The paper offers the research techniques for stable operation of discrete power supply systems in industrial enterprises. The methods for developing predictive models in control systems and decision-making support for nonlinear non-stationary objects are proposed. The methods are based on the application of associative search procedure to virtual model identification as well as Gramian techniques. The methods use intelligent process knowledge analysis. The knowledgebase is created and extended in realtime process operation. Intelligent algorithms are offered for predicting power plant dynamics in optimization tasks. The operator’s decision-making process is modeled using associative search algorithms. Gramian technique of stability analysis of discrete system is used for investigating linear virtual model stability.