The specific activity of radionuclides in the soil of the Borujerd region using high purity Germanium detector (HPGe) was measured and the associated radiological hazards were calculated. The mean specific activity of radionuclides of Ra-226, Th-232, K-40, and Cs-137 in soil was obtained at 10.99±5.11, 35.36±4.44, 324.20±10.24, and 2.93±0.60 Bq.kg-1. These values were below the global average. Also, the value of basic radiological risk parameters, such as Raeq, AEDout, AEDint, Hex, Hin, and Iγ, ranged from 52.02 to 139.54 in Bq.kg-1, from 24.98 to 68.27 and from 42.90 to 117.22 in mSv.y-1, 122.57 to 334.93, 0.14 to 0.37, 0.16 to 0.40, and 0.27 to 1.04, respectively. The range of excess lifetime cancer risk (ELCR) value for the surrounding soil samples varied from 0.15×10-3 to 0.41×10-3, in which samples S4, S14, S24, S27, S28, S29, and S30 exceeded the global average of 0.29×10-3. A radiological map of the city of Borujerd was prepared using the (GIS) program. The study showed that the level of radioactivity in the Borujerd area did not exceed the critical value and is in line with the global results.

Partial Discharge (PD) measurement is one of the best solutions for condition assessment of Gas Insulated Switchgears ((GIS)s). For having Condition-based maintenance of (GIS), online PD monitoring is of great importance. For this aim, Ultra High Frequency (UHF) PD sensors should be installed inside the (GIS) during the installation. However, in most installed (GIS)s in industries, the internal UHF PD sensors are not installed. In this paper, a new method for online defect type recognition according to external UHF PD sensors and based on the time-frequency representation of PD signal is proposed. In this case, four artificial defect types named protrusion on the main conductor, protrusion on the enclosure, free moving metal particle, and metal particle on spacer are implanted inside the 132 kV L-Shaped structure of one phase in enclosure (GIS). The signal energy at each level of the decomposed signal by Discrete Wavelet Transform (DWT) is applied for features of each defect type. The trends of signal energy variations at each frequency range of signal are applied for discriminating between each defect type. The Deep Feed Forward Network (DFFN) classifier is applied for PD pattern recognition. The results show the benefits and simplicity of the proposed method for PD signal classification, independent from the position of the PD sensor, especially in the case of online PD monitoring of (GIS).