Assessment of State Estimation Methods for Power Systems with Uncertain Parameters

2020 ◽  
Author(s):  
Francesco Conte ◽  
Bruno Gabriele ◽  
Giacomo-Piero Schiapparelli
Keyword(s):  
Power Systems ◽  
State Estimation ◽  
Estimation Methods ◽  
Uncertain Parameters

scholarly journals Robust Power System State Estimator using Projection Statistics for IEEE systems

2019 ◽  
Vol 8 (2) ◽  
pp. 1178-1182
Keyword(s):  
Power Systems ◽  
Power System ◽  
State Estimation ◽  
Load Flow ◽  
Estimation Methods ◽  
Behavior Changes ◽  
State Estimator ◽  
Good Efficiency ◽  
Environment Index ◽  
Two Stages

In Real-time applications like measurement noise in distributed power system. From model of Gaussian frequently deviates or unknown are obtained and estimated in terms of impulse pulse of noise. Under such situations, the efficiency behavior changes. State estimation (SE) methods which are conventional which have both measured and estimated values with the noise consideration is greatly depleted. In this paper, state estimation methods are used in power systems to reduce the error and noise either in two stages of operation. To achieve a linear property of the estimated output by using adjustment of weights and its measurements. The estimator considers the limits and statistical data with a good efficiency even with and without noise effects and disturbances. Tu understand the behavior of SE methods, IEEE test bench of 14-bus and 30- buses are considered. the results are simulated in MTALAB-SIMULINK environment. Index Terms: Load flow study, IEEE 14 and 30 Bus systems, Statistics, GM-estimator, MATLAB, M file.

POWER SYSTEM STATE ESTIMATION USING A ROBUST ESTIMATOR

2019 ◽  
Vol XVI (4) ◽  
pp. 53-65
Author(s):  
Zahid Khan ◽  
Katrina Lane Krebs ◽  
Sarfaraz Ahmad ◽  
Misbah Munawar
Keyword(s):  
Power Systems ◽  
State Estimation ◽  
Loss Function ◽  
Weighted Least Squares ◽  
Primary Data ◽  
Robust Estimator ◽  
System State ◽  
State Estimator ◽  
Suggested Approach ◽  
Power System State Estimation

State estimation (SE) is a primary data processing algorithm which is utilised by the control centres of advanced power systems. The most generally utilised state estimator is based on the weighted least squares (WLS) approach which is ineffective in addressing gross errors of input data of state estimator. This paper presents an innovative robust estimator for SE environments to overcome the non-robustness of the WLS estimator. The suggested approach not only includes the similar functioning of the customary loss function of WLS but also reflects loss function built on the modified WLS (MWLS) estimator. The performance of the proposed estimator was assessed based on its ability to decrease the impacts of gross errors on the estimation results. The properties of the suggested state estimator were investigated and robustness of the estimator was studied considering the influence function. The effectiveness of the proposed estimator was demonstrated with the help of examples which also indicated non-robustness of MWLS estimator in SE algorithm.

scholarly journals The New Trend of State Estimation: From Model-Driven to Hybrid-Driven Methods

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2085
Author(s):  
Xue-Bo Jin ◽  
Ruben Jonhson Robert RobertJeremiah ◽  
Ting-Li Su ◽  
Yu-Ting Bai ◽  
Jian-Lei Kong
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Future Development ◽  
Data Driven ◽  
Estimation Methods ◽  
Multiple Model ◽  
Development Trends ◽  
Research Results ◽  
Model Driven ◽  
Model Based

State estimation is widely used in various automated systems, including IoT systems, unmanned systems, robots, etc. In traditional state estimation, measurement data are instantaneous and processed in real time. With modern systems’ development, sensors can obtain more and more signals and store them. Therefore, how to use these measurement big data to improve the performance of state estimation has become a hot research issue in this field. This paper reviews the development of state estimation and future development trends. First, we review the model-based state estimation methods, including the Kalman filter, such as the extended Kalman filter (EKF), unscented Kalman filter (UKF), cubature Kalman filter (CKF), etc. Particle filters and Gaussian mixture filters that can handle mixed Gaussian noise are discussed, too. These methods have high requirements for models, while it is not easy to obtain accurate system models in practice. The emergence of robust filters, the interacting multiple model (IMM), and adaptive filters are also mentioned here. Secondly, the current research status of data-driven state estimation methods is introduced based on network learning. Finally, the main research results for hybrid filters obtained in recent years are summarized and discussed, which combine model-based methods and data-driven methods. This paper is based on state estimation research results and provides a more detailed overview of model-driven, data-driven, and hybrid-driven approaches. The main algorithm of each method is provided so that beginners can have a clearer understanding. Additionally, it discusses the future development trends for researchers in state estimation.

scholarly journals ADMM-based Distributed State Estimation for Power Systems: Evaluation of Performance

2020 ◽  
Vol 53 (5) ◽  
pp. 182-188
Author(s):  
Sergei Parsegov ◽  
Samal Kubentayeva ◽  
Elena Gryazina ◽  
Alexander Gasnikov ◽  
Federico Ibáñez
Keyword(s):  
Power Systems ◽  
State Estimation ◽  
Distributed State Estimation ◽  
Evaluation Of Performance

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.

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