Particle Swarm Optimization Technique Research Articles

Optimal distribution grid allocation of reactive power with a focus on the particle swarm optimization technique and voltage stability

A structured approach to managing reactive power is imperative within the context of power systems. Among the restructuring initiatives in the electrical sector, power systems have undergone delineation into three principal categories: generation, transmission, and distribution entities, each of which is overseen by an independent system operator. Notably, active power emerges as the predominant commodity transacted within the electrical market, with the autonomous grid operator assuming the responsibility of ensuring conducive conditions for the execution of energy contracts across the transmission infrastructure. Ancillary services, comprising essential frameworks for energy generation and delivery to end-users, encompass reactive power services pivotal in the regulation of bus voltage. Of particular significance among the array of ancillary services requisite in a competitive market milieu is the provision of adequate reactive power to uphold grid safety and voltage stability. A salient impediment to the realization of energy contracts lies in the inadequacy of reactive power within the grid, which poses potential risks to its operational safety and voltage equilibrium. The optimal allocation of the reactive power load is predicated upon presumptions of consistent outcomes within the active power market. Under this conceptual framework, generators are afforded continual compensation for the provision of reactive power indispensable for sustaining their active energy production endeavors.

GNBoost-Based Ensemble Machine Learning for Predicting Tribological Properties of Liquid-Crystal Lubricants.

The intricate development of liquid-crystal lubricants necessitates the timely and accurate prediction of their tribological performance in different environments and an assessment of the importance of relevant parameters. In this study, a classification model using Gaussian noise extreme gradient boosting (GNBoost) to predict tribological performance is proposed. Three additives, polysorbate-85, polysorbate-80, and graphene oxide, were selected to fabricate liquid-crystal lubricants. The coefficients of friction of these lubricants were tested in the rotational mode using a universal mechanical tester. A model was designed to predict the coefficient of friction through data augmentation of the initial data. The model parameters were optimized using particle swarm optimization techniques. This study provides an effective example for lubricant performance evaluation and formulation optimization.

Optimal integration and planning of PV and wind renewable energy sources into distribution networks using the hybrid model of analytical techniques and metaheuristic algorithms: A deep learning-based approach

The optimal integration of Renewable Energy Sources (RESs) into distribution networks is crucial for sustainable energy generation. However, existing approaches utilized for optimal RES integration, including metaheuristic and analytical methods, exhibit limitations. Analytical methods are simple to implement, computationally efficient, and able to model power system mechanisms, yet they face scalability issues, making them inefficient for large-scale distribution systems. On the other hand, metaheuristic approaches, while flexible for complex problems, suffer from premature convergence problems, limiting their application in real distribution networks. To address these issues, this paper introduces a novel hybrid model that combines the strengths of analytical and metaheuristic techniques. The proposed model employs a deep learning-based method called bi-directional long short-term memory (B-LSTM) network is utilized to handle the uncertainties associated with the Photovoltaic (PV) and Wind Turbine (WT) generation and load demand. Subsequently, a distinctive analytical multi-objective index-based procedure is developed to minimize the real and reactive energy loss and voltage deviation by analyzing the branch current change caused by the RES penetration in distribution networks. The optimization problem is then solved using the binary particle swarm optimization (BPSO) technique. Importantly, the presented method considers the long-term nature of optimal RES integration and reflects the seasonal impacts of the load and RES output powers. The proposed approach is applied to the IEEE 33-bus test system. The simulation results show that the introduced hybrid model, by addressing the limitations of existing approaches, offers a promising solution for efficient and effective RES integration in distribution systems while requiring low computational effort, thereby advancing the transition towards sustainable energy infrastructure.

