An Improved PMU Data Manipulation Attack Model

2021 ◽  
Author(s):  
Yuancheng Li ◽  
Haiyan Hou
Keyword(s):  
Transmission Line ◽  
Convex Relaxation ◽  
Constraint Condition ◽  
Construction Time ◽  
Data Manipulation ◽  
Attack Model ◽  
Vector Construction ◽  
Small Constant ◽  
Alternating Direction ◽  
Equivalent Impedance

The importance of Phasor Manipulation Unit (PMU) in the smart grid makes it a target for attackers who can create PMU Data Manipulation Attacks (PDMA) by adding a small constant to change the magnitude and angle of the voltage and current captured by the PMU. To prevent the attack result from being detected by PDMA detection based on the properties of equivalent impedance, this paper proposes a collaborative step attack. In this attack, the equivalent impedance’s value on the end of the transmission line is equal whether before or after been attack, which is taken as the constraint condition. The objective function of it is to minimize the number of the elements which is not 0 in attack vector but this number is not 0. Turn a vector construction problem into an optimization problem by building objective functions and constraints and then we use the Alternating Direction Method of Multipliers (ADMM) and Convex Relaxation (CR) to solve. The experiment verifies the feasibility of using the CR-ADMM algorithm to construct attack vectors from two aspects of attack vector construction time and vector sparsity. Further, it uses the constructed attack vectors to carry out attacks on PMU. The experimental results show that the measurement value of PMU will change after the attack, but the equivalent impedance value at both ends of the transmission line remains the same. The attack vector successfully bypasses the PDMA detection method based on the property of equivalent impedance and the attack model constructed based on this method was more covert than the original model.

scholarly journals A Sparse Optimization Approach for Energy-Efficient Timetabling in Metro Railway Systems

2018 ◽  
Vol 2018 ◽  
pp. 1-19 ◽  
Author(s):  
Ziyan Luo ◽  
Xiaoyu Li ◽  
Naihua Xiu
Keyword(s):  
Optimization Model ◽  
Energy Efficient ◽  
Convex Relaxation ◽  
Sparse Optimization ◽  
Computational Time ◽  
Optimization Approach ◽  
Railway Systems ◽  
Alternating Direction ◽  
Np Hard Problem ◽  
Regenerative Energy

In this paper, we propose a sparse optimization approach to maximize the utilization of regenerative energy produced by braking trains for energy-efficient timetabling in metro railway systems. By introducing the cardinality function and the square of the Euclidean norm function as the objective function, the resulting sparse optimization model can characterize the utilization of the regenerative energy appropriately. A two-stage alternating direction method of multipliers is designed to efficiently solve the convex relaxation counterpart of the original NP-hard problem and then to produce an energy-efficient timetable of trains. The resulting approach is applied to Beijing Metro Yizhuang Line with different instances of service for case study. Comparison with the existing two-step linear program approach is also conducted which illustrates the effectiveness of our proposed sparse optimization model in terms of the energy saving rate and the efficiency of our numerical optimization algorithm in terms of computational time.

scholarly journals A Greedy Multistage Convex Relaxation Algorithm Applied to Structured Group Sparse Reconstruction Problems Based on Iterative Support Detection

2014 ◽  
Vol 2014 ◽  
pp. 1-23 ◽  
Author(s):  
Liangtian He ◽  
Yilun Wang
Keyword(s):  
Numerical Experiments ◽  
Prior Information ◽  
Structural Information ◽  
Convex Relaxation ◽  
Measured Data ◽  
Alternating Direction Method ◽  
Noisy Environments ◽  
Alternating Direction ◽  
Reconstruction Quality ◽  
Relaxation Procedure

We propose a new effective algorithm for recovering a group sparse signal from very limited observations or measured data. As we know that a better reconstruction quality can be achieved when encoding more structural information besides sparsity, the commonly employedl2,1-regularization incorporating the prior grouping information has a better performance than the plainl1-regularized models as expected. In this paper we make a further use of the prior grouping information as well as possibly other prior information by considering a weightedl2,1model. Specifically, we propose a multistage convex relaxation procedure to alternatively estimate weights and solve the resulted weighted problem. The procedure of estimating weights makes better use of the prior grouping information and is implemented based on the iterative support detection (Wang and Yin, 2010). Comprehensive numerical experiments show that our approach brings significant recovery enhancements compared with the plainl2,1model, solved via the alternating direction method (ADM) (Deng et al., 2013), either in noiseless or in noisy environments.

scholarly journals The Impact of the Frequency Dependence of Soil Electrical Parameters on Lightning Overvoltages Developed in a 138 kV Transmission Line

2020 ◽  
Author(s):  
Felipe De Vasconcellos ◽  
Fernando Moreira ◽  
Rafael Alípio
Keyword(s):  
Transmission Line ◽  
Frequency Dependence ◽  
Soil Parameters ◽  
Construction Time ◽  
Frequency Dependent ◽  
Percentage Decrease ◽  
Conservative Estimation ◽  
Line Construction ◽  
The Impact ◽  
Length Reduction

This study evaluates the overvoltages developed due to direct lightning strokes to a 138-kV transmission line tower top comparing constant and frequency-dependent soil parameters. ATP(Alternative Transients Program) was used to simulate the phenomena. The inclusion of the frequency-dependent soil parameters causes a percentage decrease of the overvoltage peaks when compared with constant soil parameters of around 15% to 33% for first strokes considering values of soil resistivity of 500 Ω.m and 2.500 Ω.m. It was also studied the counterpoise cables length reduction in order to maintain equivalent overvoltage levels to those of simulations with constant parameters. This reduction ranged from 25 to 55%, which could contribute to economic gains as well as operational efficiency in the grounding systems and transmission line construction time. Therefore, disregarding the frequency dependence of the soil parameters in simulations may lead to an overly conservative estimation of the lightning performance of the transmission line.

scholarly journals Hyperspectral Image Recovery Using Non-Convex Low-Rank Tensor Approximation

2020 ◽  
Vol 12 (14) ◽  
pp. 2264
Author(s):  
Hongyi Liu ◽  
Hanyang Li ◽  
Zebin Wu ◽  
Zhihui Wei
Keyword(s):  
Hyperspectral Image ◽  
Convex Relaxation ◽  
Principal Component ◽  
Nuclear Norm ◽  
Low Rank ◽  
Low Rank Approximation ◽  
Rank Tensor ◽  
Alternating Direction ◽  
Tensor Approximation ◽  
Tensor Nuclear Norm

Low-rank tensors have received more attention in hyperspectral image (HSI) recovery. Minimizing the tensor nuclear norm, as a low-rank approximation method, often leads to modeling bias. To achieve an unbiased approximation and improve the robustness, this paper develops a non-convex relaxation approach for low-rank tensor approximation. Firstly, a non-convex approximation of tensor nuclear norm (NCTNN) is introduced to the low-rank tensor completion. Secondly, a non-convex tensor robust principal component analysis (NCTRPCA) method is proposed, which aims at exactly recovering a low-rank tensor corrupted by mixed-noise. The two proposed models are solved efficiently by the alternating direction method of multipliers (ADMM). Three HSI datasets are employed to exhibit the superiority of the proposed model over the low rank penalization method in terms of accuracy and robustness.

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