https://joape.uma.ac.ir/ Journal of Operation and Automation in Power Engineering en daily 1 Sat, 01 Aug 2026 00:00:00 +0330 Sat, 01 Aug 2026 00:00:00 +0330 https://joape.uma.ac.ir/article_3567.html Electricity pricing approaches are generally categorized into flat-rate and dynamic pricing models. Flat-rate pricing charges a fixed rate regardless of market conditions, whereas dynamic pricing adjusts rates based on system and market factors. Traditional pricing methods often lack flexibility, preventing consumers from choosing their preferred pricing plans. This study introduces a Selective Electricity Pricing (SEP) model that allows customers to select a Maximum Tolerable Price (MTP) tailored to their needs and benefit from Real-Time Pricing. The SEP model also includes a retailer-funded mechanism to shield customers from high market prices, acting as a risk hedge. Using a risk aversion function to gauge consumer preferences, the SEP method was implemented on the IEEE-24 test system. Results indicate that low-risk customers are more likely to engage in dynamic pricing. The SEP model significantly outperforms flat-rate pricing, yielding 17.27% higher retailer profits, 11.32% lower demand, and a 2.73% increase in average customer payments, compared to a 2,500MW drop under flat-rate pricing. https://joape.uma.ac.ir/article_3568.html Optimal load distribution in power systems is crucial for minimizing overall costs while adhering to technical constraints. This process becomes increasingly complex with the integration of wind energy due to the inherent uncertainty in wind turbine production caused by variable wind conditions. This paper presents a novel approach to address these uncertainties within the context of the optimal power flow (OPF) problem by employing Information Gap Decision Theory (IGDT). Unlike traditional scenario-based methods, IGDT provides a computationally efficient and reliable framework for decision-making under uncertainty without extensive probabilistic data. The methodology uses the Weibull probability density function to model wind speed, allowing for realistic estimation of wind farm output power. The Evolutionary Particle Swarm Optimization (EPSO) algorithm, an advanced version of PSO, is utilized to solve the optimization problem, reducing the risk of convergence to local optima. Results are computed under two strategies: risk-averse and risk-taking, represented by immunity functions. These strategies highlight the impact of user demand on adjusting calculation parameters. Comparative analysis with scenario-based probabilistic optimization shows that the IGDT approach enhances system load cost evaluation by 0.12%. This study provides a robust framework for optimal power allocation under uncertainty, ensuring resilient and secure power generation. https://joape.uma.ac.ir/article_3569.html The present study proposes a bidirectional DC-DC converter (BDC) that uses a non-isolated coupled-inductor (NI-CI). This converter can transfer power bidirectionally between the DC bus of a microgrid, supplied by PV or other renewable sources and energy storage system. The device exhibits a high voltage conversion ratio while using a few components. The converter applies the input inductance as a current ripple filter and uses a CI configuration to enhance the gain in boost mode. Also, the turns ratio of a coupled inductor is implemented to enhance the voltage conversion ratio to lower voltage stress. In addition, the converter’s operation is more efficient considering its soft-switching advantages. The duty cycle control is applied to generate the desired voltage on both sides of the converter by controlling the corresponding power switch. It is worth noting that the low-voltage side current ripple is not significant. Besides, the results show an increase in voltage gain throughout boost mode and a decrease in voltage gain in the buck mode. Furthermore, the converter is mathematically studied in the following, and a PID converter is designed to illustrate the converter’s stability. Finally, the practicality of the proposed NI-CI-BDC structure was validated by incorporating experimental results from a 200-watt prototype. https://joape.uma.ac.ir/article_3682.html Permanent magnet brushless DC (BLDC) motors are increasingly preferred in industrial applications, particularly for low- and medium-power scenarios, due to their commutator-free operation, higher efficiency, reduced maintenance, compact size, and versatile speed control. This work presents the development of an enhanced BLDC motor prototype with concentrated windings, specifically tailored for aircraft actuator applications. The primary objective is to maximize electromagnetic torque and torque per kilogram through a novel dimensional optimization approach. A systematic design procedure, incorporating sensitivity analysis and finite element method (FEM) modeling, was established to identify and optimize key parameters affecting overall performance. Our results demonstrate significant improvements in power density, torque-to-weight ratio, and efficiency compared to conventional designs, offering a robust solution for the demanding requirements of aerospace applications. https://joape.uma.ac.ir/article_3786.html This paper utilizes the Fast Fourier Transform (FFT) technique to extract the apparent power of DC microgrids for fault detection. The proposed method separates the real and imaginary components of power and compares the imaginary part with a predetermined threshold. To determine the relay threshold, PP and PG faults are simulated at various distances along each line connected to each bus. The Inverse Fast Fourier Transform (IFFT) is then calculated for each fault at each line and location. The relay