Thermodynamic analysis and multi-objective optimization of an ORC-based solar-natural gas driven trigeneration system for a residential area

Regarding the growing demands for energy, depletion of fossil fuel resources, and the adverse environmental consequences associated with their utilization, especially global warming, it is imperative to increase the share of renewable resources within energy systems. During the summer season, the residential sector in Khuzestan province, Iran, experiences a significant surge in energy consumption. This elevated demand is mostly attributed to the region's severe temperatures, requiring the extensive use of cooling equipment. As a result, taking into account the high potential for solar energy in the nation, this research first uses TRNSYS software to calculate the cooling and heating loads of a residential area in the province of Khuzestan. Next, a solar-natural gas Combined Cooling, Heating and Power (CCHP) system is designed and modeled using Engineering Equation Solver (EES) software, with the goal of supplying the cooling load. Then, using the particle swarm optimization technique, the system's multi-objective optimization was completed. The results showed that a fuel consumption rate of 0.04274 kg/s is required in steady state conditions to supply a cooling load of 1446.2 kW for the region with 1000 m2 of solar collecting area. A total of 465.7 kW of electric power and 3336 kW of heating are produced. The system's energy and exergy efficiencies were determined to be 186.6 % and 27.81 %, respectively. Eventually, the findings of this study demonstrated that the proposed configuration has the potential to provide multiple positive outcomes with a high level of efficiency, and improve the share of renewable energy.

Integration of Carbon-Based Nanomaterials for Hybrid Energy Storage in Lithium-Ion Batteries

Hybrid energy storage systems benefit significantly from incorporating nanoparticles containing metallic oxides, potassium phosphate, chemical compounds, and sulfides. The current technological problem is to increase the device’s electrical capacity without decreasing its energy capacity. The energy storage capacity of lithium-ion cells has been greatly improved by the use of tiny carbon particles owing to their conductive qualities, extensive specific surface area, customizable form, and intrinsic resilience. Further biologically active layouts, thinner ion-diffusion dimensions, and increased aboard carrier-/charge-transport motion are all made possible by incorporating effective bi-dimensional nanomaterials into future-oriented battery packs. For applications like lithium-ion batteries and electrochemical capacitors, this research presents carbon-based energy storage (CES) using a support vector machine (SVM) incorporating Particle Swarm Optimization (CES-SVM) to investigate the long-term stability of nanoparticle alloy forms of a specific shape and chemical makeup. This paper provides a potential improvement to the intermittent particle swarm optimization technique and its accompanying algorithmic architecture. Hybrid nanocomposites have been successfully synthesized, allowing the easy synthetic pathway and unique growth of nanostructuring technology to be used for electrodes with outstanding electrochemical effectiveness in conventional lithium-ion batteries. This paper shows how the cell’s conductivity, the electrode/electrolyte contact, and ion diffusion all affect impedance performance. Carbonaceous electrodes for lithium-ion battery systems, electrochemical storage units, and related combination devices are examined as a final area.

Particle Swarm Optimization (PSO) And Evolutionary Programming (EP) Technique for Optimal Placement and Sizing DG for Minimizing Loss in IEEE-30 Bus System

In recent years, most growing nations worldwide have experienced an unavoidably rising demand for electricity, which may result in a dropping of the voltage profile and a reduction in system stability, both of which have the potential to overload the power generation system significantly. One of the ways to reduce loss system network in this study is by distributed generation (DG) allocation, which the DG optimal location and sizing are the two most significant considerations for integration of DG. Improper placement and DG sizing in the power system not only result in increased total power losses but also affect the electrical system’s capability to be function properly. This paper provides a comparative analysis of two meta- heuristic optimization techniques, that is, Particle Swarm Optimization (PSO) and Evolutionary Programming (EP), to examine the optimal placement and sizing of distributed-generation (DG) for photovoltaic systems with the aim of minimizing the losses in interconnected distributed generation, IEEE-30 bus system. The objective function of this study is minimization the power loss with considered some constraints which are voltage limits and DG sizing limits are being covers in this study. The optimization issue has been solved by working with MATLAB/m-files software. The outcomes derived from the simulation results in this study demonstrated that PSO performance of two units of DG is better compared to EP algorithm. The voltage profile was improved drastically, and the power loss was reduced as lowest as 17.4486MW in the first case, 17.4368MW in the second case, and 17.4179MW in the third case.

