改進(jìn)SSA優(yōu)化BP神經(jīng)網(wǎng)絡(luò)的變壓器故障診斷

摘" 要： 變壓器故障類型的準(zhǔn)確診斷對(duì)保障電網(wǎng)的安全與穩(wěn)定至關(guān)重要。針對(duì)BP神經(jīng)網(wǎng)絡(luò)與麻雀搜索算法（SSA）存在收斂緩慢和易陷入局部極值導(dǎo)致無(wú)法準(zhǔn)確診斷的問題，提出將改進(jìn)的麻雀搜索算法（ISSA）優(yōu)化BP神經(jīng)網(wǎng)絡(luò)應(yīng)用于變壓器故障診斷。首先，引入非線性慣性權(quán)重和縱橫交叉策略，從而提高算法的收斂速度和全局尋優(yōu)能力；其次，將ISSA與傳統(tǒng)SSA在收斂函數(shù)上進(jìn)行對(duì)比分析，得到ISSA算法在迭代12次后以52%的準(zhǔn)確率收斂，而SSA算法迭代23次后才達(dá)到25%的準(zhǔn)確率，證明了ISSA在收斂速度和精度方面有明顯提高；最后，將ISSA?BP、SSA?BP和BP診斷模型進(jìn)行對(duì)比。實(shí)驗(yàn)結(jié)果表明，ISSA?BP模型準(zhǔn)確率達(dá)到了97%，比SSA?BP、BP神經(jīng)網(wǎng)絡(luò)模型分別提高了4%和11%，可以認(rèn)為提出的算法模型在變壓器故障診斷領(lǐng)域具有更高的精度與良好的發(fā)展前景。

關(guān)鍵詞： 麻雀搜索算法； BP神經(jīng)網(wǎng)絡(luò)； 變壓器； 故障診斷； 非線性慣性權(quán)重； 縱橫交叉策略

中圖分類號(hào)： TN919?34； TM41" " " " " " " " " " 文獻(xiàn)標(biāo)識(shí)碼： A" " " " " " " " " " " 文章編號(hào)： 1004?373X（2025）04?0145?06

Improved SSA optimized BP neural network for transformer fault diagnosis

WANG Fanrong1， 2， WANG Junhan1， JIANG Junjie1

（1. School of Electrical and Electronic Engineering， Hubei University of Technology， Wuhan 430068， China;

2. Xiangyang Industry Research Institute， Hubei University of Technology， Xiangyang 441100， China）

Abstract： Accurate diagnosis of transformer fault types is crucial to ensure the safety and stability of the power grid. In allusion to the problems that BP neural network and Sparrow search algorithm （SSA） have slow convergence speed and easy to fall into local extreme， which cannot make accurate diagnosis， an improved Sparrow search algorithm （ISSA） is proposed to optimize BP neural network for transformer fault diagnosis. The nonlinear inertia weights and vertical and horizontal crossover strategies are introduced to improve the convergence speed and global optimization ability of the algorithm. A comparative analysis of the convergence function between ISSA and traditional SSA shows that ISSA algorithm converges with an accuracy of 52% after 12 iterations， while SSA algorithm converges with an accuracy of 25% after 23 iterations， which proves that ISSA has significantly improved convergence speed and accuracy. The diagnostic models of ISA?BP， SSA?BP and BP were compared. The experimental results show that the accuracy of ISSA?BP model can reach 97%， which is 4% and 11% higher than that of SSA?BP and BP models， respectively. The proposed algorithmic model has higher accuracy and good development prospect in the field of transformer fault diagnosis.

Keywords： Sparrow search algorithm; BP neural network; transformer; fault diagnosis; nonlinear inertia weight; vertical and horizontal crossover strategy

0" 引" 言

油浸式變壓器因其工作環(huán)境復(fù)雜多變、超負(fù)荷運(yùn)行和易絕緣受潮老化等問題容易發(fā)生故障，當(dāng)變壓器出現(xiàn)故障時(shí)，只依靠實(shí)地員工的經(jīng)驗(yàn)來(lái)判斷故障的確切原因是非常困難的，這會(huì)造成非常嚴(yán)重的后果[1]。因此，對(duì)變壓器故障性質(zhì)進(jìn)行檢測(cè)，并對(duì)其故障類型進(jìn)行準(zhǔn)確和高效的診斷，對(duì)保障電網(wǎng)的安全與穩(wěn)定具有重要意義[2]。

當(dāng)變壓器發(fā)生故障時(shí)，會(huì)導(dǎo)致H2、C2H4等氣體溶解于絕緣油中，有相關(guān)研究表明，對(duì)變壓器油中溶解氣體進(jìn)行檢測(cè)能夠從側(cè)面反映出變壓器故障類型[3]。……