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The Environmental Economic Power Dispatch (EEPD) problem, a widely studied bi-objective nonlinear optimization challenge in power systems, traditionally focuses on the economic dispatch of thermal generators without considering network security constraints. However, environmental sustainability necessitates reducing emissions and increasing the penetration of renewable energy sources (RES) into the electrical grid. The integration of high levels of RES, such as wind and solar PV, introduces stability issues due to their uncertain and intermittent nature. This paper addresses these concerns by formulating and solving the Stable Environmental Economic Power Dispatch (SEEPD) problem, which includes fixed zonal reserve capacity from conventional thermal generators and uncertain reserves from RES. Uncertainties in RES and load demand are modeled using random variable generation techniques, applying Gaussian, Weibull, and log-normal probability density functions (PDFs) for load demand, wind velocity, and solar irradiance, respectively. The stochastic SEEPD problem extends to multiple periods by replicating the single-period problem for each interval in the planning horizon, linking periods through intertemporal ramping costs, physical ramp rate, and fixed zonal reserve constraints on dispatch variables. Multi-Objective Evolutionary Algorithms (MOEAs) have gained importance for solving complex nonlinear problems involving multi-objective functions. This paper applies the latest MOEAs to tackle the proposed SEEPD problem, incorporating stochastic wind and solar PV power sources. Network security constraints, such as transmission line capacities and bus voltage limits, are considered along with constraints on generator capabilities and intertemporal spinning reserves, ramp-up and ramp-down constraints for thermal generators. A bidirectional coevolutionary-based multi-objective evolutionary algorithm is employed, integrating an advanced constraint-handling technique to ensure compliance with system constraints. The simulation results show that the proposed formulation achieves a better trade-off between various conflicting objective functions compared to other state-of-the-art MOEAs.

Detection of high impedance faults (HIFs) has been traditionally a main challenge in the protection of distribution systems, since they do not generate enough current to be reliably detected by conventional over-current relays. Data-based methods are alternative HIF detection methods which avoid threshold settings by training a classification or regression model. However, most of them lack interpretability and are not compatible with various distribution networks. This paper proposes an object detection-based HIF detection method, which has higher visualization and can be easily applied to different scenarios. First, based on the analysis of HIFs, a Butterworth band-pass filter is designed for HIF harmonic feature extraction. Subsequently, based on the synchronized data provided by distribution-level phasor measurement units, global HIF feature gray-scale images are formed through combining the topology information of the distribution network. To further enhance the feature information, a locally excitatory globally inhibitory oscillator region attention mechanism (LEGIO-RAM) is proposed to highlight the critical feature regions and inhibit useless and fake information. Finally, an object detection network based You Only Look Once (YOLO) v2 is established to achieve fast HIF detection and section location. The obtained results from the simulation of the proposed approach on three different distribution networks and one realistic distribution network verify that the proposed method is highly effective in terms of reliability and generalization.

In recent years, there has been a proliferation of studies focusing on sustainable energy sources aimed at delivering reliable power with reduced greenhouse gas emissions. Among these endeavors, electrification of urban areas through incorporation of renewable energy sources (RESs) stands out as a pivotal application of RES. Increasingly, the integration of RESs like PV and wind into the power grid is being pursued as an alternative to traditional centralized power stations, offering a means to ensure continuous power supply during peak demand periods while simultaneously managing unit costs, thereby providing a systematic and readily accessible power supply to consumers. This paper presents a comprehensive examination of hybrid wind and PV with focus on achieving consistent DC bus-bar voltage through integration with microcontroller system via buck-boost converter. Additionally, hybrid system’s output voltage is tailored to accommodate necessary battery connections. However, given influence of environmental variables like fluctuations in solar irradiation and wind velocity on power output of PV and wind seamless integration of these sources becomes paramount. To address this challenge MATLAB model is developed to simulate grid integration of hybrid wind and PV, enabling a detailed analysis of integrated grid’s performance. Simulation results are then proposed with those obtained from conventional fossil fuel-based power generation methods. In this research underscores importance of integrating RES into existing power grids and highlights efficacy of hybrid wind and PV in meeting this objective. Findings presented herein contribute to advancing discourse on sustainable energy solutions, offering valuable insights for policymakers, industry stakeholders, and researchers.

