Minimization of the total transmission loss of an interconnected AC-DC grid plays an important role for the economic operation of the AC-DC grid. Different from the conventional AC grid where the transmission loss is usually minimized by reactive power regulation, the transmission loss of a meshed AC-DC grid can be optimized by adjusting the active power exchange between the AC and DC grids. Additionally, smaller DC voltage deviation after grid disturbances is very desirable since it can bring less impact to the operations of AC-DC grid. This paper firstly presents two improved sequential power flow algorithms for modular multilevel converters (MMCs) based AC-DC grid under DC power-voltage droop control. An optimization algorithm is then proposed to minimize the total loss of the AC-DC grid and the overall DC voltage deviation after the change of operating conditions. Adaptive droop control is used in the proposed optimization algorithm in which the power references are control variables solved from the optimal AC-DC power flow. The proposed algorithm is verified in an AC-DC grid consisting of a six-terminal DC grid connected to the IEEE 39-bus AC grid.

Conventional droop control methods for power-sharing in a multi-terminal high voltage DC (MTDC) grid lead to voltage deviation from the nominal value. Moreover, the power-sharing is inaccurate in the droop-controlled MTDC system. This paper proposes a secondary controller with a distributed architecture to compensate the voltage deviation and to achieve equal power sharing automatically among the converter stations. A distributed observer is used to estimate the average voltage of the converter stations. The proposed distributed controller and observer use local measurements of output powers and voltages, and only communicate with neighboring stations. Therefore, the requirement of the global information in the centralized secondary control schemes is eliminated, which reduces the communication requirement and improves the reliability of the MTDC grid. The case studies illustrate that the proposed distributed controller regulates the stations’ average voltage and shares the power mismatch accurately. Impact of the communication time-delays and the control gain on stability of the MTDC grid are also investigated using the Lyapunov-Krasovskii LMI condition. It is shown that reducing the control gain can stabilize the MTDC grid in the case of large communication time-delays.

Falls have been one of the main threats to people’s health, especially for the elderly. Detecting falls in time can prevent the long lying time, which is extremely fatal. This paper intends to show the efficacy of detecting falls using a wearable accelerometer. In the past decade, the fall detection problem has been extensively studied. However, since the hardware resources of wearable devices are limited, designing highly accurate embeddable models with feasible computational cost remains an open research problem. In this paper, different types of shallow and lightweight neural networks, including supervised and unsupervised models are explored to improve the fall detection results. Experiment results on a large open dataset show that the lightweight neural networks proposed have obtained much better results than machine learning methods used in previous work. Moreover, the storage and computation requirements of these lightweight models are only a few hundredths of deep neural networks in literature. In tested lightweight neural networks, the best one is proved to be the supervised convolutional neural network (CNN) that can achieve an accuracy beyond 99.9% with only 441 parameters. Its storage and computation requirements are only 1.2 KB and 0.008 MFLOPs, which make it more suitable to be implemented in wearable devices with restricted memory size and computation power.

The regulation of active power flowing through one or multiple DC lines plays an important role to guarantee secure and economic operations of high voltage direct current (HVDC) grids. This paper proposes a new method to regulate DC line power flow based on the adaptive DC voltage droop control strategy in which the voltage references of the voltage droop controllers vary autonomously at post-contingencies. The main advantage of the proposed method is that it can avoid installation of extra equipment and thus the associated losses and costs in the power-converter-based power flow control methods. The proposed control approach does not require to solve online global AC-DC power flow equations, leading to autonomous control. Furthermore, the adaptive droop control does not influence the stability of the HVDC grid since only the droop voltage references vary, but not the droop coefficients. Static analysis and dynamic simulations of a five-terminal HVDC grid are implemented using MATLAB/Simulink Simscape Blockset and OPAL-RT RT-LAB libraries to validate the effectiveness of the proposed autonomous control method.

This paper presents a combined control scheme that consists of two complementary control strategies, namely hierarchical control and autonomous control to handle contingencies in a multi-terminal high voltage direct current (MTDC) system. The proposed hierarchical control considers three optimal targets, i.e., equal or proportional power sharing, minimizations of transmission loss and total DC voltage variation. The primary control layer implements the droop control for voltage source converters (VSCs). While in the secondary control layer, voltage reference setpoints of the VSCs in droop control scheme are calculated by the proposed optimal power flow method with the equal or proportional power sharing as equality constraints. As the hierarchical control relies on communication system, the autonomous control for power sharing can be activated during communication system malfunctioning or rapid power variation caused by renewable energy sources. The combined control strategy can greatly increase the reliability of the MTDC system. The simulation results of a five-terminal MTDC network conducted in the MATLAB/Simulink software verify the performance of the proposed combined control strategy.