| 1. |
|
|
| 2. |
|
|
| 3. |
|
|
| 4. |
|
|
| 5. |
|
|
| 6. |
- Flores, Rafael, 1970, et al.
(author)
-
Positive and negative sequence estimation for unbalanced voltage dips
- 2003
-
In: in Proc. IEEE Power Engineering Society Summer Meeting, '03, Canada, JUL 13-17. ; , s. 2498-2502
-
Conference paper (peer-reviewed)abstract
- This paper proposes the use of a complex Kalman filter for the estimation of positive and negative sequences from three phase voltages. A complex voltage is obtained by applying the alphabeta-transform followed by the dq-transform using a rotational operator. The algorithm for three phase voltages containing K harmonics is also given.In the conventional method, estimation of positive and negative sequences is performed through two steps: the magnitude and phase-angle in each individual phase of the voltages are first estimated and the symmetrical component transformation is then applied. The proposed method offers a direct estimation of the positive and negative sequences that may reduce the estimation errors. In addition, the proposed method has a reduced computational cost since the number of state variables is reduced to 2/3 as compared to that in the conventional method. An experiment was performed on measured three phase voltage data. Results have shown that the proposed method offers a good estimation.
|
|
| 7. |
|
|
| 8. |
|
|
| 9. |
|
|
| 10. |
|
|
| 11. |
|
|
| 12. |
|
|
| 13. |
|
|
| 14. |
|
|
| 15. |
|
|
| 16. |
|
|
| 17. |
|
|
| 18. |
|
|
| 19. |
|
|
| 20. |
|
|
| 21. |
|
|
| 22. |
|
|
| 23. |
|
|
| 24. |
|
|
| 25. |
|
|
| 26. |
|
|
| 27. |
|
|
| 28. |
- Lundberg, Magnus, et al.
(author)
-
Infrared Detection of Buried Land Mines Based on Texture Modeling
- 2001
-
In: Spie Proceedings: Detection and Remediation Technologies for Mines and Minelike Targets VI. - : SPIE. ; 4394, s. 199-206, s. 199-206
-
Conference paper (peer-reviewed)abstract
- A parametric model for the infrared signature caused by a buried land mine is presented. Further, two ways ofmodeling the colored background noise, is proposed. In the first, it is assumed the noise can be approximated by anautoregressive process, while in the second, the statistics of the noise is described using recent development in texturemodeling, the so called FRAME method. Given an a priori distribution of the mine parameters in combination with atrained noise distribution, a Bayesian detector is derived. Experiments indicate that significant gains in performancecan be achieved as compared to the standard detector used, which correlates the infrared image with the knownmine shape and thresholds the square of the output.
|
|
| 29. |
|
|
| 30. |
|
|
| 31. |
|
|
| 32. |
|
|
| 33. |
|
|
| 34. |
|
|
| 35. |
|
|
| 36. |
|
|
| 37. |
|
|
| 38. |
|
|
| 39. |
|
|
