The Experts below are selected from a list of 84 Experts worldwide ranked by ideXlab platform

Fang Wei - One of the best experts on this subject based on the ideXlab platform.

  • Method for fault location in a low-resistance grounded distribution network based on multi-source information fusion
    International Journal of Electrical Power & Energy Systems, 2021
    Co-Authors: He Wang, Chenlu Huang, Jian Zhang, Fang Wei
    Abstract:

    Abstract Several uncertain factors, such as the protection and circuit breaker refusing operation and misoperation, make it difficult to obtain accurate results based on a single fault location using the switching Quantity. So this study proposes a power grid fault location method via multi-data source information fusion. The compressed sensing algorithm is used to reconstruct the Electrical signal twice, and the rough fault range and Electrical Quantity fault degree are obtained respectively. Subsequently, the Bayesian network is used to obtain the switching fault degree of each element in the rough fault range. Finally, DS evidence theory fuses these two fault degrees to obtain the location result. PSCAD software builds an IEEE 14 node system, and the effectiveness of the proposed method is verified by simulation. Furthermore, it can quickly and accurately locate the fault after its occurrence and has good application prospects with respect to the fault location of a low-resistance grounded distribution network.

Jonas Christoffer Villumsen - One of the best experts on this subject based on the ideXlab platform.

  • ISGT - Sensor placement for optimal estimation in power distribution grids
    2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2015
    Co-Authors: Francesco Fusco, Jonas Christoffer Villumsen
    Abstract:

    The problem of placing sensors in distribution grids for optimal state estimation is studied. An improved formulation is proposed where the estimation error of any resulting Electrical Quantity is optimised, as opposed to traditional methods optimising the estimation quality in the state variables only. As a result, the solution is more robust, since the result is not specific to the arbitrary choice of the state variable, and more flexible, since any desired weight can be assigned to the estimation error of all possible Electrical quantities in the network. A greedy algorithm with convergence guarantees is also introduced based on general results on submodular functions.

He Wang - One of the best experts on this subject based on the ideXlab platform.

  • Method for fault location in a low-resistance grounded distribution network based on multi-source information fusion
    International Journal of Electrical Power & Energy Systems, 2021
    Co-Authors: He Wang, Chenlu Huang, Jian Zhang, Fang Wei
    Abstract:

    Abstract Several uncertain factors, such as the protection and circuit breaker refusing operation and misoperation, make it difficult to obtain accurate results based on a single fault location using the switching Quantity. So this study proposes a power grid fault location method via multi-data source information fusion. The compressed sensing algorithm is used to reconstruct the Electrical signal twice, and the rough fault range and Electrical Quantity fault degree are obtained respectively. Subsequently, the Bayesian network is used to obtain the switching fault degree of each element in the rough fault range. Finally, DS evidence theory fuses these two fault degrees to obtain the location result. PSCAD software builds an IEEE 14 node system, and the effectiveness of the proposed method is verified by simulation. Furthermore, it can quickly and accurately locate the fault after its occurrence and has good application prospects with respect to the fault location of a low-resistance grounded distribution network.

Francesco Fusco - One of the best experts on this subject based on the ideXlab platform.

  • ISGT - Sensor placement for optimal estimation in power distribution grids
    2015 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT), 2015
    Co-Authors: Francesco Fusco, Jonas Christoffer Villumsen
    Abstract:

    The problem of placing sensors in distribution grids for optimal state estimation is studied. An improved formulation is proposed where the estimation error of any resulting Electrical Quantity is optimised, as opposed to traditional methods optimising the estimation quality in the state variables only. As a result, the solution is more robust, since the result is not specific to the arbitrary choice of the state variable, and more flexible, since any desired weight can be assigned to the estimation error of all possible Electrical quantities in the network. A greedy algorithm with convergence guarantees is also introduced based on general results on submodular functions.

Jian Zhang - One of the best experts on this subject based on the ideXlab platform.

  • Method for fault location in a low-resistance grounded distribution network based on multi-source information fusion
    International Journal of Electrical Power & Energy Systems, 2021
    Co-Authors: He Wang, Chenlu Huang, Jian Zhang, Fang Wei
    Abstract:

    Abstract Several uncertain factors, such as the protection and circuit breaker refusing operation and misoperation, make it difficult to obtain accurate results based on a single fault location using the switching Quantity. So this study proposes a power grid fault location method via multi-data source information fusion. The compressed sensing algorithm is used to reconstruct the Electrical signal twice, and the rough fault range and Electrical Quantity fault degree are obtained respectively. Subsequently, the Bayesian network is used to obtain the switching fault degree of each element in the rough fault range. Finally, DS evidence theory fuses these two fault degrees to obtain the location result. PSCAD software builds an IEEE 14 node system, and the effectiveness of the proposed method is verified by simulation. Furthermore, it can quickly and accurately locate the fault after its occurrence and has good application prospects with respect to the fault location of a low-resistance grounded distribution network.