threshold is selected based on the lowest significant value among the highest IFFT values calculated for all microgrid lines. This study proposes a novel relay threshold calculation approach, enabling precise fault detection and localization in DC microgrids. The relay threshold value is calculated at the control center and then sent to the microgrid relays. Fault detection is achieved by comparing the IFFT values obtained within the microgrid with the relay threshold value. Once the relay threshold is surpassed, the microgrid detects the fault and promptly sends a trip signal to the circuit breaker. This fault detection strategy accurately identifies the fault location by measuring the current and voltage between the terminals of the faulty section. The proposed method swiftly detects all PP and PG faults (including HIF up to 50 ohms) in grid-connected and islanded modes within 2-3 milliseconds. It accurately locates faults with minimal deviation across various positions. Rigorous simulations using MATLAB and EMTP-RV programs confirm the effectiveness of the protection scheme, emphasizing its reliable performance. https://joape.uma.ac.ir/article_3699.html Load-frequency control plays a critical role in maintaining the stability and reliability of islanded microgrids, where the absence of a large interconnected grid makes frequency regulation more challenging. With the increasing integration of renewable energy sources and energy storage systems, the stochastic and uncertain nature of µGs component’s behavior has amplified the need for advanced LFC mechanisms, making it a focal area of research for decades. This paper introduces a novel parallel process FOPI–FPOD controller optimized for robust LFC and stability in µGs. Employing time and frequency domain objective costs, a multi-objective particle swarm optimization algorithm with nonlinear time-varying coefficients generates a Pareto front, with fuzzy decision-making selecting optimal designs. The proposed controller demonstrates strong robustness by effectively handling uncertainties such as sudden load changes, RES fluctuations, and parametric variations, while maintaining stable frequency regulation. The controller's performance is evaluated under four scenarios: sudden load changes with time delays, uncertainties in RESs, parametric system uncertainties, and energy storage systems' impact. Comparative analysis with PID, FOPID, and PD(1+PI) controllers demonstrates the proposed design's superior stability and resilience, providing a robust solution for frequency stabilization in µGs. Numerical results demonstrate that the proposed FOPI–FOPD controller significantly outperforms traditional methods, achieving lower error indices, reduced frequency deviations, and more efficient utilization of energy storage systems under various scenarios and energy storage systems participation levels. These findings highlight its robust and adaptive performance in ensuring stable and efficient LFC task for an islanded µGs control. https://joape.uma.ac.ir/article_3787.html Due to recent developments in communications and the increasing penetration rate of distributed generation (DGs), new players in the energy market, known as prosumers, have emerged. Prosumers can both produce and consume power, offering benefits such as on-site power consumption, peak shaving, and postponing the power transmission network investment costs. This paper presents a two-stage day-ahead peer-to-peer pricing and power exchange model among local market participants, including the upstream grid, consumers, prosumers (equipped with rooftop solar panels), and electric vehicles. In the first stage, initial pricing is determined using the mid-market rate pricing method, taking into account each participant's declared demand and the forecasted solar production of prosumers. In the second stage, the random behavior of electric vehicles is modeled through scenario generation, and their dynamic behavior is incorporated into the pricing scheme. The proposed model aims to minimize two objectives: trading costs and electrical power losses due to the exchange of power among participants. This two-objective problem is reformulated as a single objective using the epsilon-constraint method. The resulting MINLP model is solved in GAMS using the DICOPT solver, and the best-compromised solution is identified through the Min-Max method. Simulation results indicate a 6.7% reduction in costs, with all participants benefiting economically. Additionally, on-site interactions led to a decrease in congestion on two lines connecting to the upstream grid by 5.02% and 6.66%, respectively. https://joape.uma.ac.ir/article_4258.html A mandatory issue in the control of a permanent magnet synchronous motor (PMSM) drive is to overcome its nonlinear dynamic characteristics. This challenge becomes more severe when a multilevel diode-clamped inverter (DCI) is used for supplying the PMSM drive. In this case, it is indispensable to provide an accurate speed tracking performance as well as the capacitor voltage balance. The model predictive control (MPC) approach can properly solve this issue by considering different objectives in the cost function of the controller. This paper concentrates on the model predictive current control (MPCC) of the PMSM drive fed through a 4-level DCI. A comparative assessment of the 4-level DCI and 2-level VSI is performed in the PMSM drive with the MPCC approach. Different objectives are considered in the MPCC process including the d-q current control, limitation of the stator currents, voltage balance of capacitors, mitigating of the CM voltage, and reduction of the switching frequency. Simulation results reveal the superior dynamic performance and capacitor