PSO Technique Research Articles

Emergence of an Improved Version of the PSO Technique for Optimization of Damped Input Filter for Direct Matrix Converter

Emergence of an Improved Version of the PSO Technique for Optimization of Damped Input Filter for Direct Matrix Converter

Application of C4.5 algorithm with PSO Feature Selection and Bagging Technique on Breast Cancer Classification

The second greatest cause of death for women worldwide is breast cancer. When abnormal cells in the body proliferate out of control, cancer is the result. The diagnosis of breast cancer was established using anthropometric data obtained from standard blood tests. From the UCI Machine Learning Repository, the Breast Cancer Coimbra Data Set was obtained and put to use. One popular classification decision tree method is the C4.5 approach. By selecting the appropriate features and applying the appropriate strategy to address class imbalance throughout the classification process, the performance of the C4.5 algorithm may be enhanced. To determine the accuracy of the classification, tests are conducted using a confusion matrix. Accuracy in this study is anticipated to increase with the application of the C4.5 Algorithm, the Bagging approach to address class imbalance, and the PSO feature selection method. The C4.5 Algorithm, PSO, Bagging Technique produce the best accuracy results, with an average of 86.36 percent. The C4.5 classification method has the second highest accuracy, with a PSO accuracy of 79.39 percent. Utilizing the Bagging Technique in conjunction with the C4.5 Algorithm, the accuracy of 75.0% is the third highest. Furthermore, it has a 65.71 percent accuracy with the C4.5 categorization. As a result, the increase in accuracy from before adding PSO and Bagging Technique was 20.65%, indicating that the inclusion of PSO and Bagging Technique had a substantial impact on the calculation process.

Particle swarm optimization-extreme learning machine model combined with the ADA boost algorithm for short-term wind power prediction

In our proposed approach, we integrate ADA boosting with particle swarm optimization-extreme learning machine (PSO-ELM) to enhance the accuracy of wind power estimation, addressing the inherent unpredictability and variability in wind energy. Initially, we refine the thresholds and input weights of the extreme learning machine (ELM) and then construct the PSO-ELM prediction model. ADA Boost is utilized to generate multiple weak predictors, each comprising a distinct hidden layer node. The PSO technique is then employed to optimize the input weights and thresholds for each weak predictor. The final forecast is attained by amalgamating and weighting the outcomes from each weak predictor using a robust wind power forecast model. Experimental validation utilizing data from Turkish wind turbines underscores the efficacy of our approach. Comparative analysis against contemporary techniques such as ensemble learning models and optimal neural networks reveals that our ADA-PSO-ELM model demonstrates superior accuracy and generalizability in predicting wind power output under real-world conditions. The proposed approach offers a promising framework for addressing the challenges associated with wind power estimation, thereby facilitating more reliable and efficient utilization of wind energy resources.

Arrangement for reliable power supply to emergency commercial customers with techno-economic performance improvement of PV enhanced distribution systems

Accelerating engagement of distributed energy resources unleashed the dependency on traditional electric power generation. To reduce the craving for traditional power dependency and unplanned outages the power distribution sectors are gradually adopting cleaner and reliable electrification. However, the unscheduled and unorganized engagement of stochastic generation from renewable energy sources (RES) makes the distribution system operation more vulnerable. For reliable and continuous power supply with RES, scheduled operation of BSSs and power capacitor (CAP) units can be integrated to provide emergency power supply support to consumers. In this paper, the supply of continuous and reliable power to commercial and emergency customers has been investigated subject to complete reliability (CR). The benefit-to-cost ratio (BCR) has been maximized to give reliable power to consumers by the minimum investment cost by utility, which can benefit both electric customers and power distribution operators in a single platform. The maximization of BCR has been evaluated by optimal S-CDS of the BSSs and S-S of CAP units’ operation through the PSO technique in the considered 28-bus active distribution network in different seasons. The techno-economic benefit of integrating only BSSs and BSSs with CAP units in the prospective locations has been analyzed. The improvement in battery life and decrease in SAENS, SADPL, and SADD have been obtained, which shows the efficacy of the proposed work.

