High-voltage cables are crucial components of electrical transmission networks, but their performance and longevity are significantly influenced by environmental conditions. Weather factors such as temperature, humidity, and exposure to harsh weather elements can accelerate the degradation of cable insulation, leading to failures and reduced service life. This paper focuses on monitoring the high-voltage cables condition, with an emphasis on detecting the wear-out, failure level, type based on environmental conditions and failure type. The study introduces a wear-out function for high-voltage cables, derived from the monitored data, which enables the determination of the cable's failure level. It also explores the impact of environmental conditions on cable wear-out and identifies the specific damaged section. The proposed methodology provides comprehensive approach to cable maintenance, offering real-time detection of failure levels and types in relation to environmental factors. Key innovations of this study include the establishment of a wear-out function based on monitoring data, the determination of failure levels with consideration of data limitations and environmental conditions, assessment of environmental impact on cable failure, and identification of cable failure types for maintenance interventions. This approach aims to enhance the efficiency of maintenance teams by prioritizing repairs based on environmental influences and improving cable reliability.

To find the ideal sizes of PV, wind, hydrogen, battery, and fuel cell systems with electrolysers for remote areas (like Lavan Island), this study developed a generalized mathematical model that is independent of the electricity distribution network. The goal was to provide electricity at a lower cost while taking into account the development of a reliable supply of load and a reliability index check of the system. The current study proposes a fuzzy logic system to minimize the costs of a hybrid hydroelectric, wind, solar, and battery system while simultaneously calculating two reliability indices, loss of power supply probability (LPSP) and dump energy probability (DEP). The goal is to find the optimal system size based on future challenges. Next, using the Shuffled Frog Leaping Algorithm (SFLA), Grasshopper Optimization Algorithm (GOA), and Honey Badger Algorithm (HBA) to optimize the objective function, the ultimate minimum is determined. The research presents three algorithms that are used to compare the results of the suggested approach and demonstrate its superiority and reliability over alternative modes. Some statistical analyses are performed to display the most consistent results, the lowest execution time, the highest convergence speed, and the minimum objective function for comparison.

High-voltage (HV) cable systems—particularly those insulated with cross-linked polyethylene (XLPE)—are increasingly deployed in both AC and DC applications due to their excellent electrical and mechanical performance. However, their long-term reliability is challenged by partial discharges (PD), insulation aging, space charge accumulation, and thermal and electrical stresses. This review provides a comprehensive survey of the state-of-the-art technologies and methodologies across several domains critical to the assessment and enhancement of cable reliability. It covers advanced condition monitoring (CM) techniques, including sensor-based PD detection, signal acquisition, and denoising methods. Aging mechanisms under various stressors and lifetime estimation approaches are analyzed, along with fault detection and localization strategies using time-domain, frequency-domain, and hybrid methods. Physics-based and data-driven models for PD behavior and space charge dynamics are discussed, particularly under DC conditions. The article also reviews the application of numerical tools such as FEM for thermal and field stress analysis. A dedicated focus is given to machine learning (ML) and deep learning (DL) models for fault classification and predictive maintenance. Furthermore, standards, testing protocols, and practical issues in sensor deployment and calibration are summarized. The review concludes by evaluating intelligent maintenance approaches—including condition-based and predictive strategies—framed within real-world asset management contexts. The paper aims to bridge theoretical developments with field-level implementation challenges, offering a roadmap for future research and practical deployment in resilient and smart power grids. This review highlights a clear gap in fully integrated AC/DC diagnostic and aging analyses for XLPE cables. We emphasize the need for unified physics-based and ML-driven frameworks to address HVDC space-charge effects and multi-stress degradation. These insights provide concise guidance for advancing reliable and scalable cable assessment.

