Voltage Variations Research Articles

An Application of Closest Pair of Points Algorithm to Detect the Outliers on the Fixed Solar System

Renewable energy resources are regarded as clean energy sources and effective utilization of these resources reduces ecological effects, produces little secondary waste and they are feasible in light of present and future economic and social societal demands. Solar energy is a radiant light and heat from the sun that may be harvested through a variety of methods, including solar power for producing electricity, solar thermal energy, and solar architecture. Solar technologies generate electricity from the sun, which is considered to be a clean energy source. Solar energy technologies offer a tremendous chance for mitigating greenhouse gas emissions and lowering global warming by replacing traditional energy sources. However, the technologies are not exempted from the operational challenges such as the ever-fluctuating ambient conditions. The intention of this study is to present the experimental outcomes of an application of the closest pair of points algorithm to detect outliers on the fixed solar system. The closest pair of points algorithm is used to find outliers in the system based on the measured voltage from the photovoltaic (PV) panel. The algorithm examines the PV panel’s immediate voltage and compares it to earlier voltage samples to assess whether there is a significant voltage variation that might turn into outliers. The algorithm proved to be extremely precise and efficient in detecting outliers on the fixed solar system. However, the efficiency of the algorithm should yet be verified for larger PV arrays to determine whether it will withstand the test.

Transient and steady‐state performance improvement of two interconnected areas through VSC‐based HVDC transmission line using multi‐purpose control strategies

AbstractThis paper presents various control strategies to improve operations in two interconnected areas connected by a VSC‐HVDC transmission line. The main focus is on designing a central control system (CCS) that coordinates control units in both areas. In area 1, an AC voltage control unit is connected to the CCS. In area 2, three control units including a load power control unit, a fault detection unit, and an AC voltage control unit are also connected to the CCS. The CCS receives inputs from these units and generates commands for the DC voltage and active/reactive power control units on both sides of the DC line. The first proposed strategy addresses permanent voltage drops caused by load fluctuations in area 2. It adjusts the transmitted power from area 1 based on voltage variations in area 2. The second strategy focuses on mitigating faults in area 2 by injecting active and reactive power from area 1 during such events. The third strategy resolves transient voltage oscillations in both areas by controlling the reactive power of stations on either side of the DC line. Simulations using MATLAB‐SIMULINK demonstrate that these mechanisms successfully achieve their objectives.

Multivariate signal-to-noise ratio as a metric for characterizing spectral computed tomography

Objective. With the introduction of spectral CT techniques into the clinic, the imaging capacities of CT were expanded to multiple energy levels. Due to a variety of factors, the acquired signal in spectral CT datasets is shared between these images. Conventional image quality metrics assume independence between images which is not preserved within spectral CT datasets, limiting their utility for characterizing energy selective images. The purpose of this work was to develop a metrology to characterize energy selective images by incorporating the shared information between images within a spectral CT dataset. Approach. The signal-to-noise ratio (SNR) was extended into a multivariate space where each image within a spectral CT dataset was treated as a separate information channel. The general definition was applied to the specific case of contrast to define a multivariate contrast-to-noise ratio (CNR). The matrix contained two types of terms: a conventional CNR term which characterized image quality within each image in the spectral CT dataset and covariance weighted CNR (Covar-CNR) which characterized the contrast in each image relative to the covariance between images. Experimental data from an investigational photon-counting CT scanner was used to demonstrate the insight of this metrology. A cylindrical water phantom containing vials of iodine and gadolinium (2, 4, and 8 mg ml−1) was imaged under conditions of variable tube current, tube voltage, and energy threshold. Two image series (threshold and bin images) containing two images each were defined based upon the contribution of photons to reconstructed images. Analysis of variance (ANOVA) was calculated between CNR terms and image acquisition variables. A multivariate regression was then fitted to experimental data. Main Results. Image type had a major difference on how Covar-CNR values were distributed. Bin images had a slightly higher mean and wider standard deviation (Covar-CNRlo: 3.38 ±17.25, Covar-CNRhi: 5.77 ± 30.64) compared to threshold images (Covar-CNRlo: 2.08 ±1.89, Covar-CNRhi: 3.45 ± 2.49) across all conditions. ANOVA found that each acquisition variable had a significant relationship with both Covar-CNR terms. The multivariate regression model suggested that material concentration had the largest impact on all CNR terms. Signficance. In this work, we described a theoretical framework to extend the SNR to a multivariate form that is able to characterize images independently and also provide insight regarding the relationship between images. Experimental data was used to demonstrate the insight that this metrology provides about image formation factors in spectral CT.

