Electrical Circuit Research Articles

Analysis of features of electric energy quality management and voltage regulation in housing and communal services

Subject of research: methods of improving the quality of electric energy in electric networks used for power supply mainly to municipal consumers in urban areas. The purpose of the study is to determine the most relevant methods for improving the quality of electric energy of municipal consumers, their advantages and disadvantages, as well as determining the direction of modernization. Methods and objects of research: the object of research in this material is the housing and communal complex in urban areas, in particular the electric grid complex. The work is based on such scientific research methods as analysis, synthesis, modeling and generalization. The main results of the study: the analysis of the features of electric energy quality management and voltage regulation in modern conditions. The importance and necessity of using modern intelligent systems to improve reliability, energy efficiency and quality of electric energy i s highlighted. The key prerequisites for the introduction of smart systems for monitoring the state of the electrical network are formulated. The tasks, the solution of which is necessary for the modernization of the complex, have been identified. A proposal has been made to use artificial intelligence-based systems as the main tool for modernization.

A Variant of the Growing Neural Gas Algorithm for the Design of an Electric Vehicle Charger Network

The Growing Neural Gas (GNG) algorithm constitutes an incremental neural network model based on the idea of a Self-Organizing Map (SOM), that is, unsupervised learning algorithms that reduce the dimensionality of datasets by locating similar samples close to each other. The design of an electric vehicle charging network is an essential aspect in the transition towards more sustainable and environmentally friendly mobility. The need to design and implement an efficient network that meets the needs of all users motivates us to propose the use of a model based on GNG-type neural networks for the design of the network in a specific geographical area. In this paper, a variant of this iterative neural network algorithm is used with the objective that, from an initial dataset of points in the plane, it calculates a new simplified dataset with the main characteristic that the final set of points maintains the geometric shape and topology of the original set. To demonstrate the capabilities of the algorithm, it is exemplified in a real case, in which the design of an electric vehicle charging network is proposed. This network is built by applying the algorithm, taking as the original set of points the ones formed by the nodes of the gas station network in the geographical area studied. Several tests of running the algorithm for different sizes of the final dataset are performed, showing the differences between the original network and the computationally generated one.

The effect of guided inquiry-based virtual and physical laboratories on science learning outcomes

ABSTRACT Background Developments in technology have rapidly advanced the variety, features, and applications of virtual labs (VL) in teaching. However, it is debated whether VL can be used instead of physical labs (PL) because more is needed to address the tactile and sensory senses and to gain motor skills that PL has. While many studies have compared VL and PL, few have examined the impact of these laboratories using contemporary learning methods. Purpose This study aims to comparatively examine the effects of guided inquiry-based learning of electrical circuits and Ohm’s law in VL and PL on the conceptual understanding, science process skills, problem-solving, and views of pre-service science teachers (PST). Sample The study was carried out with 26 PSTs studying in the first year of a science teaching program at a state university in Türkiye. Design and Methods The study is in a true experimental design with the pretest-posttest control group. The matched pairs of PSTs were randomly assigned to the VL (n = 13) and PL (n = 13). Three-tier simple electrical circuits concept test, science process skill test in multiple formats, problem-solving inventory, and open-ended questionnaire were used for completing the data collection process. Results Results revealed that guided inquiry-based VL and PL were equally effective in increasing PSTs’ conceptual understanding. While VL is more effective in completing knowledge gaps, PL is more effective in developing science process skills. Neither has a significant effect on problem-solving. Conclusions Both VL and PL have advantages and disadvantages in terms of the types of learning outcomes. Consequently, guided inquiry-based VL and PL are recommended to use in science teaching as complements to each other.

