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A recently created optimization algorithm named the Dynamic Hunting Leadership (DHL) algorithm was inspired by the leadership tactics used in hunting operations. The foundation of DHL is the idea that successful leadership can significantly increase hunting endeavors. Although DHL has shown to be relatively simple and successful in tackling a variety of practical optimization issues, it has been discovered it suffers with efficiently balancing global exploration and local search phase, particularly in high-dimensional numerical problems and engineering applications. Furthermore, due to drawbacks, it is vulnerable to becoming stuck in local optimal. The present study aims to tackle the aforementioned challenges by introducing a modified variant of DHL, referred to as mDHL, that utilizes the Levy Flight technique as a localized development strategy to augment each hunter’s capacity to track their prey and attain superior optimal outcomes. Moreover, local escape operator and quasi-opposition learning are synergistically incorporated to foster the hunters’ exploration and localized optimal escape techniques. These tactics foster superior knowledge sharing between leaders and hunters, resulting in a harmonious blend of exploration and development capabilities. The mDHL algorithm is shown to outperform existing optimizers across 20 function test suites with varying dimensions from 30 to 200 and CEC 2019 functions. In addition, it has been successfully applied to solve four practical engineering design cases, demonstrating its practicality. The experimental findings indicate a substantial improvement over conventional DHL, emphasizing the potential of mDHL as a competitive and efficient algorithm for addressing engineering and numerical optimization challenges.

Very recently, several novel conceptions of fractional derivatives have been proposed and employed to develop numerical simulations for a wide range of real-world configurations with memory, background, or non-local effects via an uncertainty parameter [0,1] as a confidence degree of belief. Under the complexities of the uncertainty parameter, the major goal of this paper is to develop and examine the Atangana-Baleanu derivative in the Caputo sense for a convoluted glucose-insulin regulating mechanism that possesses a memory and enables one to recall all foreknowledge. However, as compared to other existing derivatives, this is a vitally important point, and the convenience of employing this derivative lessens the intricacy of numerical findings. The Atangana-Baleanu derivative in the Caputo sense of fuzzy valued functions (FVF) in parameterized interval representation is established initially in this study. Then, it is leveraged to demonstrate that the existence and uniqueness of solutions were verified using the theorem suggesting the Banach fixed point and Lipschitz conditions under generalized Hukuhara differentiability. In order to explore the regulation of plasma glucose in diabetic patients with impulsive insulin injections and by monitoring the glucose level that returns to normal in a finite amount of time, we propose an impulsive differential equation model. It is a deterministic mathematical framework that is connected to diabetes mellitus and fractional derivatives. The framework for this research and simulations was numerically solved using a numerical approach based on the Adams-Bashforth-Moulton technique. The findings of this case study indicate that the fractional-order model's plasma glucose management is a suitable choice.

In this article, we investigate some new positivity and negativity results for some families of discrete delta fractional difference operators. A basic result is an identity which will prove to be a useful tool for establishing the main results. Our first main result considers the positivity and negativity of the discrete delta fractional difference operator of the Riemann-Liouville type under two main conditions. Similar results are then obtained for the discrete delta fractional difference operator of the Liouville-Caputo type. Finally, we provide a specific example in which the chosen function becomes nonincreasing on a time set.

We study the monotonicity method to analyse nabla positivity for discrete fractional operators of Riemann-Liouville type based on exponential kernels, where $ \left({}_{{c_0}}^{C{F_R}}\nabla^{\theta} \mathtt{F}\right)(t) > -\epsilon\, \Lambda(\theta-1)\, \bigl(\nabla \mathtt{F}\bigr)(c_{0}+1) $ such that $ \bigl(\nabla \mathtt{F}\bigr)(c_{0}+1)\geq 0 $ and $ \epsilon > 0 $. Next, the positivity of the fully discrete fractional operator is analyzed, and the region of the solution is presented. Further, we consider numerical simulations to validate our theory. Finally, the region of the solution and the cardinality of the region are discussed via standard plots and heat map plots. The figures confirm the region of solutions for specific values of $ \epsilon $ and $ \theta $.