Voltage-insensitive stochastic magnetic tunnel junctions with double free layers

Stochastic magnetic tunnel junction (s-MTJ) is a promising component of probabilistic bit (p-bit), which plays a pivotal role in probabilistic computers. For a standard cell structure of the p-bit, s-MTJ is desired to be insensitive to voltage across the junction over several hundred millivolts. In conventional s-MTJs with a reference layer having a fixed magnetization direction, however, the stochastic output significantly varies with the voltage due to spin-transfer torque (STT) acting on the stochastic free layer. In this work, we study a s-MTJ with a “double-free-layer” design theoretically proposed earlier, in which the fixed reference layer of the conventional structure is replaced by another stochastic free layer, effectively mitigating the influence of STT on the stochastic output. We show that the key device property characterized by the ratio of relaxation times between the high- and low-resistance states is one to two orders of magnitude less sensitive to bias voltage variations compared to conventional s-MTJs when the top and bottom free layers are designed to possess the same effective thickness. This work opens a pathway for reliable, nanosecond-operation, high-output, and scalable spintronics-based p-bits.

Analyzing Solar PV Integrated UPQC Performance in Micro Grid Systems

The presented work proposes a methodology for the automated transition of a solar PV array and battery integrated unified power quality conditioner (PV-B-UPQC) between standalone and grid-connected modes of operation. The system consists of a shunt and series active filters connected back-to-back with a common DC link, addressing the challenge of integrating power quality improvement with clean energy generation. The automated transition ensures continuous power supply to critical loads, even during grid unavailability. Key innovations include implementing this automated transition in the PV-B-UPQC system with minimal disturbance to local loads. The system's performance is validated through experimental evaluation under various dynamic conditions, such as automated transition, supply voltage variations, grid unavailability, changes in solar power generation, and load variations - scenarios commonly encountered in modern distribution networks. The results are verified using MATLAB/Simulink simulations. Index Terms—Power Quality, shunt compensator, series compensator, UPQC, Solar PV, MPPT.

EFFICIENT DSP-BASED REAL-TIME IMPLEMENTATION OF ANFIS REGULATOR FOR SINGLE-PHASE POWER FACTOR CORRECTOR

As a widely used technology, active power factor corrector (A-PFC) circuits face many control challenges, such as nonlinearity and uncertainties. With more and more power electrical appliances that are connected to the utility grid, the A-PFC is for the task of improving the power quality (PQ) to meet the requirement of the international standards, and this results in great challenges for keeping the total harmonic distortion (THD) as long as a power factor in the desired ranges. To this end, this paper focuses on the simulation and real-time implementation of a combined adaptive neural-fuzzy inference system (ANFIS) and predictive current controller for a single-phase PFC rectifier. The proposed control method has a simple, low-cost structure for better response and robustness. The performance of the proposed control approach was evaluated in real-time based on the dSPACE 1104 digital signal processor for different reference and load conditions. The results obtained from simulation and experimental tests validate the superiority of the proposed approach by evidencing a unity power factor, lower THD, fast dynamic response, and robustness against fast load and output voltage variations.

Estimating incident infrared radiation intensity on a horizontal surface

The study aims to characterize external IR heating sources, operating in free convection atmosphere, qualitatively and quantitatively for various applications. It employs non-invasive IR thermography to measure steady-state irradiations from a ceramic IR heater at variable voltages (60–150 V) using the surface energy budget (SEB) method which relies on only two measured parameters (surface and ambient temperature). The free convection heat transfer from the plate is demarcated between the thermal boundary layer (TBL) and buoyant plume-dominated region by temperature histogram tool. Nearly 78 ± 5 % of the plate area is occupied by the hot plume(s) in the studied voltage range. Estimated irradiation agrees fairly well with one another (within ± 10 %). Additionally, the time response of the plate was measured to be ∼ 120 s. This fast response can be made even faster, by reducing the thermal mass, eventually leading to low-cost IR sensors.

