### Abstract

AC power system state estimation process aims to produce a real-time 'snapshot' model for the network. Therefore, a grand challenge to the newly built smart grid is how to 'optimally' estimate the state with increasing uncertainties, such as intermittent wind power generation or inconsecutive vehicle charging. Mathematically, such estimation problems are usually formulated as Weighted Least Square (WLS) problems in literature. As the problems are nonconvex, current solvers, for instance the ones implementing the Newton's method, for these problems often achieve local optimum, rather than the much desired global optimum. Due to this local optimum issue, current estimators may lead to incorrect user power cut-offs or even costly blackouts in the volatile smart grid. To initialize the iterative solver, in this paper, we propose utilizing historical data as well as fast-growing computational power of Energy Management System, to efficiently obtain a good initial state. Specifically, kernel ridge regression is proposed in a Bayesian framework based on Nearest Neighbors search. Simulation results of the proposed method show that the new method produces an initial guess excelling current industrial approach.

Original language | English (US) |
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Title of host publication | 2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012 |

Pages | 599-604 |

Number of pages | 6 |

DOIs | |

State | Published - Dec 1 2012 |

Externally published | Yes |

Event | 2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012 - Tainan, Taiwan, Province of China Duration: Nov 5 2012 → Nov 8 2012 |

### Other

Other | 2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012 |
---|---|

Country | Taiwan, Province of China |

City | Tainan |

Period | 11/5/12 → 11/8/12 |

### Fingerprint

### Keywords

- historical data
- iterative algorithm
- kernel ridge regression
- Smart grid
- state estimation

### ASJC Scopus subject areas

- Computer Networks and Communications
- Communication

### Cite this

*2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012*(pp. 599-604). [6486051] https://doi.org/10.1109/SmartGridComm.2012.6486051

**A search method for obtaining initial guesses for smart grid state estimation.** / Weng, Yang; Negi, Rohit; Ilic, Marija D.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

*2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012.*, 6486051, pp. 599-604, 2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012, Tainan, Taiwan, Province of China, 11/5/12. https://doi.org/10.1109/SmartGridComm.2012.6486051

}

TY - GEN

T1 - A search method for obtaining initial guesses for smart grid state estimation

AU - Weng, Yang

AU - Negi, Rohit

AU - Ilic, Marija D.

PY - 2012/12/1

Y1 - 2012/12/1

N2 - AC power system state estimation process aims to produce a real-time 'snapshot' model for the network. Therefore, a grand challenge to the newly built smart grid is how to 'optimally' estimate the state with increasing uncertainties, such as intermittent wind power generation or inconsecutive vehicle charging. Mathematically, such estimation problems are usually formulated as Weighted Least Square (WLS) problems in literature. As the problems are nonconvex, current solvers, for instance the ones implementing the Newton's method, for these problems often achieve local optimum, rather than the much desired global optimum. Due to this local optimum issue, current estimators may lead to incorrect user power cut-offs or even costly blackouts in the volatile smart grid. To initialize the iterative solver, in this paper, we propose utilizing historical data as well as fast-growing computational power of Energy Management System, to efficiently obtain a good initial state. Specifically, kernel ridge regression is proposed in a Bayesian framework based on Nearest Neighbors search. Simulation results of the proposed method show that the new method produces an initial guess excelling current industrial approach.

AB - AC power system state estimation process aims to produce a real-time 'snapshot' model for the network. Therefore, a grand challenge to the newly built smart grid is how to 'optimally' estimate the state with increasing uncertainties, such as intermittent wind power generation or inconsecutive vehicle charging. Mathematically, such estimation problems are usually formulated as Weighted Least Square (WLS) problems in literature. As the problems are nonconvex, current solvers, for instance the ones implementing the Newton's method, for these problems often achieve local optimum, rather than the much desired global optimum. Due to this local optimum issue, current estimators may lead to incorrect user power cut-offs or even costly blackouts in the volatile smart grid. To initialize the iterative solver, in this paper, we propose utilizing historical data as well as fast-growing computational power of Energy Management System, to efficiently obtain a good initial state. Specifically, kernel ridge regression is proposed in a Bayesian framework based on Nearest Neighbors search. Simulation results of the proposed method show that the new method produces an initial guess excelling current industrial approach.

KW - historical data

KW - iterative algorithm

KW - kernel ridge regression

KW - Smart grid

KW - state estimation

UR - http://www.scopus.com/inward/record.url?scp=84876040045&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84876040045&partnerID=8YFLogxK

U2 - 10.1109/SmartGridComm.2012.6486051

DO - 10.1109/SmartGridComm.2012.6486051

M3 - Conference contribution

AN - SCOPUS:84876040045

SN - 9781467309110

SP - 599

EP - 604

BT - 2012 IEEE 3rd International Conference on Smart Grid Communications, SmartGridComm 2012

ER -