Prediction of regional wind power generation using a multi-objective optimized deep learning model with temporal pattern attention

Accurate and stable prediction of regional wind power is crucial for optimal scheduling and renewable energy utilization in the power grid. In this paper, a novel multi-objective optimized recurrent neural network with temporal pattern attention (TPA) is proposed to address the randomness and uncertainty of wind farms in regional wind power prediction. Firstly, Taguchi method is applied to select the weather variables from wind farms, reducing redundancy and improving efficiency. Then, the stacked model is constructed using a denoising autoencoder (DAE) and gated recurrent unit (GRU), to improve the robustness and temporal correlation of the hidden states. The TPA is introduced to assign different weights to the hidden states, considering the multivariate relationships at different time steps. Furthermore, the Multi-objective slime mould algorithm (MOSMA) and variable weight multi-objective loss function (VMLF) are developed to optimize DGRU-TPA under multiple objectives to realize accurate and stable prediction. Finally, the experiment results demonstrate that nRMSE, nMAPE, and nSD of the proposed model are reduced by 26.36%, 24.05%, and 21.04% respectively, and qualification rate (QR) is increased by 13.56% compared to other models. The proposed model has achieved superior performance in regional prediction, which is crucial for effective grid management with increasing wind energy

Wind Turbine Blade Icing Diagnosis Using Convolutional LSTM-GRU With Improved African Vultures Optimization

Wind farms are usually located in plateau mountains and northern coastal areas, bringing a high probability of blade icing. Blade icing even leads to blade cracks and turbine collapse. Traditional methods of blade icing diagnosis increase operating costs and have the potential risk of damaging the original mechanical structure. A data-driven model based on a novel convolutional recurrent neural network is proposed in this article. The method can effectively extract hidden features for accurate icing diagnosis. The hyperparameters of the proposed model are optimized by the improved African vultures optimization algorithm (IAVOA). To alleviate the critical data imbalance, the adaptive synthetic (ADASYN) is used to oversample the minority classes of icing status. In comparison to the state-of-the-art classification methods, the proposed method illustrates the outstanding effectiveness in blade icing diagnosis using the sensor data from supervisory control and data acquisition (SCADA) systems. The effectiveness analysis of variables, ablation study, and sensitivity analysis validates the performance of the proposed method

Trustworthy Fault Diagnosis with Uncertainty Estimation through Evidential Convolutional Neural Networks

Deep neural networks (DNNs) have been widely used for intelligent fault diagnosis under the closed world assumption that any testing data is within classes of the training data. However, in reality, out-of-distribution (OOD) cases such as new fault conditions can happen after the original trained model is deployed. Most of the current DNNs are deterministic which can misclassify with high confidence in the open-world scenario. This overconfident behavior would not guarantee the reliability and robustness of fault diagnosis results in practice. Therefore, trustworthy intelligent fault diagnosis with uncertainty estimation is crucial for real applications. In this paper, we develop a novel convolutional neural network integrating evidence theory to achieve fault classifications with prediction uncertainty estimation. The estimated prediction uncertainty can identify potential OOD samples. This approach allows a minimal modification of the state-of-the-art DNN model by using a risk-calibrated evidential loss function and Dirichlet distribution that replaces the classification probabilities. The experimental results show that the proposed approach can not only achieve accurate classification of known classes but also detect unknown classes effectively. The proposed method shows significant potential in detecting OOD patterns and provides trustworthy fault diagnosis in open and non-stationary environments

Trustworthy and intelligent fault diagnosis with effective denoising and evidential stacked GRU neural network

AbstractWith the advances in Internet-of-Things and data mining technologies, deep learning-based approaches have been widely used for intelligent fault diagnosis of manufacturing assets. However, uncertainty caused by the non-stationary process data such as vibration signal and noise interference in practical working environments will greatly affect the performance and reliability of predictions. The present paper develops a trustworthy and intelligent fault diagnosis framework based on a two-stage joint denoising method and evidential neural networks. The proposed denoising method integrating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and the independent component analysis (ICA) method can effectively reduce data uncertainty caused by noise interference. The stacked gated recurrent unit (SGRU) model has been incorporated into the evidential neural networks as a deep classifier. The proposed evidential SGRU (ESGRU) method can quantify the prediction uncertainty, which estimates the prediction trustworthiness. Predictive entropy and reliability diagrams are used as calibration methods to validate the effectiveness of uncertainty estimation. The proposed framework is validated by two case studies of rolling bearing fault diagnosis in variable noise conditions. Experimental results demonstrate that the proposed method can achieve a high denoising effect and provide reliable uncertainty prediction results which are significant for practical applications

Intelligent machine fault diagnosis with effective denoising using EEMD-ICA- FuzzyEn and CNN

With the advances in smart sensing and data mining technologies of Industry 4.0, condition monitoring of key equipment in manufacturing has brought transformations in production and maintenance management. However, in practical applications, noise from both the working environment and the sensing devices is inevitable, which causes the low performance of data-driven fault diagnosis. To address this challenge, the paper develops a robust two-stage joint denoising method by integrating ensemble empirical mode decomposition (EEMD) and independent component analysis (ICA), with fuzzy entropy discriminant as a threshold. The developed method can filter noisy components from decomposed modal components and reconstruct a new signal with denoised independent components. Moreover, an improved convolutional neural network (CNN) model based on the VGG structure has been constructed as a classifier to achieve end-to-end fault diagnosis. The experimental results demonstrate the high accuracy and superior anti-interference capability of the proposed method for rolling bearing fault diagnosis under various noise levels. Compared with state-of-the-art denoising methods and fault diagnosis methods, the proposed method achieves higher accuracy and robustness under variable noise interference. The proposed method can be applied to broader fault diagnosis tasks of production equipment in complex practical environment

