A novel optimized neural network model for cyber attack detection using enhanced whale optimization algorithm.

Cybersecurity is critical in today's digitally linked and networked society. There is no way to overestimate the importance of cyber security as technology develops and becomes more pervasive in our daily lives. Cybersecurity is essential to people's protection. One type of cyberattack known as "credential stuffing" involves using previously acquired usernames and passwords by attackers to access user accounts on several websites without authorization. This is feasible as a lot of people use the same passwords and usernames on several different websites. Maintaining the security of online accounts requires defence against credential-stuffing attacks. The problems of credential stuffing attacks, failure detection, and prediction can be handled by the suggested EWOA-ANN model. Here, a novel optimization approach known as Enhanced Whale Optimization Algorithm (EWOA) is put on to train the neural network. The effectiveness of the suggested attack identification model has been demonstrated, and an empirical comparison will be carried out with respect to specific security analysis.

Dynamic performance of rotor-side nonlinear control technique for doubly-fed multi-rotor wind energy based on improved super-twisting algorithms under variable wind speed.

The paper proposes a nonlinear controller called dual super-twisting sliding mode command (DSTSMC) for controlling and regulating the rotor side converter (RSC) of multi-rotor wind power systems that use doubly-fed induction generators. It was proposed that this controller be developed as an alternative to the direct power control (DPC), which makes use of a pulse width modulation (PWM) strategy to regulate the RSC's functioning. Overcoming the power/current quality issue with the proposed technique (DPC-DSTSMC-PWM) is characterized by great robustness and excellent performance. The designed strategy was contrasted with the standard method of control and other methods already in use. So, the unique proposed control strategy's robustness, performance, efficiency, and efficacy in enhancing system characteristics were tested and validated in Matlab/Simulink. In both tests, the proposed method resulted in significant improvements, reducing active power ripples by 83.33%, 57.14%, and 48.57% in the proposed tests. When compared with the traditional regulation method, the reduction rates of reactive power ripples are 64.06%, 52.47%, and 68.7% in the tests. However, in contrast to the conventional method, the proposed tests showed a decrease of between 72.46%, 50%, and 76.22% in the value of total harmonic distortion (THD) of the provided currents. These ratios show how effective the proposed plan is in ameliorating and enhancing aspects of the energy system.

Smart distribution network voltage estimation using PMU technology considering zero injection constraints.

To properly control the network of the power system and ensure its protection, Phasor measurement units (PMUs) must be used to monitor the network's operation. PMUs can provide synchronized real-time measurements. These measurements can be used for state estimation, fault detection and diagnosis, and other grid control applications. Conventional state estimation methods use weighting factors to balance the different types of measurements, and zero injection measurements can lead to large weighting factors that can introduce computational errors. The offered methods are designed to ensure that these zero injection criteria can be strictly satisfied while calculating the voltage profile and observability of the various distribution networks without sacrificing computing efficiency. The proposed method's viability is assessed using standard IEEE distribution networks. MATLAB coding is used to simulate the case analyses. Overall, the study provides a valuable contribution to the field of power distribution system monitoring and control by simplifying the process of determining the optimal locations for PMUs in a distribution network and assessing the impact of ZI buses on the voltage profile of the system.

Development of renewable energy fed three-level hybrid active filter for EV charging station load using Jaya grey wolf optimization.

This work develops a hybrid active power filter (HAPF) in this article to operate in conjunction with the energy storage system (ESS), wind power generation system (WPGS), and solar energy system (SES). It employs three level shunt voltage source converters (VSC) connected to the DC-bus. Optimization of the gain values of the fractional-order proportional integral derivative controller (FOPIDC) and parameter values of the HAPF is achieved using the Jaya grey wolf hybrid algorithm (GWJA). The primary objectives of this study, aimed at enhancing power quality (PQ), include: (1) ensuring swift stabilization of DC link capacitor voltage (DCLCV); (2) reducing harmonics and improving power factor (PF); (3) maintaining satisfactory performance under different combinations of loads like EV charging load, non linear load and solar irradiation conditions. The proposed controller's performance is evaluated through three test scenarios featuring different load configurations and irradiation levels. Additionally, the HAPF is subjected to design using other optimization algorithms such as genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) to assess their respective contributions to PQ improvement.

Self-filtering based on the fault ride-through technique using a robust model predictive control for wind turbine rotor current.

