CAJ | 학술논문

建立了无惩罚因子策略的数学模型，并应用改进细菌群体优化（bacterial colony chemotaxis，BCC）算法进行无功优化。该模型利用可行细菌的占比指导细菌向可行空间搜索或最小网损空间搜索，快速搜索到可行的最优值。在基本BCC算法中引入速度、感知范围的动态调整以及高斯变异机制以提高寻优精度；同时引入映射因子以改善BCC算法解决离散域问题的性能。算例结果表明，改进BCC算法具有较好寻优性能，结合无惩罚因子策略的数学模型能快速得出合理的无功优化策略。
건립료무징벌인자책략적수학모형，병응용개진세균군체우화（bacterial colony chemotaxis，BCC）산법진행무공우화。해모형이용가행세균적점비지도세균향가행공간수색혹최소망손공간수색，쾌속수색도가행적최우치。재기본BCC산법중인입속도、감지범위적동태조정이급고사변이궤제이제고심우정도；동시인입영사인자이개선BCC산법해결리산역문제적성능。산례결과표명，개진BCC산법구유교호심우성능，결합무징벌인자책략적수학모형능쾌속득출합리적무공우화책략。
A no-penalty factor strategy model is proposed and reactive power optimization is performed by use of bacterial colony chemotaxis （BCC） algorithm. In the proposed model the possible bacterial proportion is used to guide the bacteria onto the search of possible space or the smallest network loss space. The dynamic adjustment of speed and perception range as well as Gaussian mutation mechanism are led into basic BCC algorithm to improve optimizing accuracy; the mapping factors are led in to improve the performance of BCC algorithm to solve the problem of discrete domain. Case calculation results show that the improved BCC algorithm possesses a better optimizing performance, and combining with the mathematical model of no-penalty strategy a rational reactive power optimization strategy can be obtained rapidly.