A new Maximum Power Point Tracking (MPPT) method, based on Self-Adaptive Particle Swarm Optimization (SAPSO), was proposed to address the energy storage challenge in engine tandem composite turbine power generation systems. A Hybrid Energy Storage System (HESS) was introduced to augment the power capture capability of the generation system and replace single battery storage, achieving efficient and stable electrical energy storage. A control simulation model of energy storage optimization based on tandem composite turbine power generation was established using Matlab/Simulink software. The power tracking performance for various control methods and the energy storage characteristics of hybrid energy storage systems were compared and analyzed under predetermined operating conditions. Simulation results reveal that the proposed SAPSO-MPPT method outperforms the conventional P&O (Perturbation and Observation) control method, increasing power generation by 190 W and reducing response time by 0.15 s. Additionally, HESS could effectively track the demand power on the busbar, achieving power recovery efficiency of 95.3% . Finally, a test platform for the tandem composite turbine power generation system was developed using a modified Y24 engine bench to validate the fuel-saving potential of the proposed energy storage optimized control strategy. The test findings indicate that the suggested SAPSO-MPPT+HESS energy storage optimization strategy improves energy recovery efficiency by 0.53 percentage points compared to the original engine.
Serving as the bridge that links detection and containment, automatic signature extraction has played an important role in anti-worm. Traditional Internet worm signature extraction algorithms were introduced. Based on the analysis of their mechanisms and major defections, an extraction algorithm based on common feature set was presented. It supported low complexity extraction and optimization, as well as the tradeoff between sensitivity and specialization, and had remarkable superiority in dealing with background noise and cross infection.
Aiming at the disadvantages of slow convergence and easily falling into local optimum of Differential Evolution (DE) algorithm, a DE algorithm based on multi-population adaptation and historically successful parameters was proposed. Firstly, all individuals were divided into elite, medium and inferior subpopulations according to fitness value, and different mutation strategies were used for different subpopulations to improve the balance between exploitation and exploration of the algorithm. Secondly, a new mutation strategy was proposed for inferior subpopulation to improve diversity of the algorithm. Thirdly, in order to further improve the balance between exploitation and exploration of the algorithm, the range of candidate parents of random individuals in each strategy was limited, which gave full play to the advantages of different individuals and the performance of the algorithm was improved. Finally, in order to strengthen the development of the algorithm, the historical successful parameters were used to guide the adaptive selection of parameters, and make the parameters keep moving in a good direction. Based on 30 test functions of CEC2014 test set, comparative experiments were carried out. Experimental results show that in 30-dimensional and 50-dimensional problems, compared with OLELS-DE (efficient Differential Evolution algorithm based on Orthogonal Learning and Elites Local Search mechanisms for numerical optimization), the proposed algorithm has the rank level of Friedman test improved by 8.62% and 22.55% respectively. It can be seen that the performance and solution accuracy of the proposed algorithm are better, and the proposed algorithm can deal with global numerical optimization problems effectively.