Fruit Tree Legume Herb Intercropping Orchard System Is an Effective Method to Promote the Sustainability of Systems in a Karst Rocky Desertification Control Area

Karst rocky desertification control through the conversion of cropland to economic forest is vital for vegetation recovery and the alleviation of distinct contradiction between ecological conservation and economic development. To evaluate the sustainability of orchard systems from the perspectives of ecosystem and economic services, we employed emergy analysis for the comprehensive and quantitative assessment of two orchard system types: (1) mango monoculture (MM) and macadamia monoculture (NM) and (2) mango Vicia angustifolia intercropping (MVI) and macadamia Desmodium intortum intercropping (NDI). In the past, these areas were converted from a maize field (MF) in the southwest karst area of China. Our results showed that, compared to the MF, the total emergy input in monoculture orchards (NM and NM) decreased by 8.99% and 35.25%, and the economic profit (EP) increased by 20,406.57 and 114,406.32 RMB·ha−1, respectively. However, the non-renewable environmental input (energy loss of soil, SOM reduction, and irrigation water) still accounted for 43.25% and 62.01% in the total emergy input. After conversion to orchard legume herb intercropping (MVI and NDI), purchased resource inputs accounted for 86.36% and 68.20% of the total emergy input. Orchard legume herb intercropping further increased the EP, while improving ecosystem services and providing the capability for groundwater recharge, soil conservation, and soil carbon sequestration. The intercropping orchards were relatively sustainable from the view of economic and ecosystem services (EISD > 3.18), due to lower environmental loading ratios (ELR < 1.15), higher emergy yield ratio (EYR > 0.89), and economic output/input ratio (O/I ratio > 2.41). The integrated pest management simulations indicated that, compared to intercropping systems, the renewable percent (R%) and emergy sustainability index (ESI) of the scenario simulations (MVI-O and NDI-O) increased by 17.61% and 10.51%, respectively. These results suggest that integrated pest management is an effective method to improve the short-term sustainability of the orchard system. Therefore, the management of intercropped legume herb within an orchard system is an effective way to achieve sustainable development

Fruit Tree Legume Herb Intercropping Orchard System Is an Effective Method to Promote the Sustainability of Systems in a Karst Rocky Desertification Control Area

Karst rocky desertification control through the conversion of cropland to economic forest is vital for vegetation recovery and the alleviation of distinct contradiction between ecological conservation and economic development. To evaluate the sustainability of orchard systems from the perspectives of ecosystem and economic services, we employed emergy analysis for the comprehensive and quantitative assessment of two orchard system types: (1) mango monoculture (MM) and macadamia monoculture (NM) and (2) mango Vicia angustifolia intercropping (MVI) and macadamia Desmodium intortum intercropping (NDI). In the past, these areas were converted from a maize field (MF) in the southwest karst area of China. Our results showed that, compared to the MF, the total emergy input in monoculture orchards (NM and NM) decreased by 8.99% and 35.25%, and the economic profit (EP) increased by 20,406.57 and 114,406.32 RMB·ha−1, respectively. However, the non-renewable environmental input (energy loss of soil, SOM reduction, and irrigation water) still accounted for 43.25% and 62.01% in the total emergy input. After conversion to orchard legume herb intercropping (MVI and NDI), purchased resource inputs accounted for 86.36% and 68.20% of the total emergy input. Orchard legume herb intercropping further increased the EP, while improving ecosystem services and providing the capability for groundwater recharge, soil conservation, and soil carbon sequestration. The intercropping orchards were relatively sustainable from the view of economic and ecosystem services (EISD > 3.18), due to lower environmental loading ratios (ELR 0.89), and economic output/input ratio (O/I ratio > 2.41). The integrated pest management simulations indicated that, compared to intercropping systems, the renewable percent (R%) and emergy sustainability index (ESI) of the scenario simulations (MVI-O and NDI-O) increased by 17.61% and 10.51%, respectively. These results suggest that integrated pest management is an effective method to improve the short-term sustainability of the orchard system. Therefore, the management of intercropped legume herb within an orchard system is an effective way to achieve sustainable development

One-bit quantization is good for programmable coding metasurfaces

International audienceThe information-carrying programmable metasurfaces has found widespread applications in communication, sensing, and other related areas. However, there is a fundamental but unresolved problem, i.e., the rigorous understanding of the quantization of metasurface coding. Here, we theoretically investigate the performance difference between one-bit and continuous information-encoding metasurfaces. To this end, we derive analytical representations of system responses in various cases (single-input single-output, single-input multiple-output, and multiple-input multiple-output). We analyze the impact of one-bit coding (in contrast to continuous coding) in terms of the resulting channel cross-talk and reduction of information capacity in various representative scenarios from wireless communication and holography. Our main finding indicates that the one-bit coding yields a satisfactory performance in most practical scenarios; we also show that in many cases there are smart ways to mildly relax optimization constraints in order to reduce the performance gap between the one-bit and continuous coding. We expect that our results can provide theoretical guidance for a large variety of metasurface-assisted information systems for electromagnetic waves and other wave phenomena