Privacy preserving decentralized power system state estimation with phasor measurement units

Kashyap, Neelabh; Werner, Stefan; Huang, Yih-Fang; Arablouei, Reza

doi:10.1109/sam.2016.7569719

Cited by 5 publications

(8 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, operators in different areas are reluctant to share network data and measurements due to competition. The internal measurements and internal line parameters of each area are hidden from all other areas [18]. They worry about that the conventional collaborative dynamic state estimation may lead to the leakage of their trade secrets and potential competitors would learn their technical competence.…”

Section: B Our Work and Contributionsmentioning

confidence: 99%

Multi-Party Dynamic State Estimation That Preserves Data and Model Privacy

et al. 2021

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

In this paper we focus on the dynamic state estimation which harnesses a vast amount of sensing data harvested by multiple parties and recognize that in many applications, to improve collaborations between parties, the estimation procedure must be designed with the awareness of protecting participants' data and model privacy, where the latter refers to the privacy of key parameters of observation models. We develop a state estimation paradigm for the scenario where multiple parties with data and model privacy concerns are involved. Multiple parties monitor a physical dynamic process by deploying their own sensor networks and update the state estimate according to the average state estimate of all the parties calculated by a cloud server and security module. The paradigm taps additively homomorphic encryption which enables the cloud server and security module to jointly fuse parties' data while preserving the data privacy. Meanwhile, all the parties collaboratively develop a stable (or optimal) fusion rule without divulging sensitive model information. For the proposed filtering paradigm, we analyze the stabilization and the optimality. First, to stabilize the multi-party state estimator while preserving observation model privacy, two stabilization design methods are proposed. For special scenarios, the parties directly design their estimator gains by the matrix norm relaxation. For general scenarios, after transforming the original design problem into a convex semi-definite programming problem, the parties collaboratively derive suitable estimator gains based on the alternating direction method of multipliers (ADMM). Second, an optimal collaborative gain design method with model privacy guarantees is provided, which results in the asymptotic minimum mean square error (MMSE) state estimation. Finally, numerical examples are presented to illustrate our design and theoretical findings.

show abstract

Section: B Our Work and Contributionsmentioning

confidence: 99%

Multi-Party Dynamic State Estimation That Preserves Data and Model Privacy

et al. 2021

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

show abstract

“…In recent years, there have been several works describing algorithms to distribute regression problems, i.e., [5]- [19]. In particular, shrinkage methods such as ridge regression and lasso have attracted a lot of attention since they play an important role in preventing the problem from being ill-posed due to possible rank deficiency of the observation matrix.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, shrinkage methods such as ridge regression and lasso have attracted a lot of attention since they play an important role in preventing the problem from being ill-posed due to possible rank deficiency of the observation matrix. Moreover, such methods regularize the regression parameters by imposing a penalty on their size or density to avoid overfitting [6], [8], [19]- [21]. Example applications are in wireless sensor networks operating under strict power budget constraints where agents collecting and processing data are distributed over a large geographical area [8].…”

Section: Introductionmentioning

confidence: 99%

“…In the framework of vertically partitioned data, some applications related to clustering and classification have been considered in [24], [25]. The regression problem with feature partitioning has also previously been considered in [6], [19]- [21]. However, works of [20], [21] assume a proper coloring scheme of the network and cannot be extended to a general graph labeling.…”

Section: Introductionmentioning

confidence: 99%

“…However, works of [20], [21] assume a proper coloring scheme of the network and cannot be extended to a general graph labeling. The algorithm proposed in [19] is not truly distributed since its consensus constraints involve the entire network instead of each agent's local neighborhood. The algorithm in [6] is fully distributed and based on the diffusion strategy [26].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Distributed Ridge Regression with Feature Partitioning

Gratton

Venkategowda

Arablouei

et al. 2018

2018 52nd Asilomar Conference on Signals, Systems, and Computers

Self Cite

View full text Add to dashboard Cite

We develop a new distributed algorithm to solve the ridge regression problem with feature partitioning of the observation matrix. The proposed algorithm, named D-Ridge, is based on the alternating direction method of multipliers (ADMM) and estimates the parameters when the observation matrix is distributed among different agents with feature (or vertical) partitioning. We formulate the associated ridge regression problem as a distributed convex optimization problem and utilize the ADMM to obtain an iterative solution. Numerical results demonstrate that D-Ridge converges faster than its diffusion-based contender does.

show abstract

Decentralized PMU-Assisted Power System State Estimation With Reduced Interarea Communication

Kashyap

Werner

Huang

2018

IEEE J. Sel. Top. Signal Process.

Self Cite

View full text Add to dashboard Cite

This paper presents a decentralized approach to multi-area power system state estimation using a combination of conventional measurement devices and newer phasor measurement units (PMU). We employ a reduced-order approach to state estimation, wherein the PMU observable and unobservable components of the state vector are estimated separately in each control area. The estimation problem is solved by exchanging information through an improved gossip-based protocol which takes into account the weak measurement coupling between control areas. A detailed analysis of the proposed protocol reveals that the amount of information exchange is always less than the naïve gossip based scheme. We show through simulations on the IEEE 118-bus test system that, in practice, the gossip iterations converge in one iteration regardless of the number of control areas. Our simulations also show that the marginal information exchange is also considerably lower when using the proposed method.

show abstract

Privacy preserving decentralized power system state estimation with phasor measurement units

Cited by 5 publications

References 32 publications

Multi-Party Dynamic State Estimation That Preserves Data and Model Privacy

Multi-Party Dynamic State Estimation That Preserves Data and Model Privacy

Distributed Ridge Regression with Feature Partitioning

Decentralized PMU-Assisted Power System State Estimation With Reduced Interarea Communication

Contact Info

Product

Resources

About