The Sequential Importance Sampling Particle Filter with a Systematic Resampling in the State Estimation of Power Systems

2018 ◽  
Author(s):  
Gabriel Intriago ◽  
Holger Cevallos ◽  
Douglas Plaza ◽  
Raul Intriago
Keyword(s):  
Power Systems ◽  
Particle Filter ◽  
State Estimation ◽  
Importance Sampling ◽  
The State ◽  
Sequential Importance Sampling

scholarly journals Methodology for Implementing the State Estimation in Renewable Energy Management Systems

Energies ◽  
2021 ◽  
Vol 14 (8) ◽  
pp. 2301
Author(s):  
Yun-Sung Cho ◽  
Yun-Hyuk Choi
Keyword(s):  
Renewable Energy ◽  
Power Systems ◽  
State Estimation ◽  
Measurement Errors ◽  
Large Scale ◽  
Test System ◽  
The State ◽  
Inner Function ◽  
Outer Function ◽  
Bad Data

This paper describes a methodology for implementing the state estimation and enhancing the accuracy in large-scale power systems that partially depend on variable renewable energy resources. To determine the actual states of electricity grids, including those of wind and solar power systems, the proposed state estimation method adopts a fast-decoupled weighted least square approach based on the architecture of application common database. Renewable energy modeling is considered on the basis of the point of data acquisition, the type of renewable energy, and the voltage level of the bus-connected renewable energy. Moreover, the proposed algorithm performs accurate bad data processing using inner and outer functions. The inner function is applied to the largest normalized residue method to process the bad data detection, identification and adjustment. While the outer function is analyzed whether the identified bad measurements exceed the condition of Kirchhoff’s current law. In addition, to decrease the topology and measurement errors associated with transformers, a connectivity model is proposed for transformers that use switching devices, and a transformer error processing technique is proposed using a simple heuristic method. To verify the performance of the proposed methodology, we performed comprehensive tests based on a modified IEEE 18-bus test system and a large-scale power system that utilizes renewable energy.

Computational development of a practical educational tool for state estimation of power systems using the MATLAB optimization toolbox

2018 ◽  
Vol 56 (2) ◽  
pp. 105-123 ◽  
Author(s):  
EA Zamora-Cárdenas ◽  
A Pizano-Martínez ◽  
JM Lozano-García ◽  
VJ Gutiérrez-Martínez ◽  
R Cisneros-Magaña
Keyword(s):  
Power Systems ◽  
State Estimation ◽  
Electric Power ◽  
Smart Grids ◽  
Weighted Least Squares ◽  
Electric Power Systems ◽  
The State ◽  
Estimation Problem ◽  
Educational Tool ◽  
And Control

State estimation is one of the most important processes to perform a reliable monitoring and control of the steady-state operating condition of modern electric power systems; thus, it is currently a fundamental part in the development of research to enhance the monitoring and security of the smart grids operation. This important topic is taught in advanced courses of operation and control of power systems, for graduate and undergraduate power engineering students. However, the most used software packages for simulation and analysis of power systems by researchers, students, and educators have put little attention on the state estimation module. Due to this fact, this paper proposes an approach to develop the computational implementation of a practical educational tool for state estimation of electric power systems using the MATLAB optimization toolbox. In this proposal, the formulation of the state estimation problem consists of developing a general digital code to implement an objective function based on the weighted least squares method. While the lsqnonlin function of the MATLAB optimization toolbox solves the formulated state estimation problem. Simplifying both research and educational processes, this tool helps graduate and undergraduate students to improve learning, understanding, and the times of implementation and development of research in state estimation. Simulations of an equivalent model of the Mexican interconnected power system consisting of 190 buses and 46 machines are used to test and validate the proposal performance.

scholarly journals Distributed State Estimation of Multi-region Power System based on Consensus Theory

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 900 ◽  
Author(s):  
Shiwei Xia ◽  
Qian Zhang ◽  
Jiangping Jing ◽  
Zhaohao Ding ◽  
Jing Yu ◽  
...  
Keyword(s):  
Power Systems ◽  
Power System ◽  
State Estimation ◽  
Solution Method ◽  
The State ◽  
Rapid Expansion ◽  
Estimation Model ◽  
Consensus Theory ◽  
Distributed State Estimation ◽  
Bus System

Effective state estimation is critical to the security operation of power systems. With the rapid expansion of interconnected power grids, there are limitations of conventional centralized state estimation methods in terms of heavy and unbalanced communication and computation burdens for the control center. To address these limitations, this paper presents a multi-area state estimation model and afterwards proposes a consensus theory based distributed state estimation solution method. Firstly, considering the nonlinearity of state estimation, the original power system is divided into several non-overlapped subsystems. Correspondingly, the Lagrange multiplier method is adopted to decouple the state estimation equations into a multi-area state estimation model. Secondly, a fully distributed state estimation method based on the consensus algorithm is designed to solve the proposed model. The solution method does not need a centralized coordination system operator, but only requires a simple communication network for exchanging the limited data of boundary state variables and consensus variables among adjacent regions, thus it is quite flexible in terms of communication and computation for state estimation. In the end, the proposed method is tested by the IEEE 14-bus system and the IEEE 118-bus system, and the simulation results verify that the proposed multi-area state estimation model and the distributed solution method are effective for the state estimation of multi-area interconnected power systems.

