基于改进金枪鱼优化粒子滤波算法的谐波估计

Harmonic Estimation Based on Improved Tuna Optimization Particle Filter Algorithm

新能源快速发展和电力电子设备不断应用,在推动整个电力系统快速发展的同时,也加重了影响电能质量的谐波污染问题。对谐波的准确估计是有效解决谐波污染的前提,针对目前电力系统的谐波估计精度不高、适用性低、抗干扰性较弱等问题,提出了改进金枪鱼群优化粒子滤波算法(TSO-PF)。首先引入Tent混沌映射初始化粒子种群,使其个体分布更均匀;随后将金枪鱼的螺旋觅食和抛物线觅食两种狩猎行为引入粒子滤波算法中,通过加入高斯扰动因子和水波动态自适应因子改进策略,提升金枪鱼优化算法的寻优能力,并指导粒子向高似然区域移动,提高粒子的多样性;最后对IEEE-14节点系统进行仿真,结果表明,所提的算法具有较好的鲁棒性、精度和抗干扰能力。

The rapid development of new energy and the continuous application of power electronic equipment not only promote the rapid development of the whole power system,but also aggravate the harmonic pollution which affects the power quality.Accurate estimation of harmonics is the precondition for effective solution of harmonic pollution.For the low accuracy,low applicability and weak anti-interference of harmonic estimation in the current power system,an improved Tuna swarm optimization particle filtering algorithm(TSO-PF)is proposed in this paper.First,the Tent chaotic map is introduced to initialize the particle population to make its individual distribution more uniform.Second,spiral foraging and parabolic foraging behaviors of tuna are introduced into the particle filter algorithm,and the optimization ability of tuna optimization algorithm is improved by adding Gaussian perturbation factor and water wave dynamic adaptive factor,which guide the particles to move to the high likelihood region and improve the diversity of particles.Finally,the simulation results of IEEE-14 node systems show that the proposed algorithm has better robustness,accuracy and anti-interference ability.