Pub Date : 2021-04-01DOI: 10.1109/ACPEE51499.2021.9436876
Dan Yang, Xiaoxiao Wo, P. Wei, Long Li, Yang Chen, Ye Cai
With the integration between power network and information network, the observability and controllability of modern power grid are improved, which has become a typical cyber physical system, However, there is no one-to-one correspondence between the actual information network and power network, so it is difficult to realize the overall perception of the power network. In this paper, a partial information network growth and evolution model is proposed, it can make up for the imperfection of information network, and realize the overall perception of the power grid. further research shows that it improves the ability of the power cyber physical system to resist the cascading failure.
{"title":"A partial information network growth and evolution model based on power system topology","authors":"Dan Yang, Xiaoxiao Wo, P. Wei, Long Li, Yang Chen, Ye Cai","doi":"10.1109/ACPEE51499.2021.9436876","DOIUrl":"https://doi.org/10.1109/ACPEE51499.2021.9436876","url":null,"abstract":"With the integration between power network and information network, the observability and controllability of modern power grid are improved, which has become a typical cyber physical system, However, there is no one-to-one correspondence between the actual information network and power network, so it is difficult to realize the overall perception of the power network. In this paper, a partial information network growth and evolution model is proposed, it can make up for the imperfection of information network, and realize the overall perception of the power grid. further research shows that it improves the ability of the power cyber physical system to resist the cascading failure.","PeriodicalId":127882,"journal":{"name":"2021 6th Asia Conference on Power and Electrical Engineering (ACPEE)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129824130","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 : 2021-04-01DOI: 10.1109/ACPEE51499.2021.9436934
Weifu Wang, Hongda Gao, Weifeng Zhang, Zhengjun Jin, Weiting Sun
Demand response is an effective way to alleviate the gap between energy supply and demand and enhance energy utilization efficiency. It is of great significance to promote the sustainable development of multi-energy complementary systems. Firstly, a demand response model based on demand elasticity matrix is constructed for a multi-energy complementary system. With the consideration of uncertainty of clean energy output, a two-level coordinated operation optimization model of the multi-energy complementary system is constructed, where the upper layer considers the overall benefit of the multi-energy complementary system, and the lower layer focuses on the benefit of the internal generation side. A two-stage solution algorithm composed of "clean energy uncertainty processing - optimization model processing based on genetic algorithm" was adopted to carry out multi-scenario simulation example analysis.
{"title":"A two-layer coordinated operation optimization model for multi-energy complementary systems considering demand response","authors":"Weifu Wang, Hongda Gao, Weifeng Zhang, Zhengjun Jin, Weiting Sun","doi":"10.1109/ACPEE51499.2021.9436934","DOIUrl":"https://doi.org/10.1109/ACPEE51499.2021.9436934","url":null,"abstract":"Demand response is an effective way to alleviate the gap between energy supply and demand and enhance energy utilization efficiency. It is of great significance to promote the sustainable development of multi-energy complementary systems. Firstly, a demand response model based on demand elasticity matrix is constructed for a multi-energy complementary system. With the consideration of uncertainty of clean energy output, a two-level coordinated operation optimization model of the multi-energy complementary system is constructed, where the upper layer considers the overall benefit of the multi-energy complementary system, and the lower layer focuses on the benefit of the internal generation side. A two-stage solution algorithm composed of \"clean energy uncertainty processing - optimization model processing based on genetic algorithm\" was adopted to carry out multi-scenario simulation example analysis.","PeriodicalId":127882,"journal":{"name":"2021 6th Asia Conference on Power and Electrical Engineering (ACPEE)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128385815","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 : 2021-04-01DOI: 10.1109/ACPEE51499.2021.9436933
Hongyang Zhao, Qiang Zhou, Ji Wu, Zhicheng Ma, Jinping Zhang, Xiuli Wang
Power distribution system and water distribution system, which are usually operate independently in tradition, are both important infrastructure in modern society. Under the background of renewable energy penetration increasing and energy conservation becoming social concerns, the joint operation of power distribution system and water distribution system could reduce the overall operation cost, improve social welfare and improve the flexibility of system operation. In this paper, we model the power distribution system and water distribution system, and simplify this model based on piecewise linear method. Then we obtain the model of power-water distribution system, which is a mixed integer linear programming problem. While the joint system is complex and the time spent on solving problem is too long for engineering application. Thus, in this paper, we propose an adaptive time clustering method based on the characteristic of the net load curve of power and water. We use an iterative algorithm to decide whether two adjacent time clusters could be merged into one in the joint operation problem. The case studies that the joint day-ahead operation problem, which proves that the model and method proposed in this paper could reduce the solution time while maintain high precision. It could meet the requirement of engineering application.
