Electrical Circuit Research Articles

A pareto strategy based on multi-objective optimal integration of distributed generation and compensation devices regarding weather and load fluctuations

In this study, we present a comprehensive optimization framework employing the Multi-Objective Multi-Verse Optimization (MOMVO) algorithm for the optimal integration of Distributed Generations (DGs) and Capacitor Banks (CBs) into electrical distribution networks. Designed with the dual objectives of minimizing energy losses and voltage deviations, this framework significantly enhances the operational efficiency and reliability of the network. Rigorous simulations on the standard IEEE 33-bus and IEEE 69-bus test systems underscore the effectiveness of the MOMVO algorithm, demonstrating up to a 47% reduction in energy losses and up to a 55% improvement in voltage stability. Comparative analysis highlights MOMVO's superiority in terms of convergence speed and solution quality over leading algorithms such as the Multi-Objective Jellyfish Search (MOJS), Multi-Objective Flower Pollination Algorithm (MOFPA), and Multi-Objective Lichtenberg Algorithm (MOLA). The efficacy of the study is particularly evident in the identification of the best compromise solutions using MOMVO. For the IEEE 33 network, the application of MOMVO led to a significant 47.58% reduction in daily energy loss and enhanced voltage profile stability from 0.89 to 0.94 pu. Additionally, it realized a 36.97% decrease in the annual cost of energy losses, highlighting substantial economic benefits. For the larger IEEE 69 network, MOMVO achieved a remarkable 50.15% reduction in energy loss and improved voltage profiles from 0.89 to 0.93 pu, accompanied by a 47.59% reduction in the annual cost of energy losses. These results not only confirm the robustness of the MOMVO algorithm in optimizing technical and economic efficiencies but also underline the potential of advanced optimization techniques in facilitating the sustainable integration of renewable energy resources into existing power infrastructures. This research significantly contributes to the field of electrical distribution network optimization, paving the way for future advancements in renewable energy integration and optimization techniques for enhanced system efficiency, reliability, and sustainability.

Solid-Emission-Tunable Squaraine with Thermal-Promoted Aggregate-State Transitions for Fast Thermal History Sensing.

Determining thermal history is crucial in many industrial processes, but reliable and sensitive organic thermal history indicators are currently absent. Herein, we report on the development of a squaraine-based fluorescent molecule, DPEA-SQ, for the detection of thermal exposure histories up to 436 K. DPEA-SQ forms multiple single crystals (DPEA-SQ-I, DPEA-SQ-II, and DPEA-SQ-III) with different conformations and aggregate-state packing modes, contributing to their different fluorescence wavelengths, lifetimes, and efficiencies. Interestingly, DPEA-SQ-I and DPEA-SQ-III undergo aggregate-state structural transitions to form the thermodynamically more stable DPEA-SQ-II, which are accompanied by changes in their fluorescence. By taking advantage of similar aggregate-state structural transformations during heating, a high-temperature thermal exposure history of up to 436 K is recorded and reflected by their fluorescence. To demonstrate the potential practical applications of DPEA-SQ, a DPEA-SQ-Powder/PDMS film is prepared and coated on an electric circuit board, which enables real-time monitoring of localized overheating by the naked eye. Additionally, the fluorescence peaks of DPEA-SQ-Powder and DPEA-SQ-Powder/PDMS films remain unchanged after storage at 373 K for 52 days, demonstrating high aggregate-state stability. The fast and reliable responses of this system make it an excellent candidate for the detection of overtemperature traces in electronic components and circuit diagnosis.

Convex Relaxations of Maximal Load Delivery for Multi-Contingency Analysis of Joint Electric Power and Natural Gas Transmission Networks

Recent increases in gas-fired power generation have engendered increased interdependencies between natural gas and power transmission systems. These interdependencies have amplified existing vulnerabilities in gas and power grids, where disruptions can require the curtailment of load in one or both systems. Although typically operated independently, coordination of these systems during severe disruptions can allow for targeted delivery to lifeline services, including gas delivery for residential heating and power delivery for critical facilities. To address the challenge of estimating maximum joint network capacities under such disruptions, we consider the task of determining feasible steady-state operating points for severely damaged systems while ensuring the maximal delivery of gas and power loads simultaneously, represented mathematically as the nonconvex joint Maximal Load Delivery (MLD) problem. To increase its tractability, we present a mixed-integer convex relaxation of the MLD problem. Then, to demonstrate the relaxation’s effectiveness in determining bounds on network capacities, exact and relaxed MLD formulations are compared across various multi-contingency scenarios on nine joint networks ranging in size from 25 to 1191 nodes. The relaxation-based methodology is observed to accurately and efficiently estimate the impacts of severe joint network disruptions, often converging to the relaxed MLD problem’s globally optimal solution within ten seconds.

