Optimal parameterization of Kalman filter based three-phase dynamic state estimator for active distribution networks

Ćetenović, Dragan; Ranković, Aleksandar

doi:10.1016/j.ijepes.2018.04.008

Cited by 26 publications

(8 citation statements)

References 20 publications

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“…The simulation Simulink structure of the proposed FDI algorithm is shown in Figure 5, which contains three part: SMPC, SHE and FDI running synchronously. The major calculation load is solving a SMPC problem (14) quadratic constrained quadratic programming (QCQP), which costs 2.1 ms on average.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…G x ∈ R n x +n u and G u ∈ R n x +n u are coefficient matrixes in state and input constraints. The fault is not considered in the predictive model in problem (14) because this paper considers the unknown actuator fault input. The problem (14) is a quadratic constrained quadratic programming problem.…”

Section: Scenario-based Smpc Designmentioning

confidence: 99%

“…Since the problem (14) is difficult to solve directly, it is necessary to construct a scenario tree of the stochastic model to convert problem (14) into a deterministic problem. The scenario-based stochastic model predictive control problem can be solved in two steps, as shown above.…”

Section: Scenario-based Smpc Designmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…Scholars also have extensive and in-depth research on state estimation [14][15][16][17][18][19][20]. In the literature, the Kalman filter is the most commonly used method to estimate states [14]. For discrete-time state-space Markov models, Crisan [15] uses a sequential Monte Carlo method to design nested structure particle filters to approximate the posterior probability measure of the static parameters and the dynamic state variables of the system.…”

Section: Introductionmentioning

confidence: 99%

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Stochastic Hybrid Estimator Based Fault Detection and Isolation for Wind Energy Conversion Systems with Unknown Fault Inputs

et al. 2018

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Abstract:In recent years, the wind energy conversion system (WECS) has been becoming the vital system to acquire wind energy. However, the high failure rate of WECSs leads to expensive costs for the maintenance of WECSs. Therefore, how to detect and isolate the faults of WECSs with stochastic dynamics is the pressing issue in the literature. This paper proposes a novel comprehensive fault detection and isolation (FDI) method for WECSs. First, a stochastic model predictive control (SMPC) controller is studied to construct the closed-loop system of the WECS. This controller is based on the Markov-jump linear model, which could precisely establish the stochastic dynamics of the WECS. Meanwhile, the SMPC controller has satisfied control performance for the WECS. Second, based on the closed-loop system with SMPC, the stochastic hybrid estimator (SHE) is designed to estimate the continuous and discrete states of the WECS. Compared with the existing estimators for WECSs, the proposed estimator is more suitable for WECSs since it considers both the continuous and discrete states of WECSs. In addition, the proposed estimator is robust to the fault input. Finally, with the proposed estimator, the comprehensive FDI method is given to detect and isolate the actuators' faults of the WECS. Both the system status and the actuators' faults can be detected by the FDI method and it can effectively quantify the actuators' fault by the fault residuals. The simulation results suggest that the SHE could effectively estimate the hybrid states of the WECS, and the proposed FDI method gives satisfied fault detection performance for the actuators of the WECS.

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Section: Simulation Resultsmentioning

confidence: 99%

Section: Scenario-based Smpc Designmentioning

confidence: 99%

Section: Scenario-based Smpc Designmentioning

confidence: 99%