The fast growth of solar and wind power generation has led to concern about the transient stability of power grids. As the amount of solar and wind power is drastically increasing in years to come, maintaining power system stability during, for example, a short circuit fault will be more important in order to ensure power supply reliability and other important issues. New studies must be performed in order to evaluate the behavior of the solar and wind farms after severe faults and improve their designs in an efficient and economical way. Under such circumstances, the most demanding requisite for solar and wind farms is the Fault Ride-Through (FRT) capability. Solar and wind farms connected to high voltage transmission system must stay connected when a voltage dip occurs in the grid, otherwise, the sudden disconnection of great amount of solar and wind power may contribute to the voltage dip, with terrible consequences. Therefore, the dynamic and transient analyses of solar PVs and wind generators are necessary.
As a result of the rapid penetration of solar and wind energy into existing power grids, the intricacies in operating the power grid smoothly, because of grid disturbances need to be addressed. New techniques are necessary to overcome this drawback as the topologies and system control in power grids are complex nowadays. The nature of solar and wind energy is stochastic, consequently, there are bound to be constant voltage and frequency fluctuations because of the integration of solar and wind power into traditional power networks. Renewable energy system equivalence could help in solving the difficulty of large system calculation. Large-scale solar and wind farms contain a large amount of power generation units, which leads to the extremely high dimension of the small-signal model matrix. There is a dimensional problem in the calculation process and the solution is to reduce the dimension of the matrix by carrying out research on reduced dimension models of the solar photo-voltaic (PV) and wind turbines in solar and wind farms. Though, the drawback of this method is that the equivalent dimension is limited, and it is impossible to find out all solutions of the large-scale solar PV and wind turbine models. In addition, the model parameters of large-scale solar PV and wind turbines will change due to factors like light intensity, wind speed, temperature and so on. These changes require that the system needs to re-solve the model after each small disturbance, which expects extremely high storage and computing power of processors.
In this Research Topic, advances of soft computing schemes for solar PVs in solar farms and wind turbines in wind farms would be emphasized, with regards to machine learning, deep learning and cloud computing for effective control and condition monitoring of the variables, behavior and stability.
The research topic would also address the following, but not limited to:
• Overview of advanced intelligent control for condition monitoring of solar PVs and wind turbines
• Applications of machine learning and deep learning advanced intelligent control in solar PVs and wind turbines
• Improving the control capabilities of solar PVs and wind turbines using advanced intelligent control
• New trends in hardware control schemes for solar PVs and wind turbines considering advanced intelligent control
• Distributed cloud computing algorithms for solar PVs and wind turbines
• Small-signal model based on advanced intelligent control of large-scale solar and wind power
• Effects of geographical location, light intensity and wind speed, in cloud computing of reduced order-distributed system by advanced intelligent control
• Real-time analysis of the operating status of large-scale solar and wind farms using advanced intelligent control
• Advanced intelligent scheme for monitoring of wind speed and solar radiation for renewable system operation optimization.
The fast growth of solar and wind power generation has led to concern about the transient stability of power grids. As the amount of solar and wind power is drastically increasing in years to come, maintaining power system stability during, for example, a short circuit fault will be more important in order to ensure power supply reliability and other important issues. New studies must be performed in order to evaluate the behavior of the solar and wind farms after severe faults and improve their designs in an efficient and economical way. Under such circumstances, the most demanding requisite for solar and wind farms is the Fault Ride-Through (FRT) capability. Solar and wind farms connected to high voltage transmission system must stay connected when a voltage dip occurs in the grid, otherwise, the sudden disconnection of great amount of solar and wind power may contribute to the voltage dip, with terrible consequences. Therefore, the dynamic and transient analyses of solar PVs and wind generators are necessary.
As a result of the rapid penetration of solar and wind energy into existing power grids, the intricacies in operating the power grid smoothly, because of grid disturbances need to be addressed. New techniques are necessary to overcome this drawback as the topologies and system control in power grids are complex nowadays. The nature of solar and wind energy is stochastic, consequently, there are bound to be constant voltage and frequency fluctuations because of the integration of solar and wind power into traditional power networks. Renewable energy system equivalence could help in solving the difficulty of large system calculation. Large-scale solar and wind farms contain a large amount of power generation units, which leads to the extremely high dimension of the small-signal model matrix. There is a dimensional problem in the calculation process and the solution is to reduce the dimension of the matrix by carrying out research on reduced dimension models of the solar photo-voltaic (PV) and wind turbines in solar and wind farms. Though, the drawback of this method is that the equivalent dimension is limited, and it is impossible to find out all solutions of the large-scale solar PV and wind turbine models. In addition, the model parameters of large-scale solar PV and wind turbines will change due to factors like light intensity, wind speed, temperature and so on. These changes require that the system needs to re-solve the model after each small disturbance, which expects extremely high storage and computing power of processors.
In this Research Topic, advances of soft computing schemes for solar PVs in solar farms and wind turbines in wind farms would be emphasized, with regards to machine learning, deep learning and cloud computing for effective control and condition monitoring of the variables, behavior and stability.
The research topic would also address the following, but not limited to:
• Overview of advanced intelligent control for condition monitoring of solar PVs and wind turbines
• Applications of machine learning and deep learning advanced intelligent control in solar PVs and wind turbines
• Improving the control capabilities of solar PVs and wind turbines using advanced intelligent control
• New trends in hardware control schemes for solar PVs and wind turbines considering advanced intelligent control
• Distributed cloud computing algorithms for solar PVs and wind turbines
• Small-signal model based on advanced intelligent control of large-scale solar and wind power
• Effects of geographical location, light intensity and wind speed, in cloud computing of reduced order-distributed system by advanced intelligent control
• Real-time analysis of the operating status of large-scale solar and wind farms using advanced intelligent control
• Advanced intelligent scheme for monitoring of wind speed and solar radiation for renewable system operation optimization.