{"title":"Fast Dispatch Method for Integrated Energy System Based on Time Clustering Algorithm","authors":"Hongyang Zhao, Qiang Zhou, Ji Wu, Zhicheng Ma, Jinping Zhang, Xiuli Wang","doi":"10.1109/ACPEE51499.2021.9436933","DOIUrl":"https://doi.org/10.1109/ACPEE51499.2021.9436933","url":null,"abstract":"Power distribution system and water distribution system, which are usually operate independently in tradition, are both important infrastructure in modern society. Under the background of renewable energy penetration increasing and energy conservation becoming social concerns, the joint operation of power distribution system and water distribution system could reduce the overall operation cost, improve social welfare and improve the flexibility of system operation. In this paper, we model the power distribution system and water distribution system, and simplify this model based on piecewise linear method. Then we obtain the model of power-water distribution system, which is a mixed integer linear programming problem. While the joint system is complex and the time spent on solving problem is too long for engineering application. Thus, in this paper, we propose an adaptive time clustering method based on the characteristic of the net load curve of power and water. We use an iterative algorithm to decide whether two adjacent time clusters could be merged into one in the joint operation problem. The case studies that the joint day-ahead operation problem, which proves that the model and method proposed in this paper could reduce the solution time while maintain high precision. It could meet the requirement of engineering application.","PeriodicalId":127882,"journal":{"name":"2021 6th Asia Conference on Power and Electrical Engineering (ACPEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128283717","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 : 2021-04-01DOI: 10.1109/ACPEE51499.2021.9437086
N. Yao, Guangrui Shan, Xueqiong Zhu
In the object detection task of substation, the low resolution object would suffer from serious information loss problem, so some low resolution objects with potential security risks cannot be detected by object detection models such as Faster RCNN. We combine Faster RCNN model with Wasserstein GAN model, and propose Enhance RCNN model especially for the low resolution object detection in the substation. In our model, discriminator in GAN is used to distinguish the abstract feature difference between the high resolution object and the low resolution object after supplementing feature. And generator is used to supplement the abstract feature for low resolution object, so that its feature distribution is consistent with the feature distribution of high resolution object, thus improving the overall detection effect. The experimental results show that for the typical object in the substation such as person, bicycle and vehicle, Enhance RCNN model averagely improves mAP (Mean Average Precision) and IoU (Intersection-over-Union) by 7.79% and 6.57% respectively when is compared with the other models including Faster RCNN, Fast RCNN and SSD. For the low resolution object whose ratio of the object pixel to total image pixel less than 0.2%, Enhance RCNN model averagely improves mAP by 10.44%.
{"title":"Substation Object Detection Based on Enhance RCNN Model","authors":"N. Yao, Guangrui Shan, Xueqiong Zhu","doi":"10.1109/ACPEE51499.2021.9437086","DOIUrl":"https://doi.org/10.1109/ACPEE51499.2021.9437086","url":null,"abstract":"In the object detection task of substation, the low resolution object would suffer from serious information loss problem, so some low resolution objects with potential security risks cannot be detected by object detection models such as Faster RCNN. We combine Faster RCNN model with Wasserstein GAN model, and propose Enhance RCNN model especially for the low resolution object detection in the substation. In our model, discriminator in GAN is used to distinguish the abstract feature difference between the high resolution object and the low resolution object after supplementing feature. And generator is used to supplement the abstract feature for low resolution object, so that its feature distribution is consistent with the feature distribution of high resolution object, thus improving the overall detection effect. The experimental results show that for the typical object in the substation such as person, bicycle and vehicle, Enhance RCNN model averagely improves mAP (Mean Average Precision) and IoU (Intersection-over-Union) by 7.79% and 6.57% respectively when is compared with the other models including Faster RCNN, Fast RCNN and SSD. For the low resolution object whose ratio of the object pixel to total image pixel less than 0.2%, Enhance RCNN model averagely improves mAP by 10.44%.","PeriodicalId":127882,"journal":{"name":"2021 6th Asia Conference on Power and Electrical Engineering (ACPEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129251657","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 : 2021-04-01DOI: 10.1109/ACPEE51499.2021.9436868
Zhiqiang Zhang, Hejin Xiong, Chaozhi He
At present, the PID control is the most basic control method of the Active Magnetic Bearing, but the integer order PID control sometimes fails to get the reliability and stability requirements of the system. Fractional PID control is a generalization of traditional PID control and theoretically has a better control effect. In this paper, the FO-PID control method is applied to the Active Magnetic Bearing system, and the control system is studied and analyzed. The model built by Simulink compares the response performance of fractional PID and integer PID under unit step signal and the anti-interference ability under different interference. Simulation shows that fractional PID has better control effect and anti-interference ability.
