A Method for Cleaning Power Grid Operation Data Based on Spatiotemporal Correlation Constraints

Wang, Changgang; Mu, Gang; Cao, Yu

doi:10.1109/access.2020.3044051

Cited by 6 publications

(3 citation statements)

References 15 publications

(16 reference statements)

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“…The detection method of power physics side mainly depends on judging whether the electrical measurement, decision action instructions and sudden changes in system operation state comply with the physical law of power system [54]. For example, bad data detection and identification based on residual detection method or spatio-temporal correlation condition constraints [71], and doubly-fed deep learning identification methods for PMU's bad data [72]. In addition, there are multi-step bad data detection algorithms for SCADA systems and FDIAs [73,74].…”

Section: Intrusion Detection and Defence Technologymentioning

confidence: 99%

Attack and defence methods in cyber‐physical power system

Yang

Liu

2022

IET Energy Syst Integration

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With the development of digitalisation and intelligence, the power system has been upgraded from the traditional single energy transmission and conversion system to a complex cyber-physical power system (CPPS) with tightly coupled energy and information flows. The cyber-physical power system achieves the controllability and observability of the power system through ubiquitous sensing technology, advanced measurement technology, and powerful information processing technology. However, the large number of intelligent electronic device accesses and frequent information interactions in CPPS make it more vulnerable and susceptible to be attacked than any previous single structured system. Viruses and intrusions can attack the CPPS through the cyber subsystem, which in turn can deal a fatal blow to the energy supply physical system. Because of the above problems, malicious attacks against CPPS have been occurring in recent years and have generated a great deal of scholarly attention. This paper precisely focusses on this problem, by profiling the structure of CPPS and the potential threats, conducting an in-depth analysis of CPPS attack modes from the cyber and physical subsystems, and summarising the three-level security defence methods for CPPS in detail. Finally, the future technological development prospects of CPPS security research are explicitly addressed, which will provide technical support for building reliable, safe, and robust energy systems. K E Y W O R D Scoordinated cyber-physical attacks, cyber-physical power system, false data injection attacks, intrusion detection, security information and event management | INTRODUCTIONIn recent years, with the development of computer technology, communication network, and intelligent devices, the traditional energy and power system with physical equipment as the core is gradually combined more closely with information and communication technology and gradually evolved into Cyber-Physical Power System (CPPS), which is a multi-dimensional heterogeneous system integrating control, computing and communication. The structure of CPPS is shown in Figure 1.In CPPS, a large number of power physical devices are connected to the information and communication system through intelligent electronic devices (IEDs) to accomplish ubiquitous sensing data collection, edge computing state identification, and system regulation and control for optimal operation. Therefore, the CPPS includes complex sensing, communication, computation, and control, and covers all This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

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Section: Intrusion Detection and Defence Technologymentioning

confidence: 99%

Attack and defence methods in cyber‐physical power system

Yang

Liu

2022

IET Energy Syst Integration

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“…Literature [13] proposed a load data repair method based on a collaborative filtering recommendation algorithm, which calculates the load range recommendation degree according to the horizontal correlation of load changes within the distribution network area and realizes rapid data correction for abnormal loads. Literature [14] proposed a system protection method for an integrated pipe gallery power cabin based on multisource heterogeneous data fusion. The method initially integrates multiple distributed data sources from the pipe gallery power cabin using middleware technology for data layer fusion.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal Joint Cleaning of Distribution Network Measurement Data Based on Correntropy Criterion with Variable Center Unscented Kalman Filter

Sun

Liu

et al. 2022

Applied Sciences

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Measurement data cleaning is a key step of edge computing in a distribution network; it is beneficial to improve the state perception and regional autonomy level of a distribution network. According to the temporal and spatial correlation of measurement data in the distribution network, a joint cleaning method of measurement data in a distribution network is proposed based on the correntropy criterion with variable center unscented Kalman filter (CC-VC-UKF). Initially, the mean square error (MSE) in the original unscented Kalman filter (UKF) is replaced by the correntropy criterion with variable center (CC-VC) to improve the accuracy of filtering the measurement data in the distribution network with a non-Gaussian non-zero mean measurement deviation. Then, the measured data of different measuring devices located on the same section of the line are filtered based on the CC-VC-UKF algorithm according to their respective reference time series to improve the signal-to-noise ratio of the measured data. Then, the filtered measured data are filtered and cleaned based on the CC-VC-UKF algorithm according to the space–time joint filtering and cleaning technology. Finally, the method is used to test the measurement data of the distribution network obtained by a power supply company in a city in north China to solve the problem of measurement deviation caused by the existence of space distance. Results show that this method can obtain FTU measurement data with higher precision from network topology based on the filtered TTU measurement data through the media of filtered spatial measurement deviation.

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“…The reliability of measurement data directly affects the conclusions from the data-based analysis of the power system operation behavior. Only conclusions based on reliable data analysis can reflect the system operation's true status (Wang et al, 2020). However, in practice, the supervisory and data acquisition system, due to the data acquisition process, measurement process, transmission modes, storage modes, and other segments, may break down or suffer interference, which will lead to lost or missing data (Jing et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A Reconstruction Method for Missing Data in Power System Measurement Based on LSGAN

et al. 2021

Self Cite

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The integrity of data is an essential basis for analyzing power system operating status based on data. Improper handling of measurement sampling, information transmission, and data storage can lead to data loss, thus destroying the data integrity and hindering data mining. Traditional data imputation methods are suitable for low-latitude, low-missing-rate scenarios. In high-latitude, high-missing-rate scenarios, the applicability of traditional methods is in doubt. This paper proposes a reconstruction method for missing data in power system measurement based on LSGAN (Least Squares Generative Adversarial Networks). The method is designed to train in an unsupervized learning mode, enabling the neural network to automatically learn measurement data, power distribution patterns, and other complex correlations that are difficult to model explicitly. It then optimizes the generator parameters using the constraint relations implied by true sample data, enabling the trained Generator to generate highly accurate data to reconstruct the missing data. The proposed approach is entirely data-driven and does not involve mechanistic modeling. It can still reconstruct the missing data in the case of high latitude and high loss rate. We test the effectiveness of the proposed method by comparing three other GAN derivation methods in our experiments. The experimental results show that the proposed method is feasible and effective, and the accuracy of the reconstructed data is higher while taking into account the computational efficiency.

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A Method for Cleaning Power Grid Operation Data Based on Spatiotemporal Correlation Constraints

Cited by 6 publications

References 15 publications

Attack and defence methods in cyber‐physical power system

Attack and defence methods in cyber‐physical power system

Spatiotemporal Joint Cleaning of Distribution Network Measurement Data Based on Correntropy Criterion with Variable Center Unscented Kalman Filter

A Reconstruction Method for Missing Data in Power System Measurement Based on LSGAN

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