No abstract

A standalone wind/solar/battery hybrid power system, making full use of the nature complementarity between wind and solar energy, has an extensive application prospect among various newly developed energy technologies. The capacity of the hybrid power system needs to be optimised in order to make a tradeoff between power reliability and cost. In this study, each part of the wind/solar/battery hybrid power system is analysed in detail and an objective function combining total owning cost and loss of power supply probability is built. To solve the problems with non-linearity, complexity and huge computation, an improved particle swarm optimisation (PSO) algorithm is developed, which integrates the taboo list to broaden the search range and introduces 'restart' and 'disturbance' operation to enhance the global searching capability. The simulation results indicate that the proposed algorithm is more stable and provides better results in solving the optimal allocation of the capacity of the standalone wind/solar/battery hybrid power system compared with the standard PSO algorithm.

No abstract

No abstract

By analyzing the recovery and reconstruction process of various power quality single disturbances and composite disturbance signals, we proposed a set of acquisition methods suitable for power quality disturbance (PQD) signals. The proposed acquisition method is applied to the compression sensing (CS) technology for data compression, the demand for the acquisition device memory is reduced, and the transmission rate is increased. An end-to-end intelligent classification framework is designed, which can directly classify the collected data without any time-consuming data pre-processing operations. The model is designed with noise adaptation module, which can cope with the error of compressed sensing recovery and has also showed good classification performance in noise data. Simultaneously, the model applies a lot of easy-to-implement techniques, which makes the trained model have better generalization ability and classification effect. The proposed method is verified by both simulation and measured data. The method showed superior performance compared to the existing disturbance identification methods based on the classification results.