{"title":"Research on Active Magnetic Bearing Rotor System Based on Fractional PID Control","authors":"Zhiqiang Zhang, Hejin Xiong, Chaozhi He","doi":"10.1109/ACPEE51499.2021.9436868","DOIUrl":"https://doi.org/10.1109/ACPEE51499.2021.9436868","url":null,"abstract":"At present, the PID control is the most basic control method of the Active Magnetic Bearing, but the integer order PID control sometimes fails to get the reliability and stability requirements of the system. Fractional PID control is a generalization of traditional PID control and theoretically has a better control effect. In this paper, the FO-PID control method is applied to the Active Magnetic Bearing system, and the control system is studied and analyzed. The model built by Simulink compares the response performance of fractional PID and integer PID under unit step signal and the anti-interference ability under different interference. Simulation shows that fractional PID has better control effect and anti-interference ability.","PeriodicalId":127882,"journal":{"name":"2021 6th Asia Conference on Power and Electrical Engineering (ACPEE)","volume":"67 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129328436","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 : 2021-04-01DOI: 10.1109/ACPEE51499.2021.9436923
Dongliang Su, Tao Qiu, Qihui Yin, Guoliang Li
For the data monitoring and fault early warning of new energy grid connection, the equipment status data, Supervisory Control And Data Acquisition (SCADA) system data, Wide Area Measurement System (WAMS) system data and other multi-source complex data were synchronized into the remote monitoring and early warning system of new energy station. The multi-source information fusion technology was used to analyze the data, and a new energy station operation parameter prediction method based on Multiple Extremum Learning Particle Swarm Optimization (MELPSO) algorithm was proposed. The correctness and effectiveness of the monitoring and early warning system was proved by comparing the predicted results of operational parameters with the measured results. The system can monitor the operation status of equipment in new energy station in real time, and has high prediction accuracy. It can judge the fault trend in advance and reduce the impact of new energy grid connection on the safe and stable operation of power grid.
为实现新能源并网数据监测与故障预警，将设备状态数据、SCADA (Supervisory Control and data Acquisition)系统数据、WAMS (Wide Area Measurement system)系统数据等多源复杂数据同步到新能源站远程监测预警系统。利用多源信息融合技术对数据进行分析，提出了一种基于多极值学习粒子群优化(MELPSO)算法的能源站运行参数预测新方法。通过对运行参数预测结果与实测结果的比较，验证了监测预警系统的正确性和有效性。该系统能够实时监测新能源站设备运行状态，预测精度高。可以提前判断故障趋势，减少新能源并网对电网安全稳定运行的影响。
{"title":"Research on Remote Monitoring and Early Warning System of New Energy Station Based on Multi-source Information Fusion","authors":"Dongliang Su, Tao Qiu, Qihui Yin, Guoliang Li","doi":"10.1109/ACPEE51499.2021.9436923","DOIUrl":"https://doi.org/10.1109/ACPEE51499.2021.9436923","url":null,"abstract":"For the data monitoring and fault early warning of new energy grid connection, the equipment status data, Supervisory Control And Data Acquisition (SCADA) system data, Wide Area Measurement System (WAMS) system data and other multi-source complex data were synchronized into the remote monitoring and early warning system of new energy station. The multi-source information fusion technology was used to analyze the data, and a new energy station operation parameter prediction method based on Multiple Extremum Learning Particle Swarm Optimization (MELPSO) algorithm was proposed. The correctness and effectiveness of the monitoring and early warning system was proved by comparing the predicted results of operational parameters with the measured results. The system can monitor the operation status of equipment in new energy station in real time, and has high prediction accuracy. It can judge the fault trend in advance and reduce the impact of new energy grid connection on the safe and stable operation of power grid.","PeriodicalId":127882,"journal":{"name":"2021 6th Asia Conference on Power and Electrical Engineering (ACPEE)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129121700","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 power distribution internet of things (IoT) needs to solve the interconnection and interoperation problems when large-scale perception layer devices are connected. CoAP protocol is the most widely used protocol for the perception layer. The use of IEC 61850 can solve the interconnection, interoperability and interoperability issues of the power distribution IoT. After the abstract communication service interface (ACSI) subset is determined by analyzing the requirements, the direct mapping strategy is adopted to construct the method of CoAP protocol to realize the IEC 61850 services. According to the characteristics of the CoAP protocol, the resource access method is designed. By comparing the coding efficiency and resource consumption, the data coding method is determined. Based on the test platform, the real-time performance is tested. The test results show the effectiveness of the proposed method.