Mathematical improvisation of electrical equivalent circuit model for commercial dye-sensitized solar module with Gauss–Seidel, curve fit and theoretical approach

Mathematical improvisation of electrical equivalent circuit model for commercial dye-sensitized solar module with Gauss–Seidel, curve fit and theoretical approach

Radiation therapy for ventricular arrhythmias.

Ventricular arrhythmias (VA) can be life-threatening arrhythmias that result in significant morbidity and mortality. Catheter ablation (CA) is an invasive treatment modality that can be effective in the treatment of VA where medications fail. Recurrence occurs commonly following CA due to an inability to deliver lesions of adequate depth to cauterise the electrical circuits that drive VA or reach areas of scar responsible for VA. Stereotactic body radiotherapy is a non-invasive treatment modality that allows volumetric delivery of energy to treat circuits that cannot be reached by CA. It overcomes the weaknesses of CA and has been successfully utilised in small clinical trials to treat refractory VA. This article summarises the current evidence for this novel treatment modality and the steps that will be required to bring it to the forefront of VA treatment.

Integration of Superconducting Fault Current Limiter and DC Circuit Breaker for Electric Aircraft DC Network

Integration of Superconducting Fault Current Limiter and DC Circuit Breaker for Electric Aircraft DC Network

Pole-to-Pole Fault Management for Electric Aircraft DC Network With HTS Cables

Pole-to-Pole Fault Management for Electric Aircraft DC Network With HTS Cables

Evaluation of strain-sensitive behavior in elastomeric conductive nanocomposites containing carbon nanotubes-a finite element-percolation model approach

A finite element (FE)-percolation model approach is developed to predict the strain-sensitive response of the three-dimensional (3D) representative volume element (RVE) of carbon nanotube (CNT)-elastomeric nanocomposite. In the simulation model, CNTs are modeled as solid, impenetrable cylinders inside a cubic insulating matrix. FE simulation is performed to evaluate the structural response of the RVE under applied strain. The FE model updates the locations of the CNTs in the deformed RVE. The paths are found using a suitable 3D resistance network associated with different percolation paths involved in the critical distance criterion. The percolation model utilizes the paths found to identify the electrical circuit for predicting tunneling conductivity. The simulating algorithm is used to study the influence of tunneling barrier height, nanotube volume fraction, and geometrical aspects. The lowest critical distance criterion is achieved for higher volume fractions and the most heightened sensitivity is obtained for lower CNT aspect ratios.

Semiglobal interval observer‐based robust coordination control of multi‐agent systems with input saturation

AbstractThis paper investigates the problem of robust coordination control based on interval observers for multi‐agent systems in the presence of input saturation and disturbances. Firstly, in the scenario where the system state is not directly measurable, two types of interval observers are, respectively, constructed by resorting to the upper and lower bounds on external perturbances as well as the output information. Secondly, a parametric Lyapunov equation‐based low‐gain feedback control method is proposed to guarantee semiglobal bounded consensus. In contrast to the conventional approach based on parametric algebraic Riccati equations, the proposed method offers the advantage of allowing the parameters to be determined in advance. Finally, a simulation example and a practical electrical circuit model are conducted to verify the theoretical results.

Optimizing Integrated Water and Electrical Networks through a Holistic Water–Energy Nexus Approach

As water and electrical networks cannot be entirely independent, a more integrated approach, the water–energy nexus (WEN), is developed. A WEN is the basis of a smart city where water and electrical networks are interconnected and integrated by implementing efficient management strategies. Accordingly, this study develops a dynamic co-optimization model for designing and operating an integrated power and water system. The proposed co-optimization model minimizes the total annual and operational costs of a micro-WEN system while capturing its optimum design values and operating conditions and meeting the demands of the electrical and water networks. Furthermore, this work presents a plan for transitioning from thermal desalination to reverse osmosis (RO) desalination in the United Arab Emirates (UAE). The key objective is to decouple electricity and water production, effectively tackling the issue of operating the UAE’s power plants at low efficiency during the winter while ensuring an adequate water supply to meet the growing demand. The results show that the co-optimization model provides a significant reduction in the total operational cost with the integration of photovoltaic energy and shifting to RO. Most importantly, the micro-WEN system is optimized over multiple timescales to reduce the computation effort and memory requirements.

