Artificial Electric Field Research Articles

An efficient modified artificial electric field algorithm for solving optimization problems and parameter estimation of fuel cell

The artificial electric field algorithm (AEFA) is a recent physics population-based optimization approach inspired by Coulomb's law of electrostatic force and Newton's law of motion. In this paper, an alternative version of AEFA called mAEFA is proposed to boost the searchability and the balance between the explorations to the exploitation of the original AEFA. To escape dropping on the local points in the mAEFA, three efficient strategies for instance; modified local escaping operator (MLEO), levy flight (LF), and opposition-based learning (OBL), are in conjunction with the original AEFA. The convergence rate will be improved when the best agent is identified; thus, stagnation at a local solution can be efficiently avoided. To assess the performance of the proposed mAEFA, it has been evaluated over the CEC'2020 test functions. Furthermore, a robust methodology based on mAEEA is proposed to identify the best parameters of PEM fuel cell (PEMFC). The model of the PEMFC includes nonlinear characteristics that involve several unknown design variables. Thus, it is challenging to develop an accurate model. There are seven design variables to be tuned to reach the targeted dependable model. Two different types of PEMFCs: NedStack PS6 and SR-12 500 W were used to demonstrate the superiority of the mAEEA. Throughout the optimization process, the unidentified parameters of PEMFC are appointed to be decision variables. But the objective function, which necessary to be least is represented by the SSE between the calculated PEMFC voltage and the experimental one. Nine recent optimizers are used in the comparison with the proposed mAEEA. According to the main findings, the advantage of the proposed mAEEA in determining the best PEMFC parameters is verified compared to the other optimizers. Lowest SSE, lowest RMSE, minimum stranded deviation, maximum efficiency, and high coefficient of determination are achieved by the proposed mAEEA.

Energy saving and management of water pumping networks

The main consumption of energy in water systems is the pumps. Due to the different tariff of energy consumption during the one day, the operation of these pumps should be controlled to minimize their consumption and consequently decrease the cost of operation. This paper utilizes an optimization algorithm to control the on/off operation of water pumps to minimize the cost of energy consumption and number of pump switching of water networks. This objective function is subjected to some optimization and hydraulic constraints such as the tanks upper and lower limits, and water network pressure limit. The proposed methodology is an iterative combination process between an optimization algorithm and EPANet hydraulic simulator where optimization algorithm generates the schedules and the hydraulic simulator is used to check the feasibility of these schedules. The suggested optimization method is the artificial electric field algorithm (AEFA). This methodology is applied to three water networks; EPANet practical example network, Richmond network and a part from Toronto network with a variable energy consumption tariff. The AEFA is tested and trained to select the best values of its controlling parameters for each network. The results show that the energy consumption cost is significantly decreased by the optimal schedules of water pumps. Also AEFA is compared with other optimization algorithms such as the genetic and particle swarm algorithms on the same networks and energy tariff and the results show the superiority of AEFA in the convergence and saving of the cost of energy consumption.

Herbicide/pesticide sensing with metamaterial absorber in THz regime

Terahertz (THz) technology has been attracted great interest in many research areas over the years, especially THz plasmonics for sensing applications since intra- and inter-molecular vibrations are within the THz range. The sensitivity of free-space THz detection can be boosted up by use of metamaterials (MM), which are artificial structures in subwavelength scale of the incident light. These artificial materials present high electric field enhancement and high sensitivity to the change in their surroundings. In this article, residual herbicides/pesticides have been investigated both theoretically and experimentally with a polarization-insensitive THz metamaterial absorber (MMA) composed of dielectric-metal disk antennas. Our THz MMA can detect as small as 5 ppM of commonly used herbicide/pesticide, namely paraquat and glyphosate. The results show that sensitivity is greatly improved by using THz MMA, with the limit of detection (LOD) of these herbicides/pesticides reaching to 5 ppM. These results indicate that THz MMA platform could be a valuable method for highly sensitive THz applications in food quality and safety control. We believe, our sensor platform based on general c-mos technology fabrication could be a potential detection tool for herbicides/ pesticides residues in agriculture and food products.