scholarly journals Particle filter in power system state estimation – bad measurement data and branch disconnection

2015 ◽  
Vol 64 (2) ◽  
pp. 237-248
Author(s):  
Piotr Kozierski ◽  
Marcin Lis ◽  
Adam Owczarkowski ◽  
Dariusz Horla
Keyword(s):  
Power System ◽  
Particle Filter ◽  
State Estimation ◽  
Measurement Data ◽  
The State ◽  
Filter Method ◽  
System State ◽  
State Variables ◽  
Zero Bias ◽  
Power System State Estimation

Abstract An approach to power system state estimation using a particle filter has been proposed in the paper. Two problems have been taken into account during research, namely bad measurements data and a network structure modification with rapid changes of the state variables. For each case the modification of the algorithm has been proposed. It has also been observed that anti-zero bias modification has a very positive influence on the obtained results (few orders of magnitude, in comparison to the standard particle filter), and additional calculations are quite symbolic. In the second problem, used modification also improved estimation quality of the state variables. The obtained results have been compared to the extended Kalman filter method

scholarly journals Estimation Of State Variables And Model Parameters For The Evolution Of COVID-19 In The City Of Rio de Janeiro

2020 ◽  
Author(s):  
Helcio R.B. Orlande ◽  
Marcelo Colaco ◽  
George S. Dulikravich ◽  
Luiz F.S. Ferreira
Keyword(s):  
Particle Filter ◽  
State Estimation ◽  
Rio De Janeiro ◽  
Reproduction Number ◽  
The State ◽  
Estimation Problem ◽  
Evolution Model ◽  
Model Parameters ◽  
State Variables ◽  
The City

Evolution model is based on that used by Hernandez et al., which considers the following groups: Susceptible, Incubating, Asymptomatic, Symptomatic, Hospitalized, Recovered and Accumulated deaths. Evolution model considers the possibility of infections from asymptomatic, symptomatic and hospitalized individuals. Evolution model considers the possibility that individuals who have recovered from the disease become symptomatic again. Observation model accounts for underreport of cases and deaths. Observation model accounts for delays in reporting cases and deaths. Model parameters were initially estimated with the Markov Chain Monte Carlo (MCMC) method, by using the data of the city of Rio de Janeiro from February 28, 2020 to April 29, 2020. These estimations were used as initial input values for the solution of the state estimation problem for the city of Rio de Janeiro. Algorithm of Liu & West for the Particle Filter was used for the solution of the state estimation problem because it allows the simultaneous estimation of state variables and model parameters. State estimation problem was solved with the data of the city of Rio de Janeiro, from February 28, 2020 to May 05, 2020. Monte Carlo simulations were run for 20 future days, considering uncertainties in the model parameters and state variables. Initial conditions were given by the state variables and corresponding distributions estimated with the particle filter on May 05, 2020. Distributions of the model parameters were also given by the estimations obtained for this date. Data of the city of Rio de Janeiro, from May 06, 2020 to May 15, 2020, were used for the validation of the solution of the state estimation problem. The present model, with the parameters obtained with the Particle Filter, accurately fits the number of reported cases and the number of reported deaths, for 10 days ahead of the period used for the solution of the state estimation problem. The Ratio of Infected Individuals per Reported Cases was around 15 on May 05, 2020. The Indexes of Under-Reported Cases and Deaths were around 12 and 2, respectively, on May 05, 2020. The Effective Reproduction Number was around 1.6 on February 28, 2020 and dropped to around 0.9 on May 05, 2020. However, uncertainties related to this parameter are large and the effective reproduction number is between 0.3 and 1.5, at the 95% credibility level. The particle filter must be used to periodically update the estimation of state variables and model parameters, so that future predictions can be made. Day 0 is February 28, 2020.

State Estimation Based on Ensemble DA–DSVM in Power System

2019 ◽  
Vol 29 (05) ◽  
pp. 653-669
Author(s):  
Shunjiang Wang ◽  
Baoming Pu ◽  
Ming Li ◽  
Weichun Ge ◽  
Qianwei Liu ◽  
...  
Keyword(s):  
Power Systems ◽  
Power System ◽  
State Estimation ◽  
Graphics Processing Unit ◽  
Estimation Algorithm ◽  
The State ◽  
Support Vector ◽  
Processing Unit ◽  
Central Processing ◽  
Calculation Results

This paper investigates the state estimation problem of power systems. A novel, fast and accurate state estimation algorithm is presented to solve this problem based on the one-dimensional denoising autoencoder and deep support vector machine (1D DA–DSVM). Besides, for further reducing the computation burden, a partitioning method is presented to divide the power system into several sub-networks and the proposed algorithm can be applied to each sub-network. A hybrid computing architecture of Central Processing Unit (CPU) and Graphics Processing Unit (GPU) is employed in the overall state estimation, in which the GPU is used to estimate each sub-network and the CPU is used to integrate all the calculation results and output the state estimate. Simulation results show that the proposed method can effectively improve the accuracy and computational efficiency of the state estimation of power systems.

Sign in / Sign up

Export Citation Format

Share Document