This paper studies the possibility of connecting Wind Farms (WF) to the electric grid with the use of finite space model predictive command (FS-MPC) to manage wind farms to improve the quality of the current output from the doubly-fed induction generator (DFIG) with considering fault ride-through technique. This proposed system can generate active power and enhance the power factor. Furthermore, the reduction of harmonics resulting from the connection of non-linear loads to the electrical grid is achieved through the self-active filtering mechanism in DFIGs-WF, facilitated by the now algorithm proposed. FS-MPC technique has the ability to improve system characteristics and greatly reduce active power ripples. Therefore, MATLAB software is used to implement and verify the safety, performance, and effectiveness of this designed technique compared to the conventional strategy. The results obtained demonstrated the effectiveness of the proposed algorithm in handling the four operational modes (Maximum power point tracking, Delta, Fault, and Filtering). Additionally, the suggested technique exhibited flexibility, robustness, high accuracy, and fast dynamic response when compared to conventional strategies and some recently published scientific works. On the other hand, the THD value of the current was significantly reduced, obtaining at one test time the values 56.87% and 0.32% before and after filtering, respectively 27.50% and 0.26% at another time of testing, resulting in an estimated THD reduction percentage of 99.43% and 99.05%, respectively. These high percentages prove that the quality of the stream is excellent after applying the proposed strategy.

Power enhancement in PV arrays under partial shaded conditions with different array configuration.

Solar Photovoltaic systems are used for electrical power generation, and they provide an alternative source to non-renewable energy sources like coal, oil, natural gas and nuclear energy. Photovoltaic arrays used in PV systems may be subjected to partial shading conditions, thereby affecting power generation because of higher power mismatch losses. Due to an uneven distribution of irradiation condition, some of the bypass diodes turned on and affect the power generation in a photovoltaic array. The mismatch losses are due to the output from PV panels subjected to different irradiations because of non-uniform partial shading conditions. The power loss can be reduced by uniformly distributing the partially shaded condition over the entire PV array. In this work different shaped 4 × 4 array configuration is proposed to overcome the effect of partial shading condition, thereby providing lower mismatch losses. Simulations under different partial shading conditions are carried out using MATLAB Simulink, and the experimental setupis carried out for the proposed array configuration for 4 × 4 PV array and the results are discussed.

Application of fractional-order synergetic-proportional integral controller based on PSO algorithm to improve the output power of the wind turbine power system.

It is noted that the traditional direct filed-oriented control (DFOC) is widely used in the field of electric power generation from wind due to its fast response dynamic, ease of implementation and simplicity, but this strategy is characterized by the presence of large ripples at the level of both active and reactive powers. This work presents a new algorithm for DFOC strategy of an asynchronous generator (AG) in a wind power (WP) system, which is based on the use of a new nonlinear controller called fractional-order synergetic control-fractional-order proportional-integral (FOSC-FOPI) controller, where the proposed technique parameters are calculated using the particle swarm optimization (PSO) strategy. It has been observed that the DFOC-FOSC-FOPI-PSO strategy is robust and works well in case of changing generator parameters. Three tests were performed to study the behavior of the designed technique under different working conditions, where the behavior of the DFOC-FOSC-FOPI-PSO strategy was compared with the behavior of the traditional DFOC technique in terms of power ripple ratio, overshoot, steady-state error, response time, tracking reference, and current quality. The simulation of the designed technique based on the FOSC-FOPI-PSO strategy of the AG-WP system is carried out using Matlab software, where the simulation results showed that the suggested technique is better than the classical technique (with PI controller) in terms of improving response time of active power (33.33%) and reactive power (10%) in second test, reduction of the steady-state error of reactive power (96.95%) and active power (97.14) in first test, minimization of harmonic distortion of current (96.57%) in third test and significant minimization of ripples of active power (99.67%, 44.69%, and 98.95%) and reactive power (99.25%, 53.65%, and 70.50%) in the three tests. The effectiveness of the DFOC-FOSC-FOPI-PSO strategy is very high, so it can be a reliable solution for controlling various generators.

Synergetic-PI controller based on genetic algorithm for DPC-PWM strategy of a multi-rotor wind power system.

This work designs a powerful new nonlinear control technique using synergetic control (SC), proportional-integral (PI) controller, and genetic algorithm (GA) for multi-rotor wind energy (MRWE) conversion systems, whereby an asynchronous generator (AG) is used to achieve optimal energy extraction. The direct power control (DPC) technique is used based on the proposed SC-PI-GA (SPI-GA) technique to control the AG-based MRWE system, where this new nonlinear control technique is used to achieve stable control characteristics under random changes in wind speed and to provide great robustness against modeling uncertainties. Moreover, the pulse width modulation (PWM) technique is used to control the AG inverter due to its simplicity and ease of implementation. In this proposed DPC-SPI-GA technique, we need to measure current and voltage to estimate the active power and the reactive power. Also, inner loops are not used in this proposed DPC-SPI-GA technique as is the case in the field-oriented control (FOC) technique, where the proposed system in this work is characterized by an integrated structure. Three different tests are proposed to study and verify the behavior of the designed DPC-SPI-GA strategy compared to the traditional DPC technique.

