Short-term interval prediction of PV power based on quantile regression-stacking model and tree-structured parzen estimator optimization algorithm

In recent years, the photovoltaic (PV) industry has grown rapidly and the scale of grid-connected PV continues to increase. The random and fluctuating nature of PV power output is beginning to threaten the safe and stable operation of the power system. PV power interval forecasting can provide more comprehensive information to power system decision makers and help to achieve risk control and risk decision. PV power interval forecasting is of great importance to power systems. Therefore, in this study, a Quantile Regression-Stacking (QR-Stacking) model is proposed to implement PV power interval prediction. This integrated model uses three models, extreme gradient boosting (Xgboost), light gradient boosting machine (LightGBM) and categorical boosting (CatBoost), as the base learners and Quantile Regression-Long and Short Term Memory (QR-LSTM) model as the meta-learner. It is worth noting that in order to determine the hyperparameters of the three base learners and one meta-learner, the optimal hyperparameters of the model are searched using a Tree-structured Parzen Estimator (TPE) optimization algorithm based on Bayesian ideas. Meanwhile, the correlation coefficient is applied to determine the input characteristics of the model. Finally, the validity of the proposed model is verified using the actual data of a PV plant in China

Kinematics modeling of a two DOFs continuum manipulator with uniform notches

Continuum manipulators have been widely adopted for single-port laparoscopy (SPL). A novel continuum manipulator with uniform notches which has two degrees of freedom (DOFs) is presented in this paper. The arrangement of flexible beams makes it own a higher load capacity. Its kinematic model is coupled with the mechanical model. The comprehensive elliptic integral solution (CEIS) is more practical in the actual deformation of the flexible beams. Based on that method, kinematics modeling is established from the driven space to the Cartesian space. The friction coefficient is an important factor which can affect the kinematic modeling. Therefore, an experimental platform is established to obtain the friction coefficient. The kinematic modeling is verified through the prototype. Experimental results show that the model has high precision

Multiscale Adaptive Fusion Network for Hyperspectral Image Denoising

Removing the noise and improving the visual quality of hyperspectral images (HSIs) is challenging in academia and industry. Great efforts have been made to leverage local, global, or spectral context information for HSI denoising. However, existing methods still have limitations in feature interaction exploitation among multiple scales and rich spectral structure preservation. In view of this, we propose a novel solution to investigate the HSI denoising using a multiscale adaptive fusion network (MAFNet), which can learn the complex nonlinear mapping between clean and noisy HSI. Two key components contribute to improving the HSI denoising: A progressively multiscale information aggregation network and a coattention fusion module. Specifically, we first generate a set of multiscale images and feed them into a coarse-fusion network to exploit the contextual texture correlation. Thereafter, a fine fusion network is followed to exchange the information across the parallel multiscale subnetworks. Furthermore, we design a coattention fusion module to adaptively emphasize informative features from different scales, and thereby enhance the discriminative learning capability for denoising. Extensive experiments on synthetic and real HSI datasets demonstrate that the proposed MAFNet has achieved a better denoising performance than other state-of-the-art techniques

Performance Comparison of Bayesian Deep Learning Model and Traditional Bayesian Neural Network in Short-Term PV Interval Prediction

The intermittence and fluctuation of renewable energy bring significant uncertainty to the power system, which enormously increases the operational risks of the power system. The development of efficient interval prediction models can provide data support for decision making and help improve the economy and reliability of energy interconnection operation. The performance of Bayesian deep learning models and Bayesian shallow neural networks in short-term interval prediction of photovoltaic power is compared in this study. Specifically, an LSTM Approximate Bayesian Neural Network model (ABNN-I) is built on the basis of the deep learning and Monte Carlo Dropout method. Meanwhile, a Feedforward Bayesian Neural Network (ABNN-II) model is introduced by Feedforward Neural Network and the Markov Chain Monte Carlo method. To better compare and verify the interval prediction capability of the ABNN models, a novel clustering method with three-dimensional features which include the number of peaks and valleys, the average power value, and the non-stationary measurement coefficient is proposed for generating sunny and non-sunny clustering sets, respectively. Results show that the ABNN-I model has an excellent performance in the field of photovoltaic short-term interval forecasting. At a 95% confidence level, the interval coverage from ABNN-I to ABNN-II can be increased by up to 3.1% and the average width of the interval can be reduced by 56%. Therefore, with the help of the high computational capacity of deep learning and the inherent ability to quantify uncertainty of the interval forecast from Bayesian methods, this research provides high-quality interval prediction results for photovoltaic power prediction and solves the problem of difficult modeling for over-fitting that exists in the training process, especially on the non-sunny clustering sets

Design of 3D force perception system of surgical robots based on Fiber Bragg Grating

Surgical robots have been widely researched due to their features of accurate positioning, no jitter, high precision, and low error rate under certain tasks. However, it is generally relatively slow to the feedback generated by force, deformation, or sudden and arbitrary impact during operation. Besides, the feedback is always provided through a vision that cannot meet the actual operation requirements for doctors. The customized design and integration of force perception functions on different robots have become one of the research hotspots of surgical robotics-worldwide recently. A force perception sensor for surgical robots based on Fiber Bragg grating (FBG) is proposed in this paper. It can be used to measure three-dimensional force. The experimental parameters are utilized to calibrate the model through the least square method. A four DoFs experimental platform is constructed. The system errors of the sensor involved are evaluated. The effectiveness of the proposed algorithm can be proved by the experimental result