Cybersecurity Enhancement of Transformer Differential Protection Using Machine Learning

The growing use of information and communication technologies (ICT) in power grid operational environments has been essential for operators to improve the monitoring, maintenance and control of power generation, transmission and distribution, however, at the expense of an increased grid exposure to cyber threats. This paper considers cyberattack scenarios targeting substation protective relays that can form the most critical ingredient for the protection of power systems against abnormal conditions. Disrupting the relays operations may yield major consequences on the overall power grid performance possibly leading to widespread blackouts. We investigate methods for the enhancement of substation cybersecurity by leveraging the potential of machine learning for the detection of transformer differential protective relays anomalous behavior. The proposed method analyses operational technology (OT) data obtained from the substation current transformers (CTs) in order to detect cyberattacks. Power systems simulation using OPAL-RT HYPERSIM is used to generate training data sets, to simulate the cyberattacks and to assess the cybersecurity enhancement capability of the proposed machine learning algorithms

False Data Injection Attacks Against Synchronization Systems in Microgrids

Synchronization systems play a vital role in the day-to-day operation of power systems and their restoration after cascading failures. Hence, their resilience to cyberattacks is imperative. In this paper, we demonstrate that a well-planned false data injection attack against the synchronization system of a generator is capable of causing tripping subsequently leading to instability and blackout. We present an analytical framework behind the design and implementation of the proposed cyberattack. Moreover, we derive and discuss the conditions for which a cyberattack interfering with a synchronizing signal can be successful. Effective physical mitigation strategies are then proposed to improve the cyber-resilience of synchronization systems. The proposed cyberattack model and mitigation strategies are verified for a microgrid test system using an OPAL-RT real-time simulator

Towards cyber-resilient telecontrol commands using software-defined networking

Cybersecurity enhancement of power systems has become one of the main objectives of utility managers and regulatory agencies because of the increasing number of cyberattacks against critical infrastructures. In this paper, we investigate the application of software-defined networking for improving the cyber-resilience of power systems in the presence of cyberattacks using false telecontrol commands. It is first demonstrated that cyberattackers can use false telecontrol commands to separate a power plant from a power grid or trip a major transmission line. Next, it is shown that software-defined networking can significantly enhance the cyber-resilience of power systems in the presence of cyberattacks using false telecontrol commands compared to legacy communication networks. This is because the source, destination and protocol of telecontrol commands can be examined and verified in software-defined networking before communication packet forwarding actions take place. Moreover, primary and back-up routes of telecontrol commands can be pre-engineered in software-defined networking to counteract potential cyberattacks

Cyber-Physical Attacks Targeting Communication-Assisted Protection Schemes

The dependence of modern societies on electric energy is ever increasing by the emergence of smart cities and electric vehicles. This is while unprecedented number of cyber-physical hazards are threatening the integrity and availability of the power grid on a daily basis. On one hand, physical integrity of power systems is under threat by more frequent natural disasters and intentional attacks. On the other hand, the cyber vulnerability of power grids is on the rise by the emergence of smart grid technologies. This underlines an imminent need for the modeling and examination of power grid vulnerabilities to cyber-physical attacks. This paper examines the vulnerability of the communication-assisted protection schemes like permissive overreaching transfer trip to cyberattacks using a co-simulation platform. The simulation results show that the transient angle stability of power systems can be jeopardized by cyberattacks on the communication-assisted protection schemes. To address this vulnerability, two physical solutions including the deployment of communication channel redundancy, and a more advanced communicated-assisted protection scheme, i.e., directional comparison unblocking scheme (DCUB), are considered and tested. The proposed solutions address the vulnerability of the communication-assisted protection schemes to distributed denial of service attack to some extent. Yet, the simulation results show the vulnerability of the proposed solutions to sophisticated cyberattacks like false data injection attacks. This highlights the need for the development of cyber-based solutions for communication channel monitoring

A Deep Learning-Based Cyberattack Detection System for Transmission Protective Relays

