The aftermath of the COVID-19 pandemic in 2019 resulted in a decrease in Java Bali's total load instead of the usual increasing trend. The loads also exhibit different characteristics in their daily, weekly, and monthly load profiles in each region. The basic statistical coefficient method used to forecast the load introduces a higher possibility of error and inaccuracies in operational planning. A different approach is necessary to achieve higher accuracy in load forecasting. One method to predict reliable trends is deep learning, a subfield of machine learning, which can synthesize the learning curve based on available data. A method called Long-Short Term Memory (LSTM), included in Deep Learning and popularized by researchers since 2000, has shown better accuracy in forecasting. This paper focuses on reviewing the LSTM method for short-term load forecasting in the Java Bali power system using several additional inputs. The method demonstrates an accurate learning curve after the addition of several input parameters.

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This paper presents the effect of the negative sequence current control strategy of an MMC-HVDC system on the single-line-to-ground (SLG) fault response of the high AC transmission lines. Four different methods reported in the literature are compared. The paper shows that the negative sequence current suppression by the MMC-HVDC station demonstrates increased double-frequency power oscillations in the DC link and high over-voltages in the AC terminals during single line to ground faults. On the other side, the adequate control of the negative sequence current during unbalanced faults in the grid improves the observed DC link voltage and power ripples. Furthermore, the negative sequence current injection, increases the zero sequence current amplitude measured at the PCC of the MMC-HVDC station. The later, enhances the fault detection capability of protection schemes in the AC transmission during
SLG to ground faults in the vicinity of the MMC substation.@en