Special Issue on AI-empowered Smart Grid Technologies and EVs 2024

Forecasting solar PV power output holds significant importance in the realm of energy management, particularly due to the intermittent nature of solar irradiation. Currently, most forecasting studies employ statistical methods. However, deep learning models have the potential for better forecasting. This study utilises Long Short-Term Memory (LSTM), Gate Recurrent Unit (GRU) and hybrid LSTM-GRU deep learning techniques to analyse, train, validate, and test data from the Zagtouli Solar Photovoltaic (PV) plant located in Ouagadougou (longitude:12.30702o and latitude:1.63548o), Burkina Faso. The study involved three evaluation metrics: Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and coefficient of determination (R2). The RMSE evaluation criteria gave 10.799(LSTM), 11.695(GRU) and 10.629(LSTM-GRU) giving the LSTM-GRU model as the best for RMSE evaluation. The MAE evaluation provided 2.09, 2.1 and 2.0 for the LSTM, GRU and LSTM-GRU models respectively, showing that the LSTM-GRU model is superior for MAE evaluation. The R2 criteria similarly showed the LSTM-GRU model to be best with 0.999 compared to 0.998 for LSTM and 0.997 for GRU. It becomes evident that the hybrid LSTM-GRU model exhibits superior predictive capabilities compared to the other two models. These results indicate that the hybrid LSTM-GRU model has the potential to reliably predict the solar PV power output. It is therefore recommended that the authorities in charge of the solar PV Plant in Ouagadougou should consider switching to the deep learning LSTM-GRU model.

Skewing of the rotor slot of a squirrel-cage induction machine has been commonly used since the beginning. However, little guidance is given regarding the principle of the working effect of the method, but even in such rare cases, the explanation does not cover the true physical reality. Consequently no formula exists for calculation of the effect. In this study, the principle of the working effect of the rotor’s skewing is derived in accordance with the true physics of the phenomenon, for both the synchronous and the asynchronous parasitic torque. The calculation regarding synchronous parasitic torque is based on comparing the stepped MMF curve of the straight rotor slot and the trapezoidal MMF curve of the skewed slot. New formulas are provided for both type of parasitic torques. Practical cases are investigated in details. Knowing the theory and the formula of slot skewing, the skew can now be applied on a targeted manner and in some cases, can completely eliminate the dangerous torque and noise components. Consequently, a theorem is formulated on when to skew according to the stator and when acc. to the rotor slot pitch; another theorem was found regarding residual synchronous parasitic torque after skewing. The investigation is then extended to include differential leakage attenuation calculations. With this in mind, from the point of view of rotor slot skew, the topic has been reviewed in its entirety.