Short-term Load Forecasting using Genetic Algorithm based Artificial Neural Network

The electrical load Forecasting is considered one of the main and important points for the planning for the future in the electric power networks in both of transmission and distribution systems. In this paper, the genetic optimization algorithm (GA) with the artificial intelligent network (ANN) methods are proposed to forecast the electrical load of the building of Electrical Power Distribution office in the Iraq /Anbar Governorate. Moreover, the optimization method of GA is presented to train the ANN weights using Matlab software with simple programming code. Then, the proposed method is applied to the actual load and the results are compared with that obtained in traditional method such as ANN and regression trees (RG trees) methods. Hence, from the simulation results and the comparison, we can confirm that the proposed method is better than the traditional methods, and it achieved a good agreement with actual load and it provide a good total RMSE of 6% when compared with other methods

An Enhanced State-Space Modeling for Detecting Abnormal Aging in VRLA Batteries

The knowledge of battery aging is an indicator that allows controlling the performance of large battery banks. State of Health (SOH) is typically the metric used, encompassing all possible mechanisms in a percentage indicator, with the Coulomb Counting as the most common method. Hence, an in-depth study of aging based on known models provides proper information for correctly managing batteries. This article proposes an aging-sensitive 3-RC-array-equivalent electrical circuit model to characterize the behavior of batteries throughout their useful life, identifying parametric changes as complementary information to the state of health. This model was validated based on experimental tests with 2 V and 6 Ah VRLA batteries aged according to the manufacturer’s recommended use. The results reveal a proportionality through capacity degradation. Then, a control group of batteries was subjected to overcharge and over-discharge conditions. The information given by Coulomb Counting SOH and the proposed method were evaluated. The proposed method provides additional information to the SOH, enhancing the distinguishing capability between typical aging performance and misused aging performance, resulting in a useful tool capable of identifying the aging associated with parametric changes in a time-invariant system where aging is treated as an imminent multiplicative fault.

Performance of PSO-GWO, VAGWO, and GWO optimization techniques for economic dispatch problem: studied case of the southeast algerian electrical network

Economic dispatch (ED) aims to identify the most cost-effective strategy for allocating power generation while meeting demand and adhering to the physical constraints of the power system. In this paper, three algorithms are proposed to solve the ED problem in power systems, including a hybrid Particle Swarm Optimization with Grey Wolf Optimizer (PSO-GWO), modified Velocity Aided Grey Wolf Optimizer (VAGWO), and Grey Wolf Optimizer (GWO). PSO is a meta-heuristic optimization technique designed to find the optimal solution to a problem by guiding the movement of particles within a defined exploration space. GWO is also a meta-heuristic optimization algorithm based on the natural behavior of grey wolves. In this paper, to enhance the performance of GWO, it is first combined with the PSO method. VAGWO is employed to refine the GWO formulation by optimizing the convergence steps. At the beginning of the iterations, the distance between the solutions (wolves) is increased to promote exploration of the search space. As the iterations progress, the step size is reduced, allowing for a more precise and efficient convergence toward the optimal solution. These algorithms will be implemented in Matlab software to minimize the fuel cost production of the following test networks: IEEE 30-bus network, Algerian 114-bus network, and Southeast Algerian network. These methods show that the PSO-GWO algorithm offers better results when compared through VAGWO and GWO.

Energy Storage Solutions to De-Carbonize the Electric Supply in Jordan

This paper aims to estimate the size of Energy Storage Systems (ESS) required de-carbonizing the electrical network in Jordan. Load profile in addition to the PV and Wind energy profiles were studied and used as input data to the simulation model. Different cases of RE penetration levels as a percentage of load demand were considered and entered to the simulation model as well. Other factors, such as allowed over-generation and ESS efficiency also provided and calculations carried out accordingly. In addition to the technical consideration, an economic analysis approach added to the study. Results show that staying with the current level of RE penetrations, i.e. at PV 11% and W 9% (RE level 20% of load demand) and 5% curtailment, no need to install ESS to absorb the RE intermittency and variability. As RE penetration level increases, the need for ESS becomes necessary, i.e. to reach 100% RE penetration and de-carbonize the electrical network accordingly, an ESS of 138,900.00MWH/ 7,386.57MW PHES would be required at PV 65% and Wind 35% penetration level and 25% curtailment with initial CAPEX investment of JOD 15,367,314,444.10. Economic feasibility study shows that with the current RE energy prices and ESS CAPEX, reaching the 100% RE penetration level would not be the optimum solution as there will be a negative cost impact, unless these prices reduced to a certain level making the solution feasible.

