Research On Nonlinear Active Noise Control Of Transformer Based On Convex Combination Adaptive Filter

With the rapid development of China's economy,people pay more and more attention to the quality of life.Reducing the noise of substation can reduce the impact of noise for the life of people around,which will greatly improve their quality of life.The active noise control system can reduce the low frequency noise of transformer substation.Therefore,the simple,economical and effective active noise reduction system has broad application prospects in the field of power transformer noise control.At present,most research of the active noise control system is based on linear system,of which the irreconcilable conflicts between the convergence speed and steady-state error exist.In actual operation,there are non-linear factors inevitably existing in the control system,which will bring about the great reduction of control performance for those linear systems.If he nonlinear problem of the active noise control system can be solved,the system can use the cheap electro acoustic device containing nonlinear distortion features,which not only can improve the noise reduction performance,but also is the special significance to reduce the cost of the system.Therefore,in this paper,the nonlinear active noise control technology of power transformer and the coordination method of convergence speed and steady-state error are studied,which is supported by the National Natural Science Foundation of China "Research on active noise control technology of array type power transformer based on internal model control"In order to solve the contradiction between convergence speed and steady-state error,the convex combination filter is introduced to the active noise control system.Detailed theoretical derivation of the stability,convergence speed and steady-state error of the control system,and the combined algorithm used in the system are carried out,which proved that the combinatorial algorithm can achieve the comprehensive performance of steady-state error and convergence speed perfectly and acquire good overall performance.In this paper,two main methods used in the current nonlinear noise control system are studied,one is based on the function link artificial neural network(FLANN)and the other is based on the Volterra filter.The basic structure and the classical algorithm of the control system are described,and the feasibility of two methods are analyzed.The convex combination structures of the two main methods are introduced into the nonlinear active noise control system respectively,and the combinatorial algorithms adopted are deduced theoretically.In order to solve the stagnation phenomenon appeared in the process of convergence according to traditional combination algorithm,the additional instantaneous transfer structure algorithm is used to optimize the algorithm.It is found that the FLANN has the advantages of simple structure and less computation,and is mainly used in the case of the weak nonlinearity.However,the Volterra filter has strong nonlinear processing ability,but the number of the kernel function and the amount of computation increases exponentially with the length of the input signal increasing exponentially.Therefore,the two kinds of filters are introduced into the active noise control system by convex combination to combine their advantages in nonlinear processing ability and computational complexity,of which the combination structure and algorithm are deduced.In order to reduce the computational complexity,the modified versiera function is used to select the association coefficient function and the sign function is used to update the mixing coefficient,which is possible to avoid the slow or even stagnation of the mixed coefficient and prevent the association coefficient from tending to be 0 or 1 when the amount of calculation is reduced at the same time.Finally,a large number of simulation experiments are carried out to verify the research contents.