Trust assessment of power system states

Abstract Modern power systems are cyber-physical systems with increasing relevance and influence of information and communication technology. This influence comprises all processes, functional, and non-functional aspects like functional correctness, safety, security, and reliability. An example of a process is the data acquisition process. Questions focused in this paper are, first, how one can trust in process data in a data acquisition process of a highly-complex cyber-physical power system. Second, how can the trust in process data be integrated into a state estimation to achieve estimated results in a way that it can reflect trustworthiness of that input?We present the concept of an anomaly-sensitive state estimation that tackles these questions. The concept is based on a multi-faceted trust model for power system network assessment. Furthermore, we provide a proof of concept by enriching measurements in the context of the IEEE 39-bus system with reasonable trust values. The proof of concept shows the benefits but also the limitations of the approach.

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Optimization of Phasor Measurement Unit (PMU) Placement in Supervisory Control and Data Acquisition (SCADA)-Based Power System for Better State-Estimation Performance

Energies ◽

10.3390/en11030570 ◽

2018 ◽

Vol 11 (3) ◽

pp. 570 ◽

Cited By ~ 15

Author(s):

Mohammad Shahriar ◽

Ibrahim Habiballah ◽

Huthaifa Hussein

Keyword(s):

Power System ◽

Data Acquisition ◽

State Estimation ◽

Supervisory Control ◽

Phasor Measurement Unit ◽

Measurement Unit ◽

Estimation Performance ◽

Phasor Measurement ◽

Pmu Placement

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State Estimation in Electric Power Systems Leveraging Graph Neural Networks

10.36227/techrxiv.18131207.v1 ◽

2022 ◽

Author(s):

Ognjen Kundacina ◽

Mirsad Cosovic ◽

Dejan Vukobratovic

Keyword(s):

Power Systems ◽

Power System ◽

State Estimation ◽

Electric Power Systems ◽

State Variables ◽

Evaluation Phase ◽

Measurement Units ◽

Synthetic Datasets ◽

Graph Neural Networks ◽

Test Scenarios

The goal of the state estimation (SE) algorithm is to estimate complex bus voltages as state variables based on the available set of measurements in the power system. Because phasor measurement units (PMUs) are increasingly being used in transmission power systems, there is a need for a fast SE solver that can take advantage of PMU high sampling rates. This paper proposes training a graph neural network (GNN) to learn the estimates given the PMU voltage and current measurements as inputs, with the intent of obtaining fast and accurate predictions during the evaluation phase. GNN is trained using synthetic datasets, created by randomly sampling sets of measurements in the power system and labelling them with a solution obtained using a linear SE with PMUs solver. The presented results display the accuracy of GNN predictions in various test scenarios and tackle the sensitivity of the predictions to the missing input data.

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

Energies ◽

10.3390/en12050900 ◽

2019 ◽

Vol 12 (5) ◽

pp. 900 ◽

Cited By ~ 2

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.

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