The digitalization of power systems over the past decade has made the cybersecurity of substations a top priority for regulatory agencies and utilities. Proprietary communication protocols are being increasingly replaced by standardized and interoperable protocols providing utility operators with remote access and control capabilities at the expense of growing cyberattack risks. In particular, the potential of supply chain cyberattacks is on the rise in industrial control systems. In this environment, there is a pressing need for the development of cyberattack detection systems for substations and in particular protective relays, a critical component of substation operation. This paper presents a deep learning-based cyberattack detection system for transmission line protective relays. The proposed cyberattack detection system is first trained with current and voltage measurements representing various types of faults on the transmission lines. The cyberattack detection system is then employed to detect current and voltage measurements that are maliciously injected by an attacker to trigger the transmission line protective relays. The proposed cyberattack detection system is evaluated under a variety of cyberattack scenarios. The results demonstrate that a universal architecture can be designed for the deep learning-based cyberattack detection systems in substations

Quantifying the efficiency of hydroxyapatite mineralising peptides

We present a non-destructive analytical calibration tool to allow quantitative assessment of individual calcium phosphates such as hydroxyapatite (HAP) from mixtures including brushite. Many experimental approaches are used to evaluate the mineralising capabilities of biomolecules including peptides. However, it is difficult to quantitatively compare the efficacy of peptides in the promotion of mineralisation when inseparable mixtures of different minerals are produced. To address this challenge, a series of hydroxyapatite and brushite mixtures were produced as a percent/weight (0–100%) from pure components and multiple (N=10) XRD patterns were collected for each mixture. A linear relationship between the ratio of selected peak heights and the molar ratio was found. Using this method, the mineralising capabilities of three known hydroxyapatite binding peptides, CaP(S) STLPIPHEFSRE, CaP(V) VTKHLNQISQSY and CaP(H) SVSVGMKPSPRP, was compared. All three directed mineralisation towards hydroxyapatite in a peptide concentration dependent manner. CaP(V) was most effective at inducing hydroxyapatite formation at higher reagent levels (Ca2+ = 200mM), as also seen with peptide-silk chimeric materials, whereas CaP(S) was most effective when lower concentrations of calcium (20mM) and phosphate were used. The approach can be extended to investigate HAP mineralisation in the presence of any number of mineralisation promoters or inhibitors

Solid-state fermentation of oil palm frond petiole for lignin peroxidase and xylanase-rich cocktail production

In current practice, oil palm frond leaflets and stems are re-used for soil nutrient recycling, while the petioles are typically burned. Frond petioles have high commercialization value, attributed to high lignocellulose fiber content and abundant of juice containing free reducing sugars. Pressed petiole fiber is the subject of interest in this study for the production of lignocellulolytic enzyme. The initial characterization showed the combination of 0.125 mm frond particle size and 60% moisture content provided a surface area of 42.3 m2/g, porosity of 12.8%, and density of 1.2 g/cm3, which facilitated fungal solid-state fermentation. Among the several species of Aspergillus and Trichoderma tested, Aspergillus awamori MMS4 yielded the highest xylanase (109 IU/g) and cellulase (12 IU/g), while Trichoderma virens UKM1 yielded the highest lignin peroxidase (222 IU/g). Crude enzyme cocktail also contained various sugar residues, mainly glucose and xylose (0.1–0.4 g/L), from the hydrolysis of cellulose and hemicellulose. FT-IR analysis of the fermented petioles observed reduction in cellulose crystallinity (I900/1098), cellulose–lignin (I900/1511), and lignin–hemicellulose (I1511/1738) linkages. The study demonstrated successful bioconversion of chemically untreated frond petioles into lignin peroxidase and xylanase-rich enzyme cocktail under SSF condition

Influenza vaccination for immunocompromised patients: systematic review and meta-analysis from a public health policy perspective.

Immunocompromised patients are vulnerable to severe or complicated influenza infection. Vaccination is widely recommended for this group. This systematic review and meta-analysis assesses influenza vaccination for immunocompromised patients in terms of preventing influenza-like illness and laboratory confirmed influenza, serological response and adverse events