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This paper presents a trusted execution environment (TEE)-enhanced federated learning (FL) framework for condition monitoring of distributed wind systems (DWSs). DWSs have become a topic of interest with the increased energy demand. Technological advancements in wind turbine technology have paved the way for DWSs to make a massive impact on the power grid. Due to underdeveloped security, malicious groups and individuals can target individual turbines, gain control of wind farms, and ultimately threaten the overall power grid. TEE-enhanced FL offers a solution; however, there are some challenges to its implementation. The remainder of this paper will discuss the challenges further and present solutions to their respective challenges. These solutions have been validated through experimentation and confirm an effective FL framework balancing both practicality and security in DWSs. 

This paper presents a practical framework that integrates wind speed forecasting with proton exchange membrane (PEM) electrolyzer design to optimize hydrogen production. Due to wind speed fluctuations, excess electrical energy is sometimes produced and left unused. A wind-to-hydrogen system addresses this challenge by converting surplus energy into storable hydrogen using a PEM electrolyzer. The proposed approach employs a multivariate supervisory control and data acquisition (SCADA) dataset and applies a convolutional neural network with bi-directional long short-term memory (CNN-Bi-LSTM) for multivariate wind speed temporal forecasting, enabling more efficient PEM operations. Compared to standard deep learning models, the CNN-Bi-LSTM architecture reduces the root mean square error by 52.5% and the mean absolute error by 56%, thereby enhancing hydrogen production forecasting. Simulation results show that a membrane thickness of 0.0252 mm and an operating temperature of 70% achieve the highest overall PEM efficiency of 63.611%. This study demonstrates the integration of deep learning-based forecasting with electrochemical modeling and SCADA datasets as a novel approach for wind-to-hydrogen production systems. 

Predictive maintenance for underground high-pressure fluid-filled (HPFF) power cables remains a critical challenge due to the weak and intermittent nature of fault-induced signals and the limited accessibility of buried infrastructure. This paper proposes a physics-informed Seq2Seq-attentionautoencoder acoustic monitoring (Echo-AE) model for predictive maintenance in underground HPFF cable systems. The Echo-AE model is developed based on a physics-informed loss function that incorporates both physics-based constraints and prediction errors. A controlled experimental setup of underground HPFF cable systems was used to capture continuous acoustic monitoring data, where three fault severity levels were generated, resulting in 4 million acoustic samples spanning normal operations and 15 fault events, and producing an imbalanced dataset with a 117:1 normal-tofault ratio to simulate real-world scenarios in which early-stage faults are rare. Results demonstrated Echo-AE’s superior early-stage fault detection capability compared with traditional models, with an F1-score of 0.8313, precision of 0.7864, recall of 0.8816, and an accuracy of 0.9936. The model exhibits fast convergence (20 epochs) and an area under the receiver operating characteristic curve of 0.998. Threshold sensitivity analysis revealed an optimal operation point that balances false positives and false negatives. 

This study proposes an intelligent techno-economic assessment framework for wind energy end users, using a novel dual-input convolutional bidirectional long short-term memory (Dual-ConvBiLSTM) architecture to predict dynamic levelized cost of energy (LCOE). The proposed architecture separates weight matrices for wind supervisory control and data acquisition (SCADA) data and financial data. This allows the model to integrate both data streams at every time step through a custom dual-input cell. This approach is compared with five baseline architectures: Recurrent Neural Network (RNN), LSTM, BiLSTM, ConvLSTM, and ConvBiLSTM, which process data through separate parallel branches and concatenate outputs before final prediction. The Dual-ConvBiLSTM achieves an LCOE estimate of $4.0391 cents/kWh, closest to the actual value of $4.0450 cents/kWh, with a root mean squared error reduction of 51.8% compared to RNN, 47.0% to LSTM, 40.0% to BiLSTM, 36.7% to ConvLSTM, and 34.4% to ConvBiLSTM, demonstrating superior capability in capturing complex interactions between SCADA data and financial parameters. This intelligent framework potentially enhances economic assessment and enables end users to accelerate renewable energy deployment through more reliable financial prediction. 