An assessment of the strategies for the energy-critical elements necessary for the development of sustainable energy sources.

There have been several strategies developed to increase the diversified supply of energy so that it can meet all of the future demands for energy. As a result, to ensure a healthy and sustainable energy future, it is imperative to warrant reliable and diverse energy supply sources if the "green energy economy" is to be realized. The purpose of developing and deploying clean energy technologies is to improve our overall energy security, reduce our carbon footprint, and ensure that the generation of energy is secure and reliable in the future, making sure that we can spur economic growth in the future. In this paper, advancements in alternative sources of energy sustainability and strategies will be examined to ensure there will be enough fuel to supply all the future demands for energy. Several emerging clean energy technologies rely heavily on the availability of materials that exhibit unique properties that are necessary for their development. This paper examines the roles that rare earth and other energy-critical materials play in securing a clean energy economy and the development of clean energy economies in general. For the development of these technologies to be successful and sustainable, a number of these energy-critical materials are at risk of becoming unavailable. This is due to their limited availability, disruptions in supply, and a lack of suitable resources for their development. An action plan focusing on producing energy-critical materials in energy-efficient ways is discussed as part of an initiative to advance the development of clean and sustainable energy.

Detection of Virus SARS-CoV-2 Using a Surface Plasmon Resonance Device Based on BiFeO3-Graphene Layers.

Coronavirus disease (COVID-19) pandemic outbreak is being investigated by severe respirational syndrome coronavirus-2 (SARS-CoV-2) as a global health issue. It is crucial to propose sensitive and rapid coronavirus detectors. Herein, we propose a biosensor based on surface plasmon resonance (SPRE) for the detection of SARS-CoV-2 virus. To achieve improved sensitivity, a BiFeO3 layer is inserted between a metal (Ag) thin film and a graphene layer in the proposed SPRE device so that it has the structure BK7 prism/ Ag/ BiFeO3/ graphene/ analyte. It has been demonstrated that a small variation in the refractive index of the analyte can cause a considerable shift in the resonance angle caused by the remarkable dielectric properties of the BiFeO3 layer, which include a high index of refraction and low loss. The proposed device has shown an extremely high sensitivity of 293 deg/RIU by optimizing the thicknesses of Ag, BiFeO3, and the number of graphene sheets. The proposed SPRE-based sensor is encouraging for use in various sectors of biosensing because of its high sensitivity.

A surface plasmon resonance nanostructure containing graphene and BaTiO3 layers for sensitive defection of organic compounds.

Organic compound-based sensors are used in a variety of significant fields, including medical research, azeotropic calibration, vegetable oil extraction, the shoe industry and geothermal power plants. Here, a high-performance, two-dimensional material-based organic compound sensor has been proposed using a surface plasmon resonance (SPR) nanostructure consisting of a BK7 glass prism, Ag, BaTiO3, Ag, graphene and sensing layer. The reflectivity curves of the SPR device have been investigated when the sensing media are Pentane, n-Hexane, n-Heptane and n-Octane. The thickness of the BaTiO3 layer and the number of graphene sheets have been optimized to maximize the sensitivity. The highest sensitivity attained is 220.83 deg/RIU for n-Octane with 45 nm silver/10 nm BaTiO3/8 nm silver and four layers of graphene. We believe that the SPR-based sensors are simple and can replace the spectrometry, chromatography and electrochemical based sensors. The proposed design is extremely effective for diverse applications in biological, industrial and chemical detection because of its simple structure and great performance.

Optical Detection of Fat Concentration in Milk Using MXene-Based Surface Plasmon Resonance Structure.

MXene (Ti3C2Tx) has emerged very recently as an interacting material for surface plasmon resonance (SPR) configuration. It was discovered that Ti3C2Tx can facilitate the adsorption of biomolecules due to its higher binding energies, stronger interaction between matter and light, and larger surface area. In this work, a two-dimensional Ti3C2Tx and silicon layer-based SPR refractometric sensor is proposed for the sensitive and fast detection of milk fat concentration due to the high significance of this issue to people all over the world. The proposed SPR structure employs BK7 (BK7 is a designation for the most common Borosilicate Crown glass used for a variety of applications in the visible range) as a coupling prism and silver as a metal layer. The layer thicknesses and the number of Ti3C2Tx sheets are optimized for the highest performance. The highest reached sensitivity is 350 deg./RIU with 50 nm silver and 4 nm silicon with a monolayer of Ti3C2Tx, which is ultra-high sensitivity compared to the latest work that utilizes SPR configuration. The proposed SPR-based sensor's ultra-high sensitivity makes it more attractive for usage in a variety of biosensing applications.

Surface plasmon resonance biosensor based on graphene layer for the detection of waterborne bacteria.

