An improved algorithm for optimal load shedding in power systems

A blackout is usually the result of load increasing beyond the transmission capacity of the power system. A collapsing system enters a contingency state before the blackout. This contingency state is characterized by a decline in the bus voltage magnitudes. To avoid blackouts, power systems may start shedding load when a contingency state occurs called under voltage load shedding (UVLS). The success of a UVLS scheme in arresting the contingency state depends on shedding the optimum amount of load at the optimum time and location. This paper proposes a hybrid algorithm based on genetic algorithms (GA) and particle swarm optimization (PSO). The proposed algorithm can be used to find the optimal amount of load shed for systems under stress (overloaded) in smart grids. The proposed algorithm uses the fast voltage stability index (FVSI) to determine the weak buses in the system and then calculates the optimal amount of load shed to recover a collapsing system. The performance analysis shows that the proposed algorithm can improve the voltage profile by 0.022 per units with up to 75% less load shedding and a convergence time that is 53% faster than GA

A novel grid-oriented dynamic weight parameter based improved variant of Jaya algorithm

Jaya algorithm (JA) is a single-step metaheuristic optimization technique that is free from algorithm-specific parameters. Regardless of its simplicity, JA proved its effective performance against the variety of optimization algorithms (Du et al., 2018). However, like other swarm-based optimization techniques, the JA also suffers from the inadequacies of slow or premature convergence (Farah and Belazi, 2018). In this study, an improved variant of JA (IJaya) is proposed whose functioning depends on the randomly initiated bounds based grid-oriented weight parameters. Initially, aiming to balance the global exploration and local exploitation capabilities of JA, a dynamic weight parameter is introduced as a varying coefficient for the entire solution updating expression of JA. Then, to maintain the population diversity and to mitigate the complexity of parameter tuning, the introduced weight parameter is dealt with the randomly selected parameter bounds based grid-search mechanism. The proposed IJaya algorithm is benchmarked on well-known 15 unconstrained mathematical test functions, and its performance is analyzed against the standard JA, one modified variant of JA, some well-known state-of-the-art, and few newly introduced optimization algorithms. Furthermore, the non-parametric Friedman and Quade rank tests are also conducted which confirmed the superiority of proposed IJaya both in convergence rate and solution quality. The paper also presents the results obtained by IJaya in two classical structural design problems (a cantilever beam and a 3-bar truss) and a real-world electrical power engineering problem. Numerical results clearly prove the efficiency of the proposed algorithm

Cyanotriazoles are selective topoisomerase II poisons that rapidly cure trypanosome infections

Millions who live in Latin America and sub-Saharan Africa are at risk of trypanosomatid infections, which cause Chagas disease and human African trypanosomiasis (HAT). Improved HAT treatments are available, but Chagas disease therapies rely on two nitroheterocycles, which suffer from lengthy drug regimens and safety concerns that cause frequent treatment discontinuation. We performed phenotypic screening against trypanosomes and identified a class of cyanotriazoles (CTs) with potent trypanocidal activity both in vitro and in mouse models of Chagas disease and HAT. Cryo-electron microscopy approaches confirmed that CT compounds acted through selective, irreversible inhibition of trypanosomal topoisomerase II by stabilizing double-stranded DNA:enzyme cleavage complexes. These findings suggest a potential approach toward successful therapeutics for the treatment of Chagas disease

Cyanotriazoles are selective topoisomerase II poisons that rapidly cure trypanosome infections

Millions who live in Latin America and sub-Saharan Africa are at risk of trypanosomatid infections, which cause Chagas disease and human African trypanosomiasis (HAT). Improved HAT treatments are available, but Chagas disease therapies rely on two nitroheterocycles, which suffer from lengthy drug regimens and safety concerns that cause frequent treatment discontinuation. We performed phenotypic screening against trypanosomes and identified a class of cyanotriazoles (CTs) with potent trypanocidal activity both in vitro and in mouse models of Chagas disease and HAT. Cryo–electron microscopy approaches confirmed that CT compounds acted through selective, irreversible inhibition of trypanosomal topoisomerase II by stabilizing double-stranded DNA:enzyme cleavage complexes. These findings suggest a potential approach toward successful therapeutics for the treatment of Chagas disease