Voltage stability index and APFC for performance improvement of modern power systems with intense renewables

In this study, a newly developed amalgam power flow controller (APFC) is used for better controllability and voltage stability enhancement of modern power system with deep renewable penetration. A new voltage stability index is proposed to determine the potential site of APFC and then Grey Wolf optimisation based on fuzzy logic is adopted to determine the optimal parameter settings of the APFC. A quarter cosine and exponential fuzzy membership function have been used to find out membership value of diverse objectives. The multi-objective problem is formulated considering three different objectives of conflicting nature. The proposed optimisation framework is implemented on an IEEE benchmark system of 30 buses for different cases. The comparison of simulation results reveals the effectiveness of the proposed model.

[1]  Varaprasad Janamala,et al.  Optimal location and parameters of GUPFC for transmission loss minimization using PSO algorithm , 2017, 2017 Innovations in Power and Advanced Computing Technologies (i-PACT).

[2]  Pradeep Singh,et al.  Amalgam Power Flow Controller: A Novel Flexible, Reliable, and Cost-Effective Solution to Control Power Flow , 2018, IEEE Transactions on Power Systems.

[3]  N. K. Sharma,et al.  A Novel Placement Strategy for FACTS Controllers , 2002, IEEE Power Engineering Review.

[4]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[5]  George J. Klir,et al.  Fuzzy Sets, Fuzzy Logic, and Fuzzy Systems - Selected Papers by Lotfi A Zadeh , 1996, Advances in Fuzzy Systems - Applications and Theory.

[6]  H. Sasaki,et al.  A New Formulation for FACTS Allocation for Security Enhancement against Voltage Collapse , 2002, IEEE Power Engineering Review.

[7]  Narayana Prasad Padhy,et al.  Optimal location and controller design of STATCOM for power system stability improvement using PSO , 2008, J. Frankl. Inst..

[8]  F. Alvarado,et al.  SVC placement using critical modes of voltage instability , 1993, Conference Proceedings Power Industry Computer Application Conference.

[9]  Udaya Annakkage,et al.  Controlled series compensation for improving the stability of multi-machine power systems , 1995 .

[10]  Selvarasu Ranganathan,et al.  Self-adaptive firefly algorithm based multi-objectives for multi-type FACTS placement , 2016 .

[11]  Leandro dos Santos Coelho,et al.  Multi-objective grey wolf optimizer: A novel algorithm for multi-criterion optimization , 2016, Expert Syst. Appl..

[12]  P. Kessel,et al.  Estimating the Voltage Stability of a Power System , 1986, IEEE Power Engineering Review.

[13]  Khaleequr Rehman Niazi,et al.  Line collapse proximity index for prediction of voltage collapse in power systems , 2012 .

[14]  Muhammad Buhari,et al.  Maximization of Wind Energy Utilization Through Corrective Scheduling and FACTS Deployment , 2017, IEEE Transactions on Power Systems.

[15]  Chao Duan,et al.  FACTS devices allocation via sparse optimization , 2016 .

[16]  S. Gerbex,et al.  Optimal Location of Multi-Type FACTS Devices in a Power System by Means of Genetic Algorithms , 2001, IEEE Power Engineering Review.

[17]  Innocent Kamwa,et al.  Optimal placement of multiple-type FACTS devices to maximize power system loadability using a generic graphical user interface , 2013, IEEE Transactions on Power Systems.