摘要：The paper characterizes the issues related to compensation of reactive power measures, which protect the smart microgrids from the loss of voltage stability. Slower forms of voltage instability are analyzed with power distribution simulations. The simulations represent the behavior of the system after preset shutdowns; P-U and Q-U charts are drawn to assess the voltage stability reserve in the given time. The purpose of the compensation is to decrease the reactive power transmission and the losses on the smart grid related to this power. This most often translates into introduction of new sources to achieve the established goal. This paper explains the algorithm for optimization of artificial measures of reactive power compensation with the use of decision trees, which are the primary method of induction education of machines due to their high effectiveness and the capability of a simple programming implementation.
其他摘要：The paper characterizes the issues related to compensation of reactive power measures, which protect the smart microgrids from the loss of voltage stability. Slower forms of voltage instability are analyzed with power distribution simulations. The simulations represent the behavior of the system after preset shutdowns; P-U and Q-U charts are drawn to assess the voltage stability reserve in the given time. The purpose of the compensation is to decrease the reactive power transmission and the losses on the smart grid related to this power. This most often translates into introduction of new sources to achieve the established goal. This paper explains the algorithm for optimization of artificial measures of reactive power compensation with the use of decision trees, which are the primary method of induction education of machines due to their high effectiveness and the capability of a simple programming implementation.
