Particle Swarm Optimization Technique Research Articles

Particle swarm optimization technique for speed control and torque ripple minimization of switched reluctance motor using PID and FOPID controllers

Particle swarm optimization technique for speed control and torque ripple minimization of switched reluctance motor using PID and FOPID controllers

Piezoelectric impedance-based high-accuracy damage identification using sparsity conscious multi-objective optimization inverse analysis

Two elements are essential in structural health monitoring utilizing dynamic responses: response measurement with high-frequency contents, i.e., small characteristic wavelengths, that can adequately reflect damage features, and effective inverse identification analysis that is however oftentimes under-determined. The advancement of smart structure integration has led to active interrogation through frequency-sweeping piezoelectric impedance measurement at high frequency range. In this research we develop a multi-objective optimization formulation for the identification of damage location and severity utilizing piezoelectric impedance. While one optimization objective is to match the response measurement with finite element model prediction in the damage parametric space, the other is the number of locations of damage, i.e., the sparsity of damage index as the solution vector, since damage usually occurs within a small number of locations. This multi-objective formulation fits well the under-determined nature of damage identification, as it naturally provides multiple solutions as basis for further elucidation. The challenge remaining is how to find a small solution set that can include the actual damage scenario. Here we develop a novel inverse identification framework utilizing the intelligent swarm optimizer which possesses flexibility for enhancement. We first embed a sparsity enforcement process into the population generation of the optimizer, which yields a solution repository intrinsically possessing sparsity. We then apply reinforcement learning so the agents can adaptively opt for local strategies with the aim of enriching the searching patterns to diversify the solutions. Through the incorporation of a Q-table, searching toward more promising directions will be rewarded. Our case analyses employing experimental data indicate that this sparsity-conscious multi-objective particle swarm optimization technique can lead to a small solution set which generally encompasses the true damage scenario. This effectively solves the structural damage identification problem with piezoelectric impedance measurement.

Optimization of fused deposition modeling parameters to fabricate morphogenesis light weight structure with high tensile and compression strength

Fused deposition modeling (FDM) is a contemporary approach that can produce simple and complex physical models, prototype tools, and functional components. However, the challenge is choosing the right parameters, which are essential to printing a high-quality Additive Manufactured (AM) structure with acceptable mechanical attributes, like mechanical strength. In the additive manufacturing technique, fabricating a lightweight structure without compromising the weight and shape is very difficult because the significant parameter is infill density (D) %, which is directly proportional to the strength and inversely proportional to the weight. In this research based on morphogenesis, the infill density is varied at different locations wherever it is needed to print a lightweight, high-strength structure. Parameters Layer Thickness (LT), Shell Thickness (ST), and D were chosen, and experiments were conducted as per Central Composite Design (CCD). Tensile Strength (TS) and Compression Strength (CS) samples were printed with PLA and tested. The results were used to develop a multi-objective function. To predict maximum TS and CS without compromising the weight, Particle Swarm Optimization (PSO) and Response Surface Methodology (RSM) techniques were used to solve the multi-objective function. PSO predicts that the optimum printing parameter values for achieving a lightweight, high-strength structure are LT of 0.17 mm, ST of 0.91 mm, and D of 11.3%. These predicted results were confirmed through experiments.

Optimal placement and sizing of FACTS devices based on Autonomous Groups Particle Swarm Optimization technique

Optimal placement and sizing of FACTS devices based on Autonomous Groups Particle Swarm Optimization technique

Optimal Sizing of Renewable Hybrid Energy System: A Review of Methodologies

The ever-growing energy demand, the intermittent nature of renewable energy resources such as wind, photovoltaic etc coupled with issues of reliability makes the hybrid renewable energy systems more preferred. Hybrid energy sources offers better reliability and cost effectiveness than a single energy source. One of the most important issues related to hybrid renewable energy systems is obtaining the optimal size of their parts so as to utilize them efficiently and economically. Determining the optimal size of hybrid energy systems helps to avoid over-sizing or under-sizing of the system which will subsequently affect the cost and reliability of the system. Optimal sizing becomes complex when considering multiple hybrid mix while at the same time considering various objectives such as cost, CO2 gas emission and reliability. This paper aims at critically reviewing the various optimization techniques used in optimal sizing of various combination of hybrid energy sources for effective selection of the appropriate sizing method for hybrid renewable energy systems. It has been found that meta-heuristic techniques are well suited for optimal sizing of the hybrid energy systems with Genetic Algorithm and Particle Swarm Optimization techniques being the most commonly used Algorithms.

