EPE Association: EPE 2022 - LS2e: Data Analysis and Cybersecurity Techniques

EPE 2022 - LS2e: Data Analysis and Cybersecurity Techniques
You are here: EPE Documents > 01 - EPE & EPE ECCE Conference Proceedings > EPE 2022 ECCE Europe - Conference > EPE 2022 - Topic 10: > EPE 2022 - LS2e: Data Analysis and Cybersecurity Techniques

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   Data-driven decentralized volt/var control for smart PV inverters in distribution systems
By Yizhou LU [View]
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Abstract: The growing penetration of renewable energy sources (RES) in modern grids may result in severe voltage violation problems due to high stochastic features. Conventional centralized approaches could provide optimal solutions for voltage regulation while with great communication burdens. Control methods based on local information usually have non-optimal results and cannot always guarantee voltage security. This paper proposes a neural network-based decentralized strategy for volt/var control using inverter reactive power capacity. Learning from optimal power flow (OPF) results of historical data, the developed controller can provide optimal results approximate to centralized solutions and outperform local control methods in minimizing the power loss. The proposed method is tested on the IEEE 33-bus system and simulation results illustrate the effectiveness in voltage regulation and loss minimization.

   Detection of Incipient Inter-Turn Short-Circuit Faults by Artificial Intelligence Classifiers
By Osman ÖRGÜT [View]
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Abstract: This paper presents two artificial intelligence (AI) based identification methods for inter-turn short circuit fault (ISCF) detection in induction motors (IMs), driven by voltage source inverters (VSIs). It was previously observed that, for an IM driven by finite control set model predictive control (FCS-MPC), the ISCF occurrence disturbs the balanced distribution of the resultant switching vectors, which are merely the control outputs of the FCS-MPC scheme. This effect of the ISCFs is utilized for fault detection purposes. The proposed method successfully detects the ISCF using AI methods which are fed by histograms of switching vectors along with torque and speed. This is especially convenient from the motor driver's perspective since no additional sensor or hardware is required for fault detection. The dataset utilized in this paper was obtained from an experimental IM drive test setup, on which intentional ISCFs can be created. The test results proved that the average fault detection rate is 95.8\%, for an ISCF of 2-turns in a 104-turns phase winding.

   Impact of Cyber Attacks on Cost Oriented Power Routing Schemes in Microgrids
By KIRTI GUPTA [View]
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Abstract: The distributed economic dispatch (ED) algorithm carried out in an AC microgrid (MG) is a promisingsolution which guarantees flexibility, scalability and reliability over single point failure as compared tothe centralized approach. Not to mention, the integration of communication infrastructure for information exchanges on one hand adds feasibility for the distributed operation but at the same time, is a threat to the smart grid. The attackers can penetrate in the communication links and inject malicious data in order to gain economical benefits, disrupt the proper functioning of the system etc. Hence, the investigation of the effect of cyber attacks on cooperative energy management (CEM) is an important concern both theoretically and practically. This paper analyses the impact of cyber attacks on a CEM, optimizing ED to a sub-optimal value in an islanded AC MG. The response of the system over false data injection (FDI) and hijacking attacks is further demonstrated on a real-time (RT) co-simulation platform.

EPE 2022 - LS2e: Data Analysis and Cybersecurity Techniques
You are here: EPE Documents > 01 - EPE & EPE ECCE Conference Proceedings > EPE 2022 ECCE Europe - Conference > EPE 2022 - Topic 10: > EPE 2022 - LS2e: Data Analysis and Cybersecurity Techniques
	[return to parent folder]

	Data-driven decentralized volt/var control for smart PV inverters in distribution systems By Yizhou LU	[View] [Download]
Abstract: The growing penetration of renewable energy sources (RES) in modern grids may result in severe voltage violation problems due to high stochastic features. Conventional centralized approaches could provide optimal solutions for voltage regulation while with great communication burdens. Control methods based on local information usually have non-optimal results and cannot always guarantee voltage security. This paper proposes a neural network-based decentralized strategy for volt/var control using inverter reactive power capacity. Learning from optimal power flow (OPF) results of historical data, the developed controller can provide optimal results approximate to centralized solutions and outperform local control methods in minimizing the power loss. The proposed method is tested on the IEEE 33-bus system and simulation results illustrate the effectiveness in voltage regulation and loss minimization.

	Detection of Incipient Inter-Turn Short-Circuit Faults by Artificial Intelligence Classifiers By Osman ÖRGÜT	[View] [Download]
Abstract: This paper presents two artificial intelligence (AI) based identification methods for inter-turn short circuit fault (ISCF) detection in induction motors (IMs), driven by voltage source inverters (VSIs). It was previously observed that, for an IM driven by finite control set model predictive control (FCS-MPC), the ISCF occurrence disturbs the balanced distribution of the resultant switching vectors, which are merely the control outputs of the FCS-MPC scheme. This effect of the ISCFs is utilized for fault detection purposes. The proposed method successfully detects the ISCF using AI methods which are fed by histograms of switching vectors along with torque and speed. This is especially convenient from the motor driver's perspective since no additional sensor or hardware is required for fault detection. The dataset utilized in this paper was obtained from an experimental IM drive test setup, on which intentional ISCFs can be created. The test results proved that the average fault detection rate is 95.8\%, for an ISCF of 2-turns in a 104-turns phase winding.

	Impact of Cyber Attacks on Cost Oriented Power Routing Schemes in Microgrids By KIRTI GUPTA	[View] [Download]
Abstract: The distributed economic dispatch (ED) algorithm carried out in an AC microgrid (MG) is a promisingsolution which guarantees flexibility, scalability and reliability over single point failure as compared tothe centralized approach. Not to mention, the integration of communication infrastructure for information exchanges on one hand adds feasibility for the distributed operation but at the same time, is a threat to the smart grid. The attackers can penetrate in the communication links and inject malicious data in order to gain economical benefits, disrupt the proper functioning of the system etc. Hence, the investigation of the effect of cyber attacks on cooperative energy management (CEM) is an important concern both theoretically and practically. This paper analyses the impact of cyber attacks on a CEM, optimizing ED to a sub-optimal value in an islanded AC MG. The response of the system over false data injection (FDI) and hijacking attacks is further demonstrated on a real-time (RT) co-simulation platform.