Flower Pollination Heuristics for Nonlinear Active Noise Control Systems

In this paper,a novel design of the flower pollination algorithm is presented for model identification problems in nonlinear active noise control systems.The recently introduced flower pollination based heuristics is implemented to minimize the mean squared error based merit/cost function representing the scenarios of active noise control system with linear/nonlinear and primary/secondary paths based on the sinusoidal signal,random and complex random signals as noise interferences.The flower pollination heuristics based active noise controllers are formulated through exploitation of nonlinear filtering with Volterra series.The comparative study on statistical observations in terms of accuracy,convergence and complexity measures demonstrates that the proposed meta-heuristic of flower pollination algorithm is reliable,accurate,stable as well as robust for active noise control system.The accuracy of the proposed nature inspired computing of flower pollination is in good agreement with the state of the art counterpart solvers based on variants of genetic algorithms,particle swarm optimization,backtracking search optimization algorithm,fireworks optimization algorithm along with their memetic combination with local search methodologies.Moreover,the central tendency and variation based statistical indices further validate the consistency and reliability of the proposed scheme mimic the mathematical model for the process of flower pollination systems.

A hybrid data driven framework considering feature extraction for battery state of health estimation and remaining useful life prediction

Battery life prediction is of great significance to the safe operation,and reduces the maintenance costs.This paper proposes a hybrid framework considering feature extraction to achieve more accurate and stable life prediction performance of the battery.By feature extraction,eight features are obtained to fed into the life prediction model.The hybrid framework combines variational mode decomposition,the multi-kernel support vector regression model and the improved sparrow search algorithm to solve the problem of data backward,uneven distribution of high-dimensional feature space and the local escape ability,respectively.Better parameters of the estimation model are obtained by introducing the elite chaotic opposition-learning strategy and adaptive weights to optimize the sparrow search algorithm.The algorithm can improve the local escape ability and convergence performance and find the global optimum.The comparison is conducted by dataset from National Aeronautics and Space Administration which shows that the proposed framework has a more accurate and stable prediction performance.Compared with other algorithms,the SOH estimation accuracy of the proposed algorithm is improved by 0.16%–1.67%.With the advance of the start point,the RUL prediction accuracy of the proposed algorithm does not change much.

A novel insulator defect detection scheme based on Deep Convolutional Auto-Encoder for small negative samples

This paper presents a novel insulator defect detection scheme based on Deep Convolutional Auto-Encoder(DCAE)for small negative samples.The proposed DCAE scheme combines the advantages of supervised learning and unsupervised learning.In order to reduce the high cost of training Deep Neural Networks,this paper pre-trained the Convolutional Neural Networks(CNN)through open labelled datasets.Through transferring learning,the encoder part of the traditional Convolutional Auto-Encoder was replaced by the first three layers of the CNN,and a small number of defect samples were used to fine-tune the parameters.A threshold discrimination scheme was designed to evaluate the model detection,realising the self-explosion detection of insulator by judging the residual result and abnormal score.The experimental results show that compared with the existing insulator self-explosion detection schemes,the proposed scheme can reduce the model training time by up to 40%,and the recognition accuracy can reach 97%.Moreover,this model does not need a large number of insulator labelled data and is especially suitable for small negative sample application.

Multi-period Two-stage Robust Optimization of Radial Distribution System with Cables Considering Time-of-use Price

In the existing multi-period robust optimization methods for the optimal power flow in radial distribution systems,the capability of distributed generators(DGs)to regulate the reactive power,the operation costs of the regulation equipment,and the current of the shunt capacitor of the cables are not considered.In this paper,a multi-period two-stage robust scheduling strategy that aims to minimize the total cost of the power supply is developed.This strategy considers the time-ofuse price,the capability of the DGs to regulate the active and reactive power,the action costs of the regulation equipment,and the current of the shunt capacitors of the cables in a radial distribution system.Furthermore,the numbers of variables and constraints in the first-stage model remain constant during the iteration to enhance the computation efficiency.To solve the second-stage model,only the model of each period needs to be solved.Then,their objective values are accumulated,revealing that the computation rate using the proposed method is much higher than that of existing methods.The effectiveness of the proposed method is validated by actual 4-bus,IEEE 33-bus,and PG 69-bus distribution systems.

计算流体力学研究B型主动脉夹层中4D Flow MRI的应用进展

主动脉夹层是危及生命的心血管急症之一,其复杂的血流动力学特征对疾病发生和发展具有重要意义。计算流体力学方法问世后,许多学者已将其用于分析人体血管血流动力学。现有基于计算流体力学(computational fluid dynamics,CFD)的B型主动脉夹层(type B aortic dissection,TBAD)血流动力学研究不足在于,计算结果的准确性和临床意义的解释有待提高。四维流动磁共振(4D Flow MRI)与CFD相结合为解决这些问题提供了新思路。文章就CFD研究TBAD中4D Flow MRI应用进展做一综述。

Primal dual interior point dynamic programming for coordinated charging of electric vehicles

Coordinated charging of electric vehicles(EVs)is critical to provide safe and cost effective operation of distribution systems where household single phase charging of EV could contribute to imbalance of the distribution system.To date,reported researches on optimization methods for coordinated charging aiming at minimizing power losses have the disadvantages of low calculation efficiency when applied to large systems or have not taken the voltage constraints into account.The phase component and polar coordinates power flow equations of an unbalanced distribution system are derived.Primal dual interior point dynamic programming is introduced for coordinated charging of EVs to minimize distribution system losses where charging demand,voltage and current constraints have been taken into account.The proposed optimization is evaluated using an actual 423-bus case as the test system.Results are promisingwith the proposed method having good convergence under time-efficient calculations while providing optimization of power losses,lower load variance,and improvement of voltage profile versus uncoordinated scenarios.