Project-Based Module Development in the Electrical Circuit Course

Education is one of the efforts of awareness to realize cultural inheritance from past generations to new generations. Vocational education must prepare graduates who are easily absorbed by the industrial world and the business world, specifically for solving this problem, one solution is improving the learning model. The project-based learning model is able to form soft skills, gain knowledge to solve problems critically and creative thinking skills. The results of the research carried out were obtained, among other things, from each material validation statement, the experts stated that the module was valid and suitable for use. The results of media expert validation showed that the module was declared media valid. The response of lecturers in the electrical circuits course to the electrical circuits learning module gave a good response, namely the practicality level of the module was 78%, where the module was declared practical. A questionnaire distributed to students taking the Electrical Circuits course with 10 statements stated that the module received a score of 80%, where a score of 80% indicated that the module they used was practical for learning the Electrical Circuits course. The analysis carried out to see the results of the Paired T Test obtained a significant level of 0.000 < 0.05 which shows that there is a significant difference between the pretest and posttest results. It was concluded from this effectiveness test that there was a difference in the results before and after receiving treatment using Project-based Module Development in the Electrical Circuits Course.

Haque’s approach with mickens’ iteration method to find a modified analytical solution of nonlinear jerk oscillator containing displacement time velocity and time acceleration

AbstractHaque’s approach with Mickens’ iteration method has been used to obtain the modified analytical solutions of the nonlinear jerk oscillator, including displacement time velocity and acceleration. The jerk oscillator represents the features of chaotic behavior in numerous nonlinear phenomena, cosmological analysis, kinematical physics, pendulum analysis, etc., such as electrical circuits, laser physics, mechanical oscillators, damped harmonic oscillators, and biological systems. In this paper, we have used different harmonic terms for different iterative stages using the truncated Fourier series. A comparison is made between the iteration method, the improved harmonic balance method, and the homotopy perturbation method. After comparison, the suggested approach has been shown to be more precise, efficient, simple, and easy to use. Furthermore, there was remarkable accuracy in the comparison between the numerical results and the generated analytical solutions. For the third approximate period, the maximum percentage error is 0.014.

Design and Performance Evaluation of Fuzzy Controller based Energy Storage Enabled Statcom for Electrical Distribution Network

In order to fulfill the demand for power, the production of locally accessible resources, such as solar and wind energy, has increased dramatically during the past ten years. Their integration into the distribution of electrical network  (EDN) has affected the operation of the network. Variations in the real power flow and an increase in reactive power demand have impacted the EDN voltage/frequency profiles and distribution transformer strain. Hence to improve the voltage regulation and reduce the active power fluctuations, there is a need for a system network of electrical distribution (EDN). It has the ability to support both reactive and active power. Thus, the energy storage device with STATCOM is introduced i.e. an energy storage device linked to Statcom (EsSt) has been proposed in this paper. The Common Coupling Point (CCP) voltage can be adjusted by energy storage connected to Statcom (EsSt) to satisfy the amount of power required actively at EDN. The EsSt has an integrated battery storage system and may be controlled using a voltage source converter by employing the proper control strategy to deliver the necessary energy support and VAR compensation. The voltage at the dc-link has been integrated using DC-DC converter based half bridge. The use of conventional PI controllers provides poor transient response. To improve the transient performance, It is suggested that a fuzzy logic controller be used in place of the PI controller. Analysis of comparison using MATLAB. The rule-based fuzzy logic controller performs better than the PI controller, according to the Simulink environment.

Importance of cyber protection in the power grid in the face of an uncertain future

In the digital era, cybersecurity is transforming the behavior of human beings in the correct use of technology, which is why it has become a critical element in business strategy, thus giving rise to the present research, which identified the current situation of associated companies in the process of generation, transmission, distribution and public lighting of electricity in Costa Rica, in the field of cybersecurity, with special emphasis on the identification, analysis and evaluation of policies, standards, procedures, controls, regulations and infrastructure in the sector. The study approach was quantitative, descriptive, and exploratory; based on a sample of experts in information security, where a survey was used for data collection. In the findings obtained, the existence of personnel with the technical knowledge to deal with a computer incident was observed, but from the business perspective, the lack of an institutional guideline and country strategy for the protection of the electrical network was recognized. In conclusion, there is a latent risk in the infrastructure of the national power grid, which is evidenced by the lack of a clear national strategy that establishes a roadmap to protect the sector against a possible cyber-attack.

