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RISS 인기검색어
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- 저자 : Dou Zhenhai (검색결과 4 건)
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Dong Jun,Dou Zhenhai,Si Shuqian,Wang Zichen,Liu Lianxin 대한전기학회 2022 Journal of Electrical Engineering & Technology Vol.17 No.1
In order to reasonably allocate the capacity of distributed generation and realize the goal of stable, economic and clean operation of the system, a multi-objective optimization model with investment cost, environmental protection and power supply quality as indicators has been established, and the multi-objective sparrow search algorithm is used to optimize the solution. Although the multi-objective search algorithm is more effi cient than the traditional single objective algorithm, it is easy to fall into local optimum. To this end, the niche optimization technology is used to improve the optimization eff ect of multi-objective sparrow search algorithm, and the Levy fl ight strategy is introduced to enhance the ability of multi-objective sparrow search algorithm to jump out of local optimum. The calculation example uses the traditional multi-object search algorithm and the niche multi-objective sparrow search algorithm with levy disturbance to solve the proposed model. The simulation results verify the eff ectiveness of the multi-objective sparrow search algorithm improved by levy disturbance and niche optimization technology
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Grid Scheduling Strategy Considering Electric Vehicles Participating in Multi-microgrid Interaction
Yu Zexu,Dou Zhenhai,Zhao Ye,Xie Ruishuo,Qiao Mengmeng,Wang Yuanyuan,Liu Lianxin 대한전기학회 2023 Journal of Electrical Engineering & Technology Vol.18 No.3
With the expansion of the scale of power grid and the increase of the number of microgrids, the energy interaction between microgrids using contact lines will greatly increase the computation of the superior dispatching center. Therefore, the contact lines between microgrids are cancelled, so that the microgrids can realize energy interaction through the distribution network. However, this method may lead to overload of distribution network connections while reducing the computation. Due to its good transfer characteristics, the addition of electric vehicles (EVs) can alleviate the contact line pressure and realize the load transfer of microgrids. On this basis, a grid dispatching model based on the participation of EVs in microgrid interaction is proposed. Contact lines between microgrids are replaced by EVs and 100% transmission through the distribution network. The grid load is optimized with the goal of minimizing total cost, maximizing renewable energy utilization, and maximizing profit of each integrator. In the process of model optimization, aiming at the problem that the speed factor in the particle swarm optimization algorithm cannot take into account the optimal direction and the optimal step size, the adaptive time factor is added to establish a two-layer improved particle swarm optimization algorithm, which realizes the cooperative optimization of load and electricity price. The simulation results show that the total cost and underutilization of renewable energy of IEEE33-node system are reduced by 13.79% and 67.85%, compared with the traditional interaction mode, while they are reduced by 0.425% and 6.11% in IEEE43-node system.
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Residual current fault type recognition based on S3VM and KNN cooperative training
Zhang, Xiangke,Wang, Yajing,Dou, Zhenhai,Wang, Wei,Bai, Yunpeng The Korean Institute of Power Electronics 2022 JOURNAL OF POWER ELECTRONICS Vol.22 No.11
It is difficult to detect the residual current of specific fault types in low-voltage distribution networks, which results in few labeled residual current samples. Thus, it is difficult to recognize the fault types of residual current. To solve this problem, a cooperative training classification model based on an improved squirrel search algorithm (ISSA) for a semi-supervised support vector machine (S3VM) and the k-nearest neighbor (KNN) is proposed (ISSA-S3VM-KNN). First, the residual current is decomposed into k intrinsic mode functions (IMFs) by variational mode decomposition (VMD), and the characteristic parameters of the IMFs are extracted to obtain a characteristic dataset for establishing a classification model. Second, to solve the problem where it is difficult to the select parameters (such as the penalty factors, slack variables and kernel function) of a S3VM, an ISSA parameter optimization method is proposed to self-adaptively select the optimal combination of parameters for the S3VM. Finally, the KNN is used to verify the classification results of an ISSA-S3VM through cooperative training, which further improves the classification accuracy of the S3VM for unlabeled residual current samples. Classification results of measured and simulation data show that the classification accuracy of the ISSA-S3VM-KNN is higher than that of the SVM-BPNN, WE-AE-BPNN, and PSO-SVM. The ISSA-S3VM-KNN provides a certain theoretical basis for achieving fast and accurate residual current fault type recognition.
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Yu Mei,Yajing Wang,Xiangke Zhang,Shiqi Liu,Qinqin Wei,Zhenhai Dou 전력전자학회 2022 JOURNAL OF POWER ELECTRONICS Vol.22 No.8
Given the low accuracy of power quality disturbance (PQD) detection, a PQD detection method based on the wavelet packet transform (WPT) and improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is proposed in this paper. First, the wavelet packet transform is used to preprocess the signal to suppress noise interference. Then, ICEEMDAN technology is adopted to calculate the local mean value by adding adaptive noise. In addition, different intrinsic mode functions (IMFs) are obtained through residual subtraction. Furthermore, the effective IMFs are calculated by the permutation entropy method to reduce false modal components and to suppress residual noise. Finally, a Hilbert transform (HT) is performed to extract the detection signal parameters. The obtained results demonstrate that this method can improve the detection accuracy and PQD speed, which results in a strong anti-noise capability.
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