Matti Lehtonen

TL;DR: In this paper, a real-time thermal rating (RTTR) based active distribution network management framework is formulated giving hour-by-hour network capacity limits, and the relationship of stochasticities in customer loads and DG output with thermal responses of underground cables, overhead lines and distribution transformers is explained.

TL;DR: In this paper, the use of a modified neural network algorithm (MNNA) is proposed as a novel adaptive tuning algorithm to optimize the controller gains and a new mathematical modulation is introduced to promote the exploration manner of NNA without initial parameters.

TL;DR: In this paper, photoluminescence (PL) spectroscopy is introduced for the first time, for effective condition assessment of insulating oil, which involves emission processes that only occur between a narrow band of electronic states that are occupied by thermalized electrons and consequently yields a spectrum that is much narrower than that of the absorption spectrum.

TL;DR: The deep neural network model used in the proposed forecasting model has been constituted by several autoencoders and a cascade neural network, and the wavelet transform has been applied to the inputs of the proposed model.

TL;DR: The simulation results show that the MTDM gives more accurate solutions as a model to the MCSSC compared with the results reported in the literature, and the EHO outperforms CLPSO in terms of the solution quality and convergence rates.