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Circuit Breakers (CBs) play an important role in ensuring the safe operation of protection systems. Condition Monitoring (CM) devices are widely implemented to extend lifetime, and to improve the maintenance quality. The present paper proposes a cost-based prioritization approach for CBs in a network equipped withCMdevices. To this end, a math- ematical formulation is developed for the categorization and modeling of equipment failures based on their severity. This formulation quantifies the effect of the CM devices on the out- age rate of the equipment. The reliability parameters of the substations 400/132/20 KV, including the failure rate, _, average repair time, r, average outage time, U, substations, in two status of without CM and with CM of the CBs are calculated. These parameters are calculated implementing a minimal cut-set method. The outage rate of equipment with and without the CM devices is used to determine the effect of the CM devices on the reliability of the network. Finally, the prioritization of substations to install theCMdevices on the CBs has been investigated in terms of the Expected Energy Not Supplied (EENS) and costs of CM. To verify the effectiveness and applicability of the method, the proposed approach is applied to the CBs in the power transmission network in the Khorasan Regional Electricity Company (KREC) in Iran. [ABSTRACT FROM AUTHOR]

Nowadays, the maintenance scheduling of equipment has been highly improved using condition monitoring (CM) systems. This paper determines the optimal inspection rate of circuit breakers equipped with CM using a novel mathematical procedure. To achieve this goal, in the first step, it is necessary to quantify the effect of CM on the early detection of minor failures of the equipment. Therefore, a mathematical formulation is provided to categorize and model the failures of the equipment based on their severity. Moreover, a new concept termed predictive maintenance (PDM) rate will be introduced and formulated in this paper. The PDM rate is applied in the maintenance process as the smart inspection rate of the equipment. In the second step, this paper proposes a new combined preventive and predictive maintenance Markov model. By developing the formulation of the proposed Markov model, an optimization problem is formed. In this optimization problem, the annual maintenance cost serves as the objective function and the inspection rates represent the decision variables. The comprehensive statistical study on the failures of 400kv‐CB of KREC in Iran and the implementation of the proposed method indicates that the CM and optimal inspection of the CB not only enhances the lifetime of the CB by 19.48 years but also lower the annual maintenance cost by 19%. [ABSTRACT FROM AUTHOR]

Leaf area index (LAI), one of the most crucial vegetation biophysical variables, is required to evaluate the structural characteristic of plant communities. This study, therefore, aimed to evaluate the LAI of ecoregions in Iran obtained using Sentinel-2B, Landsat 8 (OLI), MODIS, and AVHRR data in June and July 2020. A field survey was performed in different ecoregions throughout Ardabil Province during June and July 2020 under the satellite image dates. A Laipen LP 100 (LP 100) field-portable device was used to measure the LAI in 822 samples with different plant functional types (PFTs) of shrubs, bushes, and trees. The LAI was estimated using the SNAPv7.0.4 (Sentinel Application Platform) software for Sentinel-2B data and Google Earth Engine (GEE) system–based EVI for Landsat 8. At the same time, for MODIS and AVHRR, the LAI products of GEE were considered. The results of all satellite-based methods verified the LAI variations in space and time for every PFT. Based on Sentinel-2B, Landsat 8, MODIS, and AVHRR application, the minimum and maximum LAIs were respectively obtained at 0.14–1.78, 0.09–3.74, 0.82–4.69, and 0.35–2.73 for shrubs; 0.17–5.17, 0.3–2.3, 0.59–3.84, and 0.63–3.47 for bushes; and 0.3–4.4, 0.3–4.5, 0.7–4.3, and 0.5–3.3 for trees. These estimated values were lower than the LAI values of LP 100 (i.e., 0.4–4.10 for shrubs, 1.6–7.7 for bushes, and 3.1–6.8 for trees). A significant correlation (p < 0.05) for almost all studied PFTs between LP 100-LAI and estimated LAI from sensors was also observed in Sentinel-2B (|r| > 0.63 and R2 > 0.89), Landsat 8 (|r| > 0.50 and R2 > 0.72), MODIS (|r| > 0.65 and R2 > 0.88), and AVHRR (|r| > 0.59 and R2 > 0.68). Due to its high spatial resolution and relatively significant correlation with terrestrial data, Sentinel-2B was more suitable for calculating the LAI. The results obtained from this study can be used in future studies on sustainable rangeland management and conservation. [ABSTRACT FROM AUTHOR]