Invisible Units Detection and Estimation Based on Random Matrix Theory

He, Xing; Chu, Lei; Qiu, Robert C.; Ai, Qian; Ling, Zenan; Zhang, Jian

doi:10.1109/tpwrs.2019.2935739

Cited by 46 publications

(21 citation statements)

References 27 publications

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“…Table 4 reveals that CBAS algorithm owns the fastest convergence rate while Table 5 shows that CBAS algorithm owns the minimum fitness function in 10 runs. Lastly, the integral of absolute error (IAE) [40][41][42] of each algorithm in three scenarios are given by Table 6, in which IAE x ¼ R T 0 jx À x � jdt and x � denotes the reference of variable x, respectively [43,44]. In particular, IAE δ of CBAS algorithm is merely 32.28%, 55.41%, 48.81%, and 56.94% of that of manual tuning [45][46][47], PSO, GA, and BAS algorithm, respectively (bold colour indicates the best results in Tables 4-6).…”

Section: Dfig Lossmentioning

confidence: 99%

Optimal coordinated control of hybrid AC/VSC-HVDC system integrated with DFIG via cooperative beetle antennae search algorithm

Jiang¹,

Huang²,

Zhang³

et al. 2020

PLoS ONE

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Nowadays, with the significant integration of various renewable energy, hybrid alternating current/ voltage source converter based high voltage direct current (AC/VSC-HVDC) system integrated with doubly-fed induction generator (DFIG) has achieved rapidly development in smart grid. A proper control system design for hybrid AC/VSC-HVDC system plays a very crucial role for a reliable and effective power transmission. Hence, this paper designs a novel cooperative beetle antenna search (CBAS) algorithm for optimal coordinated control of hybrid AC/VSC-HVDC system integrated with DFIG. Compared with original beetle antennae search (BAS) algorithm, CBAS algorithm can significantly improve searching efficiency via an efficient cooperation with a group of multiple beetles instead of a single beetle. Particularly, CBAS algorithm can effectively escape from local optimums thanks to its dynamic balance mechanism, which can maintain appropriate trade-off between global exploration and local exploitation. Moreover, three case studies are undertaken to validate the effectiveness and superiorities and effectiveness of CBAS algorithm compared against that of other traditional meta-heuristic algorithms. Especially, the average results of fitness function acquired by CBAS algorithm is merely 46.05%, 41.18%, and 47.82% of that of PSO, GA, and BAS algorithm, respectively.

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Section: Dfig Lossmentioning

confidence: 99%

Optimal coordinated control of hybrid AC/VSC-HVDC system integrated with DFIG via cooperative beetle antennae search algorithm

Jiang¹,

Huang²,

Zhang³

et al. 2020

PLoS ONE

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“…Therefore, a combination of (19), (20), (21) and (27) can be utilized to provide the warning/alarm bounds of the levitation condition for the maglev train.…”

Section: ) Asymptotic Characteristics Analysismentioning

confidence: 99%

“…As an effective universal data analysis tool, random matrix theory (RMT) can be applied to the operating condition awareness of the decoupled multi-node LCS. RMT has been introduced in the big data architecture design of smart girds for correlation analysis and anomaly detection purposes [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

A Levitation Condition Awareness Architecture for Low-Speed Maglev Train Based on Data-Driven Random Matrix Analysis

et al. 2020

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Modern low-speed maglev trains typically use multi-node decentralized levitation control modules, which results in a complex levitation control system with coupling interaction. Conducting systematic levitation condition awareness of the levitation control system is still a promising challenge. In this paper, under the hypothesis of levitation residuals following normal distribution, a levitation condition awareness architecture for the levitation control system is proposed based on data-driven random matrix analysis. The proposed architecture consists of an engineering procedure followed by a cascaded mathematical procedure. In the decentralized engineering procedure, the data-driven modeling for individual levitation control modules is achieved by nonlinear autoregressive modeling with an exogenous input neural network, and the unknown parameters are identified by a modified combinatorial genetic algorithm. On this basis, high-dimensional analysis of streaming residual random matrices for the levitation control system is conducted aided by large-dimensional random matrix theory, and the control limits of the constructed indicators are well-designed using the theorical distributions. Based on the comparative analysis of the experimental datasets, the proposed awareness architecture is verified to show the effectiveness of the systematic condition evaluation of the levitation system, and incipient train-guideway interaction vibration abnormalities can be detected in a timely manner. INDEX TERMS Low-speed maglev train, levitation control modeling, levitation condition awareness, random matrix analysis

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“…While the knowledge of Y is crucial, it may be unavailable or outdated (via TI) due to some reasons [3][4][5][6][7]. Among these reasons, the uncertainties caused by the behavior of high-penetration renewables [8,9], which are analytically intractable for most tools, are one of the main challenges. How to address these uncertainties by harnessing their jointly spatial-temporal statistical properties is at the heart of our study, and this question threads throughout the proposed hybrid framework.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Framework for Topology Identification of Distribution Grid With Renewables Integration

Qiu

et al. 2021

IEEE Trans. Power Syst.

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Topology identification (TI) is a key task for state estimation (SE) in distribution grids, especially the one with highpenetration renewables. The uncertainties, initiated by the timeseries behavior of renewables, will almost certainly lead to bad TI results without a proper treatment. These uncertainties are analytically intractable under conventional framework-they are usually jointly spatial-temporal dependent, and hence cannot be simply treated as white noise. For this purpose, a hybrid framework is suggested in this paper to handle these uncertainties in a systematic and theoretical way; in particular big data analytics are studied to harness the jointly spatial-temporal statistical properties of those randomness. With some prior knowledge, a model bank is built first to store the countable typical models of network configurations. And thus the difference between the SE outputs of each bank model and our observation is capable of being defined as a matrix variate-the so-called random matrix. In order to gain insight into the random matrix, a well-designed metric space is needed. Auto-regression (AR) model, factor analysis (FA), and random matrix theory (RMT) are tied together for the metric space design, followed by jointly temporal-spatial analysis of those matrices which is conducted in a high-dimensional (vector) space. Under the proposed framework, some big data analytics and theoretical results are obtained as a by-product to improve the TI performance. Our framework is validated using IEEE standard distribution network with some field data in practice.

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Invisible Units Detection and Estimation Based on Random Matrix Theory

Cited by 46 publications

References 27 publications

Optimal coordinated control of hybrid AC/VSC-HVDC system integrated with DFIG via cooperative beetle antennae search algorithm

Optimal coordinated control of hybrid AC/VSC-HVDC system integrated with DFIG via cooperative beetle antennae search algorithm

A Levitation Condition Awareness Architecture for Low-Speed Maglev Train Based on Data-Driven Random Matrix Analysis

A Hybrid Framework for Topology Identification of Distribution Grid With Renewables Integration

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