Review on data-driven modeling and monitoring for plant-wide industrial processes

Application of support vector machine models for forecasting solar and wind energy resources: A review

Power systems are the most complex systems that have been created by men in history To operate such systems in a stable mode, several control loops are needed Voltage frequency plays a vital role in power systems which need to be properly controlled To this end, primary and secondary frequency control loops are used to control the frequency of the voltage in power systems Secondary frequency control, which is called Load Frequency Control (LFC), is responsible for maintaining the frequency in a desirable level after a disturbance Likewise, the power exchanges between different control areas are controlled by LFC approaches In recent decades, many control approaches have been suggested for LFC in power systems This paper presents a comprehensive literature survey on the topic of LFC In this survey, the used LFC models for diverse configurations of power systems are firstly investigated and classified for both conventional and future smart power systems Furthermore, the proposed control strategies for LFC are studied and categorized into different control groups The paper concludes with highlighting the research gaps and presenting some new research directions in the field of LFC

Challenges and Opportunities of Load Frequency Control in Conventional, Modern and Future Smart Power Systems: A Comprehensive Review

A data-driven multi-model methodology with deep feature selection for short-term wind forecasting

A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and Differential Evolution algorithm

Wind speed prediction using reduced support vector machines with feature selection

A microgrid is a distributed networked generation system, which can effectively integrate various sources of distributed generation, especially renewable energy sources into the information network. The standalone wind/solar/battery power system is a typical standalone microgrid, in which the wind and solar power generations are the intermittent systems with complex dynamics and multiconstraints. Coordinated optimization between the wind power and solar power generations can effectively meet the load demand, reduce wear and tear of generating units, prolong the lifetime and, thus, guarantee the safety of the power grid. Regarding the large-scale, geographically dispersed standalone wind/solar/battery power generation system, this paper constituted a hierarchical distributed model predictive control (HDMPC). In this HDMPC, the upper layer utilizes an iterative distributed control strategy to realize the coordination of the power dispatch. It thus reaches the economic object, e.g., reducing the torsional shaft torque transmitted to gearbox in wind turbine system. The lower layer utilizes the supervisory predictive control to realize both the economic and tracking property. Under this hierarchical structure, the back-calculation from the lower control layer to the upper layer is utilized to keep the consistency of constraints. Through coordinated optimization among the subsystems, the proposed HDMPC realizes the plug and play of distributed energy. The simulation and the experiment validate the advantages of the proposed method in that it can realize the reliability, high efficiency, flexibility, and interactivity for the microgrid control.

Hierarchical Distributed Model Predictive Control of Standalone Wind/Solar/Battery Power System

Nonlinear multivariable hierarchical model predictive control for boiler-turbine system

Wind speed forecasting using deep neural network with feature selection

Reliable load frequency control (LFC) is very important for a modern power system with multi-source power generation and has been the primary focus of studies on advanced control theory and applications. In the LFC of a power system, the generation rate constraints (GRC) and position limit of the governor valve present major challenges to the control scheme because they significantly affect the dynamic responses of the system, resulting in larger overshoot and longer settling time. Model predictive control (MPC) is an attractive control strategy that systematically considers the constraints on the process inputs, states, and outputs. It is employed in LFC to cope with the GRC problem. This study proposes a distributed MPC (DMPC) for a four-area hydro–thermal interconnected power system. In the proposed scheme, the limit position of the governor valve is modelled by a fuzzy model and the local predictive controllers are incorporated into the non-linear control system. The effectiveness of the proposed non-linear constraint DMPC was demonstrated by simulations.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-cta.2015.1021

Xiaobing Kong

Papers

Wind speed prediction using reduced support vector machines with feature selection

Hierarchical Distributed Model Predictive Control of Standalone Wind/Solar/Battery Power System

Nonlinear multivariable hierarchical model predictive control for boiler-turbine system

Wind speed forecasting using deep neural network with feature selection

Distributed model predictive control for load frequency control with dynamic fuzzy valve position modelling for hydro–thermal power system