The basic goal of Combined Heat and Power Economic Dispatch (CHPED) is to find the best value for heat obtained from heat generators, power obtained from power generators, and both power and heat obtained from co-generators such that fuel costs are kept minimum while heat and power demands and constraints are met precisely. Based on enhanced discriminatory attribute, a newly Improved version of the Heap-based Technique (IHT) is to increase the searching capacity around the leader position and avoid trapping in a local optimum. Additionally, an adaptive parameter is used linearly to half of the iteration to select an effective operation for creating the new solutions. On 25 benchmark optimizing functions of unimodal or multimodal properties, the efficacy of the proposed IHT in contrast to the traditional HT is tested. Additionally, the proposed IHT in contrast to the traditional HT are employed for CHPED with small scale (seven units), medium scale (twenty-four) and two large-scale (eighty-four and ninety-six) systems with consideration of valve point loading and transmission losses constraints. According to comparisons of results obtained by the IHT with existing approaches, it is shown that the proposed IHT is particularly effective and resilient for finding optimal solutions for the CHPED.

This study proposes a reduced model based on the state space representation for identifying an accurate electric equivalent circuit of Lithium-Polymer Battery Cells. The parameter extraction process is formulated as non-linear optimization problem via three-stage procedure. The first stage estimates the state of charge (SoC) based on the non-linear characteristics associated with the battery current and the initial SoC condition. In the second stage, the open circuit voltage is estimated in terms of the resulted SoC that is employed in the first stage with varied linear and non-linear models. In the third stage, an Equilibrium Algorithm (EA), a recent optimizer, is developed for optimally identifying the battery parameters. The EA’s parameters are adjusted based on Taguchi’s design of experiment approach to reduce the computational time as well as the number of experiments that are required to get the optimum possible parameter arrangement Numerical simulations associated with experimental implementation are emulated on Li-Ion Battery to prove the high capability of the proposed EA an as efficient identification procedure. In Addition, the proposed EA is characterized with high accuracy compared with several recent optimization algorithms for ARTEMIS driving cycle profile. The solution quality improvement of the proposed reduced model is achieved with high closeness to the experimental measurements for battery voltage and SoC. Furthermore, 16 % less computational times 12 % more accuracy are obtained by the proposed reduced model compared with linear and non-linear models.

We study the HIV/AIDS infection dynamics using the new concept of piecewise fractional differential equations in the sense of Atangana–Baleanu derivative. The cases of HIV/AIDS in Indonesia have been utilized to provide better fitting to the real data. We show that the piecewise modeling approach has the better fitting to the cases in the present work compared to the previous case of the model in the Caputo sense. We study that the model is locally asymptotically stable when R0

Many studies have been made on the double-fed induction generator wind turbine system (DFIG-WTS) in recent decades due to its power management capability, speed control operation, low converter cost, and minimized energy losses. In contrast, induction machine control is a more complex task because of its multivariable and nonlinear nature. In this work, a new robust nonlinear generalized predictive control (RNGPC) is developed to maximize the extracted energy from the wind without the use of aerodynamic torque measurements or an observer. The aim of the predictive control is to produce an anticipated impact by employing explicit knowledge of the present condition. By revisiting the cost function of the conventional nonlinear generalized predictive control (NGPC), which is based on Taylor series expansion, in that way, the resilience of the system is improved. An integral action is included in the nonlinear predictive controller. As a result, if the closed loop system is stable, the suggested controller totally eliminates the steady state error, even if unknown perturbations and mismatched parameters are present. The output locating error’s convergence to the source is utilized to show the locked system’s stability. Simulation results demonstrate and verify the efficiency, the good performance, and robustness of this proposed control technique.

In this paper, Three Dimensional Space Vector Pulse Width Modulation (3D-SVPWM) is developed to obtain the performance analysis of a 3-phase matrix converter (MC). MC is used to convert 3-phase fixed AC voltage to 3-phase variable AC voltage, which provides superior performance compared to conventional AC to AC converters. The 9-bidirectional power switches in a 3-phase matrix converter are controlled by appropriate gating pulses utilizing 3D-SVPWM. In conventional AC to AC power converters, the common mode voltage is induced across the load terminal, which causes bearing failure and EMI issues and so leads to increased total harmonic distortion (THD). Implementing the 3D-SVPWM approach with the nearest switching state vector (NSV) selection mechanism alleviates these problems. In proposed system, CMV is reduced to 37 V, which is equivalent to a Vdc/8 time of the supplied AC input voltage and THD of 3-phase to 3-phase MC is reduced to 1.12% for output voltage and 2.18% for output current. The output findings of the simulation and real-time simulator are verified using the Matlab Simulink model and the HIL real-time (OPAL-RT) simulator.

