CAJ | 학술논문

为了解决变压器气相色谱分析法故障诊断中存在的操作繁琐、消耗待测气体和载气、检测周期长等缺点，提出了利用光声光谱技术检测变压器油中CH4，C2 H2，C2 H4，C2 H6，H2五种特征气体的含量并计算C2 H2／C2 H4，CH4／H2，C2 H4／C2 H6三对比值数据。将五种SVM 类型和四种核函数采用交叉组合建立20种不同的支持向量机模型，并采用启发式算法对于惩罚因子 c和g的取值进行参数寻优，以建立变压器故障诊断准确率最高、最快运行速度的支持向量机模型。启发式算法主要对比研究了粒子群算法和遗传算法在寻优精度与速度上的效果。仿真实验结果表明C-SVC模型、RBF核函数、遗传算法寻优构成的支持向量机模型对变压器故障的诊断准确率最高，测试集达到97.5％，训练集达到98.3333％，并且遗传算法的寻优速度快于粒子群算法2倍左右。该方法具有操作简单、非接触性测量、不消耗载气、检测周期短、稳定性和灵敏度高等优点。可以代替传统的气相色谱分析法进行变压器故障诊断，满足变压器故障诊断的实际工程需要。
위료해결변압기기상색보분석법고장진단중존재적조작번쇄、소모대측기체화재기、검측주기장등결점，제출료이용광성광보기술검측변압기유중CH4，C2 H2，C2 H4，C2 H6，H2오충특정기체적함량병계산C2 H2／C2 H4，CH4／H2，C2 H4／C2 H6삼대비치수거。장오충SVM 류형화사충핵함수채용교차조합건립20충불동적지지향량궤모형，병채용계발식산법대우징벌인자 c화g적취치진행삼수심우，이건립변압기고장진단준학솔최고、최쾌운행속도적지지향량궤모형。계발식산법주요대비연구료입자군산법화유전산법재심우정도여속도상적효과。방진실험결과표명C-SVC모형、RBF핵함수、유전산법심우구성적지지향량궤모형대변압기고장적진단준학솔최고，측시집체도97.5％，훈련집체도98.3333％，병차유전산법적심우속도쾌우입자군산법2배좌우。해방법구유조작간단、비접촉성측량、불소모재기、검측주기단、은정성화령민도고등우점。가이대체전통적기상색보분석법진행변압기고장진단，만족변압기고장진단적실제공정수요。
In order to solve the problems such as complex operation ,consumption for the carrier gas and long test period in tradi-tional power transformer fault diagnosis approach based on dissolved gas analysis (DGA ) ,this paper proposes a new method which is detecting 5 types of characteristic gas content in transformer oil such as CH 4 ,C2 H2 ,C2 H4 ,C2 H6 and H2 based on photoacoustic spectroscopy and C2 H2/C2 H4 ,CH4/H2 ,C2 H4/C2 H6 three-ratios data are calculated .The support vector ma-chine model was constructed using cross validation method under five support vector machine functions and four kernel functions , heuristic algorithms were used in parameter optimization for penalty factor c and g ,which to establish the best SVM model for the highest fault diagnosis accuracy and the fast computing speed .Particles swarm optimization and genetic algorithm two types of heuristic algorithms were comparative studied in this paper for accuracy and speed in optimization .The simulation result shows that SVM model composed of C-SVC ,RBF kernel functions and genetic algorithm obtain 97.5% accuracy in test sample set and 98.333 3% accuracy in train sample set ,and genetic algorithm was about two times faster than particles swarm optimiza-tion in computing speed .T he methods described in this paper has many advantages such as simple operation ,non-contact meas-urement ,no consumption for the carrier gas ,long test period ,high stability and sensitivity ,the result shows that the methods described in this paper can instead of the traditional transformer fault diagnosis by gas chromatography and meets the actual pro-ject needs in transformer fault diagnosis .