Table 6 RQ5 (Countermeasures for SG faults and failures)

F1

\(\bullet\) Spread-spectrum technique \(\bullet\) schemes using priority messages and lower duty cycles \(\bullet\) channel hopping techniques \(\bullet\) intelligent local controller switching while integrating a retransmission mechanism

 F17

\(\bullet\) Condition monitoring such as thermal modelling, dissolved gas analysis, frequency response analysis, partial discharge analysis in order to predict and prevent failures on transformers

F2

\(\bullet\) Strong point-to-point authentication schemes to avoid spoofing attacks

 F19

\(\bullet\) Increment in the current that can cause equipment overheating leading to a reduced life span of their insulation \(\bullet\) fall in voltage and frequency \(\bullet\) limited power flow

F3

\(\bullet\) Error-correction code

 F20

\(\bullet\) Identification of vulnerable links in power grid under geomagnetic storm conditions

F4

\(\bullet\) Authentication and probing

 F21

\(\bullet\) Reduced voltage operation can effectively lower the possibility of flashover in case of forest fire (specifically voltage drop to 50)

F5

\(\bullet\) Scheduling policies like round-robin, weighted round-robin or weighted fair queuing \(\bullet\) strong authentication and filtering policies for incoming communication flows

 F23

\(\bullet\) Analysis of transmission line lightning trip-out fault \(\bullet\) installation of line arresters

F8

\(\bullet\) Using information from multiple layers (physical layer of the time-synchronized measuring devices and the whole grid level) \(\bullet\) applying system stability analysis on a dynamic physical infrastructure model

 F24

\(\bullet\) Mitigation plans deployed for critical components \(\bullet\) deploying Energy Storage Systems (ESSs) \(\bullet\) shielding against EMP attacks or solar flares \(\bullet\) increasing the capabilities of relevant lines \(\bullet\) monitoring \(\bullet\) prediction of the fault chains of cascading failures (e.g. using weighted fuzzy C-means algorithm) \(\bullet\) balancing the reactive power locally and avoiding long-distance transmission of reactive power \(\bullet\) load shedding

F9

\(\bullet\) Cryptography signatures and strong authentication \(\bullet\) support vector models, machine learning, game-theoretic techniques against load redistribution attack \(\bullet\) mechanisms to detect and mitigate man-in-the-middle attacks \(\bullet\) advanced measurement units such as PMUs

 F25

\(\bullet\) Suppressing the fault current within minimum cycles with the use of a superconducting fault current limiter (SFCL), more specifically resistive SFCLs \(\bullet\) fault current hierarchical limitation to neutralize the effect of microgrid fault current on system total fault current when there is a fault in utility grid \(\bullet\) inverter-based distributed generators

F10

\(\bullet\) New product generation and replacement \(\bullet\) digital code signing \(\bullet\) monitoring of the controllers

 F26

\(\bullet\) Use of mobile transformers, temporary transmission poles \(\bullet\) use of local means of power generation, gasoline and diesel generators \(\bullet\) resilience studies

F12

\(\bullet\) Keeping data and most of the computation on the consumer’s device \(\bullet\) combining peer-to-peer communications and elements of centralized control \(\bullet\) gossip protocols combined with PKI \(\bullet\) adding noise to the meter readings and using differential privacy techniques to mask the contributions of individual meters’ measurements \(\bullet\) strong data encryption and secret key management schemes \(\bullet\) Byzantine fault-tolerant algorithms ensuring protection from malicious meters \(\bullet\) establishing a regulatory regimen of consumer protection

 F27

\(\bullet\) Fast-response energy storage

F15

\(\bullet\) Smart meter based load blocking scheme

 F28

\(\bullet\) Minimization of frequency deviation with the help of energy storage devices like a supercapacitor or a battery

F16

\(\bullet\) Hardening the smart meters \(\bullet\) installing voltage regulators at the customer’s site \(\bullet\) installing adaptable renewable generation facilities

 F30

\(\bullet\) Predictive maintenance with early identifications of wind turbine malfunctions \(\bullet\) monitoring approaches based on vibration signals \(\bullet\) monitoring wind turbine gearboxes with SCADA data (oil temperature, lubricant pressure) (e.g. with deep neural networks)