Recursive state estimation for multi‐rate time‐varying systems with multiplicative noises: Dealing with sensor resolutions

Shen, Yuxuan; Wang, Zidong; Liu, Hongjian; Dong, Hongli; Yi, Xiaojian

doi:10.1002/rnc.6128

Cited by 8 publications

(3 citation statements)

References 58 publications

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“…In addition to using the windowing method to hide cross-term influence, deep learning methods were also used to extract features automatically to mitigate the effects of noisy cross-terms. In [67], the WVD was combined with CNN to convert a one-dimensional voltage disturbance signal into a two-dimensional image file. Then a neural network was utilized to classify the images.…”

Section: Statistical Analysis Feature Techniquesmentioning

confidence: 99%

“…The requirement for using CNN is that one-dimensional interference signals must be converted into two-dimensional image files before the classification process [142]. After combining with a CNN, WVD can determine follow-up remedial actions [67]. The combination of S-transform and CNN can avoid the complex problem of window function selection with fixed window width, good noise immunity, better real-time performance, and higher accuracy [86].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Yan¹,

Chen²,

Geng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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With increasing global concerns about clean energy in smart grids, the detection of power quality disturbances (PQDs) caused by energy instability is becoming more and more prominent. It is well acknowledged that the PQD effects on power grid equipment are destructive and hazardous, which causes irreversible damage to underlying electrical/electronic equipment of the concerned intelligent grids. In order to ensure safe and reliable equipment implementation, appropriate PQD detection technologies must be adopted to avoid such adverse effects. This paper summarizes the newly proposed and traditional PQD detection techniques in order to give a quick start to new researchers in the related field, where specific scenarios and events for which each technique is applicable are also clearly presented. Finally, comments on the future evolution of PQD detection techniques are given. Unlike the published review articles, this paper focuses on the new techniques from the last five years while providing a brief recap on traditional PQD detection techniques so as to supply researchers with a systematic and state-of-the-art review for PQD detection.

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Section: Statistical Analysis Feature Techniquesmentioning

confidence: 99%

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Yan¹,

Chen²,

Geng³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

View full text Add to dashboard Cite

show abstract

“…Low sensor resolution may cause the sensor's output measurements to deviate from the actual system measurements. 34 In Reference 39, a suitable resolution model is proposed and a standard Bayesian tracking filter was designed to fix the problem of sensor resolution induction. It can be seen that it is necessary to consider the sensor resolution when studying the distributed filtering problem of MRSs.…”

Section: Introductionmentioning

confidence: 99%

Fusion estimation for state-saturated multi-rate systems with integral measurement and sensor resolution

Shi,

Xu,

et al. 2023

Preprint

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The fusion estimation problem for a class of multi-rate systems (MRS) subject to state saturation is examined in this paper. varying sensors have varying sensor sampling rates (SSRs), and in MRS, the system’s status update rate (SUR) and the SSR can differ from one another. In addition, to incorporate the requirements of actual engineering practice, the measurement output of sensors is influenced by the sensor resolution and the phenomenon of integral measurements. The aim of this paper is to design an appropriate set of local estimators for each sensor node, taking into account the effects of integral measurement, sensor resolution and multi-rate sampling mechanism. Then, Design of the upper bound on the error covariance of the estimator can be obtained by solution of the matrix difference equation, and the relevant filter parameter is determined by minimizing the obtained upper bound. For local state estimation, an acceptable fusion estimating approach based on the covariance intersection (CI) method is given, and its consistency is demonstrated. Lastly, the effectiveness and appropriateness of the suggested multi-rate fusion estimation approach are confirmed by a simulated example.

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Resolution‐Dependent State Estimation for a Class of Nonlinear Coupled Complex Networks With Stochastic Communication and Correlated Noises

Chen,

Yue,

Jia

2024

Adaptive Control & Signal

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This article proposes the design of the resolution‐dependent variance‐constrained state estimation (RDVCSE) algorithm for a class of time‐varying nonlinear coupled complex networks (TVNCCNs) with stochastic communication and correlated noises. Specifically, a continuous‐differentiable nonlinear function with bounded first partial derivative is considered during the exchange among different coupled units and a resolution‐limited model is taken into account to embody the limited data‐processing capabilities of sensors. In order to describe the principle of random allocation in engineering, a stochastic strategy is employed in the sensor/estimator shared channel. An augmented RDVCSE method is developed such that the error covariance upper bound of state estimation (ECUBSE) can be guaranteed and obtained first. Then, the estimator parameter can be concretized via optimizing the trace of ECUBSE. In addition, a sufficient criterion is provided to verify the uniform boundedness of the presented RDVCSE algorithm. Finally, a comparative simulation is carried out to illustrate the validity of the introduced RDVCSE algorithm.

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Recursive state estimation for multi‐rate time‐varying systems with multiplicative noises: Dealing with sensor resolutions

Cited by 8 publications

References 58 publications

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

A Review on Intelligent Detection and Classification of Power Quality Disturbances: Trends, Methodologies, and Prospects

Fusion estimation for state-saturated multi-rate systems with integral measurement and sensor resolution

Resolution‐Dependent State Estimation for a Class of Nonlinear Coupled Complex Networks With Stochastic Communication and Correlated Noises

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