voltage balance in the suggested MPCC of PMSM drive with the 4-level DCI. Results manifest that the current total harmonic distortion (THD) and the torque ripple are lower in the PMSM drive with a 4-level DCI than with the 2-level VSI. The current THD is reduced from 8.61% in the 2-level VSI to 4.59% in the 4-level DCI. Moreover, the torque ripple is reduced from 1.2 Nm in the 2-level VSI to 0.32 Nm in the 4-level DCI. https://joape.uma.ac.ir/article_3680.html This paper presents a comprehensive review of the operation management of multi-microgrid systems. The multi-microgrid systems focus on the coordination between neighbor microgrids in the distribution systems. The microgrids integrate different distributed energy resources to supply the required loads. Renewable resources, dispatchable resources, battery energy storage systems, and demand-side management including price-based and incentive-based demand response programs are common distributed energy resources in microgrid systems. This review paper studies the application of different distributed energy resources and provides to determine the application of which distributed energy sources are significant in microgrid systems. Besides, this paper divides the multi-microgrid systems into single and multi-carrier systems. The multi-carrier multi-microgrid systems integrate the electrical, heating, cooling, hydrogen, and water systems to increase the flexibility of the system. In this paper, the structure of multi-carrier multi-microgrid systems also has been studied. This review work lists the advantages/disadvantages of different research works in multi-microgrid fields. The outlines and results of this review show that operating costs are the main objective of multi-microgrid systems. Also, uncertainty modeling for renewable energy is an integral part of energy management. Besides, the review work shows that multi-microgrid systems are developing towards multi-carrier energy systems to improve synergy. https://joape.uma.ac.ir/article_3681.html This paper presents a comprehensive model to evaluate the potential of integrating renewable energy sources (RESs) and battery energy storage systems (BESS) in the residential sector. The proposed model is applied to real-world residential customers to determine the optimal strategy for photovoltaic (PV) installation under different pricing schemes, including the Feed-in Tariff (FIT) and Net Energy Metering (NEM) mechanisms. To this end, the model accounts for capital cost, installation cost, maintenance cost, and replacement cost of PV units and battery energy storage systems. The analysis employs the Net Present Value (NPV) metric to assess the future cash flows of a residential customer and evaluate PV installations under different tariff structures, such as actual and subsidized electricity prices. The residential customer is connected to the distribution network and can interact with the distribution operator to maximize the benefits of their resources. Furthermore, customers can utilize the BESS to increase their flexibility in energy management. The proposed model is tested on a real case study in Tehran city, and the simulation results indicate that the payback period under the FIT mechanism is less than four years, making this strategy more beneficial for residential customers. https://joape.uma.ac.ir/article_3788.html Radial flux brushless DC motors with surface-mounted permanent magnets offer several advantages, but they are also characterized by a significant drawback: high cogging torque. Mitigating cogging torque is a critical challenge in the design of brushless direct current motors, particularly in applications such as electric vehicles. This article presents three approaches to reduce cogging torque in radial flux permanent magnet brushless motors: teeth edge inset width variation, magnet tip depth variation, and a hybrid approach combining both techniques. The teeth edge inset width variation method involves reducing the inset width of the stator teeth, while the magnet tip depth variation approach addresses the depth of the magnet's edge inset on the rotor core surface. The hybrid approach integrates changes to both the stator teeth and rotor magnet poles. Additionally, the study investigates how these approaches affect the average torque and flux density distribution. Finite element analysis was conducted to simulate and analyze a 1000 W, 510 rpm radial flux brushless DC motor. The results show that the proposed methods effectively reduce cogging torque, demonstrating their potential to enhance the performance of these motors in practical applications. https://joape.uma.ac.ir/article_3954.html Accurate state estimation is crucial for the effective control and management of power grids, as it provides a comprehensive understanding of voltage magnitude and phase angle at network buses. Incorrect estimations may lead to damaging decisions and network collapse. This paper addresses the significance of precise state estimation in distribution networks and proposes an efficient algorithm for optimal measurement device allocation, aiming to minimize estimation errors. The algorithm considers both investment and technical constraints, utilizing an optimal alternative current (AC) power flow model that eliminates the need for exact values of active and reactive load demands. The proposed method identifies optimal locations for installing a specific number of phasor measurement units (PMUs) across the network. The application of the algorithm to 33-bus and 69-bus test systems demonstrates its effectiveness in enhancing state estimation accuracy. Results reveal that optimizing the number and location of measurement devices significantly improves outcomes. A comparative