Engineering of TiN/ZnO/SnO2/ZnO/Pt multilayer memristor with advanced electronic synapses and analog switching for neuromorphic computing

The two-terminal memristor is a promising neuromorphic artificial electronic device, mirroring biological synapses in structure and replicating various synaptic functions. Despite its advantages, challenges in achieving high reliability, gradual switching, and low energy consumption hinder progress in neuromorphic devices. This study explores electronic synapses and simulates analog switching in a Pt/TiN/ZnO/SnO2/ZnO/Pt multilayer (ML) configuration, featuring a ∼3 nm SnO2 layer between ZnO layers. Results show enhanced cycling endurance (more than 250 cycles), resistance window (102), tunable synaptic plasticity, and multilevel switching. ML memristors exhibit low coefficient of variation (4.5 %) in set voltage, low energy consumption (set = ∼0.12 nj, reset = ∼0.1 nj), and fast switching speeds (set = 300 ns, reset = 200 ns), suitable for high-density memory and neuromorphic systems. They successfully emulate synaptic functions, including paired-pulse facilitation (PPF), spike voltage-dependent plasticity (SVDP), spike width-dependent plasticity (SWDP), spike frequency-dependent plasticity (SFDP), and post-tetanic potentiation (PTP). Modulating pulse amplitude and width achieves multilevel conductance in long-term potentiation (LTP) and long-term depression (LTD). Using nonlinear conductance data, a 96.5 % image pattern recognition accuracy is achieved in a deconvolution neural network (DNN) simulation. These results highlight the ML memristor's potential in efficient neuromorphic computing systems.

Hardware-in-the-Loop Emulation of a SEPIC Multiplier Converter in a Photovoltaic System

This article presents the development and execution of a Single-Ended Primary-Inductor Converter (SEPIC) multiplier within a Hardware-in-the-Loop (HIL) emulation environment tailored for photovoltaic (PV) applications. Utilizing the advanced capabilities of the dSPACE 1104 platform, this work establishes a dynamic data exchange mechanism between a variable voltage power supply and the SEPIC multiplier converter, enhancing the efficiency of solar energy harnessing. The proposed emulation model was crafted to simulate real-world solar energy capture, facilitating the evaluation of control strategies under laboratory conditions. By emulating realistic operational scenarios, this approach significantly accelerates the innovation cycle for PV system technologies, enabling faster validation and refinement of emerging solutions. The SEPIC multiplier converter is a new topology based on the traditional SEPIC with the capability of producing a larger output voltage in a scalable manner. This initiative sets a new benchmark for conducting PV system research, offering a blend of precision and flexibility in testing supervisory strategies, thereby streamlining the path toward technological advancements in solar energy utilization.

Power quality enhancement in the wind energy distribution system using HHO algorithm based UPFC

ABSTRACT This study proposes using a Wind Energy Conversion System (WECS) to power a Unified Power Flow Controller (UPFC) Direct Current (DC) capacitor, maintaining appropriate voltage levels. This arrangement allows UPFC adjustment to improve Power Quality (PQ). The main issue rapid growth of nonlinear loads causes sudden variations in source voltage, resulting in a reduction in power quality within seconds. UPFC is used to address these concerns. This work uses the Harris Hawks Optimizer (HHO) to improve the performance of the UPFC. To begin, the aim function parameters are defined, including voltage, current, active power, and reactive power. Thus, these characteristics are used to generate control pulses for the series and shunt active power filters. HHO is used to find the best solution and predict the ideal UPFC control signal. Additionally, voltage instability and power dissipation decrease under different load conditions. Thus, system power supply quality improves. To evaluate the effectiveness of the proposed technique, two scenarios were identified: fault circumstances and engine speed changes. MATLAB/Simulink was used to implement the suggested strategy and compare it to established methodologies like Grey Wolf Optimization (GWO), Modified Particle Swarm Optimization (MPSO), and Whale Optimization Algorithm (WOA).

Effects of Heat Input and Intertrack Overlap on the Microstructure and Properties of Inconel 686 Weld Overlays.

