Microwave Assisted Preparation of Barium Doped Titania (Ba/TiO2) as Photoanode in Dye Sensitized Solar Cells

Pure TiO2 and barium (0.5 wt%) doped TiO2 (Ba/TiO2) nanostructures have been synthesized via facile microwave irradiation method. The pure anatase phase of synthesized photoactive material was confirmed by X-ray diffraction. Ba doping in the TiO2 host structure influenced the optical band gap as confirmed by UV-visible spectroscopy. The optical band gap increased from 3.21 eV for the TiO2 to 3.26 eV for Ba/TiO2. Morphological analysis of synthesized TiO2 and Ba/TiO2 was conducted using scanning electron microscopy. Energy dispersive X-ray spectroscopy confirmed the formation of Ba/TiO2 and no impurities were observed. Electrochemical impedance spectroscopy showed that the charge transfer resistance increased for Ba/TiO2, which reduced dark current creation in a dye-sensitized solar cell. The highest power conversion efficiency (3.24%) was achieved for Ba/TiO2 photoanode compared to 2.1% for a pure TiO2 photoanode-based device

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

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

Synthesis, Characterization and Photodegradation Studies of Copper Oxide–Graphene Nanocomposites

In this work, a simple hydrothermal method was employed to prepare a pristine sample of copper oxide (CuO) and three samples of copper oxide–graphene nanocomposites (CuO-xG) with x = 2.5, 5, and 10 mg of graphene. The synthesized samples were characterized using X-ray powder diffractometry (XRD), field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDX), Fourier-transform infrared spectroscopy (FTIR) and ultraviolet–visible (UV-Vis) spectroscopy. The XRD patterns of CuO-xG nanocomposites exhibited the diffraction peaks related to the crystal planes of monoclinic CuO and hexagonal graphite. The surface morphology of the prepared samples was investigated using FESEM images. EDX analysis was used to investigate the chemical composition of the synthesized samples. FTIR spectroscopy identified the vibrational modes of the covalent bonds present in the samples. The allowed direct optical bandgap energy was calculated for all prepared samples using UV-Vis absorption spectra. The small bandgap of CuO-xG nanocomposites indicates their potential use as an effective photocatalyst in the presence of visible light. Photocatalytic activity of the samples was explored for the degradation of methylene blue (MB) dye contaminant under visible light irradiation. The results showed that the CuO-5G sample has the highest photodegradation efficiency (~56%)

An application of heuristic optimization algorithm for demand response in smart grids with renewable energy

This work presented power usage scheduling by engaging consumers in demand response program (DRP) with and without using renewable energy generation (REG). This power usage scheduling problem was modeled as an optimization problem, which was solved using an energy scheduler (ES) based on the crossover mutated enhanced wind-driven optimization (CMEWDO) algorithm. The CMEWDO was an enhanced wind-driven optimization (WDO) algorithm, where the optimal solution returned from WDO was fed to crossover and mutation operations to further achieve the global optimal solution. The developed CMEWDO algorithm was verified by comparing it with other algorithms like the whale optimization algorithm (WOA), enhanced differential evolution algorithm (EDE), and the WDO algorithm in aspects of the electricity bill and peak to average demand ratio (PADR) minimization without compromising consumers' comfort. Also, the developed CMEWDO algorithm has a lower computational time (measured in seconds) and a faster convergence rate (measured in number of iterations) than the standard WDO algorithm and other comparative algorithms

A smoothing spline algorithm to interpolate and predict the eigenvalues of matrices extracted from the sequence of preconditioned banded symmetric Toeplitz matrices

Understanding the eigenvalue distribution of sequence Toeplitz matrices has advanced significantly in recent years. Notable contributors include Bogoya, Grudsky, Böttcher, and Maximenko, who have derived precise asymptotic expansions for these eigenvalues under certain conditions related to the generating function as the matrix size increases. Building on this foundation, the Stefano Serra-Capizzano conjectured that, under certain assumptions about $\Omega$ and $\Phi$, a similar expansion may hold for the eigenvalues of a sequence of preconditioned Toeplitz matrices $T_{n}^{-1}(\Phi) T_n(\Omega)$, given a monotonic ratio $r = \Omega/\Phi$. In contrast to current eigenvalue solvers, this work presents a novel method for efficiently calculating the eigenvalues of a sequence of large preconditioned banded symmetric Toeplitz matrices (PBST). Our algorithm uses a higher-order spline fitting extrapolation technique to gather spectral data from a smaller sequence of PBST matrices in order to forecast the spectrum of bigger matrices

