Abstract
Integrating renewable energy sources into the power distribution grid challenges protection system operation, leading to protection blinding when circuit breakers fail to trip due to fault current contribution from these sources. Communication-based adaptive protection can address this issue, but communication system components like sensors can fail. This research proposes a genetic algorithm-based approach to optimally place redundant sensors, minimizing protection blinding under communication uncertainty within a redundancy budget. The fault tolerance, measured by a new metric called redundancy degree, reflects the number of redundant components deployed. Results demonstrate the algorithm’s effectiveness in optimizing redundant sensor locations, reducing system costs, and improving fault tolerance. For the system and scenarios investigated, an average of 60% redundant sensors are relocated, reducing the average protection trip time by 36.65% compared to a baseline approach that does not consider communication uncertainty. This encourages incorporating communication component failure considerations in power system planning.
| Original language | English |
|---|---|
| Pages (from-to) | (In-Press) |
| Number of pages | 11 |
| Journal | IEEE Transactions on Network and Service Management |
| Volume | (In-Press) |
| Early online date | 13 Nov 2024 |
| DOIs | |
| Publication status | E-pub ahead of print - 13 Nov 2024 |
Bibliographical note
© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Copyright © and Moral Rights are retained by the author(s) and/ or other copyright owners. A copy can be downloaded for personal non-commercial research or study, without prior permission or charge. This item cannot be reproduced or quoted extensively from without first obtaining permission in writing from the copyright holder(s). The content must not be changed in any way or sold commercially in any format or medium without the formal permission of the copyright holders.
This document is the author’s post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.
Funder
This work was supported by the German Research Foundation DFG as part of the project “Multi-ResiServD” with the project identification number 360352892 of the priority program DFG SPP 1984 - Hybrid and multimodal energy systems: System theory methods for the transformation and operationof complex networks.
Funding
This work was supported by the German Research Foundation DFG as part of the project “Multi-ResiServD” with the project identification number 360352892 of the priority program DFG SPP 1984 - Hybrid and multimodal energy systems: System theory methods for the transformation and operation of complex networks.
| Funders | Funder number |
|---|---|
| Deutsche Forschungsgemeinschaft | 360352892 |
Keywords
- Genetic algorithm
- redundancy
- uncertainty
- adaptive protection
