Application of BP neural network to power prediction of wind power generation unit in microgrid

2014 ◽  
pp. 117-124 ◽  
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Wind Power ◽  
Bp Neural Network ◽  
Wind Power Generation ◽  
Power Prediction ◽  
Power Generation Unit ◽  
Generation Unit

scholarly journals Wind Power Prediction Based on Extreme Learning Machine with Kernel Mean p-Power Error Loss

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 673 ◽  
Author(s):  
Ning Li ◽  
Fuxing He ◽  
Wentao Ma
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Power System ◽  
Wind Power ◽  
Extreme Learning Machine ◽  
Bp Neural Network ◽  
Power Prediction ◽  
Error Loss ◽  
Wind Power Prediction ◽  
Learning Machine

In recent years, more and more attention has been paid to wind energy throughout the world as a kind of clean and renewable energy. Due to doubts concerning wind power and the influence of natural factors such as weather, unpredictability, and the risk of system operation increase, wind power seems less reliable than traditional power generation. An accurate and reliable prediction of wind power would enable a power dispatching department to appropriately adjust the scheduling plan in advance according to the changes in wind power, ensure the power quality, reduce the standby capacity of the system, reduce the operation cost of the power system, reduce the adverse impact of wind power generation on the power grid, and improve the power system stability as well as generation adequacy. The traditional back propagation (BP) neural network requires a manual setting of a large number of parameters, and the extreme learning machine (ELM) algorithm simplifies the time complexity and does not need a manual setting of parameters, but the loss function in ELM based on second-order statistics is not the best solution when dealing with nonlinear and non-Gaussian data. For the above problems, this paper proposes a novel wind power prediction method based on ELM with kernel mean p-power error loss, which can achieve lower prediction error compared with the traditional BP neural network. In addition, to reduce the computational problems caused by the large amount of data, principal component analysis (PCA) was adopted to eliminate some redundant data components, and finally the efficiency was improved without any loss in accuracy. Experiments using the real data were performed to verify the performance of the proposed method.

scholarly journals Modeling and analysis of hydrogen storage wind and gas complementary power generation system

2021 ◽  
pp. 014459872110033
Author(s):  
Zheng Li ◽  
Shaodong Hou ◽  
Xin Cao ◽  
Yan Qin ◽  
Pengju Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Hydrogen Storage ◽  
Wind Power ◽  
Bp Neural Network ◽  
Load Forecasting ◽  
Wind Power Generation ◽  
Environmental Cost ◽  
Generation System ◽  
Power Generation System

In view of the uncertainty and volatility of wind power generation and the inability to provide stable and continuous power, this paper proposes a hydrogen storage wind-gas complementary power generation system, using Matlab/Simulink to simulate and model wind generators and gas turbines. Considering the economy and power supply reliability of the wind-gas complementary power generation system, and taking the economic and environmental cost of the system as the objective function, the capacity optimization model of the wind-gas complementary power generation system is established. The brain storming algorithm (BSO) is used to solve the optimization problem, and the BSO algorithm is used to optimize the BP neural network, which improves the accuracy of the BP neural network for load forecasting. Finally, a simulation is carried out with load data in a certain area, and the simulation verification verifies that BSO-BP can improve the accuracy of load forecasting and reduce the error of load forecasting. Multi-objective optimization of system economic cost and environmental cost through BSO algorithm can make the system cost reach the most reasonable level. Through the analysis of the calculation examples, it is verified that gas-fired power generation can effectively alleviate the volatility of wind power generation, showing the role and advantages of energy complementary power generation. Therefore, the wind-gas complementary system can effectively increase energy utilization and reduce wind curtailment.

scholarly journals Forecasting Wind Power Generation Using Artificial Neural Network: “Pawan Danawi”—A Case Study from Sri Lanka

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Amila T. Peiris ◽  
Jeevani Jayasinghe ◽  
Upaka Rathnayake
Keyword(s):  
Neural Network ◽  
Power Generation ◽  
Wind Power ◽  
Wind Farm ◽  
Mean Squared Error ◽  
Wind Power Generation ◽  
Training Algorithms ◽  
Training Algorithm ◽  
Squared Error ◽  
Ann Models

Wind power, as a renewable energy resource, has taken much attention of the energy authorities in many countries, as it is used as one of the major energy sources to satisfy the ever-increasing energy demand. However, careful attention is needed in identifying the wind power potential in a particular area due to climate changes. In this sense, forecasting both wind power generation and wind power potential is essential. This paper develops artificial neural network (ANN) models to forecast wind power generation in “Pawan Danawi”, a functioning wind farm in Sri Lanka. Wind speed, wind direction, and ambient temperature of the area were used as the independent variable matrices of the developed ANN models, while the generated wind power was used as the dependent variable. The models were tested with three training algorithms, namely, Levenberg-Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian Regularization (BR) training algorithms. In addition, the model was calibrated for five validation percentages (5% to 25% in 5% intervals) under each algorithm to identify the best training algorithm with the most suitable training and validation percentages. Mean squared error (MSE), coefficient of correlation (R), root mean squared error ratio (RSR), Nash number, and BIAS were used to evaluate the performance of the developed ANN models. Results revealed that all three training algorithms produce acceptable predictions for the power generation in the Pawan Danawi wind farm with R > 0.91, MSE < 0.22, and BIAS < 1. Among them, the LM training algorithm at 70% of training and 5% of validation percentages produces the best forecasting results. The developed models can be effectively used in the prediction of wind power at the Pawan Danawi wind farm. In addition, the models can be used with the projected climatic scenarios in predicting the future wind power harvest. Furthermore, the models can acceptably be used in similar environmental and climatic conditions to identify the wind power potential of the area.

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