Electrical insulators are components used to promote electrical isolation and mechanical support for conductors in transmission lines belonging to the electric power systems (EPS). The performance of glass insulators is compromised mainly in the face of high pollution level, causing the amplitude of the leakage current to increase and the formation of superficial discharges. In this work, a methodology for estimating the surface pollution critical level of glass insulators using leakage current parameters and artificial intelligence algorithms is proposed. For this purpose, glass insulators were tested in the laboratory to simulate different levels of pollution, in order to record leakage current, voltage signals and environmental variables (e.g., humidity and temperature). The pollution level was simulated by depositing the pollution solution on the insulators, where each solution was differentiated based on the level of equivalent salt deposit density (ESDD). In the tests, the level of ESDD responsible for causing surface discharges was also recorded, as this phenomenon occurs at high levels of pollution. Pollution modeling was performed using forecasting algorithms such as multilayer perceptron (MLP) and support vectors regression (SVR). Based on the application of the best performance model and using criteria that indicate the occurrence of surface discharges, it was possible to estimate the critical level of pollution in the glass insulators string for 69 kV systems.

Lightning strikes are a leading cause of outages on overhead transmission lines, significantly compromising power system reliability. Consequently, monitoring lightning activity is critical to mitigate its impact on lines with high outage rates. This study presents an autonomous lightning strike counter system utilizing a split-core Rogowski coil for non-invasive current measurement on transmission towers. The system combines the Rogowski coil with an active integrator circuit to reconstruct the incident current waveform from the coil voltage signal. A microcontroller-based processing unit records strike occurrences and classifies them by amplitude using predefined thresholds. Laboratory tests were carried out to evaluate the performance of the Rogowski coil and integrator circuit, validating the system accuracy in detecting current pulses associated with lightning strikes. Underway field tests will assess the sensor’s reliability during long-term autonomous operation on 345-kV transmission towers. The results demonstrate that the proposed system represents a practical and cost-effective solution for lightning monitoring in remote areas, contributing to enhanced data collection for engineering studies and improved reliability of electrical infrastructure.

This paper proposes an integrated framework for predictive asset management in electric power distribution systems by combining machine learning with the Analytic Hierarchy Process (AHP). Distinct utility datasets were used according to the characteristics of each prediction task. Annual preventive- and corrective-maintenance records, including maintenance cost and frequency, covered the period from 2016 to 2023, whereas the SAIDI and SAIFI annual time series covered the period from 2014 to 2023. Remaining Useful Life (RUL) estimation was based on static asset-level records from the entire available equipment stock and therefore was not associated with a single time-series interval. Multiple machine learning algorithms were evaluated independently for each Type of Utility Component (TUC), and the best-performing models were selected according to their validation errors. The resulting predictions were incorporated into a two-level AHP hierarchy that integrates technical, economic, operational, and regulatory criteria for portfolio-level asset-renewal prioritization. For RUL estimation, the selected TUC-specific models reduced the mean MAE from 11.33 years for the historical-mean baseline to 2.70 years, corresponding to an absolute improvement of 8.63 years and a relative reduction of 76.15%. The resulting priority ranking was evaluated retrospectively using moving-average, cumulative-gain, and lift analyses. The top 20% of ranked assets captured 29.61% of the observed corrective-maintenance occurrences, corresponding to a lift of 1.4806 relative to random selection. The cumulative-gain curve yielded an area under the curve of 0.6337 and a ranking Gini coefficient of 0.2674, indicating moderate positive discrimination across the asset portfolio. These results show that the proposed ML–AHP framework can support the allocation of inspection, engineering-assessment, and renewal resources toward assets with comparatively greater maintenance demand and operational relevance. The framework provides a structured, data-driven decision-support approach for utility asset-renewal planning, while prospective field implementation remains necessary to quantify reductions in failures, corrective maintenance, continuity penalties, and total expenditure.