Nowadays, many different kinds of novel generators have been introduced to convert nature power into electrical power, such as mechanical energy, [1,2] hydraulic energy, [3] solar energy, [4,5] thermal energy, [6] etc. These generators all possess an electric field to output power, however, the large
There is a rising prospective in harvesting energy from the environment, as in situ energy is required for the distributed sensors in the interconnected information society, among which the water flow energy is the most potential candidate as a clean and abundant mechanical source. However, for microscale and unordered movement of water, achieving a sustainable direct-current generating device with high output to drive the load element is still challenging, which requires for further exploration. Herein, we propose a dynamic PN water junction generator with moving water sandwiched between two semiconductors, which outputs a sustainable direct-current voltage of 0.3 V and a current of 0.64 μA. The mechanism can be attributed to the dynamic polarization process of water as moving dielectric medium in the dynamic PN water junction, under the Fermi level difference of two semiconductors. We further demonstrate an encapsulated portable power-generating device with simple structure and continuous direct-current voltage output of 0.11 V, which exhibits its promising potential application in the field of wearable devices and the IoTs.
In an islanded microgrid with multiple distributed generations (DGs), the difference in line impedance may cause local voltage deviation, which leads to a series of problems such as lower power allocation accuracy and bus voltage drop under traditional droop control. In this respect, a method for optimizing the droop control using an improved particle swarm optimization (IPSO) is proposed. Firstly, the microgrid structure and influence of line parameters on traditional droop control strategy is analyzed. Then, an improved particle swarm optimization is proposed. Based on the basic particle swarm optimization (PSO) algorithm, a fuzzy inference system (FIS) is introduced to dynamically adjust the particle swarm optimization, which can effectively improve the global search ability and local search ability of the algorithm. After that, the improved algorithm is applied to the droop controller, simultaneously, the range of stable operation of the system is determined by small signal analysis. Finally, the simulation and experiment results show that the proposed improved droop control strategy can achieve accurate allocation of active and reactive power effectively while maintaining bus voltage and system frequency stability, enhance the dynamic performance and transient stability of the microgrid system.
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