Financially Motivated FDI on SCED in Real-Time Electricity Markets: Attacks and Mitigation

Liu, Chensheng; Zhou, Min; Wu, Jing; Long, Chengnian; Kundur, Deepa

doi:10.1109/tsg.2017.2784366

Cited by 51 publications

(15 citation statements)

References 34 publications

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“…Considering this theorem, most publications focused on FDIAs that target congested line sets without being detected by the conventional BDD. Similar to [11,[14][15][16][17], this paper assumes that FDIAs are launched without being detected by changing congested lines from their original situations. According to 2 −norm-based residual test, in order to launch an unidentified attack, the following constraint should be satisfied:…”

Section: Undetected False Data Injection Attack On State Estimationmentioning

confidence: 99%

A Novel Detection Algorithm to Identify False Data Injection Attacks on Power System State Estimation

Ganjkhani

Fallah²,

Badakhshan

et al. 2019

Energies

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This paper provides a novel bad data detection processor to identify false data injection attacks (FDIAs) on the power system state estimation. The attackers are able to alter the result of the state estimation virtually intending to change the result of the state estimation without being detected by the bad data processors. However, using a specific configuration of an artificial neural network (ANN), named nonlinear autoregressive exogenous (NARX), can help to identify the injected bad data in state estimation. Considering the high correlation between power system measurements as well as state variables, the proposed neural network-based approach is feasible to detect any potential FDIAs. Two different strategies of FDIAs have been simulated in power system state estimation using IEEE standard 14-bus test system for evaluating the performance of the proposed method. The results indicate that the proposed bad data detection processor is able to detect the false injected data launched into the system accurately.

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Section: Undetected False Data Injection Attack On State Estimationmentioning

confidence: 99%

A Novel Detection Algorithm to Identify False Data Injection Attacks on Power System State Estimation

Ganjkhani

Fallah²,

Badakhshan

et al. 2019

Energies

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show abstract

“…Existing EPF methods can be divided into three categories, namely, physical methods, statistical methods, and machine learning methods (Figure 1). Physical methods are based on safety constrained unit commitment (SCUC) and safety constrained economic dispatch (SCED) models to simulate the day-to-day power market situation based on boundary conditions and physical theory [8,9]. Although physical methods are effective from the perspective of predictive logic, their main problem is that the SCUC and SCED models require a large amount of real-time operating data, such as line transmission capacity, electricity load, and competitors' bids, which leads to very complicated calculations.…”

Section: Introductionmentioning

confidence: 99%

A Multiscale Electricity Price Forecasting Model Based on Tensor Fusion and Deep Learning

Xie

Zhang

2021

Energies

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The price of electricity is an important factor in the electricity market. Accurate electricity price forecasting (EPF) is very important to all competing electricity market parties. Decision-making in the electricity market is highly dependent on electricity prices, making an EPF model an important part of the orderly and efficient operation of the electricity market. Especially during the COVID-19 pandemic, the prices of raw materials for electricity production, such as coal, have risen sharply. Forecasting electricity prices has become particularly important. Currently, existing EPF prediction models face two main challenges: First, how to integrate multiscale electricity price data to obtain a higher prediction accuracy. Second, how to solve the problem of data noise caused by the fusion of EPF samples and multiscale data. To solve the above problems, we innovatively propose a tensor decomposition method to integrate multiscale electricity price data and use L1 regularization and wavelet transform to remove data noise. In general, this paper proposes a deep learning EPF prediction model, named the WT_TDLSTM model, based on tensor decomposition, a wavelet transform, and long short-term memory (LSTM). Among them, the LSTM method is used to predict electricity prices. We conducted experiments on three datasets. The experimental results of three data prove that the WT_TDLSTM model is better than the compared EPF model. The indicators of MSE and RMSE are 33.65–99.97% better than the comparison model. We believe that the WT_TDLSTM model is a good supplement to the EPF model.

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“…However, they both only focus on attacks that cause line overflows. In [10], a financially motivated FDI attack model is analysed and a robust incentive‐reduction strategy is proposed to deter such attacks by protecting a subset of meters. More generally, machine learning techniques are also deployed in detecting LR attacks.…”

Section: Introductionmentioning

confidence: 99%

“…Estimation–detection framework: In this study, we introduce an LR attack detection framework based on support vector models by leveraging the historical load information commonly available to system operators. While there are existing approaches in the literature to prevent attacks by installing new devices [6] or protecting specific measurements [10], guiding operators to utilise existing data available to design software‐based solutions is complementary to those existing approaches. Our method determines the existence of LR attacks directly through the estimated loads, which can be conveniently applied in conjunction with the current EMS operations.…”

Section: Introductionmentioning

confidence: 99%

Detecting load redistribution attacks via support vector models

2020

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Financially Motivated FDI on SCED in Real-Time Electricity Markets: Attacks and Mitigation

Cited by 51 publications

References 34 publications

A Novel Detection Algorithm to Identify False Data Injection Attacks on Power System State Estimation

A Novel Detection Algorithm to Identify False Data Injection Attacks on Power System State Estimation

A Multiscale Electricity Price Forecasting Model Based on Tensor Fusion and Deep Learning

Detecting load redistribution attacks via support vector models

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