In this article, the energy management of the intelligent distribution system with charging stations for battery-based electric vehicles (EVs) and plug-in hybrid EVs, hydrogen station for fuel cell-based EVs, and renewable integrated energy systems (IESs) with hydrogen storage devices in accordance with the estimation of economic, operational, security and environmental goals of distribution system operator is presented. Hydrogen storage is used to store electric energy and feed hydrogen consumers. The methodology adopted here is expressed as a multi-objective formulation to be solved. Objective functions are minimizing the cost of buying energy by distribution system from the upstream network, minimizing distribution system energy losses, minimizing environmental emissions, and maximizing voltage security in the distribution system. In this issue, AC power flow model, operation and voltage security boundaries in the network, performance model of charging station for EVs, hydrogen station model for fuel cell vehicles, and renewable IES operation model with hydrogen storage is the boundaries specific to the problem. The problem in the single-objective model uses the Pareto optimization that relies on the sum of weighted functions method. Next, the fuzzy decision-making technique extracts an optimal compromised solution between the operational, economic, security and environmental objectives of the network operator. In the present scheme, load, energy prices, renewable phenomena, electric vehicles have uncertainty. In this article, stochastic optimization based on Unscented Transform is incorporated to provide a suitable modeling of the uncertain parameters appearing in the problem. Modelling of the performance of EVs charging station and hydrogen fulling station, using hydrogen storage as electricity energy storage and feeding hydrogen loads, energy management of renewable bio-waste and tidal units in IES, considering the different objectives of network operator, and using Unscented Transform approach to model of uncertainty parameters are the innovations of this article. Findings show that the method improves the technical, environmental and economic conditions of the grid, and the integrated system with its optimal performance is able to enhance the economic, environmental, security and operation status of the distribution system up to roughly 45.8%, 38%, 32-45% and 10.6%, respectively.

Brushless direct current (BLDC) motors are widely used in industrial applications due to their high efficiency and reliability. However, these motors exhibit inherent nonlinear and chaotic behavior, which can degrade performance and cause instability under certain operating conditions. This paper proposes a fractional-order sliding mode controller (FO-SMC) for robust chaos suppression and improved stability in BLDC motor systems to address this issue. The proposed controller leverages fractional-order calculus to enhance robustness, mitigate chattering, and provide better disturbance rejection than conventional control approaches. A comprehensive Lyapunov-based stability analysis is conducted to ensure finite-time convergence and system stability under parameter uncertainties and external disturbances. The effectiveness of the proposed FO-SMC is evaluated through extensive numerical simulations, comparing its performance against the integer-order sliding mode control method. The results demonstrate that FOS-MC significantly outperforms traditional controllers regarding settling time, overshoot reduction, and robustness to external perturbations. Additionally, the study explores the practical feasibility of implementing the proposed control strategy in real-time applications using Grunwald–Letnikov fractional derivatives, which enable efficient numerical approximation and digital implementation in field-programmable gate array-based and microcontroller-driven control systems. The findings confirm that FOS-MC provides a highly adaptive and resilient solution for stabilizing BLDC motors, making it a strong candidate for advanced industrial automation and high-performance motor control applications.

This study expresses energy scheduling in intelligent distribution grid with renewable resources, charging stations and hydrogen stations for electric vehicles, and integrated energy systems. In deterministic model, objective function minimizes total operating, energy losses and environmental costs of grid. Constraints are power flow equations, network operating and voltage security limits, operating model of renewable resources, electric vehicle stations, and integrated energy systems. Scheme includes uncertainties in load, renewable resources, charging and hydrogen stations, and energy prices. Robust optimization uses to obtain an operation that is robust against the forecast error of the aforementioned uncertainties. Modeling electric vehicles station and aforementioned integrated energy systems, considering economic, operational, and environmental objectives of network operator as objective function, extracting a robust model of aforementioned uncertainties in order to extract a solution that is robust against the uncertainty prediction error, and examining ability of energy management to improve voltage security of grid are among innovations of this paper. Numerical results obtained from various cases prove the aforementioned advantages and innovations. Energy management of resources, charging and hydrogen stations, and aforementioned integrated systems lead to scheme being robust against 35% of the prediction error of various uncertainties. In these conditions, scheme has improved economic, operational, environmental, and voltage security conditions by about 33.6%, 7%-37.4%, 44.4%, and 24.7%, respectively, compared to load flow studies. By applying optimal penalty price for energy losses and pollution, pollution and energy losses in the network are reduced by about 45.15% and 34.1%, respectively.

