Local Hybrid Linear State Estimation for Electric Power Systems Using Stream Processing

The increasing penetration of renewable energy resources with highly fluctuating outputs has placed increasing concern on the accuracy and timeliness of electric power system state estimation(SE).Meanwhile,we note that only a fraction of system states fluctuate at the millisecond level and require to be updated.As such,refreshing only those states with significant variation would enhance the computational efficiency of SE and make fast-continuous update of states possible.However,this is difficult to achieve with conventional SE methods,which generally refresh states of the entire system every 4–5 s.In this context,we propose a local hybrid linear SE framework using stream processing,in which synchronized measurements received from phasor measurement units(PMUs),and trigger/timingmode measurements received from remote terminal units(RTUs)are used to update the associated local states.Moreover,the measurement update process efficiency and timeliness are enhanced by proposing a trigger measurement-based fast dynamic partitioning algorithm for determining the areas of the system with states requiring recalculation.In particular,non-iterative hybrid linear formulations with both RTUs and PMUs are employed to solve the local SE problem.The timeliness,accuracy,and computational efficiency of the proposed method are demonstrated by extensive simulations based on IEEE 118-,300-,and 2383-bus systems.

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.

Neural-network-based Power System State Estimation with Extended Observability

This paper proposes a neural-network-based state estimation(NNSE)method that aims to achieve higher time efficiency,improved robustness against noise,and extended observability when compared with the conventional weighted least squares(WLS)state estimation method.NNSE consists of two parts,the linear state estimation neural network(LSE-net)and the unobservable state estimation neural network(USE-net).The LSE-net functions as an adaptive approximator of linear state estimation(LSE)equations to estimate the nominally observable states.The inputs of LSE-net are the vectors of synchrophasors while the outputs are the estimated states.The USE-net operates as the complementary estimator on the nominally unobservable states.The inputs are the estimated observable states from LSE-net while the outputs are the estimation of nominally unobservable states.USE-net is trained off-line to approximate the veiled relationship between observable states and unobservable states.Two test cases are conducted to validate the performance of the proposed approach.The first case,which is based on the IEEE 118-bus system,shows the comprehensive performance of convergence,accuracy,and robustness of the proposed approach.The second case study adopts real-world synchrophasor measurements,and is based on the Jiangsu power grid,which is one of the largest provincial power systems in China.

Enhanced Denoising Autoencoder-aided Bad Data Filtering for Synchrophasor-based State Estimation

Due to its high accuracy and ease of calculation,synchrophasor-based linear state estimation(LSE)has attracted a lot of attention in the last decade and has formed the cornerstone of many wide area monitor system(WAMS)applications.However,an increasing number of data quality concerns have been reported,among which bad data can significantly undermine the performance of LSE and many other WAMS applications it supports.Bad data filtering can be difficult in practice due to a variety of issues such as limited processing time,non-uniform and changing patterns,and etc.To pre-process phasor measurement unit(PMU)measurements for LSE,we propose an improved denoising autoencoder(DA)-aided bad data filtering strategy in this paper.Bad data is first identified by the classifier module of the proposed DA and then recovered by the autoencoder module.Two characteristics distinguish the proposed methodology:1)The approach is lightweight and can be implemented at individual PMU level to achieve maximum parallelism and high efficiency,making it suited for real-time processing;2)the system not only identifies bad data but also recovers it,especially for critical measurements.We use numerical experiments employing both simulated and real-world phasor data to validate and illustrate the effectiveness of the proposed method.

Synchrophasor-based real-time state estimation and situational awareness system for power system operation

State estimation is a critical functionality of energy management system(EMS) to provide power system states in real-time operations. However, problems such as failure to converge, prone to failure during contingencies,and biased estimates while system is under stressed condition occur so that state estimation results may not be reliable.The unreliable results further impact downstream network and market applications, such as contingency analysis,voltage stability analysis, transient stability analysis, system alarming, and unit commitment. Thus, operators may lose the awareness of system condition in EMS. This paper proposes a fully independent and one-of-a-kind system by integrating linear state estimator into situational awareness applications based on real-time synchrophasor data. With guaranteed and accurate state estimation solution and advanced real-time data analytic and monitoring functionalities, the system is capable of assisting operators to assess and diagnose current system conditions for proactive and necessary corrective actions. The architecture, building components, and implementation of the proposed system are explored in detail. Two case studies with simulated data from the subsystems of Electric Reliability Council of Texas(ERCOT) and Los Angeles Department of Water and Power(LADWP) are presented. The test results show the effectiveness and reliability of the system, and its value for realtime power system operations.