The objective of this study was to investigate how weld overlays with nickel superalloys are important for the integrity, due the high temperatures and corrosive environments that can be experienced in mineral processing environments, of mining and processing equipment. The Ni-Cr-Mo superalloy Inconel 686 overlays are fabricated through automatic gas metal arc welding with variations in arc voltage and travel speed (i.e., heat input), and they have overlap between adjacent weld tracks for applications in the mining and minerals sector. The impact of variations in the process parameters and the size of the weld overlapping on the dilution, solidification morphology, microsegregation, and microhardness were investigated. Both geometric and chemical composition definitions were used to quantify the extent of the weld dilution. Subsequently, the weld geometry and dilution were correlated with the solidification microstructure and phase transformations. The maximum dilutions were measured to be 13.63% (1/2 overlap, 5.96 kJ·cm-1) and 15.39% (1/3 overlap, 4.77 kJ·cm-1), which shows that less of an overlap increases the dilution level. Scanning electron microscopy and chemical composition analysis revealed that an increase in weld heat input and dilution level led to higher levels of microsegregation for Mo and Cr, as well as the volume fraction of Mo- and Cr-rich phases in the interdendritic/intercellular regions in the overlay layer. Analysis of the weld overlays in the current study revealed strong and unprecedented connections between the weld overlay process conditions, the resultant metallurgy (i.e., dendrite arm spacing, microsegregation, and phase formation), and the hardness of the overlay. It was concluded that the optimal weld overlays in the processing window studied in this investigation were fabricated at mid-level heat inputs (i.e., 4-5 kJ·cm-1) and a 1/2 track overlap.

Advanced extraction of PV parameters’ models based on electric field impacts on semiconductor conductivity using QIO algorithm

This article presents a novel approach for parameters estimation of photovoltaic cells/modules using a recent optimization algorithm called quadratic interpolation optimization algorithm (QIOA). The proposed formula is dependent on variable voltage resistances (VVR) implementation of the series and shunt resistances. The variable resistances reduced from the effect of the electric field on the semiconductor conductivity should be included to get more accurate representation. Minimizing the mean root square error (MRSE) between the measured (I–V) dataset and the extracted (V–I) curve from the proposed electrical model is the main goal of the current optimization problem. The unknown parameters of the proposed PV models under the considered operating conditions are identified and optimally extracted using the proposed QIOA. Two distinct PV types are employed with normal and low radiation conditions. The VVR TDM is proposed for (R.T.C. France) silicon PV operating at normal radiation, and eleven unknown parameters are optimized. Additionally, twelve unknown parameters are optimized for a Q6-1380 multi-crystalline silicon (MCS) (area 7.7 cm2) operating under low radiation. The efficacy of the QIOA is demonstrated through comparison with four established optimizers: Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Salp Swarm Algorithm (SSA), and Sine Cosine Algorithm (SCA). The proposed QIO method achieves the lowest absolute current error values in both cases, highlighting its superiority and efficiency in extracting optimal parameters for both Single-Crystalline Silicon (SCS) and MCS cells under varying irradiance levels. Furthermore, simulation results emphasize the effectiveness of QIO compared to other algorithms in terms of convergence speed and robustness, making it a promising tool for accurate and efficient PV parameter estimation.

Consequence simulation of cyber attacks on key smart grid business cases

The increasing threat of cyber-attacks on modern power systems highlights the need for a comprehensive examination through simulations. This study conducts an in-depth simulation of cyber-attacks on critical smart grid components, including smart meters, substation automation, and battery management systems, to expose and analyze potential disruptions to power system operations. We identify vulnerabilities that can lead to severe grid instabilities, such as voltage variations, system collapses, and inverter failures. Our analysis underscores the complex interactions between cyber threats and grid components, revealing how disruptions extend beyond mere load interruptions to affect the core infrastructure. We advocate for integrating established cybersecurity frameworks like NIST, ISO/IEC 27001, and IEC 62443, essential in fortifying grid stability against these dynamic threats. Our findings highlight the urgent need for continuous adaptation and enforcement of these frameworks to enhance resilience and ensure the reliability of modern power grids against cyber-attacks.