analysis with the conventional weighted least squares (WLS) algorithm underscores the applicability of the proposed model, particularly in distribution networks with limited measurement devices. The proposed method formulates optimal meter placement problems in distribution networks based on an optimal power flow model, which has a superior performance in both accuracy and convergence without needing to exact nodal demands for state estimation. This research contributes to the advancement of state estimation procedures, offering a practical approach to enhance accuracy and reliability in power grid management. https://joape.uma.ac.ir/article_3955.html Partial discharge in the power systems equipment can cause the destruction of the insulation system and the occurrence of accidents. There are various methods to identify and measure partial discharge, one of which can be mentioned as the acoustic method. Commercial sensors in the market usually have a high price. For this issue, a low-cost piezoelectric sensor and a built-in low-noise preamplifier and filter for partial discharge measurements have been designed. In this article, the effect of the sensitivity and frequency response of the acoustic sensor on time and frequency domain measurements has been discussed. The performance of the proposed AE sensor has been investigated through simulation and experimental studies. The obtained results indicate that the proposed low-cost AE sensor has a good performance and is capable of PD monitoring within power transformers. https://joape.uma.ac.ir/article_4148.html The increasing complexity of current power systems has made improving their dependability and operational efficiency a primary focus of research. The dynamic performance and adaptability of conventional low-frequency transformers equipped with tap changers are inherently limited. As an alternative, Solid-State Transformers (SSTs) are favored due to their smaller size, higher reliability, and improved controllability. However, their nonlinear behavior, numerous switching devices, and passive elements like capacitors demand advanced, high-speed, and robust control strategies to unlock their full potential. Therefore, achieving stable output voltage and regulated input current through an effective Dual-Loop control (DLC) mechanism is essential. In this study, an optimal design of the DLC strategy is proposed for a power factor correction converter, which serves as the front-end stage of an SST. This combined control scheme merges a fractional-order proportional-integral (FOPI) controller with a conventional PI regulator. The controllers are designed to maintain voltage and current ripples within acceptable thresholds, even under load disturbances and changes in system parameters. To achieve this, DLC tuning is carried out using the Coati optimization algorithm, aiming to reduce the ISTSE cost function as much as possible and improve overall system behavior. To verify the effectiveness of the proposed strategy, a thorough comparison is carried out with other control techniques, including standard single-loop control (SLC), PI-PI-based DLC, and the proposed PI-FOPI-based DLC, under different operating conditions and using various optimization algorithms. The numerical results clearly demonstrate that the proposed design significantly enhances the converter’s dynamic behavior compared to the typical SLC configuration, achieving up to 77% better performance in terms of rise time and settling time. https://joape.uma.ac.ir/article_4174.html The integration of solar generation and Energy Storage Systems (ESSs) into ship power systems has gained increasing attention. This trend is primarily driven by stringent Marine Pollution Protocol regulations and the increasing integration of Renewable Energy Sources (RESs). Integrating RESs and BESSs into ship power systems helps reduce pollutant emissions from fossil fuel generators. However, inadequate sizing of hybrid ship power systems may result in high investment costs and elevated greenhouse gas emissions. This article introduces a Mixed-Integer Linear Programming model for identifying the optimal configuration of RESs and BESSs. The model incorporates two objective functions, aiming to minimize both costs and pollutant emissions. In the proposed model, the possibility of using four different technologies -lead-acid, nickel-cadmium, lithium-ion, and sodium-sulfur- has been considered for BESSs. For optimal energy management of a hybrid ship power system under photovoltaic radiation uncertainty along the route, Information Gap Decision Theory has been utilized. Considering the two contradictory objective functions in the proposed model, the ε-constraint method has been used to determine Pareto optimal responses, and the fuzzy inference method has been used to determine the final optimal response. The proposed model has been evaluated through four distinct case studies. The analysis of the results shows that using the optimal sizing of RESs and BESSs can lead to a simultaneous reduction in costs and emissions. https://joape.uma.ac.ir/article_4175.html In an islanded microgrid, ensuring frequency stability is essential for reliable system operation. Distributed generation (DG) and electric vehicles (EVs) make frequency stability challenging in an islanded microgrid because they increase generation and load variability and reduce system inertia. Load frequency control (LFC) is mainly used to enhance the frequency response of these types of microgrids. In addition, uncertainty in parameters and perturbations strongly impact the application of LFC. To address these challenges, this paper presents an LFC method for islanded microgrids using model predictive control (MPC) based on deep