The paper is devoted to establishing strong consistency of estimates of nonlinear characteristics of dynamic stochastic systems. To describe the shape of the nonlinearities, regression functions, i.e. conditional expectations of a random variable with respect to another one, are used. In turn, the nonlinear regression functions are estimated by using kernel-type algorithm approaches, which are suitable under fairly mild assumptions with respect to the system description. Within the approach, the key issue of the present paper is considering a case of mutually dependent observations. At the same time, conventional nonparametric approaches are based on regression estimates, which impose a number of conditions on sampled data, e.g. mutual independence, various mixing conditions, etc., while such assumptions are not always suitable for verifying within the practice of dynamic system considerations.

The paper presents an approach to design an intelligent information support system to be used as a human-operator assistant to control large complex industrial plants. Tasks and structure of such an intelligent information support system (IISS), IISS design stages, methodology of IISS design, toolkits for IISS design are considered. A flexible simulation complex (FSC) as such an intelligent toolkit has been presented. The complex is used as a “kernel” of IISS for human-operators of a nuclear power plant. A new approach to abnormal situations with regard for the heuristic regularities of human-operator thinking process is proposed. The regularities are revealed on basis of recording the motions of the human- operator eyes over the information field of the control board and processing the experimental data obtained. For data processing, a probability theoretical approach is used based on involving the notion of consistency of measures of dependence of random variables.

There is increasing demand for an effective method to analyze the stability of modern power systems that are nearing their stability boundaries. This demand stems from the burgeoning use of distributed generation and renewable energy sources. We propose a new method that facilitates small-signal stability analysis of power systems, based on the spectral decomposition of a square H2 norm of the transfer function. Compared with the dynamics of the H2 and H-infinity norms of the transfer functions, analyzing the behavior of individual eigen-components allows earlier identification of pre-fault conditions. Because each eigen-component is associated with a particular eigenvector, the potential sources of instability can be easily localized and tracked in real time. We analyze an important class of systems operating under pre-fault conditions near the boundary of stability. In such cases, we demonstrate that several weakly stable modes can increase the system energy up to a critical level much earlier, owing to their synergetic effect. In simulation experiments, the proposed method is applied to a stability analysis of an actual power grid on Russky Island, which is connected to an adjacent power grid on the mainland. The simulation results are analyzed and discussed. In particular, in numerical experiments we observe an interaction of weakly stable oscillating modes in the form of low-frequency beating.

In the nuclear power engineering, methods of detection faults and diagnostics, as well as systems of information support of human-operators in order to increase the safety and reliability of the operation play the increasing role One of tasks solved within the information support is the task of calculating technical and economical indexes. The problem of calculation of technical and economical indexes and its place in the make-up of the application software of the unit level control system of the automated process control system of the nuclear power plant “Kudankulam” is considered.

One-criterion problem of control the complex metallurgical process presented by variable parameters in vector notation was considered. In the implementation vector notation presentation transformed into canonical form by means of piecewise continuous functions optimizing control variables on a given interval [0, T]. Pontryagin's maximum principle is used to determine the optimal conditions for solving one-criterion problem, which is formulated as regular multi-criteria (maximum performance, minimum loss of ferrous and nonferrous metals and energy, etc.) optimization problem. Assumed that a piecewise continuous equations in the zone u ⊂ U, and their existence in space U ⊂ E as the main way to solve multi-criteria optimization problems of metallurgical processes is acceptable convolution of trajectory one-criterion optimum problem. In the modeling of singularly perturbed multi-criteria optimal control problems of metallurgical processes was used the method of Whitham or the search of controlled variable and control action carried out in the form of an asymptotic series in small parameter with the highest differential equations derivatives. It is shown that the expansion of control actions equations system in an asymptotic series in the small parameter allows a distributed optimal control problem for considered process to be transformed to recurrent sequence of simple regular tasks. Finding the coefficients of the asymptotic expansion of the optimal control actions for multi-criteria metallurgical process is the desired solution with given initial conditions and singular perturbations.