Evaluation of PV-based Buck-Boost and SEPIC Converters for EV Charging Applications

In recent decades, environmental issues have become an area of greatest concern due to changes in global climate conditions. The transportation sector is a major contributor to carbon dioxide emissions, accounting for more than 22.9% of total carbon dioxide emissions. At present, most vehicles run on gasoline/diesel as fuel which is unsustainable and unviable as fossil fuels produce carbon emissions and fuel costs are rising. To address these issues, electric vehicles (EVs) offer an attractive solution as alternative to internal combustion engine vehicles that use electricity as an energy source. It is logical to use renewable energy to charge vehicles, which makes renewable energy an end-to-end clean energy source. In electric vehicles, energy conversion plays an important role. In the energy conversion process, alternating current (AC) can be converted to direct current (DC), or direct current can be converted to alternating current. In EV fast charging applications, DC-to-DC conversion is used, which requires DC-to-DC converters. In this paper, a detailed evaluation of the Buck-Boost and Single-Ended Primary Inductance Converters (SEPIC) with PV as input is analyzed for EV charging applications to make it end-to-end clean energy. For this purpose, a 5-by-5 PV system with a Buck-Boost, SEPIC converters with particle swarm optimization technique is considered, which is simulated in a MATLAB/SIMULINK environment. The simulation results showed that the ripples in output are minimal in SEPIC which supports the smooth and efficient charging of EV battery.

An empirical study on the construction of sample branches in colleges and universities in promoting the development of students’ subjectivity

Abstract The development of student subjectivity constitutes a crucial dimension in achieving the comprehensive growth of students. This study examines the role of constructing model branches within higher education institutions and explores their influence on fostering student subjectivity. To this end, an enhanced K-means clustering algorithm, augmented by a particle swarm optimization technique, is employed to process the data of college students, thereby facilitating the development of model branches in these institutions. Additionally, the initial warning feature matrix within the collaborative filtering recommendation algorithm is applied to quantitatively assess student characteristic factors in the establishment of model branches, enabling the creation of an early warning system. The effect of model branch construction on the evolution of student subjectivity is subsequently analyzed through Gaussian process regression and partial least squares regression methods. The findings indicate that the extent of model branch development significantly impacts student subjectivity. Specifically, the average development level of student subjectivity in institutions with high-level model branches surpasses that in institutions lacking such structures by 17.255 points. Thus, the establishment of high-level model branches in colleges and universities emerges as a vital strategy to enhance student subjectivity and promote balanced and holistic student development.

Analysis of Optimal Power Flow Using Combined PSO-GSA Technique

Using a hybrid approach that incorporates both particle swarm optimization (PSO) and gravitational search algorithms (GSA), this project aims to find the best way for power systems to distribute their energy. A novel heuristic search optimization technique, the GSA is works on the law of gravity. While this strategy has many advantages, it suffers from sluggish search performance and memory constraints. In order to discover a solution to this problem, the PSO technique was utilized. PSO and GSA, were utilized in this investigation to discover the optimal power flow utilizing a combination of these two methodologies. The suggested optimization method merges the social thinking and local search features of particle swarm optimization with those of the GSA, therefore taking benefit of both algorithms. This study examines and evaluates an optimization technique for the optimal power flow problem, focusing on reducing fuel costs, improving the voltage profile, and minimizing real power losses. The investigation and assessment are conducted on the commonly used IEEE 30-bus test systems. The simulation findings showcase the robust and effective resolution of the optimum power transfer problem through the amalgamation of Particle Swarm Optimization (PSO) and Gravitational Search Algorithm (GSA).

