Computation of breakdown voltage of long rod-plane air gaps in large temperature and humidity range under positive standard switching impulse voltage

Recent Progress and Future Prospects on All-Organic Polymer Dielectrics for Energy Storage Capacitors

“…Then, more electrons are generated by collision ionization with the molecules or neutral atoms, which result in increased conductivity and breakdown path inside the dielectrics. − Besides, greater environmental humidity during the test can reduce the dielectric breakdown strength. Last but not the least, tested conditions also influence the breakdown phenomenon. − Generally, the breakdown strength obtained by immersing the tested sample in silicone oil is higher than that obtained in air, which is due to the avoidance of creeping discharge. − However, the precise understanding of the breakdown process of dielectrics is still not completely clear due to the numerous influencing factors and complicated mechanisms of breakdown. In addition, further research including experimental and theoretical innovation is urgently needed to understand the breakdown process of polymer dielectrics.…”

Analisis Variabel Perubahan Suhu Terhadap Karakteristik Tegangan Tembus Dielektrik Udara

“…Masalah yang sering terjadi dalam penyaluran energi listrik adalah kegagalan isolasi (Harinata et al, 2019) yang disebabkan oleh kondisi iklim, polusi dan terpaan medan listrik (Mustamin, 2011). Perubahan iklim merupakan fenomena global, dimana kondisi alam mengalami pergeseran musim atau terjadi anomali terhadap kondisi normalnya dalam rentang waktu yang panjang dimana perubahan iklim identik degan meningkatnya suhu udara (Alfiandy et al, 2022) perubahan iklim ini berdampak pada jaringan kelistrikan dalam hal ini pengaruh perubahan suhu pada daerah tropis, dapat mempengaruhi keandalan isolasi udara pada sistem distribusi (Abugalia, 2019), Selain dari pengaruh suhu, besarnya arus yang mengalir pada penghantar dapat mengakibatkan peningkatan suhu di kubikel 20 kV kemudian berkurangnya keandalan pada sistem hingga terjadinya kerusakan pada peralatan (Akhir et al, 2021) pada peralatan tegangan tinggi isolasi biasanya di gunakan unntuk memisahkan antara 2 peralatan yang tidak memiliki tegangan dengan peralatan listrik yang memiliki tegangan (Ge et al, 2020). supaya di dalam rangkaian tersebut tidak terjadi munculnya lompatan/percikan elektron (Flashover/sparkover) yang akan mengakibatkan kerusakan pada peralatan tegangan tinggi (Guerra-Garcia et al, 2020), dalam penyaluran tegangan tinggi semakin besar energi listrik yang digunakan maka semakin besar pula tegangan listrik yang disalurkan, dengan besarnya tegangan listrik tersebut mengakibatkan isolasi udara yang digunakan harus memiliki tahanan isolasi yang besar pula (Haddin & Bahtiar, 2018).…”

A machine learning‐based approach for dielectric strength prediction of long air gaps with engineering configurations

“…In recent years, the data-driven models based on artificial intelligence algorithms provide a new possibility to realize dielectric strength prediction, which is an alternative way except for the physical models and empirical formulas. Some artificial intelligence or machine learning algorithms, such as artificial neural networks (ANN) [17][18][19][20], support vector machine (SVM) [21][22][23][24][25], fuzzy logic [26,27], Gaussian process regression (GPR) [28], least squares regression (LSR) [29], and extremely randomized trees [30], etc., have been applied for breakdown voltage prediction of air gaps, transformer oils, and solid dielectrics like insulation paper and nanocomposites. These data-driven models mainly focus on the mathematical correlations between the dielectric strength and various influencing factors, and do not directly consider the discharge evolution process full of randomness and uncertainty, which is not restricted by the complex and unclear physical process.…”

“…Zhang and Niu et al [17,18] applied back propagation (BP) and Chebyshev neural networks to predict sphere gap discharge voltages under different atmospheric conditions, taking air pressure, temperature, relative humidity, wind speed, and illumination as input parameters. Ge et al [21] and Yang et al [30] took the temperature, humidity, air pressure, and gap distance as model inputs, and established machine learning models to predict the U 50 of long rod-plane air gaps, using support vector regression (SVR), decision tree, random forest, and extremely randomized trees. On this basis, Ding et al [22] conducted U 50 prediction of DC transmission tower gaps by AdaBoost-SVR model, with the inputs of atmospheric parameters, ring diameter and pipe dimeter of grading rings, tower widths, and gap distances.…”

“…Ge et al. [21] and Yang et al. [30] took the temperature, humidity, air pressure, and gap distance as model inputs, and established machine learning models to predict the U 50 of long rod‐plane air gaps, using support vector regression (SVR), decision tree, random forest, and extremely randomized trees.…”