To improve the efficiency and stability of power transaction management, a micro-grid power transaction management platform is studied. Block chain Internet technology is used to build the basic architecture of the electric energy transaction management platform. Based on the principle of low cost and high efficiency, the mathematical model of electric energy transaction is established. Combined with the distributed consensus mechanism of block chain technology, the intelligent contract of electric energy transaction is designed to realize the execution and management functions of electric energy block chain transaction. The test results show that the average time delay of the platform is about 0. 36 s for 10 groups of the random electricity trading instructions, which is real-time and efficient. For 5 random groups of users, the average electricity cost of the transaction results is 20. 42 yuan, which is feasible and economical, and lays a good foundation for the sustainable development of the micro-grid electricity trading management platform.
KEYWORDS: Reflection, Time-frequency analysis, Signal detection, Design and modelling, Signal processing, Signal generators, Signal attenuation, Head, Feature extraction, Energy transfer
Conventional cable joint fault detection methods use covariance matrices to extract fault features with high sample transformation amplitudes, resulting in significant differences between the obtained Wigner-Hough transformation peaks and the actual ones. Therefore, a new cable joint fault detection method is required based on the extended time-frequency domain reflection method. Based on the energy transfer characteristics of the cable, a transmission line schematic is drawn to extract the fault characteristics. Then the fault frequency domain is divided by combining the extended time frequency domain reflection to achieve cable joint fault detection. The experimental results show that the peak values of Wigner-Hough transitions obtained by the designed cable joint fault detection method are less different from the actual ones, which proves that its fault detection effect is good, accurate, and has a certain application value.
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