Optimal Multistage Scheduling of PMU Placement: An ILP Approach

Indian Inst. of Technol., Mumbai
IEEE Transactions on Power Delivery (Impact Factor: 1.52). 11/2008; DOI: 10.1109/TPWRD.2008.919046
Source: IEEE Xplore

ABSTRACT This paper addresses various aspects of optimal phasor measurement unit (PMU) placement problem. We propose a procedure for multistaging of PMU placement in a given time horizon using an integer linear programming (ILP) framework. Hitherto, modeling of zero injection constraints had been a challenge due to the intrinsic nonlinearity associated with it. We show that zero injection constraints can also be modeled as linear constraints in an ILP framework. Minimum PMU placement problem has multiple solutions. We propose two indices, viz, BOI and SORI, to further rank these multiple solutions, where BOI is bus observability index giving a measure of number of PMUs observing a given bus and SORI is system observability redundancy index giving sum of all BOI for a system. Results on IEEE 118 bus system have been presented. Results indicate that: (1) optimal phasing of PMUs can be computed efficiently; (2) proposed method of modeling zero injection constraints improve computational performance; and (3) BOI and SORI help in improving the quality of PMU placement.

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    ABSTRACT: This paper presents various aspects of optimal Phasor measurement unit (PMU) placement problem in Smart Grid. Improvements in power system control and protection is achieved by utilizing real time synchronized phasor measurements. Phasor Measurement Unit (PMU) technology provides phasor information (both magnitude and phase angle) in real time. The advantage of referring phase angle to a global reference time is helpful in capturing the wide area snap shot of the power system. Effective utilization of this technology is very useful in mitigating blackouts and learning the real time behaviour of the power system. In present work, Power System Analysis Toolbox (PSAT) is used for optimal placement of PMU using different methods such as Depth First, Graph Theory, Simulated Annealing, Re-Spinning Tree and Direct Spanning Tree. The proposed methods has been verified, compared and studied on IEEE 14 Bus System. The results are also compared with the results of Integer Linear Programming (ILP) Method by using TORA software. Index Terms-Integer linear programming (ILP), Phasor Measurement Unit (PMU),), PSAT, Smart grid, TORA. I: INTRODUCTION Based on physical power grid, smart grid is a new type power grid which highly integrates modern advanced information techniques, communication techniques, computer science and techniques with physical grids. It has many advantages, such as improving energy efficiency, reducing the impact to environment, enhancing the security and reliability of power supply and reducing the power loss of the electricity transmission network and so on. The objectives of smart grid are: fully satisfy customer requirements for electrical power, optimize resources allocation, ensure the security, reliability and economic of power supply, satisfy environment protection constraints, guarantee power quality and adapt to power market development [3]. Smart grid can provide customer with reliable, economical, clean and interactive power supply and value added services.
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    IEEE Transactions on Power Systems 05/2014; · 2.92 Impact Factor
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    ABSTRACT: A genetic algorithm (GA) based approach for reliability placement of phasor measurement units (PMUs) in smart grid is proposed. The algorithm combines two conflicting objectives which are maximization of the reliability of observability and minimization of the number of PMU placements for ensuring full system observability. The multi-objective problem is formulated as a nonlinear optimization problem and genetic algorithm approach is employed for solving the large scale bus systems. The optimization model is solved for IEEE 14, 30, 57, 118, and 2383 standard bus systems. The effectiveness of the proposed approach has been demonstrated by comparing results with exact algorithms for smaller problem sizes. The results suggest that by employing genetic algorithm, the system reliability of observability is improved by approximately 48% as compared to traditional optimal PMU placement. According to results, the proposed approach achieve significant cost savings (~17%-~50%) compared to available reliability based models in literature.
    Journal of Network and Innovative Computing. 06/2014; 2(1):30-40.

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