Forecasting of Wind Speed in Malang City of Indonesia using Adaptive Neuro-Fuzzy Inference System and Autoregressive Integrated Moving Average Methods

2020 ◽  
Author(s):  
Hadi Suyono ◽  
Dinda Oki Prabawanti ◽  
Mahfudz Shidiq ◽  
Rini Nur Hasanah ◽  
Unggul Wibawa ◽  
...  
Keyword(s):  
Wind Speed ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Moving Average ◽  
Inference System ◽  
Autoregressive Integrated Moving Average ◽  
Neuro Fuzzy

scholarly journals Electricity consumption forecast for Tarkwa using autoregressive integrated moving average and adaptive neuro fuzzy inference system

2021 ◽  
Vol 18 (1) ◽  
pp. 75-94
Author(s):  
Robert Ofosu ◽  
Benjamin Odoi ◽  
Mercy Asamoah
Keyword(s):  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Moving Average ◽  
Electricity Consumption ◽  
Electrical Energy ◽  
Inference System ◽  
Autoregressive Integrated Moving Average ◽  
Independent Variables ◽  
Testing Dataset ◽  
Neuro Fuzzy

Electricity has become one of the inelastic goods in our world today. The proper functioning of most equipment today relies on electricity. Taking Tarkwa which is a mining community into consideration, the various mines, schools, shops, banks and other companies in the municipality massively rely on electricity for their day to day running. Therefore, knowing the exact amount of electricity to produce and distribute for the smooth running of businesses and basic living is of great necessity. This study compared and formulated a model to forecast and predict the daily electrical energy consumption in Tarkwa for the year 2019. The data used was a monthly dataset for the year 2018 and it comprised of the temperature, wind speed, population and electricity consumption for Tarkwa. The methods used were Artificial Neuro-Fuzzy Inference System (ANFIS) and Autoregressive Integrated Moving Average (ARIMA). The ANFIS was used as a predictor to predict the electricity consumption based on the training and testing of the dependent and independent variables. The ARIMA was used to forecast the dependent and independent variables for 2019. These simulations were done using MATLand Minitab. The results of the analysis revealed that the training and testing dataset allowed ANFIS to learn and understand the system but the ANFIS could only forecast the 2019 electricity consumption after the input data to the system was changed to the ARIMA forecasted 2019 independent variables. It was observed that the amount of electricity consumed in 2019 increased by 14%.

Torque and pitch angle control of a wind turbine using multiple adaptive neuro-fuzzy control

2019 ◽  
Vol 44 (2) ◽  
pp. 125-141
Author(s):  
Satyabrata Sahoo ◽  
Bidyadhar Subudhi ◽  
Gayadhar Panda
Keyword(s):  
Wind Speed ◽  
Wind Turbine ◽  
Fuzzy Inference System ◽  
Pitch Angle ◽  
Fuzzy Inference ◽  
Adaptive Controller ◽  
Inference System ◽  
Proportional Integral ◽  
Simulink Model ◽  
Neuro Fuzzy

This article presents a multiple adaptive neuro-fuzzy inference system-based control scheme for operation of the wind energy conversion system above the rated wind speed. By controlling the pitch angle and generator torque concurrently, the generator power and speed fluctuation can be reduced and also turbine blade stress can be minimized. The proposed neuro-fuzzy-based adaptive controller is composed of both the Takagi–Sugeno fuzzy inference system and neural network. First, a step change in wind speed and then a simulated wind speed are considered in the proposed adaptive control design. A MATLAB/Simulink model of the wind turbine system is prepared, and simulations are carried out by applying the proportional integral, fuzzy-proportional integral and the proposed adaptive controller. From the obtained results, the effectiveness of the proposed adaptive controller approach is confirmed.

scholarly journals Adaptive Neuro-Fuzzy Inference System-Based Maximum Power Tracking Controller for Variable Speed WECS

Energies ◽  
2021 ◽  
Vol 14 (19) ◽  
pp. 6275
Author(s):  
Abrar Ahmed Chhipa ◽  
Vinod Kumar ◽  
R. R. Joshi ◽  
Prasun Chakrabarti ◽  
Michal Jasinski ◽  
...  
Keyword(s):  
Wind Speed ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Maximum Power ◽  
Membership Functions ◽  
Inference System ◽  
Neuro Fuzzy ◽  
Fuzzy Input ◽  
Wide Range ◽  
Mppt Controller

This paper proposes an adaptive neuro-fuzzy inference system (ANFIS) maximum power point tracking (MPPT) controller for grid-connected doubly fed induction generator (DFIG)-based wind energy conversion systems (WECS). It aims at extracting maximum power from the wind by tracking the maximum power peak regardless of wind speed. The proposed MPPT controller implements an ANFIS approach with a backpropagation algorithm. The rotor speed acts as an input to the controller and torque reference as the controller’s output, which further inputs the rotor side converter’s speed control loop to control the rotor’s actual speed by adjusting the duty ratio for the rotor side converter. The grid partition method generates input membership functions by uniformly partitioning the input variable ranges and creating a single-output Sugeno fuzzy system. The neural network trained the fuzzy input membership according to the inputs and alter the initial membership functions. The simulation results have been validated on a 2 MW wind turbine using the MATLAB/Simulink environment. The controller’s performance is tested under various wind speed circumstances and compared with the performance of a conventional proportional–integral MPPT controller. The simulation study shows that WECS can operate at its optimum power for the proposed controller’s wide range of input wind speed.

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