Combined renewable energy sources (RESs) are emerging as a competitive alternative to conventional energy production facilities due to their sustainability and zero-emission characteristics. However, determining the optimal system size is complicated by two major challenges: the cost of energy (COE) and the intermittent nature of RESs. This study introduces a novel mathematical approach to optimize the sizing of photovoltaic (PV), wind, hydrogen, battery, and fuel cell systems with electrolyzers, specifically tailored for the remote area of Lavan Island. The proposed method aims to deliver electricity without reliance on the traditional electricity distribution grid, while offering a scalable solution applicable to other geographical regions. The primary objective is to achieve cost-effective electricity generation while ensuring a reliable energy supply through the evaluation of system reliability indices. A fuzzy logic system is employed to minimize the costs of a hybrid system incorporating hydroelectric, wind, solar, and battery technologies, while simultaneously calculating two key reliability metrics: the Loss of Power Supply Probability (LPSP) and the Dump Energy Probability (DEP). To optimize the objective function, this study applies three advanced algorithms: the Shuffled Frog Leaping Algorithm (SFLA), the Grasshopper Optimization Algorithm (GOA), and the Honey Badger Algorithm (HBA). These algorithms are used to determine the global optimum, with comparative analyses conducted to highlight the performance of the proposed approach. The results are evaluated based on statistical metrics, including consistency, execution time, convergence speed, and the minimization of the objective function. The findings demonstrate the superiority and the reliability of the proposed method over alternative approaches, paving the way for cost-efficient and sustainable energy solutions in isolated regions.

Due to the extended length of transmission lines, electrical equipment is frequently exposed to various faults. To ensure a continuous supply of electricity to consumers and maintain the stability of the power grid, the implementation of reconnection schemes proves to be highly effective. In the case of transient faults, after the power is interrupted and the fault is cleared, the line can be re-energized through an automatic reconnection strategy. Upon the occurrence of a primary arc fault, the protection system is triggered, de-energizing the faulty phase by issuing commands to circuit breakers. However, due to the ionization of the air surrounding the defective phase, a secondary arc current remains at the fault location. Over time, as the insulation resistance of the arc path increases, the breakdown voltage rises, and after a few cycles, the secondary arc is fully extinguished. It is a common practice to reclose the circuit breaker after a predefined dead time; however, reclosing in the presence of permanent faults may compromise system stability. Therefore, it is essential to design a reclosing scheme capable of distinguishing between transient and permanent faults, as well as detecting the moment of secondary arc extinction. This paper aims to explore the potential of utilizing voltage phasor information from the transmission line's sending end to identify fault types and the moment of secondary arc extinction. To achieve this, various structural of the transmission line will be considered. The proposed network will be simulated using PSCAD, and the results will be analyzed accordingly.

One of the critical challenges in power system protection is ensuring proper relay coordination. To achieve optimal coordination of protection systems and ensure security, fast cleaning, and selection of types of faults is a necessary priority. This priority, when hosting synchronous distributed generations (SDGs), although technical factors like power loss reduction and voltage profile may lead to the loss of protection coordination in these networks. To address this challenge, a truth table is initially introduced to assist users in selecting the optimal settings for directional overcurrent relays (DOCR). The objective is to reduce the total operating time of the relays. Normally, the conventional coordination between the pair of primary and backup relays is achieved with two settings, which include setting the time factor and excitation current. In addition to these settings, two constant coefficients of reverse time characteristics, that is, relay characteristic and reverse curve type, are continuously considered to be optimized. Defining an index to determine the penetration of SDGs based on structure, the Thevenin impedance (Zth) of the distribution system, and the impedance of SDGs is also very important in this field. Finally, optimization of the problem, including minimizing the losses, improving the voltage and cost profile, and protection coordination indicators, will be expressed to solve the problem. In addition, DGs increase the fault current level. Hence, employing suitable fault current limiters (FCLs) can help restrict fault current levels and protect network equipment from potential damage under such conditions. The Whale Optimization Algorithm (WOA) evolution is used to achieve the solution of optimal by unique response. So, the efficiency of the proposed design is applied to the IEEE 30 and 34 busses. The obtained results show the positive effect of the proposed method in determining the amount of penetration of SDGs in distribution systems.