Neural networks and particle swarm for transformer oil diagnosis by dissolved gas analysis

The lifetime of power transformers is closely related to the insulating oil performance. This latter can degrade according to overheating, electric arcs, low or high energy discharges, etc. Such degradation can lead to transformer failures or breakdowns. Early detection of these problems is one of the most important steps to avoid such failures. More efficient diagnostic systems, such as artificial intelligence techniques, are recommended to overcome the limitations of the classical methods. This work deals with diagnosing the power transformer insulating oil by analysis of dissolved gases using new techniques. For this, we have proposed intelligent techniques based on Multilayer artificial neural networks (ANN). Thus, a multi-layer ANN-based model for fault detection is presented. To improve its classification rate, this one was optimized by a meta-heuristic technique as the particle swarm optimization (PSO) technique. Optimized ANNs have never been used in transformer insulating oil diagnostics so far. The robustness and effectiveness of the proposed model is demonstrated, and high accuracy is obtained.

Hybrid cheetah particle swarm optimization based optimal hierarchical control of multiple microgrids

The emergence of microgrids arises from the growing integration of Renewable Energy Resources (RES) and Energy Storage Systems (ESSs) into Distribution Networks (DNs). Effective integration, coordination, and control of Multiple Microgrids (MMGs) whereas navigating the complexities of energy transition within this context poses a significant challenge. The dynamic operation of MMGs is a challenge faced by the traditional distributed hierarchical control techniques. The application of Artificial Intelligence (AI) techniques is a promising way to improve the control and dynamic operation of MMGs in future smart DNs. In this paper, an innovative hybrid optimization technique that originates from Cheetah Optimization (CHO) and Particle Swarm Optimization (PSO) techniques is proposed, known as HYCHOPSO. Extensive benchmark testing validates HYCHOPSO’s superiority over CHO and PSO in terms of convergence performance. The objective for this hybridization stems from the complementary strengths of CHO and PSO. CHO demonstrates rapid convergence in local search spaces, while PSO excels in global exploration. By combining these techniques, the aim is to leverage their respective advantages and enhance the algorithm's overall performance in addressing complex optimization problems. The contribution of this paper offering a unique approach to addressing optimization challenges in microgrid systems. Through a comprehensive comparative study, HYCHOPSO is evaluated against various metaheuristic optimization approaches, demonstrating superior performance, particularly in optimizing the design parameters of Proportional-Integral (PI) controllers for hierarchical control systems within microgrids. This contribution expands the repertoire of available optimization methodologies and offers practical solutions to critical challenges in microgrid optimization, enhancing the efficiency, reliability, and sustainability of microgrid operations. HYCHOPSO achieves its optimal score within fewer than 50 iterations, unlike CHO, GWO, PSO, Hybrid-GWO-PSO, and SSIA-PSO, which stabilize after around 200 iterations. Across various benchmark functions, HYCHOPSO consistently demonstrates the lowest mean values, attains scores closer to the optimal values of the benchmark functions, underscoring its robust convergence capabilities.the proposed HYCHOPSO algorithm, paired with a PI controller for distributed hierarchical control, minimizes errors and enhances system reliability during dynamic MMG operations. Using HYCHOPSO framework, an accurate power sharing, voltage/frequency stability, seamless grid-to-island transition, and smooth resynchronization are achieved. This enhances the real application's reliability, flexibility, scalability and robustness.

Understanding Penetration Attenuation of Permeable Concrete: A Hybrid Artificial Intelligence Technique Based on Particle Swarm Optimization

Permeable concrete is a type of porous concrete with the special function of water permeability, but the permeability of permeable concrete will decrease gradually due to the clogging behavior arising from the surrounding environment. To reliably characterize the clogging behavior of permeable concrete, particle swarm optimization (PSO) and random forest (RF) hybrid artificial intelligence techniques were developed in this study to predict the permeability coefficient of permeable concrete and optimize the aggregate mix ratio of permeable concrete. Firstly, a reliable database was collected and established to characterize the input and output variables for the machine learning. Then, PSO and 10-fold cross-validation were used to optimize the hyperparameters of the RF model using the training and testing datasets. Finally, the accuracy of the developed model was verified by comparing the predicted value with the actual value of the permeability coefficients (R = 0.978 and RMSE = 1.3638 for the training dataset; R = 0.9734 and RMSE = 2.3246 for the testing dataset). The proposed model can provide reliable predictions of the clogging behavior that permeable concrete may face and the trend of its development.