A complete control structure based backstepping controller design for stacked multi-cell multi-level SPWM VSC STATCOM

Currently, the installation of FACTS (Flexible Alternative Current Transmission System) devices such as synchronous static compensators (STATCOM) represent a very useful solution to improve both network stability and power quality. The effectiveness of these devices is principally determined by the used static converter (topology and the switching strategy) and the adopted dynamic law characterizing the controller structure. This paper illustrates the stacked five-level, multi-cell STATCOM compensator performances governed by a Backstepping controller technique compared to the use of the conventional PI controller (SPWM) for controlling voltage variation (sags or overshoots) affecting a given network point. The interest in using stacked multi-cell converters topology is to increase the number of voltage levels in order to ensure a good quality voltage waveform at the specified connection point. Therefore, the proposed backstepping controller based on the use of Lyapunov control functions presents a simplified structure and a reduced computational load compared to many others advanced control approaches. The proposed system is verified by running a simulation analysis with Matlab Simulink environment. The simulation results obtained show that the proposed STATCOM configuration ensures good voltage regulation at the common connection point (PCC), correction of the unity power factor on the grid side and good response time via reactive power compensation under variable load conditions.

Disturbance rejecting PID-FF controller design of a non-ideal buck converter using an innovative snake optimizer with pattern search algorithm

The optimal design of a proportional-integral-derivative controller with two cascaded first-order low-pass filters (PID-FF) for non-ideal buck converters faces significant challenges, including effective disturbance rejection, robustness to parameter variations, and the mitigation of high-frequency signal noise, with existing approaches often struggling and leading to suboptimal performance in practical applications. This study addresses these challenges by introducing a constraint on the open-loop crossover frequency to mitigate high-frequency noise and ensuring the controller prioritizes maintaining constant output voltage and robust responsiveness to input voltage and load current variations. This study also introduces an innovative metaheuristic algorithm, the opposition-based snake optimizer with pattern search (OSOPS), designed to address these limitations. OSOPS enhances the Snake Optimizer (SO) by integrating opposition-based learning (OBL) and Pattern Search (PS), thereby improving its exploration and exploitation capabilities. The proposed algorithm design includes a crossover frequency constraint aimed at counteracting high-frequency noise and ensuring robust performance under diverse disturbances. The efficacy of the OSOPS algorithm is demonstrated through rigorous statistical box plot analysis and convergence response comparisons with the original SO algorithm. Additionally, we systematically compare the performance of the OSOPS-based PID–FF–controlled non-ideal buck converter system against systems utilizing the original SO algorithm and the classical pole placement (PP) method. This evaluation encompasses transient and frequency responses, disturbance rejection, and robustness analysis. The results reveal that the OSOPS-based system outperforms the SO- and PP-based systems with 14.21 % and 32.10 % faster rise times, along with 15.38 % and 84.95 % faster settling times, respectively. The OSOPS and SO systems also exhibit higher bandwidths, exceeding the PP-based system by 18.74 % and 17.03 %, respectively. By addressing the key challenges in PID-FF controller design for non-ideal buck converters, this study provides a substantial advancement in control strategy, promising enhanced performance in practical applications.

Numerical simulation of neural activity based on discrete Markov chains with stochastic differential equations

Abstract With the development of new numerical calculation methods and computer software science and technology, people can have a good understanding of the potential mechanisms of cerebrovascular diseases. Here, we combine the stochastic differential equation (SDE) of discrete Markov chains to numerically simulate the dynamic changes of neural signals, and find that the changes of neural signals exhibit regular fluctuations. By analyzing the variation of voltage over time, we know that the voltage change at the next moment is closely related to the previous moment and has continuity. Based on the knowledge of neural ion channel dynamics, it was found that there will be longer peak changes in voltage, exhibiting a power-law distribution, which is consistent with the actual situation and statistical data related to resignation channels. By analyzing the voltage and peak changes of ion channels, we can gain a new understanding of the transmission laws of neural information and greatly improve the biological mechanisms.