{"title":"CoAP Protocol Communication Mapping for Power Distribution Internet of Things Based on IEC 61850","authors":"Bo Li, Ruifeng Zhao, Jiangang Lu, Weinian Ouyang, Yu Chen, Yongliang Liang","doi":"10.1109/ACPEE51499.2021.9436974","DOIUrl":"https://doi.org/10.1109/ACPEE51499.2021.9436974","url":null,"abstract":"The power distribution internet of things (IoT) needs to solve the interconnection and interoperation problems when large-scale perception layer devices are connected. CoAP protocol is the most widely used protocol for the perception layer. The use of IEC 61850 can solve the interconnection, interoperability and interoperability issues of the power distribution IoT. After the abstract communication service interface (ACSI) subset is determined by analyzing the requirements, the direct mapping strategy is adopted to construct the method of CoAP protocol to realize the IEC 61850 services. According to the characteristics of the CoAP protocol, the resource access method is designed. By comparing the coding efficiency and resource consumption, the data coding method is determined. Based on the test platform, the real-time performance is tested. The test results show the effectiveness of the proposed method.","PeriodicalId":127882,"journal":{"name":"2021 6th Asia Conference on Power and Electrical Engineering (ACPEE)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123850755","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 : 2021-04-01DOI: 10.1109/ACPEE51499.2021.9436879
Ruonan Zheng, Guojie Li, Keyou Wang, Bei Han, Zhitong Chen, Mengyang Li
In order to reduce the error of short-term photovoltaic (PV) power forecast without irradiance data, a prediction model based on daily feature matrix and long short term-memory (LSTM) deep neural network is proposed. Firstly, various factors affecting PV output are analyzed to select model inputs effectively. On this basis, a new similar day selection method considering the internal and external factors under multi-source data integration scenarios is introduced. Based on weather forecast information and day-ahead PV power data, daily feature matrices can be constructed to determine similar days by calculating the distances between the matrices. Then, the similar historical PV power vector is used as an input of a LSTM deep neural network, combined with meteorological forecast information to realize the final power prediction. Finally, the feasibility of the proposed method can be validated with the actual data of residential PV systems in North America.
{"title":"Short-term Photovoltaic Power Prediction Based on Daily Feature Matrix and Deep Neural Network","authors":"Ruonan Zheng, Guojie Li, Keyou Wang, Bei Han, Zhitong Chen, Mengyang Li","doi":"10.1109/ACPEE51499.2021.9436879","DOIUrl":"https://doi.org/10.1109/ACPEE51499.2021.9436879","url":null,"abstract":"In order to reduce the error of short-term photovoltaic (PV) power forecast without irradiance data, a prediction model based on daily feature matrix and long short term-memory (LSTM) deep neural network is proposed. Firstly, various factors affecting PV output are analyzed to select model inputs effectively. On this basis, a new similar day selection method considering the internal and external factors under multi-source data integration scenarios is introduced. Based on weather forecast information and day-ahead PV power data, daily feature matrices can be constructed to determine similar days by calculating the distances between the matrices. Then, the similar historical PV power vector is used as an input of a LSTM deep neural network, combined with meteorological forecast information to realize the final power prediction. Finally, the feasibility of the proposed method can be validated with the actual data of residential PV systems in North America.","PeriodicalId":127882,"journal":{"name":"2021 6th Asia Conference on Power and Electrical Engineering (ACPEE)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121230326","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 : 2021-04-01DOI: 10.1109/ACPEE51499.2021.9437116
Chengsheng Zhang, Zhenguo Shao, Feixiong Chen
The smart grid is rapidly developing to become highly connected and automated. These advancements have been mainly attributed to the ubiquitous data communication in the grid. However, low sampling frequency will limit the utilization degree of data because low frequency measurement power data contains little information. The existing methods of reconstructing the low-frequency sampling data into the high-frequency sampling data have poor accuracy of data reconstruction since most of them failed to capture the characteristics of power data. This paper proposes a novel method based on super-resolution generative adversarial network (SRGAN) to address this issue. First, we convert power data into data-images. Furthermore, the data-images are used to train the SRGAN model. Finally, the trained generator can be used to reconstruct the low-frequency sampling data into the high-frequency sampling data. Numerical experiments have been carried out based on photovoltaic (PV) power generation time-series data from the State Grid Corporation of China with separately reconstruction of the irradiance and PV power datas. The results demonstrate the superior performance of the proposed method compared with a series of state-of-the-art methods.
