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Percorrer por autor "Monteiro, J."

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  • Convolutional neural network approach for fault detection and characterization in medium voltage distribution networks
    Publication . Shafei, Atefeh Pour; Silva, J. Fernando A.; Monteiro, J.; Elsevier
    Power outages significantly impact the power industry by disrupting social welfare and economic stability. Still, existing methods for fault detection face challenges due to load and network topology, conditions, and installed equipment. However, recent advances in artificial intelligence (AI) are enabling researchers to create alternative approaches for fault detection and location strategies. Therefore, this paper introduces a novel method for detecting, classifying, and locating faults in power systems through voltage waveform analysis using a convolutional neural network (CNN) integrated with the Piecewise Function Put Together (PFPT) algorithm for fault detection and fault zone localization in a power distribution network. Utilizing Park's transformation, noise reduction PFPT sine fitting, and CNNs, the proposed method distinguishes between 'healthy' and 'faulty' conditions. Simulation results reveal that while the voltage Park's vector time behavior of a healthy system remains stable, it exhibits circular or mixed patterns under faulty conditions. These patterns enable the identification of four types of short circuit faults—single-line-to-ground (LG), line-to-line (LL), line-to-line-to-ground (LLG), and three-line (3L) faults—by analyzing 3D voltage Park's waveforms at network buses. The study validates fault type identification through the observation of rotating Park vectors from sine fitting of time-based voltage waveforms. By converting 3D voltage waveforms into high-resolution images, the method utilizes a CNN for fault recognition, achieving an accuracy of 93.1%. This innovative approach underscores the robustness and precision of combining traditional electrical engineering techniques with modern AI.
    2024Artigo científico Acesso aberto Ver mais
  • Improving the FMEA method to failure risk assessment of transformers: a case study in Tehran electric power distribution transformers
    Publication . Shafei, Atefeh Pour; Rezaei, Alireza; Silva, J. Fernando A.; Monteiro, J.; Monteiro, Joaquim
    Distribution transformers play a pivotal role in power networks, serving as critical assets whose failure can lead to substantial expenses for replacement or repair. This study employs an improved Failure Modes and Effects Analysis (FMEA) method, utilizing transformer failures within the Tehran Electric Power Distribution Company over a decade. The Risk Priority Number (RPN) criterion assesses the failure probability associated with various failure modes for quantization within the FMEA method. Risk prioritization is determined through the RPN formulation. This paper proposes new improvements in FMEA, including an expert-based weighting method applied to the weighted RPN's sub-parameters to enhance precision and calculate RPN values based on the age group of each transformer. Subsequently, strategic corrective actions are proposed and implemented to mitigate the risk of transformer failures in the upcoming year. The culmination of this case study results in improved prediction capabilities and more effective failure prevention strategies for the company's transformers, facilitating purposeful asset management.
    2024Comunicação em conferência Acesso restrito Ver mais