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Asia Power and Energy Engineering Conference (APEEC)

作者： Zheng Jie-yun Zhang Lin-yao Lin Chang-zhui Ning Yue Hu Zhi-jian Lin Wei-wei Yi Chen-ying Chen Zhi Liu Xin State Grid Fujian Elect Power Co Ltd Econ & Technol Res Inst Fuzhou Fujian Peoples R China Wuhan Univ Sch Elect Engn & Automat Wuhan Hubei Peoples R China Fujian Yirong Informat Technol Co Ltd Fuzhou Fujian Peoples R China

ISBN: (纸本)9781728115900

This paper presents an hierarchical planning model for Active Distribution Network (ADN) based on game theory, considering the coordination multi-agent interest in "source-network-load". The upper layer of the model considers the decisions of each agents of "Source-Network-Load"(S-N-L), aiming at achieving the equilibrium in the game mode for each interest body. The lower layer considers the active management strategies such as the adjustment of Distributed generation (DG) reduction and On-load Tap Changer (OLTC), aiming at minimizing the expected value of annual load cutting of DG. The iterative search algorithm is used to search the multi-agent equilibrium solution in the upper layer, and the improved particle swarm optimization algorithm is applied in all the decision optimization process of the model. The simulation analysis of the improved IEEE 33-node example showed the feasibility of the model and that the difference of the action order of the game at has an impact on the game process and results which can give suggestions for the ADN planning problem of multi-interests coordination in the open environment of electricity market.

关键词： active distribution network planning source-net-load game theory hierarchical planning iterative search method improved particle swarm optimization algorithm

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Distribution Network Planning Study with Distributed Generation Based on Steiner Tree Model 6

Distribution Network Planning Study with Distributed Generat...

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IEEE PES Asia-Pacific Power and Energy Engineering Conference (IEEE PES APPEEC)

作者： Han, Xiangyu Liu, Jun Liu, Dichen Liao, Qingfen Hu, Jingzhu Yang, Yunlei Wuhan Univ Sch Elect Engn Wuhan Peoples R China Taizhou Power Supply Co Taizhou Peoples R China

ISBN: (纸本)9781479975372

The traditional methods used for distribution network planning are challenged because of the access of the stochastic distributed generation. A new distribution network planning model is proposed by converting the distribution network planning with distributed generation into solving the Steiner tree problem. Moreover, in order to solve the problem that the time of calculation multiplied along with the increase in the amount of Steiner nodes, an improved particle swarm optimization algorithm is proposed to solve the Steiner tree Non-deterministic Polynomial problem. In this algorithm the problem of premature convergence is solved by applying a small disturbance to the global optimum and the searching results are more reliable and accurate by improve the search ability for global optimum. Typical results of the proposed algorithm and the traditional algorithm are further illustrated and compared through an actual 54 nodes system to prove the validity of the methods in this paper.

关键词： distribution network planning distributed generation Steiner tree improved particle swarm optimization algorithm

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Optimal Design of Passive Harmonic Filter with High Power Factor 3

Optimal Design of Passive Harmonic Filter with High Power Fa...

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3rd Asia Energy and Electrical Engineering Symposium (AEEES)

作者： Zhang, Ming Cheng, Chen Wang, Yang Xu, Jiaxiong He, Shunfan Hu, Yisheng Wuhan Text Univ Sch Elect & Elect Engn Wuhan Peoples R China South Cent Univ Nationalities Coll Comp Sci Wuhan Peoples R China Hawk Elect Pte Ltd Wuhan Peoples R China

ISBN: (纸本)9781665425513

Nowadays, power electronic devices and other nonlinear loads are widely used in low-voltage power distribution system (LVPDS), which will produce a large number of harmonics, reduce the quality of voltage and current, and may also affect the normal operation of other equipments. Passive harmonic filter (PHF) is a main method to suppress harmonics in LVPDS. However, especially under the condition of high power factor, how to give consideration to both harmonic suppression and reactive power compensation must make a compromise between several contradictory objectives. For the above reason, this paper proposes a optimization design method of PHF bank based on improved particle swarm optimization algorithm for LVPDS with high power factor, in order to improve the overall performance of PHF bank. Finally, a design example of PHF bank of LVPDS in a beverage production company is given to illustrate the superiority and feasibility of the proposed method, which has a certain value of popularization and application.

关键词： high power factor passive harmonic filter improved particle swarm optimization algorithm optimal design

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improved adaptive compression factor PSO algorithm for coordinated thrust distribution of hovercrafts 43

Improved adaptive compression factor PSO algorithm for coord...

