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Power transformers are the most important component in power system. Exposing these transformers to the harmonic distortions causes additional heat losses, insulation stress, decrease in lifetime of insulation, and reduced power factor with decrease in efficiency of the system. The lifespan of distribution transformers is influenced by the fragility of power quality in power networks. Harmonic mitigation filters with robust control technique are required to reduce the harmonic effects on power transformer. Traditionally, synchronous reference frame (DQ) is employed to control the shunt active power filter (APF) for mitigation of harmonics in power transformers. DQ control of Shunt APF lacks merits of fast response, delayed operation due to phased lock loop under abnormal grid conditions leading to insufficient harmonic elimination. A developed DQ method based on detecting the positive and negative sequence components is proposed to precisely control the shunt APF for reliable operation of power transformer. This detection technique improves the response time, mitigate the harmonics effecting the operation of transformer and overall power factor. The proposed control system is evaluated under different abnormal operating scenarios and compared with traditional DQ method. The results and analysis confirm the efficacy of the developed DQ method in improving the power transformer performance. [ABSTRACT FROM AUTHOR]

Recent statistics indicate that as of 2023, the global renewable energy capacity has reached new heights, with Saudi Arabia significantly contributing to this growth through its strategic initiatives. The kingdom is particularly focusing on harnessing solar power, given its abundant sunlight, and is also exploring wind energy, leveraging its vast desert landscapes. These efforts are part of Saudi Arabia's broader strategy to become a global leader in renewable energy. This paper provides a detailed exploration of Saudi Arabia's ambitious journey from a fossil-fuel-dominated energy sector to a more sustainable, renewable-energy-driven future. Anchored in the broader context of global energy trends, the study emphasizes the critical shift toward renewable resources, with a particular focus on Saudi Arabia's unique position in this global movement. Key to this transition is Saudi Arabia's Vision 2030, a strategic framework that guides the country's renewable energy policies and initiatives. A comprehensive review of these policies, including other governmental and international collaborations that support renewable energy development, is offered. The current state of renewable energy in Saudi Arabia is assessed, including an overview of existing projects and production statistics. The paper also explores emerging technologies such as energy storage systems and smart grid solutions, emphasizing their role in Saudi Arabia's energy transition. An impact assessment is conducted to understand the environmental, economic, and social effects of this energy shift. The paper also includes a comparative analysis of other countries' renewable energy transitions, extracting lessons and best practices applicable to the Saudi context. Strategic recommendations and reflections on Saudi Arabia's future role in the global energy landscape are provided for stakeholders in energy policy, environmental planning, and sustainable development. [ABSTRACT FROM AUTHOR]

This paper presents a control method for a system composed of a photovoltaic (PV) array, five-phase impedance source inverter, five-phase induction motor and centrifugal pump. This method is based on controlling the motor speed to control the pump power as the insolation level or temperature change to attain the maximum power extraction from the PV-array. The motor speed is controlled by using artificial neural network (ANN) which is trained to provide the desired inverter frequency and modulation index at any insolation level and temperature to attain the maximum PV operating power. The data of the neural network are based on the operation of the induction motor at constant air gap flux and perturb and observe method for maximum power point tracking. Simulation results are obtained using MATLAB Simulink to verify the proposed control method. [ABSTRACT FROM AUTHOR]

Incorporating photovoltaic (PV) inverters in power distribution systems via static synchronous compensators (PV-STATCOM) during the nighttime has lately been described as a solution to improve network performance. Hunter prey optimization (HPO) is introduced in this study for efficient PV-STATCOM device allocation in distribution systems. HPO generates numerous scenarios for how animals could act when hunting, some of which have been expanded into stochastic optimization. The PV-STATCOM device allocation issue in distribution networks is structured to simultaneously minimize the electrical energy losses and improve the voltage profile while accounting for variable 24 h loadings. The impacts of varying the number of installed PV-STATCOM devices are investigated in distribution systems. It is tested on two IEEE 33-node and 69-node distribution networks. The effectiveness of the proposed HPO is demonstrated in comparison to the differential evolution (DE) algorithm, particle swarm optimization (PSO), artificial rabbits algorithm (ARA), and golden search optimizer (GSO). The simulation results demonstrate the efficiency of the proposed HPO in adequately allocating the PV-STATCOM devices in distribution systems. For the IEEE 33-node distribution network, the energy losses are considerably decreased by 57.77%, and the voltages variance sum is significantly reduced by 42.84%. The energy losses in the IEEE 69-node distribution network decreased by 57.89%, while voltage variations are reduced by 44.69%. Additionally, the suggested HPO is highly consistent than the DE, PSO, ARA, and GSO. Furthermore, throughout the day, the voltage profile at all distribution nodes surpasses the minimum requirement of 95%. [ABSTRACT FROM AUTHOR]