Exploring the energy landscape of graphynes for noble gas adsorption using swarm intelligence

Gas adsorption on one-atom-thick membranes is a growing technology for separation applications owing to its excellent energy efficiency. Herein, we investigate the adsorption of the noble gases, Ne, Ar and Kr on graphynes (GYs), a novel class of one-atom-thick carbon membranes using a swarm intelligence technique, namely particle swarm optimization (PSO). Modeling the adsorption of noble gas clusters on two-dimensional substrates requires a thorough examination of the energy landscape. The high dimensionality of the problem makes it tricky to employ ab initio methods for such studies, necessitating the use of a metaheuristic global optimization technique such as PSO. We explored the adsorption of 1–30 atoms of Ne, Ar and Kr on α-, β-, γ- and rhombic-GYs to predict the most suitable form of GY for the adsorption of each of the gases. Employing the dispersion-corrected density functional theory (DFT-D) data for the adsorption of single gas atoms as the reference data, we parametrized two empirical pairwise potentials, namely, Lennard-Jones (LJ) and improved Lennard-Jones (ILJ) potentials. We then analyzed the growth pattern as well as the energetics of adsorption using the parametrized potentials, in combination with the PSO technique, which enabled us to predict the best possible membrane for the adsorption of the noble gases: α-GY for Ne and γ-GY for Ar and Kr. The accuracy of our modeling approach is further validated against DFT-D computations thereby establishing that PSO, when combined with the ILJ potential, can serve as a computationally feasible approach for modeling gas adsorption on GYs.

A Dual-Objective Approach for Allocation of Virtual Machine with improved Job Scheduling in Cloud Computing

In Cloud Computing (CC) environment, requests of user are maintained via workloads that are allocated to Virtual Machines (VMs) using scheduling techniques which primarily focus on reducing the time for processing by generating efficient schedules of smaller lengths. The efficient processing of requests also needs larger usage of resources that incurs higher overhead in the form of utilization of energy and optimization of cost utilized by Physical Machines (PMs). Assignment of VMs optimally in the environment of CC for jobs submitted by users is a challenge. In order to obtain better solution involving scheduling of jobs to VMs, considering two parameters utilization of energy and cost, we present a dual-objective approach for VM allocation with improved scheduling of jobs in CC environment. The proposed work aimed to build a dual-objective scheduling model for improved job scheduling, focusing on minimization of cost and utilization of energy at a time. For evaluating performance of dual-objective approach, we utilized two types of benchmark datasets and compared with existing approaches such as Whale, Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) and Metaheuristic Dynamic VM Allocation (MDVMA) techniques. The results obtained from simulation demonstrated that dual-objective approach performs better in the form of minimization of utilization of energy and cost

A Comparative Analysis of Metaheuristic Algorithms Tuned Supper Twisting Sliding Mode Control of a Self-balancing Segway

A self-balancing two-wheel Segway is a multivariable, nonlinear, coupled and unstable personal transport that is among the benchmarks of under-actuated systems to test different control schemes. The proper operation of the system needs stable and robust controllers for the balancing and direction of the Segway. Stable and robust performance of such systems can be achieved using adaptive, optimal and non-linear control schemes. A conventional sliding mode controller is a nonlinear controller, considered a robust controller except for the chattering problem that can be solved using higher order sliding mode controllers such as a supper twisted sliding mode controller (STSMC). In this research, STSMC is proposed for balancing and PID controller for direction control of a self-balancing Segway. To solve the impacts of design technique, skill, and experience of the designer on the control schemes, controllers are tuned using metaheuristic optimization algorithms, namely Grey wolf algorithm (GWO), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) technique. The overall system has been implemented in MATLAB/Simulink, and performance evaluations have been executed for the time domain specifications of settling and rise times as comparison criteria for the Segway operating conditions of variable driver mass and tilt angles. The results show relatively quick responses - for initial pitch angel of 0.3 rad, and initial yaw angle of 0.1 rad – of GWO-STSMC and GWO-PID controllers. For variable driver mass and constant initial tilt angles, the pitch response is found to be less sensitive to the increase in mass of the driver for GWO-STSMC, while the yaw angle response shows a slight sensitivity decrease for GWO-PID controller. For variable initial tilt angles and constant mass of the drive, both GWO-STSMC and GWO-PID controllers show the capability of tracking the reference pitch and yaw angles.

