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The sliding window method is commonly used for non-intrusive load disaggregation. However, it is difficult to choose the appropriate window size, and the disaggregation effect is poor in low-frequency industrial environments. To better handle low-frequency industrial load data, in this paper, we propose a vertical non-intrusive load disaggregation model that is different from the sliding window method. By training multiple electrical parameters at a single point on the bus end with the corresponding load data at the branch end, the proposed method, called multiple electrical parameters to point (Mep2point), takes the electrical parameter data sampled at a single point on the bus end as its input and outputs the load data of the target device sampled at the corresponding point. First, the electrical parameters of the bus end are processed, and each item is normalized to the range from 0–1. Then, the electrical parameters are vertically arranged by their time point, and a convolutional neural network (CNN) is used to train the model. The proposed method is analyzed on low-frequency industrial user data sampled at a frequency of 1/120 Hz in the real world. We compare our method with three advanced sliding window methods, achieving an average improvement ranging from 9.23% to 22.51% in evaluation metrics, while showing substantial superiority in the actual decomposed images. Compared with three classical machine learning algorithms, our model, using the same amount of data, significantly outperforms these methods. Finally, we also compared our method with the multi-channel low window sequence-to-point (MLSP) method, which also selects multiple electrical parameters. Our model's complexity is much less than that of the MLSP model, and its performance remains high. The superiority of our model, as presented in this paper, is fully verified by experimental analysis, which can produce better actual load decomposition results from each branch and contribute to the analysis and monitoring of loads in industrial environments. [ABSTRACT FROM AUTHOR]

Accurate power load forecasting can provide crucial insights for power system scheduling and energy planning. In this paper, to address the problem of low accuracy of power load prediction, we propose a method that combines secondary data cleaning and adaptive variational mode decomposition (VMD), convolutional neural networks (CNN), bi-directional long short-term memory (BILSTM), and adding attention mechanism (AM). The Inner Mongolia electricity load data were first cleaned use the K-means algorithm, and then further refined with the density-based spatial clustering of applications with the noise (DBSCAN) algorithm. Subsequently, the parameters of the VMD algorithm were optimized using a multi-strategy Cubic-T dung beetle optimization algorithm (CTDBO), after which the VMD algorithm was employed to decompose the twice-cleaned load sequences into a number of intrinsic mode functions (IMFs) with different frequencies. These IMFs were then used as inputs to the CNN-BILSTM-Attention model. In this model, a CNN is used for feature extraction, BILSTM for extracting information from the load sequence, and AM for assigning different weights to different features to optimize the prediction results. It is proved experimentally that the model proposed in this paper achieves the highest prediction accuracy and robustness compared to other models and exhibits high stability across different time periods. [ABSTRACT FROM AUTHOR]

Accurate and reliable PV power probabilistic-forecasting results can help grid operators and market participants better understand and cope with PV energy volatility and uncertainty and improve the efficiency of energy dispatch and operation, which plays an important role in application scenarios such as power market trading, risk management, and grid scheduling. In this paper, an innovative deep learning quantile regression ultra-short-term PV power-forecasting method is proposed. This method employs a two-branch deep learning architecture to forecast the conditional quantile of PV power; one branch is a QR-based stacked conventional convolutional neural network (QR_CNN), and the other is a QR-based temporal convolutional network (QR_TCN). The stacked CNN is used to focus on learning short-term local dependencies in PV power sequences, and the TCN is used to learn long-term temporal constraints between multi-feature data. These two branches extract different features from input data with different prior knowledge. By jointly training the two branches, the model is able to learn the probability distribution of PV power and obtain discrete conditional quantile forecasts of PV power in the ultra-short term. Then, based on these conditional quantile forecasts, a kernel density estimation method is used to estimate the PV power probability density function. The proposed method innovatively employs two ways of a priori knowledge injection: constructing a differential sequence of historical power as an input feature to provide more information about the ultrashort-term dynamics of the PV power and, at the same time, dividing it, together with all the other features, into two sets of inputs that contain different a priori features according to the demand of the forecasting task; and the dual-branching model architecture is designed to deeply match the data of the two sets of input features to the corresponding branching model computational mechanisms. The two a priori knowledge injection methods provide more effective features for the model and improve the forecasting performance and understandability of the model. The performance of the proposed model in point forecasting, interval forecasting, and probabilistic forecasting is comprehensively evaluated through the case of a real PV plant. The experimental results show that the proposed model performs well on the task of ultra-short-term PV power probabilistic forecasting and outperforms other state-of-the-art deep learning models in the field combined with QR. The proposed method in this paper can provide technical support for application scenarios such as energy scheduling, market trading, and risk management on the ultra-short-term time scale of the power system. [ABSTRACT FROM AUTHOR]

With the global objectives of achieving a "carbon peak" and "carbon neutrality" along with the implementation of carbon reduction policies, China's industrial structure has undergone significant adjustments, resulting in constraints on high-energy consumption and high-emission industries while promoting the rapid growth of green industries. Consequently, these changes have led to an increasingly complex power system structure and presented new challenges for electricity demand forecasting. To address this issue, this study proposes a 24-step multivariate time series short-term load forecasting algorithm model based on KNN data imputation and BiTCN bidirectional temporal convolutional networks combined with BiGRU bidirectional gated recurrent units and attention mechanism. The Kepler adaptive optimization algorithm (KOA) is employed for hyperparameter optimization to effectively enhance prediction accuracy. Furthermore, using real load data from a wind farm in Xinjiang as an example, this paper predicts the electricity load from 1 January to 30 December in 2019. Experimental results demonstrate that our comprehensive short-term load forecasting model exhibits lower prediction errors and superior performance compared to traditional methods, thus holding great value for practical applications. [ABSTRACT FROM AUTHOR]

Aiming at the problem of accurate AC series arc fault detection, this paper proposes a low voltage AC series arc fault intelligent detection model based on deep learning. According to the GB/T 31143—2014 standard, an experimental platform was established. This system comprises a lower computer (slave computer) and an upper computer (master computer). It facilitates the acquisition of experimental data and the detection of arc faults during the data acquisition process. Based on a one-dimensional Convolutional Neural Network (CNN), Residual model (Res) and RIME optimization algorithm (RIME) are introduced to optimize the CNN. The current signals collected using high-frequency current, low-frequency coupled current, and high-frequency coupled current are used to construct an arc fault feature set for training of the necessary detection model. The experimental results indicate that the RIME optimization algorithm delivers the best performance when optimizing a one-dimensional CNN detection model with an introduced Res. This model achieves a detection accuracy of 99.42% ± 0.13% and a kappa coefficient of 95.69% ± 0.96%. For collection methods, high-frequency coupled current signals are identified as the optimal choice for detecting low-voltage AC series arc faults. Regarding feature selection, random forest-based feature importance ranking proves to be the most effective method. [ABSTRACT FROM AUTHOR]

