Electrical Engineering Problems Research Articles

Optimal coordination of directional overcurrent protection relays using hybrid optimizer

In order to minimize or maximize an objective function, electrical engineering problems might benefit from optimization methodologies inspired by nature. This study employs a hybrid particle swarm optimizer (HPSO) to get rid of local optimum solutions and find the best global one for directional overcurrent protection relays (DOPR) protection. DOPR coordination is a highly constrained, mixed-integral nonlinear optimization problem. An adaptive defense method has been created to counteract the intentionally challenging design. Therefore, design variables such as time multiplier setting (TMS) and plug setting (PS) are utilized for each relay in the circuit. The goal function is to reduce the amount of time spent to trace the fault by all the relays. Reducing the time required for each relay to trace the fault is the aim of function. After analyzing and comparing two test studies of the IEEE benchmark using various optimization strategies, we found that the results for the IEEE 15-bus system has minimum time of 8.9 s, which is improved by 22.92% to 82.89%, and those for the IEEE 30-bus system minimum time of 22.45 s that is improved by 4.41% to 39.6%. In terms of overall DOPR operation and computational time needed to get the global optimum solution, the acquired results are demonstrably superior to those obtained using conventional and state-of-the-art methods.

Mitigation of numerical issues appearing in transient analyses when applying fractional derivative approximations

Due to the potential decrease of the computation time for problems with fractional order derivatives, and due to the extension of the range of applicable solvers for a given problem, the approximation of fractional derivatives (e.g., using Oustaloup’s method) is an important and frequently discussed topic. A significant problem that can occur (although one that is not discussed very often) when approximations are applied concerns the potential numerical issues that can arise when applying these approximations in a transient analysis, e.g., as shown in the paper, in a time-stepping solver. This paper examines five different methods for approximating the fractional derivative (Oustaloup’s method, a refined Oustaloup method, a modified continued fraction expansion method, Matsuda’s method and Charef’s method). An important preliminary step in the analysis was the establishment of the general form of FDAE (fractional differential-algebraic equations), the general form of the applied approximations and the general form of the DAE (differential-algebraic equations) resulting from the approximation. This allowed for a precise description of the equations according to which the transformation from the FDAE to the approximating DAE takes place. Three problems derived from studies in electrical engineering have been introduced into the analysis (a linear circuit problem, a circuit problem with nonlinear fractional elements, and a problem featuring nonlinearities and pregenerated noise). For these problems, the conditions in which numerical issues appear have been studied in detail. They are the main motivation of this study as these issues are so significant that they often result in zero-solutions or solutions tending to infinity (giving an impression as if the system is unstable). Modifications and alternatives of the transformation into the DAE, that aim at the mitigation of these numerical errors, are mentioned later. The final result is very satisfactory, where the algorithm for the transformation of the FDAE to the approximating DAE practically eliminates the most important barriers (which was the impossibility of using approximations above a certain order due to the mentioned numerical issues). The study presented in this paper is motivated by problems in electrical engineering but due to its generality it is also applicable in other fields where fractional calculus is used.

Electrical Engineering and its Aspects in the Modern Concept of Technical Education

This paper focuses on some aspects in the concept of teaching electrical engineering in technical education. It is presented the issues of possible approaches and tools allowing to implement the teaching of electrical engineering in a demonstrative and entertaining way using contemporary means based on digital technologies. In this framework, the possibilities of motivating students are also presented and some findings from selected scientific papers, domestic and foreign research are also summarized.
 The introductory part presents the issues related to the definition of subject or subject didactics of electrical engineering as a prerequisite for successful teaching by teachers. The next part is devoted to a research investigation focused on the application of new approaches to teaching electrical engineering in school practice, the possibility of linking virtual or augmented reality in a demonstrative approach to teaching, the use of online applications to solve electrical engineering problems and thus linking the practical form of electrical engineering with the theory presented in the classroom with the support of digital technologies.
 The conclusion then summarizes the possibilities associated with the teaching of electrical engineering and the search for new paths that are common to both students preparing for the teaching profession and the students who will one day be guided by these teachers through the secrets of electrical engineering as one of the most important fields of human activity.

