Correlation Mining for Reconstruction Measures and Performance Indexes of Distribution Network Planning Based on BP Neural Network

Since the traditional correlation analysis based on complex power flow calculation cannot meet the requirements of performance evaluation of current distribution network planning, the BP neural network (Back Propagation Neural Network, the BPNN) based correlation mining is proposed in this paper. With the reconstruction measures and performance indexes as the training sample sets, the corresponding correlation model through the offline learning of sample data can be obtained. As a result, when given reconstruction measures in practical application, the neural network training can give the results of performance indexes quickly and accurately. In addition, in order to improve the generalization mapping capability of BPNN, the genetic algorithm is used to optimize the weights and thresholds of the BPNN. Experimental result based on the IEEE 33 node network shows the accuracy and effectiveness of the presented methodology.

[1]  C. Borges,et al.  Active distribution network integrated planning incorporating distributed generation and load response uncertainties , 2011, 2012 IEEE Power and Energy Society General Meeting.

[2]  Yong Liu,et al.  Optimal active distribution system management considering aggregated plug-in electric vehicles , 2016 .

[3]  Sheng Siqin.  Multi-objective Planning of Distribution Network Based on Pareto Optimality , 2014 .

[4]  Zou Jian-xiao.  Distributed Control Strategy for Variable Speed Constant Frequency Wind Power Unit Based on Intelligent Multi-agents , 2010 .

[5]  Feng Mingca.  Reliability Planning of Distribution Power Network Considering the Characteristics of Short-Term Peak Load , 2015 .

[6]  Zhanfeng Huang,et al.  Based on Fuzzy Grey Correlation Method in the Relation Analysis between Safety Investment and Benefit , 2010, 2010 4th International Conference on Bioinformatics and Biomedical Engineering.

[7]  Qin Jian-guang.  Reliability Evaluation for Large-Scale Distribution Network Based on Component Groups , 2012 .

[8]  Wei Wu,et al.  Implementation of Web Mining Algorithm Based on Cloud Computing , 2017, Intell. Autom. Soft Comput..

[9]  Mauricio E. Samper,et al.  Investment Decisions in Distribution Networks Under Uncertainty With Distributed Generation—Part I: Model Formulation , 2013, IEEE Transactions on Power Systems.

[10]  M. Hernandez,et al.  Reducing Under-Frequency Load Shedding in Isolated Power Systems Using Neural Networks. Gran Canaria: A Case Study , 2016, IEEE Transactions on Power Systems.

[11]  Yang Jiashi.  A Search Strategy for Optimal Configuration of Distributed Generation Considering Centroid Mapping and Path Analysis , 2012 .

[12]  Song Li,et al.  A Novel Wavelet-Based Ensemble Method for Short-Term Load Forecasting with Hybrid Neural Networks and Feature Selection , 2016, IEEE Transactions on Power Systems.

[13]  G. Ledwich,et al.  Integrated Distribution Systems Planning to Improve Reliability Under Load Growth , 2012, IEEE Transactions on Power Delivery.

[14]  Chen Zhiba,et al.  A Model of Very Short-term Photovoltaic Power Forecasting Based on Ground-based Cloud Images and RBF Neural Network , 2015 .

[15]  Junyong LIU,et al.  Active energy management strategies for active distribution system , 2015 .

[16]  Vassilios G. Agelidis,et al.  Reliability Worth Analysis of Distribution Systems Using Cascade Correlation Neural Networks , 2018, IEEE Transactions on Power Systems.

[17]  Wan Lingyun.  A Reliability Level Tree Algorithm for Reliability Evaluation of Medium Voltage Distribution Networks , 2011 .

[18]  Luo Xian-jue,et al.  New distribution network planning algorithm based on multilayer Hopfield neural network , 2006 .

[19]  Arindam Ghosh,et al.  Optimal distribution network reinforcement considering load growth, line loss and reliability , 2013, 2013 IEEE Power & Energy Society General Meeting.

[20]  Junyong Liu,et al.  Robust Energy Management of Microgrid With Uncertain Renewable Generation and Load , 2016, IEEE Transactions on Smart Grid.

[21]  Liu Sige,et al.  Monte Carlo Simulation Based Reliability Evaluation of Distribution System Containing Microgrids , 2011 .

[22]  Andy M. Yip,et al.  A regularized k-means and multiphase scale segmentation , 2011 .

[23]  Norman Mariun,et al.  A Novel Implementation for Generator Rotor Angle Stability Prediction Using an Adaptive Artificial Neural Network Application for Dynamic Security Assessment , 2013, IEEE Transactions on Power Systems.

[24]  Hamid Lesani,et al.  Integrated distribution network expansion planning incorporating distributed generation considering uncertainties, reliability, and operational conditions , 2015 .