For small scale isolated PV-wind-diesel-battery hybrid generation system, the capacity optimization needs to determine capacity of each component. This paper proposes an optimization method that takes economic and tec...
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For small scale isolated PV-wind-diesel-battery hybrid generation system, the capacity optimization needs to determine capacity of each component. This paper proposes an optimization method that takes economic and technical indexes into account, which optimize the capacity of the battery and diesel generators under the premise of satisfying the technical indexes, and also optimize capacity of wind turbine (WT) and photovoltaic array (PV) by considering the economic index. Among them, the economic index of the system is to minimize the life-cycle cost (LCC). According to the wind-solar resources of the installed site, the mathematical model of the system is built after analyzing the output characteristics of each micro-source and the energy dispatching strategy. Finally, the particle swarm optimization (pso) algorithm is adopted to obtain the minimum cost and the optimal capacity configuration scheme by MATLAB simulation software. The influence of capacity of WT and PV on LCC is analyzed too.
There are some defects in the traditional distribution network like high power loss, unbalanced power flow and vary kinds of negative effects brought by the integration of distributed generators. To overcome these def...
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There are some defects in the traditional distribution network like high power loss, unbalanced power flow and vary kinds of negative effects brought by the integration of distributed generators. To overcome these deficiencies, based on power electronical devices, a device called power exchange node that can actively control power flow in the distribution network is proposed. Its topology, working principle as well as its operation mode in the distribution network are preliminarily discussed. Matlab/Simulink is used to establish the model of the proposed power exchange node to verify its fast and stable ability of power exchange. Finally, pso algorithm is used in an example of a distribution network containing a power exchange node to verify its ability of power loss reduction.
With the development of Internet technology, the large number of network nodes and dynamic structure makes network security detection more complex, which requires the use of a multi-layer feedforward neural network to...
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With the development of Internet technology, the large number of network nodes and dynamic structure makes network security detection more complex, which requires the use of a multi-layer feedforward neural network to build a security threat detection model to improve network security protection. Therefore, the entropy model is adopted to optimize the particle swarm algorithm to decode particles, and then the single-peak and multi-peak functions are used to test and compare the particle entropy and fitness values to optimize the weights and thresholds in the multi-layer feedforward neural network. Finally, Suspicious Network Event Recognition Dataset discovered by data mining is sampled and applied to the entropy model particle swarm optimization for training. The test results show that there are four functions for the optimal mean and standard deviation in this algorithm, with values of 5.712e - 02, 4.805e - 02, 4.914e - 01, 1.066e - 01, 1.577e - 01, 1.343e - 01, and 2.089e + 01, 5.926, respectively. Overall, the algorithm proposed in the study is the best. Finally, the detection rate of attack types is calculated. The multi-layer feedforward neural network algorithm is 83.80%, the particle swarm optimization neural network algorithm is 91.00%, and the entropy model particle swarm optimization algorithm is 95.00%. The experiment shows that the research model has high accuracy in detecting network security threats, which can provide technical support and theoretical assistance for network security protection.
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