A Crossover Optimization-Based Control for Making Strides in BLDC Motor Utilizing Renewable Sources

This study explores the eco-friendly and versatile applications of renewable energy sources, with a focus on solar modules adaptable to varying conditions. The objective is to minimize switching loss and enhance Total Harmonic Distortion (THD) outcomes. Solar energy harnessed from PV panels is utilized to power a BLDC motor, employing two methods to amplify PV panel output. In the initial approach, Maximum Power Point Tracking (MPPT) is implemented using a hybrid PSO and cuckoo-searches (HPSO_CSO) technique. The second method integrates an AC load through DC-DC converters connected to a BLDC motor and a multilayer inverter. Comparative analyses encompassing maximum power, stator current, THD, and duty cycle are conducted for both the conventional PSO-based BLDC and the proposed HPSO_CSO BLDC systems. The findings reveal that the suggested HPSO_CSO converter with BLDC motor outperforms existing methods, exhibiting the most favorable outcomes. The entire system is simulated using the SIMULINK model, with MATLAB 2021a employed for implementation. The proposed HCSO-PSO-MPPT approach achieves an impressive THD of just 1.08%, signifying superior power conversion performance in contrast to existing techniques.

Mitigation of Grid Current Harmonics by ABC- ANN based Shunt Active Power Filter

Harmonics are being introduced into power system networks as a result of the increasing use of nonlinear devices. These harmonics cause distortion of current and voltage signals, which in turn causes damage to power distribution systems. As a result, the suppression of harmonics is of extreme significance in power systems. This paper proposes Shunt Active Power Filters (SAPF) based on neural network algorithms like Artificial Neural Network (ANN) as a feasible approach to mitigating harmonic distortion and raising power quality in electrical distribution systems. This research shows that using shunt active power filters (SAPF), which use the Artificial Bee Colony Optimized Artificial Neural Network Controller (ABC-ANN), is an efficient method for enhancing power quality and minimizing harmonic distortion in distribution systems. The ABC-ANN algorithms have been produced for SAPF with the goal of improving system performance by reducing Grid current Harmonics .In the first stage of this work, PSO Technique is used to tune a standard PI controller to its optimum gain values (Ki ,Kp). Then, these target and input data of PSO tuned PI controller will be supplying inputs to the ANN controller. To find the optimal weight and bias values, this ANN controller has been tuned with the help of the ABC algorithm. Using MATLAB/SIMULINK software, we compare the performance of the proposed algorithm to that of other optimization algorithm like Differential Evaluation (DE) algorithm, as well as the PSO tuned PI controller and traditional PI controller. The findings from the simulation suggest that a SAPF utilizing an ABC trained ANN controller could improve THD in the supplying current while maintaining harmonics within IEEE-519 accepting levels.

Research on the construction of enterprise financial management information based on feature set

Abstract This study suggests a PSO-SVM financial crisis early warning model aid organizations in developing an efficient financial crisis early warning model for early financial crisis avoidance. For feature set selection, a wrapper method is utilized, in which the classifier’s learning algorithm and feature set selection are combined. The SVM is utilized as the classifier, and its feature set and kernel function parameters (C and δ 2) are employed as the placements of the particles in the PSO, with the SVM’s classification results being used as the adaption values. By achieving the near-optimal feature subset and kernel function parameters, the results are close to the model’s ideal predictions. The PSO technique is used to concurrently optimize the feature set and the kernel function parameters to remove unnecessary or redundant features. According to the findings of the model comparison, the SVM’s accuracy is 80.31%, which is less than the accuracy of the PSO-SVM model, which is 94.55%. The accuracy and recall of the PSO-SVM training set are 71.39% and 81.87%, respectively, while the accuracy and recall of the test set are both 70.53% and 72.29%, respectively. In comparison, the SVM model’s test set accuracy rate is just 34.57%, while its training set accuracy rate is only 44.01%. This suggests that the PSO-SVM model put forward in this research has seen significant improvements in both the accuracy of indicator selection and indicator interpretability.

Reduction of Power Losses in the Distribution System by Controlling Tap Changing Transformer using the PSO Algorithm

Energy is an essential commodity for everyone, with electrical energy being the most preferred form. Unfortunately, non-renewable energy resources are gradually depleting, and renewable energy sources take several years to establish. To mitigate this problem, technology has shifted from non-renewable energy sources to electrical devices and machines, including household appliances like washing machines and air conditioners. However, the generation of electricity is still inadequate to meet the growing demand. This leads to two critical issues: Excessive power loss and inadequate voltage stability, making it difficult for power distribution companies to ensure a consistent and reliable power supply. The objective of this study is to tackle the issue of reduction and minimization of power dissipation By employing the PSO technique, adjusting the transformer tap settings. The proposed approach uses the 14-bus system as a reference and calculates losses for this system using the backward-forward sweeping technique.