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43rd Chinese Control Conference (CCC)

作者： Wang, Yuanhui Yin, Peidong Ren, Zheda Zhang, Xiaoyue Wang, Chenglong Harbin Engineer Univ Coll Intelligent Syst Sci & Engn Nantong St 145 Harbin 150001 Peoples R China Harbin Engn Univ Sanya Nanhai Innovat & Dev Base Harbin Peoples R China

ISBN: (纸本)9798350366907;9789887581581

To solve the unreasonable thrust distribution, turning safety, control flexibility and excessive energy consumption of hovercrafts. A coordinated thrust distribution method of multiple control surfaces for hovercrafts named improved adaptive compression factor particle swarm optimization (PSO) algorithm is proposed. A coordinated thrust distribution model with vector nozzle is established to describe the relationship between each control surface and its generated force(moment). More over, an improved adaptive compression factor PSO algorithm is proposed to distribute the resulting forces (moment) reasonably to each control surface. The salient features are as follows. First, an adaptive compression factor is introduced to improve the global search ability in the early algorithm stage. Second, an adaptive function is introduced to enhance the local search and convergence ability. Simulation results are given to demonstrate the proposed method can effectively distribute the resulting force (moment) to each control surface, improving the turning safety, control performance, and reducing energy consumption for hovercrafts.

关键词： Hovercrafts improved particle swarm optimization algorithm coordinated thrust distribution vector nozzle

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optimization in Engineering Sciences 1

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2014年

作者： Stefanoiu Dan Borne Pierre Popescu Dumitru Filip Florin Gheorghe El Kamel Abdelkader

ISBN: (数字)9781118648773

ISBN: (纸本)9781848214989

The purpose of this book is to present the main metaheuristics and approximate and stochastic methods for optimization of complex systems in Engineering Sciences. It has been written within the framework of the European Union project ERRIC (Empowering Romanian Research on Intelligent Information Technologies), which is funded by the EU’s FP7 Research Potential program and has been developed in co-operation between French and Romanian teaching researchers. Through the principles of various proposed algorithms (with additional references) this book allows the reader to explore various methods of implementation such as metaheuristics, local search and populationbased methods. It examines multi-objective and stochastic optimization, as well as methods and tools for computer-aided decision-making and simulation for decision-making.

关键词： optimization in Engineering Sciences: Approximate and Metaheuristic Methods Pierre Borne F. G. Philip Dumitru Popescu Dan Stefanoiu Abdelkader El Kamel Vose-Liepins algorithm with Infinite Population Genetics algorithms Biologic Breviary Nix-Vose algorithm with finite Population How to implement a Genetic algorithms improved particle swarm optimization algorithm Calculation of Solution to the Stochastic Problem

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Research on Multi-Objective Dynamic Reactive Power optimization of Distribution Network with Renewable Energy Access 11

Research on Multi-Objective Dynamic Reactive Power Optimizat...

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11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Wu, Xiaomeng Jing, Mengyi Li, Xiaoxiao Luo, Yingying School of Electric Engineering Xi'An Shiyou University Xi'an China Key Laboratory of Measurement and Control Technique of Oil and Gas Wells of Shaanxi Province Xi'an China

ISBN: (纸本)9798350333664

With the large-scale access of distributed power to the distribution network, the structure of the original distribution network becomes complicated. Due to the volatility and randomness of wind power and photovoltaic power generation output, the power flow direction of the distribution network is changed, and the quality of power is also reduced. In view of the above situation, a reactive power optimization model with the minimum active network loss, voltage deviation and average fluctuation rate of node voltage is established. In addition, the uncertainty of wind power and photovoltaic output is simulated and solved by improved particle swarm optimization algorithm. Finally, the example simulation is carried out in the IEEE33 node distribution system. The results show that the improved particle swarm optimization algorithm and optimization model proposed in this paper can effectively reduce the network loss and prevent the node voltage from exceeding the limit. © 2023 IEEE.

关键词： distributed power supply improved particle swarm optimization algorithm multi-objective reactive power optimization

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Optimal Power Flow Research on Distributed Network Considering Distributed Generation

Optimal Power Flow Research on Distributed Network Consideri...

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China International Conference on Electricity Distribution (CICED)

作者： Liu Zhijian Yan Jun Song Qi Kunming Univ Sci & Technol Coll Elect Engn Kunming 650224 Yunnan Peoples R China

ISBN: (纸本)9781467390682

Because more distribution generations are added to the distribution network, the power flow of the distribution network is changed. The analysis and Research on the power flow of distribution network with distributed generation is an important means to analyze the influence of distributed generation on distribution network. First, Newton-Raphson algorithm is applyed to calculate the power flow of the distribution network with distributed generation. Then the optimal power flow model of the distribution network is set up based on the objective function of the minimum loss in the network. Finally, the optimization problem is solved by using the improved particle swarm optimization algorithm. The simulation is tested and verified by using IEEE14 node system in the MATLAB software. The test results show that the optimal power flow research on distribution network with distributed generation can reduce the loss of the network and improve the economy of the system.

