Open AccessModel‐predictive control‐based hybrid optimized load frequency control of multi‐area power systems
Author(s) -
MV Mahendran,
V Vijayan
Publication year - 2021
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/gtd2.12119
Subject(s) - control theory (sociology) , controller (irrigation) , electric power system , automatic frequency control , particle swarm optimization , pid controller , benchmark (surveying) , computer science , automatic generation control , power (physics) , limit (mathematics) , control engineering , engineering , control (management) , mathematics , temperature control , telecommunications , mathematical analysis , physics , geodesy , quantum mechanics , artificial intelligence , machine learning , geography , agronomy , biology
It is distinguished that there should be a balance between power generation and load demand, thereby maintaining the frequency and tie‐line power of the multi‐source multi‐area interconnected power system (MS‐MA‐IPS) in a determined limit to safeguard the entire system from failure. Hence, load frequency control (LFC) by adjusting the megawatt output of generators is applied, which has been considered as the most recent research work in this field. Under these circumstances, this paper intends to construct a dual‐mode‐switch‐controller‐based LFC in a multi‐area power system, which obviously considers the impact of incremental control action along with the system dynamic constraints such as capacitive energy storage, generation rate constraint, and governor with dead band. In order to achieve this effect, in this research work, the operations of the proposed controller of the MS‐MA‐IPS are based on the dual‐mode switch, and here, switching is carried out with respect to a threshold value ξ . Based on switching, the control varies between proportional–integral (PI) control and model‐predictive control. Moreover, in order to make the performance elegant, the proportional gain of the PI controller K p gand the threshold of the switch ξ are optimally tuned by introducing a novel optimisation algorithm referred to as particle updated dragonfly algorithm, which is the conceptual hybridisation of the traditional dragonfly algorithm and particle swarm optimisation. Finally, the performance of the proposed model is evaluated by varying the control parameters such as C g , T C D , T r , and T r bto minimize the undesired deviations in power flows between control areas.