Open AccessAdaptive LMBP training‐based icosϕ control technique for DSTATCOM
Author(s) -
Mangaraj Mrutyunjaya,
Panda Anup Kumar,
Penthia Trilochan,
Dash Asish Ranjan
Publication year - 2020
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/iet-gtd.2018.6295
Subject(s) - matlab , harmonics , control theory (sociology) , controller (irrigation) , voltage , power factor , computer science , backpropagation , power (physics) , capacitor , engineering , electronic engineering , control engineering , artificial neural network , control (management) , artificial intelligence , electrical engineering , agronomy , physics , quantum mechanics , biology , operating system
In this study, the design and implementation of a new hybrid soft computing control technique called Levenberg–Marquardt backpropagation (LMBP)‐based icosϕ is proposed. Two different compensation techniques are evaluated for the performance analysis of three‐phase three‐wire (3P3W) voltage source converter (VSC)‐based DSTATCOM. The first one is the conventional icosϕ control technique whereas the other one is called the LMBP‐based icosϕ control technique. The better power quality of the system is obtained using the proposed control algorithm by maintaining a less voltage across the capacitor as compared to the conventional one. So, the reduction in the size of the DSTATCOM is realised by the LMBP‐based control algorithm. Furthermore, the performance parameters such as load balancing, harmonics elimination, power factor improvement, and voltage regulation are evaluated under both balanced and unbalanced loading conditions as per the IEEE guidelines. The effectiveness of the proposed controller is studied in the MATLAB/Simulink environment and also validated with a low power rated prototype experimental results.