Pub Date : 2021-11-08DOI: 10.1109/eGRID52793.2021.9662133
Yidong Shi, Zeng Liu, Jiazhi Wang, Jinjun Liu
To deal with the voltage deviation problem at the point of common coupling (PCC), a decentralized secondary control based on the injection of an extra small-AC-signal (SACS) into the output voltage of each inverter is proposed in this paper. Equal compensation value from the secondary control (SC) can be guaranteed and the voltage at the PCC can be restored to the nominal value without communication links. Moreover, the proposed method can also endure the inaccurate line impedance and the start-up delay of SC making it suitable for practical applications. The effectiveness of the proposed method is demonstrated by the simulation and experimental results.
{"title":"A Decentralized PCC Voltage Secondary Control Method Based on Small-AC-Signal Injection for Parallel Inverters in Islanded Microgrids","authors":"Yidong Shi, Zeng Liu, Jiazhi Wang, Jinjun Liu","doi":"10.1109/eGRID52793.2021.9662133","DOIUrl":"https://doi.org/10.1109/eGRID52793.2021.9662133","url":null,"abstract":"To deal with the voltage deviation problem at the point of common coupling (PCC), a decentralized secondary control based on the injection of an extra small-AC-signal (SACS) into the output voltage of each inverter is proposed in this paper. Equal compensation value from the secondary control (SC) can be guaranteed and the voltage at the PCC can be restored to the nominal value without communication links. Moreover, the proposed method can also endure the inaccurate line impedance and the start-up delay of SC making it suitable for practical applications. The effectiveness of the proposed method is demonstrated by the simulation and experimental results.","PeriodicalId":198321,"journal":{"name":"2021 6th IEEE Workshop on the Electronic Grid (eGRID)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124153019","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 : 2021-11-08DOI: 10.1109/eGRID52793.2021.9662151
Huoming Yang, Malte Eggers, Peter Teske, S. Dieckerhoff
In recent years, continuous efforts have been made on the modeling and stability analysis of converter-dominated grids (CDGs) to guarantee efficient, stable and resilient operations. The literature has tried to reveal the mechanism behind abnormal instability and resonances caused by the interaction between multiple time-scale control loops within a single converter, different types of converters and power networks. It is commonly assumed that CDGs are three-phase balanced systems and supply only passive loads. In reality, CDGs can experience imbalance caused by asymmetric networks, loads or faults. Moreover, induction motor (IM) loads, which exhibit highly nonlinear couplings between dynamics of power, voltage and frequency, typically account for a large portion of electric loads. Ignoring the impact of imbalance and dynamic loads in the modeling and stability analysis of CDGs can lead to unrealistic stability assessment results. To fill the gap, this paper presents a general small-signal modeling framework for CDGs in the presence of IM loads. Linear time-periodic (LTP) eigenvalue analysis is performed to investigate the impact of the interaction between IM loads, grid-following (GFL) converters and virtual synchronous generator (VSG) converters on the system stability. The time-domain simulation and experimental results validate the theoretical analysis.