The rollout of China Pakistan Economic Corridor (CPEC) has benefited Pakistan in getting a faster pace in terms of economic and financial stability. Pakistan has a significant geographical location in the Asian continent. It possesses an enormous renewable energy sources (RES) potential, primarily wind, in its Sindh, Balochistan, and Khyber Pakhtun-Khuwa (KPK) provinces. This study concentrates on the availability of wind power potential at the selected twelve sites located in three different provinces of the country, using the Weibull probability distribution (WPD) technique. Five parametric estimation techniques were simulated on the data sets of the selected wind sites to find the optimal results. The obtained results showed that the annual wind energy density was found to be 3668.28 kWh/m2 at Lucky Energy, 3297.21 kWh/m2 at Sujawal, 3180.03 kWh/m2 at Tando Ghulam Ali, 2836.76 kWh/m2 at Sanghar, 2821.09 kWh/m2 at Baburband, 2684.32 kWh/m2 at Ketibandar, 2618.78 kWh/m2 at Umerkot, 1759.2 kWh/m2 Hawksby, 1253.76 kWh/m2 at Gwadar, 896.41 kWh/m2 at Quetta, 558.59 kWh/m2 at Haripur and 376.07 kWh/m2 at Peshawar respectively. From the results, it is found that sites present in Sindh province are suitable for commercial-scale wind farm development and the selected wind sites of Balochistan are appropriate for utility-scale wind power development. In contrast, the selected wind sites of Khyber Pakhtunkhwa province are unsuitable for commercial scale due to low wind speeds around the year.

To reduce the Carbon footprint and reduce emissions from the globe, the world has kicked-off to leave reliance of fossil fuels and generate electrical energy from renewable energy sources. The MOOPF problem is becoming more complex, and the number of decision variables is increasing, with the introduction of power electronics-based Flexible AC Transmission Systems (FACTS) devices. These power system components can all be used to increase controllability, effectiveness, stability, and sustainability. The added uncertainty and variability that FACTS devices and wind generation provide to the power system makes it challenging to find the right solution to MOOPF issues. In order to determine the best combination of control and state variables for the MOOPF problem, this paper develops three cases of competing objective functions. These cases include minimizing the total cost of power produced as well as over- and underestimating the cost of wind generation, emission rate, and the cost of power loss caused by transmission lines. In the case studies, power system optimization is done while dealing with both fixed and variable load scenarios. The proposed algorithm was tested on three different cases with different objective functions. The algorithm achieved an expected cost of $833.014/h and an emission rate of conventional thermal generators of 0.665 t/h in the case 1. In Case 2, the algorithm obtained a minimum cost of $731.419/h for active power generation and a cost of power loss is 124.498 $/h for energy loss. In Case 3, three objective functions were minimized simultaneously, leading to costs of $806.6/h for emissions, 0.647 t/h, and $214.9/h for power loss.

The growing popularity of electric vehicles presents a significant challenge to current electric grids since the rising number of these vehicles places additional strain on power systems inside distribution networks. A proposed paradigm is presented for electric vehicles (EVs), which is subsequently partitioned into three distinct dispatching areas to assess its practicality. This study is structured around two primary objectives. The first objective focuses on EV owners aiming to minimize their electricity consumption costs while also receiving compensation for providing services. The second objective involves using an aggregator to establish distinct tariffs for each dispatching area. Additionally, the aggregator aims to shift the charging load demand from peak to off-peak hours and distribute the charging demand to each agent. The authors of this study propose the utilization of a charging and discharging coordination method, specifically the Multi-Agents Charging and Discharging (MACD) algorithm, as a means to successfully tackle the issue of charging demand during peak hours. The objective of the proposed algorithm is to effectively handle the increased charging requirements during peak periods by using vehicle-to-grid (V2G) technologies within the context of Smart Grid systems and electric vehicle (EV) batteries. Importantly, this reduction is achieved without compromising the performance of electric vehicles (EVs) or the convenience experienced by EV owners. The algorithm under consideration demonstrates reduced power charging costs for various sectors. Specifically, it achieves a decrease of 15% for households, 14.6% for corporate buildings, and 14.5% for Industrial Park EV aggregators.

