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- A reconfigurable electro-optic photonic integrated circuit architecturePublication . Moreira, André; Velázquez, Ernesto; Lourenço, Paulo; Fantoni, AlessandroIn this work, we present the design and numerical validation of a reconfigurable electro-optic photonic integrated circuit architecture for dynamic 1 & times; 4 optical routing, based on electro-optic phase shifters and a chain of cascaded multimode interference structures, and implemented in an amorphous silicon platform. By exploiting recent developments in symmetry-engineered silicon photonics to enable phase control in a platform compatible with complementary metal-oxide-semiconductor fabrication requirements, this approach addresses the increasing demand for ultra-fast, compact and energy efficient reconfigurable photonic integrated circuits. The photonic circuit design bases its functionality on self-imaging theory and has been optimized through simulations implementing the beam propagation method, while optical performance and electro-optical behavior have been validated through finite difference time-domain simulations and multiphysics modeling. The numerical results obtained confirm the operational performance of the individual building blocks and demonstrate the proposed architecture as being able to perform as a reconfigurable electro-optic platform and provide the dynamic 1 & times; 4 optical routing. Hence, this architecture provides a scalable platform for reconfigurable silicon photonics, complementary metal-oxide-semiconductor compatible, and the fabrication-ready layout establishes a practical path to experimental validation and future reconfigurable photonic integrated circuits.
- Optimized wavelet-denoised temporal convolutional network for multi-step ahead predictions in power systemsPublication . Buratto, William Gouvea; Gonzalez, Gabriel Villarrubia; Yamaguchi, Cristina Keiko; Nied, Ademir; Yow, Kin-Choong; Stefenon, Stefano FrizzoAccurate forecasting of power generation is essential for ensuring the reliable and cost-efficient operation of modern electrical grids. This paper introduces an optimized wavelet temporal convolutional network (TCN) designed for multi-step-ahead prediction of thermal power generation in Brazil. The proposed model, called optimized-wavelet-TCN, integrates a discrete wavelet transform denoising stage to effectively filter high-frequency noise from operational data while preserving underlying signal trends, followed by a TCN optimized via a tree-structured Parzen estimator for hyperparameter tuning. Evaluated on real-world hourly data spanning multiple years, the optimized-wavelet-TCN achieves superior forecasting performance with a mean absolute percentage error of 10.2%, 7.24%, and 8.54% for 15, 30, and 60-step ahead predictions, respectively. The model significantly reduces computational time to only 1.54 s for a 30-step forecast, markedly outperforming state-of-the-art transformer-based and recurrent neural network baselines in both accuracy and efficiency.
- Diffusion-augmented YOLO26-Swin cascaded framework with hybrid SHAP-CAM for autonomous power grid inspectionPublication . Stefenon, Stefano Frizzo; Matos-Carvalho, João Pedro; Mariani, Viviana Cocco; Coelho, Leandro dos Santos; Yow, Kin-Choong; Stefenon, Stefano Frizzo; Springer NatureDeep learning-based autonomous inspection of power grid insulators is challenged by data imbalance and model opacity. This paper presents an end-to-end solution integrating advanced data synthesis, detection, classification, and explainability. First, a conditional diffusion model generates realistic synthetic fault images to balance the dataset. A two-stage architecture based on You Only Look Once version 26 (YOLO26) extra-large and Shifted windows (Swin)-V2-B, called YOLO26-Swin, fine-tuned with Bayesian optimization, performs robust insulator detection and then fault classification. Finally, a novel SHapley Additive exPlanations with Class Activation Mapping (SHAP-CAM) method provides intuitive visual explanations for model predictions. Extensive experiments validate our framework’s superiority: it achieves an F1-score of 0.98149 and a mean Average Precision (mAP)@[0.5] of 0.98951, exceeding leading detection and classification models. This work highlights the efficacy of diffusion models for data augmentation in critical infrastructure and advances the interpretability of vision-based inspection systems.