FORECASTING THE CONSUMPTION OF ELECTRICAL ENERGY ON THE CEB ELECTRICAL NETWORK IN LOME IN TOGO BY APPROACHES: SIMPLE LINEAR REGRESSION, RECURRENT NEURAL NETWORKS AND GENETIC ALGORITHMS

Forecasting the electrical power to be consumed requires planning production on several levels. In this work we used data from the Electric Community of Benin. Temperature, relative humidity, wind speed, normal direct irradiance, precipitation and diffuse radiation are the meteorological variables that made it possible to analyze the forecasts. The objective is to do learning with genetic algorithms, LSTM recurrent neural networks and simple linear regression after a characterization and a correlation study and then to submit the results to performance evaluation criteria. The results of the characterization made it possible to understand that certain variables are significant and influence the consumption of electrical energy. The study of the correlation gives 94% between direct normal irradiance and diffuse irradiance. Both give with the temperature 67% for one and 68% for the other. Regarding modeling, the results are bad with genetic algorithms if we take into account the correlation coefficient (R² = 28.84%), good with simple linear regression (R² = 69.08%) and very interesting for networks of recurrent neurons where we find: MAE = 0.11 MSE = 0.02 MAPE = 18.50% RMSE = 13.09% RRMSE = 18.25% and R² = 96.11%. Given these results, we deduce that short- and long-term memory recurrent neural networks (LSTM) are very well suited to predicting the electrical power consumed on the CEB electrical network.

Fuzzy-Fractional Modeling and Simulation of Electric Circuits using Extended He-Laplace-Carson Algorithm

Abstract In literature, most of the electric circuits are found to be at integer order, which are unable to capture the involved uncertainties and memory effects. To understand these effects, the current study is focused on modeling and analysis of fuzzy-fractional LC circuit, RC circuit, RL circuit, and RLC circuit. Triangular fuzzy numbers are utilized to introduce uncertainties in circuits. The memory effects are considered through fractional derivative in Caputo sense. A hybrid homotopy perturbation method (HPM) is established for solution purpose, in which standard HPM is combined with Laplace-Carson transformation in fuzzy-Caputo sense. This leads to an excellent and well-organized approach to calculate numerical solutions in the aspect of fuzzy logic and fractional calculus. The results obtained
through proposed approach are validated by comparing them with already existing results in literature. Residual errors are also calculated across the domain of r at fractional order to determine the convergence and stability of electric circuit models. To study the dynamics of inductor, resistor, and capacitor on current along with fractional and fuzzy parameters involved in the electric circuits, contour and three-dimensional plots are constructed. Several plots specifying the fuzzy membership function of models across various parameter values are also demonstrated. The results achieved through this study indicate that He-Laplace-Carson method can assist engineers and researchers when dealing with electric circuits characterized by fuzzy and fractional dynamics.

Examining electrical equivalent circuit models of insulators in transmission lines under pollution conditions: a comprehensive study and analysis

This research presents a comprehensive study investigating the behavior of insulators in transmission lines under various pollution conditions. Utilizing equivalent electrical circuits, the analysis aims to understand the impact of pollution on insulator performance, which is crucial for maintaining the reliability and efficiency of power transmission systems. By providing valuable insights into the relationship between pollution and insulator behavior, this study contributes to the development of improved strategies for mitigating the effects of pollution on transmission lines, ultimately enhancing the overall safety and stability of electrical power networks. The study focuses on a digital model that enables the visualization of potential and electric field distribution within a series of glass insulators utilized in Algerian electrical grids. Initially, a parallel RC network equivalent circuit was devised and its parameters were determined through finite element analysis using Comsol Multiphysics. This equivalent circuit was subsequently integrated into ATP/EMTP software to simulate leakage currents, yielding satisfactory outcomes. The model was then incorporated into the isolator's equivalent circuit. Results from various simulations indicate that the occurrence of discharge along the dry strip impacts leakage current. Furthermore, the re-initiation of discharge on the contaminated insulator's surface is influenced by the pollutant deposit's conductivity and distribution. Therefore, understanding the pollution level is vital for accurately evaluating and sizing the insulator chain at the installation site.