{"title":"A Power Data Reconstruction Method Based on Super-Resolution Generative Adversarial Network","authors":"Chengsheng Zhang, Zhenguo Shao, Feixiong Chen","doi":"10.1109/ACPEE51499.2021.9437116","DOIUrl":"https://doi.org/10.1109/ACPEE51499.2021.9437116","url":null,"abstract":"The smart grid is rapidly developing to become highly connected and automated. These advancements have been mainly attributed to the ubiquitous data communication in the grid. However, low sampling frequency will limit the utilization degree of data because low frequency measurement power data contains little information. The existing methods of reconstructing the low-frequency sampling data into the high-frequency sampling data have poor accuracy of data reconstruction since most of them failed to capture the characteristics of power data. This paper proposes a novel method based on super-resolution generative adversarial network (SRGAN) to address this issue. First, we convert power data into data-images. Furthermore, the data-images are used to train the SRGAN model. Finally, the trained generator can be used to reconstruct the low-frequency sampling data into the high-frequency sampling data. Numerical experiments have been carried out based on photovoltaic (PV) power generation time-series data from the State Grid Corporation of China with separately reconstruction of the irradiance and PV power datas. The results demonstrate the superior performance of the proposed method compared with a series of state-of-the-art methods.","PeriodicalId":127882,"journal":{"name":"2021 6th Asia Conference on Power and Electrical Engineering (ACPEE)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116308168","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 : 2021-04-01DOI: 10.1109/ACPEE51499.2021.9436959
Zhu Cunhao, Ma Shiying, Zheng Chao, Li Penghua
The emergence of wide area measurement system (WAMS) provides an opportunity for online transient stability assessment of power system. Based on the theory of complementary cluster energy barrier criterion (CCEBC), a transient stability control scheme based on real-time power angle trajectory fitting is proposed: The specific instability mode of the system is determined by the composite power angle, the system is transformed into a single machine infinite bus system; The real-time response information of WAMS is used to fit the power-angle trajectory of the disturbed system, and the instability judgment point of the extended phase trajectory is used as the end point of the measured fitting; Combined with extended equal area criterion (EEAC) theory, the tripping capacity of generation is calculated, and the controlled object is determined by the relative kinetic energy of each unit; In order to ensure the transient stability of the disturbed system, at the point of maximum acceleration power point and the point of instability judgment, the tripping control is performed respectively; Finally, a simulation example on Sanhua planning power grid verifies the effectiveness of the proposed emergency control strategy.
{"title":"Transient Stability Emergency Control Based on Real-time Power Angle Trajectory Fitting","authors":"Zhu Cunhao, Ma Shiying, Zheng Chao, Li Penghua","doi":"10.1109/ACPEE51499.2021.9436959","DOIUrl":"https://doi.org/10.1109/ACPEE51499.2021.9436959","url":null,"abstract":"The emergence of wide area measurement system (WAMS) provides an opportunity for online transient stability assessment of power system. Based on the theory of complementary cluster energy barrier criterion (CCEBC), a transient stability control scheme based on real-time power angle trajectory fitting is proposed: The specific instability mode of the system is determined by the composite power angle, the system is transformed into a single machine infinite bus system; The real-time response information of WAMS is used to fit the power-angle trajectory of the disturbed system, and the instability judgment point of the extended phase trajectory is used as the end point of the measured fitting; Combined with extended equal area criterion (EEAC) theory, the tripping capacity of generation is calculated, and the controlled object is determined by the relative kinetic energy of each unit; In order to ensure the transient stability of the disturbed system, at the point of maximum acceleration power point and the point of instability judgment, the tripping control is performed respectively; Finally, a simulation example on Sanhua planning power grid verifies the effectiveness of the proposed emergency control strategy.","PeriodicalId":127882,"journal":{"name":"2021 6th Asia Conference on Power and Electrical Engineering (ACPEE)","volume":"60 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114031118","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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