This paper presents a new hybrid algorithm of Harris hawks’ optimization with sequential quadratic programming (HHO-SQP) for optimal coordination of directional overcurrent relays to find optimal relays settings. The SQP procedure is employed in the hybrid algorism as a local search mechanism to enhance the performance of the original HHO method. The optimization problem is described based on a developed objective function as a non-linear and highly constrained optimization problem to minimize the total operating time for primary relays at the same time of maximizing the backup relays operating time. The developed objective function is subject to some constraints related to the coordination process including the absence of any miss coordination between primary and backup relays. The performance of the proposed algorithm based on the new objective function is implemented for two different test systems. The results of the proposed algorithm is compared with those obtained from other recent meta-heuristic techniques. The results show that the new hybrid algorithm outperforms the recently published meta-heuristic algorithms.

Abstract The current paper presents a multi‐objective manta ray foraging algorithm (MO‐MRFA) for efficient operation of hybrid AC and multi‐terminal direct current (MTDC) power grids. The multi‐objective framework aims at achieving economical, technical and environmental benefits by minimising the total production fuel costs, minimising the transmission power losses and minimising the environmental emissions in the AC/MTDC transmission systems. The MRFA imitates three separate independent foraging organisations of the manta rays. It is updated incorporating an additional Pareto archive to preserve the non‐dominated solutions. A dynamic adaptation of the fitness feature is employed by iteratively varying the form of the employed fitness function. Furthermore, a fuzzy decision‐making technique is activated to finally pick the appropriate operating point of the AC/MTDC power grids. The proposed technique is compared with other reported algorithms in the literatures. The applications are conducted on three test systems. These systems are IEEE 30‐bus, IEEE 57‐bus test power systems in addition to real part of the Egyptian grid at West Delta region. Numerical results demonstrate that the proposed MO‐MRFA has great effectiveness and robustness indices over the others. Nevertheless, the proposed MO‐MRFA is successfully extracting several Pareto solutions that meet the techno‐economic requirements with accepted environmental concerns.

In this paper, a wind turbine service sustainability is achieved by assessing grounding grids under steady-state and lightning-based transient conditions. An effective grounding grid is designed concerning IEC61400-24 standard, where Taif city, Saudi Arabia is considered a farming place. However, high soil resistivity is a challenge during the design. To accomplish the study, apparent resistivities are measured using Wenner method, in the field. A framework of multi-layer soil modeling is performed using the Sunde model, and it is optimally solved using the genetic algorithm. Using the optimal stratified soil, the wind turbine grounding system is assessed considering grounding resistances, electric potentials, and step voltages. The break-point effect is evaluated considering the designed grounding system. Then, the lightning-based transient study is investigated for both the healthy grid and break-point considerations. COMSOL Multiphysics and ATP/EMTP were used to accomplish simulations and modeling. The results ensure the effectiveness design of the assessed grounding system.

Proposing a new precise price forecasting method is still a challenging task as electricity price signals generally exhibit various complex features. In this paper, a new approach for electricity price forecasting called hybrid local general regression neural network, and harmony search algorithm (LGRNN-HSA) is proposed. The proposed LGRNN-HSA is developed by combining the coordinate delay (CD) method, local forecasting paradigm, general regression neural network (GRNN) and harmony search algorithm (HSA). The CD is employed in the proposed method to reconstruct the time series dataset. Then the local forecasting paradigm is utilized with GRNN to predict the future price based on the nearest neighbours only so that the shortcomings in global forecasting methods can be overcome. To enhance the forecasting accuracy, the HSA is employed to optimize not only the parameters of local forecasting paradigm but also the smooth parameter which has a great effect on the accuracy of GRNN. In LGRNN-HSA, a different smooth parameter for every training point is utilized instead of utilizing a constant value in GRNN. To verify the forecasting accuracy of the LGRNN-HSA, a real-world price and demand dataset is employed. The simulation results prove that the LGRNN-HSA significantly improved forecasting accuracy compared to other methods.