Synthesis and characterization of silver nanoparticles by electrochemical method

In this work, silver nanoparticles (Ag-NP) were synthesized by electrochemical method, using a metallic silver cathode and anode in deionized water. The polarity was inverted every 60 seconds generating a voltage variation in order to obtain three different concentrations of Ag nanoparticles: 16 ppm, 24 ppm and 34 ppm. The synthesized nanoparticles were structurally, optically and morphologically characterized by XRD, UV-Vis and Transmission Electron Microscopy (TEM), respectively. XRD patterns showed the peaks at 38.11° and 64.42° from Ag. The surface plasmon resonance peak in absorption spectra on silver colloidal solution showed absorption from 422 nm to 429 nm. TEM measurement provided an average particle size for the concentrations of 16 ppm, 24 ppm and 34 ppm of 4.23 nm, 3.95 nm and 3.27 nm, respectively. The test with the E. coli bacteria showed that there is an inhibition in the growth of the bacteria with the presence of silver nanoparticles.

Output voltage control of DAB converters based on uncertainty and disturbance estimation.

The dual active bridge (DAB) converter is a power electronic device commonly used for DC voltage regulation and stabilization. However, during its control process, external disturbances, load variations, input voltage variations, switching tube voltage drops, dead time, etc. lead to errors in the control output, thus reducing the control accuracy of the system. Therefore, this article propose a robust control scheme for the output voltage based on uncertainty and disturbance estimator. In this article, an average small-signal model of the dual active bridge converter was established in terms of the basic principles and operation mechanisms, simplifying the controller's design. Then, the basic principles of the uncertainty and disturbance estimator (UDE) are introduced. The small-signal model of the dual active bridge (DAB) converter is applied to the UDE to minimize output voltage error by enabling the controller to directly regulate the shift ratio. Finally, this article discusses the application and effectiveness of the uncertainty and disturbance estimator (UDE) in the simulation and control of dual active bridge (DAB) converters. A series of experimental comparative studies are conducted, demonstrating that this scheme offers significant advantages in suppressing system uncertainties and disturbances.

Effect of rotation on achieving constant voltage in three-phase self-excited induction generator for small scale wind turbines application

Three-phase Self-Excited Induction Generators (SEIGs) are commonly employed for electricity generation in remote or isolated areas. SEIGs are preferred in such regions due to their ability to create a magnetic field by adding a capacitor to one of their terminals. Nevertheless, a significant challenge in utilizing SEIGs is maintaining a consistent output voltage in the presence of load fluctuations. This study aims to investigate the impact of generator rotation on the SEIG's output voltage and determine the optimal rotation speed required for achieving a stable output voltage. Ensuring stable voltage regulation is crucial to guarantee the proper functioning of all loads connected to the SEIG. Furthermore, operating the SEIG in parallel with other generators is advantageous. The methodology employed in this study involves varying the load supplied by the SEIG at different capacitor values. Unwanted voltage variations occur due to load fluctuations within a generating system or SEIG. Adjustments to the generator's rotation speed are made to uphold a uniform voltage level. The variables considered in this study include the generator's rotation speed, capacitor size, and load fluctuations. Simulation results demonstrate that the SEIG's output voltage is affected by the generator's rotation speed, and maintaining a consistent voltage necessitates appropriate adjustments to capacitor values and generator speed. This research enhances understanding of SEIG characteristics and offers guidance on effective settings for maintaining a stable output voltage at various generator rotation speeds. Future research can focus on practically implementing these findings to enhance the performance of SEIGs in real-world applications

Automatic Switching of Three Phase Induction Motor during Fault Condition

Abstract: Three-Phase Induction Motor Automated Transition in Defective Conditions Maintaining the effective operation of three-phase induction motors is crucial in a variety of industrial contexts. However, malfunctions such as phase inconsistency, voltage drop, and overcurrent can seriously impair motor performance, leading to loss of operation and possible damage. This article introduces an automatic switching method designed to improve the fault-tolerance capacity of induction motors in order to address these issues. The system uses advanced fault detection algorithms to identify abnormal conditions quickly and enable a seamless transition to backup power or other configurations. It also incorporates preventive measures to guard against phase abnormalities and voltage variations that could harm the motor. Through simulation and real-world testing, the suggested system's dependability and effectiveness are confirmed, highlighting its potential to guarantee continuous motor running in industrial applications, a three-phase induction motor must be automatically switched on. A two-motor system a primary motor and a backup motor is shown in this study. If the primary motor fails, the backup motor is automatically turned on. Microcontroller design controls the entire switching process.