Physics-Informed Neural Networks for Solving 2-D Magnetostatic Fields

Physics-informed neural network (PINN) has shown great potential in the inverse and parametric designing problems in electrical engineering. Moreover, most existing works on PINN are dedicated to computational fluids, and very few attentions have been paid to static and low-frequency electromagnetic near fields with multiple media in electrical engineering applications. In this work, a PINN for solving two-dimensional (2D) magnetostatic fields in electromagnetic devices and systems is proposed. The magnetic field intensity and the magnetic vector potential are solved by training a neural network which encodes partial differential equations and boundary conditions as residuals. The computation of the spatial derivatives of media constitutive parameters, which negatively impacts the training of PINN, is eliminated. A mesh-assisted non-uniform sampling method for the selection of collocation points is proposed to further improve the performance of PINN. The proposed PINN is verified by comparing its results with those of the finite element method in two 2D magnetostatic case studies. It is expected that this work will promote further applications of PINN in the modelling, numerical analysis, and parametric design of electromagnetic devices and systems.

EXTREME LEARNING MACHINE AND CHAOTIC BASED SPHYRAENA CHRYSOTAENIA OPTIMIZATION ALGORITHMS FOR LOSS LESSENING AND POWER STABILITY MAGNIFICATION

In this paper Extreme Learning Machine and Chaotic based Sphyraena Chrysotaenia Optimization Algorithms are applied for solving the Real Power loss lessening problem. Key objective of this work are Real power loss decreasing, power divergence restraining, and power constancy amplification. Extreme Learning machine and chaotic are integrated in the algorithm to obtain the better solutions. Candidate solutions in the projected Sphyraena Chrysotaenia optimization are Sphyraena Chrysotaenia and population in the inspection region is quixotically enthused. Spasmodically impressive solutions can be erroneous while restructuring the position of inspection agents and renewed positions may be inadequate one than the previous positions so magnificent selection is engaged. Domination comprises recurrence the self-effacing fitting solution to ensuing generation. In Extreme Learning Machine based Sphyraena Chrysotaenia Optimization Algorithm (ELMSC) initial phases of iteration, the Sphyraena Chrysotaenia Optimization Algorithm contestants are diversified in position and exponential standby generates unrestricted impulsive calculations which endow the rudiments to accommodate the entire revelation area. Compatibly, all over end stage of iterations, fundamentals are enclosed by Sphyraena Chrysotaenia Optimization Algorithm contestants and all an optimal condition with equivalent scheme. Chaotic sequences are combined into the Sphyraena Chrysotaenia Optimization Algorithm (CSCO). This amalgamation will augment the Exploration and Exploitation. Tinkerbell chaotic map fabricating tenets are employed. Proposed ELMSC and CSCO are corroborated in IEEE 30, 57, 118, 300, and 354 bus test systems. True power loss lessening, power divergence curtailing, and power constancy augmentation has been achieved. In future proposed ELMSC and CSCO can be applied to solve the others problems in Electrical engineering and also can be applied to resolve the problems in other engineering domains.

A QUBO Formulation for Minimum Loss Network Reconfiguration

We introduce a novel quadratic unconstrained binary optimization (QUBO) formulation for a classical problem in electrical engineering – the optimal reconfiguration of distribution grids. For a given graph representing the grid infrastructure and known nodal loads, the problem consists in finding the spanning tree that minimizes the total link ohmic losses. A set of constraints is initially defined to impose topologically valid solutions. These constraints are then converted to a QUBO model as penalty terms. The electrical losses terms are formulated as QUBO, taking advantage of the relationship between several model variables in order to reduce the overall losses expression. Finally, these terms are added to the model as the objective function to minimize. In order to achieve the best possible performance of solution searching with classical solvers, with hybrid quantum-classical solvers and with quantum annealers, our QUBO formulation has the goal of being very efficient in terms of variables usage. A standard 33-node test network is used as an illustrative example of our general formulation. Model metrics for this example are presented and discussed. Finally, the optimal solution for this example was obtained and validated through comparison with the optimal solution from an independent method.

Event-triggered fuzzy integral control for a class of nonlinear singularly perturbed systems

A fuzzy integral controller with an event-triggered strategy for a class of nonlinear continuous singularly perturbed systems is proposed in this manuscript. Since the singularly perturbed systems are characterized by the parasitic parameter ɛ, which leads the systems to have multi-time-scale dynamics, a state feedback controller is proposed and designed for manipulating the effects of the parasitic parameter with the ɛ free functions by utilizing the Takagi–Sugeno fuzzy model, integral feedback, and event-triggered mechanism. As a result, we demonstrate that the proposed method maintain almost the same performance as the conventional controllers with less steady-state error while producing fewer events, which makes the communication channels between the plant system and the controller more available, through the examples of electrical engineering problems.

