A New Fast and Efficient Artificial Neural Network Based State Estimator Incorporated into a Linear Optimal Regulator for Power System Control Enhancement

Abstract—Fast and accurate state estimation has a crucial role in practical implementation of state feedback-based controllers. But most of the states are not accessible or economically feasible to measure and must be estimated. Although such controllers are normally designed offline, their dynamic performance is evaluated online. Hence, having high speed with acceptable accuracy is an essential feature for estimators. In this article, a state estimator based on an artificial neural network incorporated into a linear optimal regulator is introduced. First, an extended Kalman filter is designed, then an estimator based on a proposed feed-forward neural network structure is elaborated after much effort on promising neural network structures. Different neural networks are trained using the data collected from the extended Kalman filter, and a qualified and shapeable alternative for the extended Kalman filter in reducing its drawbacks is obtained. The optimum structure is identified when minimum state estimation error is achieved. Significant speed and sufficient accuracy are the main advantages of the proposed structure to be used online for state estimation. Dynamic performance of the system equipped with the linear optimal regulator plus different estimators is examined and compared to a single-machine/infinite-bus power system in MATLAB (The MathWorks, Natick, Massachusetts, USA).

[1]  Giorgio Battistelli,et al.  Moving-horizon state estimation for nonlinear systems using neural networks , 2008, CDC.

[2]  Y. H. Ku,et al.  Electric Power System Dynamics , 1983 .

[3]  Goran Strbac,et al.  A recursive Bayesian approach for identification of network configuration changes in distribution system state estimation , 2011, 2011 IEEE Power and Energy Society General Meeting.

[4]  Jian Wang,et al.  Short, medium and long term load forecasting model and virtual load forecaster based on radial basis function neural networks , 2010 .

[5]  Chee Kheong Siew,et al.  Universal Approximation using Incremental Constructive Feedforward Networks with Random Hidden Nodes , 2006, IEEE Transactions on Neural Networks.

[6]  I. Kamwa,et al.  Simultaneous state and input estimation of a synchronous machine using the Extended Kalman Filter with unknown inputs , 2011, 2011 IEEE International Electric Machines & Drives Conference (IEMDC).

[7]  Elyas Rakhshani,et al.  Intelligent Linear-Quadratic Optimal Output Feedback Regulator for a Deregulated Automatic Generation Control System , 2012 .

[8]  Babu Narayanan,et al.  Power system stability and control , 2007 .

[9]  Majid Poshtan,et al.  Detection of broken rotor bars in induction motors using nonlinear Kalman filters. , 2010, ISA transactions.

[10]  M. Jazaeri,et al.  Multi-mode genetic algorithm based linear optimal control design for power systems , 2009, 2009 International Conference on Sustainable Power Generation and Supply.

[11]  Z. Moravej, D. N. Vishwakarma, S. P. Singh,et al.  ANN-Based Protection Scheme for Power Transformer , 2000 .

[12]  Yuan Liao,et al.  Online Optimal Transmission Line Parameter Estimation for Relaying Applications , 2009, IEEE Transactions on Power Delivery.

[13]  Vassilios G. Agelidis,et al.  Undersampled on-line ANN-EKF based estimation of harmonics/interharmonics in power systems , 2010, IEEE PES General Meeting.

[14]  Giorgio Battistelli,et al.  Moving-horizon state estimation for nonlinear systems using neural networks , 2008, 2008 47th IEEE Conference on Decision and Control.

[15]  A. Karami,et al.  Power system transient stability margin estimation using neural networks , 2011 .

[16]  Sukumar Kamalasadan,et al.  A new intelligent algorithm for online voltage stability assessment and monitoring , 2009 .

[17]  Z. Dong,et al.  A Statistical Approach for Interval Forecasting of the Electricity Price , 2008, IEEE Transactions on Power Systems.

[18]  George N Korres,et al.  A Distributed Multiarea State Estimation , 2011, IEEE Transactions on Power Systems.

[19]  M. Ehsani,et al.  Simple Derivative-Free Nonlinear State Observer for Sensorless AC Drives , 2006, IEEE/ASME Transactions on Mechatronics.

[20]  D. N. Vishwakarma,et al.  ANN-Based Protection Scheme for Power Transformer , 2000 .

[21]  Carlos A. Castro,et al.  Artificial neural networks for load flow and external equivalents studies , 2010 .

[22]  D. Simon.  Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches , 2006 .

[23]  D. C. Macdonald,et al.  The design of turbine-generator optimal controllers including the effect of torsional modes of oscillation , 1988 .

[24]  E. Fallah,et al.  A new method for speed sensorless vector control of single-phase induction motor using Extended Kalman Filter , 2011, 2011 19th Iranian Conference on Electrical Engineering.

[25]  V. Tikhomirov.  On the Representation of Continuous Functions of Several Variables as Superpositions of Continuous Functions of one Variable and Addition , 1991 .

[26]  Seema Singh,et al.  Harmonics estimation in emerging power system: Key issues and challenges , 2011 .

[27]  L Giovanini,et al.  A fault detection and isolation filter for discrete linear systems. , 2003, ISA transactions.

[28]  Jeffrey K. Uhlmann,et al.  Unscented filtering and nonlinear estimation , 2004, Proceedings of the IEEE.

[29]  Imad M. Jaimoukha,et al.  A Study on LQG/LTR Control for Damping Inter-Area Oscillations in Power Systems , 2007, IEEE Transactions on Control Systems Technology.