Inter-Turn Short Circuit (ITSC) faults do not necessarily produce high temperatures but exhibit distinct heat distribution and characteristics. This paper proposes a novel fault diagnosis and identification scheme utilizing an improved You Look Only Once Vision 8 (YOLOv8) algorithm, enhanced with an infrared image slicing-aided hyper-inference (SAHI) technique, to automatically detect ITSC fault trajectories in dry-type transformers. The infrared image acquisition system gathers data on ITSC fault trajectories and captures images with varying contrast to enhance the robustness of the recognition model. Given that the fault trajectory constitutes a small portion of the overall infrared image and is subject to significant background interference, traditional recognition algorithms often misjudge or omit faults. To address this, a YOLOv8-based visual detection method incorporating Dynamic Snake Convolution (DSConv) and the Slicing-Aided Hyper-Inference algorithm is proposed. This method aims to improve recognition precision and accuracy for small targets in complex backgrounds, facilitating accurate detection of ITSC faults in dry-type transformers. Comparative tests with the YOLOv8 model, Fast Region-based Convolutional Neural Networks (Fast-RCNNs), and Residual Neural Networks (Retina-Nets) demonstrate that the enhancements significantly improve model convergence speed and fault trajectory detection accuracy. The approach offers valuable insights for advancing infrared image diagnostic technology in electrical power equipment. [ABSTRACT FROM AUTHOR]

Short-term load forecasting is a challenging research problem and has a tremendous impact on electricity generation, transmission, and distribution. A robust forecasting algorithm can help power system operators to better tackle the ever-changing electric power demand. This paper presents a novel deep neural network for short-term electric load forecasting for the St. John's campus of Memorial University of Newfoundland (MUN). The electric load data are obtained from the Memorial University of Newfoundland and combined with metrological data from St. John's. This dataset is used to formulate a multivariate time-series forecasting problem. A novel deep learning algorithm is presented, consisting of a 1D Convolutional Neural Network, which is followed by an encoder–decoder-based network with attention. The input used for this model is the electric load consumption and metrological data, while the output is the hourly prediction of the next day. The model is compared with Gated Recurrent Unit (GRU) and Long Short Term Memory (LSTM)-based Recurrent Neural Network. A CNN-based encoder–decoder model without attention is also tested. The proposed model shows a lower mean absolute error (MAE), mean squared error (MSE), mean absolute percentage error (MAPE), and higher R2 score. These evaluation metrics show an improved performance compared to GRU and LSTM-based RNNs as well as the CNN encoder–decoder model without attention. [ABSTRACT FROM AUTHOR]

With the development of the power system, power users begin to use their own power supply in order to improve the power economy, but this also leads to the occurrence of the risk of self-provided power supply. The actual distribution network has few samples of power supply risk and it is difficult to identify the power supply risk by using conventional deep learning methods. In order to achieve high accuracy of self-provided power supply risk identification with small samples, this paper proposes a combination of transfer learning, convolutional block attention module (CBAM), and convolutional neural network (CNN) to identify the risk of self-provided power supply in an active distribution network. Firstly, in order to be able to further identify whether or not a risk will be caused based on completing the identification of the faulty line, we propose that it is necessary to identify whether or not the captive power supply on the faulty line is in operation. Second, in order to achieve high-precision identification and high-efficiency feature extraction, we propose to embed the CBAM into a CNN to form a CBAM-CNN model, so as to achieve high-efficiency feature extraction and high-precision risk identification. Finally, the use of transfer learning is proposed to solve the problem of low risk identification accuracy due to the small number of actual fault samples. Simulation experiments show that compared with other methods, the proposed method has the highest recognition accuracy and the best effect, and the risk recognition accuracy of active distribution network backup power is high in the case of fewer samples. [ABSTRACT FROM AUTHOR]

Since the advent of smart agriculture, technological advancements in solar energy have significantly improved farming practices, resulting in a substantial revival of different crop yields. However, the smart agriculture industry is currently facing challenges posed by climate change. This involves multi-timeframe forecasts for greenhouse operators covering short-, medium-, and long-term intervals. Solar energy not only reduces our reliance on non-renewable electricity but also plays a pivotal role in addressing climate change by lowering carbon emissions. This study aims to find a method to support consistently optimal solar energy use regardless of changes in greenhouse conditions by predicting solar energy (kWh) usage on various time steps. In this paper, we conducted solar energy usage prediction experiments on time steps using traditional Tensorflow Keras models (TF Keras), including a linear model (LM), Convolutional Neural Network (CNN), stacked—Long Short Term Memory (LSTM), stacked-Gated recurrent unit (GRU), and stacked-Bidirectional—Long Short —Term Memory (Bi-LSTM), as well as Tensor-Flow-based models for solar energy usage data from a smart farm. The stacked-Bi-LSTM outperformed the other DL models with Root Mean Squared Error (RMSE) of 0.0048, a Mean Absolute Error (MAE) of 0.0431, and R-Squared ( R 2) of 0.9243 in short-term prediction (2-h intervals). For mid-term (2-day) and long-term (2-week) forecasting, the stacked Bi-LSTM model also exhibited superior performance compared to other deep learning models, with RMSE values of 0.0257 and 0.0382, MAE values of 0.1103 and 0.1490, and R 2 values of 0.5980 and 0.3974, respectively. The integration of multi-timeframe forecasting is expected to avoid conventional solar energy use forecasting, reduce the complexity of greenhouse energy management, and increase energy use efficiency compared to single-timeframe forecasting models. [ABSTRACT FROM AUTHOR]

Deep learning models have demonstrated potential in Condition-Based Monitoring (CBM) for rotating machinery, such as induction motors (IMs). However, their performance is significantly influenced by the size of the training dataset and the way signals are presented to the model. When trained on segmented signals over a fixed period, the model's accuracy can decline when tested on signals that differ from the training interval or are randomly sampled. Conversely, models utilizing data augmentation techniques exhibit better generalization to unseen conditions. This paper highlights the bias introduced by traditional training methods towards specific periodic waveform sampling and proposes a new method to augment phase current signals during training using a shifting window technique. This approach is considered as a practical approach for motor current augmentation and is shown to enhance classification accuracy and improved generalization when compared to existing techniques. [ABSTRACT FROM AUTHOR]

Magnetic field is one of the basic data for constructing a transformer digital twin. The finite element transient simulation takes a long time and cannot meet the real-time requirements of a digital twin. According to the nonlinear characteristics of the core and the timing characteristics of the magnetic field, this paper proposes a fast calculation method of the spatial magnetic field of the transformer, considering the nonlinear characteristics of the core. Firstly, based on the geometric and electrical parameters of the single-phase double-winding test transformer, the corresponding finite element simulation model is built. Secondly, the key parameters of the finite element model are parametrically scanned to obtain the nonlinear working condition data set of the test transformer. Finally, a deep learning network integrating a convolutional neural network (CNN) and a long short-term memory network (LSTM) is built to train the mapping relationship between winding voltage, current, and the spatial magnetic field so as to realize the rapid calculation of the transformer magnetic field. The results show that the calculation time of the deep learning model is greatly shortened compared with the finite element model, and the model calculation results are consistent with the experimental measurement results. [ABSTRACT FROM AUTHOR]

