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DC Field | Value | Language |
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dc.contributor.author | Theodorakatos, Nikolaos P | - |
dc.contributor.author | Babu, Rohit | - |
dc.contributor.author | Theodoridis, Christos A | - |
dc.contributor.author | Moschoudis, Angelos P | - |
dc.date.accessioned | 2024-08-29T05:43:37Z | - |
dc.date.available | 2024-08-29T05:43:37Z | - |
dc.date.issued | 2024 | - |
dc.identifier.citation | Vol. 17, No. 5 | en_US |
dc.identifier.issn | 1999-4893 | - |
dc.identifier.uri | https://doi.org/10.3390/a17050191 | - |
dc.identifier.uri | https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/16595 | - |
dc.description.abstract | Phasor measurement units (PMUs) are deployed at power grid nodes around the transmission grid, determining precise power system monitoring conditions. In real life, it is not realistic to place a PMU at every power grid node; thus, the lowest PMU number is optimally selected for the full observation of the entire network. In this study, the PMU placement model is reconsidered, taking into account single- and multi-capacity placement models rather than the well-studied PMU placement model with an unrestricted number of channels. A restricted number of channels per monitoring device is used, instead of supposing that a PMU is able to observe all incident buses through the transmission connectivity lines. The optimization models are declared closely to the power dominating set and minimum edge cover problem in graph theory. These discrete optimization problems are directly related with the minimum set covering problem. Initially, the allocation model is declared as a constrained mixed-integer linear program implemented by mathematical and stochastic algorithms. Then, the (Formula presented.) integer linear problem is reformulated into a non-convex constraint program to find optimality. The mathematical models are solved either in binary form or in the continuous domain using specialized optimization libraries, and are all implemented in YALMIP software in conjunction with MATLAB. Mixed-integer linear solvers, nonlinear programming solvers, and heuristic algorithms are utilized in the aforementioned software packages to locate the global solution for each instance solved in this application, which considers the transformation of the existing power grids to smart grids. © 2024 by the authors. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Algorithms | en_US |
dc.publisher | Multidisciplinary Digital Publishing Institute (MDPI) | en_US |
dc.subject | Algorithms | en_US |
dc.subject | Binary-Integer Programming | en_US |
dc.subject | Channel Limit Capacity | en_US |
dc.subject | Multi-Channel Pmus | en_US |
dc.subject | Nonlinear Programming | en_US |
dc.subject | Observability | en_US |
dc.subject | Optimal Pmu Placement (Opp) | en_US |
dc.subject | Optimization | en_US |
dc.subject | Phasor Measurement Unit (Pmu) | en_US |
dc.subject | Sufficient Conditions For Optimality | en_US |
dc.title | Mathematical Models for the Single-Channel and Multi-Channel Pmu Allocation Problem and Their Solution Algorithms | en_US |
dc.type | Article | en_US |
Appears in Collections: | Journal Articles |
Files in This Item:
File | Size | Format | |
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algorithms-17-00191-v2.pdf | 3.58 MB | Adobe PDF | View/Open |
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