Missing-sensor-fault-tolerant control for SSSC facts device with real-time implementation

Control of power systems relies on the availability and quality of sensor measurements. However, measurements are inevitably subjected to faults caused by sensor failure, broken or bad connections, bad communication, or malfunction of some hardware or software. These faults in turn may cause the failure of power system controllers and consequently severe contingencies in the power system. To avoid such contingencies, this paper presents a sensor evaluation and (missing sensor) restoration scheme (SERS) by using auto-associative neural networks (auto-encoders) and particle swarm optimization (PSO). Based on the SERS, a missing-sensor-fault-tolerant control (MSFTC) is developed for controlling a static synchronous series compensator (SSSC) connected to a power network. This MSFTC improves the reliability, maintainability and survivability of the SSSC and the power network. The effectiveness of the MSFTC is demonstrated by a real-time implementation of an SSSC connected to the IEEE 10-machine 39-bus system on the Real Time Digital Simulator (RTDS) and TMS320C6701 digital signal processor platform. The proposed fault-tolerant control can be readily applied to many existing controllers in power systems.

[1]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[2]  R.J. Marks,et al.  On the contractive nature of autoencoders: application to missing sensor restoration , 2003, Proceedings of the International Joint Conference on Neural Networks, 2003..

[3]  H. Bourlard,et al.  Auto-association by multilayer perceptrons and singular value decomposition , 1988, Biological Cybernetics.

[4]  F. Blaabjerg,et al.  Single current sensor technique in the DC-link of three-phase PWM-VS inverters. A review and the ultimate solution , 1996, IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting.

[5]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[6]  Laszlo Gyugyi,et al.  Static Synchronous Series Compensator: A Solid-State Approach to the Series Compensation of Transmission Lines , 1997 .

[7]  R.G. Harley,et al.  Wind Speed Estimation Based Sensorless Output Maximization Control for a Wind Turbine Driving a DFIG , 2008, IEEE Transactions on Power Electronics.

[8]  Riccardo Poli,et al.  Particle swarm optimization , 1995, Swarm Intelligence.

[9]  H. Glavitsch,et al.  Estimation of steady-state unbalanced system conditions combining conventional power flow and fault analysis software , 1995 .

[10]  Ron J. Patton,et al.  Fault-Tolerant Control: The 1997 Situation , 1997 .

[11]  R. G. Harley,et al.  Analysis of a power oscillation damping scheme using a voltage-source inverter , 2002 .

[12]  W. A. Kirk,et al.  An Introduction to Metric Spaces and Fixed Point Theory , 2001 .

[13]  F. Alvarado,et al.  Stability Analysis of Interconnected Power Systems Coupled with Market Dynamics , 2001, IEEE Power Engineering Review.

[14]  Wei Qiao,et al.  Indirect Adaptive External Neuro-Control for a Series Capacitive Reactance Compensator Based on a Voltage Source PWM Converter in Damping Power Oscillations , 2007, IEEE Transactions on Industrial Electronics.

[15]  Mohammed H. Haque.  Damping improvement by FACTS devices: A comparison between STATCOM and SSSC , 2006 .

[16]  Paul J. Werbos,et al.  Backpropagation Through Time: What It Does and How to Do It , 1990, Proc. IEEE.

[17]  Mark A. Kramer,et al.  Autoassociative neural networks , 1992 .

[18]  Ronald G. Harley,et al.  An improved control scheme for a series capacitive reactance compensator based on a voltage source inverter , 1996, IAS '96. Conference Record of the 1996 IEEE Industry Applications Conference Thirty-First IAS Annual Meeting.

[19]  R. Podmore,et al.  A Practical Method for the Direct Analysis of Transient Stability , 1979, IEEE Transactions on Power Apparatus and Systems.

[20]  O. Alsaç,et al.  Generalized state estimation , 1998 .

[21]  Zhi Gao,et al.  Robust neuro-identification of nonlinear plants in electric power systems with missing sensor measurements , 2008, Eng. Appl. Artif. Intell..

[22]  R. Kuffel,et al.  Real time digital simulation for control and protection system testing , 2004, 2004 IEEE 35th Annual Power Electronics Specialists Conference (IEEE Cat. No.04CH37551).