This paper introduces a novel convolutional neural network (CNN) architecture tailored for state of charge (SoC) estimation in battery management systems (BMS), accompanied by an advanced optimization technique to enhance training efficiency. The proposed CNN architecture comprises multiple one-dimensional convolutional (Conv1D) layers followed by batch normalization and one-dimensional max-pooling (MaxPooling1D) layers, culminating in dense layers for regression-based SoC prediction. To improve training effectiveness, we introduce an advanced dynamic k-decay learning rate scheduling method. This technique dynamically adjusts the learning rate during training, responding to changes in validation loss to fine-tune the training process. Experimental validation was conducted on various drive cycles, including the dynamic stress test (DST), Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), United States 2006 Supplemental Federal Test Procedure (US06), and Worldwide Harmonized Light Vehicles Test Cycle (WLTC), spanning four temperature conditions (−5 °C, 5 °C, 25 °C, 45 °C). Notably, the test error of DST and US06 drive cycles, the CNN with optimization achieved a mean absolute error (MAE) of 0.0091 and 0.0080, respectively at 25 °C, and a root mean square error (RMSE) of 0.013 and 0.0095, respectively. In contrast, the baseline CNN without optimization yielded higher MAE and RMSE values of 0.011 and 0.014, respectively, on the same drive cycles. Additionally, training time with the optimization technique was significantly reduced, with a recorded time of 324.14 s compared to 648.59 s for the CNN without optimization at room temperature. These results demonstrate the effectiveness of the proposed CNN architecture combined with advanced dynamic learning rate scheduling in accurately predicting SoC across various battery types and drive cycles. The optimization technique not only improves prediction accuracy but also substantially reduces training time, highlighting its potential for enhancing battery management systems in electric vehicle applications. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) microgrids comprise a multitude of small PV power stations distributed across a specific geographical area in a decentralized manner. Computational services for forecasting the output power of power stations are crucial for optimizing resource deployment. This paper proposes a deep-learning-based architecture for short-term prediction of PV power. Firstly, in order to make full use of the spatial information between different power stations, a spatio–temporal feature fusion method is proposed. This method is capable of exploiting both the power information of neighboring power stations with strong correlations and meteorological information with the PV feature data of the target power station. By using a multiscale convolutional neural network–long short-term memory (CNN-LSTM) network model, it is capable of generating a PV feature dataset containing spatio–temporal attributes that expand the data source and enhance the feature constraints. It is capable of predicting the output power sequences of power stations in PV microgrids with high model generalization and responsiveness. To validate the effectiveness of the proposed framework, an extensive numerical analysis is also conducted based on a real-world PV dataset. [ABSTRACT FROM AUTHOR]

With a large proportion of wind farms connected to the power grid, it brings more pressure on the stable operation of power systems in shorter time scales. Efficient and accurate scheduling, operation control and decision making require high time resolution power forecasting algorithms with higher accuracy and real-time performance. In this paper, we innovatively propose a high temporal resolution wind power forecasting method based on a light convolutional architecture—DC_LCNN. The method starts from the source data and novelly designs the dual-channel data input mode to provide different combinations of feature data for the model, thus improving the upper limit of the learning ability of the whole model. The dual-channel convolutional neural network (CNN) structure extracts different spatial and temporal constraints of the input features. The light global maximum pooling method replaces the flat operation combined with the fully connected (FC) forecasting method in the traditional CNN, extracts the most significant features of the global method, and directly performs data downscaling at the same time, which significantly improves the forecasting accuracy and efficiency of the model. In this paper, the experiments are carried out on the 1 s resolution data of the actual wind field, and the single-step forecasting task with 1 s ahead of time and the multi-step forecasting task with 1~10 s ahead of time are executed, respectively. Comparing the experimental results with the classical deep learning models in the current field, the proposed model shows absolute accuracy advantages on both forecasting tasks. This also shows that the light architecture design based on simple deep learning models is also a good solution in performing high time resolution wind power forecasting tasks. [ABSTRACT FROM AUTHOR]

Under the premise of ensuring the safe and stable operation of a wind–thermal–storage power system, this paper proposes an optimization model aimed at improving its overall economic efficiency and effectively reducing the peak-to-valley load difference. The model transforms the multi-objective optimization problem to solve a feasible interval of weight coefficients. We introduce a novel fusion model, where a Convolutional Neural Network (CNN) is melded with a Long Short-Term Memory Network (LSTM) to form the target network structure. Additionally, for datasets with limited samples, we incorporate a Self-Attention Mechanism (SAM) into the Model-Agnostic Meta-Learning (MAML). Ultimately, we build an MAML-SAM-CNN-LSTM network model to solve the interval of weight coefficients. An arithmetic validation of a modified IEEE 30-node system demonstrates that the MAML-SAM-CNN-LSTM network proposed in this paper can adeptly solve the feasible intervals of weight coefficients in the optimization model of the wind-thermal storage system. This is achieved under the constraints of the specified wind-thermal storage power system operation indexes. The evaluation indexes of the network model, including its accuracy, precision, recall, and F1 score, all exceed 98.72%, 98.57%, 98.30%, and 98.57%, respectively. This denotes a superior performance compared to the other three network models, offering an effective reference for optimizing decision-making and facilitating the enhanced realization of multi-objective, on-demand scheduling in the wind-thermal storage power system. [ABSTRACT FROM AUTHOR]

This document discusses the challenges of achieving high-power quality in modern power grids with a large share of distributed power production, particularly due to the integration of renewable energy sources (RES). The variable nature of RES can lead to disturbances and power quality degradation. The document highlights the importance of voltage source converters and control systems in enabling the flexible operation of RES. It also mentions the concept of microgrids, which are closely connected to power quality and other factors such as the Internet of Things and cyber security. The document includes eight research papers covering various topics related to power quality analysis and control of renewable energy sources in power grids. These topics include power factor correction, supercapacitor parameters identification, pulse width modulation, disturbance analysis, vibration analysis of motors, control of converters, phasor measurement units, and optimization of controller parameters. The document concludes by expressing gratitude to the authors, staff, and reviewers involved in the research papers. [Extracted from the article]

Recent advancements in the integration of artificial intelligence (AI) and machine learning (ML) with physical sciences have led to significant progress in addressing complex phenomena governed by nonlinear partial differential equations (PDEs). This paper explores the application of novel operator learning methodologies to unravel the intricate dynamics of flame instability, particularly focusing on hybrid instabilities arising from the coexistence of Darrieus–Landau (DL) and Diffusive–Thermal (DT) mechanisms. Training datasets encompass a wide range of parameter configurations, enabling the learning of parametric solution advancement operators using techniques such as parametric Fourier Neural Operator (pFNO) and parametric convolutional neural networks (pCNNs). Results demonstrate the efficacy of these methods in accurately predicting short-term and long-term flame evolution across diverse parameter regimes, capturing the characteristic behaviors of pure and blended instabilities. Comparative analyses reveal pFNO as the most accurate model for learning short-term solutions, while all models exhibit robust performance in capturing the nuanced dynamics of flame evolution. This research contributes to the development of robust modeling frameworks for understanding and controlling complex physical processes governed by nonlinear PDEs. [ABSTRACT FROM AUTHOR]