Artificial electric field algorithm with inertia and repulsion for spherical minimum spanning tree

Artificial electric field algorithm (AEFA) is a potential global optimization algorithm proposed in recent years and has been successfully applied to various engineering optimizations. However, precocious convergence tends to occur when solving complex engineering optimization problems. To avoid premature convergence to some extent, an artificial electric field algorithm with inertia and repulsion (IRAEFA) is proposed. The IRAEFA algorithm introduces the inertia mechanism and the repulsion between charges, expands the search space, increases the diversity of population, balances the exploration and development ability of the algorithm, and avoids the algorithm falling into the local optimal solution. Finally, the IRAEFA algorithm is used to solve the spherical mining spanning tree (MST) problem, and the results obtained are compared and analyzed with the results of other well-known metaheuristics optimization algorithms. Experimental results show that the proposed algorithm has better performance than other algorithms in solving spherical MST problems.

Optimal Design of an Isolated Hybrid Microgrid for Enhanced Deployment of Renewable Energy Sources in Saudi Arabia

Hybrid microgrids are presented as a solution to many electrical energetic problems. These microgrids contain some renewable energy sources such as photovoltaic (PV), wind and biomass, or a hybrid of these sources, in addition to storage systems. Using these microgrids in electric power generation has many advantages such as clean energy, stability in supplying power, reduced grid congestion and a new investment field. Despite all these microgrids advantages, they are not widely used due to some economic aspects. These aspects are represented in the net present cost (NPC) and the levelized cost of energy (LCOE). To handle these economic aspects, the proper microgrids configuration according to the quantity, quality and availability of the sustainable source of energy in installing the microgrid as well as the optimal design of the microgrid components should be investigated. The objective of this paper is to design an economic microgrid system for the Yanbu region of Saudi Arabia. This design aims to select the best microgrid configuration while minimizing both NPC and LCOE considering some technical conditions, including loss of power supply probability and availability index. The optimization algorithm used is Giza Pyramids Construction (GPC). To prove the GPC algorithm’s effectiveness in solving the studied optimization problem, artificial electric field and grey wolf optimizer algorithms are used for comparison purposes. The obtained results demonstrate that the best configuration for the selected area is a PV/biomass hybrid microgrid with a minimum NPC and LCOE of $319,219 and $0.208/kWh, respectively.

Minimizing the Total Cost of WAMS Using Artificial Electric Field Algorithm

Wide area monitoring system (WAMS) is one of the most important technologies in modern power systems, it gathers the synchronized time stamped measurements from wide area across power system through a high reporting measuring device such as synchronized phasor measurement units (PMUs).Due to the high cost of PMU itself and its installation cost, it is often difficult to install PMU at each bus in the power system. However, there are several approaches to solve the optimal PMU placement (OPP) with various optimization algorithms. In this paper, a realistic cost model has been proposed to provide optimal PMUs placement (OPP). The proposed model has been solved using artificial electric field algorithm (AEFA), which is a novel optimization algorithm, it was proposed by 2019. In this paper, a binary form of AEFA has been proposed to solve the proposed cost model. AEFA has been implemented in MATALB programming environment, and the results demonstrate that the proposed cost model provides a realistic view of WAMS cost. Additionally, the results have been compared with other optimization algorithm, and it demonstrates that the AEFA is efficient to solve the OPP problem.