As a result of the risks that waterborne bacteria bring to the human body, identifying them in drinking water has become a global concern. In this article, a highly sensitive surface plasmon resonance (SPR) biosensor consisting of prism, Ag, graphene, affinity layer and sensing medium is proposed for rapid detection of the waterborne bacteria. Four SPR-based sensors are first studied with the structures prism/Ag/sensing medium, prism/Ag/affinity layer/sensing medium, prism/Ag/graphene/sensing medium, and prism/Ag/graphene/affinity layer/sensing medium. The latter structure is found to have the highest sensitivity so it is considered for further investigations. Four different commonly used prisms are then demonstrated which are N-FK51A, 2S2G, SF10 and BK7. The structure with the prism N-FK51A is found to correspond to the highest sensitivity so it is considered for further investigations. The structure parameters are then optimized. The proposed SPR sensor can achieve high sensitivity of about 221.63 °/RIU for Escherichia coli and 178.12 °/RIU for Vibrio cholera bacteria with an average value of 199.87 °/RIU. We believe that the proposed structure will open a new window in the field of microorganism detections.

Induction motor control system with a Programmable Logic Controller (PLC) and Profibus communication for industrial plants - An experimental setup.

Existing monitoring and control of an induction motor systems must be interoperable with next generation products to ensure smart integration in order to update existing system by reducing the cost and increasing the reliability. The best method must be chosen for efficient use of energy in some cases such as speed control, switching-on or switching-off, or in controlling the speed of induction motors, which make up a large part of the load group in industrial field. In this study, the controlling and monitoring of an induction motor parameters were carried out over a computer by using the Profibus (Process Field Bus) communication method through the TIA (Totally Integrated Automation) Portal program used to program all Siemens PLCs (Programmable Logic Controllers). The induction motor parameters were obtained over the frequency converter without using additional cards such as current, voltage, frequency and speed measurement. In this way, cables and electronic cards confusion were avoided and a safer, faster and more functional application was implemented compared to classical methods. KEPServerEx OPC (Ole-for Processing Control) Server program by OPC Foundation was preferred for the communication of computer with a PLC. Profi-Lab editor program was used for monitoring and controlling the system over a computer.

Optimal energy management of distributed generation in micro-grids using artificial bee colony algorithm.

The use of renewable energy sources in energy distribution networks as distributed generation sources for dispersed and low consumption loads in remote areas such as remote villages and islands with low population can be a proper solution for reducing economic costs, reducing environmental pollutions and increasing energy efficiency. The purpose of this paper is optimal operation management of micro-grids by considering the existing capacities in the electricity market. In fact the microgrid operator, which is responsible for the safe operation of the network, should consider a process for planning in the network that takes into account all benefits of micro-grid's components. In other words, enough reliability for generation resources in these networks should be created in order to reduce costs and environmental pollution from energy production. In this paper, the artificial bee colony (ABC) algorithm has been used to minimize the costs and environmental pollutions by providing the optimal production power of distributed generation.

Cancer cell detector based on a slab waveguide of anisotropic, lossy, and dispersive left-handed material.

Cancer is a disease that takes place when human cells grow uncontrollably. When detected and cured early, it can be non-life-threatening. It becomes life-threatening in case of late discovery where it affects the ability of an organ to function. In this work, a symmetric slab waveguide sensor is analyzed for the detection of cancer cells. The covering layers are assumed anisotropic lossy dispersive left-handed materials. Different from other sensors in which the analyte is located in the cladding region where the evanescent field exists, the cancerous cell is placed in the guiding film region that supports the oscillating field. Hence, the proposed sensor avoids the acute weakness of conventional optical waveguide sensors. Due to the high localization of the electromagnetic wave in the analyte region, the proposed sensor shows unusual sensitivity enhancement. The results revealed that the sensitivities obtained are 110%, 325%, and 450% for the first, second, and third modes, respectively. The enhancement of the sensitivity of the third mode relative to the conventional waveguide sensors is nearly a factor of 18.

Analysis and experimental evaluation of shunt active power filter for power quality improvement based on predictive direct power control.

This paper discusses the use of the concept of classical and predictive direct power control for shunt active power filter function. These strategies are used to improve the active power filter performance by compensation of the reactive power and the elimination of the harmonic currents drawn by non-linear loads. A theoretical analysis followed by a simulation using MATLAB/Simulink software for the studied techniques has been established. Moreover, two test benches have been carried out using the dSPACE card 1104 for the classic and predictive DPC control to evaluate the studied methods in real time. Obtained results are presented and compared in this paper to confirm the superiority of the predictive technique. To overcome the pollution problems caused by the consumption of fossil fuels, renewable energies are the alternatives recommended to ensure green energy. In the same context, the tested predictive filter can easily be supplied by a renewable energy source that will give its impact to enhance the power quality.