Improving quality of electricity generated by grid-tied inverters in solar power plants in low generated power mode using frequency adaptive PWM

Abstract The article presents the results of a study of the dependence of the quality indicators of electricity generated by solar power plants in three-phase electrical networks on the level of generated power. In the course of the study, it was established that when the generated power is reduced, the grid voltage inverters that are part of the solar power plants cause an increase in the share of higher harmonic currents to the electric grid. An analysis of international standards regarding the quality requirements of the generated current was performed, which showed that when the level of generated power decreases, the quality of electric energy decreases and may not meet the requirements of the standard. The algorithm of frequency-adaptive pulse-width modulation is proposed, which allows to improve the quality indicators of the electricity generated by the grid inverter of the voltage of the solar power plant to the electrical grid.

Smart Grid in the Arctic City

This article presents the evolution of a smart grid in an artic city, and we analyze its development using the Smart Grid Reference Architecture (SGAM) and from historical, technological, and social points of view in an Arctic context. We illustrate the emergence of smart grid application and its relation to energy consumption habits and sustainability. The study includes observations from empirical research conducted using a mixed methods approach. This included two months of organizational ethnographies consisting of interviews with specialists at an electricity utility, shadowing the workers, participant observation in the control center, and a questionnaire survey among local residents. Three stages of transformation in the electricity network were observed: 1) the conversion from analog to digital in network operations, with residents as passive receivers of electricity; 2) introducing digital communications and a digital local area network that allowed residents to report consumption; 3) the electrical network as an evolving smart grid with a digital platform enabling two-ways communication among network actors. People became more active when smart grid applications were introduced in the electricity network and middle-aged men can now better manage their energy consumption, even though their motivation is financial, rather than environmental. At the same time, unplugging from the smart grid becomes increasingly difficult.

Cloud Network Collaborative SFC Deployment Under Heterogeneous Resource Of Power White Box Network

The construction of new electric power systems has led to a multiplication in the number of access nodes for electric power IoT information communication, accompanied by a deepening trend in business cloudification. However, due to the insufficient resource linkage and regulation capabilities of the electric power communication network on the cloud side, network side, and edge side, coupled with the lack of a convenient and unified cloud-network resource convergence and control mechanism, the existing electric power communication architecture is gradually finding it difficult to fully meet the demand for deterministic multi-service bearer. Therefore, it is necessary to evolve the original network equipment within the network toward a white box model, opening up interfaces for fine-grained sensing and full-area control, in order to provide more accurate and controllable network transmission services. In this paper, we first propose a cloud-network cooperative resource scheduling architecture for the electric power white box network. This architecture establishes a bearer network to achieve high consistency and cooperativeness of cloud-side end computing power through white box switches, uniformly schedules the global resources of the cloud-network convergence network, and improves the network service quality between cloud-side and side-side. Furthermore, we design a multi-dimensional re-source scheduling method for cloud-network synergy in the electric power white box network. This involves constructing a white box network virtualized heterogeneous resource (VHR) model and a heterogeneous resource control flow (HRCF) model. We transform the problem of merged deployment of service function chains (SFCs) into an SFC merger deployment problem by jointly controlling heterogeneous information, communication, storage, and arithmetic resources, along with the white box switch-specific pipelining resources in the white box network. We then carry out merger optimization of the same kind of function chains. Additionally, we propose a G-GS algorithm based on heuristic methods. Simulation results demonstrate that the method presented in this paper can significantly reduce the cost consumption of white box pipeline resources in the electric power white box network, thereby reducing the processing delay of SFC and improving the quality of electric power service bearing.

Power domination in generalized Mycielskian of spiders

The power domination problem in the graph was introduced to model the monitoring problem in electric networks. It is to find a set of vertices with minimum cardinality, called a power dominating set (PDS), that monitors the whole set $ V $ after applying the rules, domination and propagation. In this paper, we discuss the power domination problem in generalized Mycielskian of spiders. We characterize spiders with $\gamma _P(\mu _m(T))= 1$. If $\gamma _P(\mu _m(T))\neq 1 $ and $ m $ is even then $ \gamma _P(\mu _m(T))=\frac {m}{2} +1$. We also show that if $ m $ is odd and $ \gamma _P(\mu _m(T))\neq 1 $, then $ \lceil \frac {m+1}{2}\rceil \leq \gamma _P(\mu _m(T))\leq \lceil \frac {m}{2}\rceil +1$.