Electricity theft (ET), which endangers public safety, creates a problem with the regular operation of grid infrastructure and increases revenue losses. Numerous machine learning, deep learning, and mathematical-based algorithms are available to find ET. Still, these models do not produce the best results due to problems like the dimensionality curse, class imbalance, improper hyper-parameter tuning of machine learning and deep learning models, etc. We present a hybrid deep learning model for effectively detecting electricity thieves in smart grids while considering the abovementioned concerns. Pre-processing techniques are first employed to clean up the data from the smart meters. Then, the feature extraction technique, like AlexNet, addresses the curse of dimensionality. The effectiveness of the proposed method is evaluated through simulations using a real dataset of Chinese intelligent meters. To conduct a comparative analysis, various benchmark models are implemented as well. Our proposed model achieves accuracy, precision, recall, and F1, up to 86%, 89%, 86%, and 84%, respectively.

The increasing demand for solar renewable energy resources, driven by the global energy crisis and the depletion of conventional energy sources, has underscored the importance of harnessing solar energy. Solar photovoltaic (PV) systems, however, exhibit nonlinear output power due to their weather-dependent nature, impacting overall system efficiency. This study focuses on the development and comparative analysis of three intelligent Maximum Power Point Tracking (MPPT) controllers using the MATLAB Simulink. The controllers employ distinct methodologies, namely, Artificial Neural Networks (ANN), Adaptive Neural and Fuzzy Inference System (ANFIS), and Fuzzy Logic Controller (FLC). The results demonstrate that ANFIS achieved the highest accuracy at 99.50%, surpassing ANN and FLC with accuracies of 97.04% and 98.50%, respectively, thus establishing ANFIS as the superior MPPT controller. Additionally, the positives and negatives of all three MPPT-based algorithms are also compared in this work.

Wireless Power Transfer (WPT) using inductive and magnetic resonance coupling developing at enormous pace due to its diversity of applications, such as electric vehicles (EV), biomedical implants, consumer electronics, robotics and so on. This review presents historical background together with applications of low power and high power WPT systems. The review emphasizes on two main design facets of WPT system including compensation networks and coil structure. All up-to-date compensation topologies are comprised in the review along with latest coil designs. Load-independent operation using compensation topologies is also reviewed. The review provides all available methods to analyze the circuit structure of WPT system theoretically and it also outlines the software used for circuit simulations as well as coil design. This paper is diverse from existing reviews in a pattern that it presents not only complete critical intuition on the vital design features, recent advancements, contemporary problems and challenges of low power and high power WPT systems but also provides the available theoretical methods as well as software to analyze the WPT system in wholesome. This paper will enrich the knowledge of the researchers to investigate the WPT systems using existing techniques and to solve the present-day design glitches of WPT systems.

In this study, the experimental implementation of a Hybrid Controller (HC) for a Wind Turbine Emulator (WTE) is presented. Conventionally, the speed control of a WTE is based on two Proportional Integral (PI) controllers used for the outer (speed) loop and inner (torque) loop of a DC motor drive. However, based on the extensive literature survey, with the PI controllers alone, significant overshoot and substantial settling time in the speed response are unavoidable. The overshoot cannot be completely removed without sacrificing speediness. To eliminate the overshoot and concurrently improve the settling time, an HC, combination of Adaptive Neuro-Fuzzy Inference System (ANFIS) and PI controller for the outer and inner loops, respectively, is proposed, designed, implemented and analyzed. The actual wind turbine is emulated using a 2-quadrant DC motor drive based on DS1104 R&D controller board from dSPACE. These two control schemes, namely PI (both controllers are PI), and HC (PI controller for inner loop and ANFIS controller for outer loop) for the WTE are extensively simulated based on the small-scale wind turbine model and eventually implemented using a small-scale laboratory set-up. When implemented in the WTE, with a step-change in wind speed, the hybrid control scheme produces a better result. It is found out the Root Mean Square Error (RMSE) for the WTE rotor speed of the HC is 0.495, which is lower compared to PI control scheme RMSE of 0.7273. Finally, to verify the validity of the WTE based on HC, a random wind speed with wind turbine parameters extracted from the actual wind turbine is applied. The results indicated that the WTE perfectly emulated the wind turbine rotor speed with an RMSE of 0.5004.

