Performance and Accuracy Investigation of the Two-Step Algorithm for Power System State and Line Temperature Estimation

Data concerning actual temperatures of line conductors constitutes essential information for the power system operator. The temperature of the power lines can be used to improve the accuracy of the power system model, thereby increasing the accuracy of the state estimation. This article presents a two-step algorithm for the power system state and line temperature estimation. In its second stage, the proposed method searches for a line temperatures vector, which corrects the uncertain power system base model and allows for further minimization of an objective function. As a result, a more accurate estimation is obtained along with a more precise model of the estimated system. The derived model can then be used for more accurate optimization. The presented method enhances standard procedures of power system state estimation, and its advantage is that it does not require direct measurements performed by phasor measurement units or measurements of line conductor temperatures and weather conditions realized by dynamic line rating systems. The results of simulations made on various test models have been examined, confirming the convergence of the procedure to the point at which the average temperature of the line wires together with the voltage values and phase angles are achieved. The algorithm’s performance and improvement method have also been presented. An advantage of the investigated approach is the possibility to calculate the temperature of line wires with the use of primary measurements in the power system. The presented and examined method, however, is sensitive to the measuring device errors. Additionally, an analysis of the method’s errors and ways of reducing them has been performed.

[1]  M. Majidi,et al.  Distribution system state estimation using compressive sensing , 2017 .

[2]  Fred C. Schweppe,et al.  Power System Static-State Estimation, Part I: Exact Model , 1970 .

[3]  William A. Chisholm,et al.  Key Considerations for the Selection of Dynamic Thermal Line Rating Systems , 2015, IEEE Transactions on Power Delivery.

[4]  Alfredo Vaccaro,et al.  Dynamic loading of overhead lines by adaptive learning techniques and distributed temperature sensing , 2007 .

[5]  Sermsak Uatrongjit,et al.  Power System State and Transmission Line Conductor Temperature Estimation , 2017, IEEE Transactions on Power Systems.

[6]  V. Cecchi,et al.  Incorporating Temperature Variations Into Transmission-Line Models , 2011, IEEE Transactions on Power Delivery.

[7]  Cong Ling,et al.  A Robust WLS Power System State Estimation Method Integrating a Wide-Area Measurement System and SCADA Technology , 2015 .

[8]  Ali Abur,et al.  Robust State Estimation Against Measurement and Network Parameter Errors , 2018, IEEE Transactions on Power Systems.

[9]  Jake P. Gentle,et al.  Improvement of Transmission Line Ampacity Utilization by Weather-Based Dynamic Line Rating , 2018, IEEE Transactions on Power Delivery.

[10]  Valentina Cecchi,et al.  System impacts of temperature-dependent transmission line models , 2014, 2014 IEEE PES General Meeting | Conference & Exposition.

[11]  M. M. Morcos,et al.  An Application of Dynamic Thermal Line Rating Control System to Up-Rate the Ampacity of Overhead Transmission Lines , 2013, IEEE Transactions on Power Delivery.

[12]  A.G. Exposito,et al.  Planning and Operational Issues Arising From the Widespread Use of HTLS Conductors , 2007, IEEE Transactions on Power Systems.

[13]  Ali Abur,et al.  LAV Based Robust State Estimation for Systems Measured by PMUs , 2014, IEEE Transactions on Smart Grid.

[14]  V. Vittal,et al.  Mechanical State Estimation for Overhead Transmission Lines With Level Spans , 2008, IEEE Transactions on Power Systems.

[15]  Piotr Kacejko,et al.  Overhead Transmission Line Sag Estimation Using a Simple Optomechanical System with Chirped Fiber Bragg Gratings. Part 1: Preliminary Measurements , 2018, Sensors.

[16]  M. M. Werneck,et al.  Hybrid Optoelectronic Sensor for Current and Temperature Monitoring in Overhead Transmission Lines , 2012, IEEE Sensors Journal.

[17]  Vijay Vittal,et al.  Mechanical State Estimation of Overhead Transmission Lines Using Tilt Sensors , 2010, IEEE Transactions on Power Systems.

[18]  V. T. Morgan,et al.  Effects of alternating and direct current, power frequency, temperature, and tension on the electrical parameters of ACSR conductors , 2003 .

[19]  Christian Rehtanz,et al.  Application of a combined electro-thermal overhead line model in power flow and time-domain power system simulations , 2017 .

[20]  Vincent T. Morgan The Current Distribution, Resistance and Internal Inductance of Linear Power System Conductors—A Review of Explicit Equations , 2013, IEEE Transactions on Power Delivery.

[21]  Keith Lindsey,et al.  Real-Time Overhead Transmission-Line Monitoring for Dynamic Rating , 2016, IEEE Transactions on Power Delivery.

[22]  Gabriele D'Antona,et al.  Power System Static-State Estimation With Uncertain Network Parameters as Input Data , 2016, IEEE Transactions on Instrumentation and Measurement.

[23]  Fred C. Schweppe,et al.  Power System Static-State Estimation, Part II: Approximate Model , 1970 .

[24]  Fred C. Schweppe,et al.  Power System Static-State Estimation, Part III: Implementation , 1970 .

[25]  R D Zimmerman,et al.  MATPOWER: Steady-State Operations, Planning, and Analysis Tools for Power Systems Research and Education , 2011, IEEE Transactions on Power Systems.

[26]  Katarzyna Mazur,et al.  Secure and Time-Aware Communication of Wireless Sensors Monitoring Overhead Transmission Lines , 2017, Sensors.