This chapter focuses on vibration energy harvesting using electrostatic converters. It synthesizes the various works carried out on electrostatic devices, from concepts, models and up to prototypes, and covers both standard (electret-free) and electret-based electrostatic vibration energy harvesters (VEH).Comment: This is an author-created, un-copyedited version of a chapter accepted for publication in Small-Scale Energy Harvesting, Intech. The definitive version is available online at: http://dx.doi.org/10.5772/51360 Please cite as: S. Boisseau, G. Despesse and B. Ahmed Seddik, Electrostatic Conversion for Vibration Energy Harvesting, Small-Scale Energy Harvesting, Intech, 201

Predictive maintenance for power electronic converters has emerged as a critical area of research and development. With the rapid advancements in deep-learning techniques, new possibilities have emerged for enhancing the performance and reliability of power converters. However, addressing challenges related to data resources, physical consistency, and generalizability has become crucial in achieving optimal strategies. This comprehensive review article presents an insightful overview of the recent advancements in the field of predictive maintenance for power converters. It explores three paradigms: model-based approaches, data-driven techniques, and the emerging concept of physics-informed machine learning (PIML). By leveraging the integration of physical knowledge into machine-learning architectures, PIML holds great promise for overcoming the aforementioned concerns. Drawing upon the current state-of-art, this review identifies common trends, practical challenges, and significant research opportunities in the domain of predictive maintenance for power converters. The analysis covers a broad spectrum of approaches used for parameter identification, feature engineering, fault detection, and remaining useful life estimation. This article not only provides a comprehensive survey of recent methodologies but also highlights future trends, serving as a resource for researchers and practitioners involved in the development of predictive maintenance strategies for power converters.

This paper presents a small-scale airflow energy harvester built on an axial turbine architecture and exploiting an electret-based electrostatic converter. When the airflow velocity is high enough, the windmill starts rotating and creates a periodic relative motion between a stator and a rotor which induces variations of capacitance. These ones are directly converted into electricity thanks to the use of Teflon electrets charged at −1400 V which polarize the variable capacitors. We focus our study on a 4-blade axial turbine with a diameter of D = 40 mm, a depth of W = 10 mm, for a total volume of 12.6 cm ³ . This windmill has been tested with various blade angles and different types of electrostatic converters and output powers up to 90 μ W at 1.5 m s ⁻¹ (7.5 μ W cm ⁻³ ) and 1.8 mW at 10 m s ⁻¹ (111 μ W cm ⁻³ ) have been obtained so far. The coefficient of power reaches C _p = 5.8% and among the small-scale airflow energy harvesters previously reported, this one has the lowest cut-in speed (1.5 m s ⁻¹ ).

This paper presents a fully autonomous integrated circuit (IC) dedicated to piezoelectric harvesters. The novel Multi-Shot Synchronous Electric Charge Extraction (MS-SECE) performed by the IC optimizes the energy transfer from a highly charged piezoelectric harvester to a low voltage storage element. The system deals with piezoelectric powers in the 10 μW to 1 mW range and handles very high piezoelectric voltage values ( >100 V) thanks to the off-chip components around the IC. The IC has been fabricated in AMS 0.35 μm 3.3 V technology and its low power consumption (1 μW @ 5 Hz) is particularly suitable for low frequency harvesters. MS-SECE increases the extracted power compared to the Maximum Power Point Tracking (MPPT) technique but it also improves the efficiency by up to 25% compared to standard SECE. Furthermore, MS-SECE allows the use of small off-chip components: an efficiency of 61% has been reached with a 125 mm 3 coupled-inductor. Finally, the proposed power management circuit self-starts, works without any battery and its overall volume is expected to be less than 1 cm 3 .