Multiple Parameters Determination Method of Hardening Soil Model Based on Particle Swarm Optimization

The utilization of numerical analytic techniques has become essential in evaluating the environmental consequences of excavation in foundation pits. The careful selection of suitable soil constitutive models has significant importance in this regard. The Hardening Soil (HS) model has become widely utilized in the numerical analysis of foundation pits among the several models considered. The essence of a constitutive model is in the identification and determination of its calculation parameters. The stress–strain curve is derived by conducting triaxial testing, utilizing the inherent properties of the particle swarm optimization (PSO) technique. The objective function entails quantifying the disparity between stress–strain curves obtained from various parameters and experimental curves while iteratively searching for five crucial parameters of soil‐hardening models: effective internal friction angle φ, effective cohesion, failure ratio Rf, stiffness level‐dependent power exponent, and standard triaxial‐drained test secant stiffness . The objective of this research is to create a computational software utilizing the Python programming language to execute PSO with the purpose of determining sets of five fundamental soil constitutive parameters. This research employs optimized parameters acquired from PSO to construct a numerical model for foundation pits and performs numerical computations. Accurate findings may be reached by comparing the measured deformation in the numerical simulation with that seen in actual foundation pits. The results demonstrate a high degree of consistency between the experimental curves and the five parameter combinations obtained through the PSO algorithm, with an error rate not exceeding 8.2%. Moreover, the optimized curves exhibit closer alignment with theoretical expectations. The settlement values calculated using a numerical model based on optimized parameters show only a 6.12% deviation from actual measurements while closely following the trend of the observed curve. This outcome has the potential to greatly alleviate the burden of conducting indoor tests and holds considerable practical value in engineering applications. The observable optimization impact of PSO also serves as evidence of its viability in improving soil constitutive parameters, providing new perspectives and sources of information for future progress in this domain.

Performance comparison of PSO, HGSO, and DE optimization techniques for computation of directional overcurrent relay coordination in power systems

Directional over current relays (DOCRs) have been in use for ensuring the complete protection of the power system network. However, the modern power system is getting complex with the increased penetration of Renewable Energy Source (RES) based distributed generators (DGs), as the current flow become bidirectional. Hence, the system demands faster operation of the protection devices to prevent any major outages. Optimal values of all the DOCR settings viz, TDS and Ip need to be found out such as to minimize the overall time of operation of all the relays in the network. This study focuses on the comparative study of the optimization problem carried out using the prominent optimization algorithms – Particle Swarm Optimization (PSO), Henry Gas Solubility Optimization (HGSO) and Differential Evolution (DE). The ability of techniques is established on IEEE 6 – bus & WSCC 9 – bus test systems for mid-point line faults. The Protection Coordination Problem (PCP) is formulated as non-linear programming (NLP) problem, and the optimal settings of the relays TDS and Ip are achieved using MATLAB R2021a platform and validated the results in POWER WORLD simulator software. The results depict that the HGSO method shows a significant reduction in relays' time of operation compared to PSO, but DE gives superior results compared to other techniques with a minimum computational time.

Improvement of photovoltaic systems with tracking of the maximum power point in low-irradiation atmospheric conditions

This paper discusses the efficient use of photovoltaic energy in areas with low solar irradiation. To extract the maximum power at low irradiation, we used a maximum power point tracking (MPPT) algorithm based on the combination of fuzzy logic (FL) and the sliding mode (SM) associated with a Proportionnel-Intégral (PI) regulator. The system parameters are calculated using the particle swarm optimization (PSO) technique, which thus ensures the stability of the controller. The performance of the proposed technique is compared with the conventional perturb and observe (P&O) technique in terms of tracking time and tracking efficiency at low irradiation. The simulation results prove that the technique has high tracking efficiency and less convergence time under low irradiation, with fewer power oscillations, low ripple and no overshoot.

