False Data Injection Detection for Phasor Measurement Units

Cyber-threats are becoming a big concern due to the potential severe consequences of such threats is false data injection (FDI) attacks where the measures data is manipulated such that the detection is unfeasible using traditional approaches. This work focuses on detecting FDIs for phasor measurement units where compromising one unit is sufficient for launching such attacks. In the proposed approach, moving averages and correlation are used along with machine learning algorithms to detect such attacks. The proposed approach is tested and validated using the IEEE 14-bus and the IEEE 30-bus test systems. The proposed performance was sufficient for detecting the location and attack instances under different scenarios and circumstances

A Novel Technique to Detect False Data Injection Attacks on Phasor Measurement Units

The power industry is in the process of grid modernization with the introduction of phasor measurement units (PMUs), advanced metering infrastructure (AMI), and other technologies. Although these technologies enable more reliable and efficient operation, the risk of cyber threats has increased, as evidenced by the recent blackouts in Ukraine and New York. One of these threats is false data injection attacks (FDIAs). Most of the FDIA literature focuses on the vulnerability of DC estimators and AC estimators to such attacks. This paper investigates FDIAs for PMU-based state estimation, where the PMUs are comparable. Several states can be manipulated by compromising one PMU through the channels of that PMU. A Phase Locking Value (PLV) technique was developed to detect FDIAs. The proposed approach is tested on the IEEE 14-bus and the IEEE 30-bus test systems under different scenarios using a Monte Carlo simulation where the PLV demonstrated an efficient performance.Peer reviewe

Integrin-linked kinase expression in myeloid cells promotes colon tumorigenesis

Colorectal cancer (CRC) is one of the most common forms of cancer worldwide and treatment options for advanced CRC, which has a low 5-year survival rate, remain limited. Integrin-linked kinase (ILK), a multifunctional, scaffolding, pseudo-kinase regulating many integrin-mediated cellular processes, is highly expressed in many cancers. However, the role of ILK in cancer progression is yet to be fully understood. We have previously uncovered a pro-inflammatory role for myeloid-specific ILK in dextran sodium sulfate (DSS)-induced colitis. To establish a correlation between chronic intestinal inflammation and colorectal cancer (CRC), we investigated the role of myeloid-ILK in mouse models of CRC. When myeloid-ILK deficient mice along with the WT control mice were subjected to colitis-associated and APCmin/+-driven CRC, tumour burden was reduced by myeloid-ILK deficiency in both models. The tumour-promoting phenotype of macrophages, M2 polarization, in vitro was impaired by the ILK deficiency and the number of M2-specific marker CD206-expressing tumour-associated macrophages (TAMs) in vivo were significantly diminished in myeloid-ILK deficient mice. Myeloid-ILK deficient mice showed enhanced tumour infiltration of CD8+ T cells and reduced tumour infiltration of FOXP3+ T cells in colitis-associated and APCmin/+-driven CRC, respectively, with an overall elevated CD8+/FOXP3+ ratio suggesting an anti-tumour immune phenotypes. In patient CRC tissue microarrays we observed elevated ILK+ myeloid (ILK+ CD11b+) cells in tumour sections compared to adjacent normal tissues, suggesting a conserved role for myeloid-ILK in CRC development in both human and animal models. This study identifies myeloid-specific ILK expression as novel driver of CRC, which could be targeted as a potential therapeutic option for advanced disease

Investigating integrin-linked kinase (ILK) as a therapeutic target in colorectal cancer

The mortality rate in colorectal cancer patients is high due to the limited benefits of current treatments including immunotherapy which in turn lead to high demand for identifying novel molecular regulators to be targeted therapeutically. Here we have identified that integrin-linked kinase (ILK) is associated with cancer prognosis and characterizes the immunosuppressive tumor microenvironment. ILK expression is also implicated in cancer cell growth and therapy-induced senescence as well as is involved in promoting a resistance of cancer cells against immune cell cytotoxicity. These findings suggest possible clinical avenues of targeting ILK combined with current colorectal cancer therapy, specifically immunotherapy

Hybrid Fault-Tolerant Control for Air-Fuel Ratio Control System of Internal Combustion Engine Using Fuzzy Logic and Super-Twisting Sliding Mode Control Techniques

