Distributed Fast Fault Diagnosis for Multimachine Power Systems via Deterministic Learning

Chen, Tianrui; Hill, David J.; Wang, Cong

doi:10.1109/tie.2019.2917367

Cited by 32 publications

(13 citation statements)

References 35 publications

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“…is capability allows detecting and isolating early developing failures and predicting failure propagation, which can allow preventive maintenance or even as a countermeasure to the possibility of catastrophic events due to failures. Chen et al [1] proposed a distributed fast fault diagnosis approach for multimachine power systems based on deterministic learning (DL) theory. In the study of Mousavi et al [2], an efficient strategy for fault detection and isolation (FDI) of an Industrial Gas Turbine is introduced based on ensemble learning methods.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Industrial Control Equipment Safety Fault Diagnosis Method in Industrial Internet of Things

Zhang

Meng

et al. 2021

Security and Communication Networks

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With the rapid development of intelligent manufacturing and Industrial Internet of Things, many industrial control systems have high requirements for the security of the system itself. Failures of industrial control equipment will cause abnormal operation of industrial control equipment and waste of resources. It is very meaningful to detect and identify potential equipment abnormalities and failures in time and implement effective fault tolerance strategies. In the Industrial Internet of Things environment, the instructions and parameters of industrial control equipment often change due to changes in actual requirements. However, it is impractical to customize the learning method for each parameter value. Aiming at the problem, this paper proposes a fault diagnosis model based on ensemble learning and proposes a method of updating voting weights based on dynamic programming to assist decision-making. This method is based on Bagging strategy and combined with dynamic programming voting weight adjustment method to complete fault type prediction. Finally, this paper uses different loads as dynamic conditions; the diagnostic capability of the Bagging-based fault diagnosis integrated model in a dynamically changing industrial control system environment is verified by experiments. The fault diagnosis model of industrial control equipment based on ensemble learning effectively improves the adaptive ability of the model and makes the fault diagnosis framework truly intelligent. The voting weight adjustment method based on dynamic programming further improves the reliability of voting.

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Section: Related Workmentioning

confidence: 99%

An Adaptive Industrial Control Equipment Safety Fault Diagnosis Method in Industrial Internet of Things

Zhang

Meng

et al. 2021

Security and Communication Networks

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“…where k n is a design constant, W F is the learned constant neural weight vector, which is obtained in Theorem 2 on the basis of (36). Then better tracking performance and the closed-loop stability will be achieved under (41) as well as (14) and (17) when performing the same (or similar) control task, in which the update law (27)is no longer needed.…”

Section: Lc Using Experiencesmentioning

confidence: 99%

“…Figure 6 also shows that excellent convergence is obtained when t = [140, 150] seconds. According to (36), we choose W F = mean t∈[t 140 ,t 150 ]Ŵ F (t). Using the learned constant neural weights W F , the transformed unknown IDC dynamics u * (Z) can be accurately approximated/learned by the constant RBF NN W F S(Z) as shown in Figure 7.…”

Section: Simulation Of Learning From Ancmentioning

confidence: 99%

“…Based on the works of Wang and Hill, 26,27 DL approach was extended to the learning control (LC) for several classes of uncertain nonlinear systems, [28][29][30][31][32] as well as some applications such as rapid fault diagnosis. [33][34][35][36] In the work of Wang and Wang, 37 the DL approach was used to achieve learning for the pure-feedback system based on the traditional backstepping and recursive design, where the complexity of the controllers increases drastically as the system order increases, which will make it difficult to obtain learning in the practical implementation.…”

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confidence: 99%

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Deterministic learning from neural control for uncertain nonlinear pure‐feedback systems by output feedback