Performance optimization of multiple stage evaporator using interior-point method and metaheuristic approaches in environment of real-time plant complexities

ABSTRACT This paper aims to maximize the energy efficiency of Multiple Stage Evaporator (MSE) to increase the solid concentration of the black liquor in the Kraft recovery process. The incorporation of different energy reduction schemes ensures a considerable magnification in energy efficiency, which is realized by developing a set of nonlinear energy models of the MSE. These models are optimized to attain the optimum process parameters that estimate the optimum energy efficiency in Steam Economic (SE) and Steam Consumption (SC). The formulated optimization problems are efficiently solved using three novel approaches, Interior Point Method, Particle Swarm Optimization, and Artificial Electric Field Algorithm. With these algorithms’ advent, the obtained results are approaching efficient outcomes (SE increased by 77.58% and SC decreased by 26.99% compared to real-time plant data). Further, the present work investigates the influence of Boiling Point Elevation and Fouling’s effect on energy efficiency, which causes a reduction in SE (7.189). Finally, this study extends up to utilizing the waste steam as a heat source, collected from the condensate to accelerate the evaporation process, enhancing the SE by 2.94%.

Artificial electric field and electron hydrodynamics

The authors study how an artificial electric field generated by electron-electron interaction or electron-phonon interaction modifies electrons' hydrodynamic properties in an inversion-breaking system.

A multi-objective artificial electric field optimization algorithm for allocation of wind turbines in distribution systems

This paper presents wind turbine allocation in distribution systems to reduce active power loss and voltage deviations using a multi-objective Artificial Electric Field Algorithm (MOAEFA). The proposed method is a mathematical algorithm which is suitably capable to find optimal solutions based on the Pareto solution set using a fuzzy decision-making method. The proposed problem is implemented on 10, 33 and 69 bus IEEE radial distribution networks. The installation location, size and power factors of wind turbines are determined optimally using the MOAEFA method. Single and multi-objective allocation problem of wind turbines is implemented using AEFA, GWO, PSO and MOAEFA, MOGWO, MOPSO methods. The obtained the results of AEFA method achieves less power loss and voltage deviations compared to the conventional GWO and PSO methods. Moreover, the results of multi-objective fuzzy allocation show that there is a compromise between single-objective results and MOAEFA method provides better performance given the loss power and voltage deviation reduction in distribution networks. Furthermore, MOAEFA method has found a better voltage profile in the allocation of wind turbines in the distribution network compared to the other methods. The performance comparison between MOAEFA method and the previous methods given in the literature verifies the superiority of the MOAEFA method.

Optimal Selection of Features Using Artificial Electric Field Algorithm for Classification

The high-dimensional features in the data may affect the performance of the classification model as all of them are not useful. The selection of relevant optimal features is a tedious task, especially the data in which the number of features is high. This paper proposed a new feature selection (FS) approach based on the artificial electric field algorithm (AEFA) called FSAEFA, to select the most suitable optimal features for classification. The AEFA is an efficient and effective population-based optimization technique inspired by the principle of Coulomb's law of electrostatic force (CLEF) to solve optimization problems. The proposed FSAEFA approach can search the most suited subset of features by attracting the worst individual features towards the best individual features by using the attraction of CLEF. The proposed FSAEFA approach has been evaluated and compared with some other FS approaches on ten publicly available benchmark datasets. The experimental result has been indicated that the performance of the proposed method is superior over the existing methods in most of the cases. The significance of the proposed FS approach along with their counter parts have been statistically measured and compared by using Friedman test and Holm procedure. It has been determined that the proposed FSAEFA approach is found to be more efficient than the existing FS approaches.

Impact of communication time delays on combined LFC and AVR of a multi-area hybrid system with IPFC-RFBs coordinated control strategy

In this paper, the impact of communication time delays (CTDs) on combined load frequency control (LFC) and automatic voltage regulation (AVR) of a multi-area system with hybrid generation units is addressed. Investigation reveals that CTDs have significant effect on system performance. A classical PID controller is employed as a secondary regulator and its parametric gains are optimized with a differential evolution - artificial electric field algorithm (DE-AEFA). The superior performance of the presented algorithm is established by comparing with various optimization algorithms reported in the literature. The investigation is further extended to integration of redox flow batteries (RFBs) and interline power flow controller (IPFC) with tie-lines. Analysis reveals that IPFC and RFBs coordinated control enhances system dynamic performance. Finally, the robustness of the proposed control methodology is validated by sensitivity analysis during wide variations of system parameters and load.

