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The paper proposes and presents a comprehensive and integrated circuit model for investigating the behaviour of partial discharges occurring in voids inside the solid insulations of medium and high voltage cables.

The model is based on the well‐known three capacitors model, which is remarkably improved to handle physical parameters such as cavity size, position, shape and pressure, environmental parameters such as cable temperature, in addition to operational parameters such as the contributions of the avalanche of free electrons inside the cavity through considering stochastic time delays.

A complete, flexible and reliable model for partial discharges in voids inside the solid insulation of medium and high voltage cables is presented whose output agrees with experimental reported results.

The proposed model deals only with single voids, and the semiconductor layers in the insulation of cables are not considered.

The model can be used in different physical, environmental and operational conditions in order to investigate the characteristics of partial discharge signals to be used as the bases for partial discharge detection and classification in power cables.

This paper presents a novel comprehensive and integrated circuit model with controlling functions to propose the behaviour of partial discharge occurring in voids inside the solid insulation of power cables. The model provides the contribution of geometrical parameters of the void, and operational conditions such as cable temperature and source frequency in partial discharge analysis.

The purpose of this paper is to investigate the condition of insulation in high-voltage equipments using partial discharge (PD) measurements. It proposes the methods to eliminate several noises like white noise, random noise and discrete spectral interferences which severely pollutes the PD signals. The study aims to remove these noises from the PD signal effectively by preserving the signal features.

This paper employs fast Fourier transform, discrete wavelet transform and translational invariant wavelet transform (TIWT) for denoising the PD signals. The simulated damped exponential pulse and damped oscillatory pulse with low- and high-level noises and a measured PD signal are considered for this analysis. The conventional wavelet denoising approach is also improved by estimating the automated global optimum threshold value using genetic algorithm (GA). The statistical parameters are evaluated and compared. Among these methods, GA-based TIWT approach provides robustness and reduces computational complexity.

This paper provides effective condition monitoring of power apparatus using GA-based TIWT approach. This method provides the low value of mean square error, pulse amplitude distortion and also high reduction in noise level due to its robustness and reduced computational complexity. It suggests that this approach works well for both signals immersed in noise as well as for noise immersed in signals.

Because of the chosen PD signals, the research results may lack for multiple discharges. Therefore, researchers are encouraged to test the proposed propositions further.

The paper includes implication for the development of online testing for equipment analysis and diagnostics during normal operating condition. Corrective actions can be planned and implemented, resulting in reduced unscheduled downtime.

This PD-based analysis often present well in advance of insulation failure, asset managers can monitor it over time and make informed strategic decisions regarding the repair or replacement of the equipment. These predictive diagnostics help society to prioritize investments before an unexpected outage occurs.

This paper provides an enhanced study of condition monitoring of HV power apparatus by which life time of insulation can be increased by taking preventive measures.