Hydraulic Power Plant Machine Dynamic Diagnosis

Nuclear Power Thermal and Hydraulic Power Electric Power Distribution

Unsupervised Clustering for Fault Diagnosis in Nuclear Power Plant Components

The development of empirical classification models for fault diagnosis usually requires a process of training based on a set of examples. In practice, data collected during plant operation contain signals measured in faulty conditions, but they are ‘unlabeled’, i.e., the indication of the type of fault is usually not available. Then, the objective of the present work is to develop a methodology...

Dynamic Voltage Stability Analysis in Multi-machine Power Systems

This paper presents a novel approach for power system dynamic voltage stability analysis based on the multi-input multi-output (MIMO) transfer function. The MIMO system is defined taking into account the critical nodal voltages as outputs and possible control variables as the input. Based on the modal analysis and singular value analysis, the dynamic voltage stability analysis is carried out. T...

Dynamic mitigation of EDFA power excursions with machine learning.

Dynamic optical networking has promising potential to support the rapidly changing traffic demands in metro and long-haul networks. However, the improvement in dynamicity is hindered by wavelength-dependent power excursions in gain-controlled erbium doped fiber amplifiers (EDFA) when channels change rapidly. We introduce a general approach that leverages machine learning (ML) to characterize an...

A Dynamic Programming Model of U.S. Nuclear Power Plant Operations

This paper presents a dynamic programming (DP) model of an electric utility’s optimal policy for operating a nuclear power plant (NPP). The utility chooses the level of capacity utilization of the NPP as a function of signals about the NPP’s current operating state. In each period the utility must determine whether or not to operate the reactor, or shut it down for preventive maintenance or ref...