Hybrid artificial electric field algorithm for assembly line balancing problem with equipment model selection possibility

Existence of different models of equipment of the tasks of an assembly line has two major direct effects on the task operational times, and the equipment purchasing cost. This issue is reflected for the first time in this study in a straight assembly line balancing problem with single model product. The problem is formulated first as a non-linear model and then is linearized. Because of the NP-hard nature of the problem some meta-heuristic algorithms are proposed to solve it efficiently. For this aim a complete encoding–decoding​ scheme and the artificial electric field algorithm as a most recent algorithm of the literature and also the simulated annealing algorithm are considered. In order to fit to the problem of this study, some modified and hybrid versions of these algorithms are proposed and are tuned by the Taguchi experimental design method. A very deep computational experiments using 24 modified test problems of the literature show the superiority of the artificial electric field algorithm when is hybridized by the simulated annealing algorithm and a local search phase.

Day-ahead optimal reactive power ancillary service procurement under dynamic multi-objective framework in wind integrated deregulated power system

This paper deals with the investigation of a day-ahead reactive power ancillary service procurement problem to minimize cost and voltage deviation under wind power generation uncertainties in a pool-based deregulated system. This reactive power procurement problem is formulated as a dynamic bi-objective optimization problem and is solved using a developed Pareto-based multi-objective artificial electric field algorithm (MO-AEFA). The developed MO-AEFA utilizes nondominated sorting principle and an external archive to store Pareto optimal solutions. Sign and reorder mutation operators are used to avoid trapping in local optima and enhance population diversity. The numerical results obtained from MO-AEFA are compared with other algorithms to validate the efficacy of proposed approach. The optimization algorithm utilizes a fast Newton power flow employing sparse matrix techniques to diminish computational burden. The capacitor switching is decided with consideration of marginal prices. The proposed methodology has been tested on modified IEEE 30-bus and IEEE 118-bus test systems. The performance of both test systems is analyzed for two studies namely, VAr dispatch without wind integration, and VAr dispatch under wind integration. The analysis with wind integration is further investigated for different wind penetration levels. The convergence characteristic of Pareto solutions is measured through statistical distance and diversity metrics.

A Novel Hybridized Fuzzy PI-LADRC Based Improved Frequency Regulation for Restructured Power System Integrating Renewable Energy and Electric Vehicles

In this article, an Improved Frequency Regulation (IFR) for an interconnected hybrid power system under a deregulated scenario is proposed. The analyzed test system comprises of a thermal power system, biogas plant, and Distributed Generation (DG). The impact of the integration of Renewable Energy Sources (RES) has been examined by considering DG system with solar and wind power generation. In order to make the system more realistic, appropriate non-linearities are incorporated into the thermal and biogas system. Electric Vehicle (EV) is also employed to take care of some portion of uncontracted demand. A maiden hybridized fuzzy-Proportional Integral (FPI)-Linear Active Disturbance Rejection Control (LADRC) is proposed and successfully implemented for IFR. Also, a new Quasi-Opposition-based Artificial Electric Field Algorithm (QO-AEFA) is proposed to acquire the optimal controller gain parameters of the tested system. A pertinent performance analysis is also examined for other employed algorithm in this study, to figure out the eminence of the proposed control algorithm. A comprehensive examination and comparison of the proposed controller with other controllers shows its effectiveness for the proposed test system. In order to ensure the reliability of the proposed controller, sensitivity analysis is also carried out for system parameters that reveal its robust nature. Further, case studies for random load variations and comparison with the works in previous literature also manifest the puissance of the current research work. Moreover, the improved results under random weather conditions illustrate the effectiveness of the proposed IFR under deregulation.