learning. The deep learning technique is used to enhance MPC controller performance against uncertainties and disturbances. The proposed method is validated through experiments, especially in the presence of disturbances and parameter instability. It is then compared with other methods, including linear active disturbance rejection control (LADRC), fractional-order PID (FOPID), and several others. The results show that the MPC method based on deep learning outperforms these approaches in terms of disturbance rejection, frequency response improvement, and system inertia enhancement. https://joape.uma.ac.ir/article_4176.html This paper presents a novel power management scheme for hybrid AC/DC microgrids (HMGs), focusing on improving power sharing, voltage control and system stability using Virtual Synchronous Generator (VSG)-based interlinking converters (ILCs). The proposed approach integrates a multi-layered control framework that employs adaptive droop control to coordinate the AC and DC subsystems in real time, responding to load variations and bidirectional power flow. The system is equipped with two ILCs: the first (ILC1) facilitates interlinking and energy exchange between the AC and DC sub-grids, while the second (ILC2), integrated with a bidirectional DC/DC converter, manages the DC-link voltage and enables efficient power transfer. A Battery Energy Storage System (BESS) is placed between ILC2 and the DC-link to stabilize power fluctuations. The VSG method, leveraging virtual inertia, is employed to counteract the negative impacts of fluctuating renewable energy sources, such as wind power and photovoltaic (PV), thereby enhancing the system's dynamic behavior and stability. This strategy mimics synchronous generator inertia, ensuring reliable frequency and voltage regulation. Simulation studies in MATLAB/Simulink validate the effectiveness of the proposed scheme, demonstrating significant improvements in power sharing efficiency and power quality. https://joape.uma.ac.ir/article_4177.html The increasing integration of inverter-based sources in microgrids demands advanced control strategies to maintain power quality by mitigating harmonics and distortion. This study proposes an enhanced control system for grid-connected inverters, integrating a proportional-integral (PI) controller in the synchronous rotating frame with a repetitive control (RC) compensator. A particle swarm optimization (PSO) algorithm is employed to optimize the controller parameters, ensuring effective harmonic suppression. The proposed PI+RC controller is evaluated through simulation studies under various scenarios, including linear and nonlinear loads as well as grid voltage distortion. Results demonstrate a significant reduction in total harmonic distortion (THD), with the proposed controller achieving a reduction from 37.5% to 5.6% under nonlinear load conditions and from 49.5% to 5% when both nonlinear loads and voltage distortion are present. Additionally, the proposed method effectively stabilizes the active and reactive power outputs, surpassing conventional PI controllers. https://joape.uma.ac.ir/article_4196.html The adoption of photovoltaic (PV) energy is growing rapidly, leading to the installation of numerous solar power plants to meet rising electricity demand. Among the various operational challenges, partial shading significantly reduces the power output of PV panels. During routine maintenance, panels are typically cleaned to remove debris such as dust, dirt, or bird droppings—common causes of shading. However, shading severity varies across panels, especially in large PV installations, making uniform cleaning inefficient. To address this, the paper proposes a machine learning-based methodology for detecting and quantifying partial shading at the panel level. By analyzing power output, irradiance, and ambient temperature data, the proposed approach identifies the panels most affected by shading and classifies the severity into low, medium, and high categories. This enables smart maintenance prioritization and early fault prediction, preventing issues such as hotspots and module degradation while reducing operational costs. In a comparative study of eleven Machine Learning models, the ensemble bagged trees algorithm achieved the best performance 90% accuracy in identifying the most affected panels and 93.5% accuracy in classifying shading levels. The proposed solution is well-suited for real-time deployment in solar parks, offering an effective tool for predictive maintenance and optimized plant operations. https://joape.uma.ac.ir/article_4248.html Insulators of overhead transmission lines are continuously exposed to various environmental conditions. Factors such as pollution, humidity, temperature, and electrical stress negatively affect their performance. Environmental stresses can reduce surface resistance, increase leakage current, and ultimately lead to the flashover voltage of insulators. As a result, overhead transmission lines and some electrical equipment become disconnected from the power network. This can cause interruptions in energy transmission and reduce electrical grid reliability. In this paper, the flashover voltage of porcelain and glass insulators with artificial/natural pollution, as well as under clean conditions (non-pollution) and different levels of humidity and temperature, is measured, and the results are evaluated and analyzed. The experimental findings show that there is a mathematical correlation between the flashover voltage of the polluted insulators and the various levels of humidity and temperature. The coefficients of the model are determined in such a way that the results of the presented model are in good agreement. Besides, the experimental