电网技术
電網技術
전망기술
POWER SYSTEM TECHNOLOGY
2012年
2期
170-175
,共6页
姚建刚%关石磊%陆佳政%蒋正龙%赵纯%夏德分%钱艳萍
姚建剛%關石磊%陸佳政%蔣正龍%趙純%夏德分%錢豔萍
요건강%관석뢰%륙가정%장정룡%조순%하덕분%전염평
红外热像%相对温度分布特征%图像去噪%图像分割%人工神经网络%零值绝缘子识别%高电压与绝缘技术
紅外熱像%相對溫度分佈特徵%圖像去譟%圖像分割%人工神經網絡%零值絕緣子識彆%高電壓與絕緣技術
홍외열상%상대온도분포특정%도상거조%도상분할%인공신경망락%령치절연자식별%고전압여절연기술
infrared thermal image%relative temperature distribution characteristics%image denoising%image segmentation%artificial neural network%zero resistance insulator identification%high voltage and insulation technology
提出利用绝缘子串相对温度分布特征和人工神经网络模型相结合的方法识别不同污秽等级、不同湿度条件下的零值绝缘子。试验获取模拟110kV线路悬式绝缘子的红外运行图像,经图像去噪、分割等预处理后,提取绝缘子串区域相对温度分布特征参数作为识别零值绝缘子的温度信息特征量,并结合环境相对湿度、等值附盐密度作为识别模型的输入向量,将实际测定绝缘子串是否含零值的状态分类信息作为输出向量,通过训练得到优化的识别模型,并用于零值绝缘子识别。试验结果验证该方法准确性高,可为输电线路瓷绝缘设备的故障检修提供参考和方法借鉴。
提齣利用絕緣子串相對溫度分佈特徵和人工神經網絡模型相結閤的方法識彆不同汙穢等級、不同濕度條件下的零值絕緣子。試驗穫取模擬110kV線路懸式絕緣子的紅外運行圖像,經圖像去譟、分割等預處理後,提取絕緣子串區域相對溫度分佈特徵參數作為識彆零值絕緣子的溫度信息特徵量,併結閤環境相對濕度、等值附鹽密度作為識彆模型的輸入嚮量,將實際測定絕緣子串是否含零值的狀態分類信息作為輸齣嚮量,通過訓練得到優化的識彆模型,併用于零值絕緣子識彆。試驗結果驗證該方法準確性高,可為輸電線路瓷絕緣設備的故障檢脩提供參攷和方法藉鑒。
제출이용절연자천상대온도분포특정화인공신경망락모형상결합적방법식별불동오예등급、불동습도조건하적령치절연자。시험획취모의110kV선로현식절연자적홍외운행도상,경도상거조、분할등예처리후,제취절연자천구역상대온도분포특정삼수작위식별령치절연자적온도신식특정량,병결합배경상대습도、등치부염밀도작위식별모형적수입향량,장실제측정절연자천시부함령치적상태분류신식작위수출향량,통과훈련득도우화적식별모형,병용우령치절연자식별。시험결과험증해방법준학성고,가위수전선로자절연설비적고장검수제공삼고화방법차감。
A method is proposed to identify zero resistance insulators under various pollution levels and humidity conditions by combining relative temperature distribution characteristics of insulator string with artificial neural network (ANN) model. The infrared image of suspension insulator string being operated in 110 kV transmission line is achieved by simulation tests and after the preprocessing of image denoising and segmentation the extracted characteristic parameters of relative temperature distribution in the region of insulator string are taken as temperature information characteristics to identify zero resistance insulator, and taking environmental relative humidity and equivalent salt deposit density as input vectors of identification model and regarding the state classification information that whether the actually measured insulation string contains zero resistance insulator as the output vector the optimized identification model is obtained by training and applied to the identification of zero resistance insulator. Testing results show that the zero resistance insulator identification by the proposed method is accurate, so it is available for reference to corrective maintenance and troubleshooting of porcelain insulation equipments for transmission lines.