Power quality and electromagnetic compatibility: special report, session 2

The scope of Session 2 (S2) has been defined as follows by the Session Advisory Group and the Technical Committee: Power Quality (PQ), with the more general concept of electromagnetic compatibility (EMC) and with some related safety problems in electricity distribution systems. Special focus is put on voltage continuity (supply reliability, problem of outages) and voltage quality (voltage level, flicker, unbalance, harmonics). This session will also look at electromagnetic compatibility (mains frequency to 150 kHz), electromagnetic interferences and electric and magnetic fields issues. Also addressed in this session are electrical safety and immunity concerns (lightning issues, step, touch and transferred voltages). The aim of this special report is to present a synthesis of the present concerns in PQ&EMC, based on all selected papers of session 2 and related papers from other sessions, (152 papers in total). The report is divided in the following 4 blocks: Block 1: Electric and Magnetic Fields, EMC, Earthing systems Block 2: Harmonics Block 3: Voltage Variation Block 4: Power Quality Monitoring Two Round Tables will be organised: - Power quality and EMC in the Future Grid (CIGRE/CIRED WG C4.24, RT 13) - Reliability Benchmarking - why we should do it? What should be done in future? (RT 15

Phasor Measurement Units Optimal Placement and Performance Limits for Fault Localization

In this paper, the performance limits of faults localization are investigated using synchrophasor data. The focus is on a non-trivial operating regime where the number of Phasor Measurement Unit (PMU) sensors available is insufficient to have full observability of the grid state. Proposed analysis uses the Kullback Leibler (KL) divergence between the distributions corresponding to different fault location hypotheses associated with the observation model. This analysis shows that the most likely locations are concentrated in clusters of buses more tightly connected to the actual fault site akin to graph communities. Consequently, a PMU placement strategy is derived that achieves a near-optimal resolution for localizing faults for a given number of sensors. The problem is also analyzed from the perspective of sampling a graph signal, and how the placement of the PMUs i.e. the spatial sampling pattern and the topological characteristic of the grid affect the ability to successfully localize faults. To highlight the superior performance of presented fault localization and placement algorithms, the proposed strategy is applied to a modified IEEE 34, IEEE-123 bus test cases and to data from a real distribution grid. Additionally, the detection of cyber-physical attacks is also examined where PMU data and relevant Supervisory Control and Data Acquisition (SCADA) network traffic information are compared to determine if a network breach has affected the integrity of the system information and/or operations

Security Analysis of Interdependent Critical Infrastructures: Power, Cyber and Gas

abstract: Our daily life is becoming more and more reliant on services provided by the infrastructures power, gas , communication networks. Ensuring the security of these infrastructures is of utmost importance. This task becomes ever more challenging as the inter-dependence among these infrastructures grows and a security breach in one infrastructure can spill over to the others. The implication is that the security practices/ analysis recommended for these infrastructures should be done in coordination. This thesis, focusing on the power grid, explores strategies to secure the system that look into the coupling of the power grid to the cyber infrastructure, used to manage and control it, and to the gas grid, that supplies an increasing amount of reserves to overcome contingencies. The first part (Part I) of the thesis, including chapters 2 through 4, focuses on the coupling of the power and the cyber infrastructure that is used for its control and operations. The goal is to detect malicious attacks gaining information about the operation of the power grid to later attack the system. In chapter 2, we propose a hierarchical architecture that correlates the analysis of high resolution Micro-Phasor Measurement Unit (microPMU) data and traffic analysis on the Supervisory Control and Data Acquisition (SCADA) packets, to infer the security status of the grid and detect the presence of possible intruders. An essential part of this architecture is tied to the analysis on the microPMU data. In chapter 3 we establish a set of anomaly detection rules on microPMU data that flag "abnormal behavior". A placement strategy of microPMU sensors is also proposed to maximize the sensitivity in detecting anomalies. In chapter 4, we focus on developing rules that can localize the source of an events using microPMU to further check whether a cyber attack is causing the anomaly, by correlating SCADA traffic with the microPMU data analysis results. The thread that unies the data analysis in this chapter is the fact that decision are made without fully estimating the state of the system; on the contrary, decisions are made using a set of physical measurements that falls short by orders of magnitude to meet the needs for observability. More specifically, in the first part of this chapter (sections 4.1- 4.2), using microPMU data in the substation, methodologies for online identification of the source Thevenin parameters are presented. This methodology is used to identify reconnaissance activity on the normally-open switches in the substation, initiated by attackers to gauge its controllability over the cyber network. The applications of this methodology in monitoring the voltage stability of the grid is also discussed. In the second part of this chapter (sections 4.3-4.5), we investigate the localization of faults. Since the number of PMU sensors available to carry out the inference is insufficient to ensure observability, the problem can be viewed as that of under-sampling a "graph signal"; the analysis leads to a PMU placement strategy that can achieve the highest resolution in localizing the fault, for a given number of sensors. In both cases, the results of the analysis are leveraged in the detection of cyber-physical attacks, where microPMU data and relevant SCADA network traffic information are compared to determine if a network breach has affected the integrity of the system information and/or operations. In second part of this thesis (Part II), the security analysis considers the adequacy and reliability of schedules for the gas and power network. The motivation for scheduling jointly supply in gas and power networks is motivated by the increasing reliance of power grids on natural gas generators (and, indirectly, on gas pipelines) as providing critical reserves. Chapter 5 focuses on unveiling the challenges and providing solution to this problem.Dissertation/ThesisDoctoral Dissertation Electrical Engineering 201

High performance platform to detect faults in the Smart Grid by Artificial Intelligence inference

Inferring faults throughout the power grid involves fast calculation, large scale of data, and low latency. Our heterogeneous architecture in the edge offers such high computing performance and throughput using an Artificial Intelligence (AI) core deployed in the Alveo accelerator. In addition, we have described the process of porting standard AI models to Vitis AI and discussed its limitations and possible implications. During validation, we designed and trained some AI models for fast fault detection in Smart Grids. However, the AI framework is standard, and adapting the models to Field Programmable Gate Arrays (FPGA) has demanded a series of transformation processes. Compared with the Graphics Processing Unit platform, our implementation on the FPGA accelerator consumes less energy and achieves lower latency. Finally, our system balances inference accuracy, on-chip resources consumed, computing performance, and throughput. Even with grid data sampling rates as high as 800,000 per second, our hardware architecture can simultaneously process up to 7 data streams.10.13039/501100000780-European Commission (Grant Number: FEDER) 10.13039/501100003086-Eusko Jaurlaritza (Grant Number: ZE-2020/00022 and ZE-2021/00931) 10.13039/100015866-Hezkuntza, Hizkuntza Politika Eta Kultura Saila, Eusko Jaurlaritza (Grant Number: IT1440-22) 10.13039/501100004837-Ministerio de Ciencia e Innovación (Grant Number: IDI-20201264 and IDI-20220543