USE OF ORDINARY DIFFERENTIAL EQUATIONS APPLIED TO MECHANICAL AND ELECTRICAL PROBLEMS WITH FIRST ORDER LINEAR SYSTEMS

In recent years there has been an increased interest in the study of mathematics applied to engineering to study aspects of its teaching. In the present research work, a compilation of analytical concepts of differential equations applied to the solution of problems of electrical circuits, a branch of electrical engineering and mechanical engineering problems, specifical problems of spring-mass mechanical systems, through the description of the analytical solutions of direct current electrical circuits and spring-mass mechanical systems, applying differential equations. This method stands out among the diverse mathematical treatments used to solve this type of engineering problem, both for its demonstrative elegance and capacity to offer a total answer. Likewise, practical engineering examples related to the studied subject were presented and the results were obtained and analyzed by using the mathematical software, Wolfram Mathematica.

Adjoint variable method for transient nonlinear electroquasistatic problems

Many optimization problems in electrical engineering consider a large number of design parameters. A sensitivity analysis identifies the design parameters with the strongest influence on the problem of interest. This paper introduces the adjoint variable method as an efficient approach to study sensitivities of nonlinear electroquasistatic problems in time domain. In contrast to the more common direct sensitivity method, the adjoint variable method has a computational cost nearly independent of the number of parameters. The method is applied to study the sensitivity of the field grading material parameters on the performance of a 320 kV cable joint specimen, which is modeled as a finite element nonlinear transient electroquasistatic problem. Special attention is paid to the treatment of quantities of interest, which are evaluated at specific points in time or space. It is shown that the method is a valuable tool to study this strongly nonlinear and highly transient technical example.

Relation between the Branch-to-Node Incidence and Triangular Matrices in Radial Distribution Networks

The power flow solution is a classical problem in electrical engineering that has been studied for more than 60 years [1]. One of the most widely used methods corresponds to the Newton-Raphson approach, which is currently employed for analyzing power systems with meshed configurations and multiple generation sources, i.e., it is typically employed for power systems in high-voltage levels [2].

Non-standard analysis in electrical engineering. Transient analysis in second-order electrical circuits with violation of switching laws

For the first time, the authors proposed the use of the mathematical apparatus of non-standard analysis to solve certain non-trivial problems of theoretical electrical engineering. It was established that the axiomatics of non-standard analysis allows to simplify the analysis of transient processes in high-order electric circuits when the switching laws are violated. It is shown that the application of non-standard analysis methods in theoretical electrical engineering provides an opportunity to use the traditional classical method of transient processes analysis of circuits with violation of switching laws. Only by using methods of non-standard analysis, it is possible to strictly prove the fulfillment of the law of energy conservation in such schemes. Also, examples of solving such tasks are given. It is recommended to expand the scope of application of non-standard methods of analysis in problems of various branches of science and technology, which use differential calculus and boundary transitions, and the solution of which is limited or impossible by standard approaches.

Research and Analysis of Small Current Grounding Line Selection System

In the small current grounding system, because the short-circuit fault of single-phase grounding accounts for more than 80% of the total short-circuit fault, the selection of single-phase grounding fault line is still the main problem in electrical engineering. This paper studies the principles, advantages and disadvantages of various line selection technical methods in case of single-phase grounding fault, and puts forward the improved method of comprehensive line selection for the problem of misjudgment of fault line selection. On this basis, it puts forward the improved method of comprehensive line selection, verifies the line selection device of 10kV transmission line through the relevant data obtained from design calculation and simulation experiment, and puts forward a more reasonable design scheme for the hardware circuit and software circuit of the line selection device, The fault tolerance and accuracy of small current grounding line selection system are improved.