关键词： Distributed Generation Distributed Network Optimal Power Flow Newton-Raphson algorithm improved particle swarm optimization algorithm

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Hybrid SVM-CIPSO methods for optimal operation of reservoir considering unknown future condition

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APPLIED SOFT COMPUTING 2020年 95卷 106572-106572页

作者： Moeini, Ramtin Babaei, Mohammad Univ Isfahan Fac Civil Engn & Transportat Dept Civil Engn Esfahan *** Iran

In this paper, new hybrid methods have been proposed to solve reservoir operation optimization problem for uncertain water inflows condition by equipping improved particle swarm optimization (IPSO) algorithm with support vector machine (SVM) method. The constrained version of IPSO algorithm (CIPSO) has been used here to improve the efficiency of the IPSO algorithm. In CIPSO algorithm, the problem constraints have been explicitly satisfied leading to smaller search space and finally smaller computational cost. Two approaches have been considered to propose the hybrid methods. In the first approach, named SVM-CIPSO1, water inflows into the dam reservoir have been predicted using SVM model and these predicted values have been used to solve reservoir operation optimization problem using CIPSO algorithm. However, in the second approach, named SVM-CIPSO2, at first, the CIPSO algorithm has been applied to solve reservoir operation optimization problem using the historical data and finally the optimal water release values have been used as input and output data to create a SVM model for predicting optimal water release from reservoir for the future condition. For comparison purpose, the ANN model has been also used to predict the water inflow or release values for the future condition and the standard form of IPSO algorithm has been also used to solve the optimization problem. Here, to evaluate the proposed approaches, the optimal water release values form Zayandehroud dam reservoir have been obtained using proposed methods and the reliability, resiliency, vulnerability and sustainability indexes have been computed. Comparison of the results indicates the capability of the proposed methods to predict the optimal water release values for future condition with acceptable accuracy. In other words, the RMSE values of SVM model for test, validation and training processes are 9.7104 (23. 56196), 11.2553 (42.69093), and 7.9556 (47.9346) MCM, respectively, which are obtained using

关键词： Optimal operation of reservoir Prediction Support vector machine improved particle swarm optimization algorithm Artificial neural networks

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Hybrid forecasting model for non-stationary daily runoff series: A case study in the Han River Basin, China

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JOURNAL OF HYDROLOGY 2019年 577卷 123915-000页

作者： Xie, Tuo Zhang, Gang Hou, Jinwang Xie, Jiancang Lv, Meng Liu, Fuchao Xian Univ Technol State Key Lab Ecohydraul Northwest Arid Reg China Xian 710048 Shaanxi Peoples R China Xian Univ Technol Res Ctr Ecohydraul & Sustainable Dev New Style Think Tank Shaanxi Univ Xian 710048 Shaanxi Peoples R China State Grid Gansu Elect Power Co Gansu Elect Power Res Inst Lanzhou 730050 Gansu Peoples R China

Accurate and reliable short-term runoff prediction is of great significance to the management of water resources optimization and reservoir flood operation. In order to improve the accuracy of short-term runoff forecasting, a hybrid model-based "feature decomposition-learning reconstruction" named VMD-DBN-IPSO was proposed. In this paper, variational mode decomposition (VMD) is first used to decompose the original daily runoff series into a set of sub-sequence for improving the frequency resolution. Partial autocorrelation function (PACF) is then applied to determine the input variables of each sub-sequence. The improved particle swarm optimization (IPSO) algorithm is combined with the deep belief network (DBN) model to predict each sub-sequences and finally reconstruct the ensemble forecasting result. Three quantitative evaluation indicators, mean absolute error (MAE), root mean square error (RMSE) and Nash-Sutcliffe efficiency coefficient (NSE), were used to evaluate and compare the established models using the historical daily runoff data (1/1/1988-31/12/2017) at Yangxian and Ankang hydrological station in the Han River Basin of China. Meanwhile, a comparative analysis of the performance of VMD-DBN-IPSO model under different forecast periods (1-, 3-, 5- and 7-day lead time) was performed. In addition, the prediction ability of peak runoff of the VMD-DBN-IPSO model is further verified by analyzing the 10 peak flows during the testing data-series. The results indicate that the VMD-DBN-IPSO model can always achieve the best performance in the training and testing stage, and has good stability and representativeness, the NSE coefficient remains above 0.8, and the prediction error of peak flow is within 20%. It is a preferred data-driven tool for forecasting daily runoff.

关键词： Daily runoff forecasting Variational mode decomposition Deep belief network improved particle swarm optimization algorithm The Han River Basin

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Research on Fault Early Warning of Wind Turbine Based on IPSO-DBN

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ENERGIES 2022年第23期15卷 9072-9072页

作者： Zhang, Zhaoyan Wang, Shaoke Wang, Peiguang Jiang, Ping Zhou, Hang Hebei Univ Coll Elect Informat Engn Baoding 071002 Peoples R China

Aiming at the problem of wind turbine generator fault early warning, a wind turbine fault early warning method based on nonlinear decreasing inertia weight and exponential change learning factor particle swarm optimization is proposed to optimize the deep belief network (DBN). With the data of wind farm supervisory control and data acquisition (SCADA) as input, the weights and biases of the network are pre-trained layer by layer. Then the BP neural network is used to fine-tune the parameters of the whole network. The improved particle swarm optimization algorithm (IPSO) is used to determine the number of neurons in the hidden layer of the model, pre-training learning rate, reverse fine-tuning learning rate, pre-training times and reverse fine-tuning training times and other parameters, and the DBN predictive regression model is established. The experimental results show that the proposed model has better performance in accuracy, training time and nonlinear fitting ability than the DBN model and PSO-DBN model.

关键词： wind turbine deep belief network improved particle swarm optimization algorithm wind power generator

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