Underground high-voltage transmission cables, especially high-pressure fluid-filled (HPFF) pipe-type cable systems, are critical components of urban power networks. These systems consist of insulated conductor cables housed within steel pipes filled with pressurized fluids that provide essential insulation and cooling. Despite their reliability, HPFF cables experience faults caused by insulation degradation, thermal expansion, and environmental stressors, which, due to their subtle and gradual nature, complicate incipient fault detection and subsequent fault localization. This study presents a novel, proactive, and retrofit-friendly predictive condition monitoring method. It leverages distributed accelerometer sensors non-intrusively mounted on the HPFF steel pipe within existing manholes to continuously monitor vibration signals in real time. A physics-enhanced convolutional neural network–long short-term memory (CNN–LSTM) deep learning architecture analyzes these signals to detect incipient faults before they evolve into critical failures. The CNN–LSTM model captures temporal dependencies in acoustic data streams, applying time-series analysis techniques tailored for the predictive condition monitoring of HPFF cables. Experimental validation uses vibration data from a scaled-down HPFF laboratory test setup, comparing normal operation to incipient fault events. The model reliably identifies subtle changes in sequential acoustic patterns indicative of incipient faults. Laboratory experimental results demonstrate a high accuracy of the physics-enhanced CNN–LSTM architecture for incipient fault detection with effective data feature extraction. This approach aims to support enhanced operational resilience and faster response times without intrusive infrastructure modifications, facilitating early intervention to mitigate service disruptions. 

Abstract The transition to carbon-neutral energy has increased the reliance upon renewable sources of energy, e.g., wind power, placing added demands on resilience and stability of the power grid. Wind-to-hydrogen production systems can be a solution for addressing these demands. By converting excess wind energy into hydrogen via electrolysis, these systems can effectively store the intermittent energy generated by wind turbines. This study discusses the application of two time-series prediction models, i.e., long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), in forecasting the energy of wind farms, subsequently used for an assessment of hydrogen production means of a proton exchange membrane (PEM) electrolyzer. Using a dataset comprising wind speed, active power, and wind direction, inputs were normalized, and wind direction was transformed into sine and cosine components to retain circular characteristics. Bi-LSTM demonstrated superior accuracy with lower testing RMSE than LSTM. Integrating wind forecasts with a PEM electrolyzer model, incorporating critical electro-chemical parameters, revealed an optimal efficiency of 63.611% at a membrane thickness of 0.00254 cm and a temperature of 70°C. Bi-LSTM forecasts boosted hydrogen production by 5% to 8% compared to LSTM. 

Abstract This paper presents a trusted execution environment (TEE)-enhanced federated learning (FL) framework for condition monitoring of distributed wind systems (DWSs). DWSs have become a topic of interest with the increased energy demand. Technological advancements in wind turbine technology has paved the way for DWSs to make a massive impact on the power grid. Due to underdeveloped security, malicious groups and individuals can target individual turbines, gain control of wind farms, and ultimately threaten the overall power grid. TEE-enhanced FL offers a solution, however, there are some challenges to their implementation. The remainder of this paper will discuss the challenges further and present solutions to their respective challenges. These solutions have been validated through experimentation and confirm an effective FL framework balancing both practicality and security in DWSs. 

This paper presents an innovative approach to wind energy forecasting through the implementation of an extended long short-term memory (xLSTM) model. This research addresses fundamental limitations in time-sequence forecasting for wind energy by introducing architectural enhancements to traditional LSTM networks. The xLSTM model incorporates two key innovations: exponential gating with memory mixing and a novel matrix memory structure. These improvements are realized through two variants, i.e., scalar LSTM and matrix LSTM, which are integrated into residual blocks to form comprehensive architectures. The xLSTM model was validated using SCADA data from wind turbines, with rigorous preprocessing to remove anomalous measurements. Performance evaluation across different wind speed regimes demonstrated robust predictive capabilities, with the xLSTM model achieving an overall coefficient of determination value of 0.923 and a mean absolute percentage error of 8.47%. Seasonal analysis revealed consistent prediction accuracy across varied meteorological patterns. The xLSTM model maintains linear computational complexity with respect to sequence length while offering enhanced capabilities in memory retention, state tracking, and long-range dependency modeling. These results demonstrate the potential of xLSTM for improving wind power forecasting accuracy, which is crucial for optimizing turbine operations and grid integration of renewable energy resources.