Truncation Error of the Network Simulation Method: Chaotic Dynamical Systems in Mechanical Engineering

This article focuses on the study of local truncation errors (LTEs) in the Network Simulation Method (NSM), specifically when using the trapezoidal method and Gear’s methods. The NSM, which represents differential equations through electrical circuit elements, offers advantages in solving nonlinear dynamic systems such as the van der Pol equation. The analysis compares the performance of these numerical methods in terms of their stability and error minimization, with particular emphasis on LTE. By leveraging circuit-based techniques prior to numerical application, the NSM improves convergence. This study evaluates the impact of step size on LTE and highlights the trade-offs between accuracy and computational cost when using the trapezoidal and Gear methods.

Two-step nested optical-electrical Monte-Carlo approach to analyze the influence of tolerances on Micro-CPV module performance

In manufacturing and product optimization, understanding the influence of tolerances, which are inevitable variations in production processes, is crucial for enhancing performance while managing costs. However, previous analytical approaches lacked the capability to quantitatively assess the cumulative effect of multiple tolerances due to their random combination and statistical independence. In this work, we introduce a novel method that overcomes these limitations by effectively modeling complex dependencies among tolerances through a two-step nested Monte-Carlo approach. We apply this model to a micro-CPV module developed at Fraunhofer ISE. First, we randomly select and combine tolerances in a cell-lens unit using ray tracing. Then, we randomly select and combine these units in a full 690-cell module using an electrical network model considering different angles of incidence. The considered tolerances include deviations in component geometries and displacements and are based on measurements. The model predicts the acceptance angle and allows to identify the optimal interconnection schemes. Further, it is capable to determine the maximum tolerances permissible for maintaining a certain module power. While tolerances lead to a distribution in current generation among the cell-lens units, we find that parallel interconnections can compensate for such variations. Further, we identify that the positions of secondary lens and micro solar cell are the most sensitive parameters for achieving high module power. These findings are crucial for refining module design cost-effectively. Moreover, the model facilitates a quantitative assessment of optimization potentials, guiding decision-making in product development and manufacturing, and a techno-economic optimization.

Temporal asynchrony analysis for dynamic operation of hydraulic-thermal-electricity multiple energy networks based on holomorphic embedding method

Analyzing the operational states of multiple energy networks (MEN) in multi-energy systems is crucial for ensuring system stability. The dynamic operational characteristics of different energy flows pose challenges for computational analysis. Traditional steady-state methods are inadequate for addressing the dynamics of MEN, especially when dealing with temporal discrepancies between hydraulic and thermal flows in thermal networks (TN) and the heterogeneity between TN and electrical networks. Therefore, this paper proposes a novel holomorphic embedding method (HEM) based on multi-stage decomposition method. The developed HEM constructs a time coefficient matrix and utilize inner-outer loop recursion to handle the time lag between thermal flow and hydraulic flow in the TN. Additionally, we reconstruct a holomorphic matrix, integrating hydraulic flow to bridge thermal and electric power flows, thereby improving the operational heterogeneity among different networks. Real-case simulations show that when the Taylor expansion order in HEM is equal to 4, the proposed method achieves a mere 1% discrepancy from actual operational data, enhancing computational efficiency by 60% compared to the Newton-Raphson method. Moreover, in this real-case, the TN exhibits a maximum delay response time of 180s compared to electrical networks. Exploiting this delay time effectively increases renewable energy generation within multi-energy systems by 961.58kW per day.