- DC-DC converter with Buck-Boost characteristics and high voltage gain based on the differential conceptPublication . Pires, V. Fernão; Foito, Daniel; Cordeiro, Armando; Monteiro, Joaquim; Pinto, Sónia; Silva, J. Fernando; Monteiro, Joaquim; Cordeiro, Armando; IEEEThis paper introduces a novel DC-DC converter based on the differential concept. This topology can operate in interleaved mode, which, together with the differential concept, allows for a reduction in the size of the inductors. This converter is also characterized by their continuous input current. Moreover, it is also characterized by an extended voltage range when compared with the classical Buck-Boost converter. Another aspect, is that the voltage stress across the switches is reduced when compared with the output voltage. The same regarding the voltage stress across the capacitors. The proposed converter will be tested through the one of the most used simulation tools, as well as, by a laboratory prototype.
- Decoupled Direct Power Compensators for a Dual-Inverter Based UPFCPublication . Monteiro, Joaquim; Silva, J. Fernando; Pinto, Sónia; Pires, V. Fernão; Monteiro, Joaquim; IEEEIn recent decades, Unified Power Flow Controllers (UPFCs) have become essential devices for enhancing the flexibility and controllability of modern electrical transmission networks. These devices facilitate the simultaneous control of local bus voltage and the optimization of power flow within electrical power transmission systems. The multilevel converter capability to operate with high voltage levels while generating low-distortion AC voltages, makes multilevel topologies suitable for UPFCs. Within this framework, this paper presents a novel multilevel converter topology and control strategy specifically designed for UPFC applications. The proposed solution employs a dual inverter topology driven by sliding mode direct power controllers, which are controlled via decoupled Proportional-Integral (PI) compensators. The characteristics, capabilities, and resulting flexibility of the proposed UPFC and control subsystem is validated through a series of simulation tests.
- Convolutional neural network approach for fault detection and characterization in medium voltage distribution networksPublication . Shafei, Atefeh Pour; Silva, J. Fernando A.; Monteiro, J.; ElsevierPower 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.
- Backstepping sliding mode controller for MPPT in DC microgrid connected Quadratic Boost converters fed from PV systemsPublication . Monteiro, Joaquim; Silva, J. Fernando; Cordeiro, Armando; Pinto, Sónia; Pires, V. Fernão; Monteiro, Joaquim; Cordeiro, Armando; IEEEPhotovoltaic (PV) systems are crucial for harnessing solar energy; however, environmental factors and the variability of the connected load significantly influence their energy generation capacity. In this context, the effectiveness of the controller used for maximum power point tracking (MPPT) to enhance efficiency relies on a thorough analysis of the nonlinear models of PV systems. Over these last decades, many control techniques have been developed to enhance the energy extraction from PV cells and improve system efficiency. Since real-world systems have real-time variations, it is difficult to continuously update both the system and the controller. The changes can produce steady state errors in the output and reduce the efficiency of the controller. As a solution to this issue, in this work a nonlinear backstepping sliding mode controller (BSMC) is proposed to improve the performance of the MPPT system. The BSMC MPPT, coupled to a DC-DC quadratic boost converter (QBC), is used as the interface between a PV array and a microgrid. Backstepping control ensures global asymptotic stability of the system by achieving the optimal power point, following Lyapunov stability criteria. Simulation and experimental results of the proposed MPPT solution are presented confirming that the system operates as expected.
- End-to-end decomposition-aware neural architecture search with sparse expert transformers for thermal energy forecastingPublication . Seman, Laio Oriel; Stefenon, Stefano Frizzo; Matos-Carvalho, João Pedro; Nied, Ademir; Yow, Kin-Choong; Stefenon, Stefano Frizzo; IEEEThis study presents a unified framework for thermal energy forecasting that integrates signal decomposition, Neural Architecture Search (NAS), and sparse transformer modeling. The method employs a hyperparameter-optimized hierarchical Variational Mode Decomposition (VMD) to enhance signal representation through adaptive noise suppression and mode separation. A differentiable preprocessing module, referred to as HeadComposer, performs joint search over normalization, decomposition, and convolutional transformations within a bilevel optimization procedure. The forecasting backbone consists of a transformer architecture incorporating gated residual connections for adaptive token selection and Mixture-of-Experts (MoE) feedforward layers for conditional computation. Experiments are conducted on real-world thermal power generation data comprising approximately 10,000 test samples, using an input look-back window of 96 time steps and a multi-step prediction horizon of 24 steps ahead. Results demonstrate consistent improvements over state-of-the-art baselines, achieving a mean absolute percentage error of 5.94%, corresponding to reductions of approximately 12-15% relative to competing methods. The proposed design demonstrates the effectiveness of combining decomposition-based preprocessing (VMD and HeadComposer) with NAS-driven architecture adaptation and sparse expert routing (based on MoE) for modeling non-stationary thermal generation processes.
