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Author

Tengfei Zhang

Bio: Tengfei Zhang is an academic researcher from Nanjing University of Posts and Telecommunications. The author has contributed to research in topics: Microgrid & Computer science. The author has an hindex of 9, co-authored 45 publications receiving 223 citations.

Papers published on a yearly basis

Papers
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Journal ArticleDOI
TL;DR: This article investigates resilient event-triggered load frequency control (LFC) of multiarea power systems under nonideal network environments under the sample-data framework and provides a better way to balance the control performance and communication resource by reasonably choosing the parameters of QEC.
Abstract: This article investigates resilient event-triggered load frequency control (LFC) of multiarea power systems under nonideal network environments. Under the sample-data framework, a novel QoS-dependent event-triggered communication (QEC) scheme is presented to deal with nonideal network environments while preserving the desired control performance and improving the communication efficiency. In comparison with some traditional state-dependent event-triggered communication schemes, since the proposed QEC depends not only on the state of controlled plant but also on the QoS of communication network, higher communication efficiency can be achieved. Then, a resilient LFC is well developed based on the proposed QEC, where “resilient” implies that: 1) for a normal QoS case, less packets are transmitted to save the communication resources and 2) for an abnormal QoS case, more packets are transmitted to mitigate the influence of nonideal QoS. Moreover, the proposed method provides a better way to balance the control performance and communication resource by reasonably choosing the parameters of QEC. Finally, the simulation results show the effectiveness of the proposed method.

52 citations

Journal ArticleDOI
TL;DR: In this paper, a dynamic scale-free network-based differential evolution (DSNDE) is developed by considering the demands of convergent speed and the ability to jump out of local minima.
Abstract: Some recent research reports that a dendritic neuron model (DNM) can achieve better performance than traditional artificial neuron networks (ANNs) on classification, prediction, and other problems when its parameters are well-tuned by a learning algorithm. However, the back-propagation algorithm (BP), as a mostly used learning algorithm, intrinsically suffers from defects of slow convergence and easily dropping into local minima. Therefore, more and more research adopts non-BP learning algorithms to train ANNs. In this paper, a dynamic scale-free network-based differential evolution (DSNDE) is developed by considering the demands of convergent speed and the ability to jump out of local minima. The performance of a DSNDE trained DNM is tested on 14 benchmark datasets and a photovoltaic power forecasting problem. Nine meta-heuristic algorithms are applied into comparison, including the champion of the 2017 IEEE Congress on Evolutionary Computation (CEC2017) benchmark competition effective butterfly optimizer with covariance matrix adapted retreat phase (EBOwithCMAR). The experimental results reveal that DSNDE achieves better performance than its peers.

49 citations

Journal ArticleDOI
TL;DR: An improved rough k-means clustering based on weighted distance measure with Gaussian function is proposed in this paper and the validity of this algorithm is demonstrated by simulation and experimental analysis.
Abstract: Rough k-means clustering algorithm and its extensions are introduced and successfully applied to real-life data where clusters do not necessarily have crisp boundaries. Experiments with the rough k-means clustering algorithm have shown that it provides a reasonable set of lower and upper bounds for a given dataset. However, the same weight was used for all the data objects in a lower or upper approximate set when computing the new centre for each cluster while the different impacts of the objects in a same approximation were ignored. An improved rough k-means clustering based on weighted distance measure with Gaussian function is proposed in this paper. The validity of this algorithm is demonstrated by simulation and experimental analysis.

46 citations

Journal ArticleDOI
TL;DR: A three-stage multi-view stacking ensemble (TMSE) machine learning model based on hierarchical time series feature extraction (HTSF) methods are proposed to solve the anomaly detection problem.
Abstract: Anomaly detection of power consumption, mainly including electricity stealing and unexpected power energy loss, has been one of the essential routine works in power system management and maintenance. With the help of Industrial Internet of Things technologies, power consumption data was aggregated from distributed various power devices. Hence, the power consumption anomaly was able to be detected by machine learning algorithms. In this paper, a three-stage multi-view stacking ensemble (TMSE) machine learning model based on hierarchical time series feature extraction (HTSF) methods are proposed to solve the anomaly detection problem: HTSF is a novel systematic time series feature engineering method to represent the given data numerically and as input data for machine learning algorithms, while TMSE is designed to ensemble meta-models to archive more accurate performance by using multi-view stacking ensemble method. Performance evaluation in real-world data shows that the proposed method outperforms the existing time series feature extraction means and dramatically decreases the time consumed for ensemble learning process.

40 citations

Journal ArticleDOI
TL;DR: A PV power forecasting model based on the dendritic neuron networks, which seeks to improve the computational efficiency and prediction accuracy, and results obtained through simulation demonstrate significant improvement in terms of accuracy and efficiency.