Today, micro-grids (MGs) include all kinds of energy storage systems (ESSs), wind turbines (WTs), photovoltaic (PV), combined heat and power (CHP), etc., also demand response are active on the demand side. In this paper, single-level robust methods for partitioning and planning the active distribution network (ADN) into several MGs are presented. According to the desired purpose, the objective function of the model is investment costs minimization for installing the capacity of distributed generations (DGs) and switches, the activity of responsive loads based on the forecast of the generation of non- DG, losses and the risk of the load points of the costumers. On the other hand, maximizing the income from the MGs energy sales to the upstream grid and the technical constraints include optimal power flow (OPF) equations. The mentioned problem is a complex nonlinear model, and therefore, the improved genetic algorithm (GA) is used. In order to validate the efficiency, the improved method has been used on a 25-bus ADN including five switches. The simulation results obtained from the case studies prove the fact that the use of the retrofitted model increases the investment costs of the MG, especially in the case of an island operation, in contrast to the active presence of responsive loads that significantly reduce costs.

This paper presents a model that explores the strategic bidding equilibrium within a microgrid and its interactions with other strategic and non-strategic rivals in a joint energy and balancing market. The model utilizes a tri-level mathematical program with equilibrium constraints (MPEC) to represent the behavior of each strategic producer. Upper level maximizes the profit of each strategic producer, including microgrid and another strategic rival. The first lower-level problem involves maximizing social welfare through the day-ahead market clearing process, while the second lower-level problem deals with the balancing market clearing process. These objectives form the core of the proposed model. To simplify the tri-level problem, duality theory and the Karush-Kuhn-Tucker (KKT) optimally conditions are employed to transform it into a mixed integer linear programming (MILP) problem. By simultaneously solving all MPECs, an equilibrium problem with equilibrium constraints (EPEC) is formulated. The resulting EPEC is then addressed using a diagonalization algorithm and game theory to obtain a market Nash equilibrium, which constitutes the secondary objective of this paper. The effectiveness of the model is evaluated using the 6-bus test system as a case study. The results indicate that, at the equilibrium point, both the microgrid and rivals experience reduced profits compared to the initial state.

Power system researcher have turned to Micro-Grids (MG) for higher reliability, greater flexibility, lower operating costs and losses, and lower CO2 emissions at the distribution system. This paper presents a single-level stochastic optimization framework for planning and partitioning of a distribution system including Multiple Micro-Grids (MMGs). The main objective is to minimize the total cost of the system including investment, operation, total losses and reliability costs of the distribution network. The proposed model takes into account the viewpoints of MG owners and distribution system operators, simultaneously. The voltage stability index is introduced to identify the optimal site of MG investment. To deal with uncertainties caused by renewable generations, the Firefly Algorithm (FA) and probability-tree method is used to create various operation scenarios of Photo-Voltaic (PVs) and Wind Turbines (WTs). This model is solved through the genetic algorithm in MATLAB, and to evaluate its effectiveness, numerical studies have been carried out on the experimental IEEE distribution network with four specified locations for investing MGs and seven Tie Switches (TS) for network partitioning. Simulation results reveal that optimal locations for MG investment are determined in such a way all MGs connect to the buses near the beginning of the feeder and as a result, load point reliability is improved, total active power losses are reduced, and the energy program becomes more optimized.

In the proposed protection coordination scheme, the depreciation of the operation time of the entire relay in the primary and backup protection modes for all possible fault locations is considered as the objective function. The limitations of this problem include the equations for calculating the operation time of the relays in both forward and reverse directions, the limitation of the coordination time interval, the limitation of the setting parameters of the proposed relays, the restriction of the size of the reactance that limits the fault current, and the limitation of the standing time of distributed generation per small signal fault. The operation time of the relays depends on the short circuit current passing through them, so it is necessary to calculate the network variables before the fault occurs. For this purpose, optimal daily power distribution should be used in the micro-grid, because micro-grids consist of storage and renewable resources. The proposed plan includes the uncertainties of consumption and generation capacity of renewable resources. Then, to achieve a reliable answer with a low standard deviation, the refrigeration optimization algorithm is used to solve the proposed problem. Finally, the proposed design is implemented on the standard test system in the MATLAB software, and then the capabilities of the proposed design are examined.