Optimal Reactive Power Dispatch With Time-Varying Demand and Renewable Energy Uncertainty Using Rao-3 Algorithm

The appropriate control and management of reactive power is of great relevance in the electrical reliability, stability, and security of power grids. This issue is considered in order to increase system efficiency and to maintain voltage under the acceptable value range. In this regard, novel technologies as FACTS, renewable energies, among others, are varying conventional grid behavior leading to unexpected limit capacity reaching due to large reactive power flow. Thus, optimal planning of this must be considered. This paper proposes a new application for a simple and easy implementation optimization algorithm, called Rao-3, to solve the constrained non-linear optimal reactive power dispatch problem. Moreover, the integration of solar and wind energy as the most applied technologies in electric power systems are exploited. Due to the continuous variation and the natural intermittence of wind speed and solar irradiance as well as load demand fluctuation, the uncertainties which have a global concern are investigated and considered in this paper. The proposed single-objective and multi-objective deterministic/stochastic optimal reactive power dispatch algorithms are validated using three standard test power systems, namely IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus. The simulation results show that the proposed optimal reactive power dispatch algorithms are superior compared with two recent algorithms (Artificial electric field algorithm (AEFA) and artificial Jellyfish Search (JS) algorithm) and other optimization algorithms used for solving the same problem.

Developed Approach Based on Equilibrium Optimizer for Optimal Design of Hybrid PV/Wind/Diesel/Battery Microgrid in Dakhla, Morocco

In this paper, a new application of Equilibrium Optimizer (EO) is proposed for design hybrid microgrid to feed the electricity to Dakhla, Morocco, as an isolated area. EO is selected to design the microgrid system due to its high effectiveness in determining the optimal solution in very short time. EO is presented for selecting the optimal system design which can minimize the cost, improve the system stability, and cover the load at different climate conditions. Microgrid system consists of photovoltaic (PV), wind turbine (WT), battery, and diesel generator. The objective function treated in this paper is to minimize the net present cost (NPC), respecting several constraints such as the reliability, availability, and renewable fraction. The sensitivity analysis is conducted in two stages: Firstly, the impact of wind speed, solar radiation, interest rate, and diesel fuel on the NPC, and levelized cost of energy (LCOE) is analyzed. Secondly, the influence of size variation on loss of power supply probability (LPSP) is investigated. The results obtained by EO are compared with those obtained by recent metaheuristics optimization algorithms, namely, Harris Hawks Optimizer (HHO), Artificial Electric Field Algorithm (AEFA), Grey Wolf Optimizer (GWO), and Sooty Tern Optimization Algorithm (STOA). The results show that the optimal system design is achieved by the proposed EO, where renewable energy sources (PV and WT) represent 97% of the annual contribution and fast convergence characteristics are obtained by EO. The best NPC, LCOE, and LPSP are obtained via EO achieving 74327 $, 0.0917 $/kWh, and 0.0489, respectively.

Coordinated SMES and TCSC Damping Controller for Load Frequency Control of Multi Area Power System with Diverse Sources

This paper investigates the load frequency analysis (LFC) of two area interconnected realistic power system with multi-fuel generating units. Each area consists of thermal, hydro and gas power generating plants. A new evolutionary algorithm is proposed, named Hybrid artificial electric field (HAEFA) optimization algorithm and integral square error (ISE) performance index is utilized to find classical PI/PID controller gains. Later, total analysis is carried out in presence of PID an account of its superiority functioning rather than PI. Moreover, the efficacy of the presented algorithm is deliberated by testing on two area conventional power system model of thermal unit with structure of non-reheat turbines and also on sphere benchmark function. As the load variation is dynamic in nature, mitigating the area frequency fluctuations and tie-line power variations could not been fulfilled by primary regulator and secondary controller. Effective governing needs additional devices. Therefore, superconducting magnetic energy storage (SMES) devices are incorporated in both areas in addition to Thyristor controlled series capacitor (TCSC) is connected in tie-line. Results, shows the system performance has been significantly improved with SMES and TCSC in the presence of HAEFA based PID controller. The potency of the HAEFA algorithm is compared with other optimizations covered in literature.