Performance Analysis of Automatic Generation Control for a Multi-Area Interconnected System Using Genetic Algorithm and Particle Swarm Optimization Technique

The primary focus of this paper is to assess an interconnected power system using different optimization techniques. The main purpose is to employ different optimization techniques, including genetic algorithms (GA) and particle swarm optimization (PSO), to systematically enhance the performance of a multi-area or two-area automatic generation control (AGC) system, aiming to optimize the three PID controllers gain values and improve system performance under diverse loading conditions. Two case studies are conducted exploring different loading conditions in the megawatt (MW) range, including increasing load demand and decreasing load demand. The analysis involves four scenarios, covering without any kind of controller, another with solely a proportional integral derivative (PID) controller, a PID controller enhanced through a genetic algorithm (GA), and lastly, a PID controller improved through particle swarm optimization (PSO). The optimization process utilizes the integral time absolute error (ITAE) as the objective function to evaluate the system's performance. The simulation outcomes for ITAE, settling time, overshoot, and undershoot for frequency deviation of area one, area two, and power deviation in the tie-line are compared with previous similar studies to assess the novelty of this work. The article highlights the importance of the multi-area AGC system and the significance of different optimization techniques in improving its performance.

MSCO: Mobility-aware Secure Computation Offloading in blockchain-enabled Fog computing environments

Fog computing has evolved as a promising computing paradigm to support the execution of latency-sensitive Internet of Things (IoT) applications. The mobile devices connected to the fog environment are resource constrained and non-stationary. In such environments, offloading mobile user’s computational task to nearby fog servers is necessary to satisfy the QoS requirements of time-critical IoT applications. Moreover, the fog servers are also susceptible to numerous attacks which induce security and privacy issues.Offloading computation task to a malicious fog node affects the integrity of users’ data. Despite the fact that there are many integrity-preserving strategies for fog environments, the majority of them rely on a reliable central entity that might have a single point of failure. Blockchain is a promising strategy that maintains data integrity in a decentralized manner. The state-of-art blockchain offloading mechnanisms have not considered the mobility during secure offloading process. Besides, it is necessary to ensure QoS constraints of the IoT applications while considering mobility of user devices. Hence, in this paper, Blockchain assisted Mobility-aware Secure Computation Offloading (MSCO) mechanism is proposed to choose the best authorized fog servers for offloading task with minimal computational and energy cost. To address the optimization issue, a hybrid Genetic Algorithm based Particle Swarm Optimization technique is employed. Experimental results demonstrated the significant improvement of MSCO when compared to the existing approaches in terms of on average 11 % improvement of total cost which includes the parameters of latency and energy consumption.

PSO-enhanced discrete-time integrated sliding mode-based control of three-level NPC converter for grid-connected PV-FC distributed generation system

This study proposes the control of a three-level NPC converter applied in a PV-FC hybrid generation system based on discrete-time integral sliding mode control (DISMC) combined with the particle swarm optimization (PSO) technique. First, the comprehensive depiction and modeling of the system's main components are initially presented. Then, the controller's detailed design procedure is given.  The sliding manifold is designed to have a fast dynamic response, and its stability analysis is verified using the Lyapunov direct method. Next, the optimization procedure is introduced to calculate the optimal values of the DISMC gains. Furthermore, a power management strategy is examined within the proposed control system to maximize the utility of the power produced by the hybrid system; the control is done through the designed PSO-DISMC to allow decoupled control of the active and reactive powers in two distinct modes of operation (the feeder-flow control (FFC) and the unit-power control (UPC) modes). The simulation of the approach is conducted in MATLAB/Simulink, and the findings demonstrate the effectiveness and robustness of the proposed control strategy.

