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Li, Fangyu

Publications and source records attributed to Li, Fangyu.

Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution Systems

Development of a cyber security strategy for the active distribution systems is challenging due to the inclusion of distributed renewable energy generations. Here this paper proposes an adaptive hierarchical cyber attack detection and localization framework for distributed active distribution systems via analyzing electrical waveforms. Cyber attack detection is based on a sequential deep learning model, via which even minor cyber attacks can be identified. The two-stage cyber attack localization algorithm first estimates the cyber attack sub-region, and then localize the specified cyber attack within the estimated subregion. We propose a modified spectral clustering-based network partitioning method for the hierarchical cyber attack ‘coarse’ localization. Next, to further narrow down the cyber attack location, a normalized impact score based on waveform statistical metrics is proposed to obtain a ‘fine’ cyber attack location by characterizing different waveform properties. Finally, compared with classical and state-of-art methods, a comprehensive quantitative evaluation with two case studies shows promising estimation results of the proposed framework.

42 ENGINEERING↗

Vulnerability Assessments for Power-Electronics-Based Smart Grids

Here, in this paper, a novel method is proposed to evaluate the cyber security of the power-electronics-based smart grids (PESG). The proposed method considers the performance and stability of both the individual inverter and the grid. To our knowledge, this is a first attempt to evaluate the performance and stability of PESG due to cyber attacks. We first develop impedance-based modeling and cyber-attack modeling for PESG. Then we propose innovative two security criteria to evaluate the security of PESG, including stability-based and metrics-based. For metrics-based criteria, we propose to use both total harmonic distortion (THD) and space phasor model (SPM) to evaluate the inverter performance. The simulation results with a two-inverter-based power grid verify the validity and accuracy of the proposed security evaluation method. Results have shown that the performance and stability of PESG are significantly affected by cyber attacks, and thus there is indeed a need to further study cyber security issues of PESG.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Data-driven Cyberattack Detection for Photovoltaic (PV) Systems through Analyzing Micro-PMU Data

With increasing exposure to software-based sensing and control, Photovoltaic (PV) systems are facing higher risks of cyber attacks. Here, to ensure the system stability and minimize potential economic losses, it is imperative to monitor operating states and detect attacks at the early stage. To meet this demand, Micro-Phasor Measurement Units (μPMU) are increasingly popular in monitoring distribution networks. However, due to the relatively low sampling rate, μPMU has not yet been used to detect and classify cyber-attacks in power electronics enabled smart grid. To our knowledge, this is one of the first attempts to use μPMU to detect cyber attacks that degrade the performance of power electronics systems. We propose to apply data-driven methods on micro-PMU data to implement attack detection. We have evaluated data-driven methods, including decision tree (DT), K-nearest neighbor (KNN), support vector machine (SVM), artificial neural network (ANN), long short-term memory (LSTM) and convolutional neural network (CNN). The proposed CNN model achieves the required performances with the highest 99.23% accuracy and 0.9963 F 1 score.

14 SOLAR ENERGY↗