Multi-type energy conversion for managing the consumption by enhancing the resiliency of electrical distribution networks

The climatic circumstances of the world have altered due to the world warming up, and this issue has increased high-impact and low-probability (HILP) events more than before. Supplying energy requirements has turned into one of the main challenges of utilities especially electrical distribution companies considering the frequency and intensity of HILP events. On the other hand, developments in storing electricity have varied expectations and will change the solutions leading to resilient electrical distribution networks (EDNs). Some researchers have studied and analyzed numerous aspects of resilient EDNs but hybridization of different types of energy storage systems (ESSs) has not evaluated before. This paper considers energy management of emergency-operated EDNs equipped with two different types of energy storage systems which are batteries and flywheels. Convex equations in all parts of the problem, including different types of energy storage systems are proposed and modeled as an MIQCP to optimize the resilient networks considering all limitations. The proposed framework is developed in GAMS software and the results are provided in the form of Pareto optimal solutions. Applicability of the conducted model is evaluated by the IEEE 33-bus test system aiming at outstanding the effects of flywheels in improving the resiliency of electrical distribution networks. The proposed model analyzed by various energy storing scenarios based on technical and economical limitations. Results showed that among the considered case studies, the 50 % of the cases included with flywheel while batteries participated in 30 % that were the most expensive ones. On the other hand, the lowest amount of objective function belongs to the case that is only included with flywheels. Accordingly, by considering flywheels as a newly born energy storage system in the emergency-operated EDNs, the flexibility of energy management is facilitated and can be developed economically.

СИНХРОНИЗАЦИЯ ПОДАЧИ СЕМЯН И РАБОЧЕЙ ЖИДКОСТИ В КАМЕРУ СМЕШИВАНИЯ ПРОТРАВЛИВАТЕЛЯ

The results of production tests of the PS-20K-4 seed dresser with a modified electrical control circuit in order to synchronize the supply of seeds and working fluid to the mixing chamber of the treater when it is put into operation are presented. At each start-up of the dressers, the supply of seeds to the chamber is ahead of the supply of working fluid to it due to the inertia of the working bodies of the hydraulic system, especially this is manifested on dressing machines equipped with centrifugal pumps for supplying working fluid (PS-20K-4, PS-22, etc.). To ensure the synchronization of the supply of seeds and working fluid to the mixing chamber, a device is proposed, the main part of which is the PVI-11 time delay relay for switching on the seed disc drive. Before starting the dresser, the time of movement of the liquid from the pump to the sprayer is determined. For the time obtained in this way, a delay is set on the PVI-11 attachment. Production tests have shown the advantage of the treater with changes made to the electrical control circuit. With the factory control scheme of the treater, the total weight of untreated seeds and seeds with reduced completeness of treatment, which are defective, increases with the increase in the installed capacity of the unit from 5 to 20 t/h, respectively from 26.4 to 48 kg at the beginning of each machine start-up. With the established synchronization of the supply of seeds and working fluid to the mixing chamber when starting the dressing machine, there were no defective seeds. Operational, technological, technical and economic evaluations of the unit showed that the shift time utilization factor was τ_c = 0.83, Shift productivity increased by 25 % due to a reduction in the time for additional treatment of untreated and insufficiently treated seeds. The indicators of technical and operational reliability were Ktn = 0.98 and Ken = 0.94, respectively. Reduced costs by 35%, reducing labor costs by 25%. The annual economic effect with 140 hours of operating amounted to 29915.6 thousand rubles.

Исследование влияния отклонений параметров моделей синхронных генераторов на результаты расчета динамической устойчивости электроэнергетической системы

One of the important tasks solved in planning the electric power system (EPS) operation is the assessment of its transient stability. Use of incorrect parameters for EPS element models, in particular synchronous generators (SG), contributes to the occurrence of errors when determining the operating conditions of power systems, and, therefore, can lead to a decrease in the reliability of EPS operation. This article is devoted to the study of the impact of errors in SG model parameters on the transient stability assessment results. The analysis presented in this article allows us to substantiate the relevance of the SG parameter estimation task using a “passive experiment”, such as synchronized phasor measurements. This research is based on the theory of electrical circuits, mathematical description of electrical machines and transients in EPS, as well as statistical analysis. Computational experiments have been carried out in the software package MATLAB using the simulation modeling environment “Simulink”. During the research, two different multi-machine EPS models have been used, including the well-known “IEEE 9-bus” system. To quantitatively assess the influence of SG parameter uncertainties on transient stability analysis, the values of the maximum permissible fault clearance time are determined, as well as the values of the maximum power output of generating equipment in prefault conditions. The authors have carried out a comprehensive analysis of the influence of deviations in SG model parameters on the maximum fault clearance time, as well as on the maximum power output of generating equipment in the prefault conditions for a given fault duration. During the study, two approaches have been considered; in each case, the EPS transient stability assessment has been performed almost automatically, and quantitative indicators have been computed for the influence of SG model parameter deviations on the outcome of EPS transient stability analysis. Analysis of the computational experiment results has demonstrated a significant influence of the SG model errors on the errors in calculating certain transient stability-related parameters. For the considered scenarios, the spread of errors in calculating the maximum fault clearance time reached 100 % relative to a base-case (errorless) result, and the error in calculating the maximum power output of generating equipment in prefault conditions for a given fault duration may reach 20 % relative to the errorless result.

