Reconstructing Missing Data in State Estimation With Autoencoders

Miranda, Vladimiro; Krstulovic, Jakov; Keko, Hrvoje; Moreira, Carlos; Pereira, Jorge

doi:10.1109/tpwrs.2011.2174810

Cited by 90 publications

(54 citation statements)

References 23 publications

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“…MDI means to estimate plausible values in order to substitute the missing ones. There are several ways to estimate the value to be imputed, from naïve approaches (such as: mean and mode substitution), to machine learning and statistical based methods (such as multiple imputation [20], Bayesian imputation [15], k-nearest neighbors imputation [3], autoenconders neural networks imputation [24]). …”

Section: Theoretical Backgroundmentioning

confidence: 99%

See 1 more Smart Citation

An Evolutionary Missing Data Imputation Method for Pattern Classification

Lobato

Tadaiesky

Araújo

et al. 2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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Data analysis plays an important role in our InformationEra; however, most of statistical and machine learning algorithms were not developed to tackle the ubiquitous issue of missing values. In pattern classification, several strategies have been proposed to handle this problem, where missing data imputation is the most used one, which can be viewed as an optimization problem where the goal is to reduce the bias imposed by the absence of information. Although most imputation methods are restricted to one type of variable only (categorical or numerical), they usually ignore information within incomplete instances. To fill these gaps, we propose an evolutionary missing data imputation method for pattern classification, based on a genetic algorithm, which is suitable for mixed-attribute datasets and takes into account information from incomplete instances and model building -more specifically, the classification accuracy. To assess the performance of our method, we used three algorithms in order to represent the three groups of classification methods: 1) rule induction learning, 2) approximate models and 3) lazy learning. Experiments have shown that the proposed method outperforms some well-established missing value treatment methods.

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Section: Theoretical Backgroundmentioning

confidence: 99%

“…At this point, these studies can be divided into two groups: 1) methods that apply evolutionary algorithms to improve the convergence of other imputation methods [24,1] and 2) the ones that use EA to perform the imputation itself. The latter will be discussed in this subsection.…”

Section: Evolutionary Approachesmentioning

confidence: 99%

An Evolutionary Missing Data Imputation Method for Pattern Classification

Lobato

Tadaiesky

Araújo

et al. 2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

View full text Add to dashboard Cite

show abstract

“…2. Probabilistic recursive Bayesian approach [7] [57], fuzzy-based pattern recognition [58], auto-encoders [59], PMU voltage time-series [60], voting technique ("vote" for the best candidate structure) [61], correlation analysis [62], and maximum likelihood estimation [63], are a few of the proposed topology search methods.…”

Section: Network Topology and Configurationmentioning

confidence: 99%

A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

Dehghanpour

Wang

et al. 2019

IEEE Trans. Smart Grid

334

173

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This paper presents a review of the literature on State Estimation (SE) in power systems. While covering some works related to SE in transmission systems, the main focus of this paper is Distribution System State Estimation (DSSE). The paper discusses a few critical topics of DSSE, including mathematical problem formulation, application of pseudomeasurements, metering instrument placement, network topology issues, impacts of renewable penetration, and cyber-security. Both conventional and modern data-driven and probabilistic techniques have been reviewed. This paper can provide researchers and utility engineers with insights into the technical achievements, barriers, and future research directions of DSSE.

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“…Traditional state estimators usually generate pseudo-measurements from historical data or context of the generation of pseudo-measurements to replace the missing data [12]. As for the most widely studied static state estimation, it could not capture the dynamics very well after disturbances [12,13]. Therefore the dynamic state estimation is studied [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Measurement‐based correlation approach for power system dynamic response estimation

Bai

Liu

et al. 2015

IET gener. transm. distrib.

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Understanding power system dynamics is essential for online stability assessment and control applications. Global positioning system-synchronised phasor measurement units and frequency disturbance recorders (FDRs) make power system dynamics visible and deliver an accurate picture of the overall operation condition to system operators. However, in the actual field implementations, some measurement data can be inaccessible for various reasons, for example, most notably failure of communication. In this study, a measurement-based approach is proposed to estimate the missing power system dynamics. Specifically, a correlation coefficient index is proposed to describe the correlation relationship between different measurements. Then, the auto-regressive with exogenous input identification model is employed to estimate the missing system dynamic response. The US Eastern Interconnection is utilised in this study as a case study. The robustness of the correlation approach is verified by a wide variety of case studies as well. Finally, the proposed correlation approach is applied to the real FDR data for power system dynamic response estimation. The results indicate that the correlation approach could help select better input locations and thus improve the response estimation accuracy.

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Reconstructing Missing Data in State Estimation With Autoencoders

Cited by 90 publications

References 23 publications

An Evolutionary Missing Data Imputation Method for Pattern Classification

An Evolutionary Missing Data Imputation Method for Pattern Classification

A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems

Measurement‐based correlation approach for power system dynamic response estimation

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