The shape design of the Tubular Linear Synchronous Motor (TLSM) is a critical engineeri ng optimization problem which was handled as single- and multi-objective optimization frameworks. However, the different practical constraints for the TLSM design must be efficiently guaranteed. This paper proposes a developed multi-objective shape design of the TLSM to maximize the operating force and minimize the flux saturation. In this regard, a Multi-objective Grey Wolf Optimizer (MGWO) is developed, including an outside archive with a predetermined size that is integrated for storing and retrieving Pareto optimal solutions. Using this knowledge, the grey wolf social structure would then be established, and, in the multi-objective searching environments, grey wolf hunting behavior would then be replicated. The superiority and effectiveness of the developed MGWO is assessed in comparison to the Multi-objective Flower Pollination Algorithm (MFPA), Multi-objective Lichtenberg Algorithm (MOLA), and Multi-objective Grasshopper Optimization Algorithm (MGOA). The outcomes illustrate that the developed MGWO provides an average improvement of 73.46%, 19.07%, and 15.15% compared to MFPA, MOLA, and MGOA, respectively. The validation of the developed MGWO is extended for a multi-objective form of welded beam design (WBD) by simultaneously minimizing the deflection and the manufacturing costs. Similar findings are obtained with different reference points, the developed MGWO provides an average improvement of 2.8%, 0.7%, and 3.04% compared to MFPA, MOLA, and MGOA, respectively. [ABSTRACT FROM AUTHOR]

Due to uncertain photovoltaic (PV) power generation, analyzing the voltage stability of transmission networks with a large PV plant is challenging. The stability of PV output power is a critical factor in establishing PV penetration levels in active transmission networks when assessing loading capabilities. Therefore, this article contributes to enhancing the understanding of how a static synchronous compensator (STATCOM) could be used as part of the power system network. STATCOMs are often used to improve the static and transient voltage, maintain transmission limits, and reduce low-frequency disturbances. With the help of a STATCOM unit, a proper investigation and analysis of dynamic voltage stability in a transmission system are presented. The validity of the study is evaluated by the Saudi Arabia grid code (SAGC). The system being tested is comprised of a 300 MW PV plant in Sakaka, Saudi Arabia, integrated into a 17-bus transmission power network and a STATCOM, which is employed at one of the buses. PSAT software is used to evaluate a number of case studies to determine the transient voltage stability with and without the STATCOM and the PV plant. The simulation findings demonstrate how STATCOM functions to improve the quality of the power system in accordance with the SAGC. In addition, the analysis of voltage stability demonstrates that the power network's resilience to short circuits at the PV system's grid connection point significantly impacts the voltage's dynamic behavior. [ABSTRACT FROM AUTHOR]

A major problem in power systems is achieving a match between the load demand and generation demand, where security, dependability, and quality are critical factors that need to be provided to power producers. This paper proposes a proportional–integral–derivative (PID) controller that is optimally designed using a novel artificial rabbits algorithm (ARA) for load frequency control (LFC) in multi-area power systems (MAPSs) of two-area non-reheat thermal systems. The PID controller incorporates a filter with such a derivative coefficient to reduce the effects of the accompanied noise. In this regard, single objective function is assessed based on time-domain simulation to minimize the integral time-multiplied absolute error (ITAE). The proposed ARA adjusts the PID settings to their best potential considering three dissimilar test cases with different sets of disturbances, and the results from the designed PID controller based on the ARA are compared with various published techniques, including particle swarm optimization (PSO), differential evolution (DE), JAYA optimizer, and self-adaptive multi-population elitist (SAMPE) JAYA. The comparisons show that the PID controller's design, which is based on the ARA, handles the load frequency regulation in MAPSs for the ITAE minimizations with significant effectiveness and success where the statistical analysis confirms its superiority. Considering the load change in area 1, the proposed ARA can acquire significant percentage improvements in the ITAE values of 1.949%, 3.455%, 2.077% and 1.949%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. Considering the load change in area 2, the proposed ARA can acquire significant percentage improvements in the ITAE values of 7.587%, 8.038%, 3.322% and 2.066%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. Considering simultaneous load changes in areas 1 and 2, the proposed ARA can acquire significant improvements in the ITAE values of 60.89%, 38.13%, 55.29% and 17.97%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. [ABSTRACT FROM AUTHOR]

Featured Application: This paper is concerned with a multi-criteria technical and economical allocation procedure of microgrids in smart cities. Microgrids have the potential to provide reliable electricity to key components of a smart city's critical infrastructure after a disaster, hence boosting the microgrid power system's resilience. Policymakers and electrical grid operators are increasingly concerned about the appropriate configuration and location of microgrids to sustain post-disaster critical infrastructure operations in smart cities. In this context, this paper presents a novel method for the microgrid allocation problem that considers several technical and economic infrastructure factors such as critical infrastructure components, geospatial positioning of infrastructures, power requirements, and microgrid cost. In particular, the geographic allocation of a microgrid is presented as an optimization problem to optimize a weighted combination of the relative importance of nodes across all key infrastructures and the associated costs. Furthermore, the simulation results of the formulated optimization problem are compared with a modified version of the heuristic method based on the critical node identification of an interdependent infrastructure for positioning microgrids in terms of the resilience of multiple smart critical infrastructures. Numerical results using infrastructure in the city of Pittsburgh in the USA are given as a practical case study to illustrate the methodology and trade-offs. The proposed method provides an effective method for localizing renewable energy resources based on infrastructural requirements. [ABSTRACT FROM AUTHOR]