This paper proposes a modified crow search optimizer (MCSO) for solving the combined economic emission power flow (EEPF) problem. In the proposed approach, the local search ability is enhanced into the crow search optimizer (CSO) and aggregated with a novel bat algorithm (NBA). Close accord between CSO, NBA, and MCSO is employed for solving the single and multi-objective frameworks. Moreover, the proposed MCSO incorporates external archive and dominance comparison to handle multi-objective frameworks while the best compromise solution is extracted by using a fuzzy based mechanism. The proposed MCSO, CSO, and NBA are developed and tested to on IEEE 30 bus and West Delta power grid (WDPG) systems. Added to the that, the proposed methodology is tested on a large-scale power system, IEEE 118-bus test system, for measure the scalability of the proposed method. Their output results are compared with the reported algorithms in the literature to demonstrate the MCSO outperformance in terms of solution quality and robustness. Significant economical solutions of the EEPF problem are achieved with respecting the environment concerns at acceptable emission levels. Added to that, the multi objective framework is assessed with hypervolume indictor that show the high capability of the proposed MCSO compared with CSO.

Nowadays, distribution utilities expend large investments on Distributed System Automation (DSA) based on smart secondary substations at load, capacitor, and distributed generator points with installed automatic sectionalizing switches on their branches. This article addresses the optimal control and operation of distribution systems that minimize the wasted energy and introducing quantitative and qualitative power services to meet consumers' satisfaction. Simultaneous allocations of Distributed Generators (DGs) and Capacitor Banks (CBs) are handled at peak loading condition. Then, the DSA is optimally activated for optimal Distribution Network Reconfiguration (DNR), optimal DGs commitment, and optimal CBs switching for losses minimization in coordination with different loading conditions. Practical daily load variation is applied to simulate the dynamic operation of automated distribution systems. For achieving these targets, the Manta Ray Foraging Optimization Algorithm (MRFOA) is adopted. MRFOA is an effective and simple structure optimizer that emulates three various individual manta rays foraging organizations. The capability of the MRFOA is applied to the IEEE 33-bus, 69-bus and practical distribution network of 84-bus due to the Taiwan Power Company (TPC). A comparison with recent techniques has been conducted to prove the effectiveness of MRFOA. The accomplished results demonstrate that the proposed MRFOA has great effectiveness and robustness among other optimization techniques.

Long-term load forecasting (LTLF) is an essential process for strategical planning of the future needed extension in the power systems of any country. Besides, deep learning has become the heart of the machine learning paradigm, which is wildly used nowadays in many fields, and it also has become the current revolution in Artificial Intelligence (AI). In this paper, an optimized deep learning model based on Stacked Long Short-Term Memory Network (SLSTMN) is proposed. The architecture of the model is optimized to get the best configuration using Genetic Algorithm (GA). In addition, the hyperparameters of the model network are optimized using many deep learning techniques. During the optimization process, hundreds of model configurations are tested. The accuracy of this model is compared with many deep learning models and is compared against the related work in the field of LTLF. The dataset of the South Australia State (SA) power system is used to test the compared models. This data includes maximum daily load, daily maximum temperature, daily minimum temperature, weekday, the month, and holidays for 12 years from 2005 to 2016. SLSTMN achieves excellent accuracy and the lowest error value (almost 1%) when compared with other models on the same dataset and with related work models on different datasets.

All over the world, the operators of the power distribution networks (DNs) are still looking for improving the efficiency of their networks. The performance of DNs and lifetime of its component have been significantly affected by its capability of varying their topologies with accurate load gathering via smart grid functions. This paper investigates making use of the smart DNs features and proposes a model of handling the capability of re-allocating the capacitors integrating with configuring the DNs topology. Using the developed formulation, the efficiency of DNs can be improved not only by minimizing the operational costs related to the network losses but also by optimizing the investment costs associated with capacitor re-allocations. Also, various load patterns are employed in the developed formulation to imitate the daily load variations over a year. The improved sunflower optimization algorithm (ISFOA) is proposed in this paper to get the optimal solution of the presented problem. The standard IEEE 33-node feeder and practical 84-node system of Taiwan Power Company (TPC) are the considered test systems. Besides, the uncertainties due to a distributed generation of wind power are investigated via Monte Carlo simulation involved with the proposed ISFOA. Furthermore, to verify the ability of ISFOA to obtain better solutions compared with different recent optimizers, a statistical comparison is carried out based on a large scale 118-node distribution systems. The simulation results reveal that significant technical and economic benefits are obtained by applying the proposed algorithm with higher superiority and effectiveness.