Zhang

Wang

2020

Intl J Robust & Nonlinear

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The essence of intelligence lies in the acquisition/learning and utilization of knowledge. However, how to implement learning in dynamical environments for nonlinear systems is a challenging issue. This article investigates the deterministic learning (DL) control problem for uncertain pure-feedback systems by output feedback, which achieves the human-like learning and control in a simple way. To reduce the complexity of control design and analysis, first, by combining an appropriate system transformation, the original pure-feedback system is transformed into a simple normal nonaffine system. An observer is then introduced to estimate the transformed system states. Based on the backstepping and dynamic surface control techniques, a simple adaptive neural control scheme is first developed to guarantee the finite time convergence of the tracking error using only one neural network (NN) approximator. Second, through DL, the exponential convergence of the NN weights is obtained with the satisfaction of partial persistent excitation condition. Thus, locally accurate approximation/learning of the transformed unknown system dynamics is achieved and stored as constant NNs. Finally, by utilizing the stored knowledge, an experience-based controller is constructed and a novel learning control scheme is further proposed to improve the control performance without any further adaptation online for the estimate neural weights. Simulation results have been given to illustrate that the proposed scheme not only can learn and memorize knowledge like humans but also can utilize experience to achieve superior control performance. K E Y W O R D Sadaptive control, deterministic learning, neural networks, pure-feedback nonlinear systems INTRODUCTIONOver the past decades, a number of approximation-based adaptive neural/fuzzy control approaches have been developed for uncertain nonlinear systems based on the concepts of backstepping design. [1][2][3][4] However, in contrast to the strict-feedback systems with affine form, 5-8 only a few results have been obtained for nonaffine pure-feedback systems since lack of mathematical tools, which possess a more general system form and can represent many practical applications, such as Duffing oscillator, 9 mechanical systems, 10 continuous stirred tank reactor (CSTR) system, 11 the system to Int J Robust Nonlinear Control. 2020;30:2701-2718. wileyonlinelibrary.com/journal/rnc

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“…As a result, they tend to give high false positives or false negatives in the fault detection process. Recently, data-driven approaches are receiving considerable attention due to the following reasons: 1) various newly developed intelligent electronic devices (IEDs) have been installed ubiquitously in the system [3]- [5], leading to enormous amount of data available at plenty of nodes across the entire system; and 2) compared with conventional model-based approaches, data-driven approaches are more robust against measurement errors and system model inaccuracy, more flexible in their implementations, and more adaptive to system evolution and changes [6]- [9].…”

Section: Introduction a Backgroundmentioning

confidence: 99%

One-Class Classifier Based Fault Detection in Distribution Systems With Varying Penetration Levels of Distributed Energy Resources

et al. 2020

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The integration of Distributed Energy Resources (DERs) into distribution systems greatly increases the system complexity and introduces two-way power flow. Conventional protection schemes are based upon local measurements and simple linear system models, and are thus not capable of handling the new complexity and power flow patterns in systems with high DER penetration. In this paper, we propose a data-driven protection framework to address the challenges introduced by DERs. Firstly, considering the limited available data under fault conditions, we adopt the Support Vector Data Description (SVDD) method, a commonly used one-class classifier, for distribution system fault detection, which only requires the normal data for its training process. Secondly, incremental learning is incorporated into the proposed SVDD-based protection framework to accommodate variations of the integration level of DERs in distribution systems over time. In particular, the artificial uniform-hyperspherical data generation model is incorporated into the incremental SVDD to boost the training speed. Finally, we validate the proposed method under the IEEE 123-node test feeder. Simulation results demonstrate that our proposed SVDD-based fault detection framework significantly improves the robustness and resilience against DERs in comparison with conventional protection systems. Meanwhile, the proposed online updating model outperforms the existing incremental SVDD models in terms of successful training speed.

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Distributed Fast Fault Diagnosis for Multimachine Power Systems via Deterministic Learning

Cited by 32 publications

References 35 publications

An Adaptive Industrial Control Equipment Safety Fault Diagnosis Method in Industrial Internet of Things

An Adaptive Industrial Control Equipment Safety Fault Diagnosis Method in Industrial Internet of Things

Deterministic learning from neural control for uncertain nonlinear pure‐feedback systems by output feedback

One-Class Classifier Based Fault Detection in Distribution Systems With Varying Penetration Levels of Distributed Energy Resources

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