A review of wind speed and wind power forecasting with deep neural networks

Skin cancer is a type of disease in which malignant cells are formed in skin tissues. However, skin cancer is a dangerous disease, and an early detection of this disease helps the therapists to cure this disease. In the present research, an automatic computer-aided method is presented for the early diagnosis of skin cancer. After image noise reduction based on median filter in the first stage, a new image segmentation based on the convolutional neural network optimized by satin bowerbird optimization (SBO) has been adopted and its efficiency has been indicated by the confusion matrix. Then, feature extraction is performed to extract the useful information from the segmented image. An optimized feature selection based on the SBO algorithm is also applied to prune excessive information. Finally, a support vector machine classifier is used to categorize the processed image into the following two groups: cancerous and healthy cases. Simulations have been performed of the American Cancer Society database, and the results have been compared with ten different methods from the literature to investigate the performance of the system in terms of accuracy, sensitivity, negative predictive value, specificity, and positive predictive value.

https://www.degruyter.com/document/doi/10.1515/med-2020-0131/pdf

Computer-aided diagnosis of skin cancer based on soft computing techniques

Skin cancer diagnosis based on optimized convolutional neural network

This paper presents a new prediction model based on empirical mode decomposition, feature selection and hybrid forecast engine. The whole structure of proposed model is based on nonstationarity and non-convex nature of wind power signal. The hybrid forecast engine consists of three main stages as; empirical mode decomposition, an intelligent algorithm and back propagation neural network. All parameters of proposed neural network will be optimized by intelligent algorithm. Effectiveness of the proposed model is tested with real-world hourly data of wind farms in Spain and Texas. In order to demonstrate the validity of the proposed model, it is compared with several other wind speed and power forecast techniques. Obtained results confirm the validity of the developed approach.

Application of hybrid forecast engine based intelligent algorithm and feature selection for wind signal prediction

A data-driven strategy for short-term electric load forecasting using dynamic mode decomposition model

A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting

The invention discloses a distribution network layering load forecasting method based on topology. A distribution network is layered according to a topology connection relation, forecasting is conducted on the layers respectively, a total forecasting result is obtained by summing in an optimizing mode, summarizing load data of a 10 kV outgoing line switch are read, and contrastive analysis is conducted on the data and the forecasting result. Forecasting accuracy is improved obviously, an intelligent distribution network system is utilized effectively, deep mining based on big data is achieved, the immunity to the data is improved, and lean management of the distribution network is achieved easily.

Distribution network layering load forecasting method based on topology

The selection of distribution network faults is of great significance to accurately identify the fault location, quickly restore power and improve the reliability of power supply. This paper mainly studies the fault phase selection method of distribution network based on wavelet singular entropy and deep belief network (DBN). Firstly, the basic principles of wavelet singular entropy and DBN are analyzed, and on this basis, the DBN model of distribution network fault phase selection is proposed. Firstly, the transient fault current data of the distribution network is processed to obtain the wavelet singular entropy of the three phases, which is used as the input of the fault phase selection model; then the DBN network is improved, and an artificial neural network (ANN) is introduced to make it a fault Select the phase classifier, and specify the output label; finally, use Simulink to build a simulation model of the IEEE33 node distribution network system, obtain a large amount of data of various fault types, generate a training sample library and a test sample library, and analyze the neural network. The adjustment of the structure and the training of the parameters complete the construction of the DBN model for the fault phase selection of the distribution network.

Fault phase selection method of distribution network based on wavelet singular entropy and DBN

In view of the limitation of the method of line selection based on single fault characteristic in distribution network, this paper proposes a distribution ground fault line selection method based on time-frequency domain energy matrix. Firstly, the zero sequence current transient signal detected by the intelligent pole switch when the fault occurs is extracted, and the fault panoramic information matrix containing time frequency energy multi-dimensional features is constructed through LCD decomposition. Then the similarity of panoramic information matrix of adjacent detection points is calculated, and the reliable line selection of the fault line is realized according to the similarity. The simulation results show that this method comprehensively utilizes the time-frequency domain characteristic information of transient signals, and is less affected by high resistance fault conditions, distribution network structure and other factors. It can effectively improve the accuracy of distribution network fault line selection, has high adaptability and reliability, and has simple algorithm principle and fast calculation speed.

Grounding Fault Line Selection Method of Distribution Network Based on Time-Frequency Domain Energy Matrix

With the development of the new power system, new energy, energy storage, electric vehicles, etc. are connected to the distribution network through power electronic devices on a large scale, making the distribution network present the "double high" feature of high proportion of new energy and high proportion of power electronics. The access of distributed photovoltaic has changed the characteristics of single power supply and unidirectional power flow in the distribution network. Thus, the voltage and current transient characteristics of distribution network change under different fault types. In this paper, the influence of distributed photovoltaic access on the fault transient characteristics of distribution network is analyzed. To verify the analysis, the simulation model is built based RTDS hardware-in-kind simulation platform.

Jiran Zhu

Papers

A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed-loop forecasting

Distribution network layering load forecasting method based on topology

Fault phase selection method of distribution network based on wavelet singular entropy and DBN

Grounding Fault Line Selection Method of Distribution Network Based on Time-Frequency Domain Energy Matrix

Transient Characteristics Analysis of Distribution Network with Distributed PV