Lithium-based batteries operation is related to some safety risks of dangerous flaming, integrity destruction, or even explosion. Gas leakage is an early and reliable indicator for such irreversible malfunctioning of electrical accumulators. Often, accurate gas emission source location sensing is difficult especially in heavy operational conditions, related to temperature changes, vibrations, movements, accelerations, etc. In this paper we propose a gas detection system, with catalytic type sensor array, and a numerical reconstruction method for precise gas emission source location inside the battery pack. The detection system employs a distributed array of CO sensors. Proposed sensor array configurations significantly reduce the number of sensing nodes inside the battery pack and fewer sensors than the protected battery elements are used. This way, data acquisition process by sensor nodes is also simplified. Several array configurations are considered according to their measurement efficiency and accuracy. Reconstruction algorithm is based on fast interpolation technique very suitable for real-time data processing. Estimation of reconstruction method accuracy is made by computational model of the gas diffusion inside the pack.

As a major component in electric power systems, power transformers are one of the most expensive and important pieces of electrical equipment. The trouble-free operation of power transformers is an important criterion for safety and stability in a power system. Technical diagnostics of electrical equipment are a mandatory part of preventing accidents and ensuring the continuity of the power supply. In this study, a complex diagnostic methodology was proposed and applied for special power transformers’ risk estimation. Twenty special power transformers were scored with the proposed risk estimation methodology. For each transformer, dissolved gas analysis (DGA) tests, transformer oil quality analysis, visual inspections of all current equipment on-site and historical data for the operation of each electrical research were conducted. All data were collected and analyzed under historical records of malfunctioning events. Statistical data for expected fault risk, based on long-term records, with such types of transformers were used to make more precise estimations of the current state of each machine and expected operational resource. The calculated degree of insulation polymerization was made via an ANN-assisted predictive method. Assessment of the collected data was applied to allow detailed information of the state of the power transformer to be rated. A method for risk assessment and reliability estimation was proposed and applied, based on the health index (HI) for each transformer.

Purpose: The purpose of this paper is to develop an inverse approach for 3D thermal sources determination.Design/methodology/approach: The developed approach is based on the Green's function for Poison's equation. Forward and inverse couple electromagnetic‐thermal field problems are formulated. Finite elements models are built and applied. Thermal field data are acquired by thermo vision camera. The thermal field sources are determined inside of the investigated inaccessible volume object using modeled and measured data with the developed approach.Findings: The presented method and implemented examples demonstrate the possibilities of the developed approach for inverse source problem solution and determination of thermal field distributions of electrical devices.Originality/value: The proposed inverse method uses the Green's function for Poison's equation for solution of thermal field problem taking into account the couple electromagnetic‐thermal problems. Proposed inverse method is very fast, accurate and can be used in many practical activities for electrical current determination and visualization in inaccessible regions only by measured external thermal field. Thermal field data needed for the method are easily acquired by thermo vision camera.

Reduction of eddy current losses is essential for magnetic gears efficiency enhancement. These losses are resulting in output torque reduction, especially in high rotational speeds and high gear ratios. In this paper, we developed an approach for determination of the eddy current losses of a coaxial magnetic gear. A time dependent two dimensional finite element model has been utilized. Permanent magnets such as NdFeB, SmCo, Alnico and ferrite are used in order to evaluate the eddy currents and the torque transmission losses for a magnetic gear performance.