Doubly-fed Deep Learning Method for Bad Data Identification in Linear State Estimation

With more data-driven applications introduced in wide-area monitoring systems(WAMS),data quality of phasor measurement units(PMUs)becomes one of the fundamental requirements for ensuring reliable WAMS applications.This paper proposes a doubly-fed deep learning method for bad data identification in linear state estimation,which can:(1)identify bad data under both steady states and contingencies;(2)achieve higher accuracy than conventional pre-filtering approaches;(3)reduce iteration burden for linear state estimation;(4)efficiently identify bad data in a parallelizable scheme.The proposed method consists of four key steps:(1)preprocessing filter;(2)online training of short-term deep neural network;(3)offline training of long-term deep neural network;(4)a decision merger.Through delicate design and comprehensive training,the proposed method can effectively differentiate the bad data from event data without relying on real-time topology information.An IEEE 39-bus system simulated by DSATools TSAT and a provincial electric power system with real PMU data collected are used to verify the proposed method.Multiple test scenarios are applied,which include steady states,three-phase-to-ground faults with(un)successful auto-reclosing,low-frequency oscillation,and low-frequency oscillation with simultaneous threephase-to-ground faults.The proposed method demonstrates satisfactory performance during both the training session and the testing session.

Rational Identification of Lightning Derivative Disasters by Strong Convective Weather Monitoring Data in Southern China——A Case Study in Guangzhou Development Region

Analytic method and identification direction for rational identification of lightning derivative disasters by strong convective weather monitoring data in southern China were introduced. Taking identification cases of lightning disaster in Guangzhou Development Region as the background,according to the characteristics in the region that large high-precision enterprises were more,lightning derivative disasters occurred frequently in thunderstorm season,and the actual situation that time of the affected enterprise applying for lightning disaster scene identification lagged,combining Technical Specifications of Lightning Disaster Investigation（ QX / T103-2009）,qualitative analysis method of lightning derivative disaster was put forward under the weather condition of strong convection in southern China by using weather monitoring data（ Doppler sounding radar data,lightning positioning monitoring data,atmospheric electric field data,rainfall data,wind direction and force）,and was optimized by technical means（ ＂ metallographic method＂ and ＂ remanence law＂）. The research could put forward efficient and convenient analytical thinking and method for lightning derivative disaster,and further optimize accuracy and credibility of lightning disaster investigation.

RECONSTRUCTION OF PART OF AN ACTUAL BLAST-WAVE FLOW FIELD TO AGREE WITH EXPERIMENTAL DATA BY USING NUMERICAL METHOD WITH HIGH IDENTIFICATION

In this paper, on the basis of experimental data of two kinds of chemical explosions, the piston-pushing model of spherical blast-waves and the second-order Godunov-type scheme of finite difference methods with high identification to discontinuity are used to the numerical reconstruction of part of an actual hemispherical blast-wave flow field by properly adjusting the moving bounary conditions of a piston. This method is simple and reliable. It is suitable to the evaluation of effects of the blast-wave flow field away from the explosion center.

A MODEL IDENTIFICATION METHOD OF VIBRATING STRUCTURES FROM INCOMPLETE MODAL INFORMATION

The accurate mathematical models for complicated structures are very difficult to construct.The work presented here provides an identification method for estimating the mass.damping,and stiffness matrices of linear dynamical systems from incomplete experimental data.The mass,stiffness and damping matrices are assumed to be real,symmetric,and positive definite The partial set of experimental complex eigenvalues and corresponding eigenvectors are given.In the proposed method the least squares algorithm is combined with the iteration technique to determine systems identified matrices and corresponding design parameters.Seeveral illustative examples,are presented to demonstrate the reliability of the proposed method .It is emphasized that the mass,damping and stiffness matrices can be identified simultaneously.

Bad Data Detection Algorithm for PMU Based on Spectral Clustering

Phasor measurement units(PMUs) can provide real-time measurement data to construct the ubiquitous electric of the Internet of Things. However, due to complex factors on site, PMU data can be easily compromised by interference or synchronization jitter. It will lead to various levels of PMU data quality issues, which can directly affect the PMU-based application and even threaten the safety of power systems. In order to improve the PMU data quality, a data-driven PMU bad data detection algorithm based on spectral clustering using single PMU data is proposed in this paper. The proposed algorithm does not require the system topology and parameters. Firstly, a data identification method based on a decision tree is proposed to distinguish event data and bad data by using the slope feature of each data. Then, a bad data detection method based on spectral clustering is developed. By analyzing the weighted relationships among all the data, this method can detect the bad data with a small deviation. Simulations and results of field recording data test illustrate that this data-driven method can achieve bad data identification and detection effectively. This technique can improve PMU data quality to guarantee its applications in the power systems.

Synchrophasor-based Online State Estimated in Large-scale Power Grid

With integration of a larger amount of clean power sources and power electronic equipment,operation and dynamic characteristics of the power grid are becoming more and more complicated and stochastic.Therefore,it is necessary and urgent to obtain accurate real-time states,which is difficult from traditional state estimation.This paper systematically develops a phasor measurement unit(PMU)based real-time state estimator for a realistic large-scale power grid for the first time.The estimator mainly relies on three refined algorithms,i.e.,an improved linear state estimation algorithm,a practical bad data identification method and a distributed topology check technique.Furthermore,a novel system architecture is designed and implemented for the China Southern Power Grid.Numerical simulations and extensive field operation results of the state estimator recorded under both normal and abnormal situations are presented.All the tests and field results demonstrate the advantages of the proposed algorithms in terms of online system monitoring and feasibility of refreshing the states of the whole system at intervals of tens of milliseconds.

Robust State Estimation of Active Distribution Networks with Multi-source Measurements

The volatile and intermittent nature of distributed generators(DGs) in active distribution networks(ADNs) increases the uncertainty of operating states. The introduction of distribution phasor measurement units(D-PMUs) enhances the monitoring level. The trade-offs of computational performance and robustness of state estimation in monitoring the network states are of great significance for ADNs with D-PMUs and DGs. This paper proposes a second-order cone programming(SOCP) based robust state estimation(RSE) method considering multi-source measurements. Firstly, a linearized state estimation model related to the SOCP state variables is formulated. The phase angle measurements of D-PMUs are converted to equivalent power measurements. Then, a revised SOCP-based RSE method with the weighted least absolute value estimator is proposed to enhance the convergence and bad data identification. Multi-time slots of D-PMU measurements are utilized to improve the estimation accuracy of RSE. Finally, the effectiveness of the proposed method is illustrated in the modified IEEE 33-node and IEEE 123-node systems.

State Estimation Approach for Combined Heat and Electric Networks

State estimation(SE)is essential for combined heat and electric networks(CHENs)to provide a global and selfconsistent solution for multi-energy flow analysis.This paper proposes an SE approach for CHEN based on steady models of electric networks(ENs)and district heating networks(DHNs).A range of coupling components are considered.The performance of the proposed estimator is evaluated using Monte Carlo simulations and case studies.Results show that a relationship between the measurements from ENs and DHNs can improve the estimation accuracy for the entire network by using the combined SE model,especially when ENs and DHNs are strongly coupled.The coupling constraints could also provide extra redundancy to detect bad data in the boundary injection measurements of both networks.An analysis of computation time shows that the proposed method is suitable for online applications.