Insulators are considered one of the most significant parts of power systems which can affect the overall performance of high-voltage (HV) transmission lines and substations. High-voltage (HV) insulators are critical for the successful operation of HV overhead transmission lines, and a failure in any insulator due to contamination can lead to flashover voltage, which will cause a power outage. However, the electrical performance of HV insulators is highly environment sensitive. The main cause of these flashovers in the industrial, agricultural, desert, and coastal areas, is the insulator contamination caused by unfavorable climatic conditions such as dew, fog, or rain. Therefore, the purpose of this work is to review the different methods adopted to identify the contamination level on high-voltage insulators. Several methods have been developed to observe and measure the contamination level on HV insulators, such as leakage current, partial disgorgement, and images with the help of different techniques. Various techniques have been discussed alongside their advantages and disadvantages on the basis of the published research work in the last decade. The major high-voltage insulator contamination level classification techniques discussed include machine learning, fuzzy logic, neuro–fuzzy interface, detrended fluctuation analysis (DFA), and other methods. The contamination level data will aid the scheduling of the extensive and costly substation insulator, and live line washing performed using high-pressured water. As a result, considerable benefits in terms of improved power system reliability and maintenance cost savings will be realized. This paper provides an overview of the different signal processing and machine-learning methods adopted to identify the contamination level on high-voltage insulators. Various methods are studied, and the advantages and disadvantages of each method are discussed. The comprehensive review of the islanding methods will provide power utilities and researchers with a reference and guideline to select the best method to be used for contamination level identification based on their effectiveness and economic feasibility.

This paper presents a fault-tolerant control (FTC) scheme for nonlinear systems which are connected in a networked control system. The nonlinear system is first transformed into two subsystems such that the unobservable part is affected by a fault and the observable part is unaffected. An observer is then designed which gives state estimates using a Luenberger observer and also estimates unknown parameter of the system; this helps in fault estimation. The FTC is applied in the presence of sampling due to the presence of a network in the loop. The controller gain is obtained using linear-quadratic regulator technique. The methodology is applied on a mechatronic system and the results show satisfactory performance.

This paper presents a novel stator inter-turn fault diagnosis method for Line Start Permanent Magnet Synchronous Motors (LSPMSMs) using the frequency analysis of acoustic signals resulting from asymmetrical faults. In this method, acoustic data are experimentally collected from a 1 hp interior mount LSPMSM for different inter-turn fault cases and motor loading levels, while including the background noise. The signals are collected using a smartphone at a sampling rate of 48,000 samples per second. The signal for each case is analyzed using fast Fourier transform (FFT), which results in the decomposition of the signal into its frequency components. The results indicate that, for both no-load and full-load conditions, 39 components are observed to be affected by the faults, whereby their amplitudes increase with the fault severity. The 40-turns fault shows the highest difference in the component amplitudes compared with the healthy condition acoustic signal. Therefore, this diagnostic method is able to detect the stator inter-turn fault for interior mount LSPMSMs. Moreover, the method is simple and cheap since it uses a readily available sensor.