Study on Fault Detection for Line Section in Active Distribution Network Based on the Generalized State-space Model

In this article, a novel approach for model-based sensor fault detection and estimation of gas turbine is presented. The proposed method includes driving a state-space model of gas turbine, designing a novel L1-norm Lyapunov-based observer, and a decision logic which is based on bank of observers. The novel observer is designed using multiple Lyapunov functions based on L1-norm, reducing the estimation noise while increasing the accuracy. The L1-norm observer is similar to sliding mode observer in switching time. The proposed observer also acts as a low-pass filter, subsequently reducing estimation chattering. Since a bank of observers is required in model-based sensor fault detection, a bank of L1-norm observers is designed in this article. Corresponding to the use of the bank of observers, a two-step fault detection decision logic is developed. Furthermore, the proposed state-space model is a hybrid data-driven model which is divided into two models for steady-state and transient conditions, according to the nature of the gas turbine. The model is developed by applying a subspace algorithm to the real field data of SGT-600 (an industrial gas turbine). The proposed model was validated by applying to two other similar gas turbines with different ambient and operational conditions. The results of the proposed approach implementation demonstrate precise gas turbine sensor fault detection and estimation.

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A General State-Space Formulation for Online Scheduling

10.20944/preprints201710.0125.v1 ◽

2017 ◽

Author(s):

Dhruv Gupta ◽

Christos T. Maravelias

Keyword(s):

State Space ◽

State Space Model ◽

Online Scheduling ◽

Model Formulation ◽

Diverse Application ◽

Space Formulation ◽

General State Space ◽

Generalized State Space ◽

Generalized State ◽

Natural Way

We present a generalized state-space model formulation particularly motivated by an online scheduling perspective. Through these proposed generalizations, we enable a natural way to handle routinely encountered disturbances and a rich set of corresponding counter-decisions. Thereby, greatly simplifying and extending the possible application of mathematical programming based online scheduling solutions to diverse application settings.

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WAMs Based Eigenvalue Space Model for High Impedance Fault Detection

Applied Sciences ◽

10.3390/app112412148 ◽

2021 ◽

Vol 11 (24) ◽

pp. 12148

Author(s):

Gian Paramo ◽

Arturo S. Bretas

Keyword(s):

Fault Detection ◽

State Space ◽

Distribution Systems ◽

State Space Model ◽

Real Life ◽

Learning Tools ◽

Space Model ◽

High Impedance ◽

High Impedance Fault ◽

High Impedance Faults

High impedance faults present unique challenges for power system protection engineers. The first challenge is the detection of the fault, given the low current magnitudes. The second challenge is to locate the fault to allow corrective measures to be taken. Corrective actions are essential as they mitigate safety hazards and equipment damage. The problem of high impedance fault detection and location is not a new one, and despite the safety and reliability implications, relatively few efforts have been made to find a general solution. This work presents a hybrid data driven and analytical-based model for high impedance fault detection in distribution systems. The first step is to estimate a state space model of the power line being monitored. From the state space model, eigenvalues are calculated, and their dynamic behavior is used to develop zones of protection. These zones of protection are generated analytically using machine learning tools. High impedance faults are detected as they drive the eigenvalues outside of their zones. A metric called eigenvalue drift coefficient was formulated in this work to facilitate the generalization of this solution. The performance of this technique is evaluated through case studies based on the IEEE 5-Bus system modeled in Matlab. Test results are encouraging indicating potential for real-life applications.

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