电力系统保护与控制
電力繫統保護與控製
전력계통보호여공제
POWER SYSTM PROTECTION AND CONTROL
2014年
19期
37-42
,共6页
改进粒子群算法%小波神经网络%变压器%故障诊断
改進粒子群算法%小波神經網絡%變壓器%故障診斷
개진입자군산법%소파신경망락%변압기%고장진단
improved particle swarm algorithm%wavelet neural network%transformer%fault diagnosis
针对变压器故障征兆和故障类型的非线性特性，结合油中气体分析法，设计了一种改进粒子群算法的小波神经网络故障诊断模型。模型采用3层小波神经网络，并用一种改进粒子群算法对其进行训练。该算法在标准粒子群算法的基础上，通过引入遗传算法中的变异算子、惯性权重因子和高斯加权的全局极值，加快了小波神经网络训练速度，提高了其训练的精度。仿真实验证明这种改进粒子群算法的小波神经网络可以有效地运用到变压器故障诊断中，为变压器故障诊断提供了一条新途径。
針對變壓器故障徵兆和故障類型的非線性特性，結閤油中氣體分析法，設計瞭一種改進粒子群算法的小波神經網絡故障診斷模型。模型採用3層小波神經網絡，併用一種改進粒子群算法對其進行訓練。該算法在標準粒子群算法的基礎上，通過引入遺傳算法中的變異算子、慣性權重因子和高斯加權的全跼極值，加快瞭小波神經網絡訓練速度，提高瞭其訓練的精度。倣真實驗證明這種改進粒子群算法的小波神經網絡可以有效地運用到變壓器故障診斷中，為變壓器故障診斷提供瞭一條新途徑。
침대변압기고장정조화고장류형적비선성특성，결합유중기체분석법，설계료일충개진입자군산법적소파신경망락고장진단모형。모형채용3층소파신경망락，병용일충개진입자군산법대기진행훈련。해산법재표준입자군산법적기출상，통과인입유전산법중적변이산자、관성권중인자화고사가권적전국겁치，가쾌료소파신경망락훈련속도，제고료기훈련적정도。방진실험증명저충개진입자군산법적소파신경망락가이유효지운용도변압기고장진단중，위변압기고장진단제공료일조신도경。
In view of non-linear characteristics between fault symptoms and fault types of transformers, a wavelet neural network fault diagnosis model based on improved particle swarm algorithm is designed with the data of dissolved gas analysis. The model, constructed by three-layer wavelet neural networks, is trained by an improved particle swarm algorithm. By introducing the mutation operator of genetic algorithm, inertia weight factor and Gaussian-weighted global extremes on the basis of the standard particle swarm algorithm, it can accelerate the training speed of wavelet neural network and improve the accuracy of training. The simulation experiments show that this improved particle swarm algorithm wavelet neural network can be effectively applied to transformer fault diagnosis and provides a new way for transformer fault diagnosis.
