Application of swarm based intelligent computing algorithms for dynamic evaluation of maximum loadability of transmission network

Abstract This work proposes the application of soft computing techniques to evaluate the optimal location and control settings of Flexible AC Transmission System (FACTS) devices inorder to improve the Maximum Loadability (ML) of pool, hybrid model of deregulated power system during scheduled outages like single (N-1) and double (N-2) contingencies. Particle Swarm Optimization (PSO), Hybrid Particle Swarm Optimization (HPSO) algorithms are applied for optimizing the solution. In this case study, three different FACTS devices are considered namely, series type Thyristor Controlled Series Compensator (TCSC), shunt type Static Var Compensator (SVC) and series – shunt type Unified Power Flow Controller (UPFC). To reduce the Installation Cost (IC) of these devices, they are optimally located in the system, based on priority indices like Contingency Severity Index (CSI) and Fast Voltage Stability Index (FVSI). This proposed approach uses Newton Raphson Power flow equations with voltage, real and VAr power, settings of FACTS devices as constraints. Hybrid structure is modeled using bilateral transactions. The problem is simulated using IEEE 57 bus system. The ML of transmission system obtained for various numbers, settings and locations of FACTS devices are compared both numerically and graphically. It is concluded that HPSO provides better result with minimum Time of Computation (ToC) and quicker convergence.

[1]  L Yathisha,et al.  Optimal switching control strategy for UPFC for wide range of operating conditions in power system , 2017, 2017 Indian Control Conference (ICC).

[2]  Aysen Demiroren,et al.  Robust Voltage/VAR Control Using PSO Based STATCOM: A Case Study in Turkey , 2016 .

[3]  S. V. N. L. Lalitha,et al.  Analysis of Maximum Loadability of a Bus for Different Load Models using New Algorithms , 2016 .

[4]  A. Elmitwally,et al.  Planning of multi-type FACTS devices in restructured power systems with wind generation , 2016 .

[5]  N. Mithulananthan,et al.  Loss reduction and loadability enhancement with DG: A dual-index analytical approach , 2014 .

[6]  Subir Datta.  Effectiveness of Fuzzy Logic-based FACTS Controller and Classical Controller in Enhancement of Power System Dynamic Stability – A Comparative Study , 2016 .

[7]  Ismail H. Altas,et al.  A novel modified hybrid PSOGSA based on fuzzy logic for non-convex economic dispatch problem with valve-point effect , 2015 .

[8]  Pallavi Choudekar,et al.  Optimal location of SVC for improvement in voltage stability of a power system under normal and contingency condition , 2017, Int. J. Syst. Assur. Eng. Manag..

[9]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[10]  N. Kamaraj,et al.  Comparison of computational intelligence algorithms for loadability enhancement of restructured power system with FACTS devices , 2012, Swarm Evol. Comput..

[11]  Hosam K. M. Youssef,et al.  Maximum loadability of power systems using hybrid particle swarm optimization , 2006 .

[12]  Mazhar Ali,et al.  Computation of loadability limit in power system based on newton-bisection algorithm , 2016, 2016 IEEE International Conference on Power System Technology (POWERCON).

[13]  T. K. Sunil Kumar,et al.  Optimal power flow-based congestion management in restructured power systems , 2016 .

[14]  Ravinder Kumar,et al.  Impact of STATCOM Control Parameters on Available Transfer Capability Enhancement in Energy Markets , 2015 .

[15]  Lyne Woodward,et al.  A new approach to Particle Swarm Optimization for dynamic systems with multiple units , 2016, IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society.

[16]  Samir Mbarki,et al.  A New Robust Blind Watermarking Scheme Based on Steerable pyramid and DCT using Pearson product moment correlation , 2014, J. Comput..

[17]  R. Kavasseri,et al.  A faster continuation power flow in rectangular coordinates for voltage stability assessment , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[18]  M. Shahidehpour,et al.  Restructured Electrical Power Systems: Operation: Trading, and Volatility , 2001 .

[19]  Nirbhow Jap Singh,et al.  Application of Particle Swarm Optimization , 2012 .

[20]  Hirotaka Yoshida,et al.  A PARTICLE SWARM OPTIMIZATION FOR REACTIVE POWER AND VOLTAGE CONTROL CONSIDERING VOLTAGE STABILITY , 2000 .

[21]  N. Arunkumar,et al.  Automatic Detection of Epileptic Seizures Using Permutation Entropy, Tsallis Entropy and Kolmogorov Complexity , 2016 .

[22]  Hongbin Sun,et al.  Transmission Contingency Analysis Based on Integrated Transmission and Distribution Power Flow in Smart Grid , 2015, IEEE Transactions on Power Systems.

[23]  N. Arunkumar,et al.  Automatic Detection of Epileptic Seizures Using New Entropy Measures , 2016 .

[24]  S. Nagalakshmi,et al.  On-line evaluation of loadability limit for pool model with TCSC using back propagation neural network , 2013 .

[25]  Belkacem Mahdad,et al.  Adaptive differential search algorithm for optimal location of distributed generation in the presence of SVC for power loss reduction in distribution system , 2016 .

[26]  Akanksha Mishra,et al.  A Fast Contingency Management Approach with Optimal Placement and Tuning of Interline Power Flow Controller Using Differential Evolution Algorithm , 2016 .

[27]  M. Saravanan,et al.  Application of particle swarm optimization technique for optimal location of FACTS devices considering cost of installation and system loadability , 2007 .