Power and Energy

Algorithms that adjust the reactive power injection of converter-connected RES to minimize losses may compromise the converters’ fault-ride-through capability. This can become crucial for the reliable operation of the distribution grids, as they could lose valuable resources to support grid voltage at the time they need them the most. This paper explores how two novel loss-minimizing algorithms can both achieve high reduction of the system losses during normal operation and remain connected to support the voltage during faults.

This paper proposes two new monitoring methods capable of detecting electrical disturbances in low voltage grids. Both approaches rely on machine learning techniques that classify voltage signals in the frequency domain. The first technique here proposed uses the Fourier Transform (FT) of the voltage waveform and classifies the corresponding complex coefficients through a Multilayer neural network with Multi-Valued Neurons (MLMVN). In this case, the structure of the classifier has three layers and a small number of neurons in the hidden layer.

The presented dataset is a hybrid solution between the simulated datasets and the ones based on real data. Furthermore, the main advantages and uniquenesses of the proposed work are: i) data variability, in terms of the operating states of the electrical loads and the adoption of appropriate consumption models; ii) the total number of available electrical parameters (433 in our case) enormously larger than the above-cited datasets, as the full analysis of frequency behaviors and harmonic computations; iii) the seasonality of the data.

The failure of some electrical equipment is not due to high temperature but particular heat distribution and characteristics.