Demand-side load forecasting in smart grids using machine learning techniques.

Electrical load forecasting remains an ongoing challenge due to various factors, such as temperature and weather, which change day by day. In this age of Big Data, efficient handling of data and obtaining valuable information from raw data is crucial. Through the use of IoT devices and smart meters, we can capture data efficiently, whereas traditional methods may struggle with data management. The proposed solution consists of two levels for forecasting. The selected subsets of data are first fed into the "Daily Consumption Electrical Networks" (DCEN) network, which provides valid input to the "Intra Load Forecasting Networks" (ILFN) network. To address overfitting issues, we use classic or conventional neural networks. This research employs a three-tier architecture, which includes the cloud layer, fog layer, and edge servers. The classical state-of-the-art prediction schemes usually employ a two-tier architecture with classical models, which can result in low learning precision and overfitting issues. The proposed approach uses more weather features that were not previously utilized to predict the load. In this study, numerous experiments were conducted and found that support vector regression outperformed other methods. The results obtained were 5.055 for mean absolute percentage error (MAPE), 0.69 for root mean square error (RMSE), 0.37 for normalized mean square error (NRMSE), 0.0072 for mean squared logarithmic error (MSLE), and 0.86 for R2 score values. The experimental findings demonstrate the effectiveness of the proposed method.

An effective gradient jellyfish search algorithm for optimal reactive power dispatch in electrical networks

AbstractAn effective optimization technique, called gradient jellyfish search optimizer (GJSO), is introduced here to address the optimal reactive power dispatch (ORPD) issue in electric networks. The ORPD problem is a complex non‐linear optimization issue involving integrated variables, aimed at achieving safe and cost‐effective operation of the system by determining optimal values for generator voltage, tap changers of transformers, and reactive power compensation. The performance of the original JSO technique is enhanced by integrating the local escaping operator into the GJSO approach. The effectiveness of the GJSO methodology is evaluated via comparison with two existing methodologies: the original jellyfish search optimizer and the equilibrium optimizer. Jellyfish search optimizer is a meta‐heuristic optimization algorithm inspired by the movement of jellyfish in the water, while equilibrium optimizer draws inspiration from game theory and equilibrium concepts. Simulations were conducted using typical IEEE‐30 bus and IEEE‐57 bus systems to validate the performance of the GJSO methodology. Two versions of the objective function are examined: minimizing line power loss and minimizing total voltage deviations at the buses. The simulation results demonstrated that the GJSO algorithm exhibited superior performance in terms of accuracy and stability compared to the standard jellyfish search optimizer and equilibrium optimizer algorithms.

Solar shunt active power filter based on optimized direct power control strategy with disturbance rejection principle

Introduction. This paper focuses on a renewable energy system coupled to a dual purpose power grid via a parallel active power filter for injecting photovoltaic energy into the grid and improving the power quality in the presence of the non-linear load. The novelty of the work consists in the combination of two advanced techniques – Fuzzy Logic Controller (FLC) and the optimized Anti-Windup Fractional Order Proportional-Integral Differentiator (AW-FOPID) regulator based on Particle Swarm Optimization with the Spreading Factor (PSO-SF) algorithm, applied to the improved Direct Power Control (DPC) strategy under different conditions related to climate changes and healthy or infected electrical network. Purpose. Its main role is to improve the power quality and reject the perturbations deforming the electrical network under distorted, unbalanced and balanced grid voltage conditions. Besides, the FLC is employed the Maximum Power Point Tracking (MPPT) under any weather conditions. In addition, the optimized AW-FOPID controller leads to keep the DC bus voltage at its reference value with small undershoots and overshoots in the voltage with a short response time in steady or dynamic states. Methods. The rejection of disturbances affecting the grid is offered by the improved DPC. On the other hand, an intelligent method based on fuzzy logic was used MPPT under any weather conditions. Furthermore, an AW-FOPID regulator based on PSO-SF algorithm is used to keep the DC bus voltage at its reference value with small undershoots and overshoots in the voltage, while keeping a fast response time. Results. The proposed system control is evaluated in various states of power source: distorted, unbalanced, and balanced by simulation using MATLAB/Simulink. The simulation results illustrate the effectiveness and performance of the studied control strategies. References 26, tables 8, figures 16.