A New PSO Technique Used for the Optimization of Multiobjective Economic Emission Dispatch

Most power is generated using fossil fuels like coal, natural gas, and diesel. The contribution of coal to power generation is very high compared to other sources. Almost all thermal power plants use coal as a fuel for power generation. Such sources of fossil fuels are limited and thus the cost of power generation increases. At the same time, the induced toxic gases due to these fossil fuels pollute the environment. The objective of this work is to solve the economic emission dispatch problem. Economic emission dispatch helps to find out how to operate power plants at the minimum cost and induce the minimum emissions at a thermal power plant. Economic emission dispatch with constraints is a nonlinear optimization problem. For the solution of such nonlinear economic emission load dispatch problems, this work considers a new particle swarm optimization technique. The proposed new PSO gives the best solution for economic emission load dispatch and handles the constraints. For the testing of the proposed new PSO algorithm, this work considered a case study of a system of six generating units, and it was tested for load demands of 700 MW, 800 MW, and 1000 MW. The results of the new PSO for the three load demands considered give the minimum generation cost, minimum emission, and minimum total cost compared to other optimization algorithms. The proposed techniques are effective, and they can help obtain the minimum generation cost and minimize emissions.

Implementation of particle swarm optimization strategy in Venlo‐type greenhouse climate to make energy‐efficient process

AbstractThis study appears to be the first to use a MATLAB simulator to illustrate Particle Swarm Optimization with multiple input–output restrictions. This proposed study's overarching objective was to make the entire process energy efficient, which provides improved performance with high accuracy and minimizes the operating cost by incorporating energy, ventilation, and CO2. Further, to reduce the complexity of the system, the optimization technique was divided into control and controlled variables. Meanwhile, to define state constraints for variables used in the objective function was to make the overall process cost‐effective, composing energy, CO2 supply, and ventilation cost. The chosen technique effectively decreased operating costs while maintaining the appropriate ranges for temperature (14–26°C), relative humidity (0–90%), and CO2 concentration (400–2000 ppm), according to simulation results. Off‐peak, standard, and peak energy cost levels were R1080.26, R748.56, and R7078.4, respectively. On the other hand, it was found through comparative analysis that the standard and off‐peak energy consumption figures decreased by 65.4 and 8.1%, respectively, as compared to the peak tariff (2279.9 kWh). The suggested PSO technique is implied to be a viable means of increasing greenhouse energy efficiency and achieving sustainable, cleaner manufacturing.

Coordinated framework for active distribution system planning integrating active system management schemes

AbstractTo counter different technical challenges with large‐scale renewable energy sources (RESs) integration, distribution systems (DS) are recently undergoing the transition process from passive to active DS (ADS), which has introduced further complications for system planning. In such context, this study proposes an ADS expansion planning (ADSEP) framework in coordination with active system management (ASM) schemes. A multi‐stage ADSEP model, unlike the traditional single‐stage model, is designed where the planning horizon is divided into several stages to make the planning investments gradually. For proper coordination of different planning alternatives, an effective bi‐level model is formulated based on upper‐level and lower‐level planning. In accordance with recent energy market scenario, RESs are assumed to be invested by RES Investor (RESI) and to ensure the desired financial benefits of RESI, the Distribution Company (DISCOM) offers bus‐wise contract price (BCP) for purchasing RESs generated energy. Moreover, a computationally efficient 2 m point estimate method (PEM) is applied to model the uncertainties of load and RESs. The proposed planning framework with multiple objectives is solved using the multi‐objective PSO technique considering 69‐bus test system.

Research on Improved PSO Algorithm for Multi-peak to Photovoltaic Maximum Power Tracking Control

To ensure the real-time output maximum power of a photovoltaic power producing system and improve the multi-peak MPPT management of a solar array under partial shadow, a maximum power tracking control strategy based on an Improved PSO algorithm is presented. To increase the stability of maximum power tracking and to promote the coordination performance of local development and global search, the adaptive inertia weight and learning factor are developed regarding the basic PSO algorithm. Besides, simulation studies are used to evaluate the MPPT control effects of the method with the fundamental PSO algorithm in various situations. The findings show that the improved PSO algorithm outperforms the traditional PSO technique in terms of tracking effectiveness. It can not only converge to the GMPP quickly in the case of various local shading and light mutations but also has higher power generation efficiency.