To enhance the operational efficiency and fault detection accuracy of hydroelectric units, this paper proposes a parallel convolutional neural network model that integrates the Gramian angular summation field (GASF) with an Improved coati optimization algorithm–parallel convolutional neural network (ICOA-PCNN). Additionally, to further improve the model's accuracy in fault identification, a multi-head self-attention mechanism (MSA) and support vector machine (SVM) are introduced for a secondary optimization of the model. Initially, the GASF technique converts one-dimensional time series signals into two-dimensional images, and a COA-CNN dual-branch model is established for feature extraction. To address the issues of uneven population distribution and susceptibility to local optima in the COA algorithm, various optimization strategies are implemented to improve its global search capability. Experimental results indicate that the accuracy of this model reaches 100%, significantly surpassing other nonoptimized models. This research provides a valuable addition to fault diagnosis technology for modern hydroelectric units. [ABSTRACT FROM AUTHOR]

For the advancement of smart grids, solar power generation predictions have become an important research topic. In the case of using traditional modeling methods, excessive computational costs may be incurred and it is difficult for these methods to learn the multi-variable dependencies of the data. Therefore, in this paper, a deep learning model was used to combine convolutional neural networks and long short-term memory recurrent network predictions. This method enables hourly power generation one day into the future. Convolutional neural networks are used to extract the features of multiple time series, while long short-term memory neural networks predict multivariate outcomes simultaneously. In order to obtain more accurate prediction results, we performed feature selection on meteorological features and combined the selected weather features to train the prediction model. We further distinguished sunny- and rainy-day models according to the predicted daily rainfall conditions. In the experiment, it was shown that the method of combining meteorological features further reduced the error. Finally, taking into account the differences in climate conditions between the northern and southern regions of Taiwan, the experimental results of case studies involving multiple regions were evaluated to verify the proposed method. The results showed that training combined with selected meteorological features can be widely used in regions with different climates in Taiwan. [ABSTRACT FROM AUTHOR]

The charging behavior of electric vehicle users is highly stochastic, which makes the short-term prediction of charging load at electric vehicle charging stations difficult. In this paper, a data-driven hybrid model optimized by the improved dung beetle optimization algorithm (IDBO) is proposed to address the problem of the low accuracy of short-term prediction. Firstly, the charging station data are preprocessed to obtain clear and organized load data, and the input feature matrix is constructed using factors such as temperature, date type, and holidays. Secondly, the optimal CNN-BiLSTM model is constructed using convolutional neural network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM), which realizes the feature extraction of the input matrix and better captures the hidden patterns and regularities in it. Then, methods such as Bernoulli mapping are used to improve the DBO algorithm and its hyperparameters; for example, hidden neurons of the hybrid model are tuned to further improve the model prediction accuracy. Finally, a simulation experiment platform is established based on MATLAB R2023a to validate the example calculations on the historical data of EV charging stations in the public dataset of ANN-DATA, and comparative analyses are carried out. The results show that compared with the traditional models such as CNN, BiLSTM and PSO-CNN-BiLSTM, the coefficient of determination of the model exceeds 0.8921 and the root mean square error is maintained at about 4.413 on both the training and test sets, which proves its effectiveness and stability. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) is a crucial and intricate electrical occurrence observed in various types of electrical equipment. Identifying and characterizing PDs is essential for upholding the integrity and reliability of electrical assets. This paper proposes an ensemble methodology aiming to strike a balance between the model complexity and the predictive performance in PD pattern recognition. A simple convolutional neural network (SCNN) was constructed to efficiently decrease the model parameters (quantities). A quadratic support vector machine (QSVM) was established and ensembled with the SCNN model to effectively improve the PD recognition accuracy. The input for QSVM consisted of the circular local binary pattern (CLBP) extracted from the enhanced image. A testing prototype with three types of PD was constructed and 3D phase-resolved pulse sequence (PRPS) spectrograms were measured and recorded by ultra-high frequency (UHF) sensors. The proposed methodology was compared with three existing lightweight CNNs. The experiment results from the collected dataset emphasize the benefits of the proposed method, showcasing its advantages in high recognition accuracy and relatively few mode parameters, thereby rendering it more suitable for PD pattern recognition on resource-constrained devices. [ABSTRACT FROM AUTHOR]

Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications. [ABSTRACT FROM AUTHOR]

To enhance the accuracy of theft detection for electricity consumers, this paper introduces a novel strategy based on the fusion of the dual-time feature and deep learning methods. Initially, considering electricity-consumption features at dual temporal scales, the paper employs temporal convolutional networks (TCN) with a long short-term memory (LSTM) multi-level feature extraction module (LSTM-TCN) and deep convolutional neural network (DCNN) to parallelly extract features at these scales. Subsequently, the extracted features are coupled and input into a fully connected (FC) layer for classification, enabling the precise detection of theft users. To validate the method's effectiveness, real electricity-consumption data from the State Grid Corporation of China (SGCC) is used for testing. The experimental results demonstrate that the proposed method achieves a remarkable detection accuracy of up to 94.7% during testing, showcasing excellent performance across various evaluation metrics. Specifically, it attained values of 0.932, 0.964, 0.948, and 0.986 for precision, recall, F1 score, and AUC, respectively. Additionally, the paper conducts a comparative analysis with mainstream theft identification approaches. In the comparison of training processes, the proposed method exhibits significant advantages in terms of identification accuracy and fitting degree. Moreover, with adjustments to the training set proportions, the proposed method shows minimal impact, indicating robustness. [ABSTRACT FROM AUTHOR]

Partial discharge (PD) is a common issue in power transformers that can lead to catastrophic failures if left undetected. Time reversal (TR) is a well-known technique in signal processing that can reconstruct signals by reversing the direction of time. The paper investigates the use of time reversal and the integration of time reversal with convolution neural networks (CNNs) for diagnosing PD in power transformers. We compare the performance of these techniques on a dataset of PD signals collected from power transformers. We propose a novel method of using time reversal as a pre-processing step to improve the accuracy of CNNs on noisy or distorted signals. Our experimental results demonstrate that this approach can significantly enhance the performance of CNNs on various datasets, including speech, audio, and image datasets. This paper provides a novel approach to signal processing and demonstrates the potential of time reversal as a pre-processing step in CNNs. [ABSTRACT FROM AUTHOR]