Cogeneration systems economic dispatch (CSED) provides an optimal scheduling of heat/ power generating units. The CSED aims to minimize the whole fuel cost (WFC) of the cogeneration units taking into consideration their technical and operational limits. Then, the current paper examines the first implementation of dominant bio-inspired metaheuristic called heap-based optimization algorithm (HBOA). The HBOA is powered by an adaptive penalty functions for getting the optimal operating points. The HBOA is inspired from the organization hierarchy, where the mechanism consists of the interaction among the subordinates and their immediate boss, the interaction among the colleagues, and the employee’s self-contribution. Based on the infeasible solutions’ remoteness from the nearest feasible point, HBOA penalizes them with various degrees. Four case studies of the CSED are implemented and analyzed, which comprise of 4, 24, 84 and 96 generating units. The HBOA is proposed to solve CSED problem with consideration of transmission losses and the valve point impacts. An investigation with the recent optimization algorithms, which are supply demand optimization (SDO), jellyfish search optimization algorithm (JFSOA), and marine predators’ optimization algorithm (MPOA), the improved MPOA (IMPOA) and manta ray foraging (MRF), is developed and elaborated. From the obtained results, it is clearly observed that the optimal solutions gained, in terms of WFC, reveal the feasibility, capability, and efficiency of HBOA compared with other optimizers especially for large-scale systems. case

Power system operators and planners have progressively shown an interest in maximizing distribution automation technologies. The automated distribution systems (ADS) provide the capability of efficient and reliable control which require an optimal operation strategy to control the status of the line switches and also dispatch the controllable devices. Therefore, this paper introduces an efficient and robust technique based on Jellyfish Search Algorithm (JFSA) for optimal Volt/VAr coordination in ADSs based on joint distribution system reconfiguration (DSR), distributed generation units (DGs) integration and Distribution static VAr compensators (SVCs) operation. The suggested technique is used for the dynamic operation of ADS in order to minimize losses and reduce emissions when considering regular daily loading conditions. The 33-bus and 69-bus delivery DSs have been subjected to a variety of scenarios. These situations are mostly concerned with achieving optimum distribution system operation and control, as well as validating the proposed methodology. Despite the problem’s complexity, the proposed technique based on JFSA is shown to be the best solution in all of the cases considered. Furthermore, a comparison of the proposed JFSA with other similar approaches demonstrates its usefulness as a method to be used in modern ADS control centers.

This paper presents an Enhanced Marine Predators Algorithm (EMPA) for simultaneous optimal distribution system reconfigurations (DSRs) and distributed generations (DGs) addition. The proposed EMPA recognizes the changes opportunity in environmental and climatic conditions. The EMPA handles a multi-objective model to minimize the power losses and enhance the voltage stability index (VSI) at different loading levels. The proposed EMPA is performed on IEEE 33-bus and large-scale 137-bus distribution systems (DSs) where three distinct loading conditions are beheld through light, nominal and heavy levels. For the 33-bus DS, the proposed EMPA successfully reduces the cumulative losses by 72.4% compared to 70.36% for MPA for the three-loading levels simultaneously. As a result, significant voltage improvement is achieved for heavy, nominal and light loadings to be 95.05, 97, 98.3%, respectively. For the 137-bus DS, it successfully minimizes the losses of 81.16% under small standard deviation 4.76%. Also, the 83-bus test system is considered for fair comparative between the proposed and previous techniques. The simulation outputs revealed significant improvements in the standard MPA and demonstrated the superiority and effectiveness of the proposed EMPA compared to other reported results by recent algorithms for DSRs associated with DGs integration.

Due to the great impact of the penetration and locations of distributed generators (DG) on the performance of the distribution system, this paper proposes a modified moth flame optimization (MMFO) algorithm. Two modifications are proposed in MMFO to enhance the exploration and exploitation balance and overcome the shortcomings of the original MFO. The proposed MMFO is used to find the optimal location and sizing of DG units based on renewable energy sources in the distribution system. The main objective function is to minimize the total operating cost of the distribution system by considering the minimization of the total active power loss, voltage deviation of load buses, the DG units cost, and emission. This multi-objective function is converted to a coefficient single objective function with achieving different constraints. Also, the bus location index is employed to introduce the sorting list of locations to accomplish the narrow candidate buses list. Based on the candidate buses, the proposed MMFO is used to get the optimal location and sizing of DG units. The proposed MMFO algorithm has been applied to the IEEE 69-bus test distribution system and the results are compared with other published algorithms to prove its effectiveness and superiority.

