Optimal placement of PMU for complete observability of the interconnected power network considering zero-injection bus: A numerical approach

Abstract

<div data-canvas-width="397.3623059866966">This paper presents an approach to place the phasor measurement unit (PMU)</div><div data-canvas-width="397.3623059866962">optimally, which minimizes the setup cost of PMU. This methodology attains complete state estimation of the interconnected power networks. An integer linear programming (ILP) method is explored for the optimal PMU placement problem. It is used to determine the optimal location and minimum number of PMUs necessary to make the interconnected power network completely observable. ILP may provide many solutions if acquainting buses to zero injection buses are unhandled. In the case of more than one solution, a bus observability redundancy index and total system observability redundancy index is proposed to find the most promising solutions set for redundancy measurement. The proposed algorithm is applied to benchmark the optimal PMU placement solutions for the IEEE 14-bus, IEEE 30-bus, New England 39-bus, IEEE 118-bus, and NRPG 246-bus test systems. The obtained results of the proposed approach are compared with the existing standard algorithm, and it is observed that the proposed approach achieves complete observability of the interconnected power network under base-load conditions.</div>

Multi-objective fault-tolerant optimization algorithm for deployment of IoT applications on fog computing infrastructure

“…For instance, Refs. [13][14][15][16][17] were presented in literature to solve single objective engineering problems with heuristic and exact approaches. Some meta-heuristics GA-based [18][19][20][21][22][23], PSO-based [3,24,25], SA-based [26][27][28] have been developed to solve optimization problems in engineering domain.…”

“…In the aforementioned problem formulation, the Eqs. (16,17) are objective functions to be minimized at the same time the constraints drawn in Eqs. (18)(19)(20)(21)(22)(23)(24) must be met.…”

“…Algorithm 5 is called to assign two fitness values to each individual based on Eqs. (16,17) since it is a multiobjective problem. The main loop of proposed MOCSA starts in line 14 and ends in line 27.…”

“…In this paper, fitness function is adjusted based on total power consumption and overall latency which are in Eqs. (16) and (17). The proposed fitness function is depicted in Algorithm 5.…”

“…Mega Node which MOCSA extracts is depicted in Fig. 15b; it shows the optimal deployment plan and the selected fog nodes are 8,10,13,17,18,19,20,24,30,35,38,39,40,42,45,49,51,52,53,58, 64 and 66. In addition to, Fig.…”