A neighborhood-aware multi-Markovian switching particle swarm optimization technique for solving complex and expensive problems

A neighborhood-aware multi-Markovian switching particle swarm optimization technique for solving complex and expensive problems

Stabilization of isolated hybrid microgrids with electric vehicle-based energy storage systems using a fractional order proportional-integral-derivative control

ABSTRACT Electric vehicles (EVs) are considered to be the most promising technology upgrades in the coming years to reduce the carbon emission, since the EVs could replace the conventional combustion vehicles as well as mitigate power fluctuations in power grids. This paper focuses on addressing the frequency stability issues in isolated hybrid grids (IHGs) by introducing a fractional order proportional-integral-derivative (FOPID) control for grid-connected power converters. A particle swarm optimization (PSO) technique is adopted to optimize the parameters of the controllers. The advantages of the proposed control method over the conventional proportional-integral-derivative (PID) control, proportional-integral with proportional-derivative (PI-PD) control, and proportional-integral-derivative with filter (PIDF) control schemes are verified by simulation case studies. Besides, a modified virtual rotor concept (MVRC) is proposed in this paper to enhance the inertia of the system. Furthermore, the performance of using EVs to regulate the frequency of IHGs is compared to the superconducting magnetic energy storage (SMES) units, capacitive energy storage (CES) units, and redox flow batteries (RFB) under fixed and variable load conditions.

PERFORMANCE ANALYSIS OF P&O AND PSO MPPT ALGORITHMS FOR PV SYSTEMS UNDER PARTIAL SHADING

Photovoltaic (PV) modules are devices that transform photon energy into electrical energy. The output power of the PV modules is influenced by the intensity of solar radiation and the ambient temperature. Non-uniform shading can cause variations in the extent of sunlight absorbed by PV modules, resulting in a decrease in power output. Maximum power point tracking (MPPT) techniques are employed to optimize the power output of PV modules by operating them at their maximum power point (MPP). The main objective of the study is to investigate the performance analysis of Perturb and Observe (P&O) and Particle Swarm Optimization (PSO) MPPT strategies in uniform and partially shaded conditions with equally and unequally different irradiance differences. Simulation studies were conducted on the PV circuit model using Matlab/Simulink, and the results were evaluated. MPPT algorithms are compared based on their tracking efficiency and convergence speed when solar radiation conditions vary. The findings of the simulation indicate that the P&O is unable to determine global MPP and gets trapped in one of the local MPPs. However, the PSO is very effective in tracking MPP under different partial shading patterns with more than 96% tracking efficiency. In the first partial shading configuration where the sunlight intensity of the PV modules is uniformly distributed, the PSO technique has reduced steady-state oscillations around the MPP. However, the P&O technique demonstrates superior response time and convergence speed in comparison to the PSO technique.

Application of an optimal tilt controller in a partial loading schedule of multi-area power system considering HVDC link and virtual inertia

Maintaining the reliability of the modern power system is becoming increasingly challenging due to the continuous growth in its size and complexity. One of the most significant challenges PS faces is frequency regulation, which is essential for maintaining a stable and reliable operation of PS. This article investigates the automatic generation control (AGC) of a multi-area PS considering partial loading scheduled in a hydro and thermal generating unit. A bird swarm algorithm (BSA) based two-degree of freedom tilt integral derivative (2DOF-TID) controller has been suggested. The superiority of the optimal 2DOF-TID controller has been selected by comparing the system dynamics with other conventional controllers. Also, the ascendancy of suggested BSA has been shown over the grasshopper algorithm and particle swarm optimization techniques. The investigation on partial loading schedules for the hydro-thermal system showed that the system's dynamic response deteriorates when loading on hydro units exceeds their nominal loading. Also, system dynamics are enhanced when thermal units are loaded beyond their capacity. The investigations are also conducted to analyse the effect of high voltage direct current (HVDC) link and virtual inertia emulation with an energy storage system. The studies with alternating current (AC)/HVDC and virtual inertia revealed that dynamic responses are better with the latter when compared with AC and HVDC links alone.

