Condition Monitoring of Transformer Breather Using a Capacitive Moisture Sensor

Empirical analysis of machine learning-based moisture sensing platforms for agricultural applications: A statistical perspective

“…For instance, in terms of accuracy as observed from figure 5, DBN RBM [2], CRNS [3], SMAP RF DN [19], GOFCHS [27], TDR [28], and P Band & L Band [34] models outperform other models, thus, they can be used for highly accurate moisture detection applications. Similarly, cost of deployment & computational complexity is visualized from figure 6, wherein it is observed that HPCM [6], HF RFID TFS [9], PWM [10], PMMA [15], FFCSM [16], MHPS [21], ECT [24], PQCWC [25], and HSAAA [32] require lowest deployment cost, while HPCM [6], PHS [17], ECT [24], and PQCWC [25] have lower computational complexity when compared with other models. Due to which these models must be used for applications which require lower complexity and lower computational costs.…”

“…This score will allow readers to identify models that have higher accuracy, better scalability, lower complexity, lower cost & lower computational delays. Based on this ARS value, it can be observed that ECT [24], PQCWC [25], MHPS [21], FoS [20], HPCM [6], PHS [17], HF RFID TFS [9], PMMA [15], FFCSM [16], HSAAA [32], PWM [10], FTO [35], CRNS [3] and CM [11] have better overall performance, thus can be used for efficient moisture sensing applications. LEWS LR [1] LR RBM [2] CRNS [3] MWMS [5] GPS [7] HF RFID TFS [9] CM [11] PLMR [12] MSR [14] FFCSM [16] SMAP [18] FoS [20] PRS [22] PQCWC [25] GOFCHS [27] SAR [29] CSMOS [31] SSMDI [33] FTO [35] PBG [38] MSOCCML [40] CRNS [42] Accuracy of moisture sensing models…”

“…While highly accurate sensing interfaces are costly, but can be used for high-speed moisture sensing applications. Specifically, DBN RBM, CRNS, SMAP LEWS LR [1] DBN RBM [2] LR RBM [2] BP RBM [2] CRNS [3] FBG [4] MWMS [5] HPCM [6] GPS [7] UHF RFID [8] HF RFID TFS [9] PWM [10] CM [11] PPMR [12] PLMR [12] RFID UHF [13] MSR [14] PMMA [15] FFCSM [16] PHS [17] SMAP [18] SMAP RF DN [19] FoS [20] MHPS [21] PRS [22] ECT [24] PQCWC [25] HDES [26] GOFCHS [27] TDR [28] SAR [29] SMI MODIS [30] CSMOS [31] HSAAA [32] SSMDI [33] P Band & L Band [34] FTO [35] eSMAP [36] PBG [38] MSNs [39] MSOCCML [40] SMAP TFC [41] CRNS [42] kCRNS [43] Computational RF DN, GOFCHS, TDR, and P Band & L Band models outperform other models; thus, they can be used for highly accurate moisture detection applications. While, HPCM, HF RFID TFS, PWM, PMMA, FFCSM, MHPS, ECT, PQCWC, ...…”

“…It showcases better performance when compared with fringe capacitance sensor (FCS), soil impedance sensor (SIS) and 𝑇𝑖𝑂 2 Nano particles sensor (TNPS), thus making it useful for a wide variety of application scenarios. Performance of this method must be validated under different soil types, and can be improved via use of pulse-width modulation (PWM) [10] with capacitive sensing interfaces.…”

Optimal Techno-economic Sequence-based Set of Diagnostic Tests for Distribution Transformers Using Genetic Algorithm

“…The monitoring of power transformers to improve their lifetime has received a great deal of attention [5]. By implementing appropriate monitoring systems, it is possible to decrease the Loss Of Life (LOL) of power transformers and the corresponding costs [6]. Moreover, the power system reliability is dramatically affected due to transformer failures and interruptions [7].…”