35 citations


Cited by
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Dissertation
01 Jul 2016
TL;DR: In this paper, a clustering-based under-sampling strategy was proposed to balance the imbalance between the minority class and the majority class, where the number of clusters in the majority classes is set to be equal to the number in the minority classes.
Abstract: Abstract Class imbalance is often a problem in various real-world data sets, where one class (i.e. the minority class) contains a small number of data points and the other (i.e. the majority class) contains a large number of data points. It is notably difficult to develop an effective model using current data mining and machine learning algorithms without considering data preprocessing to balance the imbalanced data sets. Random undersampling and oversampling have been used in numerous studies to ensure that the different classes contain the same number of data points. A classifier ensemble (i.e. a structure containing several classifiers) can be trained on several different balanced data sets for later classification purposes. In this paper, we introduce two undersampling strategies in which a clustering technique is used during the data preprocessing step. Specifically, the number of clusters in the majority class is set to be equal to the number of data points in the minority class. The first strategy uses the cluster centers to represent the majority class, whereas the second strategy uses the nearest neighbors of the cluster centers. A further study was conducted to examine the effect on performance of the addition or deletion of 5 to 10 cluster centers in the majority class. The experimental results obtained using 44 small-scale and 2 large-scale data sets revealed that the clustering-based undersampling approach with the second strategy outperformed five state-of-the-art approaches. Specifically, this approach combined with a single multilayer perceptron classifier and C4.5 decision tree classifier ensembles delivered optimal performance over both small- and large-scale data sets.

336 citations

01 Jan 2013
TL;DR: An appropriate control scheme is now developed for controlling the interlinking converter to keep the hybrid microgrid in autonomous operation with active power proportionally shared among its distributed sources.
Abstract: The coexistence of ac and dc subgrids in a hybrid microgrid is likely given that modern distributed sources can either be ac or dc. Linking these subgrids is a power converter, whose topology should preferably be not too unconventional. This is to avoid unnecessary compromises to reliability, simplicity, and industry relevance of the converter. The desired operating features of the hybrid microgrid can then be added through this interlinking converter. To demonstrate, an appropriate control scheme is now developed for controlling the interlinking converter. The objective is to keep the hybrid microgrid in autonomous operation with active power proportionally shared among its distributed sources. Power sharing here should depend only on the source ratings and not their placements within the hybrid microgrid. The proposed scheme can also be extended to include energy storage within the interlinking converter, as already proven in simulation and experiment. These findings have not been previously discussed in the literature, where existing schemes are mostly for an ac or a dc microgrid, but not both in coexistence.

271 citations

Journal ArticleDOI
TL;DR: An in-depth review of existing anomaly detection frameworks for building energy consumption based on artificial intelligence is presented, in which a comprehensive taxonomy is introduced to classify existing algorithms based on different modules and parameters adopted.

198 citations

01 Jan 2009
TL;DR: In this article, an extended topological method has been proposed by incorporating several specific features of power systems such as electrical distance, power transfer distribution factors and line flow limits, starting from the extended metric for efficiency named as net-ability, an extended betweenness and proposes a joint method of extended and betweenness to rank the most critical lines and buses in an electrical power grid.
Abstract: Vulnerability analysis of power systems is a key issue in modern society and many efforts have contributed to the analysis. Complex network metrics for the assessment of the vulnerability of networked systems have been recently applied to power systems. Complex network theory may come in handy for vulnerability analysis of power systems due to a close link between the topological structure and physical properties of power systems. However, a pure topological approach fails to capture the engineering features of power systems. So an extended topological method has been proposed by incorporating several of specific features of power systems such as electrical distance, power transfer distribution factors and line flow limits. This paper defines, starting from the extended metric for efficiency named as net-ability, an extended betweenness and proposes a joint method of extended betweenness and net-ability to rank the most critical lines and buses in an electrical power grid. The method is illustrated in the IEEE-118-bus, IEEE-300-bus test systems as well as the Italian power grid.

144 citations

Journal ArticleDOI
TL;DR: This article aims to provide a comprehensive survey of distributed control and communication strategies in NMGs and advances in several promising communication and computation technologies and their potential applications in N MGs.
Abstract: Networked microgrids (NMGs) provide a promising solution for accommodating various distributed energy resources (DERs) and enhancing the system performance in terms of reliability, resilience, flexibility, and energy efficiency. With the penetration of MGs, the communication-based distributed control is playing an increasingly important role in NMGs for coordinating a multitude of heterogeneous and spatially distributed DERs, which feature enhanced efficiency, reliability, resilience, scalability, and privacy-preserving as compared with conventional centralized control. This article aims to provide a comprehensive survey of distributed control and communication strategies in NMGs. We provide thorough discussions and elaborate on: 1) Essential merits of MGs and NMGs, and their practical implementations; 2) Distributed communication network characteristics and specific operation objectives of NMGs; 3) Classifications of distributed control strategies in NMGs and their salient features; 4) Communication reliability issues concerning data timeliness, data availability, and data accuracy, and the development of countermeasures; 5) Advancements in several promising communication and computation technologies and their potential applications in NMGs.

138 citations