Data-Driven Multi-Hidden Markov Model-Based Power Quality Disturbance Prediction That Incorporates Weather Conditions

Power quality (PQ) disturbance in power systems has been a concern for operators and customers. The purpose is to locate and forecast the presence of PQ disturbances to suppress or avoid their negative effects on power grid and appliances. This study, using a multi-hidden Markov model (MHMM), motivates data-driven tools to achieve situation awareness of PQ disturbance. We first design a modified adaptive-sorted neighborhood method that consists of blocking and merging phases to locate PQ disturbance sources from a large volume of PQ records. We then group and discretize the data on disturbance sources and weather conditions according to regions. The capability of MHMM-based tools to predict future PQ disturbance level can be improved by clustering the training set of time series of PQ disturbance levels based on weather conditions. A Hadoop-based PQ analysis framework is proposed to reduce computational times, considering the volume of PQ data in a realistic power grid is large. We utilize numerical case studies that use real data collected from a power system of a Chinese city to investigate the correctness and feasibility of the proposed method.

[1]  Zhiyong Yuan,et al.  Visualization of wide area measurement information from the FNET system , 2011, 2011 IEEE Power and Energy Society General Meeting.

[2]  Qian Ai,et al.  Research on dynamic load modelling based on power quality monitoring system , 2013 .

[3]  Pablo Aravena,et al.  A Simple Predictive Method to Estimate Flicker , 2014 .

[4]  V. Katie,et al.  Effects of low power electronics & computer equipment on power quality at distribution grid-measurements and forecast , 2004, 2004 IEEE International Conference on Industrial Technology, 2004. IEEE ICIT '04..

[5]  Lawrence R. Rabiner,et al.  A tutorial on hidden Markov models and selected applications in speech recognition , 1989, Proc. IEEE.

[6]  Kun-Huang Huarng,et al.  A neural network-based fuzzy time series model to improve forecasting , 2010, Expert Syst. Appl..

[7]  G.W. Chang,et al.  On Tracking the Source Location of Voltage Sags and Utility Shunt Capacitor Switching Transients , 2008, IEEE Transactions on Power Delivery.

[8]  Sheng-Tun Li,et al.  A Stochastic HMM-Based Forecasting Model for Fuzzy Time Series , 2010, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[9]  Robin Girard,et al.  Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production , 2018, IEEE Transactions on Sustainable Energy.

[10]  Tomáš Vantuch,et al.  The Power Quality Forecasting Model for Off-Grid System Supported by Multiobjective Optimization , 2017, IEEE Transactions on Industrial Electronics.

[11]  Ian Dobson,et al.  Evidence for self-organized criticality in a time series of electric power system blackouts , 2004, IEEE Transactions on Circuits and Systems I: Regular Papers.

[12]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .

[13]  Maozhen Li,et al.  Hadoop-based framework for big data analysis of synchronised harmonics in active distribution network , 2017 .

[14]  Ali Dastfan,et al.  Modeling and Forecasting Nonstationary Voltage Fluctuation Based on Grey System Theory , 2017, IEEE Transactions on Power Delivery.

[15]  Seung-Il Moon,et al.  Topological Locating of Power Quality Event Source , 2006 .

[16]  Lasantha Meegahapola,et al.  Characterisation of flicker emission and propagation in distribution networks with bi-directional power flows , 2014 .

[17]  Bart Kosko,et al.  Fuzzy function approximation with ellipsoidal rules , 1996, IEEE Trans. Syst. Man Cybern. Part B.

[18]  João Gama,et al.  Predicting Ramp Events with a Stream-Based HMM Framework , 2012, Discovery Science.

[19]  Henrik Madsen,et al.  Spatio‐temporal analysis and modeling of short‐term wind power forecast errors , 2011 .

[20]  B. Vahidi,et al.  A New Stochastic Model of Electric Arc Furnace Based on Hidden Markov Model: A Study of Its Effects on the Power System , 2012, IEEE Transactions on Power Delivery.

[21]  Qingqing Huang,et al.  Dynamic Detection of Transmission Line Outages Using Hidden Markov Models , 2016, IEEE Transactions on Power Systems.

[22]  Arash Asrari,et al.  A Hybrid Algorithm for Short-Term Solar Power Prediction—Sunshine State Case Study , 2017, IEEE Transactions on Sustainable Energy.

[23]  M. Kezunovic,et al.  Integrated Fault Location and Power-Quality Analysis in Electric Power Distribution Systems , 2016, IEEE Transactions on Power Delivery.

[24]  Jovica V. Milanovic,et al.  Assessment of the Economic Value of Voltage Sag Mitigation Devices to Sensitive Industrial Plants , 2015, IEEE Transactions on Power Delivery.

[25]  G. W. Chang,et al.  Forecasting Flicker Severity by Grey Predictor , 2012, IEEE Transactions on Power Delivery.

[26]  Eamonn J. Keogh,et al.  A symbolic representation of time series, with implications for streaming algorithms , 2003, DMKD '03.

[27]  Dong-xiao Niu,et al.  Mid-long Term Load Forecasting Using Hidden Markov Model , 2009, 2009 Third International Symposium on Intelligent Information Technology Application.

[28]  B. Vahidi,et al.  A new stochastic method based on Hidden Markov Models to transformer differential protection , 2008, 2008 11th International Conference on Optimization of Electrical and Electronic Equipment.

[29]  Saeed Jazebi,et al.  A Novel Algorithm for Fault Type Fast Diagnosis in Overhead Transmission Lines Using Hidden Markov Models , 2011 .

[30]  C. Lee Giles,et al.  Adaptive sorted neighborhood methods for efficient record linkage , 2007, JCDL '07.

[31]  Xiaodong Liu,et al.  Fuzzy forecasting based on automatic clustering and axiomatic fuzzy set classification , 2015, Inf. Sci..

[32]  Kui Wu,et al.  A Machine Learning Approach to Meter Placement for Power Quality Estimation in Smart Grid , 2016, IEEE Transactions on Smart Grid.

[33]  Drago Dolinar,et al.  Detection of voltage sag sources based on instantaneous voltage and current vectors and orthogonal Clarke's transformation , 2008 .

[34]  S. Jazebi,et al.  Power Quality Disturbance Classification Using S-transform and Hidden Markov Model , 2012 .

[35]  Najeh Chaâbane,et al.  A novel auto-regressive fractionally integrated moving average–least-squares support vector machine model for electricity spot prices prediction , 2014 .

[36]  Johan Driesen,et al.  Assessment of voltage sag indices based on scaling and wavelet coefficient energy analysis , 2013, 2013 IEEE Power & Energy Society General Meeting.

[37]  Tom White,et al.  Hadoop: The Definitive Guide , 2009 .

[38]  James S. Thorp,et al.  Synchronized Phasor Measurement Applications in Power Systems , 2010, IEEE Transactions on Smart Grid.

[39]  Minwu Chen,et al.  Harmonic modelling and prediction of high speed electric train based on non-parametric confidence interval estimation method , 2017 .

[40]  Chia-Nan Ko,et al.  Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter , 2013 .

[41]  S. Mishra,et al.  Detection and Classification of Power Quality Disturbances Using S-Transform and Probabilistic Neural Network , 2008, IEEE Transactions on Power Delivery.