{"title":"Modeling and Stability Analysis of Converter-Dominated Grids with Dynamic Loads","authors":"Huoming Yang, Malte Eggers, Peter Teske, S. Dieckerhoff","doi":"10.1109/eGRID52793.2021.9662151","DOIUrl":"https://doi.org/10.1109/eGRID52793.2021.9662151","url":null,"abstract":"In recent years, continuous efforts have been made on the modeling and stability analysis of converter-dominated grids (CDGs) to guarantee efficient, stable and resilient operations. The literature has tried to reveal the mechanism behind abnormal instability and resonances caused by the interaction between multiple time-scale control loops within a single converter, different types of converters and power networks. It is commonly assumed that CDGs are three-phase balanced systems and supply only passive loads. In reality, CDGs can experience imbalance caused by asymmetric networks, loads or faults. Moreover, induction motor (IM) loads, which exhibit highly nonlinear couplings between dynamics of power, voltage and frequency, typically account for a large portion of electric loads. Ignoring the impact of imbalance and dynamic loads in the modeling and stability analysis of CDGs can lead to unrealistic stability assessment results. To fill the gap, this paper presents a general small-signal modeling framework for CDGs in the presence of IM loads. Linear time-periodic (LTP) eigenvalue analysis is performed to investigate the impact of the interaction between IM loads, grid-following (GFL) converters and virtual synchronous generator (VSG) converters on the system stability. The time-domain simulation and experimental results validate the theoretical analysis.","PeriodicalId":198321,"journal":{"name":"2021 6th IEEE Workshop on the Electronic Grid (eGRID)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126432010","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 : 2021-11-08DOI: 10.1109/eGRID52793.2021.9662134
Sahand Liasi, R. Hadidi, Narges S. Ghiasi
In recent decades, the increasing use of nonlinear loads has caused many problems in terms of power quality. These problems include low power factor, and voltage and current harmonics. The distorted voltage can result in increasing temperature of wires and cables, inappropriate performance of protective devices and disturbance in telecommunication lines. Therefore, it would be essential to install filters to omit or damp these distortions. Conventionally, passive filters were used to maintain harmonics under a sensible level. Nevertheless, this kind of filters has many problems such as large size and resonance issues. In recent years, by improvements in power electronics, passive filters have been replaced with active power filters (APF). Controlling APFs using PI, deadbeat, and predictive controllers have been discussed in different works. However, they all need an accurate model of the system or information about the converters. In this paper, we will provide two control strategies: first, an artificial neural network (ANN)-based control method which mimic conventional control methods; second, ANN-based recognition and control method, which does not require any information about the system model. This control method can be well suiting any system because it can control the whole system only based on the effects on the input on the output of the system. In this paper, ANN-based control methods have been discussed. Then, a control method based on ANN recognition and control will be introduced and developed. The simulation results will be brought, discussed, and compared to show the proficiency of the proposed method over the existent methods.
{"title":"Current Harmonic Compensation by Active Power Filter Using Neural Network-Based Recognition and Controller","authors":"Sahand Liasi, R. Hadidi, Narges S. Ghiasi","doi":"10.1109/eGRID52793.2021.9662134","DOIUrl":"https://doi.org/10.1109/eGRID52793.2021.9662134","url":null,"abstract":"In recent decades, the increasing use of nonlinear loads has caused many problems in terms of power quality. These problems include low power factor, and voltage and current harmonics. The distorted voltage can result in increasing temperature of wires and cables, inappropriate performance of protective devices and disturbance in telecommunication lines. Therefore, it would be essential to install filters to omit or damp these distortions. Conventionally, passive filters were used to maintain harmonics under a sensible level. Nevertheless, this kind of filters has many problems such as large size and resonance issues. In recent years, by improvements in power electronics, passive filters have been replaced with active power filters (APF). Controlling APFs using PI, deadbeat, and predictive controllers have been discussed in different works. However, they all need an accurate model of the system or information about the converters. In this paper, we will provide two control strategies: first, an artificial neural network (ANN)-based control method which mimic conventional control methods; second, ANN-based recognition and control method, which does not require any information about the system model. This control method can be well suiting any system because it can control the whole system only based on the effects on the input on the output of the system. In this paper, ANN-based control methods have been discussed. Then, a control method based on ANN recognition and control will be introduced and developed. The simulation results will be brought, discussed, and compared to show the proficiency of the proposed method over the existent methods.","PeriodicalId":198321,"journal":{"name":"2021 6th IEEE Workshop on the Electronic Grid (eGRID)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133529956","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 : 2021-11-08DOI: 10.1109/eGRID52793.2021.9662140
S. F. Zarei, M. A. Ghasemi, S. Peyghami, F. Blaabjerg
This paper proposes a fault detection scheme for microgrids with grid-forming inverters. In this paper, a superimposed phase-current scheme with a voltage-restraint element is proposed which identifies the faults in an islanded microgrid with grid-forming inverters. In the proposed method, different factors including the implemented fault-ride-through strategy, the fault current limiting scheme, and the control structure of the grid-forming inverter are considered. Furthermore, the moving window concept is included, which considerably increases the detection speed. The severity and type of short circuit fault do not affect the functionality of the proposed method, and both symmetrical/asymmetrical short circuit faults are properly identified by the proposed scheme. Finally, the performance of the proposed scheme is demonstrated by applying different symmetrical/asymmetrical faults in a test system.
