• Energy Research
• CN close
• Sensors close

Virtual power plant (VPP) composed of a large number of distributed energy resources (DERs) has become a regional multienergy aggregation model to realize the large-scale integration of renewable energy generation into the grid. Due to the characteristics of centralized management, the existing energy operation mode is difficult to simply apply to distributed energy resources transactions. The decentralization, transparency, contract execution automation and traceability of blockchain technology provide a new solution to the aggregation of decentralized resources and the opacity of transactions in VPP. In this paper, the existing problems of virtual power plants are analyzed, and the virtual power plant trading model is designed, which realizes the transparent benefit distribution and message transmission of virtual power plants. The virtual power plant blockchain network based on blockchain technology in this model solves the DERs coordination problem in VPP and the security and efficiency problems in information transmission. Combined with the actual situation of virtual power plant, the blockchain network collaboration mechanism (BNCM), which is convenient to reach agreement, is designed. Compared with the traditional practical Byzantine fault tolerance (PBFT) consensus algorithm, this mechanism can make DERs reach a consensus quickly. Finally, simulation experiments on the consensus algorithm show that the algorithm can reduce the collaboration time between DERs under the premise of ensuring the same fault tolerance rate and is more suitable for VPP scenarios with a large number of DERs.

Microbial fuel cells (MFCs) are of increasing interest as bioelectrochemical systems for decomposing organic materials and converting chemical energy into electricity. The main challenge for this technology is that the low power and voltage of the devices restricts the use of MFCs in practical applications. In this paper, a power management system (PMS) is developed to store the energy and export an increased voltage. The designed PMS successfully increases the low voltage generated by an individual MFC to a high potential of 5 V, capable of driving a wireless temperature and humidity sensor based on nRF24L01 data transmission modules. With the PMS, MFCs can intermittently power the sensor for data transmission to a remote receiver. It is concluded that even an individual MFC can supply the energy required to power the sensor and telemetry system with the designed PMS. The presented PMS can be widely used for unmanned environmental monitoring such as wild rivers, lakes, and adjacent water areas, and offers promise for further advances in MFC technology.

The ankle joint is one of the important joints of the human body to maintain the ability to walk. Diseases such as stroke and ankle osteoarthritis could weaken the body’s ability to control joints, causing people’s gait to be out of balance. Ankle–foot orthoses can assist users with neuro/muscular or ankle injuries to restore their natural gait. Currently, passive ankle–foot orthoses are mostly designed to fix the ankle joint and provide support for walking. With the development of materials, sensing, and control science, semi-active orthoses that release mechanical energy to assist walking when needed and can store the energy generated by body movement in elastic units, as well as active ankle–foot orthoses that use external energy to transmit enhanced torque to the ankle, have received increasing attention. This article reviews the development process of ankle–foot orthoses and proposes that the integration of new ankle–foot orthoses with rehabilitation technologies such as monitoring or myoelectric stimulation will play an important role in reducing the walking energy consumption of patients in the study of human-in-the-loop models and promoting neuro/muscular rehabilitation.

While deep learning has found widespread utility in gearbox fault diagnosis, its direct application to wind turbine gearboxes encounters significant hurdles. Disparities in data distribution across a spectrum of operating conditions for wind turbines result in a marked decrease in diagnostic accuracy. In response, this study introduces a tailored dynamic conditional adversarial domain adaptation model for fault diagnosis in wind turbine gearboxes amidst cross-condition scenarios. The model adeptly adjusts the importance of aligning marginal and conditional distributions using distance metric factors. Information entropy parameters are also incorporated to assess individual sample transferability, prioritizing highly transferable samples during domain alignment. The amalgamation of these dynamic factors empowers the approach to maintain stability across varied data distributions. Comprehensive experiments on both gear and bearing data validate the method’s efficacy in cross-condition fault diagnosis. Comparative outcomes demonstrate that, when contrasted with four advanced transfer learning techniques, the dynamic conditional adversarial domain adaptation model attains superior accuracy and stability in multi-transfer tasks, making it notably suitable for diagnosing wind turbine gearbox faults.

Li-ion batteries are expected to become the mainstream devices for green energy storage or power supply in the future due to their advantages of high energy and power density and long cycle life. Monitoring the temperature and strain change characteristics of Li-ion batteries during operation is conducive to judging their safety performance. The hinged differential lever sensitization structure was used for strain sensitization in the design of an FBG sensor, which also allowed the simultaneous measurement of strain and temperature. The temperature and strain variation characteristics on the surface of a Li-ion soft-packed battery were measured using the des.igned sensor. This report found that the charging and discharging processes of Li-ion batteries are both exothermic processes, and exothermic heat release is greater when discharging than when charging. The strain on the surface of Li-ion batteries depends on electrochemical changes and thermal expansion effects during the charge and discharge processes. The charging process showed an increasing strain, and the discharging process showed a decreasing strain. Thermal expansion was found to be the primary cause of strain at high rates.

To overcome high periodic maintenance requirements, difficult replacement, and large application limitations of wireless sensor nodes powered by chemical batteries during the vibration control process of stiffened plates, a two-degree-of-freedom diagonal beam piezoelectric vibration energy harvester was proposed. Multidimensional energy harvesting and broadband work are integrated into one structure through the combined action of oblique angle, mass blocks, and piezoelectric beam. The mechanical model of the beam is established for theoretical analysis; the output characteristics of the structure are analyzed by finite element simulation; a piezoelectric energy harvesting experimental bench is built. The results show that: The structure has a wider harvesting band, multi-order resonant frequency, multi-dimensional energy harvesting, and higher output voltage and power than the traditional cantilever structures. The output performance of the specimens with 45° oblique angle, 5 g:5 g mass ratio, and 0.2 mm thickness of piezoelectric substrate is good in the frequency band of 10~40 Hz. When the excitation frequency is 28 Hz, the output voltage of the sextuple array structure reaches 19.20 V and the output power reaches 7.37 mW. The field experiments show that the harvester array can meet the requirements of providing auxiliary energy for wireless sensor nodes in the process of active vibration control of stiffened plates.

An ultra-low power CMOS image sensor with on-chip energy harvesting and power management capability is introduced in this paper. The photodiode pixel array can not only capture images but also harvest solar energy. As such, the CMOS image sensor chip is able to switch between imaging and harvesting modes towards self-power operation. Moreover, an on-chip maximum power point tracking (MPPT)-based power management system (PMS) is designed for the dual-mode image sensor to further improve the energy efficiency. A new isolated P-well energy harvesting and imaging (EHI) pixel with very high fill factor is introduced. Several ultra-low power design techniques such as reset and select boosting techniques have been utilized to maintain a wide pixel dynamic range. The chip was designed and fabricated in a 1.8 V, 1P6M 0.18 µm CMOS process. Total power consumption of the imager is 6.53 µW for a 96 × 96 pixel array with 1 V supply and 5 fps frame rate. Up to 30 μW of power could be generated by the new EHI pixels. The PMS is capable of providing 3× the power required during imaging mode with 50% efficiency allowing energy autonomous operation with a 72.5% duty cycle.