Bad data detection in state estimation using Decision Tree technique

State estimation is a useful study in every control and dispatching center that its outcome is used for other programs like optimal power flow and load frequency control. State estimation uses network model and a group of measurements to calculate the best estimation of state variables of system. Measurements may get amounts of error in data reading or transmitting. One of important issue of state estimator is bad data detection. A famous method for bad data detection is Chi-square method but it has some defections. In this paper Probabilistic Neural Network (PNN) and Decision Tree (DT) are used to detect bad data detection in state estimation. The proposed method was simulated on IEEE30 buses network and Chi-square, PNN and DT methods are applied to scenarios and results is compared.

[1]  A. Abur,et al.  Multi area state estimation using synchronized phasor measurements , 2005, IEEE Transactions on Power Systems.

[2]  Nikolaos M. Manousakis,et al.  A state estimator including conventional and synchronized phasor measurements , 2012, Comput. Electr. Eng..

[3]  Mohamad Musavi,et al.  Bad Data Detection and Identification Using Neural Network-Based Reduced Model State Estimator , 2013, 2013 IEEE Green Technologies Conference (GreenTech).

[4]  S.M.T. Bathaee,et al.  Prediction of unplanned islanding using an energy based strategy , 2016 .

[5]  Turaj Amraee.  Loss-of-field detection in synchronous generators using decision tree technique , 2013 .

[6]  Elias Kyriakides,et al.  Estimation of transmission line parameters using PMU measurements , 2015, 2015 IEEE Power & Energy Society General Meeting.

[7]  R.P. Maheshwari,et al.  Power Transformer Differential Protection Based On Optimal Probabilistic Neural Network , 2010, IEEE Transactions on Power Delivery.

[8]  Babak Mozafari,et al.  Optimal placement of PMUs to maintain network observability using a modified BPSO algorithm , 2011 .

[9]  Shyh-Jier Huang,et al.  Enhancement of power system data debugging using GSA-based data-mining technique , 2002 .

[10]  A.M. Ranjbar,et al.  Optimal Placement of Phasor Measurement Units: Particle Swarm Optimization Approach , 2007, 2007 International Conference on Intelligent Systems Applications to Power Systems.

[11]  M. Tarafdar Haque,et al.  Application of Neural Networks in Power Systems; A Review , 2007 .

[12]  Turaj Amraee,et al.  Blackout prediction in interconnected electric energy systems considering generation re-dispatch and energy curtailment , 2017 .

[13]  Allen J. Wood,et al.  Power Generation, Operation, and Control , 1984 .

[14]  Shyh-Jier Huang,et al.  Artificial neural network enhanced by gap statistic algorithm applied for bad data detection of a power system , 2002, IEEE/PES Transmission and Distribution Conference and Exhibition.

[15]  A. G. Expósito,et al.  Power system state estimation : theory and implementation , 2004 .

[16]  Soheil Ranjbar,et al.  Transient Instability Prediction Using Decision Tree Technique , 2013, IEEE Transactions on Power Systems.

[17]  A. Abur,et al.  Bad Data Identification When Using Phasor Measurements , 2007, 2007 IEEE Lausanne Power Tech.

[18]  A. Abur,et al.  A modified Chi-Squares test for improved bad data detection , 2015, 2015 IEEE Eindhoven PowerTech.

[19]  Babak Mozafari,et al.  Relay logic for islanding detection in active distribution systems , 2015 .

[20]  E. Kyriakides,et al.  PMU Measurement Uncertainty Considerations in WLS State Estimation , 2009, IEEE Transactions on Power Systems.

[21]  S.P. Teeuwsen,et al.  Neural network based multi-dimensional feature forecasting for bad data detection and feature restoration in power systems , 2006, 2006 IEEE Power Engineering Society General Meeting.

[22]  A. Abur,et al.  Effect of Phasor Measurements on the Choice of Reference Bus for State Estimation , 2007, 2007 IEEE Power Engineering Society General Meeting.

[23]  J. P. Pandey,et al.  Topology identification, bad data processing, and state estimation using fuzzy pattern matching , 2005, IEEE Transactions on Power Systems.

[24]  Qing Zhang,et al.  Impact of PMU Measurement Buffer Length on State Estimation and its Optimization , 2013, IEEE Transactions on Power Systems.

[25]  Donald F. Specht,et al.  Probabilistic neural networks , 1990, Neural Networks.