{"title":"A Fault Detection Scheme for Islanded-Microgrid with Grid-Forming Inverters","authors":"S. F. Zarei, M. A. Ghasemi, S. Peyghami, F. Blaabjerg","doi":"10.1109/eGRID52793.2021.9662140","DOIUrl":"https://doi.org/10.1109/eGRID52793.2021.9662140","url":null,"abstract":"This paper proposes a fault detection scheme for microgrids with grid-forming inverters. In this paper, a superimposed phase-current scheme with a voltage-restraint element is proposed which identifies the faults in an islanded microgrid with grid-forming inverters. In the proposed method, different factors including the implemented fault-ride-through strategy, the fault current limiting scheme, and the control structure of the grid-forming inverter are considered. Furthermore, the moving window concept is included, which considerably increases the detection speed. The severity and type of short circuit fault do not affect the functionality of the proposed method, and both symmetrical/asymmetrical short circuit faults are properly identified by the proposed scheme. Finally, the performance of the proposed scheme is demonstrated by applying different symmetrical/asymmetrical faults in a test system.","PeriodicalId":198321,"journal":{"name":"2021 6th IEEE Workshop on the Electronic Grid (eGRID)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130115030","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 : 2021-11-08DOI: 10.1109/eGRID52793.2021.9662142
N. Tan, Filippo Savi, G. Buticchi, Sulaiha Ahmad, C. Gerada
The paper shows the idealized performance and a design methodology for the isolated version of the SEPIC DC/DC converter, including a non-dissipative snubber. This paper compares the use of a Single-Ended Primary-Inductor Converter (SEPIC) and a flyback converter as Photovoltaic (PV) charge controllers for battery charging applications. A simulation based study is also presented, comparing key performance metrics, like efficiency, input voltage and output current ripple, between the proposed architecture and an industry-standard flyback converter.
{"title":"SEPIC and Flyback Converters for Isolated Photovoltaic Battery Charging Application","authors":"N. Tan, Filippo Savi, G. Buticchi, Sulaiha Ahmad, C. Gerada","doi":"10.1109/eGRID52793.2021.9662142","DOIUrl":"https://doi.org/10.1109/eGRID52793.2021.9662142","url":null,"abstract":"The paper shows the idealized performance and a design methodology for the isolated version of the SEPIC DC/DC converter, including a non-dissipative snubber. This paper compares the use of a Single-Ended Primary-Inductor Converter (SEPIC) and a flyback converter as Photovoltaic (PV) charge controllers for battery charging applications. A simulation based study is also presented, comparing key performance metrics, like efficiency, input voltage and output current ripple, between the proposed architecture and an industry-standard flyback converter.","PeriodicalId":198321,"journal":{"name":"2021 6th IEEE Workshop on the Electronic Grid (eGRID)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133040217","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 : 2021-11-08DOI: 10.1109/eGRID52793.2021.9662158
Syed. R. B. Alvee, Bohyun Ahn, Taesic Kim, Ying Su, Y. Youn, Myung-Hyo Ryu
Ransomware has become a serious threat to the current computing world, requiring immediate attention to prevent it. Ransomware attacks can also have disruptive impacts on operation of smart grids including digital substations. This paper provides a ransomware attack modeling method targeting disruptive operation of a digital substation and investigates an artificial intelligence (AI)-based ransomware detection approach. The proposed ransomware file detection model is designed by a convolutional neural network (CNN) using 2-D grayscale image files converted from binary files. The experimental results show that the proposed method achieves 96.22% of ransomware detection accuracy.
