On reliability optimization for power generation systems

The reliability level of a power generation system is an important problem which is concerned by both electricity producers and electricity consumers. Why？ It is known that the high reliability level may result in additional utility cost, and the low reliability level may result in additional consumer＇s cost, so the optimum reliability level should be determined such that the total cost can reach its minimum. Four optimization models for power generation system reliability are constructed, and the proven efficient solutions for these models are also given.

Robust H_∞ controller design for a class of nonlinear fuzzy time-delay systems

The problem of fuzzy modeling for state and input time-delays systems with a class of nonlinear uncertainties by fuzzy T-S model is addressed.By using the linear matrix inequality（LMI） method, the problem of fuzzy robust H ∞ controller design for the system is studied.Assuming that the nonlinear uncertain functions in the model considered are gain-bounded, a sufficient condition for the robustly asymptotic stability of the closed-loop system is obtained via Lyapunov stability theory.By solving the LMI, a feedback control law which guarantees the robustly asymptotic stability of the closed-loop system is constructed and the effect of the disturbance input on the controlled output is ruduced to a prescribed level.

Robust H_∞ filtering for discrete-time systems with Markovian switching and time-delays

The robust H∞ filtering problem for uncertain discrete-time Markovian jump linear systems with mode- dependent time-delays is investigated. Attention is focused on designing a Markovian jump linear filter that ensures robust stochastic stability while achieving a prescribed H∞ performance level of the resulting filtering error system, for all admissible uncertainties. The key features of the approach include the introduction of a new type of stochastic Lyapunov functional and some free weighting matrix variables. Sufficient conditions for the solvability of this problem are obtained in terms of a set of linear matrix inequalities. Numerical examples are provided to demonstrate the reduced conservatism of the proposed approach.

Design and realization of threshold secret sharing scheme with random weights

A new threshold secret sharing scheme is constructed by introducing the concept of share vector, in which the number of shareholders can be adjusted by randomly changing the weights of them. This kind of scheme overcomes the limitation of the static weighted secret sharing schemes that cannot change the weights in the process of carrying out and the deficiency of low efficiency of the ordinary dynamic weighted sharing schemes for its resending process. Thus, this scheme is more suitable to the case that the number of shareholders needs to be changed randomly during the scheme is carrying out.

Fault diagnosis of the bushing infrared images based on mask R‐CNN and improved PCNN joint algorithm

Bushings are served as an important component of the power transformers;it's of great significance to keep the bushings in good insulation condition.The infrared images of the bushing are proposed to diagnose the fault with the combination of image segmentation and deep learning,including object detection,fault region extraction,and fault diagnosis.By building an object detection system with the frame of Mask Region convolutional neural network(CNN),the bushing frame can be exactly extracted.To distinguish the fault region of bushings and the background,a simple linear iterative clustering‐based pulse coupled neural network is proposed to improve the fault region segmentation performance.Then,two infrared image feature parameters,the relative position and area,are explored to classify fault type effectively based on the K‐means cluster technique.With the proposed joint algorithm on bushing infrared images,the accuracy reaches 98%,compared with 44%by the conventional CNN classification method.The integrated algorithm provides a feasible and advantageous solution for the field application of bushing image‐based diagnosis.

AN INTEGRABLE HIERARCHY AND ITS EXPANDING LAX INTEGRABLE MODEL

In general, Liouville integrable hierarchies of evolution equations were obtained by choosing proper U in zero curvature frame Ut - Vx + [U, V] = 0 first. But in the present paper, a new Liouville integrable hierarchy possessing bi-Hamiltonian structure is obtained by choosing V with derivatives in x and spectral potentials. Then integrable coupling, i.e. expanding Lax integrable model of the hierarchy obtained is presented by constructing a subalgebra of loop algebra A2.