Optimal distribution grid allocation of reactive power with a focus on the particle swarm optimization technique and voltage stability.

A structured approach to managing reactive power is imperative within the context of power systems. Among the restructuring initiatives in the electrical sector, power systems have undergone delineation into three principal categories: generation, transmission, and distribution entities, each of which is overseen by an independent system operator. Notably, active power emerges as the predominant commodity transacted within the electrical market, with the autonomous grid operator assuming the responsibility of ensuring conducive conditions for the execution of energy contracts across the transmission infrastructure. Ancillary services, comprising essential frameworks for energy generation and delivery to end-users, encompass reactive power services pivotal in the regulation of bus voltage. Of particular significance among the array of ancillary services requisite in a competitive market milieu is the provision of adequate reactive power to uphold grid safety and voltage stability. A salient impediment to the realization of energy contracts lies in the inadequacy of reactive power within the grid, which poses potential risks to its operational safety and voltage equilibrium. The optimal allocation of the reactive power load is predicated upon presumptions of consistent outcomes within the active power market. Under this conceptual framework, generators are afforded continual compensation for the provision of reactive power indispensable for sustaining their active energy production endeavors.

Using feed-forward perceptron Artificial Neural Network (ANN) model to determine the rolling force, power and slip of the tandem cold rolling.

In this paper, an Artificial Neural Network (ANN) is used to investigate the influence of rolling parameters such as thickness reduction, inter-strand tension, rolling speed and friction on the rolling force, rolling power, and slip of tandem cold rolling. For this reason, the rolling power was derived for 195 various experiments through a series of observation tests. The network is trained and tested using real data collected from a practical tandem rolling line. The best topology of the ANN is determined by Broyden-Fletcher-Goldfarb-Shanno (BFGS) training algorithm and error, and nine neurons in the hidden layer had the best performance. The average of the training, testing, and validating correlation coefficients data sets are mentioned 0.947, 0.924, and 0.943, respectively. The obtained results show MSE value 4.2 × 10-4 for predicting slip. In addition, the effect of friction and angular velocity condition on the cold rolling critical slip phenomena are investigated. The results show that ANNs can accurately predict the cold rolling parameters considered in this study.