results indicate that the increase in temperature has a significant effect on the behavior of the insulators examined under different pollution and humidity conditions. The results show that the flashover voltage of the insulators decreases between 4% and 41% under constant pollution and humidity with different temperatures. It was also revealed that it is possible to estimate the flashover voltage of the insulators under different humidities using the correction factor of the flashover voltage. https://joape.uma.ac.ir/article_4259.html     Among different renewable resources, wind power and solar photovoltaics (PVs) have the most desirable technical and economic prospects. However, the significant penetration of such low inertia power plants in power grids causes a decrease in the system's total inertia and thereby leads to a decrease in system frequency deviation (SFD) more than the acceptable range. This paper presents a distributed (D-SCUC) problem in which system frequency deviation is considered as a constraint to prevent system frequency deviation more than the pre-determined level. With the proposed method, the system partitions into several areas wherein a SCUC problem is separately solved, and the analytical target cascading (ATC) method is used to coordinate these sub-systems. To avoid the masking effect, a modified penalty function is used. A simple 6-bus network and the modified IEEE RTS 24-bus test system are used as the case study. The results show the effectiveness of the D-SCUC technique, especially in large power systems, and therefore, the system operator's concern about the system frequency is relieved. https://joape.uma.ac.ir/article_4260.html This paper presents and investigates a high step-up non-isolated DC-DC converter with an integrated transformer for photovoltaic applications. Unlike most non-isolated DC-DC converters that rely on coupled inductors, the proposed converter uses an integrated transformer combined with a modified voltage multiplier cell to achieve a high voltage conversion ratio. Additionally, a passive voltage clamp is employed to reduce voltage stress across the switch and recycle energy stored in the leakage inductance of the integrated transformer. This approach allows for the use of a switch with low on-resistance (RDS-ON). Other notable advantages of the proposed structure include continuous input current with low ripple and a shared common ground between the input source, switch, and load, making it an attractive solution for photovoltaic-based systems. The relatively low voltage across the diodes also mitigates reverse recovery issues. The proposed topology is derived from the SEPIC converter and is studied in detail. Furthermore, the proposed configuration is compared to various previously presented DC-DC converters to demonstrate its advantages. Finally, a 250-W laboratory prototype is developed, and experimental results are presented. These results validate the theoretical analysis and confirm the functionality and feasibility of the proposed converter. https://joape.uma.ac.ir/article_4262.html This paper introduces a novel protection method for identifying and locating faults in DC microgrids, which is aimed at overcoming the challenges faced by modern power systems. A two dimensional current modeling technique is utilized to detect faults, in which even minimal changes in the sampled data result in rapid detection due to the model's sensitivity. Additionally, the method differentiates between transient and permanent faults and is robust against noise in sampled signals. Furthermore, a deep learning model based on long short term memory layers, optimized using the whale optimization algorithm, is applied for fault location. The deep learning model's layers are fully aligned with the data, and the optimization process enhances the model's accuracy. The proposed scheme operates without relying on extensive communication links, making it practical for real world applications. Comparative evaluations demonstrate that the system outperforms existing methods in terms of accuracy, speed, and reliability, confirming its effectiveness in DC microgrid protection. The deployment of the proposed method effectively identifies and pinpoints faults at various locations within the microgrid in as little as 1 millisecond and within PV and EV components in up to 11 milliseconds. This capability has been validated across a range of fault types and impedances. Additionally, the method has demonstrated reliable performance despite noisy conditions, maintaining accuracy with a signal to noise ratio of 40 dB. https://joape.uma.ac.ir/article_4263.html The distance relays on transmission lines connecting Doubly Fed Induction Generator (DFIG) wind farms with a Static Synchronous Compensator (STATCOM) face challenges in ensuring reliable protection due to the system unique fault characteristics and varying operating modes. This work presents a new relaying method that integrates Particle Swarm Optimization (PSO) with Variational Mode Decomposition (VMD) and Dominant Mode Filtering-Teager-Kaiser Energy Operator (DMF-TKEO) for fault detection. For fault classification, it employs a combination of PSO-VMD and a Modified Jellyfish Optimization-tuned Random Vector Functional Link (MJO-RVFL) network. The fault detection technique focuses on optimizing the parameters (α and K) of the existing variational mode decomposition by minimizing the mean envelope entropy. The optimal Intrinsic Mode Functions (IMFs) are then derived, from which the dominant mode is identified using the Pearson correlation and fault detection is accomplished through the Teager-Kaiser energy operator. In the proposed fault classification framework, the grid-side currents are