Name
Abstract | ||
|---|---|---|
To ensure the safe operation of a power system, it is necessary to conduct its state estimation continuously. In this paper, a novel quantum genetic algorithm (QGA) is combined with unscented Kalman filter (UKF) for dynamic state estimation of power systems. Firstly, an innovation matrix is used to improve the estimation accuracy by constructing an adaptive correction factor for correcting the prediction covariance matrix in real time. The prediction error of constant Holt's two-parameter model is then analysed for adaptive optimization, and QGA is employed to adjust the parameters dynamically. Finally, simulation tests are carried out on IEEE 30 bus system and the results indicate that the proposed approach, namely QGA-UKF, has good estimation accuracy and stability that are higher than GA-UKF and UKF. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-22796-8_26 | ADVANCES IN NEURAL NETWORKS - ISNN 2019, PT I |
Keywords | Field | DocType |
Power system, Dynamic state estimation, Unscented Kalman filter, Quantum genetic algorithm | Mean squared prediction error,Adaptive optimization,Computer science,Matrix (mathematics),Quantum genetic algorithm,Electric power system,Algorithm,Kalman filter,Covariance matrix | Conference |
Volume | ISSN | Citations |
11554 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lihua Zhou | 1 | 18 | 7.71 |
| Minrui Fei | 2 | 1003 | 117.54 |
| Dajun Du | 3 | 214 | 25.76 |
| Wenting Li | 4 | 1 | 0.70 |
| Huosheng Hu | 5 | 2009 | 220.95 |
| Aleksandar Rakic | 6 | 1 | 1.36 |