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Using PMU data, the fault location algorithm has gradually been trended in the literature. Besides the optimality of the algorithm, the fault location application should directly receive the data of the faulty line from the PMU devices to have the best performance; using state estimation to create the input data of fault location algorithm not only wastes the golden time, but it also decreases the reliability. Given, it is not possible to install PMU devices in all buses of the system, it is better to install them for the lengthy lines having the most probability of the failure. Although various approaches have been presented in the literature to solve optimal PMU placement (OPP), the fault location observability has less been considered in OPP formulation Accordingly, this paper considers a new index, named fault location observability (FLO), as a second objective function in its OPP formulation. Using this index, in addition to minimizing PMU installation cost, the number of lengthy lines being directly observable are also maximized. Using the ɛ-constraint method, the multi-objective problem has been solved several times as single-objective optimizations. Given the constraints and objective functions are linear, the work presents a mixed integer linear programming (MILP) formulation for the placement problem. Using the GAMS under CPLEX solver, the global optimal solutions of MILP model corresponding to the various ɛ-constraints yields to a Pareto optimal front. The simulation results, next, implements the proposed method on the IEEE 300 bus test system as well as the practical OMAN grid to prove the effectiveness of the proposed method.

Distribution transformers (DTs) are deemed as one of the major and high-priced equipment of electrical grids and their destruction negatively affects the stability and security of the network. The insulation status of the transformer depends on the hotspot and oil temperatures. Accordingly, controlling and, if possible, reduction of transformer oil temperature will improve the insulation status. In this paper, the effect of using nano-oil on oil temperature and loading capacity increment (LCI) of DTs has been studied via the electro-thermal resistance model (E-TRM). The studied nanofluids are two volumetric concentrations of multi-walled carbon nanotubes (MWCNTs) and three volumetric concentrations of diamond nanoparticles dispersed in pure mineral oil (MO). First, the numerical results gained from the E-TRM method are compared and verified with the experiential results of a 500 kVA DT. As well as, the effect of using MWCNT, diamond and proposed ONF nanoparticles in the heat transfer capacity of the transformer are investigated and compared. The results demonstrate that the highest temperature reduction in comparison with MO among the studied nanofluids is about 1 °C and for nanofluid ODI2. While the use of hypothetical ONF nanofluid reduces the oil temperature by 2.7 °C. Finally, the LCI of DTs caused by the use of nanofluids is investigated by the proposed novel equation. The use of nanofluids, especially the proposed ONF nanofluid, leads to the LCI up to 5%.