The chemical corrosion of metals in large industries such as oil and gas is a fundamental and costly problem. Gas transmission and distribution pipes and the other structures submerged in the soil and in an electrolyte, according to the existing conditions and according to the metallurgical structure, are corroded, and after a period of work, they disrupt an active system and process and lead to loss. The worst corrosion that occurs for metals embedded in the soil is where there are stray electric currents. Based on this, the cathodic protection of metal pipes is known as the most effective protection method to prevent the corrosion of structures buried in the ground, which is widely used to protect the corrosion distribution and transmission pipes of gas, oil, and water. In gas networks, current and voltage measurements for cathodic protection are carried out and recorded in specific periods according to the standards approved by the National Gas Company. The effect of stray currents on the obtained results is significant. The reason for this is that the available data is recorded as a time series, and as a result, the critical value of this time series will significantly impact the remaining life of the gas pipelines. Therefore, the purpose of this article is to investigate the stray currents effect on failure rate using normal probability distribution. In the following, the estimation of the remaining useful life of gas pipelines under cathodic protection is obtained using neural networks and compared with the results of the failure probability to check the accuracy of the results. According to the data history of the equipment, the amount of failure and the remaining useful life of the gas pipelines will be obtained.

Todays, Micro-Grids (MGs) include various types of distributed energy resources such as Wind Turbines (WT), PhotoVoltaics (PVs), Energy Storage Systems (ESSs), Combined Heat and Power units (CHPs), and demand-responsive loads. The variable nature of WT and PV resources and single contingencies of CHP units jeopardize the reliability of MG's customers during operation periods. Demand-side response program manages the time-consumption pattern of responsive loads (RLs) to overcome these uncertainties. As a distribution network is often divided into multiple MGs therefore this paper proposes a bi-level reliability-based model for the planning and partitioning of them in presence of RLs. At the first level, Tie Switches (TSs) and energy resources placement are determined to meet the annual peak demand to minimize the total costs. Output results of first level feed to next level as input data until the power exchange between MGs and the upstream distribution system and also RLs participation considering the desirable risk for all MGs customers are optimally calculated to maximize multiple MGs benefit. Since the second-level outputs can affect on first-level results, a bi-level model is applied. A genetic algorithm is used to solve each level's problem. For validation, numerical studies are applied to a 25-Bus test distribution network with three MGs and five TSs. The simulation results show that MG planning in islanded mode causes more investment costs depending on the installation of DERs with higher capacities. In addition, the participation of RLs in planning islanded or grid-connected MGs leads to a significant decrease in the system investment and operation, loss, and reliability costs.

Despite improving various technical indices such as operation and voltage profile, the integration of synchronous distributed generations (SDGs) may lead to the loss of protection coordination in the distribution network (DN). To tackle the problem, this paper presents a multi-objective siting and sizing of SDGs on DN considering operation, economic and protection coordination indices simultaneously. The proposed scheme is structured in the framework of a four-objective optimization problem, which aims to minimize the energy losses, the worst voltage security index (VSI), planning cost of SDGs and the protection index (PI) (the deviation of coordination time interval). Therefore, it is constrained to power flow equations, VSI, and protection coordination of overcurrent relays (OCRs). Then, the Pareto optimization based on the method of the weighted functions summation obtains an integrated single-objective problem for the proposed scheme. Next, a hybrid evolutionary algorithm formed by merging particle swarm optimization (PSO) and crow search algorithm (CSA) is incorporated to achieve the optimal solution with unique response approximate conditions. Eventually, the suggested scheme is applied on distribution portion of IEEE 30-bus ring and IEEE 69-bus radial DN and numerical results confirm the efficient performance of SDG planning in terms of technical indicators and protective devices coordination.