Minimization of fuel cost for generator systems with valve point effect using modified PSO

The goal of this research is to lay the groundwork for understanding the particle swarm optimization technique. The applicability of this technique is proven by solving a variety of Constraint and unconstraint optimization problems. During the operation of the power system, economic load dispatch is a vital optimization activity for allocating generation between the committed units to divide the power while meeting various system constraints economically. In this work, the modified PSO method's application to the electrical power system's economic load dispatch problem on 3-generator, 5-generator and 13-generator systems are also investigated. In this research work Exterior penalty method is applied to convert constraint optimization problem into unconstraint optimization problem

Reliability analysis in distribution system by deep belief neural network

Rapid increase in the usage of intermittent renewable energy, ongoing changes in electrical power system structure and operational needs posing growing problems while ensuring adequate service reliability and retaining the quality of power. Power system reliability is a pertinent factor to consider while planning, designing, and operating distribution systems. utilities are obligated to offer their customers uninterrupted electrical service at the least cost while maintaining a satisfactory level of service quality. The important metrics for gauging the effect of distributed renewable energy on distribution networks is reliability analysis. Reliability analysis in distribution systems involves evaluating the performance and robustness of electrical distribution networks. An artificial intelligence approach is implemented in this paper to improve reliability analysis with dispersed generations in distribution network. Deep belief neural networks (DBNNs) are a type of artificial neural network that can be used for various tasks, including analyzing complex data such as those found in power distribution systems. This paper integrated a DBNN using a particle swarm optimization (PSO) technique. The proposed model performance is assessed using mean square error, mean absolute error, root mean square error, and R squared error. The findings reveal that reliability analysis with this novel technique is more accurate.

Comparative Study of MPPT Techniques in Solar PV Systems: A Comprehensive Review

Abstract: Maximum power point tracking(MPPT) is a very important technique which is employed in photovoltaic systems to extract maximum available power from the Photovoltaic panels under varying operating conditions. This paper presents a comparative study of different MPPT techniques, including traditional methods like Perturb and Observe (P&O), Incremental Conductance (Inc Con), Constant voltage method, as well as intelligent methods like Fibonacci algorithm-based search MPPT and Particle Swarm Optimization (PSO) Technique. The advantages and disadvantages of each technique are discussed in detail, along with their suitability for different applications. The paper concludes by highlighting the limitations of existing techniques and proposing a problem statement for developing a novel MPPT technique that addresses these limitations. Traditional MPPT techniques like Perturb and Observe , Incremental Conductance, and constant voltage method are straightforward to deploy and computationally economical. However, they can encounter issues such as being trapped in local maxima and exhibiting slower tracking rates during swift changes in irradiance. Intelligent MPPT techniques, such as Fibonacci algorithm-based search MPPT, and Particle Swarm Optimization (PSO) Technique are more adaptable to complex operating conditions and have faster tracking speeds, but they are also more computationally complex and expensive to implement. The limitations of existing MPPT techniques include sensitivity to partial shading conditions, limited efficiency under dynamic irradiance conditions, and the requirement for accurate system parameters. To address these limitations, a novel MPPT technique should be developed that is efficient under partial shading and dynamic irradiance conditions, robust to inaccurate system parameters, and has low computational complexity and implementation cost.

Arc Quality Index Based on Three-Phase Cassie–Mayr Electric Arc Model of Electric Arc Furnace

In steel recycling, the optimization of Electric Arc Furnaces (EAFs) is of central importance in order to increase efficiency and reduce costs. This study focuses on the optimization of electric arcs, which make a significant contribution to the energy consumption of EAFs. A three-phase equivalent circuit integrated with the Cassie–Mayr arc model is used to capture the nonlinear and dynamic characteristics of arcs, including arc breakage and ignition process. A particle swarm optimization technique is applied to real EAF data containing current and voltage measurements to estimate the parameters of the Cassie–Mayr model. Based on the Cassie–Mayr arc parameters, a novel Arc Quality Index (AQI) is introduced in the study, which can be used to evaluate arc quality based on deviations from optimal conditions. The AQI provides a qualitative assessment of arc quality, analogous to indices such as arc coverage and arc stability. The study concludes that the AQI serves as an effective operational tool for EAF operators to optimize production and increase the efficiency and sustainability of steel production. The results underline the importance of understanding electric arc dynamics for the development of EAF technology.

