Real-time robust forecasting-aided state estimation of power system based on data-driven models

Abstract This paper presents a real-time robust power system forecasting-aided state estimation method based on the Bayesian framework, deep learning, and Gaussian mixture model to dynamically estimate the non-Gaussian measurement noise in the real-time power system. The approach is data driven and model independent. A non-linear mapping function between measurement and state is formulated based on the historical operating data of the power system and the Gaussian process. Then combine the anomaly detection technology in machine learning and the Gaussian mixture model to accurately judge and delete the abnormal data in measurement information. Thus, a power system state forecasting model based on long-short term memory neural network is established, which can solve the problem of missing data combining power flow calculation. Numerical simulations carried out on the IEEE 118-bus and IEEE 300-bus test system reveal that the proposed method has high accuracy and robustness.

[1]  Shuai Lu,et al.  Estimation of the Dynamic States of Synchronous Machines Using an Extended Particle Filter , 2013, IEEE Transactions on Power Systems.

[2]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[3]  Yi Wang,et al.  Robust Forecasting-Aided State Estimation for Power System Against Uncertainties , 2020, IEEE Transactions on Power Systems.

[4]  Xiaodong Wang,et al.  Distributed Point-Based Gaussian Approximation Filtering for Forecasting-Aided State Estimation in Power Systems , 2016, IEEE Transactions on Power Systems.

[5]  Gang Wang,et al.  Robust and Scalable Power System State Estimation via Composite Optimization , 2017, IEEE Transactions on Smart Grid.

[6]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[7]  Chao Ren,et al.  A Fully Data-Driven Method Based on Generative Adversarial Networks for Power System Dynamic Security Assessment With Missing Data , 2019, IEEE Transactions on Power Systems.

[8]  Ketan Rajawat,et al.  An Asynchronous Decentralized Forecasting-Aided State Estimator for Power Systems , 2019, IEEE Transactions on Power Systems.

[9]  Lamine Mili,et al.  Robust Unscented Kalman Filter for Power System Dynamic State Estimation With Unknown Noise Statistics , 2019, IEEE Transactions on Smart Grid.

[10]  Heng Tao Shen,et al.  Video Captioning With Attention-Based LSTM and Semantic Consistency , 2017, IEEE Transactions on Multimedia.

[11]  N. S. Vichare,et al.  Robust state estimation based on projection statistics [of power systems] , 1996 .

[12]  Shaobu Wang,et al.  Fast robust power system dynamic state estimation using model transformation , 2020 .

[13]  E. Handschin,et al.  Static state estimation in electric power systems , 1974 .

[14]  Lang Tong,et al.  Bayesian State Estimation for Unobservable Distribution Systems via Deep Learning , 2018, IEEE Transactions on Power Systems.

[15]  Ali Abur,et al.  Power System Dynamic State Estimation: Motivations, Definitions, Methodologies, and Future Work , 2019, IEEE Transactions on Power Systems.

[16]  Kit Po Wong,et al.  Forecasting-Aided Imperfect False Data Injection Attacks Against Power System Nonlinear State Estimation , 2016, IEEE Transactions on Smart Grid.

[17]  Jiandong Duan,et al.  Unscented Kalman Filter With Generalized Correntropy Loss for Robust Power System Forecasting-Aided State Estimation , 2019, IEEE Transactions on Industrial Informatics.

[18]  L. Mili,et al.  A Robust Iterated Extended Kalman Filter for Power System Dynamic State Estimation , 2017, IEEE Transactions on Power Systems.

[19]  Kai Sun,et al.  Dynamic State Estimation for Multi-Machine Power System by Unscented Kalman Filter With Enhanced Numerical Stability , 2015, IEEE Transactions on Smart Grid.

[20]  Mohamed E. El-Hawary,et al.  Optimized Neural Network Parameters Using Stochastic Fractal Technique to Compensate Kalman Filter for Power System-Tracking-State Estimation , 2019, IEEE Transactions on Neural Networks and Learning Systems.

[21]  Weng Khuen Ho,et al.  Robust Power System State Estimation Using t-Distribution Noise Model , 2020, IEEE Systems Journal.

[22]  Jingrui Zhang,et al.  A Distributed Robust Power System State Estimation Approach Using $t$-Distribution Noise Model , 2021, IEEE Systems Journal.

[23]  Frank K. Soong,et al.  Effective Spectral and Excitation Modeling Techniques for LSTM-RNN-Based Speech Synthesis Systems , 2017, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[24]  Ankush Sharma,et al.  Unscented Rauch–Tung–Streibel smoother-based power system forecasting-aided state estimator using hybrid measurements , 2019 .

[25]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[26]  Victoria J. Hodge,et al.  A Survey of Outlier Detection Methodologies , 2004, Artificial Intelligence Review.

[27]  Zuyi Li,et al.  Distribution System State Estimation: A Semidefinite Programming Approach , 2019, IEEE Transactions on Smart Grid.

[28]  Lei Ding,et al.  A hybrid robust forecasting-aided state estimator considering bimodal Gaussian mixture measurement errors , 2020 .

[29]  Gang Wang,et al.  Real-Time Power System State Estimation and Forecasting via Deep Unrolled Neural Networks , 2018, IEEE Transactions on Signal Processing.

[30]  Zhao Yang Dong,et al.  Robust Forecasting Aided Power System State Estimation Considering State Correlations , 2018, IEEE Transactions on Smart Grid.

[31]  Jovica V. Milanović,et al.  Voltage Sag Estimation in Sparsely Monitored Power Systems Based on Deep Learning and System Area Mapping , 2018, IEEE Transactions on Power Delivery.

[32]  J.C.S. de Souza,et al.  Forecasting-Aided State Estimation—Part I: Panorama , 2009 .

[33]  Yuan Zhang,et al.  Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network , 2019, IEEE Transactions on Smart Grid.

[34]  Zhenyu Huang,et al.  Assessing Gaussian Assumption of PMU Measurement Error Using Field Data , 2018, IEEE Transactions on Power Delivery.

[35]  Kai Chen,et al.  Training Deep Bidirectional LSTM Acoustic Model for LVCSR by a Context-Sensitive-Chunk BPTT Approach , 2016, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[36]  Lamine Mili,et al.  A Robust Generalized-Maximum Likelihood Unscented Kalman Filter for Power System Dynamic State Estimation , 2018, IEEE Journal of Selected Topics in Signal Processing.

[37]  William W. Hager,et al.  Updating the Inverse of a Matrix , 1989, SIAM Rev..