A novel out of step relaying algorithm based on wavelet transform and a deep learning machine model

Desai, Jigneshkumar P.; Makwana, Vijay H.

doi:10.1186/s41601-021-00221-y

Cited by 18 publications

(14 citation statements)

References 16 publications

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“…• Convolutional layer contains several convolution kernels to generate new feature maps, which convolves the network weight with the receptive field of the feature map of the previous layer, and uses the activation function to form the feature map of the next convolutional layer (Yamashita et al, 2018). • Fully connected layer is often used for high-level inference, which maps the features processed by the convolution layers to the output layer (Desai and Makwana, 2021). • Output layer is the final outputs of the NPCNN.…”

Section: Figurementioning

confidence: 99%

A hybrid deep learning model with error correction for photovoltaic power forecasting

Zhang

et al. 2022

Front. Energy Res.

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The penetration of photovoltaic (PV) power into modern power systems brings enormous economic and environmental benefits due to its cleanness and inexhaustibility. Therefore, accurate PV power forecasting is a pressing and rigid demand to reduce the negative impact of its randomness and intermittency on modern power systems. In this paper, we explore the application of deep learning based hybrid technologies for ultra-short-term PV power forecasting consisting of a feature engineering module, a deep learning-based point prediction module, and an error correction module. The isolated forest based feature preprocessing module is used to detect the outliers in the original data. The non-pooling convolutional neural network (NPCNN), as the deep learning based point prediction module, is developed and trained using the processed data to identify non-linear features. The historical forecasting errors between the forecasting and actual PV data are further constructed and trained to correct the forecasting errors, by using an error correction module based on a hybrid of wavelet transform (WT) and k-nearest neighbor (KNN). In the simulations, the proposed method is extensively evaluated on actual PV data in Limburg, Belgium. Experimental results show that the proposed hybrid model is beneficial for improving the performance of PV power forecasting compared with the benchmark methods.

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Section: Figurementioning

confidence: 99%

A hybrid deep learning model with error correction for photovoltaic power forecasting

Zhang

et al. 2022

Front. Energy Res.

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“…However, in operation, planetary gearboxes are prone to failure and have high maintenance costs under dynamic load and frequently changing operating conditions [3]. Therefore, accurate gearbox fault diagnosis is of great significance to improve the safety, reliability and economy of WTs [4].…”

Section: Introductionmentioning

confidence: 99%

A dynamic-model-based fault diagnosis method for a wind turbine planetary gearbox using a deep learning network

Zhao

2022

Prot Control Mod Power Syst

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The planetary gearbox is a critical part of wind turbines, and has great significance for their safety and reliability. Intelligent fault diagnosis methods for these gearboxes have made some achievements based on the availability of large quantities of labeled data. However, the data collected from the diagnosed devices are always unlabeled, and the acquisition of fault data from real gearboxes is time-consuming and laborious. As some gearbox faults can be conveniently simulated by a relatively precise dynamic model, the data from dynamic simulation containing some features are related to those from the actual machines. As a potential tool, transfer learning adapts a network trained in a source domain to its application in a target domain. Therefore, a novel fault diagnosis method combining transfer learning with dynamic model is proposed to identify the health conditions of planetary gearboxes. In the method, a modified lumped-parameter dynamic model of a planetary gear train is established to simulate the resultant vibration signal, while an optimized deep transfer learning network based on a one-dimensional convolutional neural network is built to extract domain-invariant features from different domains to achieve fault classification. Various groups of transfer diagnosis experiments of planetary gearboxes are carried out, and the experimental results demonstrate the effectiveness and the reliability of both the dynamic model and the proposed method.

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“…Therefore, the level of wind power penetration into modern power grids has correspondingly increased in the past decades (Anjaiah et al, 2022). However, the randomness, volatility, and reverse load characteristics of wind power will definitely aggravate the power supply-consumption imbalance, thus bringing great challenges to the economic operation, stability, and security of the electric energy system (Desai and Makwana, 2021). Wind speed forecasting affects not only the reserve capacity and maintenance plan of the energy system but also energy market transactions and charge and discharge plans of the storage stations (Wang et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Prediction Intervals of Wind Speed Based on Explainable Neural Network

Huang

Hong²,

Wang³

2022

Front. Energy Res.

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With the rapid growth of wind power penetration into modern power grids, wind speed forecasting plays an increasingly significant role in the planning and operation of electric power and energy systems. However, the existing wind speed forecasting methods are modeled as black boxes, which are very complicated and cannot be written down explicitly due to the complex fluctuation characteristics of wind speed series. To this end, this study proposes a novel direct method based on an explainable neural network (xNN) for deterministic and probabilistic wind speed forecasting. It can theoretically extract the nonlinear mapping features in wind speed, thereby providing a clear explanation of the relationship between the input and the output of the forecasting model. Then, the uncertainties in wind speed are statistically synthesized via the kernel density estimation method. Finally, we use wind speed data from real wind farms in Belgium to verify the feasibility and effectiveness of the proposed method. The simulation results demonstrate that it is not only able to accurately extract the non-stationary feature in the wind speed series but also superior to other benchmark algorithms in prediction accuracy. Therefore, the proposed method has a high potential for practical applications in real electric power and energy systems.

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A novel out of step relaying algorithm based on wavelet transform and a deep learning machine model

Cited by 18 publications

References 16 publications

A hybrid deep learning model with error correction for photovoltaic power forecasting

A hybrid deep learning model with error correction for photovoltaic power forecasting

A dynamic-model-based fault diagnosis method for a wind turbine planetary gearbox using a deep learning network

Probabilistic Prediction Intervals of Wind Speed Based on Explainable Neural Network

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