decomposed using the particle swarm optimization-based variational mode decomposition. The resulting optimal IMFs are employed to identify the most appropriate IMF, which is subsequently used to extract energy features. These features are then provided as input to MJO-RVFL network for fault classification. To assess the effectiveness of the proposed protection scheme, different fault and non -fault scenarios are created on a two-bus test power system through MATLAB/Simulink. The results demonstrate the effectiveness of the proposed protection scheme, confirming its suitability for securing such critical transmission lines. The proposed method gives 100% fault detection accuracy and 99.97% accuracy for fault classification. Furthermore, the proposed classifier achieves the performance metrics like Precision (0.995), Recall (0.992) and F1-Score (0.994), providing quantitative insights into its accuracy and dependability Finally, a proposed algorithm is compared with similar works in literature. https://joape.uma.ac.ir/article_4264.html The increasing integration of photovoltaic (PV) panels and electric vehicles (EVs), both direct current (DC) sources and loads, has introduced significant transformations in modern distribution networks (DNs). Consequently, the DN now accommodates both alternating current (AC) and DC loads and generators. To effectively manage this evolving structure, the implementation of an AC-DC distribution network (ADDN) has emerged as a promising solution. However, due to the random nature of load demands and the variable output of renewable distributed generations (DGs), the network may face challenges such as line loading violations and bus voltage constraint breaches, posing significant operational risks. To address these uncertainties, the K-means algorithm has been employed for uncertainty modeling. Additionally, extreme events characterized by high costs but low probabilities of occurrence have been incorporated into the analysis. This paper investigates the AC-DC distribution network planning (ADDNP) under such conditions, focusing on minimizing the financial impact of extreme events. The average value at risk (AVaR) criterion has been applied to identify and manage these high-impact scenarios. The proposed model is validated on a 13-bus DN, considering varying confidence levels and voltage tolerance values, demonstrating its effectiveness in mitigating operational risks. https://joape.uma.ac.ir/article_4265.html     Excessive reliance on conventional energy sources and inadequate load management strategies have triggered severe climatic impacts and created an imbalance between energy supply and demand. To address this issue, integrating smart, controllable devices with renewable energy sources and battery energy storage systems within the distribution grid enables consumers to participate actively in demand-side management strategies. Demand-side management enhances demand flexibility, reduces energy procurement costs, minimizes peak load fluctuations, and decreases emissions. This study introduces a novel demand-side management framework that includes a life cycle analysis of hybrid renewable energy systems optimized using the Modified Coati Optimization Algorithm. A hybrid renewable energy system comprising solar photovoltaic panels, wind turbines, and battery energy storage systems is deployed across the residential, commercial, and industrial sectors to assess the impact of demand-side management on cost savings, peak demand reduction, and emissions mitigation. The proposed optimization method is compared with Particle Swarm Optimization, Grey Wolf Optimization, and the standard Coati Optimization Algorithm. The results indicate that the Modified Coati Optimization Algorithm achieves cost savings of 31.45%, 31.89%, and 28.21% in the residential, commercial, and industrial sectors, respectively. Peak load reductions of 37.64%, 29.37%, and 25.63% are also achieved. Significant emission reductions are documented as totaling 3,743.11 kilograms of carbon dioxide, 28.37 kilograms of sulfur dioxide, and 17.25 kilograms of nitrogen oxides. The improved convergence of the Modified Coati Optimization Algorithm strengthens the implementation of demand-side management, making it a vital tool for optimizing energy management in smart grids. https://joape.uma.ac.ir/article_4266.html This study presents an advanced Frequency Adaptive Sliding Discrete Fourier Transform-based Phase-Locked Loop (FASDFT-PLL) for grid synchronization and power quality enhancement in distribution networks. Traditional PLL techniques, such as SDFT-PLL and MSTOGI-PLL, often struggle with phase inaccuracies, slow convergence, and poor tracking under distorted grid conditions or frequency variations. The proposed FASDFT-PLL dynamically adjusts the observation window size in real time, enabling accurate extraction of the fundamental voltage component, phase angle, and frequency, even with various zero crossings, harmonics, and nonlinear loads. To validate its performance, the method is tested under multiple conditions. Compared to conventional techniques, FASDFT-PLL exhibits faster convergence, higher phase tracking accuracy, and improved robustness against frequency deviations and harmonic distortions. The proposed method is further integrated with a shunt active power filter (SAPF), demonstrating its effectiveness in maintaining power quality and reducing harmonic distortion. Simulation results confirm that FASDFT-PLL significantly outperforms existing PLL algorithms, making it a promising solution for modern power systems. https://joape.uma.ac.ir/article_4267.html Accurate solar photovoltaic power (SPVP) generation forecasting is vital for integrating solar energy into the power