It is no doubt that the optimal power flow (OPF) has great importance in electric power systems. It aims at assigning the adequate operating levels in order to meet the required demands with the objective of minimizing combined economic and environmental concerns. Integration of emerged technologies of voltage source converter (VSC) stations in AC meshed power systems changes foremost their corresponding operation and control features. The VSC stations are usually connected with each other through HVDC lines and consequently a multi-terminal direct current (MDC) system is established. This paper presents an improved manta ray foraging optimizer (IMRFO) for solving the OPF in electric power systems with and without emerged technologies of VSC stations. The proposed IMRFO aims at minimizing the total fuel costs, the total environmental emissions, and the total electrical losses. The MRFO simulates the foraging behaviors of the manta rays. MRFO is improved to handle multi-objectives by incorporating an outward store for the non-dominated Pareto individuals. The form of the fitness function is adaptively varied by iteratively changing their weights. Furthermore, a technique for order preference by similarity to ideal solution (TOPSIS) is applied to extract a suitable operating point among the resulted Pareto set. Several applications of the proposed IMRFO are presented for conventional IEEE 30-bus system, as an AC meshed power system, and modified IEEE 30-bus with emerged VSC stations, as a hybrid AC/MDC meshed power system. Simulation results declare that the proposed algorithm has great effectiveness and robustness features compared to the others. Also, various well-distributed Pareto solutions are obtained based on the proposed algorithm with adequate techno-economic-environmental characteristics.

The combined heat and power (CHP) generator not only generates electrical power but also generates heat energy in a single process, which decreases the emission level significantly. The integration of wind power into the power system will lead to an impact on the economic operation of the system as well as the bus voltage and transmission losses. In this paper, a formulation of optimal power flow (OPF) problem of a power system incorporating stochastic wind power is presented. To solve this problem, a modified version of the moth swarm algorithm (MMSA) is proposed. Three objective functions, which are the minimization of operating cost, the minimization of transmission power loss, and the voltage profile improvement, are considered in this paper. To minimize the operating cost, the direct, overestimation, and underestimation costs of wind power units are considered. Two test systems are considered to prove the effectiveness and the superiority of the proposed MMSA in comparison with other methods. The comparison with other methods proves the efficiency and the superiority of the proposed MMSA.

Modeling of solar generating systems (SGSs) is necessary for recognizing their performance under various conditions of solar irradiance, temperature, and loading. There are nine unbeknown parameters (UPs) in the three-diode model (3-DM); if they are accurately determined, it can exactly identify the real characteristics of SGSs. Parametrization of the 3-DM of SGSs is a nonlinear problem that can be solved via optimization due to its effectivity in determining the optimal parameters to a variety of symmetrical and asymmetrical problems with nonlinearity. Root-mean-squared errors amongst measured and extracted electric current points are the fitness functions to be minimized. The main contributions of this article are the innovative utilization of the barnacles mating optimization algorithm (BMOA) for precise parametrizing of the 3-DM of SGSs and the experimental validation of the SGS. The optimization procedure is based on real measurements of I/V at specific circumstances, in which BMOA is employed to identify the nine UPs of 3-DM of SGSs. Two SGSs are under study, the first of which (Kyocera KC200GT) is widely utilized in the literature for performing comparisons, and the second (Copex P-120) is experimentally set up during different sun irradiances and temperatures. The results of BMOA emphasize its preference over other optimizers for identifying the nine UPs of 3-DM of SGSs.

Recently, the integration of distributed generators (DGs) in radial distribution systems (RDS) has been widely evolving due to its sustainability and lack of pollution. This study presents an efficient optimization technique named the honey badger algorithm (HBA) for specifying the optimum size and location of capacitors and different types of DGs to minimize the total active power loss of the network. The Combined Power Loss Sensitivity (CPLS) factor is deployed with the HBA to accelerate the estimation process by specifying the candidate buses for optimal placement of DGs and capacitors in RDS. The performance of the optimization algorithm is demonstrated through the application to the IEEE 69-bus standard RDS with different scenarios: DG Type-I, DG Type-III, and capacitor banks (CBs). Furthermore, the effects of simultaneously integrating single and multiple DG Type-I with DG Type-III are illustrated. The results obtained revealed the effectiveness of the HBA for optimizing the size and location of single and multiple DGs and CBs with a considerable decline in the system’s real power losses. Additionally, the results have been compared with those obtained by other known algorithms.