Maximizing Efficiency in Energy Trading Operations through IoT-Integrated Digital Twins.

The Internet of Things (IoT) has brought about significant transformations in multiple sectors, including healthcare and navigation systems, by offering essential functionalities crucial for their operations. Nevertheless, there is ongoing debate surrounding the unexplored possibilities of the IoT within the energy industry. The requirement to better the performance of distributed energy systems necessitates transitioning from traditional mission-critical electric smart grid systems to digital twin-based IoT frameworks. Energy storage systems (ESSs) used within nano-grids have the potential to enhance energy utilization, fortify resilience, and promote sustainable practices by effectively storing surplus energy. The present study introduces a conceptual framework consisting of two fundamental modules: (1) Power optimization of energy storage systems (ESSs) in peer-to-peer (P2P) energy trading. (2) Task orchestration in IoT-enabled environments using digital twin technology. The optimization of energy storage systems (ESSs) aims to effectively manage surplus ESS energy by employing particle swarm optimization (PSO) techniques. This approach is designed to fulfill the energy needs of the ESS itself as well as meet the specific requirements of participating nano-grids. The primary objective of the IoT task orchestration system, which is based on the concept of digital twins, is to enhance the process of peer-to-peer nano-grid energy trading. This is achieved by integrating virtual control mechanisms through orchestration technology combining task generation, device virtualization, task mapping, task scheduling, and task allocation and deployment. The nano-grid energy trading system's architecture utilizes IoT sensors and Raspberry Pi-based edge technology to enable virtual operation. The evaluation of the proposed study is carried out through the examination of a simulated dataset derived from nano-grid dwellings. This research analyzes the efficacy of optimization approaches in mitigating energy trading costs and optimizing power utilization in energy storage systems (ESSs). The coordination of IoT devices is crucial in improving the system's overall efficiency.

CalyPSO: An Enhanced Search Optimization based Framework to Model Delay-based PUFs

Delay-based Physically Unclonable Functions (PUFs) are a popular choice for “keyless” cryptography in low-power devices. However, they have been subjected to modeling attacks using Machine Learning (ML) approaches, leading to improved PUF designs that resist ML-based attacks. On the contrary, evolutionary search (ES) based modeling approaches have garnered little attention compared to their ML counterparts due to their limited success. In this work, we revisit the problem of modeling delaybased PUFs using ES algorithms and identify drawbacks in present state-of-the-art genetic algorithms (GA) when applied to PUFs. This leads to the design of a new ES-based algorithm called CalyPSO, inspired by Particle Swarm Optimization (PSO) techniques, which is fundamentally different from classic genetic algorithm design rationale. This allows CalyPSO to avoid the pitfalls of textbook GA and mount successful modeling attacks on a variety of delay-based PUFs, including k-XOR APUF variants. Empirically, we show attacks for the parameter choices of k as high as 20, for which there are no reported ML or ES-based attacks without exploiting additional information like reliability or power/timing side-channels. We further show that CalyPSO can invade PUF designs like interpose-PUFs (i-PUFs) and (previously unattacked) LP-PUFs, which attempt to enhance ML robustness by obfuscating the input challenge. Furthermore, we evolve CalyPSO to CalyPSO++ by observing that the PUF compositions do not alter the input challenge dimensions, allowing the attacker to investigate cross-architecture modeling. This allows us to model a k-XOR APUF using a (k − 1)-XOR APUF as well as perform cross-architectural modeling of BRPUF and i-PUF using k-XOR APUF variants. CalyPSO++ provides the first modeling attack on 4 LP-PUF by reducing it to a 4-XOR APUF. Finally, we demonstrate the potency of CalyPSO and CalyPSO++ by successfully modeling various PUF architectures on noisy simulations as well as real-world hardware implementations.