grid. This paper presents an advanced forecasting model using a Bayesian enhanced Long Short-Term Memory neural network (BLSTM NN) model optimized by the Particle Swarm Optimization (PSO) algorithm to elevate the accuracy and reliability of SPVP generation forecasting. The hyperparameters of the BLSTM NN are optimized using the PSO algorithm, resulting in improved forecasting performance. The model is evaluated using a comprehensive dataset comprising five years of historical data from a 1 MW SPVP plant in South India, sampled at 15-minute intervals. Key performance indicators, including Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and R-squared, are used for various analyses. The performance of the proposed model is evaluated through accuracy, uncertainty, scalability, and sensitivity analyses. Experimental results highlight a 16.02% reduction in RMSE, a 22.84% reduction in MAPE, and a 24\% improvement in R² over the conventional baseline DB model. The outcomes underscore the capability of the proposed model to deliver superior forecasting accuracy. The approach helps integrate reliable and efficient solar power into grid planning.         https://joape.uma.ac.ir/article_4294.html The growing integration of renewable energy sources (RESs) has reduced the rotational inertia of power grids, traditionally supplied by large rotating generators. This reduction makes grids more vulnerable to frequency variations. To address this challenge, this paper introduces a new fractional derivative virtual inertia and damping control (FDVIDC) strategy for enhancing frequency stability in an islanded microgrid (IMG) network. A fractional-order proportional integral derivative (FOPID) controller is employed to regulate the active power output in a biopower-dominated microgrid system. The parameters of both the FDVIDC and FOPID controllers are optimized using a new meta-heuristic algorithm called the Multi-population African Vultures Optimization (MAVO), with the integral time absolute error (ITAE) criterion as the performance index. The effectiveness of the proposed MAVO algorithm is demonstrated using standard benchmark test functions and is compared with the original African Vultures Optimization (AVO), Whale Optimization Algorithm (WOA), and Particle Swarm Optimization (PSO). Simulation results confirm that MAVO achieves a 90.74% reduction in ITAE, with a settling time of 9.573 seconds and a steady-state error of 2.809, indicating its superior convergence and control accuracy over PSO as the baseline. Time-domain analysis further confirms that the FOPID controller outperforms conventional PI and PID controllers. The robustness of the proposed control strategy is assessed under various operating scenarios. Finally, the proposed technique is validated through the OPAL-RT platform. https://joape.uma.ac.ir/article_4354.html This paper proposes a robust active disturbance rejection control (ADRC) scheme for a push–pull DC–DC converter, addressing challenges posed by parameter uncertainties, load variations, and external disturbances. Unlike most existing studies on other converter topologies, this work develops and validates an ADRC controller specifically designed for the push–pull converter. First, the average model of the converter is presented, and then the system’s flatness property is used to design an ADRC controller based on a linear extended state observer (LESO) for total disturbance compensation. Next, a Lyapunov-based stability analysis proves the ultimate boundedness of the observer and tracking errors using a separation argument. Simulation and experimental results validate the effectiveness and superior performance of the proposed controller compared to proportional–derivative (PD) and proportional–integral–derivative (PID) controllers under various operating conditions, providing a fair comparison rarely addressed in existing studies, while maintaining the same control loop specifications.     https://joape.uma.ac.ir/article_4545.html Smart home energy management (SHEM) strategies effectively overcome the limitations of traditional methods by automating tasks through smart meters, appliances, and home automation systems, thus reducing manual effort and enhancing efficiency. Using Internet of Things (IoT) and artificial intelligence (AI) technologies, SHEM systems provide real-time optimization and precise adjustments, leading to quick identification and reduction of energy waste. They facilitate the integration and optimization of renewable energy sources like solar panels, improving sustainability and reducing reliance on grid electricity. Additionally, SHEM systems are scalable, accommodating the needs of larger or more complex homes while offering significant energy savings and enhanced convenience. Recent advancements include integrating advanced metering infrastructure, smart sensors, and home energy storage systems with supervisory control and data acquisition (SCADA) to manage energy generation, transmission, and distribution effectively. Despite the potential benefits, challenges remain, such as system complexity and the need for optimal control strategies. Thus, continued research and development are crucial for refining smart solutions and algorithms, ultimately enhancing energy efficiency, cost savings, and user comfort. The evolving role of smart homes and grids underscores the importance of collaboration among researchers, industry stakeholders, and policymakers to achieve a more sustainable, efficient, and secure future. This review explores SHEM systems, highlighting recent studies using advanced technologies like smart meters, IoT, AI, and other tools to improve energy efficiency, reduce costs, and integrate renewable energy, while addressing challenges.