Applied machine learning in wind speed prediction and loss minimization in unbalanced radial distribution system

ABSTRACT

Environmental parameter consideration has always prompted wind power to be used as renewable energy. However, the biggest challenge lies in wind energy integration to the power grid due to wind intermittency. Wind speed or wind power forecasting is one of the approach to manage this intermittency. Numerous prediction methods have been reported in previous literatures over few years. In this work, multivariate wind speed forecasting using Machine Learning framework in a python environment is executed. Several statistical models and neural network models are examined to best predict the wind speed of Surat, India [22.2587° N, 71.1924° E]. The model efficiency is tested in terms of measurements of correlation factors and Mean Absolute Error values. The predicted wind speed value is further considered for the power generation from the wind farm and integrated to the distribution system. Load Impedance Matrix method is implemented for Distribution System Load Flow analysis for being robust and simple with single-step computation. IEEE-19 bus and IEEE-25 bus-unbalanced radial distribution systems are considered for finding the power losses in the branches with wind power as Distributed Generation. An efficient and effective optimization technique, Teaching Learning-Based Optimization, is used to obtain the optimal location and capacity of Distributed Generation to minimize the power loss in the distribution lines.

KEYWORDS:

• Distribution System Load Flow (DSLF)
• loss Minimization
• teaching Learning-Based Optimization (TLBO)
• machine Learning (ML)

Highlight Section

In this work, few technical points are emphasized as,

• The objective of the work is focused on minimizing the line losses using Distributed Generation in IEEE-19 and IEEE-25 bus unbalanced Radial Distribution System (RDS).

• Wind Energy as DG is employed to the system after wind speed forecasting using multivariate (effect of temperature, humidity, visibility, and old wind speed data) Machine Learning Algorithms (MLAs).

• Several algorithms such as linear Regression (LR), Decision Tree Regressor (DTR), Ada Boost Regressor (ABR), Gradient Boosting Regressor (GBR) and Random Forest Regressor (RFR), Multi-Layer Perceptron (MLP),Long Short Term Memory (LSTM) and Recurrent Neural Network (RNN) are applied to the hourly wind data from year 2010 to 2019, extracted from any meteorological website for a site under study.

• The optimum position and capacities of DGs (three) are decided by using Teaching Learning-Based Optimization (TLBO) technique for which the system will give the most minimum line losses being the primary objective of the work.

• The active power loss evaluation of the system with DG is monitored with Load Impedance Matrix (LIM) based power flow technique.

Additional information

Notes on contributors

Aliva Routray

Aliva Routray received B.Tech in Electrical and Electronics Engineering from Biju Pattnaik University of Tehnology, Odisha in 2012, M.Tech in Power System Engineering from Veer Surendra Sai University of Technology, Sambalpur (Odisha) in 2016. Currently, she is a Ph.D scholar in Department of Electrical Engineering at Sardar Vallabhbhai National Institute of Technology, Surat, India. Her research work is primarily concerned with Power Loss Minimization in Distribution Systems, Application of Renewable Energy as Distribution Generation, Power System Stability Analysis and Optimization Techniques.

Khyati D Mistry

Khyati D Mistry received B.E. degree in Electrical Engineering and Master degree in power system from the Sardar Patel University, Vallabh Vidhyanagar, Anand, India in 2004 and 2006, respectively. She received Ph.D. degree in electrical engineering from Sardar Vallabhbhai National Institute of Technology, Surat, India in 2015. She is joined as Assistant Professor, Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat in September 2007. January 2019, he is promoted as Associate Professor in same institute. Her research interest is in optimization techniques and analysis of power system, Power system protection.

Sabha Raj Arya

Sabha Raj Arya received Bachelor of Engineering degree in Electrical Engineering from Government Engineering College Jabalpur, in 2002, Master of Technology in Power Electronics from Motilal National Institute of Technology, Allahabad, in 2004 and Ph.D. degree in Electrical Engineering from Indian Institute of Technology (I.I.T) Delhi, New Delhi, India, in 2014. He is joined as Assistant Professor, Department of Electrical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat. January 2019, he is promoted as Associate Professor in same institute. His fields of interest include Power electronics, power quality, design of power filters and distributed power generation.

He received Two National Awards namely INAE Young Engineer Award from Indian National Academy of Engineering, POSOCO Power System Award from Power Grid Corporation of India in the year of 2014 for his research work. He is also received Amit Garg Memorial Research Award-2014 from I.I.T Delhi from the high impact publication in a quality journal during the session 2013-2014. At present, he has published more than Hundred research paper in internal national Journals and conferences in field of electrical power quality. He also serves as an Associate Editor for the IET (U.K.) Renewable Power Generation.

B. Chittibabu

B. Chitti Babureceived the Ph.D. in Electrical Engineering from National Institute of Technology Rourkela, India in 2012. He had been with National Institute of Technology Rourkela, India as an Assistant Professor in Electrical engineering department from 2007 to 2013. Subsequently He had two post-doc research appointments with Wroclaw University of Science & Technology, Poland from Dec 2013–June 2014 and VSB-Technical University of Ostrava, Czech Republic from Sep 2014 to Sep 2015 and both the appointments have been sponsored by European Commission, U.K. In September 2016, he was appointed as an Assistant Professor in the department of electrical & electronics engineering, The University of Nottingham Malaysia Campus, Malaysia. His research interests include power electronics applications in smart distribution grid containing renewable energy sources and low-power electronics design, including photovoltaic energy systems.

Paper ID UESO-2020-1644.R1 (Old title: Machine Learning Approach to Forecast Wind Speed for Loss Minimization in Unbalanced Radial Distribution System)

Paper New Title: “Applied Machine Learning in Wind Speed Prediction and Loss Minimization in Unbalanced Radial Distribution System”