TOPOLOGY ERROR IDENTIFICATION WITH LIMITED NUMBER OF MEASUREMENTS IN POWER DISTRIBUTION SYSTEMS

Date

2023-9-11

Author

Parlaktuna, Kemal

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Electricity has become an indispensable part of the modern human. Traditionally electricity was generated, transmitted and distributed in different grid levels. Generation and distribution of electricity was not intertwined. Monitoring the distribution network was not necessary as the power was flowing unidirectionally and the loads were stationary, meaning that the distribution system operator had enough information to operate the system. However, with increasing popularity of renewable energy sources and mobile load technologies such as electric vehicles, the roles are not clearly defined for each grid level. These new advancements also bring new challenges and business opportunities for distribution system operator. The operator should improve its situational awareness on the system by conducting several analysis such as power flow or state estimation. Both of these methods rely on correct topology both in terms of parameters and switch statuses. Several switching actions occur daily in power distribution systems. Not all switches are equipped with communication devices that relay the current switch status. Even if the switches are equipped with communication devices, due to malfunctions or communication errors the known switch status might be different than the actual switch status. Moreover, state estimation also requires redundant number of measurements which are not available in distribution level. Due to large system size in terms of bus number, not all elements of the system are equipped with both measurement instruments and devices to telemeter the gathered measurements. This thesis proposes a method for power distribution system with limited measurements to identify switch status mismatches using Direct Load Flow (DLF) and Weighted Least Absolute Values (WLAV) estimator.

Subject Keywords

state estimation, direct load flow, situational awareness, distribution systems, topology identification

URI

https://hdl.handle.net/11511/105472

Collections

Graduate School of Natural and Applied Sciences, Thesis