Cancer detection using convolutional neural network optimized by multistrategy artificial electric field algorithm

AbstractRecently, image processing schemes are widely used to improve disease detection performance in many medicinal fields. Cancer is considered as one of the most deadly disease and early diagnosis of cancer is the complicated task in the field of medicine. In this paper, we present the two pretrained convolutional neural network (CNN) based on ensemble models such as VGG19 and VGG16 for cancer diagnosis that classifies both normal and abnormal images. The dilemma associated with CNN hyperparameter tuning complicates while diagnosing cancer. Hence, we propose multistrategy based artificial electric field (M‐AEF) algorithm for hyper‐parameter tuning in CNN thereby finding the optimal values. The exponentially decaying learning rates are more helpful to train CNN and prevent it from a local minimum. Thus, random minority over‐sampling and random majority under‐sampling address the imbalanced issue present in the dataset. The images are obtained from three different datasets namely the Kaggle dataset, International Collaboration on Cancer Reporting (ICCR) dataset, and cancer programming dataset for cancer detection. The experimental results are executed in MATLAB software and various performance analyses are carried out. Finally, the proposed method demonstrated better and higher cancer detection performance than other methods.

Combined frequency and voltage stabilisation of multi-area multisource system by DE-AEFA optimised PID controller with coordinated performance of IPFC and RFBs

This paper introduces a new-fangled coordinated control mechanism for simultaneous stabilisation of frequency and voltage in multi-area system. A new differential evolution- artificial electric field (DE-AEFA) algorithm is suggested for fine tuning of classical PID as regulator. Viability of DE-AEFA based control scheme is established by counterpointing its dynamic responses with other algorithms and also tested on a widely accepted test system of dual area thermal plant with turbines of non-reheat structure. Further, the dynamic responses of the considered system has been examined by placing interline power flow controller (IPFC) in tie-line and redox flow batteries (RFBs) in both the areas. This investigation depicts the superiority of IPFC and RFBs in mitigating frequency, tie-line and voltage deviations by coordinating with the proposed controller. It is deduced that the proposed coordinated control strategy is highly efficient and surpasses prevalent approaches. Eventually, sensitivity analysis is performed to test control strategy robustness.

An Intelligent Model for the Detection of White Blood Cells using Artificial Intelligence

Background and Objective: The automatic detection and counting of white blood cells (WBCs) play a vital role in the diagnosis of hematological diseases. Computer-aided methods are prevalent in the detection of WBCs because the manual process involves several complexities. In this article, a complete automatic detection algorithm to recognize the WBCs embedded in cluttered and complicated smear images of blood is designed.Methods: The proposed algorithm uses the ellipse detection approach to approximate the presence of WBCs in the Blood. A newly designed artificial electric field algorithm with novel velocity and position bound (AEFA-C) is employed for this purpose. The problem of detection of WBCs is transformed into an optimization problem where the random candidate solutions (ellipses) are efficiently mapped. These candidate ellipses are mapped onto the edge map of the smear image, and a complete mapping is obtained using the AEFA-C algorithm.Results: The effectiveness of the AEFA-C based detector is tested over the 60 smear images of the blood, having all the five types of WBCs or leukocytes. The developed algorithm obtained an overall detection accuracy of 96.90%. Further, the robustness test is performed on the same dataset which justifies that the technique can handle the different noises with the detection accuracy of 90.33%. Also, the comparative study of the proposed detection algorithm with the state-of-art detection algorithms is carried out.Conclusions: The experimental results demonstrate the efficiency of the proposed scheme for the detection of the WBCs in terms of detection accuracy, stability, and robustness and its outperformance over the state-of-art algorithms.