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Footfall contains the highest harvestable biomechanical energy from the human body, which can attain 67 W, showing great potential as a pervasive and sustainable power source for wearable bioelectronics in the era of the Internet of Things. Developing an effective technology for robust and efficient energy harvesting from human walking remains highly desired. Here, we present a waterproof smart insole, based on a triboelectric nanogenerator, for highly efficient and robust human biomechanical energy harvesting. This insole was rationally designed as a composite structure to fully utilize the pressure distribution of a footfall for wearable electricity generation and to deliver a power output reaching 580 μW. The insole was additionally able to withstand use in harsh environments, including pluvial conditions, without affecting the power output consistency. A total of 260 light-emitting diodes were lit up with perspiring feet and water on the floor, and a capacitor of 88 μF was charged to 2.5 V in 900 s. This work represents a practical approach to developing a highly efficient and robust smart insole that can be used as a sustainable power source for wearable bioelectronics.

The daily load profiles modeling is of great significance for the economic operation and stability analysis of the distribution network. In this paper, a flow-based generative network is proposed to model daily load profiles of the distribution network. Firstly, the real samples are used to train a series of reversible functions that map the probability distribution of real samples to the prior distribution. Then, the new daily load profiles are generated by taking the random number obeying the Gaussian distribution as the input data of these reversible functions. Compared with existing methods such as explicit density models, the proposed approach does not need to assume the probability distribution of real samples, and can be used to model different loads only by adjusting the structure and parameters. The simulation results show that the proposed approach not only fits the probability distribution of real samples well, but also accurately captures the spatial-temporal correlation of daily load profiles. The daily load profiles with specific characteristics can be obtained by simply classification.INDEX TERMS Daily load profiles, distribution network, generative network. BIRGITTE BAK-JENSEN (Senior Member, IEEE) received the M.Sc. degree in electrical engineering and the Ph.D. degree in modeling of high voltage components from the

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The technological advances of intelligent electric substations have significantly improved the operational performance of power utilities by incorporating advanced monitoring and control functionalities. The data traffic patterns in substation communication network (SCN) need to be better understood to improve the SCN performance against different forms of cyberattacks. To this end, this study presents a fractional auto-regressive integrated moving average (FARIMA)-based threshold model to characterise the SCN traffic flow based on the IEC 61850 protocol and carry out anomaly detection. The performance of the proposed anomaly detection solution is assessed and validated through numerical analysis under the condition of the cyber storm based on the collected SCN data traffic from a real 110 kV substation, and the numerical results clearly confirmed its effectiveness.