Presenting a meta-heuristic solution for optimal resource allocation in fog computing

Given that cloud computing is a relatively new field of study, there is an urgent need for comprehensive approaches to resource provisioning and the allocation of Internet of Things (IoT) services across cloud infrastructure. Other challenging aspects of cloud computing include IoT resource virtualization and disseminating IoT services among available cloud resources. To meet deadlines, optimize application execution times, efficiently use cloud resources, and identify the optimal service location, service placement plays a crucial role in installing services on existing virtual resources within a cloud-based environment. To achieve load balance in the fog computing infrastructure and ensure optimal resource allocation, this work proposes a meta-heuristic approach based on the cat swarm optimization method. For more clarity in the difference between the work presented in this research and other similar works, we named the proposed technique MH-CSO. The algorithm incorporates a resource check parameter to determine the accessibility and suitability of resources in different situations. This conclusion was drawn after evaluating the proposed solution in the ifogsim environment and comparing it with particle swarm and ant colony optimization techniques. The findings demonstrate that the proposed solution successfully optimizes key parameters, including runtime and energy usage.

Application of SSSC for Voltage Stability Improvement in the Nigerian 330 kV Transmission System using Particle Swarm Optimization Technique

Load imbalance has always been a major cause of voltage instability of the power system network and increase in voltage instability affects the power system reliability. In this paper, static synchronous series capacitor (SSSC) FACTS device was implemented to improve the voltage stability in the Nigerian 330 kV transmission network using particle swarm optimization (PSO) technique. Modelled power system network in the power system analysis toolbox (PSAT) is simulated to determine the existing power flow analysis and the level of instability in the power system generation plants situated in the Northern region. The outcome of the power flow is thereafter, optimized with PSO by generating the relationship with a high prediction accuracy between voltage at each location and an inter-linked transmission line distance of the 330kV. The model was generated with least square method and the high prediction accuracy determination was carried out with R-square value of the polynomial order. The best model was optimized and the entire computations were carried out in MatLab resulting in the determination of the optimal location which was between Jebba transmission and Shiroro generation stations. SSSC FACTS controller was implemented on the optimized transmission line and the simulation results showed that the introduction of SSSC improved the voltage stability of the power system network.

Constructing one-dimensional supramolecular polymer structures using particle swarm optimization technique

Constructing one-dimensional supramolecular polymer structures using particle swarm optimization technique

Understanding the effects of machinability properties of Incoloy 800 superalloy under different machining conditions using artificial intelligence methods

Incoloy 800 is frequently used in high-temperature applications as it has the ability to retain good metallurgical stability at elevated temperatures. Due to the nature of the applications used for, parts made from Incoloy 800 usually require different machining processes such as milling and turning. Therefore, the current study aims to investigate the milling performance of Incoloy 800 under different cutting parameters (75–150 m/min and 0.075–0.15 mm/rev) and cooling conditions namely dry, flood, Minimum Quantity Lubrication (MQL) and Cryogenic (Cryo)+MQL. It was observed that all machinability metrics improved in the MQL+Cryo C/L environment. It is noticeable that the surface roughness value improved by 30% in this environment. In addition, a model based on artificial neural networks (ANN) and particle swarm optimization (PSO) was proposed to analyze the results and predict optimum cutting parameters. It appears that Cryo+MQL strategies are the best option for all cutting parameters. It was found that the estimations for surface roughness, flank wear, and cutting temperature with the proposed ANN architecture are achieved with overall relative error of 6.08%, 12.38%, and 8.32%, respectively. The proposed model resulted in good performance between the experimental test data and the predicted values. The developed model made the most efficient predictions for the MQL+Cryo cutting environment. It was observed that the estimations of the different input parameters in the MQL+Cryo cutting environment present a relative error of 8.36%, 1.46%, and 2.38% for surface roughness, flank wear, and cutting temperature, respectively. By utilizing the predictive capability of the trained ANN model, the optimization of the input parameters was carried out with the PSO technique. Thus, with the developed PSO-ANN model, promising findings were obtained in overcoming important handicaps such as time and cost in experimental studies.