgrids when the main power is not in service. The reconfiguration of the distribution system is then applied in the second stage using the DA to supply the unrestored critical loads, considering minimum losses. The proposed method is tested on the IEEE 123-node feeder with 5 DERs. Simulation results show that the unrestored critical loads in the first stage can be re-energized by using the proposed method, hence the resiliency is enhanced.
{"title":"Microgrids Formation for Resiliency Improvement of Distribution Systems Considering Reconfiguration","authors":"Shaimaa Zalat, H. A. Abdelsalam, N. Abbasy","doi":"10.1109/MEPCON55441.2022.10021785","DOIUrl":"https://doi.org/10.1109/MEPCON55441.2022.10021785","url":null,"abstract":"Distribution systems are exposed to many events, such as natural disasters which may lead to a major failure in feeding many loads as a result of the destruction of its poles, lines or other parts. The power supply may be affected and cannot operate. This paper proposes an approach to supply the critical loads from distributed energy resources (DERs) using mixed-integer linear programming (MILP) load restoration method combined with the reconfiguration dragonfly algorithm (DA) to restore the maximum number of critical loads. The first stage of the proposed approach is using the DERs to form microgrids when the main power is not in service. The reconfiguration of the distribution system is then applied in the second stage using the DA to supply the unrestored critical loads, considering minimum losses. The proposed method is tested on the IEEE 123-node feeder with 5 DERs. Simulation results show that the unrestored critical loads in the first stage can be re-energized by using the proposed method, hence the resiliency is enhanced.","PeriodicalId":174878,"journal":{"name":"2022 23rd International Middle East Power Systems Conference (MEPCON)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130840265","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 : 2022-12-13DOI: 10.1109/MEPCON55441.2022.10021783
A. Hady, M. Mokhtar, M. Attia, Mariam A. Sameh
Due to its great benefits, the implementation of DC Microgrids has been increasing over the past few years. This led to attracting the researchers to improve its control to maximize its performance as possible. Droop resistance control is one of the most used control approaches. In this paper, Local Unimodal Sampling (LUS) optimization is used to optimize the values of droop resistance to ensure the best current sharing. Also, optimization is carried out to optimize the gains of fractional order PID controller of the droop controller which grantee ideal current sharing and minimized voltage deviation.
{"title":"Optimized Strategy for Enhancing DC-Microgrid's Performance using Local Unimodal Sampling (LUS) optimization algorithm","authors":"A. Hady, M. Mokhtar, M. Attia, Mariam A. Sameh","doi":"10.1109/MEPCON55441.2022.10021783","DOIUrl":"https://doi.org/10.1109/MEPCON55441.2022.10021783","url":null,"abstract":"Due to its great benefits, the implementation of DC Microgrids has been increasing over the past few years. This led to attracting the researchers to improve its control to maximize its performance as possible. Droop resistance control is one of the most used control approaches. In this paper, Local Unimodal Sampling (LUS) optimization is used to optimize the values of droop resistance to ensure the best current sharing. Also, optimization is carried out to optimize the gains of fractional order PID controller of the droop controller which grantee ideal current sharing and minimized voltage deviation.","PeriodicalId":174878,"journal":{"name":"2022 23rd International Middle East Power Systems Conference (MEPCON)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128877717","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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