- Patch-based transformer with mixture of experts and conformal inference for curtailment time series forecastingPublication . Heidrich, Mikhail Zimmer; Reis, Mari Aurora Favero; Yamaguchi, Cristina Keiko; Seman, Laio Oriel; Stefenon, Stefano Frizzo; Stefenon, Stefano Frizzo; IEEEThe growth of variable renewable energy sources has increased the occurrence of curtailment in power systems, particularly in regions affected by transmission constraints and limited operational flexibility. Reliable forecasting of curtailment time series, together with well-calibrated uncertainty estimates, is therefore essential to support operational planning, market decisions, and risk management. This paper proposes a curtailment forecasting framework that combines a high-capacity neural time series model with distribution-free conformal prediction to jointly address accuracy and uncertainty quantification. The forecasting backbone is a Transformer-based encoder-decoder architecture enhanced with reversible instance normalization to mitigate distribution shift, patch-based tokenization to reduce sequence length and improve temporal representation, and a Mixture of Experts feed-forward module to enable conditional computation and improved generalization. The proposed method is evaluated against a wide range of state-of-the-art models, including NBEATS, NBEATSx, NHITS, PatchTST, LSTM, GRU, TFT, Informer, and Autoformer, across multiple forecasting horizons. The model achieves an MSE of 0.0059 for a horizon equal to 3h, corresponding to a 13.2% reduction relative to the second-best methods. For a horizon equal to 5h, the MSE is reduced to 0.0119, representing a 29.6% improvement, while for a horizon equal to 10, the model attains an MSE of 0.0461, outperforming NHITS by 22.9%. Ablation analysis confirms the importance of patch embedding, the Mixture of Experts mechanism, and reversible instance normalization. The conformal prediction module achieves an empirical coverage of 91.27% for a nominal 90% level, with adaptive and practically useful interval widths.
- Multi-scale convolutional Chebyshev Kolmogorov-Arnold networks using conformal prediction for photovoltaic power forecastingPublication . Takara, Lucas de Azevedo; Souza, Rodrigo Clemente Thom de; Mariani, Viviana Cocco; Coelho, Leandro Dos Santos; Stefenon, Stefano Frizzo; Stefenon, Stefano Frizzo; IEEEAccurate photovoltaic (PV) power forecasting remains challenging due to nonlinear dynamics, rapid weather variations, and multi-scale temporal dependencies. This paper proposes ConvChebyKAN, a hybrid deep learning architecture that integrates multi-scale causal convolutions, Chebyshev polynomial parameterized Kolmogorov Arnold networks (KANs), and conformal prediction to improve both deterministic and probabilistic forecasting performance. The multi-scale convolutional module captures short-term fluctuations and long-term temporal dependencies, while the Chebyshev-based KAN improves nonlinear representation capability and numerical stability. A multi-objective hyperparameter optimization strategy based on Pareto efficiency and TOPSIS ranking is used to jointly optimize forecasting accuracy, prediction interval sharpness, and coverage reliability. The model is evaluated using real photovoltaic generation and meteorological data from four seasonal datasets and forecasting horizons from 1 to 48 hours ahead. ConvChebyKAN reaches an MAE of 149.13 kWh, RMSE of 227.34 kWh, R2 of 98.2%, and sMAPE of 6.21%, outperforming KAN and other machine learning models. These results show that the proposed architecture effectively captures multi-scale temporal patterns while providing reliable uncertainty estimates, supporting PV forecasting applications in power system operation and energy management.