Power loss minimization with simultaneous location and sizing of distribution generation units using artificial algae algorithm

Power loss is one <span lang="EN-US">of the important pointers used to measure the performance of distributions networks. Many optimization algorithms have been proposed to solve various optimal power flow problems in Electrical Engineering. In this paper, a novel technique, artificial algae algorithm is developed to robustly detect the optimal location and size of distributed generation (DG) units for minimization of total power losses without violating the equality and inequality constraints. The main objective of optimal power flow (OPF) is to maximize or minimize the objective function using various constraint so that steady-state operation point is achieved. The concept of optimal power flow in power system helps to minimize real power loss. In the proposed approach, various control variables like generator bus, voltage magnitudes, and transformer tap settings are considered. The proposed algorithm is simulated in MATLAB and effectiveness is carried on IEEE 33 bus radial distribution system and satisfactory results are achieved when compared with other optimization techniques. A notable improvement in reduction of active power losses with 3 DG operating at different power factors are 65.5%, 42.4%, and 77.8% respectively, were achieved in comparison to the system without DGs and as compared with other research papers.</span>

Economic load dispatch using memetic sine cosine algorithm.

In this paper, the economic load dispatch (ELD) problem which is an important problem in electrical engineering is tackled using a hybrid sine cosine algorithm (SCA) in a form of memetic technique. ELD is tackled by assigning a set of generation units with a minimum fuel costs to generate predefined load demand with accordance to a set of equality and inequality constraints. SCA is a recent population based optimizer turned towards the optimal solution using a mathematical-based model based on sine and cosine trigonometric functions. As other optimization methods, SCA has main shortcoming in exploitation process when a non-linear constraints problem like ELD is tackled. Therefore, beta -hill climbing optimizer, a recent local search algorithm, is hybridized as a new operator in SCA to empower its exploitation capability to tackle ELD. The proposed hybrid algorithm is abbreviated as SCA-beta HC which is evaluated using two sets of real-world generation cases: (i) 3-units, two versions of 13-units, and 40-units, with neglected Ramp Rate Limits and Prohibited Operating Zones constraints. (ii) 6-units and 15-units with Ramp Rate Limits and Prohibited Operating Zones constraints. The sensitivity analysis of the control parameters for SCA-beta HC is initially studied. The results show that the performance of the SCA-beta HC algorithm is increased by tuning its parameters in proper value. The comparative evaluation against several state-of-the-art methods show that the proposed method is able to produce new best results for some tested cases as well as the second-best for others. In a nutshell, hybridizing beta HC optimizer as a new operator for SCA is very powerful algorithm for tackling ELD problems.

Convex invertible cones and Nevanlinna-Pick interpolation: The suboptimal case

Nevanlinna-Pick interpolation developed from a topic in classical complex analysis to a useful tool for solving various problems in control theory and electrical engineering. Over the years many extensions of the original problem were considered, including extensions to different function spaces, nonstationary problems, several variable settings and interpolation with matrix and operator points. Here we discuss a variation on Nevanlinna-Pick interpolation for positive real odd functions evaluated in real matrix points. This problem was studied by Cohen and Lewkowicz using convex invertible cones and the Lyapunov order, but was never fully resolved. In this paper we present a solution to this problem in a special case that we refer to as ‘suboptimal’ based on connections with the classical case. The solution requires a representation of linear matrix maps going back to R.D. Hill and an analysis of when positive linear matrix maps are completely positive, on which we reported in earlier work and which we will briefly review here.

Electrical Automation Engineering Based on AI Technology

In the research of electrical automation engineering, AI technology has been widely used in industrial production and scientific research. It can help people do deeper and higher quality work, and also improve our ability to develop artificial intelligence systems. Therefore, this paper designs an electrical automation engineering system based on AI technology. Firstly, this paper introduces the concept and composition of electrical automation engineering, then expounds the definition, characteristics and classification of AI technology, then studies the existing problems of electrical automation engineering, designs the electrical automation engineering system framework based on AI technology, and tests the function of the system. Finally, the test results show that in terms of the stability of each module function of the system, the shortest stability time is at least 24 hours, which can ensure the stable operation of the system. In terms of the time spent connecting to the network, the DNS lookup time is within 3 seconds, the server processing time is within 2-3 seconds, and the content data transmission time is within 4 seconds. This shows that the system can run stably.

Using Synthesis and Analysis for Design in Systems Engineering: an Integrated Approach

ABSTRACTThis article presents a complete and general approach for the preliminary design of systems in the framework of systems engineering by relying mainly on synthesis. It is completed by a validation step through analysis to reduce furthermore the space of admissible solutions. This approach focuses on the modeling of design problems in electrical engineering and of their solving by including non‐functional requirements right from the start through a single process. Starting from the specification of the requirements, the proposed approach develops the construction of synthesis models of design problems and their solving using the DEPS problem modeling language and the DEPS Studio environment based on Constraint Programming and Object‐oriented modeling. Concepts related to this approach include in particular the issue of reusability of models, notions of subdefinite systems and of problem, knowledge and solutions spaces.