Power generation in the global power sector has recently seen a paradigm shift from centralized bulk generation to distributed generation. In this regard, there are two transmission technologies available: high-voltage alternating current (HVAC) and high-voltage direct current (HVDC). The technical and economic benefits of HVDC transmission technology have enabled many countries to implement it widely for long-distance bulk power transmission, particularly between remote locations and densely populated areas. HVDC technology, however, is still relatively new in Pakistan. Despite this, HVDC technology holds great promise in Pakistan, specifically in utilizing and integrating distributed renewable energy sources with the national grid, as well as enabling cross-border energy trade. This study aims to provide a first-ever scientometric analysis of the HVDC technology field, highlighting key research themes and trends. A correlation is established between HVDC research and its adoption in Pakistan by extracting articles from the Scopus database and analyzing them with the VOS viewer, a modern data analysis tool. The scientometric analysis indicates a minimal contribution from Pakistani authors in the field of HVDC technology as compared to authors of the neighboring nations, highlighting a significant gap in knowledge transfer and technology adoption. Moreover, an overview of HVDC technology is provided, along with a comparison between HVAC and HVDC technologies. Last but not least, Pakistan’s existing and proposed HVDC projects, prospects, and challenges associated with HVDC adoption are discussed.

Load modeling is essential to distribution system analysis, planning, and control. Therefore, in this work, effect of non-linear load models has been considered for the optimal site and size of DG and SC allocation. A new and efficient modified branch and bus ordering-based forward-backward load flow method has been applied to solve load flow problem in radial distribution system. Recently implemented several state-of-the-art evolutionary algorithms (EAs) are employed to solve optimal site and size of DG and SC allocation problems and it is shown that performance of multi-operator/multimethod is better than other algorithms that are based on a single operator and/or algorithm. Therefore, a new hybrid EA based on various state-of-the-art operators such as GA, DE, and PSO is designed and applied to solve optimal site and size of DG and SC allocation problems. Various technical objective functions (index of active and reactive power loss and voltage deviation index) are considered to show the impacts of non-linear load models. From the simulation results, it is shown that DG and SC allocation problem is multi-objective. Therefore, further weighted sum multi-objective technical, economic, and environmental functions are formulated to find the solution to DG and SC allocation problems. The gathered results demonstrate that the proposed methodology significantly minimizes the cost of energy supplied by grid, total operating cost, and active and reactive power losses. Consequently, it can be stated that the suggested methodology has considerable economic and technological benefits and may be used to address many optimization issues in various distribution networks.

Single objective constant PQ load models were extensively considered for site and size of distributed generation (DG) and shunt capacitor (SC) allocation. Which may lead to single non-dominated solution of unpredictable and misleading results about their site and size, loss reduction and payback period. Therefore, primary objective of this study is to investigate the effects of seven nonlinear voltage-dependent load models for the siting and sizing of DG and SC considering various conflicting Multiobjective functions. These objective functions are minimization of active power loss, voltage deviation, cost of energy loss per year, the total cost of installed DG and SC, and emission. Three study cases of simultaneous optimization of two and three objective functions are intended to find optimal integration of DG and SC in the standard 33 and 118-bus radial distribution network considering seven nonlinear voltage-dependent load models. A new Multiobjective evolutionary algorithm (MOEA) called Bidirectional Coevolutionary (BiCo) is applied to the proposed study cases to demonstrate the impact of load model on DG and SC allocation. Further, to show the superiority and performance of proposed algorithm, six state-of-the-art MOEAs are implemented and statistically compared with proposed algorithm using a representative hypervolume indicator (HVI). The maximum savings in annual cost of annual energy loss reaches 58.99% in case1 of PQ load model, 59.4% in case 2 and 64.96% in case 3 of industrial load model considering only DG allocation, whereas, 93.673% in constant PQ load model of case1, 78.908% in constant current load model of case2 and 93.403% of case3 of PQ load model considering simultaneous DG and SC. Simulation results show that the proposed algorithm is adept and suitable to find a better trade-off between various conflicting objective functions compared to other recently designed MOEAs.