This paper presents an interesting hybrid solution to a challenging estimation and control problem of the Permanent Magnet Synchronous Motor (PMSM). Apart from the inherently nonlinear nature of the PMSM, which makes this problem particularly challenging, is the unavailability of the measurements, rotor position, and speed. In an effort to efficiently cope with such issues along with the random noise environment, the Unscented Kalman Filter (UKF) is chosen to estimate the states of the PMSM dynamic system and the Model Predictive Control (MPC) is utilized to control the state space vector in Pulse Width Modulation (PWM). Additionally, the MPC has also been implemented in combination with the Extended Kalman Filter (EKF) and also with Sliding Mode Control (SMC), in order to vigorously compare these hybrid approaches in terms of accuracy, robustness, and transient response. The MPC-UKF, a combination that has never been implemented before, outperforms the other two by efficiently dealing with the issues of high nonlinearities, by accurately estimating the states while the measurements were practically unavailable, and coping with the fast dynamics of the PMSM.

Induction motors are popularly used in various applications because of the proposed modest construction, substantiated process, and limited size of specific power. The traditional AC traction drives are experimentally analyzed. There is a high circulating current due to the high Common-Mode Voltage (CMV). The high Circulating Bearing Current (CBC) is a major problem in conventional two-level voltage source inverter fed parallel-connected sensor-based induction motors for traction applications. A sensorless method is well known for shrinking costs and enhancing the reliability of an induction motor drive. The modified artificial neural network-based model reference adaptive system is designed to realize speed estimation methods for the sensorless drive. Four dissimilar multilevel inverter network topologies are being implemented to reduce CBC in the proposed sensorless traction motor drives. The multilevel inverter types are T-bridge, Neutral Point Clamped Inverter (NPC), cascaded H-bridge, and modified reduced switch topologies. The four methods are compared, and the best method has been identified in terms of 80% less CMV compared to the conventional one. The modified cascaded H-bridge inverter reduces the CBC of the proposed artificial neural network-based parallel connected induction motor; it is 50% compared to the conventional method. The CBC of the modified method is analyzed and associated with the traditional method. Finally, the parallel-connected induction motor traction drive hardware is implemented, and the performance is analyzed.

Human civilization has changed forever since induction motors were invented. Induction motors are widely used and have become the most prevalent electrical componentsdue to their beneficial characteristics. Many control strategies have been developed for their performance improvement, starting from scalar to vector to direct torque control. The latter, which is a class of vector control, was proposed as an alternative to ensure separate flux and torque control while remaining completely in a stationary reference frame. This technique allows direct inverter switching and reasonable simplicity compared to other vector control techniques, and it is less sensitive to parameter variation. Yet, the use of hysteresis controllers in conventional DTC involves undesired ripples in the stator current, flux, and torque, which lead to bad performances. This paper aims to minimize the ripple level and ensure the system’s performance in terms of robustness and stability. To generate the appropriate reference control voltages, the proposed method is an improved version of DTC, which combines the power of fuzzy logic, neural networks, and an increased number of sectors. Satisfactory results were obtained by numerical simulation in MATLAB/Simulink. The proposed method was proven to be a fast dynamic decoupled control that robustly responds to external disturbance and system uncertainties.

In this paper, the Archimedes optimization algorithm (AOA) is applied as a recent metaheuristic optimization algorithm to reduce energy losses and capture the size of incorporating a battery energy storage system (BESS) and photovoltaics (PV) within a distribution system. AOA is designed with revelation from Archimedes’ principle, an impressive physics law. AOA mimics the attitude of buoyant force applied upward on an object, partially or entirely dipped in liquid, which is relative to the weight of the dislodged liquid. Furthermore, the developed algorithm is evolved for sizing several PVs and BESSs considering the changing demand over time and the probability generation. The studied IEEE 69-bus distribution network system has different types of the load, such as residential, industrial, and commercial loads. The simulation results indicate the robustness of the proposed algorithm for computing the best size of multiple PVs and BESSs with a significant reduction in the power system losses. Additionally, the AOA algorithm has an efficient balancing between the exploration and exploitation phases to avoid the local solutions and go to the best global solutions, compared with other studied algorithms.