Towards Intelligent Power Electronics-Dominated Grid via Machine Learning Techniques

Abu-Rub, Omar H.; Fard, Amin Y.; Umar, Muhammad Farooq; Hosseinzadehtaher, Mohsen; Shadmands, Mohammad B.

doi:10.1109/mpel.2020.3047506

Cited by 61 publications

(12 citation statements)

References 16 publications

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“…These attacks can cause severe harm to power electronics-based grid systems that exhibit low virtual inertia [127]. The low virtual inertia nature of power electronics-based systems when interconnected with traditional synchronous generator-based grid systems creates a new opportunity (attack surface) for the attacker to inflict more harm within a short amount of time [127], [128]. As a result of these attacks, these systems can easily and quickly become unstable before the intrusion is detected and acted upon.…”

Section: Network and Firmware Security Detection And Mitigation Metho...mentioning

confidence: 99%

A Review of Cyber–Physical Security for Photovoltaic Systems

You-hai

Giani

Elasser

et al. 2022

IEEE J. Emerg. Sel. Topics Power Electron.

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Section: Network and Firmware Security Detection And Mitigation Metho...mentioning

confidence: 99%

A Review of Cyber–Physical Security for Photovoltaic Systems

You-hai

Giani

Elasser

et al. 2022

IEEE J. Emerg. Sel. Topics Power Electron.

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“…The environmentally friendly power assets acquired notoriety overall in the space of dispersed age. The power issue is tended to by empowering the huge scope environmentally friendly power assets and gave an option in contrast to the regular age practices of power concentrated in [20]. The acquaintance of energy informatics in with the field of power rolled out extreme improvements given in [21].…”

Section: Related Workmentioning

confidence: 99%

Smart Grid Sensor Monitoring Based on Deep Learning Technique with Control System Management in Fault Detection

Awasthi¹,

Mahesh²,

Joshi³

et al. 2022

Int. j. commun. netw. inf. secur.

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The smart grid environment comprises of the sensor for monitoring the environment for effective power supply, utilization and establishment of communication. However, the management of smart grid in the monitoring environment isa difficult process due to diversifieduser request in the sensor monitoring with the grid-connected devices. Presently, context-awaremonitoring incorporates effective management of data management and provision of services in two-way processing and computing. In a heterogeneous environment context-aware, smart grid exhibits significant performance characteristics with the grid-connected communication environment for effective data processing for sustainability and stability. Fault diagnoses in the automated system are formulated to diagnose the fault separately. This paper developed anoptimized power grid control model (OPGCM) model for fault detection in the control system model for grid-connected smart home appliances. OPGCM model uses the context-aware power-awarescheme for load management in grid-connected smart homes. Through the adaptive smart grid model,power-aware management is incorporated with the evolutionary programming model for context-awareness user communication. The OPGCM modelperforms fault diagnosis in the grid-connected control system initially, Fault diagnosis system comprises of a sequential process with the extraction of the statistical features to acquirea sustainable dataset with effective signal processing. Secondly, the features are extracted based on the sequential process for the acquired dataset with a reduction of dimensionality. Finally, the classification is performed with the deep learning model to predict or identify the fault pattern. With the OPGCM model, features are optimized with the whale optimization model to acquire features to perform fault diagnosis and classification. Simulation analysis expressed that the proposed OPGCM model exhibits ~16% improved classification accuracy compared with the ANN and HMM model.

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“…With all the benefits the PEDG introduces, it brings up some challenges that need to be properly addressed prior to full implementation of such a complex system in the real world. These challenges include control, privacy, stability, planning, resilience, and cyber-physical security aspects [2].…”

Section: Introductionmentioning

confidence: 99%

“…Existing solutions mostly focus on detecting FDI attacks and they neglect the internal failure of the DERs [2]. Also, the existing literature, mostly focuses on detection only and they are not proposing any solutions after the detection is executed in real-time [2]. Additional utility is presented in classifying FDI attacks and circuit faults with a single NN.…”

Section: Introductionmentioning

confidence: 99%

Real-Time AI-Based Anomaly Detection and Classification in Power Electronics Dominated Grids

Baker

Fard

Althuwaini

et al. 2023

IEEE J. Emerg. Sel. Top. Ind. Electron.

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Real-time anomaly detection system (ADS) and anomaly classification system (ACS) techniques are becoming a crucial need for future power electronic dominated grid (PEDG). Artificial intelligence techniques such as recurrent neural networks, specifically long short-term memory (LSTM) provide a promising solution to detect anomalies in power grids. The main challenge is the implementation of these methods for real-time detection and classification for preventing catastrophic failure in PEDG. This article is addressing the challenge for detection and classification of anomalies in real-time in PEDG. The proposed approach is based on integration of model predictive control (MPC) and LSTM for realizing real-time ADS and ACS. The LSTM detection network can utilize the same time-series input data as the MPC, allowing for anomaly classification and correction. The proposed integrated LSTM-MPC approach has features of power electronics internal failure detection and corrective actions, which is an important aspect in future PEDG to differentiate inverters internal failures versus anomalies. Such internal failures include open circuit fault that needs to be detected and classified from a potential cyber-attack, allowing resilient operation of PEDG. The proposed integrated LSTM-MPC scheme for real-time ADS and ACS scheme is tested on a realistic 14-bus system dominated with inverters forming PEDG.

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Towards Intelligent Power Electronics-Dominated Grid via Machine Learning Techniques

Cited by 61 publications

References 16 publications

A Review of Cyber–Physical Security for Photovoltaic Systems

A Review of Cyber–Physical Security for Photovoltaic Systems

Smart Grid Sensor Monitoring Based on Deep Learning Technique with Control System Management in Fault Detection

Real-Time AI-Based Anomaly Detection and Classification in Power Electronics Dominated Grids

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