Abstract An Optimal power flow (OPF) is non‐linear and constrained multi‐objective problem. OPF problems are expensive and evolutionary algorithms (EAs) are computationally complex to obtain uniformly distributed and global Pareto front (PF). Therefore, here, hybrid two‐phase algorithm integrated with parameter less constraint technique is applied to solve OPF problem. Proposed technique combines single and multi‐objective EAs to find better convergence and evenly distributed PF. For the validation and effectiveness of proposed algorithm, various conflicting objective functions are formulated and implemented on IEEE 30 and 300‐bus network. Each case is independently run twenty times. Hyper volume indicator technique is employed to find the best PF, and the best‐compromised solution is obtained by using fuzzy decision‐making technique. Recently, maximum integration of wind and solar power is highly encouraged. Complexity of OPF is increased with the integration of uncertain renewable energy resources. Hence, 30‐bus test system is modified by replacing some conventional generators with the wind and solar generation. Uncertainties in wind, solar and load demand are modelled by appropriate probability distribution functions. Simulation results show that the proposed method can find the near global PF of highly complex problems subject to satisfying all the operational constraints.

Degradation of components and system failure within the microgrid system is deteriorating the performance of electrification. The aim of this study is to discuss the relationship and connections between issues resulting from degradation and deterioration in the microgrid system, in addition to introducing the prominent impacts which may eventually lead to the premature termination of the microgrid system. This study explored the microgrid degradation and deterioration issues within four microgrid sections: generation section, storage section, transmission section, and distribution section. Subsequently, this study analyzes, derives, and classifies all emerging issues into four types of prominent impacts. The degradation and deterioration invoked many component performance issues into four main damaging outcomes, namely (i) deteriorated transmission line yielded issues regarding expected energy not achieved; (ii) energy deficit and unpredicted blackout come after the depth of discharge (DOD) reduction and invoke a loss of power supply; (iii) a shorter battery life cycle, shorter transformer lifespan, and decreased DG lifetime concluded as a shorter microgrid life expectancy; and (iv) rapid microgrid broke down and the crash of the key component inadvertently fastened the time to failure and gave rise to the early failure of a microgrid system. It is envisaged that the discussion in this study can provide useful mapped information for the researcher, stakeholder, operator, and other parties for thoroughly addressing various degradation and deterioration issues and anticipating the early termination of the microgrid system.

Due to the nonlinear dynamics of direct current (DC) microgrids, the existence of input constraints, and their multi-input multi-output (MIMO) nature, classical linear controllers cannot provide an appropriate performance in a wide range of operations. In this paper, to address these issues, nonlinear suboptimal controllers are systematically developed in the primary layer of DC microgrids by employing a state-dependent Riccati equation (SDRE) methodology. To this end, the whole complexities of the nonlinear dynamics and input constraints are considered in the design procedure of the proposed SDRE controllers. After designing the controllers, and for a fast yet effective fault detection/isolation, an artificial neural network (ANN) is trained to identify the closed-loop microgrid at its nominal condition. Then, the trained ANN is employed to design a fault detection/isolation mechanism. Simulation results of the developed SDRE control scheme augmented by the ANN-based fault detection/isolation mechanism demonstrate the merits of the proposed scheme.