{"title":"Ransomware Attack Modeling and Artificial Intelligence-Based Ransomware Detection for Digital Substations","authors":"Syed. R. B. Alvee, Bohyun Ahn, Taesic Kim, Ying Su, Y. Youn, Myung-Hyo Ryu","doi":"10.1109/eGRID52793.2021.9662158","DOIUrl":"https://doi.org/10.1109/eGRID52793.2021.9662158","url":null,"abstract":"Ransomware has become a serious threat to the current computing world, requiring immediate attention to prevent it. Ransomware attacks can also have disruptive impacts on operation of smart grids including digital substations. This paper provides a ransomware attack modeling method targeting disruptive operation of a digital substation and investigates an artificial intelligence (AI)-based ransomware detection approach. The proposed ransomware file detection model is designed by a convolutional neural network (CNN) using 2-D grayscale image files converted from binary files. The experimental results show that the proposed method achieves 96.22% of ransomware detection accuracy.","PeriodicalId":198321,"journal":{"name":"2021 6th IEEE Workshop on the Electronic Grid (eGRID)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132984844","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 : 2021-11-05DOI: 10.1109/eGRID52793.2021.9662132
Hussain Sarwar Khan, Ihab S. Mohamed, K. Kauhaniemi, Lantao Liu
The rapid growth of renewable energy technology enables the concept of microgrid (MG) to be widely accepted in the power systems. Due to the advantages of the DC distribution system such as easy integration of energy storage and less system loss, DC MG attracts significant attention nowadays. The linear controller such as PI or PID is matured and extensively used by the power electronics industry, but their performance is not optimal as system parameters are changed. In this study, an artificial neural network (ANN) based voltage control strategy is proposed for the DC-DC boost converter. In this paper, the model predictive control (MPC) is used as an expert, which provides the data to train the proposed ANN. As ANN is tuned finely, then it is utilized directly to control the step-up DC converter. The main advantage of the ANN is that the neural network system identification decreases the inaccuracy of the system model even with inaccurate parameters and has less computational burden compared to MPC due to its parallel structure. To validate the performance of the proposed ANN, extensive MATLAB/Simulink simulations are carried out. The simulation results show that the ANN-based control strategy has better performance under different loading conditions comparison to the PI controller. The accuracy of the trained ANN model is about 97%, which makes it suitable to be used for DC microgrid applications.
{"title":"Artificial Neural Network-Based Voltage Control of DC/DC Converter for DC Microgrid Applications","authors":"Hussain Sarwar Khan, Ihab S. Mohamed, K. Kauhaniemi, Lantao Liu","doi":"10.1109/eGRID52793.2021.9662132","DOIUrl":"https://doi.org/10.1109/eGRID52793.2021.9662132","url":null,"abstract":"The rapid growth of renewable energy technology enables the concept of microgrid (MG) to be widely accepted in the power systems. Due to the advantages of the DC distribution system such as easy integration of energy storage and less system loss, DC MG attracts significant attention nowadays. The linear controller such as PI or PID is matured and extensively used by the power electronics industry, but their performance is not optimal as system parameters are changed. In this study, an artificial neural network (ANN) based voltage control strategy is proposed for the DC-DC boost converter. In this paper, the model predictive control (MPC) is used as an expert, which provides the data to train the proposed ANN. As ANN is tuned finely, then it is utilized directly to control the step-up DC converter. The main advantage of the ANN is that the neural network system identification decreases the inaccuracy of the system model even with inaccurate parameters and has less computational burden compared to MPC due to its parallel structure. To validate the performance of the proposed ANN, extensive MATLAB/Simulink simulations are carried out. The simulation results show that the ANN-based control strategy has better performance under different loading conditions comparison to the PI controller. The accuracy of the trained ANN model is about 97%, which makes it suitable to be used for DC microgrid applications.","PeriodicalId":198321,"journal":{"name":"2021 6th IEEE Workshop on the Electronic Grid (eGRID)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122888527","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：