With the rapid growth in the proportion of renewable energy access and the structural complexity of distributed energy systems, traditional microgrid (MG) scheduling methods that rely on mathematical optimization models and expert experience are facing significant challenges. Therefore, it is essential to present a novel scheduling technique with high intelligence and fast decision-making capacity to realize MGs' automatic operation and regulation. This paper proposes an optimal scheduling decision-making method for MGs based on deep neural networks (DNN). Firstly, a typical mathematical scheduling model used for MG operation is introduced, and the limitations of current methods are analyzed. Then, a two-stage optimal scheduling framework comprising day-ahead and intra-day stages is presented. The day-ahead part is solved by mixed integer linear programming (MILP), and the intra-day part uses a convolutional neural network (CNN)—bidirectional long short-term memory (Bi LSTM) for high-speed rolling decision making, with the outputs adjusted by a power correction balance algorithm. Finally, the validity of the model and algorithm of this paper are verified by arithmetic case analysis. [ABSTRACT FROM AUTHOR]

This document is a correction notice for a research paper titled "A Drone Based Transmission Line Components Inspection System with Deep Learning Technique." The authors have identified errors in the figures and main body paragraphs of the paper and have provided corrected versions of the figures. They have also made changes to the abstract and introduction sections. The paper discusses the use of a drone to detect and analyze transmission line components for defects, utilizing deep learning techniques and convolutional neural networks. The authors acknowledge the errors in the original paper and apologize for any confusion caused. The research is funded by the Korean Ministry of the Interior and Safety. The authors of the paper are Zahid Ali Siddiqui and Unsang Park. [Extracted from the article]

Photovoltaic (PV) fault detection is crucial because undetected PV faults can lead to significant energy losses, with some cases experiencing losses of up to 10%. The efficiency of PV systems depends upon the reliable detection and diagnosis of faults. The integration of Artificial Intelligence (AI) techniques has been a growing trend in addressing these issues. The goal of this systematic review is to offer a comprehensive overview of the recent advancements in AI-based methodologies for PV fault detection, consolidating the key findings from 31 research papers. An initial pool of 142 papers were identified, from which 31 were selected for in-depth review following the PRISMA guidelines. The title, objective, methods, and findings of each paper were analyzed, with a focus on machine learning (ML) and deep learning (DL) approaches. ML and DL are particularly suitable for PV fault detection because of their capacity to process and analyze large amounts of data to identify complex patterns and anomalies. This study identified several AI techniques used for fault detection in PV systems, ranging from classical ML methods like k-nearest neighbor (KNN) and random forest to more advanced deep learning models such as Convolutional Neural Networks (CNNs). Quantum circuits and infrared imagery were also explored as potential solutions. The analysis found that DL models, in general, outperformed traditional ML models in accuracy and efficiency. This study shows that AI methodologies have evolved and been increasingly applied in PV fault detection. The integration of AI in PV fault detection offers high accuracy and effectiveness. After reviewing these studies, we proposed an Artificial Neural Network (ANN)-based method for PV fault detection and classification. [ABSTRACT FROM AUTHOR]

The load forecasting research for an NPS faces challenges including a high model accuracy, non-sharing of data, and a high communication cost. This paper proposes a load forecasting method for an NPS, based on efficient federated transfer learning (FTL). The adversarial feature extractor is added on the basis that FTL can effectively transfer the parameter features of the non-mask load to the local load data, and make up for the loss of mask load prediction accuracy. In order to improve the efficiency of the gradient compression of federated learning (FL), a depth dynamic threshold compression sensing method is proposed, which replaces the sparse signal in compressed sensing via the U-Net model and achieves an observation dimension reduction through a convolutional neural network (CNN). The experimental results show that the mean absolute percentage error (MAPE) and the root-mean-square error (RMSE) of the load forecasting method proposed in this paper are reduced by 9.6% and 2.31 kW, on average, when the load data are covered up to different degrees. Compared with the traditional FL model, the proposed compression algorithm saves 23.5% of the communication cost, without changing the accuracy of the model. The proposed prediction framework is easily interpretable, and robust under different validation metrics. [ABSTRACT FROM AUTHOR]

Predicting the remaining useful life (RUL) of lithium-ion batteries is crucial for enhancing their reliability and safety. Addressing the issue of inaccurate RUL predictions caused by the nonlinear decay resulting from capacity regeneration, this paper proposes an indirect lithium-ion battery RUL prediction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and convolutional neural network (CNN)–bidirectional gated recurrent unit (BiGRU). The method extracts Health Indicators (HI) from the battery-charging stage and employs CEEMDAN to decompose HI into several components. These components are then input into a component prediction model for forecasting. Finally, the predicted component results are fused and input into a capacity prediction model to achieve indirect RUL prediction. Validation is conducted using the lithium-ion battery dataset provided by NASA. The results indicate that, under prediction starting points (STs) of 80 and 100, the maximum average absolute errors do not exceed 0.0096 and 0.0081, and the maximum root mean square errors do not exceed 0.0196 and 0.0115, demonstrating high precision and reliability. [ABSTRACT FROM AUTHOR]

The complex environmental impact makes it difficult to predict wind speed with high precision for multiple wind turbines. Most existing research methods model the temporal dependence of wind speeds, ignoring the spatial correlation between wind turbines. In this paper, we propose a multi-wind turbine wind speed prediction model based on Weighted Diffusion Graph Convolution and Gated Attention Network (WDGCGAN). To address the strong nonlinear correlation problem among multiple wind turbines, we use the maximal information coefficient (MIC) method to calculate the correlation weights between wind turbines and construct a weighted graph for multiple wind turbines. Next, by applying Diffusion Graph Convolution (DGC) transformation to the weight matrix of the weighted graph, we obtain the spatial graph diffusion matrix of the wind farm to aggregate the high-order neighborhood information of the graph nodes. Finally, by combining the DGC with the gated attention recurrent unit (GAU), we establish a spatio-temporal model for multi-turbine wind speed prediction. Experiments on the wind farm data in Massachusetts show that the proposed method can effectively aggregate the spatio-temporal information of wind turbine nodes and improve the prediction accuracy of multiple wind speeds. In the 1h prediction task, the average RMSE of the proposed model is 28% and 33.1% lower than that of the Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN), respectively. [ABSTRACT FROM AUTHOR]

The electricity market is constantly evolving, being driven by factors such as market liberalization, the increasing use of renewable energy sources (RESs), and various economic and political influences. These dynamics make it challenging to predict wholesale electricity prices. Accurate short-term forecasting is crucial to maintaining system balance and addressing anomalies such as negative prices and deviations from predictions. This paper investigates short-term electricity price forecasting using historical time series data and employs advanced deep learning algorithms. First, four deep learning models are implemented and proposed, which are a convolutional neural network (CNN) with an integrated attention mechanism, a hybrid CNN followed by a gated recurrent unit model (CNN-GRU) with an attention mechanism, and two ensemble learning models, which are a soft voting ensemble and a stacking ensemble model. Also, the optimized version of a transformer model, the Multi-Head Attention model, is introduced. Finally, the perceptron model is used as a benchmark for comparison. Our results show excellent prediction accuracy, particularly in the hybrid CNN-GRU model with attention, thereby achieving a mean absolute percentage error (MAPE) of 6.333%. The soft voting ensemble model and the Multi-Head Attention model also performed well, with MAPEs of 6.125% and 6.889%, respectively. These findings are significant, as previous studies have not shown high performance with transformer models and attention mechanisms. The presented results offer promising insights for future research in this field. [ABSTRACT FROM AUTHOR]