Wind is one of the world’s most rapidly expanding and environmentally friendly renewable energy sources (RES). The aim of this study is to analyze the wind potential availability in Sanghar and Gwadar cities through the wind characteristics’ analysis. To analyze the wind potential at the both sites, one-year average wind speed data (including annual, monthly, day-night, and seasonal variations) for 2020 were used. A Weibull distribution parametric approach with five different technique was applied. Along with an analysis of the wind potential, an economic assessment was also out to estimate the installation cost, turbine-cost, capacity factor of ten different wind turbines with different rated power were used at the selected sites. The results show that at the Sanghar site, the maximum power of 1612.82 kW was generated by Vestas V126/3300 whereas the minimum power 383.44 kW was generated by Nordex n60/1300. At the Gwadar site, Vestas V126/3300 generated the maximum power of 745.10 kW and least power of 157.98 kW was generated by Nordex n60/1300. With respect to an economic assessment, Vestas V126/3300 and Suzlon S66/1250 had the highest and lowest installation cost of turbines respectively at both sites. At the Sanghar site, the lowest value i.e., 0.0422 kWh/ $\$ $ , 0.0893 kWh/ $\$ $ and the highest values 0.081 kWh/ $\$ $ , 0.2028 kWh/ $\$ $ were shown by Goldwind GW121/2500 and Nordex n80/2500 at the Gwadar and Sanghar sites respectively.

This paper provides a review of the implementation of different materials and how they have impacted the efficiency of solar cells. This work elaborates on all solar generation methods that have been developed in the past and covers disparate technologies that are being implemented in different generations. A review of the characterization and factors involved in these processes are also discussed briefly. Furthermore, the economic, environmental, and technical perspectives related to solar cells have also been expounded. This paper also provides some insights into potential research directions that can be pursued in the field of solar energy. Energy demands are increasing all over the world, and substantial amounts of fossil fuels are currently exhausted all over the world in order to meet those needs, which in turn contaminates our environment; moreover, non-renewable sources of energy are diminishing at higher rates as well. Solar energy is of prime importance in all renewable energy sources as the Sun shines at the Earth for 8 to 10 h on average. Thus, heat can be harnessed to generate electricity, but solar cells are not substantially efficient because the materials used in them are quite costly and waste a considerable amount of energy, mostly as heat, which subsequently reduces the efficiency of the cell and increases the overall price as well. These challenges can be dealt with by designing more efficient, economical systems of storage and manufacturing PV cells with high efficacy. Scientists and engineers are more inclined toward advanced technologies and material manipulation to enhance the efficiency of solar energy and reduce its cost. In this regard, substantial research is being carried out, especially on the structure of materials and advanced materials like nanomaterials and quantum dots. Due to their distinct electromechanical and material properties, carbon-based nanomaterials like carbon nanotubes, graphene, fullerene, and nanohybrids are being employed as the electrodes, transport layers, active layers, or intermediate (interfacial) layers of solar cells in this regard.

After the occurrence of a DC-side feeder faults on HVDC transmission systems, protection and fault detection systems are anticipated to minimize their onerous effects, by initiating fault-clearing actions such as selective tripping of circuit breakers. Following the successful fault clearance, a subsequent action of significant importance, is the meticulous estimation of its location as a means to accelerate the line restoration, reduce down-time, limit recovery and repair costs, and hence elevate the overall availability and reliability of the transmission system. In order to capture DC-side fault transients for protection and fault location applications, measuring equipment is required to be placed on HVDC installations. This paper focuses primarily on reviewing the available technologies from the perspective of enabling protection, fault location and automation applications in HVDC systems. The review constitutes a mapping of protection and fault location functions, against the available voltage and current measuring technologies, ultimately unlocking insights for selecting measuring equipment based on the desirable characteristics of protection and fault location systems. The review also revealed that the frequency characteristics of each sensing scheme, primarily refers to the bandwidth of the primary sensor, whereas the overall bandwidth of the complete measuring scheme may be further restricted by the secondary converter and corresponding data acquisition system and signal processing electronics. It was also identified that the use of RC voltage dividers has prevailed for voltage measurements for HVDC applications, due to their superior advantages. The choice of a suitable device for current measurement, depends mainly on the fault detection method used and the frequency range it operates. In particular, the review revealed that fault detection and protection methods are mainly concentrated in a frequency spectrum ranging from a few kHz to 100 kHz, while fault location methods require measurements with a frequency range from 100 kHz up to 2 MHz.