Power converter faults represent about 12% of electrical system faults. More than 31% of these converter faults are caused by the transistors in the converters. These transistor failures are classified into open‐gate and short‐gate faults. A fault diagnostic and tolerant control system is proposed in this study for an open‐gate fault of a three‐phase voltage‐source inverter (VSI). The system under study is an induction motor fed by a VSI. The diagnostic technique uses a detection method of positive and negative sequence components, which requires only the measurement of motor currents. The diagnostic signals are derived from the negative sequence components of the motor currents. These signals are then compared using logic functions to trigger the tolerant control. In the case of an open‐gate fault, the tolerant system converts the inverter topology from six switches to four switches where the faulty inverter leg is disconnected. The proposed control method was evaluated under steady‐state and transient conditions through simulation and experiments. The results confirm an effective and reliable performance of the motor under an open‐gate fault. [ABSTRACT FROM AUTHOR]

The subject of power quality is gaining much interest nowadays. With the sophisticated sensitive electrical devices available in the market, power stations should ensure qualified and eminent power to the point of utilization. Bad voltage conditions in a power system can cause a malfunction at the point of utilization. Dynamic voltage restorer (DVR) is a reliable power electronic offsetting device which when engaged in the power system is effectual in the operation of compensating voltage sag/swell. In this research work, a DVR for offsetting multiple voltage disturbances in a power system is presented and a novel control strategy to trigger power switches in DVR is proposed. The proposed novel control methodology is compared with Synchronous Reference Frame (SRF) theory-controlled DVR. The simulation model is designed and the analysis with MATLAB/SIMULINK is presented. [ABSTRACT FROM AUTHOR]

The main aim of this work was the maximization of the energy saving of balanced and unbalanced distribution power systems via system reconfiguration and the optimum capacitor's bank choice, which were estimated by using a new algorithm: modified Tabu search and Harper sphere search (MTS-HSSA). The results demonstrated that the proposed method is appropriate for energy saving and improving performance compared with other methods reported in the literature for IEEE 33-bus adopted systems, including large scale systems such as IEEE 119 and the IEEE 123 unbalanced distribution system. Moreover, it can be used for unbalanced distribution systems distributed generators (DGs). The results demonstrated that the proposed method (the optimal choice of shunt capacitor (SC) banks and the optimal reconfiguration via the proposed algorithm) is appropriate for energy saving compared with different strategies for energy saving, which included distributed generation (DG) at different cost levels. [ABSTRACT FROM AUTHOR]

The paper is concerned with designing an effective controller for a linear tubular homopolar (LT-H) motor type. The construction and operation of the LT-H motor are first described in detail. Then, the motor model is represented in the direct-quadrature (d-q) axes in order to facilitate the design of the control loops. The designed control system consists of two main loops: the current control loop and velocity adaptation loop. The determination of the regulator's gains is accomplished through deriving and analyzing the transfer functions of the loops. To enhance the system's robustness, a robust variable estimator is designed to observe the velocity and stator resistance. Different performance evaluation tests are performed using MATLAB/Simulink software to validate the controller's robustness for variable-speed operation and load force changes as well. The obtained results reveal the appropriate dynamics of the motor thanks to the well-designed control system. [ABSTRACT FROM AUTHOR]

Shunt active power filters (APFs) are used to address power quality issues such as harmonic distortion and power system imbalance. Many control systems for shunt APFs demand measurements of voltages and currents for the determination of instantaneous real and reactive power signals. From these signals, the reference currents of the shunt APF are calculated. A method of control for the APF shunt that only measures currents is proposed in this paper. The control method includes a modified technique to extract the positive and negative sequence components in the time domain. The reference currents in the shunt APF are calculated by the negative sequence components of the measured currents. Simulation and experimental results confirm the efficiency of this control scheme under ideal and non-ideal conditions. [ABSTRACT FROM AUTHOR]

Arrhythmia is a dangerous disease in which the heart rhythm varies and it may be very fast or very slow. Rapid heartbeats can lead to shortness of breath, chest pain, and sudden weakness, whereas slow heartbeats can lead to dizziness, problems with concentration, and constant stress. Finding an effective treatment for arrhythmia has become a very important endeavor for researchers and clinicians. In this article, we review the latest methodologies used in arrhythmia diagnosis and treatment. They include the application of five different types of artificial neural networks trained by machine learning and powered by artificial intelligence: convolutional, recurrent, feedforward, radial basis function, and modular neural network. Some of these methodologies are merged to enhance accuracy and efficacy. This review suggests that more research needs to be carried out in merging neural network types for their application in electrocardiogram (ECG).