Intelligent technologies for nuclear power systems: heuristic and neural tools

The consecutive, partly overlapping emergence of expert systems and then neural computation methods among intelligent technologies, is reflected in the evolving scene of their application to nuclear engineering. This paper provides a bird's eye view of the state of the application in the domain, along with a review of a particular task, the one perhaps economically more important: refueling design in nuclear power reactors.

Feasibility of online collaborative voltage stability control of power systems

This paper presents the results of feasibility study of a novel concept of power system on-line collaborative voltage stability control. The proposal of the on-line collaboration between power system controllers is to enhance their overall performance and efficiency to cope with the increasing operational uncertainty of modern power systems. In the paper, the framework of proposed on-line collaborative voltage stability control is firstly presented, which is based on the deployment of multi-agent systems and real-time communication for on-line collaborative control. Then two of the most important issues in implementing the proposed on-line collaborative voltage stability control are addressed: (1) Error-tolerant communication protocol for fast information exchange among multiple intelligent agents; (2) Deployment of multi-agent systems by using graph theory to implement power system post-emergency control. In the paper, the proposed on-line collaborative voltage stability control is tested in the example 10-machine 39-node New England power system. Results of feasibility study from simulation are given considering the low-probability power system cascading faults.

Linear indicator for voltage collapse in power systems

A new linear indicator is presented together with a comparative study with other published works. The salient advantage of the linear characteristic is emphasised. The new index is tested utilising the IEEE 30 bus test power system

Nonlinear time series prediction based on a power-law noise model

In this paper we investigate the influence of a power-law noise model, also called noise, on the performance of a feed-forward neural network used to predict time series. We introduce an optimization procedure that optimizes the parameters the neural networks by maximizing the likelihood function based on the power-law model. We show that our optimization procedure minimizes the mean squared leading to an optimal prediction. Further, we present numerical results applying method to time series from the logistic map and the annual number of sunspots demonstrate that a power-law noise model gives better results than a Gaussian model.