Noise Estimation in Measurements to Improve the State Estimation of Electric Power Systems

Martínez-Parrales, R.; Fuerte‐Esquivel, Claudio R.

doi:10.1109/iceee.2019.8884579

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…Estimators play a crucial role by providing essential information about a system's state, allowing for the real-time assessment of voltage phasors at different locations within the system. This task involves handling extensive data volumes at a control center, which, in turn, leads to increased costs related to economic and computational resources [8]. Within the estimation process, it becomes essential to analyze measurement redundancy to mitigate the risk of deviations or errors in the applied algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…They enable the real-time assessment of voltage phasors at various points within the system. However, this task involves the management of substantial data volumes at a central control center, leading to increased costs related to economic and computational resources [8]. Within the estimation process, it is essential to scrutinize measurement redundancy to mitigate the risk of deviations or errors in the applied algorithm.…”

Section: Introductionmentioning

confidence: 99%

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The Efficiency of the Kalman Filter in Nodal Redundancy

Moyano,

Vargas

2024

Energies

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The growing integration of distributed energy resources underscores the critical importance of having precise insights into the dynamics of an electrical power system (EPS). Consequently, an estimator must align with the EPS dynamics to enhance the overall reliability, safety, and system stability. This alignment ensures that operators can make informed decisions during system operations. An initial step in gaining insight into the system’s state involves examining its state vector, which is represented by voltage phasors. These results are derived through the application of a distributed state-estimation process in large-scale systems. This study delved into the effectiveness of Bayesian filters, with a particular emphasis on the extended Kalman filter (EKF) algorithm in the context of distributed state estimation. To analyze the outcomes, the nodal partitioning process was incorporated within the distributed state-estimation framework. The synergy between the EKF algorithm and the partitioning method was evaluated using the IEEE118 test system.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Efficiency of the Kalman Filter in Nodal Redundancy

Moyano,

Vargas

2024

Energies

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“…In this context, a state estimation process is a tool that provides reliable information for the operation of an electric power system (EPS), calculating in real time the best estimate of the current situation of a system. To carry out this task, it is necessary to acquire and process large volumes of information in a control center, which increases the cost of the economic and computational resources available [8]. A good estimation of the state improves the supervision and control of an electrical power system, it being necessary to consider an estimation criterion, redundancy of the measurements, and the probability distribution of random errors.…”

Section: Introductionmentioning

confidence: 99%

A Novel Multi-Area Distribution State Estimation Approach with Nodal Redundancy

Vargas,

Moyano

2023

Energies

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State estimators based on load flows and applied in electrical power systems (EPS) are a basic and crucial function in energy management systems (EMS), since they must guarantee the quality of their results for decision-making. In this research, we propose a new method for partitioning an electrical system within distributed estimation processes. This method is developed under the concept of nodal redundancy and considers the number of measurements associated with each bus of the electrical system. By distributing the measurements in subsystems, such that each redundancy is evenly distributed, the proposed method aims to improve the performance of both centralized and distributed estimation techniques developed in the literature. We evaluate the proposed method by using the IEEE 14-bus and IEEE 118-bus systems, considering several operating cases and a wide array of measurements of the electrical power system. Results demonstrate the quality of the estimate and the processing time for both traditional and distributed estimates under the proposed methodology.

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Noise Estimation in Measurements to Improve the State Estimation of Electric Power Systems

Cited by 2 publications

References 11 publications

The Efficiency of the Kalman Filter in Nodal Redundancy

The Efficiency of the Kalman Filter in Nodal Redundancy

A Novel Multi-Area Distribution State Estimation Approach with Nodal Redundancy

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