An Optimization Approach to Multiarea State Estimation

Conejo, Antonio J.; Torre, S. de la; Canas, M.

doi:10.1109/tpwrs.2006.887964

Cited by 133 publications

(91 citation statements)

References 22 publications

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“…In the case of DSE each control center needs to estimate those phase angles that are related to its measurements, but it has to cooperate with neighboring control centers, typically by exchanging the state variables of the border buses, to ensure that the power flows on the tie lines are correctly estimated. In most of the recently proposed DSE algorithms, e.g., [11], [12], [13], [15], state variables are exchanged at the beginning or at the end of every iteration, and are used as an input when calculating the next state vector update. For the purpose of our study, we consider a state-of-the-art algorithm proposed in [15], which is highly robust and obtains accurate estimates of the power flows on the tie lines.…”

Section: A Distributed State Estimation (Dse)mentioning

confidence: 99%

“…However, the information exchange is very limited in practice due to the sensitivity of the data, and it typically includes only the data needed for a consistent and correct estimate of power flows on the lines connecting two regions. While today the SE in interconnected power systems is mostly done hierarchically, there is an increasing interest for fully distributed SE (DSE) for future smart grids [11], [12], [13], [14], [15], as it eliminates the need for a central authority. DSE is effectively an extension of the basic SE [1], [2], and it can obtain a consistent state estimate for the entire interconnected power system.…”

Section: Introductionmentioning

confidence: 99%

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Security of Fully Distributed Power System State Estimation: Detection and Mitigation of Data Integrity Attacks

Vuković

Dán

2014

IEEE J. Select. Areas Commun.

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Abstract-State estimation plays an essential role in the monitoring and supervision of power systems. In today's power systems state estimation is typically done in a centralized or in a hierarchical way, but as power systems will be increasingly interconnected in the future smart grid, distributed state estimation will become an important alternative to centralized and hierarchical solutions. As the future smart grid may rely on distributed state estimation, it is essential to understand the potential vulnerabilities that distributed state estimation may have. In this paper, we show that an attacker that compromises the communication infrastructure of a single control center in an interconnected power system can successfully perform a denial of service attack against state-of-the-art distributed state estimation, and consequently it can blind the system operators of every region. As a solution to mitigate such a denial of service attack, we propose a fully distributed algorithm for attack detection. Furthermore, we propose a fully distributed algorithm that identifies the most likely attack location based on the individual regions' beliefs about the attack location, isolates the identified region, and then reruns the distributed state estimation. We validate the proposed algorithms on the IEEE 118 bus benchmark power system.

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Section: A Distributed State Estimation (Dse)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Security of Fully Distributed Power System State Estimation: Detection and Mitigation of Data Integrity Attacks

Vuković

Dán

2014

IEEE J. Select. Areas Commun.

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“…A modified Lagrangian decomposition technique based on the decomposition of the first order optimality condition is proposed in [27]. This method has an excellent performance over the traditional Lagrangian decomposition approach and has been applied to many multi-area optimal power flow and state estimation problems in recent years [28], [29], [30] and [31].…”

Section: Fig3 Decomposed Model Of a Two Area Systemmentioning

confidence: 99%

“…The optimality condition decomposition (OCD) is a modified Lagrangian decomposition approach in which the global optimization problem is decomposed into several sub-problems in such a way that if the first-order Karush-Kuhn-Tucker (KKT) optimality conditions of every sub-problem are joined together, they are identical to the first-order optimality conditions of the global problem [30]. The area sub-problem is obtained by relaxing all the complicating constraints of other areas through adding them to the objective function of the area subproblem and maintaining its own complicating constraints.…”

Section: Proposed Optimality Condition Decompositionmentioning

confidence: 99%

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Multi‐agent receding horizon control with neighbour‐to‐neighbour communication for prevention of voltage collapse in a multi‐area power system

Islam

Muttaqi

Sutanto

2014

IET Generation, Transmission & Distribution

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-agent receding horizon control with neighbour-to-neighbour communication for prevention of voltage collapse in a multi-area power system," IET Generation, Transmission and Distribution, vol. 8, (9) pp. 1604-1615, 2014 Multi-agent receding horizon control with neighbour-to-neighbour communication for prevention of voltage collapse in a multi-area power system AbstractIn this study, a multi-agent receding horizon control is proposed for emergency control of long-term voltage instability in a multi-area power system. The proposed approach is based on a distributed control of intelligent agents in a multi-agent environment where each agent preserves its local information and communicates with its neighbours to find an optimal solution. In this study, optimality condition decomposition is used to decompose the overall problem into several subproblems, each to be solved by an individual agent. The main advantage of the proposed approach is that the agents can find an optimal solution without the interaction of any central controller and by communicating with only its immediate neighbours through neighbour-toneighbour communication.The proposed control approach is tested using the Nordic-32 test system and simulation results show its effectiveness, particularly in terms of its ability to provide solution in distributed control environment and reduce the control complexity of the problem that may be experienced in a centralised environment. The proposed approach has been compared with the traditional Lagrangian decomposition method and is found to be better in terms of fast convergence and real-time application. Abstract:In this paper, a multi-agent receding horizon control is proposed for emergency control of long-term voltage instability in a multi-area power system. The proposed approach is based on a distributed control of intelligent agents in a multi-agent environment where each agent preserves its local information and communicates with its neighbours to find an optimal solution. In this paper, optimality condition decomposition (OCD) is used to decompose the overall problem into several sub-problems, each to be solved by an individual agent. The main advantage of the proposed approach is that the agents can find an optimal solution without the interaction of any central controller and by communicating with only its immediate neighbours through neighbour-to-neighbour communication.The proposed control approach is tested using the Nordic-32 test system and simulation results show its effectiveness, particularly in terms of its ability to provide solution in distributed control environment and reduce the control complexity of the problem that may be experienced in a centralized environment. The proposed approach has been compared with the traditional Lagrangian decomposition method and is found to be better in terms of fast convergence and real-time application.

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Multi‐area state estimation in a distribution network using Takagi‐Sugeno model estimated by Kalman filter

Ghadikolaee

Kazemi

Shayanfar

2020

Int Trans Electr Energ Syst

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Summary This study addressed the problem of multi‐area state estimation in a clustered distribution system. Distribution networks are inherently expansive and comprise a multitude of nodes. This issue increases the state estimation computation time and makes it inapplicable for control of sophisticated distribution networks. Multi‐area state estimation is a technique to reduce computation time while concerning computation accuracy. Many efforts are required to reach a perfect algorithm, followed by the optimization of different parameters of the proposed algorithms. This paper performed a precise mathematical analysis of the impact made by the common (shared) node exchanged information between the areas in the multi‐area state estimation algorithm. Furthermore, a new iterative multi‐area state estimation algorithm equipped with machine learning tools was designed based on analytical detections for enhancing the convergence speed and accuracy of the estimation results. The improvement was evaluated in two clustered networks with 356 and 711 nodes. The results indicated the benefits provided by the proposed modification in terms of convergence speed and accuracy with minimum data exchanges in an iterative multi‐area distribution network.

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An Optimization Approach to Multiarea State Estimation

Cited by 133 publications

References 22 publications

Security of Fully Distributed Power System State Estimation: Detection and Mitigation of Data Integrity Attacks

Security of Fully Distributed Power System State Estimation: Detection and Mitigation of Data Integrity Attacks

Multi‐agent receding horizon control with neighbour‐to‐neighbour communication for prevention of voltage collapse in a multi‐area power system

Multi‐area state estimation in a distribution network using Takagi‐Sugeno model estimated by Kalman filter

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