Wind farms in Tamil Nadu's Coimbatore district have identified several issues, including flickering emissions, frequent generator tripping, and power evacuation issues caused by a weak grid. Taking into consideration the scarcity of research on flickering emissions, this work focuses on the causes of short-term flicker severity (Pst) in wind farms that export generated power to industrial loads. To identify the scenarios that cause flickering, simulation models of Fixed Speed Wind Farm (FSWF) and Variable Speed Wind Farm (VSWF) with controllers were developed using DIgSILENT power factory software. The flicker emissions were measured at the wind farm substations using Fluke and Dranetz PX5.8 power quality analyzers in accordance with the IEC 61400-21 standard. To validate the simulation model, the results from the flickermeter during the simulation and the field measurements were compared. According to the results of this research, both fixed and variable speeds produce flicker emissions that exceed the IEC standard limit, that causes the power electronics-based industrial drives to fail to operate. The controllers were developed to improve the performance of wind farms that will benefit the current and future wind energy-efficient conversion systems (WECS). Keywords FSWF. VSWG. Flickering. Point of Common Coupling. Power Quality Analyzer [40] Abulanwar, Sayed, Abdelhady Ghanem, Mohammad EM Rizk, Weihao Hu. (2019) A proposed flicker mitigation scheme of DFIG in weak distribution networks.
An intensi ed research is going on worldwide to increase renewable energy sources like solar and wind to reduce emissions and achieve the worldwide targets and also to address the depleting fossil fuels resources and meet the increasing energy demand of the population. The Solar Radiation (SR) is intermittent, forecasting the solar radiation beforehand is a must. The objective of this research is to use Modern Machine Techniques for different climatic conditions to forecast SR with higher accuracy.The required dataset is collected from National Solar Radiation Database having features as temperature, pressure, relative humidity, dew point, solar zenith angle, wind speed and direction, with respect to the yparameter Global Horizontal Irradiance GHI (W/m 2 ). The collected data is rst split based on different types of climatic conditions. Each climatic model will be trained on various Machine Learning (ML) algorithms like Multiple Linear Regression(MLR), Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR), Gradient Boosting Regression(GBR), Lasso and Ridge Regression and Deep Learning Algorithm especially Long-short Term Memory (LSTM) using Google Colab Platform. From our analysis, LSTM has the least error approximation of 0.0040 loss at the 100th epoch and of all ML models, Gradient Boosting and RFR top high, when it comes to the Hot weather season -Gradient Boosting leads 2% than RFR and similarly for Cold weather, Autumn and monsoon climate -RFR has 1% higher accuracy than Gradient Boosting. This high accuracy model is deployed in a User Interface (UI) that will be more useful for real-time solar prediction, load operators for maintenance scheduling, stock commitment and load dispatch centers for engineers to decide on setting up solar panels, for household clients and future researchers.
An intensified research is going on worldwide to increase renewable energy sources like solar and wind to reduce emissions and achieve the worldwide targets and also to address the depleting fossil fuels resources and meet the increasing energy demand of the population. The Solar Radiation (SR) is intermittent, forecasting the solar radiation beforehand is a must. The objective of this research is to use Modern Machine Techniques for different climatic conditions to forecast SR with higher accuracy.The required dataset is collected from National Solar Radiation Database having features as temperature, pressure, relative humidity, dew point, solar zenith angle, wind speed and direction, with respect to the y-parameter Global Horizontal Irradiance GHI (W/m2). The collected data is first split based on different types of climatic conditions. Each climatic model will be trained on various Machine Learning (ML) algorithms like Multiple Linear Regression(MLR), Support Vector Regression (SVR), Decision Tree Regression (DTR), Random Forest Regression (RFR), Gradient Boosting Regression(GBR), Lasso and Ridge Regression and Deep Learning Algorithm especially Long-short Term Memory (LSTM) using Google Colab Platform. From our analysis, LSTM has the least error approximation of 0.0040 loss at the 100th epoch and of all ML models, Gradient Boosting and RFR top high, when it comes to the Hot weather season – Gradient Boosting leads 2% than RFR and similarly for Cold weather, Autumn and monsoon climate –RFR has 1% higher accuracy than Gradient Boosting. This high accuracy model is deployed in a User Interface (UI) that will be more useful for real-time solar prediction, load operators for maintenance scheduling, stock commitment and load dispatch centers for engineers to decide on setting up solar panels, for household clients and future researchers.
Wind farms in Tamil Nadu's Coimbatore district have identified several issues, including flickering emissions, frequent generator tripping, and power evacuation issues caused by a weak grid. Taking into consideration the scarcity of research on flickering emissions, this work focuses on the causes of short-term flicker severity (Pst) in wind farms that export generated power to industrial loads. To identify the scenarios that cause flickering, simulation models of Fixed Speed Wind Farm (FSWF) and Variable Speed Wind Farm (VSWF) with controllers were developed using DIgSILENT power factory software. The flicker emissions were measured at the wind farm substations using Fluke and Dranetz PX5.8 power quality analyzers in accordance with the IEC 61400-21 standard. To validate the simulation model, the results from the flickermeter during the simulation and the field measurements were compared. According to the results of this research, both fixed and variable speeds produce flicker emissions that exceed the IEC standard limit, that causes the power electronics-based industrial drives to fail to operate. The controllers were developed to improve the performance of wind farms that will benefit the current and future wind energy-efficient conversion systems (WECS).
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