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

Sensitive Electrochemical Detection of 4-Nitrophenol with PEDOT:PSS Modified Pt NPs-Embedded PPy-CB@ZnO Nanocomposites

In this study, a selective 4-nitrophenol (4-NP) sensor was developed onto a glassy carbon electrode (GCE) as an electron-sensing substrate, which decorated with sol–gel, prepared Pt nanoparticles- (NPs) embedded polypyrole-carbon black (PPy-CB)/ZnO nanocomposites (NCs) using differential pulse voltammetry. Characterizations of the NCs were performed using Field Emission Scanning Electron Microscopy (FESEM), Energy-Dispersive Spectroscopy (EDS), X-ray Photoelectron Spectroscopy (XPS), Ultraviolet–visible Spectroscopy (UV–vis), Fourier Transform Infrared Spectroscopy (FTIR), High Resolution Transmission Electron Microscopy (HRTEM), and X-ray Diffraction Analysis (XRD). The GCE modified by conducting coating binders [poly(3,4-ethylenedioxythiophene) polystyrene sulfonate; PEDOT:PSS] based on Pt NPs/PPy-CB/ZnO NCs functioned as the working electrode and showed selectivity toward 4-NP in a phosphate buffer medium at pH 7.0. Our analysis of 4-NP showed the linearity from 1.5 to 40.5 µM, which was identified as the linear detection range (LDR). A current versus concentration plot was formed and showed a regression co-efficient R2 of 0.9917, which can be expressed by ip(µA) = 0.2493C(µM) + 15.694. The 4-NP sensor sensitivity was calculated using the slope of the LDR, considering the surface area of the GCE (0.0316 cm2). The sensitivity was calculated as 7.8892 µA µM−1 cm−2. The LOD (limit of detection) of the 4-NP was calculated as 1.25 ± 0.06 µM, which was calculated from 3xSD/σ (SD: Standard deviation of blank response; σ: Slope of the calibration curve). Limit of quantification (LOQ) is also calculated as 3.79 µM from LOQ = 10xLOD/3.3. Sensor parameters such as reproducibility, response time, and analyzing stability were outstanding. Therefore, this novel approach can be broadly used to safely fabricate selective 4-NP sensors based on nanoparticle-decorated nanocomposite materials in environmental measurement

An optimal power flow solution for a power system integrated with renewable generation

Integrating Green Renewable Energy Sources (GRES) as substitutes for fossil fuel-based energy sources is essential for reducing harmful emissions. The GRES are intermittent and their integration into the conventional IEEE 30 bus configuration increases the complexity and nonlinearity of the system. The Grey Wolf optimizer (GWO) has excellent exploration capability but needs exploitation capability to enhance its convergence speed. Adding particle swarm optimization (PSO) with excellent convergence capability to GWO leads to the development of a novel algorithm, namely a Grey Wolf particle swarm optimization (GWPSO) algorithm with excellent exploration and exploitation capabilities. This study utilizes the advantages of the GWPSO algorithm to solve the optimal power flow (OPF) problem for adaptive IEEE 30 bus systems, including thermal, solar photovoltaic (SP), wind turbine (WT), and small hydropower (SHP) sources. Weibull, Lognormal, and Gumbel probability density functions (PDFs) are employed to forecast the output power of WT, SP, and SHP power sources after evaluating 8000 Monte Carlo possibilities, respectively. The multi-objective green economic optimal solution consisted of 11 control variables to reduce the cost, power losses, and harmful emissions. The proposed method to address the OPF problem is validated using an adaptive IEEE bus system. The proposed GWPSO algorithm is evaluated by comparing it with PSO and GWO optimization algorithms in terms of achieving an optimal green economic solution for the adaptive IEEE 30 bus system. This evaluation is conducted within the confines of the same test system using identical system constraints and control variables. The integration of a small SHP with WT and SP sources, along with the proposed GWPSO algorithm, led to a yearly cost reduction ranging from $\$$19,368 to $\$$30,081. Simulation findings endorsed that the proposed GWPSO algorithm executes fruitfully compared to alternative algorithms regarding a consistent convergence curve and robustness, proving its potential as a viable choice for achieving cost-effective solutions in power systems incorporating GRES