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Su, Hongsheng,Zhang, Zezhong The Korean Institute of Electrical Engineers 2018 Journal of Electrical Engineering & Technology Vol.13 No.2
As distributed generation (DG) is connected to grid, there is new node-type occurring in distribution network. An efficient algorithm is proposed in this paper to calculate power flow for weakly meshed distribution network with DGs in different load models. The algorithm respectively establishes mathematical models focusing on the wind power, photovoltaic cell, fuel cell, and gas turbine, wherein the different DGs are respectively equivalent to PQ, PI, PQ (V) and PV node-type. When dealing with PV node, the algorithm adopts reactive power compensation device to correct power, and the reactive power allocation principle is proposed to determine reactive power initial value to improve convergence of the algorithm. In addition, when dealing with the weakly meshed network, the proposed algorithm, which builds path matrix based on loop-analysis and establishes incident matrix of node voltage and injection current, possesses good convergence and strong ability to process the loops. The simulation results in IEEE33 and PG&G69 node distribution networks show that with increase of the number of loops, the algorithm's iteration times will decrease, and its convergence performance is stronger. Clearly, it can be effectively used to solve the problem of power flow calculation for weakly meshed distribution network containing different DGs.
A Cooperative Coevolution Algorithm Based on ABC and NMSM
Hongsheng Su,Kaile Yin,Zihan Wu 보안공학연구지원센터 2016 International Journal of Hybrid Information Techno Vol.9 No.12
Considering that the existing artificial bee colony (ABC) algorithm can not give simultaneously attention to evolution speed and solution quality, an improved ABC algorithm is proposed based on nelder-mead simplex method (NMSM) in this paper, and defined ad NMSM-ABC. In the process of iteration, the algorithm periodically passes the best individual vertex from NMSM operator into ABC, or migrates the optimal food source information from ABC to NMSM can get away from local minimum with aid of ABC. Hence, the proposed algorithm can realize cooperative co-evolution of the two so that the desired properties are obtained. In addition, the sensitivity analysis of the key parameter of NMSM-ABC is also conducted and the best value is suggested. Finally, Numerical experiments and comparisons on 6 benchmark functions are conducted with other ABC algorithms, and the results indicate that the proposed algorithm effectively overcomes the local minimum, and dramatically enhances the global searching ability and convergence speed, and is a good cooperative co-evolution algorithm.
Probabilistic Load Flow Analysis Based on Sparse Polynomial Chaotic Expansion
Hongsheng Su,Xiaoyang Dong,Xiaoying Yu 대한전기학회 2020 Journal of Electrical Engineering & Technology Vol.15 No.2
To aim at the defciencies of the traditional second-order polynomial chaotic expansion with more input variables and larger calculation quantities applied in power systems probabilistic load fow (PLF) calculation, an improved PLF method is proposed based on sparse polynomial chaotic expansion theory in this paper. In this method, to obtain the sparse expression of the polynomial chaotic expansion (PCE) and reduce calculation quantity, the quadratic cross terms in the second-order PCE are eliminated by Sobol sensitivity analysis. And then, Nataf transformation is used to control the correlation among nonnormal input variables. Through comparison on computational efciency between probabilistic collocation point method and random sampling method in the sparse polynomial chaotic expansion, the probabilistic collocation point method is selected to suit samples allocation. By applying in IEEE-9, IEEE-30, and IEEE-118 standard test systems, respectively, the results show that the proposed method can dramatically reduce the deterministic PLF calculations quantity compared with the traditional stochastic response surface method (SRSM), and possesses the same accuracy as Monte Carlo method, and is an efective solving method for some high-dimensional input variable systems.
Hongsheng Su,Zezhong Zhang 대한전기학회 2018 Journal of Electrical Engineering & Technology Vol.13 No.2
As distributed generation (DG) is connected to grid, there is new node-type occurring in distribution network. An efficient algorithm is proposed in this paper to calculate power flow for weakly meshed distribution network with DGs in different load models. The algorithm respectively establishes mathematical models focusing on the wind power, photovoltaic cell, fuel cell, and gas turbine, wherein the different DGs are respectively equivalent to PQ, PI, PQ (V) and PV node-type. When dealing with PV node, the algorithm adopts reactive power compensation device to correct power, and the reactive power allocation principle is proposed to determine reactive power initial value to improve convergence of the algorithm. In addition, when dealing with the weakly meshed network, the proposed algorithm, which builds path matrix based on loop-analysis and establishes incident matrix of node voltage and injection current, possesses good convergence and strong ability to process the loops. The simulation results in IEEE33 and PG&G69 node distribution networks show that with increase of the number of loops, the algorithm’s iteration times will decrease, and its convergence performance is stronger. Clearly, it can be effectively used to solve the problem of power flow calculation for weakly meshed distribution network containing different DGs.
Hongsheng Su,Yan Yan 보안공학연구지원센터 2015 International Journal of Security and Its Applicat Vol.9 No.8
Current probabilistic power flow calculation methods mostly consider the uncertainties of loads and the random failures of generators without thinking about the changing of the grids structure. Hence, this paper proposes a new probabilistic power flow calculation method comprehensively considering the influences of the uncertainties of wind farms, loads, generators, and grids structure on power flow calculation. The linear relationships are deduced between the nodes injection power and the branch active power as the circuits being at failures, and the cumulative probability distribution of each branches power flow is calculated by using semi-invariant and Gram-Charlier series expansion, and such that the complicated convolution operation is avoided. Combining compensation method and the conditional probability theory to deal with network structure changes of random factors, the paper establishes a probability flow calculation model comprehensively considering diverse factors such as random outputting power of the wind farms, random changes of the loads, and random failures of the generators, and the random variation of the grid structures and so on, the probability distribution function and probability density function of each branch can be quickly obtained by the model. Through the analysis on IEEE 14-node system, the uncertainty of grid structure has a remarkable effect on the probability distribution of the quantity to be solved. Hence, applying the proposed method can provide planners with more accurate and comprehensive information.