Non-intrusive load monitoring (NILM) enables disaggregation of appliance power usage through data gathered from the incoming power line. This disaggregation assists householders in reducing electricity expenses and aids system operators in implementing demand-side responses. However, despite advancements in power measurement, processing household power data remains a significant challenge. Existing methods struggle to accurately disaggregate appliances with brief operation times and exhibit large errors in load curve regression tasks. To address these issues, a novel time-power hybrid (TPH) algorithm is proposed to enhance the accuracy of dataset preprocessing for NILM model training. Additionally, the temporal convolutional auto-encoder model (TCAE) is introduced to improve disaggregation performance. Both the TPH and TCAE are evaluated using a public dataset in comparison to other representative methods. The results demonstrate that the proposed TPH algorithm exhibits superior load activation extraction performance. The TCAE model displays significant advantages in both curve regression and load ON/OFF detection for various appliances. Remarkably, the TCAE achieves a detection precision of 99.9%, particularly for appliances with short operation times, which other models are unable to accomplish. Furthermore, the TCAE necessitates less computational space compared to existing models.

This paper proposes an online power system voltage stability index (PVSI_Online) that quantifies power system voltage stability in real time between an AC network and LCC HVDC using only local measurements. Previous methods relied on using telecommunications to inform a change in a predetermined AC network model. The proposed method uses local measurements of voltage and current from the HVDC using a newly developed, numerically robust technique called Moving Window Covariance to detect a change in the AC network in real time. These network parameters are fed to a newly devised index that accurately models and captures the dynamics of the interaction between an LCC HVDC and AC network. The index is used to quantify stability, but also to advise network operation. In an onset of instability due to network degradation, the proposed index can provide a power order value for the HVDC to restore stability. All as a self-contained module that can be implemented within a HVDC scheme.