After the North China grid and the Central China grid get into connection with the UHVAC demonstration, a new phenomenon is discovered according to some simulations. That is, the faults at the remote end of the UHV in...After the North China grid and the Central China grid get into connection with the UHVAC demonstration, a new phenomenon is discovered according to some simulations. That is, the faults at the remote end of the UHV interconnected grid will result in significant power fluctuation and voltage drop on the UHV transmission line and even system splitting. But the faults near the UHV line only have marginal effects. Further, the simulation results also indicate that the short-circuit current of the buses near the UHV line is larger than that of the buses far away from the UHV line. This phenomenon is divergent from the traditional view. In this paper, the detail will be introduced, and the factors influencing the system stability after faults are presented and analyzed. The results indicate that transmission power of the UHV line and of the lines between the remote end and the major grid influence the fluctuation on UHV line. The load model and the grid structure of the remote end also have effect on it. Finally, corresponding control scheme is presented to improve the operation conditions of the UHV interconnected grid and ensure its security and stability.展开更多
With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intri...With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.展开更多
It has been recognized recently that when injecting renewable energy source power to a load bus which connected to a distributed feeder in a power grid system, a stability problem occurs particularly when having high ...It has been recognized recently that when injecting renewable energy source power to a load bus which connected to a distributed feeder in a power grid system, a stability problem occurs particularly when having high fault duties that exceeding the circuit breaker ratings at some substations. In this paper an analysis of power flow, short circuit, stability and protection is given in detail to an example of limited 7-bus power grid system. Comparison is illustrated between power grid with and without distributed generators regarding bus voltages, fault currents, critical power angles, selected current transformers and over current relay settings in each bus.展开更多
文摘After the North China grid and the Central China grid get into connection with the UHVAC demonstration, a new phenomenon is discovered according to some simulations. That is, the faults at the remote end of the UHV interconnected grid will result in significant power fluctuation and voltage drop on the UHV transmission line and even system splitting. But the faults near the UHV line only have marginal effects. Further, the simulation results also indicate that the short-circuit current of the buses near the UHV line is larger than that of the buses far away from the UHV line. This phenomenon is divergent from the traditional view. In this paper, the detail will be introduced, and the factors influencing the system stability after faults are presented and analyzed. The results indicate that transmission power of the UHV line and of the lines between the remote end and the major grid influence the fluctuation on UHV line. The load model and the grid structure of the remote end also have effect on it. Finally, corresponding control scheme is presented to improve the operation conditions of the UHV interconnected grid and ensure its security and stability.
基金supported by the National Natural Science Foundation China under Grants number 62073232,and the Science and Technology Project of Shenzhen,China(KCXST20221021111402006,JSGG20220831105800002)and the“Nanling Team Project”of Shaoguan city,and the Science and Technology project of Tianjin,China(22YFYSHZ00330)+1 种基金and Shenzhen Excellent Innovative Talents RCYX20221008093036022,Shenzhen-HongKong joint funding project(A)(SGDX20230116092053005)the Shenzhen Undertaking the National Major Science and Technology Program,China(CJGJZD20220517141405012).
文摘With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.
文摘It has been recognized recently that when injecting renewable energy source power to a load bus which connected to a distributed feeder in a power grid system, a stability problem occurs particularly when having high fault duties that exceeding the circuit breaker ratings at some substations. In this paper an analysis of power flow, short circuit, stability and protection is given in detail to an example of limited 7-bus power grid system. Comparison is illustrated between power grid with and without distributed generators regarding bus voltages, fault currents, critical power angles, selected current transformers and over current relay settings in each bus.