Identification of characteristics of conceptual prototype of microprocessor resource-saving relay protection system

The object of the study is the conceptual prototype of a microprocessor resource-saving relay protection system. Currently, relay protection ensures electrical networks reliable and efficient work, however, the traditional architecture is proprietary, not allowing to fix and replace damaged parts without a company specialist. Therefore, the open-architecture relay protection is a very pressing issue, but the problem lies in meeting the relay protection requirements. The data transmission protocols nRF and ESP-NOW, Hall sensors evaluation for AC current measurement determination and sensor accuracy improvement was implemented. Experimental validation demonstrated that nRF and ESP-NOW protocols meet the delay and reliability requirements, however, the nRF protocol is more suitable due to its flexibility and obstacle penetration. The data demonstrated that the most effective conditions are without obstacles at 15 meters from the modem and with obstacles 5 meters from the modem. The experiment of Hall sensors characteristics determination demonstrated the accuracy of current measurement with the set values of the opening and closing currents. Nevertheless, it is not accurate (12.45 %) for the relay protection application. Therefore, the application of the changing values of the opening and closing currents is more effective and accuracy reaches 6.92 %. As a result, the service life of the Hall sensor was determined, and even after 10 million openings, the open state time remained unchanged. Therefore, the approximation function for current amplitude determination depending on open state time was found. On the other hand, Hall sensors may suffer from temperature drift and require further optimization to be fully reliable. The study limitation is the current range from 0 to 800 A

Power Quality State Estimation for Distribution Grids Based on Physics-Aware Neural Networks—Harmonic State Estimation

In the transition from traditional electrical energy generation with mainly linear sources to increasing inverter-based distributed generation, electrical power systems’ power quality requires new monitoring methods. Integrating a high penetration of distributed generation, which is typically located in medium- or low-voltage grids, shifts the monitoring tasks from the transmission to distribution layers. Compared to high-voltage grids, distribution grids feature a higher level of complexity. Monitoring all relevant nodes is operationally infeasible and costly. State estimation methods provide knowledge about unmeasured locations by learning a physical system’s non-linear relationships. This article examines a new flexible, close-to-real-time concept of harmonic state estimation using synchronized measurements processed in a neural network. A physics-aware approach enhances a data-driven model, taking into account the structure of the electrical network. An OpenDSS simulation generates data for model training and validation. Different load profiles for both training and testing were utilized to increase the variance in the data. The results of the presented concept demonstrate high accuracy compared to other methods for harmonic orders 1 to 20.

Spark: A Statistical Comparison and Evaluation of Classification Algorithms for Fault Prediction in Electrical Secondary Distribution Network

Managing faults in the electrical secondary distribution network is a challenging task given the nature, size, and complexity. Predicting faults early before they occur helps in increasing the safety and reliability of the power distribution system. Various statistical and machine learning techniques are being used to predict different types of faults. This study applies classification algorithms available in the big data framework Apache Spark through its python interface PySpark to predict electrical secondary distribution network faults. The study evaluates and compares nine algorithms: Decision tree, Gradient-boosted tree, Logistic regression, Naïve Bayes, Multilayer perceptron, Random forest, Linear Support Vector Machine, One-versus-rest and Factorization machines. The research uses Friedman’s test followed by the Nemenyi post hoc test to find the significance of performance differences among the algorithms. The results show significant differences among the algorithms. Gradient-boosted tree and One-versus-rest with Gradient-boosted tree had the best performance for binary and multiclass classification, respectively, while Naïve Bayes had the worst performance.