Power system state estimation based on Iterative Extended Kalman Filtering and bad data detection using normalized residual test

Alamin, Abubeker; Khalid, Haris M.; Peng, Jimmy Chih-Hsien

doi:10.1109/peci.2015.7064881

Cited by 12 publications

(5 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results show that ACKF has a consistently better computation performance compared with the WLS and IEKF methods. It is also found that IEKF has a heavier computation compared with WLS, which is consistent with the previous research [36].…”

Section: Computation Efficiencysupporting

confidence: 92%

Measurement Sensitivity and Estimation Error in Distribution System State Estimation using Augmented Complex Kalman Filter

Louis

Ledwich

Walker

et al. 2020

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

Distribution state estimation (DSE) is an essential part of an active distribution network with high level of distributed energy resources. The challenges of accurate DSE with limited measurement data is a well-known problem. In practice, the operation and usability of DSE depend on not only the estimation accuracy but also the ability to predict error variance. This paper investigates the application of error covariance in DSE by using the augmented complex Kalman filter (ACKF). The Kalman filter method inherently provides state error covariance prediction. It can be utilized to accurately infer the error covariance of other parameters and provide a method to determine optimal measurement locations based on the sensitivity of error covariance to measurement noise covariance. This paper also proposes a generalized formulation of ACKF to allow scalar measurements to be incorporated into the complex-valued estimator. The proposed method is simulated by using modified IEEE 34-bus and IEEE 123-bus test feeders, and randomly generates the load data of complex-valued Wiener process. The ACKF method is compared with an equivalent formulation using the traditional weighted least squares (WLS) method and iterated extended Kalman filter (IEKF) method, which shows improved accuracy and computation performance.

show abstract

Section: Computation Efficiencysupporting

confidence: 92%

Measurement Sensitivity and Estimation Error in Distribution System State Estimation using Augmented Complex Kalman Filter

Louis

Ledwich

Walker

et al. 2020

Journal of Modern Power Systems and Clean Energy

View full text Add to dashboard Cite

show abstract

“…The IEKF algorithm to enhance its bad data detection capability of the IEKF algorithm is enhanced when LRNT was applied. IEKF was unable to accurately identify the location of bad data eventhough the detection scheme was able to determine faulty measurements [20].…”

Section: Alternative Formulation Of Bddimentioning

confidence: 99%

Power System State Estimation Bad Data Detection and Identification: A Review on Issues and Alternative Formulations

Gulcharan

Nor

Ibrahim

et al. 2017

IJEECS

View full text Add to dashboard Cite

State Estimation (SE) is the main function of power system where Energy Management System (EMS) is obliged to estimate the available states. Power system is a quasi-static system and hence changes slowly with time. Dynamic State Estimation (DSE) technique represents the time deviation nature of the system, which allows the forecasting of state vector in advance. Various techniques for DSE are available in the literature. This paper presents a review on different methodologies and developments in DSE, based on comprehensive survey of the available literature. From the survey it can be concluded that there are still areas in the developing DSE that can still be improved in terms of system computational time, redundancy and robustness of the system.

show abstract

“…The integration of renewable energy further reduces lifecycle emissions, resulting in a net environmental benefit. Climate Change Mitigation: Carbon Footprint Reduction: The adoption of renewable energy and electric vehicles plays a crucial role in mitigating climate change by reducing emissions of greenhouse gases, particularly carbon dioxide (CO2) [87]. By transitioning to renewable electricity for EV charging, businesses can significantly reduce their carbon footprint and contribute to global efforts to limit global warming and its associated impacts.…”

Section: Environmental and Economic Impactsmentioning

confidence: 99%

Sustainable Solutions: Integrating Renewable Energy and Electric Vehicles for Cleaner Operations

Rossi,

Bianchi

2024

JENRR

View full text Add to dashboard Cite

In the face of escalating environmental concerns and the urgent need to mitigate carbon emissions, integrating renewable energy and electric vehicles (EVs) has emerged as a promising avenue for achieving cleaner and more sustainable operations across various sectors. This paper explores the synergistic potential of combining renewable energy sources with EV technology to revolutionize energy consumption and transportation systems. The first part of the paper discusses the pressing environmental challenges posed by traditional energy sources and internal combustion engine vehicles, emphasizing the need for a transition towards cleaner alternatives. It highlights the detrimental effects of fossil fuel combustion on air quality, public health, and climate change, underscoring the imperative for sustainable energy solutions. Next, the paper delves into the advantages of renewable energy sources such as solar, wind, and hydropower in providing clean and abundant energy. It examines the advancements in renewable energy technologies, including decreasing costs and increasing efficiency, making them increasingly viable alternatives to fossil fuels. Subsequently, the paper explores the pivotal role of electric vehicles in reducing greenhouse gas emissions and dependency on fossil fuels in the transportation sector. It discusses the environmental benefits of EVs, including zero tailpipe emissions and lower lifecycle emissions compared to conventional vehicles.

show abstract

Power system state estimation based on Iterative Extended Kalman Filtering and bad data detection using normalized residual test

Cited by 12 publications

References 11 publications

Measurement Sensitivity and Estimation Error in Distribution System State Estimation using Augmented Complex Kalman Filter

Measurement Sensitivity and Estimation Error in Distribution System State Estimation using Augmented Complex Kalman Filter

Power System State Estimation Bad Data Detection and Identification: A Review on Issues and Alternative Formulations

Sustainable Solutions: Integrating Renewable Energy and Electric Vehicles for Cleaner Operations

Contact Info

Product

Resources

About