Adaptive Robust Control of Variable Speed Wind Turbine Generator

In this work we want to propose a control strategy to maximize the wind energy captured in a variable speed wind turbines, for this goal the speed of turbine should keep in optimum speed when the wind speed is changing. Many control approach has been suggested that is base on approximate models that it causes unsuitable behavior of system because of Uncertainty parameters of the system. Hence at this work we use adaptive robust control approach that it can to compensate Uncertain of the parameters and present a smooth system with maximum energy production. Numerical simulations are given to illustrate the effectiveness and validity of the proposed approach

Adaptive Robust Control of Variable Speed Wind Turbine Generator

In this work we want to propose a control strategy to maximize the wind energy captured in a variable speed wind turbines, for this goal the speed of turbine should keep in optimum speed when the wind speed is changing. Many control approach has been suggested that is base on approximate models that it causes unsuitable behavior of system because of Uncertainty parameters of the system. Hence at this work we use adaptive robust control approach that it can to compensate Uncertain of the parameters and present a smooth system with maximum energy production. Numerical simulations are given to illustrate the effectiveness and validity of the proposed approach

Spatial‐temporal learning structure for short‐term load forecasting

Abstract In the power system operational/planning studies, it is a crucial task to provide the load consumption information in the look‐ahead times. The huge variation of the power system infrastructure in recent years has led to significant changes in the consumers’ consumption pattern. Therefore, short‐term load forecasting (STLF) is transformed to a more complicated problem in recent years. To address this issue, this paper proposes a graph‐based deep neural network to capture full spatial‐temporal features and be able to oversee high volatility time series including load sequence. The proposed spatial deep learning structure benefits from learning the spatial feature using Gabor filter‐oriented layers and full understanding the temporal behaviour based on bidirectional networks. The designed learning‐based system is developed as a graph‐based learning system to improve the accuracy considering the meteorological information behaviour. To verify the performance of the designed deep graph network, the actual load data of Shiraz, Iran, is used. Besides, to demonstrate the superiority and effectiveness of the proposed, the designed deep graph network is compared with three well‐known shallow and deep networks in different cases including yearly performance, seasonal performance, and sensitivity analysis on the meteorological data