A Levy Flight motivated meta-heuristic approach for enhancing maximum loadability limit in practical power system

Most of the practical engineering optimization problems are highly nonlinear, nonconvex, and sometimes discontinuous. Classical optimization techniques, mostly being differential calculus based, either fail to find the optimal solution for practical problems or provide solution after relaxing the nonlinearities. Over the time, population-based meta-heuristic techniques have gained enough popularity among research fraternity due to their unrestricted performance on the nature of the optimization problems. Despite their better performances, they sometimes suffer from the problem of trapping into local optima. Hence, suitable strategies should be adopted to overcome the above said issues. In view of this, a new Levy Flight (LF) based Adaptive Particle Swarm Optimization (APSOLF) technique is designed and proposed in this work to solve complex, nonlinear optimization problems. The performance of the proposed algorithm is gauged by applying it on various mathematical benchmark functions. The technique is also applied to solve the practical electrical engineering problems where the task of proposed algorithm is to optimize the Static Synchronous Series Compensator (SSSC) parameters to improve the Maximum Loadability Limit (MLL) of some standard test power systems viz. IEEE 14, 30, 57, 118 and a practical Indian southern region 205 buses. The results obtained are compared with other variants of PSO. Furthermore, the robustness of the proposed algorithm is tested by performing the statistical analysis (both parametric and nonparametric tests). The results confirm better efficiency, robustness, and consistency of the proposed algorithm. The simulations are performed on MATLAB environment.

A survey on dragonfly algorithm and its applications in engineering

The dragonfly algorithm was developed in 2016. It is one of the algorithms used by researchers to optimize an extensive series of uses and applications in various areas. At times, it offers superior performance compared to the most well-known optimization techniques. However, this algorithm faces several difficulties when it is utilized to enhance complex optimization problems. This work addressed the robustness of the method to solve real-world optimization issues, and its deficiency to improve complex optimization problems. This review paper shows a comprehensive investigation of the dragonfly algorithm in the engineering area. First, an overview of the algorithm is discussed. Besides, we also examined the modifications of the algorithm. The merged forms of this algorithm with different techniques and the modifications that have been done to make the algorithm perform better are addressed. Additionally, a survey on applications in the engineering area that used the dragonfly algorithm is offered. The utilized engineering applications are the applications in the field of mechanical engineering problems, electrical engineering problems, optimal parameters, economic load dispatch, and loss reduction. The algorithm is tested and evaluated against particle swarm optimization algorithm and firefly algorithm. To evaluate the ability of the dragonfly algorithm and other participated algorithms a set of traditional benchmarks (TF1-TF23) were utilized. Moreover, to examine the ability of the algorithm to optimize large-scale optimization problems CEC-C2019 benchmarks were utilized. A comparison is made between the algorithm and other metaheuristic techniques to show its ability to enhance various problems. The outcomes of the algorithm from the works that utilized the dragonfly algorithm previously and the outcomes of the benchmark test functions proved that in comparison with participated algorithms (GWO, PSO, and GA), the dragonfly algorithm owns an excellent performance, especially for small to intermediate applications. Moreover, the congestion facts of the technique and some future works are presented. The authors conducted this research to help other researchers who want to study the algorithm and utilize it to optimize engineering problems.

Coupled and Characteristic Modes of a Wideband Slot Antenna Using FEKO

Coupled Mode Theory (CMT) is a classic model that addresses many diverse problems in physics and electrical engineering. Although CMT is well-established in areas such as directional coupler design, its significance in antenna engineering is less well-recognized. Recently, Characteristic Mode Analysis (CMA) has shown how CMT quantitatively models a wide variety of multi-mode patch structures. In this paper, we extend these ideas to a double-tuned slot antenna utilizing some unique features of the method of moments code FEKO. The modeled slot antenna has a stable radiation pattern throughout its 50% impedance bandwidth. CMA decomposition indicates the antenna consists of two resonators: the slot and a lumped LC resonator that represents the feed probe. The two are electromagnetically coupled to produce broad impedance bandwidth. An equivalent circuit whose values are derived directly from the CMA results is given.