In this study, the lightning protection system and grounding system of one plant of the petrochemical industry were investigated, evaluated, and improved. The methods used in this study were rolling sphere and angle protection methods. The grounding system of the building under study was modeled and simulated using ATP/EMTP (Alternative Transient Program/Electromagnetic Transient Program) software. The results show that the external lightning protection system of the prilling tower studied does not adhere to IEC 62305 and IEC 1024-1-1 standards. Moreover, the grounding configuration of the DCS building was not appropriate for protecting sensitive equipment inside. Lightning causes an enormous potential difference between lightning ground rods in the grounding system. Additionally, disabling the existing surge protective device (SPD) causes an increase in the magnitude of Ground Potential Rise at the DCS building. Improvement of the lightning protection system of the prilling tower and DCS building on Plant 1 of this petrochemical company as well as some other recommendations for improvements are proposed. This paper also shows evidence that external lightning protection, internal lightning protection, and the grounding system need to be connected to make an exemplary lightning protection system.

Contaminants in transformer oil insulation can float when meeting several conditions. Then, the presence of floating contaminants affects the electrical characteristics of oil insulation. Therefore, the pre-breakdown (corona) and breakdown characteristics due to metallic floating particles in transformer oil insulation would be investigated. This test used DC high voltage stress. A 56 Ω resistor was connected to the oscilloscope to detect the corona currents. The camera was used to capture the images of corona light emission. In addition, the electric field between the electrode and particles was simulated. The variables were the particle size, including its shape, and the distance between the particles and the grounded electrode. The experimental results show that the average value of corona inception and breakdown was inversely proportional to the size of floating particles. The peak value of corona current was directly proportional to the particle size. The lowest breakdown voltage was found when the particle was close to the electrode, but they were not in contact.

Polymer is an insulating substance that has become increasingly popular in recent years due to its benefits. Light density, superior dielectric and thermal properties, and water-resistant or hydrophobic properties are only a few of the benefits. The presence of impurities or pollutants on the insulator’s surface lowers its dielectric capacity, which can lead to current leakage. The influence of seawater and fly ash pollutants on the distribution of the electric field and the current density of the insulator was simulated in this study. The finite element method was used to execute the simulation (FEM). Polymer insulators are subjected to testing in order to gather current leakage statistics. The tested insulator is exposed to seawater pollution, which varies depending on the equivalent salt density deposit value (ESDD). The pollutant insulator for fly ash varies depending on the value of non-soluble deposit density (NSDD). The existence of a layer of pollutants increased the value of the electric field and the value of the surface current density, according to the findings. Both in simulation and testing, the ESDD value of seawater pollutants and the NSDD value of fly ash contaminants influenced the value of the leakage current that flowed. The greater the ESDD and NSDD values are, the bigger the leakage current will be.

The broken rotor bar is the fault that most often occurs in induction motors. This paper proposes a method to identify the broken rotor bar fault using a combination of Empirical Mode Decomposition and Artificial Neural Networks (ANNs). The motor current signal is processed using EMD analysis resulting in the Intrinsic Mode Function (IMF) signal. The zero crossing point of the IMF signal is recorded to obtain the Time Successive between Zero Crossing (TSZC). The Probability Density Function (PDF) of the TZSC is used as the ANN classifier input. The PDF properties of peak, width, and standard deviation are selected as the input variables. Two ANNs were designed as fault detection and severity identification systems. The experimental testing also considers the load level variation. The experiment of the broken rotor bar fault diagnostics shows that the ANN-based fault detection system is able to detect faults with accuracy up to 94.2%. Moreover, the ANN-based severity identification successfully identified 76.09% of the cases. In addition, the experiment on load variations reveals that the fault diagnostic is more effective at higher loads.