Hybrid Optimization Strategies for Global Optimality in Non-Convex Programming Using SQP and IPMs: Avoiding the Maratos Effect in PMU Placement – A Case Study

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Publikašuvnnas:Journal of Physics: Conference Series vol. 3027, no. 1 (Jun 2025), p. 012041
Váldodahkki: Theodorakatos, Nikolaos P
Eará dahkkit: Babu, Rohit
Almmustuhtton:
IOP Publishing
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Liŋkkat:Citation/Abstract
Full Text - PDF
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024 7 |a 10.1088/1742-6596/3027/1/012041  |2 doi 
035 |a 3222635682 
045 2 |b d20250601  |b d20250630 
100 1 |a Theodorakatos, Nikolaos P  |u School of Electrical and Computer Engineering, National Technical University of Athens (NTUA) , Iroon Polytechniou 9, Zografou 15780, Athens, Greece 
245 1 |a Hybrid Optimization Strategies for Global Optimality in Non-Convex Programming Using SQP and IPMs: Avoiding the Maratos Effect in PMU Placement – A Case Study 
260 |b IOP Publishing  |c Jun 2025 
513 |a Journal Article 
520 3 |a This study investigates the optimum configuration of Phasor Measurement Units (PMUs) for strategic placement within power transmission networks, utilizing nonlinear programming to obtain complete power network observability and maximize the coverage of observed buses throughout the grid. To address this complex engineering problem, we first solve a 0-1 integer linear programming model. We then apply solution methods based on sequential quadratic programming and interior-point approaches in solving equivalent nonlinear programming models, incorporating a necessary and sufficient stopping criterion to ensure global optimality while avoiding the Maratos effect. Our approach avoids these infeasibility issues by leveraging a piecewise convex feasible region, permitting the optimizer solver to simultaneously satisfy both feasibility and optimality without the need for a penalty function to perform in the iterative process. This leads to the successful rejection of the Maratos effect, a crucial key factor in ensuring the accuracy and effectiveness of the optimization process without destroying the super-linear convergence close to optimality. Representative numerical results are presented, along with a discussion of optimality conditions essential for achieving the best solution. The minimization problem is solved in MATLAB in a two-stage process. Initially, an objective function with a single product is minimized to determine the number of PMUs required for wide-area monitoring, control, and state estimation. A second product is subsequently added to the objective function to maximize the observability of network buses. The optimal PMU placement set solutions are produced to give all network buses observed directly or indirectly. In order to find out globally optimal solutions, IEEE power networks are performed with the mathematical algorithms in MATLAB. 
653 |a Linear programming 
653 |a Placement 
653 |a Integer programming 
653 |a Measuring instruments 
653 |a Convexity 
653 |a Matlab 
653 |a Quadratic programming 
653 |a Optimization 
653 |a State estimation 
653 |a Phasors 
653 |a Feasibility 
653 |a Observability (systems) 
653 |a Nonlinear programming 
653 |a Penalty function 
700 1 |a Babu, Rohit  |u Department of Electrical and Electronics Engineering Alliance University , Anekal, Bengaluru, India 
773 0 |t Journal of Physics: Conference Series  |g vol. 3027, no. 1 (Jun 2025), p. 012041 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3222635682/abstract/embedded/75I98GEZK8WCJMPQ?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3222635682/fulltextPDF/embedded/75I98GEZK8WCJMPQ?source=fedsrch