Optimization of Minimum Negative Current BCM Synchronous Buck Converter

Non-isolated DC-DC converters are widely used in renewable energy applications, such as the hybrid energy storage system (HESS), charging and discharging system of batteries and supercapacitors and the photovoltaic power generation. With the advantages of achieving zero-voltage-switching (ZVS), high efficiency and power density, low cost, and fast dynamic response, the converters operating in boundary current mode (BCM) have caught researchers' eyes recently. Taking the synchronous buck converters as an example, this paper briefly introduces the working principle and advantages of BCM converter realize soft switching. Firstly, it is pointed out that the phenomenon of circulating energy exists in the BCM converter. Then, the relationship between circulating energy and negative current is analyzed, and an optimal control strategy of negative current minimization is proposed to reduce the circulating energy. In the condition of realizing ZVS, negative current minimization not only improves the efficiency of the converter, but also reduces the ripple of inductor current to a certain extent. Finally, the experimental platform of 100 W synchronous buck converter is built. Experimental results validate that the optimal control of minimum negative current has good effect on improving efficiency of converter and reducing the ripple of inductor current

Health Status Assessment for Wind Turbine with Recurrent Neural Networks

In order to improve the safety, efficiency, and reliability in large scale wind turbines, a great deal of statistical and machine-learning models for wind turbine health monitoring system (WTHMS) are proposed based on SCADA variables. The data-driven WTHMS have been performed widely with the attentions on predicting the failures of the wind turbine or primary components. However, the health status of wind turbine often degrades gradually rather than suddenly. Thus, the SCADA variables change continuously to the occurrence of certain faults. Inspired by the ability of recurrent neural network (RNN) in redefining the raw sensory data, we introduce a hybrid methodology that combines the analysis of variance for each sequential SCADA variable with RNN to assess the health status of wind turbine. First, each original sequence is split by different variance ranges into several categories to improve the generalized ability of the RNN. Then, the long short-term memory (LSTM) is procured on the normal running sequence to learn the gradually changing situations. Finally, a weighted assessment method incorporating the health of primary components is applied to judge the health level of the wind turbine. Experiments on real-world datasets from two wind turbines demonstrate the effectiveness and generalization of the proposed model

Multistep Wind Speed and Wind Power Prediction Based on a Predictive Deep Belief Network and an Optimized Random Forest

A variety of supervised learning methods using numerical weather prediction (NWP) data have been exploited for short-term wind power forecasting (WPF). However, the NWP data may not be available enough due to its uncertainties on initial atmospheric conditions. Thus, this study proposes a novel hybrid intelligent method to improve existing forecasting models such as random forest (RF) and artificial neural networks, for higher accuracy. First, the proposed method develops the predictive deep belief network (DBN) to perform short-term wind speed prediction (WSP). Then, the WSP data are transformed into supplementary input features in the prediction process of WPF. Second, owing to its ensemble learning and parallelization, the random forest is used as supervised forecasting model. In addition, a data driven dimension reduction procedure and a weighted voting method are utilized to optimize the random forest algorithm in the training process and the prediction process, respectively. The increasing number of training samples would cause the overfitting problem. Therefore, the k-fold cross validation (CV) technique is adopted to address this issue. Numerical experiments are performed at 15-min, 30-min, 45-min, and 24-h to indicate the superiority and signal advantages compared with existing methods in terms of forecasting accuracy and scalability

Variable speed control of wind turbines based on the quasi-continuous high-order sliding mode method

The characteristics of wind turbine systems such as nonlinearity, uncertainty and strong coupling, as well as external interference, present great challenges in wind turbine controller design. In this paper, a quasi-continuous high-order sliding mode method is used to design controllers due to its strong robustness to external disturbances, unmodeled dynamics and parameter uncertainties. It can also effectively suppress the chattering toward which the traditional sliding mode control method is ineffective. In this study, the strategy of designing speed controllers based on the quasi-continuous high order sliding mode method is proposed to ensure the wind turbine works well in different wind modes. First, the plant model of the variable speed control system is built as a linearized model; and then a second order speed controller is designed for the model and its stability is proved. Finally, the designed controller is applied to wind turbine pitch control. Based on the simulation results from a simulation of 1200 s which contains almost all wind speed modes, it is shown that the pitch angle can be rapidly adjusted according to wind speed change by the designed controller. Hence, the output power is maintained at the rated value corresponding to the wind speed. In addition, the robustness of the system is verified. Meanwhile, the chattering is found to be effectively suppressed

Study on preprocessing of surface defect images of cold steel strip

The image preprocessing is an important part in the field of digital image processing, and it’s also the premise for the image detection of cold steel strip surface defects. The factors including the complicated on-site environment and the distortion of the optical system will cause image degradation, which will directly affects the feature extraction and classification of the images. Aiming at these problems, a method combining the adaptive median filter and homomorphic filter is proposed to preprocess the image. The adaptive median filter is effective for image denoising, and the Gaussian homomorphic filter can steadily remove the nonuniform illumination of images. Finally, the original and preprocessed images and their features are analyzed and compared. The results show that this method can improve the image quality effectively

Application of Parameter Optimized Variational Mode Decomposition Method in Fault Feature Extraction of Rolling Bearing

The decomposition effect of variational mode decomposition (VMD) mainly depends on the choice of decomposition number K and penalty factor α. For the selection of two parameters, the empirical method and single objective optimization method are usually used, but the aforementioned methods often have limitations and cannot achieve the optimal effects. Therefore, a multi-objective multi-island genetic algorithm (MIGA) is proposed to optimize the parameters of VMD and apply it to feature extraction of bearing fault. First, the envelope entropy (Ee) can reflect the sparsity of the signal, and Renyi entropy (Re) can reflect the energy aggregation degree of the time-frequency distribution of the signal. Therefore, Ee and Re are selected as fitness functions, and the optimal solution of VMD parameters is obtained by the MIGA algorithm. Second, the improved VMD algorithm is used to decompose the bearing fault signal, and then two intrinsic mode functions (IMF) with the most fault information are selected by improved kurtosis and Holder coefficient for reconstruction. Finally, the envelope spectrum of the reconstructed signal is analyzed. The analysis of comparative experiments shows that the feature extraction method can extract bearing fault features more accurately, and the fault diagnosis model based on this method has higher accuracy