In recent decades, the rapid rise in electricity demand has compelled transmission and distribution systems to operate at almost their maximum capacity. This can pose numerous technical challenges such as excessive power losses, voltage and transient instabilities, as well as reduced power quality and reliability. Employment of Flexible Alternating Current Transmission System (FACTS) devices can be an effective approach to obviate such challenges and reinforce the power system functionality. Nevertheless, FACTS devices require a high initial investment, and hence their optimal allocation in terms of various aspects such as type, size and location is of utmost importance. This cannot be achieved without the deployment of optimization techniques. The aim of this paper is to provide a comprehensive review of the existing proposals for the enhancement of power system performance adopting FACTS devices. Adhering to that, an in-depth analysis is carried out, in which the most pertinent options are classified into specific groups based on their optimization objectives. Finally, a comparative analysis is accomplished in which the main attributes and drawbacks of each optimization technique are presented.

The increasing penetration of Distributed Generators (D.G.) into the existing power system has brought some real challenges regarding the transient response of electrical systems. The injection of D.G.s and abrupt load changes may cause massive power, current, and voltage overshoots/undershoots, which consequently affects the equilibrium of the existing power system and deteriorate the performance of the connected electrical appliances. A robust and intelligent control strategy is of utmost importance to cope with these issues and enhance the penetration level of D.G.s into the existing power system. This paper presents a Modified Particle Swarm Optimization (MPSO) algorithm-based intelligent controller for attaining a desired power-sharing ratio between the M.G. and the main grid with an optimal transient response in a grid-tied Microgrid (M.G.) system. The proposed MPSO algorithm includes an additional parameter named best neighbor particles (rbest) in the velocity updating equation to convey additional information to every individual particle about all its neighbor particles, consequently leading to the increased exploration capability of the algorithm. The MPSO algorithm optimizes P.I. parameters for transient and steady-state response improvement of the studied M.G. system. The main dynamic response evaluation parameters are the overshoot and settling time for active and reactive power during the D.G. connection and load change. Furthermore, the performance of the proposed controller is compared with the PI-PSO-based MG controller, which validates the effectiveness of the proposed M.G. control scheme in maintaining the required active and reactive power under different operating conditions with minimum possible overshoot and settling time.

The proportional integral derivative (PID) controller is one of the most robust and simplest configuration controllers used for industrial applications. However, its performance purely depends on the tuning of its proportional (KP), integral (KI) and derivative (KD) gains. Therefore, a proper combination of these gains is primarily required to achieve an optimal performance of the PID controllers. The conventional methods of PID tuning such as Cohen-Coon (CC) and Ziegler–Nichols (ZN) generate unwanted overshoots and long-lasting oscillations in the system. Owing to the mentioned problems, this paper attempts to achieve an optimized combination of PID controller gains by exploiting the intelligence of the whale optimization algorithm (WOA) and one of its recently introduced modified versions called improved whale optimization algorithm (IWOA) in an automatic voltage regulator (AVR) system. The stability of the IWOA-AVR system was studied by assessing its root-locus, bode maps, and pole/zero plots. The performance superiority of the presented IWOA-AVR design over eight of the recently explored AI-based approaches was validated through a comprehensive comparative analysis based on the most important transient response and stability metrics. Finally, to assess the robustness of the optimized AVR system, robustness analysis was conducted by analyzing the system response during the variation in the time constants of the generator, exciter, and amplifier from −50% to 50% range. The results of the study prove the superiority of the proposed IWOA-based AVR system in terms of transient response and stability metrics.

Abstract Current Transformer (CT) modeling, by which CT’s characteristics can be studied has a significant importance in CT selection and design. In spite of numerous studies about closed-core CT model, only a few works have been conducted on air-gapped ones with the following problems: models of which required data is easily-accessible, have poor simulations of magnetization process; on the contrary, models which have satisfactory simulations, are hard to be established because of the hard-to-get required data. Therefore, based on Preisach Theory, a novel air-gapped CT model is deduced from the closed core CT model. The proposed model is accurate and can be established easily. The saturation and remanence properties of closed-core CT and air-gapped CT are simulated and compared.