This paper presents a separation method to discern broken rotor bar fault from low frequency load torque oscillation thorough radial leakage flux spectrums in induction motors (IMs). Broken rotor bar fault can usually be detected using classical motor current based analysis (MCSA), but it may not provide reliable results since its performance depend on motor topology, stator winding and load type. Particularly, if a motor is subjected to load fluctuation, then oscillation related signatures exhibit similar behavior that of broken bar which leads misleading signatures. In this paper, radial leakage flux spectrum is exhaustively analyzed thorough a fluxgate sensor to discern these two effects in IMs. It is shown that there are some additional characteristics broken bar signatures such as 3sf _s and (f _s -f _r )±2sf _s in radial leakage flux which do not appear in low frequency load torque oscillation case. A 2-Dimensional (2D) finite element analysis and experiments are carried to show that using leakage flux can provide a separation method and more reliable results than classical MCSA in IMs.

Lightning surge analysis of protective structures is significant for the design of system. However, simulation models are limited. In this study, a Faraday cage with four air terminals and 2 × 6 grounding rods built to protect a switchyard control building in a 380 kV substation is simulated in alternative transient program for the analysis of lightning surges. Distributed line modelling is used for cage conductors. Down conductors are represented by considering non‐uniform variation of the conductor parameters. Current waveforms through the system and voltages at some critical points after a lightning stroke are computed. The traditional lightning parameters needed in structural protection such as lightning peak current, maximum current derivative, current rise time and current duration are determined. The effects of mesh size and some system parameters such as grounding resistance and lightning surge impedance are investigated.

In power transformers, lightning strikes, switching operations, and short circuit faults can deform the winding insulation, resulting in an electric arc between the windings. If the arc is not detected in its initial phase, it may lead to a solid short circuit and damage the transformer, potentially causing an explosion due to overheating and high pressure. In this study, winding arcs in the transformer are identified from the terminal current and voltage signals. A 3D magnetic model of a 15 MVA power transformer is constructed in Ansys@Maxwell, and the nonlinear arc model is simulated in Matlab@Simulink. The transient voltage and current signals related to the arcing conditions at five different points in the high-voltage side winding are obtained by running Ansys and Matlab simultaneously using ANSYS@Simplorer (Twin Builder). These signals are transformed into the frequency domain using Fast Fourier Transform (FFT). The arcs are detected from the transient-generated frequency components of the transformer voltage and current signals.

The amount of electric energy produced by photovoltaic panels depends on air temperature, humidity rate, wind velocity, photovoltaic module temperature, and particularly solar radiation. Being aware of the behaviour patterns of the panels to be used in project and planning works regarding photovoltaic applications will set forth a realistic expense form; therefore, erroneous investments will be avoided, and the country budget will benefit from added value. The power ratings obtained from the photovoltaic panels and the environmental factors were measured and recorded for a year by the measurement stations established in three diverse regions (Adiyaman-Malatya-Sanliurfa). In the developed artificial neural network models, the estimation accuracy was 99.94%. Furthermore, by taking the data of the General Directorate of Meteorology as a reference, models of artificial neural networks were developed using the data from Adiyaman province for training; by using Malatya and Sanliurfa as test data, 99.57% estimation accuracy was achieved. With the artificial neural network models developed as a result of the study, the energy efficiency for the photovoltaic energy systems desired to be established by using meteorological parameters such as temperature, humidity, wind, and solar radiation of various regions anywhere in the world can be estimated with high accuracy.