AC-DC hybrid distribution grids realize power transmission through tie lines. Accurately characterizing the power exchange capacity between regional grids while ensuring safe grid operation is the basis for the coordinated scheduling of resources in interconnected distribution grids. However, most of the current AC/DC hybrid models are linear, and it is challenging to ensure the accuracy criteria of the obtained feasible regions. In this paper, a two-stage multi-segment boundary approximation method is proposed to characterize the feasible region of hybrid distribution grid tie line operation. Information such as security operation constraints are mapped to the feasible region of the boundary tie line to accurately characterize the transmission exchange capacity of the tie line. To avoid the limitations of linear models, the method uses a nonlinear model to iteratively search for boundary points of the feasible region. This ensures high accuracy in approximating the real feasible region shape and capacity limitations. A convolutional neural network (CNN) is then utilized to map the given boundary and cost information to obtain an estimated equivalent operating cost function for the contact line, overcoming the inability of previous methods to capture nonlinear cost relationships. This provides the necessary cost information in a data-driven manner for the economic dispatch of hybrid AC-DC distribution networks. Numerical tests demonstrate the effectiveness of the method in improving coordination accuracy while preserving regional grid privacy. The key innovations are nonlinear modeling of the feasible domain of the contact line and nonlinear cost fitting for high-accuracy dispatch. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach for optimizing wind farm control through the utilization of a combined model predictive control method. In contrast to conventional methods of controlling active and reactive power in wind farms, the suggested approach integrates a wind power prediction model driven by a neural network and a state-space model for wind turbines. This combination facilitates a more precise forecast of active power, thereby enabling the dynamic prediction of the range of reactive power output from the wind turbines. When combined with the equation of state in wind farm space, it is possible to accurately optimize the reactive power of a wind farm. Furthermore, the impact of active power on voltage fluctuations in the wind farm collector system was examined. The utilization of model predictive control enhances voltage regulation, optimizes system redundancy, and increases the reactive capacity. Sensitivity coefficients were calculated using analytical methods to enhance computational efficiency and to resolve issues related to convergence. In order to validate the proposed methodology and control scheme, a wind farm simulation model comprising 20 turbines was developed to assess the feasibility of the scheme. [ABSTRACT FROM AUTHOR]

Accurate estimation of Li-ion battery capacity is critical for a battery management system (BMS). This paper proposes an innovative method which combines a convolutional neural network and incremental capacity analysis (ICA). In the present approach, the voltage and temperature, which significantly affect the ICA curve during the discharging process, are adopted as the inputs for CNN. Rather than extracting feature parameters of an IC curve, as is carried out in the available research, the present method uses the whole ICA curve as the input to avoid complicated feature extraction and correlation analysis. The results show that the maximum error of capacity estimation is less than 4.7%, the rectified mean squared error is less than 1.3% for each battery, and the overall RMSE is below 1.12%. [ABSTRACT FROM AUTHOR]

This research delineates a pivotal advancement in the domain of sustainable energy systems, with a focused emphasis on the integration of renewable energy sources—predominantly wind and solar power—into the hydrogen production paradigm. At the core of this scientific endeavor is the formulation and implementation of a deep-learning-based framework for short-term localized weather forecasting, specifically designed to enhance the efficiency of hydrogen production derived from renewable energy sources. The study presents a comprehensive evaluation of the efficacy of fully connected neural networks (FCNs) and convolutional neural networks (CNNs) within the realm of deep learning, aimed at refining the accuracy of renewable energy forecasts. These methodologies have demonstrated remarkable proficiency in navigating the inherent complexities and variabilities associated with renewable energy systems, thereby significantly improving the reliability and precision of predictions pertaining to energy output. The cornerstone of this investigation is the deployment of an artificial intelligence (AI)-driven weather forecasting system, which meticulously analyzes data procured from 25 distinct weather monitoring stations across Latvia. This system is specifically tailored to deliver short-term (1 h ahead) forecasts, employing a comprehensive sensor fusion approach to accurately predicting wind and solar power outputs. A major finding of this research is the achievement of a mean squared error (MSE) of 1.36 in the forecasting model, underscoring the potential of this approach in optimizing renewable energy utilization for hydrogen production. Furthermore, the paper elucidates the construction of the forecasting model, revealing that the integration of sensor fusion significantly enhances the model's predictive capabilities by leveraging data from multiple sources to generate a more accurate and robust forecast. The entire codebase developed during this research endeavor has been made available on an open access GIT server. [ABSTRACT FROM AUTHOR]

Deep learning (DL) networks are a popular choice for short-term load forecasting (STLF) in the residential sector. Hybrid DL methodologies based on convolutional neural networks (CNNs) and long short-term memory networks (LSTMs) have a higher forecasting accuracy than conventional statistical STLF techniques for different types of single-household load series. However, existing load forecasting methodologies are often inefficient when a high load demand persists for a few hours in a day. Peak load consumption is explicitly depicted as a tail in the probability distribution function (PDF) of the load series. Due to the diverse and uncertain nature of peak load demands, DL methodologies have difficulty maintaining consistent forecasting results, particularly when the PDF of the load series has a longer tail. This paper proposes a multihousehold load forecasting strategy based on the collective moment measure (CMM) (which is obtained from the PDF of the load series), data augmentation, and a CNN. Each load series was compared and ordered through CMM indexing, which helped maintain a minimum or constant shifting variance in the dataset inputted to the CNN. Data augmentation was used to enlarge the input dataset and solve the existing data requirement issues of the CNN. With the ordered load series and data augmentation strategy, the simulation results demonstrated a significant improvement in the performance of both single-household and multihousehold load forecasting. The proposed method predicts day-ahead multihousehold loads simultaneously and compares the results based on a single household. The forecasting performance of the proposed method for six different household groups with 10, 20, 30, 50, 80, and 100 household load series was evaluated and compared with those of existing methodologies. The mean absolute percentage error of the prediction results for each multihousehold load series could be improved by more than 3%. This study can help advance the application of DL methods for household load prediction under high-load-demand conditions. [ABSTRACT FROM AUTHOR]

A large proportion of photovoltaic (PV) power generation is connected to the power grid, and its volatility and stochasticity have significant impacts on the power system. Accurate PV power forecasting is of great significance in optimizing the safe operation of the power grid and power market transactions. In this paper, a novel dual-channel PV power forecasting method based on a temporal convolutional network (TCN) is proposed. The method deeply integrates the PV station feature data with the model computing mechanism through the dual-channel model architecture; utilizes the combination of multihead attention (MHA) and TCN to extract the multidimensional spatio-temporal features between other meteorological variables and the PV power; and utilizes a single TCN to fully extract the temporal constraints of the power sequence elements. The weighted fusion of the dual-channel feature data ultimately yields the ideal forecasting results. The experimental data in this study are from a 26.52 kW PV power plant in central Australia. The experiments were carried out over seven different input window widths, and the two models that currently show superior performance within the field of PV power forecasting: the convolutional neural network (CNN), and the convolutional neural network combined with a long and short-term memory network (CNN_LSTM), are used as the baseline models. The experimental results show that the proposed model and the baseline models both obtained the best forecasting performance over a 1-day input window width, while the proposed model exhibited superior forecasting performance compared to the baseline model. It also shows that designing model architectures that deeply integrate the data input method with the model mechanism has research potential in the field of PV power forecasting. [ABSTRACT FROM AUTHOR]

