A Mn(I) complex (1)
bearing a proton responsive hydroxy
unit on 1,8-naphthyridine-N-oxide scaffold (L
1
H) was synthesized. The molecular
structure of 1 revealed the lactim form of the ligand.
The corresponding deprotonated lactam complexes [18-C-6-K·2] and 3 were prepared and structurally characterized.
The acid–base equilibrium between the lactim and lactam forms
was studied by 1H NMR and UV–vis spectra. The catalytic
efficiency of 1 was evaluated by performing α-alkylation
reaction of ketones with primary alcohols. The scope of the α-alkylation
reaction is broad in terms of both ketones and alcohols. The efficacy
of the protic catalyst is demonstrated in the alkylation of the bioactive
steroids progesterone and pregnenolone. A controlled catalyst [Mn(L2)(CO)3Br] (4), which is structurally
similar to 1 but devoid of the proton responsive hydroxy
unit, shows significantly reduced catalytic efficiency validating
the crucial role of the hydroxy functionality in 1. Kinetic
study, control reactions, and deuterium labeling experiments were
conducted to gain mechanistic insights.
Large-scale blackouts that have occurred in the past few decades have necessitated the need to do extensive research in the field of grid security assessment. With the aid of synchrophasor technology, which uses phasor measurement unit (PMU) data, dynamic security assessment (DSA) can be performed online. However, existing applications of DSA are challenged by variability in system conditions and unaccounted for measurement errors. To overcome these challenges, this research develops a DSA scheme to provide security prediction in real-time for load profiles of different seasons in presence of realistic errors in the PMU measurements. The major contributions of this paper are: (1) develop a DSA scheme based on PMU data, (2) consider seasonal load profiles, (3) account for varying penetrations of renewable generation, and (4) compare the accuracy of different machine learning (ML) algorithms for DSA with and without erroneous measurements. The performance of this approach is tested on the IEEE-118 bus system. Comparative analysis of the accuracies of the ML algorithms under different operating scenarios highlights the importance of considering realistic errors and variability in system conditions while creating a DSA scheme.
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