Safety and critical applications employ fault-tolerant control systems (FTCS) to increase reliability and availability in the event of a failure of critical components. Process facilities may employ these technologies to cut down on production losses caused by equipment failures that occur on an irregular or unscheduled basis. Air–fuel ratio (AFR) adjustment in the fuel system of internal combustion engines (ICE) is crucial for enhancing engine efficiency, saving fuel energy, and safeguarding the environment. This paper proposes a novel hybrid fault-tolerant control system (HFTCS) for controlling the AFR in ICEs that combines the features of both an active fault-tolerant control system (AFTCS) and a passive fault-tolerant control system (PFTCS). The fault detection and isolation (FDI) unit is designed using fuzzy logic (FL) as part of an AFTCS to give estimated sensor values to the engine controller when the sensor becomes faulty. Super-twisting sliding mode control (ST-SMC) is implemented as part of a PFTCS to maintain AFR by adjusting the throttle actuator in the fuel supply line under faulty conditions. Lyapunov stability analysis is also performed to make sure that the system remains stable in both normal and faulty conditions. According to the results in the Matlab/Simulink environment, the suggested system stays robust and stable during sensor faults. In faulty situations, it also maintains the AFR at 14.6 without any degradation, and a comparison with previous studies is carried out. The study shows that the suggested approach is an innovative and highly dependable solution for AFR control in ICEs, preventing engine shutdown and output loss for higher profitability

Advanced Fault-Tolerant Anti-Surge Control System of Centrifugal Compressors for Sensor and Actuator Faults

Faults frequently occur in the sensors and actuators of process machines to cause shutdown and process interruption, thereby creating costly production loss. centrifugal compressors (CCs) are the most used equipment in process industries such as oil and gas, petrochemicals, and fertilizers. A compressor control system called an anti-surge control (ASC) system based on many critical sensors and actuators is used for the safe operation of CCs. In this paper, an advanced active fault-tolerant control system (AFTCS) has been proposed for sensor and actuator faults of the anti-surge control system of a centrifugal compressor. The AFTCS has been built with a dedicated fault detection and isolation (FDI) unit to detect and isolate the faulty part as well as replace the faulty value with the virtual redundant value from the observer model running in parallel with the other healthy sensors. The analytical redundancy is developed from the mathematical modeling of the sensors to provide estimated values to the controller in case the actual sensor fails. Dual hardware redundancy has been proposed for the anti-surge valve (ASV). The simulation results of the proposed Fault-tolerant control (FTC) for the ASC system in the experimentally validated CC HYSYS model reveal that the system continued to operate in the event of faults in the sensors and actuators maintaining system stability. The proposed FTC for the ASC system is novel in the literature and significant for the process industries to design a highly reliable compressor control system that would continue operation despite faults in the sensors and actuators, hence preventing costly production loss

Design of Active Fault-Tolerant Control System for Air-Fuel Ratio control of Internal Combustion engine using nonlinear regression-based observer model.

Internal Combustion (IC) engines are prevalent in the process sector, and maintaining sufficient Air-Fuel Ratio (AFR) regulation in their fuel system is crucial for enhanced engine performance, fuel economy, and environmental safety. Faults in the AFR system's sensors cause the engine to shut down, hence, fault tolerance is essential. In order to avoid engine shutdown, this paper offers a novel Active Fault-Tolerant Control System (AFTCS) for air-fuel ratio control of an Internal Combustion (IC) engine in a process plant. In the Fault Detection and Isolation (FDI) unit, the proposed AFTCS uses a nonlinear regression-based observer model for analytical redundancy. The suggested system was simulated in the MATLAB / Simulink environment. The proposed system was tested at two different speeds (300 r/min and 600 r/min) and the results show that the system's response is within the acceptable bound without compromising the stability. The findings also demonstrate the higher fault tolerance capability for sensor defects of the AFR control system, particularly for the MAP sensor (at 300 r/min) in terms of reduced oscillatory response in comparison to the current literature. Compared to the linear regression-based and Genetic Algorithm (GA) based model, the nonlinear regression-based model results in a more accurate estimation of the faulty sensors. The proposed model is also efficient in terms of computation power and response time

A Comparative Study of Design of Active Fault-Tolerant Control System for Air–Fuel Ratio Control of Internal Combustion Engine Using Particle Swarm Optimization, Genetic Algorithm, and Nonlinear Regression-Based Observer Model

In this article, three distinct strategies for designing an Active Fault-Tolerant Control System (AFTCS) for Air-Fuel Ratio (AFR) control of an Internal Combustion (IC) engine in a process plant to avoid engine shutdown, are presented. The proposed AFTCS employs a Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and a Nonlinear Regression (NLR)-based observer model in the Fault Detection and Isolation (FDI) unit for analytical redundancy. A comparison between these three proposed techniques is carried out to determine the least expensive and most accurate approach. The results show that the nonlinear regression produces highly accurate results by consuming very low computational power, and its response time is also very low as compared to GA and PSO. The results obtained show that NLR requires 99.6% and 93.1% less computational time for throttle and MAP estimation, respectively, by reducing the estimation error to as low as 0.01. The simulation of the proposed system is carried out in the MATLAB/Simulink environment. The results prove the superior fault tolerance performance for sensor faults of the AFR control system, especially for the Manifold Absolute Pressure (MAP) sensor in terms of less oscillatory response as compared to that reported in existing literature