With the steady increase in the use of renewable energy sources in the energy sector, new challenges arise, especially the unpredictability of these energy sources. This uncertainty complicates the management, planning, and development of energy systems. An effective solution to these challenges is short-term forecasting of the output of photovoltaic power plants. In this paper, a novel method for short-term production prediction was explored which involves continuous photography of the sky above the photovoltaic power plant. By analyzing a series of sky images, patterns can be identified to help predict future photovoltaic power generation. A hybrid model that integrates both a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) for short-term production forecasting was developed and tested. This model effectively detects spatial and temporal patterns from images and power output data, displaying considerable prediction accuracy. In particular, a 74% correlation was found between the model's predictions and actual future production values, demonstrating the model's efficiency. The results of this paper suggest that the hybrid CNN-LSTM model offers an improvement in prediction accuracy and practicality compared to traditional forecasting methods. This paper highlights the potential of Deep Learning in improving renewable energy practices, particularly in power prediction, contributing to the overall sustainability of power systems. [ABSTRACT FROM AUTHOR]

Central air conditioning in large buildings is an important demand-response resource due to its large load power and strong controllability. Demand-response-oriented air conditioning load modeling needs to calculate the room temperature. The room temperature calculation models commonly used in the existing research cannot easily and accurately calculate the room temperature change of large buildings. Therefore, in order to obtain the temperature change of a large building and its corresponding power potential, this paper first proposes a building model based on CNN (convolutional neural network). Then, in order to fully apply the demand-response potential of the central air conditioning load, this paper puts forward an evaluation method of the load-reduction potential of the central air conditioning cluster based on pre-cooling and develops an economic load-reduction strategy according to the different energy consumption of different buildings in the pre-cooling stage. Finally, multiple building examples with different building parameters and temperature comfort ranges are set up, and the economic advantages of the proposed strategy are illustrated by Cplex solution examples. [ABSTRACT FROM AUTHOR]

An arc fault is the leading cause of electrical fire. Aiming at the problems of difficulty in manually extracting features, poor generalization ability of models and low prediction accuracy in traditional arc fault detection algorithms, this paper proposes a fault arc detection method based on the fusion of channel attention mechanism and residual network model. This method is based on the channel attention mechanism to perform global average pooling of information from each channel of the feature map assigned by the residual block while ignoring the local spatial data to enhance the detection and recognition rate of the fault arc. This paper introduces a one-dimensional depth separable convolution (1D-DS) module to reduce the network model parameters and shorten the time of single prediction samples. The experimental results show that the F1 score of the network model for arc fault detection under mixed load conditions is 98.07%, and the parameter amount is reduced by 46.06%. The method proposed in this paper dramatically reduces the parameter quantity, floating-point number and time complexity of the network structure while ensuring a high recognition rate, which improves the real-time response ability to detect arc fault. It has a guiding significance for applying arc fault on the edge side. [ABSTRACT FROM AUTHOR]

In nuclear power plants, the loss-of-coolant accident (LOCA) stands out as the most prevalent and consequential incident. Accurate breach size diagnosis is crucial for the mitigation of LOCAs, and identifying the cause of an accident can prevent catastrophic consequences. Traditional methods mostly focus on combining model algorithms and utilize intricate composite model neural network architectures. However, it is crucial to investigate whether greater complexity necessarily leads to better performance. In addition, the consideration of the impact of dataset construction and data preprocessing on model performance is also needed for model building. This paper proposes a framework named DeepLOCA-Lattice to experiment with different preprocessing approaches to fundamental deep learning models for a comprehensive analysis of the diagnosis of LOCA breach size. The DeepLOCA-Lattice involves data preprocessing via the lattice algorithm and equal-interval partitioning and deep-learning-based models, including the multi-layer perceptron (MLP), recurrent neural networks (RNNs), convolutional neural networks (CNNs), and the transformer model in LOCA breach size diagnosis. After conducting rigorous ablation experiments, we have discovered that even rudimentary foundational models can achieve accuracy rates that exceed 90%. This is a significant improvement when compared to the previous models, which yield an accuracy rate of lower than 50%. The results interestingly demonstrate the superior performance and efficacy of the fundamental deep learning model, with an effective dataset construction approach. It elucidates the presence of a complex interplay among diagnostic scales, sliding window size, and sliding stride. Furthermore, our investigation reveals that the model attains its highest accuracy within the discussed range when utilizing a smaller sliding stride size and a longer sliding window length. This study could furnish valuable insights for constructing models for LOCA breach size estimation. [ABSTRACT FROM AUTHOR]

In this paper, an innovative approach for the fast estimation of the mutual inductance between transmitting and receiving coils for Dynamic Wireless Power Transfer Systems (DWPTSs) is implemented. To this end, a Convolutional Neural Network (CNN) is used; an image representing the geometry of two coils that are partially misaligned is the input of the CNN, while the output is the corresponding inductance value. Finite Element Analyses are used for the computation of the inductance values needed for CNN training. This way, thanks to a fast and accurate inductance estimated by the CNN, it is possible to properly manage the power converter devoted to charge the battery, avoiding the wind up of its controller when it attempts to transfer power in poor coupling conditions. [ABSTRACT FROM AUTHOR]

Wind turbine energy generators operate in a variety of environments and often under harsh operational conditions, which can result in the mechanical failure of wind turbines. In order to ensure the efficient operation of wind turbines, the detection of any abnormality in the mechanics is particularly important. In this paper, a method for detecting abnormalities in the bearings of wind turbine energy generators, based on the cascade deep learning model, is proposed. First, data on the mechanics of wind turbine generators were collected, and the correlation between the data was studied in order to select the parameters related to the bearing temperature. Then, the logical relationship between the observation parameters and the target parameters was established based on a one-dimensional convolutional neural network (CNN) and a long short-term memory (LSTM) network, and the difference between the predicted temperature and the actual temperature was assessed using the root mean square error evaluation model. Finally, a numerical example was used to verify the operational data from a wind farm unit in northwest China. The results show that the CNN-LSTM model proposed in this paper can detect abnormalities earlier in the state of the main bearing than the LSTM model, and the CNN-LSTM model can detect abnormalities in the main bearing that the LSTM network cannot find. [ABSTRACT FROM AUTHOR]

