The Role of Learning Methods in the Dynamic Assessment of Power Components Loading Capability

Abstract

Abstract-The need for dynamic loading of power components in the deregulated electricity market demands reliable assessment models that should be able to predict the thermal behavior when the load exceeds the nameplate value.When assessing network load capability, the hot-spot temperature of the components is known to be the most critical factor. The knowledge of the evolution of the hot-spot temperature during overload conditions is essential to evaluate the loss of insulation life and to evaluate the consequent risks of both technical and economical nature. This paper discusses an innovative grey-box architecture for integrating physical knowledge modeling (a.k.a. whitebox) with machine learning techniques (a.k.a. black-box). In particular, we focus on the problem of forecasting the hot-spot temperature of a mineral-oil-immersed transformer. We perform a set of experiments and we compare the predictions obtained by the grey-, white-, and black-box approaches.

Recurrent Neurofuzzy Network in Thermal Modeling of Power Transformers

“…The data were collected from measurements performed in an experimental power transformer whose characteristics are summarized in Table I. Measurements were collected via fiber-optical-based sensors that measures: 1) the hot-spot temperature of the medium-voltage and low-voltage windings and 2) the top oil temperature [7]. The data logging system records the temperature acquired from each sensor at 5-min intervals and different loading current profiles.…”

“…An alternative recently proposed in the literature is based on the combination of the deterministic model of the system with an adaptative blackbox model, such as an ANN [7]. This approach shows an increase of the modeling's performance, when compared with only the deterministic model.…”

Reliability Analysis and Overload Capability Assessment of Oil-Immersed Power Transformers

“…Thus, the residual value ε defined as below is used as the first objective function in this paper just as Liu et al did in [16]:…”

Reproducing human decisions in reservoir management: the case of lake Lugano

“…The LL package used in this work has been already shown to achieve high accuracy in non-linear modeling problems, such as prediction of air pollution (Corani, 2005) and prediction of temperature of power components (Villacci et al, 2005).…”