In addition to the many advantages of Distributed Generation (DG) for the distribution networks, they change the direction and increase the level of fault current. These changes may cause the loss of protection coordination of reclosers and fuses in distribution networks. Usually, the operation time of reclosers is determined by two main settings, TDS and IP, but two other parameters (A and B), which are defined by the standards of traditional reclosers, are also influential on the operation time of reclosers. These two parameters can be modified in digital reclosers. In this article, these two characteristics are optimized with TDS and IP to restore protection coordination. For this purpose, first, the location and the size of the DGs are specified, after that to alleviate the effects of the DGs, the location and the impedance of FCLs are optimized by a multi-objective optimization function to reduce the loss, improve voltage profile, and reduce the effects of changes in the feeders’ fault current while DGs are connected to the distribution network. Then, to restore the protection coordination, and to reduce the operating time of the protection equipment, fuse, and recloser, the parameters A, B, TDS, and IP are optimized using a multi-function optimization. To validate the proposed approach, the IEEE 33-bus network in the presence of synchronous DGs and resistive SFCL has been simulated in DIgSILENT software.

Abstract Chaos is a dynamic phenomenon that occurs over time in discrete and continuous nonlinear systems for some parameters, and chaotic systems are very sensitive to initial conditions. Controlling a chaotic system means eliminating its chaotic behavior and bringing the system to its origin equilibrium point or another desirable point. Moreover, as most natural systems have fractional dynamics, there is a clear need to study fractional systems. Nowadays, Brushless Direct Current (BLDC) electric motors are widely used as actuator components in many industries. Controlling these nonlinear and multivariable systems is of great importance. Additionally, these systems are often accompanied by parameter uncertainties and external disturbances, which may lead to undesirable and even unstable system behavior. In this research, a three-state-variable chaotic model is presented, and with the help of fractional-order sliding mode control strategy, the performance of the system is improved and controlled compared to conventional sliding mode control. It can be seen that the resistance of the fractional-order BLDC system with fractional-order sliding mode control is significantly higher than that of the conventional BLDC system with conventional sliding mode control against parameter uncertainties and external disturbances. Finally, the controller's performance is evaluated using MATLAB software.

In this paper, a new approach is proposed for the optimal operation of the strategic microgrids in the day-ahead market in the presence of rivals. The method describes a bi-level multi-objective operation of price maker participants (microgrids) that are going to maximize their profit in the presence of rivals and minimize their emissions. The upper-level problem in a bi-level mathematical optimization problem with equilibrium constraints (MPEC) seeks to maximize the profit of price maker participants, and the lower level maximizes the social welfare. Karush-Kuhn-Tucker (KKT) conditions and duality theory are utilized to convert the bi-level model to a mixed-integer linear programming model. The Epsilon constraint method is used to solve the bi-level-multi-objective problem in order to maximize the profit of the linearized model and reduce the emissions of price maker participants. The method generates some Pareto optimal solutions, and the fuzzy decision-making method is used to find the best solution among Pareto optimal solutions. To show the effect of the network on the studies, the power transmission distribution factor (PTDF) method has been used. The proposed method is tested on a 6-bus system in various scenarios. The results show the good performance of the proposed method.

Today, the chemical corrosion of metals is one of the main problems of large productions, especially in the oil and gas industries. Due to massive downtime connected to corrosion failures, pipeline corrosion is a central issue in many oil and gas industries. Therefore, the determination of the corrosion progress of oil and gas pipelines is crucial for monitoring the reliability and alleviation of failures that can positively impact health, safety, and the environment. Gas transmission and distribution pipes and other structures buried (or immersed) in an electrolyte, by the existing conditions and due to the metallurgical structure, are corroded. After some time, this disrupts an active system and process by causing damage. The worst corrosion for metals implanted in the soil is in areas where electrical currents are lost. Therefore, cathodic protection (CP) is the most effective method to prevent the corrosion of structures buried in the soil. Our aim in this paper is first to investigate the effect of stray currents on failure rate using the condition index, and then to estimate the remaining useful life of CP gas pipelines using an artificial neural network (ANN). Predicting future values using previous data based on the time series feature is also possible. Therefore, this paper first uses the general equipment condition monitoring method to detect failures. The time series model of data is then measured and operated by neural networks. Finally, the amount of failure over time is determined.