Nuclear power is a type of clean and green energy; however, there is a risk of radioactive material leakage when accidents occur. When radioactive material leaks from nuclear power plants, it has a great impact on the environment and personnel safety. In order to enhance the safety of nuclear power plants and support the operator's decisions under accidental circumstances, this paper proposes a fault diagnosis method for nuclear power plants based on the sparrow search algorithm (SSA) optimized by the CNN-LSTM network. Firstly, the convolutional neural network (CNN) was used to extract features from the data before they were then combined with the long short-term memory (LSTM) neural network to process time series data and form a CNN-LSTM model. Some of the parameters in the LSTM neural network need to be manually tuned based on experience, and the settings of these parameters have a great impact on the overall model results. Therefore, this paper selected the sparrow search algorithm with a strong search capability and fast convergence to automatically search for the hand-tuned parameters in the CNN-LSTM model, and finally obtain the SSA-CNN-LSTM model. This model can classify the types of accidents that occur in nuclear power plants to reduce the nuclear safety hazards caused by human error. The experimental data are from a personal computer transient analyzer (PCTRAN). The results show that the classification accuracy of the SSA-CNN-LSTM model for the nuclear power plant fault classification problem is as high as 98.24%, which is 4.80% and 3.14% higher compared with the LSTM neural network and CNN-LSTM model, respectively. The superiority of the sparrow search algorithm for optimizing model parameters and the feasibility and accuracy of the SSA-CNN-LSTM model for nuclear power plant fault diagnosis were verified. [ABSTRACT FROM AUTHOR]

To solve the problem of feature selection and error correction after mode decomposition and improve the ability of power load forecasting models to capture complex time series information, a two-stage short-term power load forecasting method based on recursive feature elimination with a cross validation (RFECV) algorithm and time convolution network–efficient channel attention mechanism–long short-term memory network (TCN–ECA–LSTM) is presented. First, the load sequence is decomposed into a relatively stable set of modal components using variational mode decomposition. Then, the RFECV-based method filters the feature set of each modal component to construct the best feature set. Finally, a two-stage prediction model based on TCN–ECA–LSTM is established. The first stage predicts each modal component and the second stage reconstructs the load forecast based on the predicted value of the previous stage. This paper takes actual data from New South Wales, Australia, as an example, and the results show that the method proposed in this paper can build the feature set reliably and efficiently and has a higher accuracy than the conventional prediction model. [ABSTRACT FROM AUTHOR]

This paper presents a small and efficient model for predicting NOx emissions from coal-fired boilers. The raw data collected are processed by the min–max scale method and converted into a multivariate time series. The overall model's architecture is mainly based on building blocks consisting of separable convolutional neural networks and efficient channel attention (ECA) modules. The experimental results show that the model can learn good representations from sufficient data covering different operation conditions. These results also suggest that ECA modules can improve the model's performance. The comparative study shows our model's strong performance compared to other NOx prediction models. Then, we demonstrate the effectiveness of the model proposed in this paper in terms of predicting NOx emissions. [ABSTRACT FROM AUTHOR]

In recent years, lithium-ion batteries have gained significant attention due to their crucial role in various applications, such as electric vehicles and renewable energy storage. Accurate prediction of the remaining useful life (RUL) of these batteries is essential for optimizing their performance and ensuring reliable operation. In this paper, we propose a novel methodology for RUL prediction using an individual control chart (ICC) to identify and remove degraded data, a convolutional neural network (CNN) to smooth the noise of sensor data and long short-term memory (LSTM) networks to effectively capture both spatial and temporal dependencies within battery data, enabling accurate RUL estimation. We evaluate our proposed model using a comprehensive dataset, and experimental results demonstrate its superior performance compared to existing methods. Our findings highlight the potential of ICC-CNN-LSTM for RUL prediction in lithium-ion batteries and provide valuable insights for future research. [ABSTRACT FROM AUTHOR]

Lithium-ion batteries are a key technology for the electrification of the transport sector and the corresponding move to renewable energy. It is vital to determine the condition of lithium-ion batteries at all times to optimize their operation. Because of the various loading conditions these batteries are subjected to and the complex structure of the electrochemical systems, it is not possible to directly measure their condition, including their state of charge. Instead, battery models are used to emulate their behavior. Data-driven models have become of increasing interest because they demonstrate high levels of accuracy with less development time; however, they are highly dependent on their database. To overcome this problem, in this paper, the use of a data augmentation method to improve the training of artificial neural networks is analyzed. A linear regression model, as well as a multilayer perceptron and a convolutional neural network, are trained with different amounts of artificial data to estimate the state of charge of a battery cell. All models are tested on real data to examine the applicability of the models in a real application. The lowest test error is obtained for the convolutional neural network, with a mean absolute error of 0.27%. The results highlight the potential of data-driven models and the potential to improve the training of these models using artificial data. [ABSTRACT FROM AUTHOR]

In this paper, we address several issues with existing methods for predicting the residual electrical life of railway relays. These issues include the difficulty of single feature prediction in characterizing the degradation process, the neglect of temporal and backward–forward correlations in the degradation process, and low prediction accuracy. To overcome these challenges, we propose a novel approach that combines convolutional neural networks (CNNs) and bidirectional long short-term memory (BiLSTM) to facilitate the life prediction of railway relays and provide an accurate data basis for the maintenance of railway relays. Firstly, we collected voltage and current signals from railway relay electrical life tests and extracted feature parameters that captured the relay's operating state. Next, we applied Spearman correlation coefficient analysis combined with random forest importance analysis to perform double-feature selection. This process eliminates redundant feature parameters and identifies the optimal feature subset. Finally, we constructed a convolutional neural network bidirectional long short-term memory (CNN BiLSTM) prediction model to accurately predict the remaining electrical life of the railway relay. Through our analysis of the prediction results, we observed that the CNN BiLSTM model achieves an effective prediction accuracy of 96.3%. This accuracy is significantly higher, more stable, and more practical compared to other prediction models such as recurrent neural networks (RNNs), long short-term memory (LSTM), and BiLSTM models. Overall, our proposed CNN BiLSTM model offers higher accuracy, better stability, and greater practicality in predicting the remaining electrical life of railway relays. [ABSTRACT FROM AUTHOR]

The thickness of the indium tin oxide (ITO) layer is a critical parameter affecting the performance of solar cells. Traditional measurement methods require sample collection, leading to manufacturing interruptions and potential quality issues. In this paper, we propose a real-time, non-contact approach using deep learning and optical interference phenomena to estimate the thickness of ITO layers in solar cells. We develop a convolutional neural network (CNN) model that processes microscopic images of solar cells and predicts the ITO layer thickness. In addition, mean absolute error (MAE) and mean squared error (MSE) loss functions are combined to train the model. Experimental results demonstrate the effectiveness of our approach in accurately estimating the ITO layer thickness. The integration of computer vision and deep learning techniques provides a valuable tool for non-destructive testing and quality control in the manufacturing of solar cells. The loss of the model after training is reduced to 0.83, and the slope of the test value in the scatter plot with the true value of the ellipsometer is approximately equal to 1, indicating the high reliability of the model. [ABSTRACT FROM AUTHOR]