Intelligent Diagnosis of Abnormal Charging for Electric Bicycles Based on Improved Dynamic Time Warping

Shuai, Chunyan; Sun, Yu; Zhang, Xiaoqi; Yang, Fang; Ouyang, Xin; Chen, Zheng

doi:10.1109/tie.2022.3206702

Cited by 7 publications

(5 citation statements)

References 33 publications

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“…Through this innovative manner, some hidden hazardous charging faults can be diagnosed in time. Compared with our similar study results based on the longest similar substring and dynamic time warping model, 27 the model is more robust and does not need to manually define charge abnormal features. In particular, the designed method does not entail the detailed state of battery from the DC measure, resulting in easy online implementation potential.…”

Section: Discussionmentioning

confidence: 77%

Promoting charging safety of electric bicycles via machine learning

et al. 2023

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Section: Discussionmentioning

confidence: 77%

Promoting charging safety of electric bicycles via machine learning

et al. 2023

Self Cite

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“…x and Γ m ∈ ℝ (n 2 −n u )×r u are the Lagrange factor matrices. Θ m+1 and b are given by ( 45)- (46). The matrices Θ ∈ ℝ m r r x n u ×m r r x n u and Υ ∈ ℝ n u ×m r r x are given by ( 63)-( 64).…”

Section: Solution Of the Rffc Problemmentioning

confidence: 99%

“…Remark Once the minimization problem (56) is formulated, there are various optimal methods that can solve the RFFC problem, for example, genetic algorithm, support vector machine [44], neural networks algorithm [45], intelligent algorithm [46], harmony search algorithm [47, 48], dynamic fireworks algorithm [49]. …”

Section: Solution Of the Rffc Problemmentioning

confidence: 99%

Robust model reference tracking control for high‐order descriptor linear systems subject to parameter uncertainties

Gao,

Duan

2023

IET Control Theory & Appl

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This paper investigates the robust model reference tracking control problem for high‐order descriptor linear systems (HODLS) subject to norm‐bounded parameter uncertainties. The problem is divided into two subproblems: a robust proportional plus derivative state feedback stabilization (RPPDSFS) problem and a robust feed‐forward compensation (RFFC) problem. In the presence of uncertainties, by using the controller consisting of RPPDSFS part and RFFC part, all the signals of the closed‐loop system are bounded. The latter is equivalent to seeking three coefficient matrices such that a series of linear matrix equations are satisfied, and simultaneously the effect caused by uncertainties is minimized. Based on the right coprime factorization, the RFFC problem is further converted into a minimization problem with a quadratic performance index and some linear constraints. Thereafter, a linear matrix equation that determines the optimal solution is achieved. The restrictive condition that the coefficient matrix of the augmented reference system is non‐defective is relaxed, which reduces the conservativeness. A numerical example and the application of a three‐axis dynamic flight motion simulator manifest the effectiveness and the practicability of the proposed algorithm.

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“…Traditional similarity measures like the Euclidean distance method, though computationally simple, face challenges in recognizing data shape deformations and requiring equal-length time-series data (Ezugwu et al, 2022). The dynamic time warping (DTW) algorithm, known for addressing shape deformations and unequal time-series data (Holder et al, 2023), has been introduced in power system analysis (Gunawan and Huang, 2021;Shuai et al, 2023). However, due to the high-dimensional nonlinear characteristics of the IES source and load data, direct clustering calculations can be complex (Liu and Chen, 2019).…”

Section: Introductionmentioning

confidence: 99%

An integrated energy system day-ahead scheduling method based on an improved dynamic time warping algorithm

2024

Front. Energy Res.

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With the construction and development of the new energy system, the integrated energy system (IES) has garnered significant attention as a crucial energy carrier in recent years. Therefore, to address the scheduling challenges of IES influenced by uncertainty in source load and mitigate the conservatism of scheduling schemes while enhancing clustering accuracy, a method for day-ahead top-note scheduling of IES is proposed. First, by improving dynamic time warping (DTW) for hierarchical clustering of wind, solar, and load data in IES, typical scenarios of IES are derived. Second, using the interval method to model wind, solar, and load data in IES along with their coupled devices and considering the conservatism issue of interval optimization, the established IES interval model undergoes affine processing. Finally, with the goal of minimizing the operating costs of IES, a day-ahead interval affine scheduling model is established, which is solved using the CPLEX Solver and INTLAB toolbox, and scheduling schemes for all typical scenarios are provided. Through comparative analysis of calculation examples, it is found that the method proposed in this paper can enhance clustering accuracy and reduce the conservatism of system scheduling schemes.

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Intelligent Diagnosis of Abnormal Charging for Electric Bicycles Based on Improved Dynamic Time Warping

Cited by 7 publications

References 33 publications

Promoting charging safety of electric bicycles via machine learning

Promoting charging safety of electric bicycles via machine learning

Robust model reference tracking control for high‐order descriptor linear systems subject to parameter uncertainties

An integrated energy system day-ahead scheduling method based on an improved dynamic time warping algorithm

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