A Novel Algorithm for Optimal Harmonic Load Flow including Harmonic Compensation

Load flow analysis is widely used for finding voltage at various parts of a large, interconnected power system. The exponential increase in the use of power electronic devices and the noticeable percentage of integration of renewable energy sources in modern power systems result in the occurrence of non-sinusoidal voltages at various parts of the power system. By reformation of the Newton-Raphson load flow method, a few harmonic load flow algorithms have been developed for modern transmission systems. However, for larger transmission systems, complexity and heavy computational burden are often encountered in the load flow solution process due to the increased size of the Jacobean matrix that further increases with multiple non-linear load buses and compensation requirements. This leads to convergence problems and increased execution time. In this paper, an optimal harmonic load flow algorithm is proposed, that employs a modified PSO technique to select control variables and the harmonic load flow method to find solutions for load flow analysis with a reduced size of the Jacobean matrix. The formulated method uses a simple technique to take into account nonlinear loads and results in faster convergence. The novelty of the algorithm is that an optimal load flow solution with the desired amount of reactive and harmonic compensation currents is obtained. The algorithm can help in getting load flow solutions in future power systems with distorted voltage and currents with compensation. The performance of the proposed algorithm is tested on a modified IEEE 30-bus system with multiple non-linear load buses and is validated with the Simulink model of the system.

Optimised backstepping sliding mode controller with integral action for MPPT-based photovoltaic system using PSO technique

Optimised backstepping sliding mode controller with integral action for MPPT-based photovoltaic system using PSO technique

Optimised backstepping sliding mode controller with integral action for MPPT-based photovoltaic system using PSO technique

Optimised backstepping sliding mode controller with integral action for MPPT-based photovoltaic system using PSO technique

Power maximization in standalone photovoltaic system: an adaptive PSO approach

A control strategy for power maximization which is an important mechanism to extract maximum power under changing environmental conditions using Adaptive Particle Swarm Optimization (APSO) is proposed in this paper. An Adaptive Inertia Weighting Factor (AIWF) is utilized in the velocity update equation of traditional PSO for the improvement in speed of convergence and precision in tracking Maximum Power Point (MPP) in standalone Photovoltaic system. Adaptation of weights based on the success rate of particles toward maximum power extraction is the most promising feature of AIWF. The inertia weight is kept constant in traditional PSO for the complete duration of optimization process. The MPPT in PV system poses a dynamic optimization problem and the proposed APSO approach paves way not only to track MPP under uniform irradiation conditions, but also to track MPP under non-uniform irradiation conditions. Simulations are done in MATLAB/Simulink environment to verify the effectiveness of proposed technique in comparison with the existing PSO technique. With change in irradiation and temperature, the APSO technique is found to provide better results in terms of tracking speed and efficiency. Hardware utilizing dSPACE DS1104 controller board is developed in the laboratory to verify the effectiveness of APSO method in real time.

Identification of fractional order Wiener-Hammerstein systems based on adaptively fuzzy PSO and data filtering technique

Identification of fractional order Wiener-Hammerstein systems based on adaptively fuzzy PSO and data filtering technique

Particle Swarm Optimization tuned Hybrid Sliding Mode Controller based static synchronous compensator with LCL filter for Power Quality improvement

One of the supreme striking areas in electrical system design is power quality analysis and its associated problems. All the electronic devices are exceptionally susceptible to a slight variation in the power supply. Among the Flexible Alternating Current Transmission System (FACTS) devices, the Static Synchronous Compensator (STATCOM) is the most effective device, since it has much faster dynamic response for the load changes. A PSO Optimized Hybrid Sliding Mode Controller based STATCOM with LCL Filter is used in the proposed work for enhancing power factor and decreasing the Total Harmonic Detection (THD) value. The Sliding Mode Controller (SMC) is used to operate this STATCOM, power quality was tuned using the PSO technique. The effectiveness of the conventional PI controller with SMC in regulating reactive power at the Point of Common Coupling (PCC) is compared with the proposed SMC-based controller. Applying soft computing techniques for efficient grid integration and cleaner energy production is the aim of this research. The overall design and analysis are done in MATLAB Simulink with nonlinear and unbalanced loads. With the proposed controller, the power factor is improved to 0.98 with the THD of 2.35 % for nonlinear inductive load. The proposed controller outperforms well with the 2.66 % overshoot and 5.61 ms settling time.

Optimal Design of Fractional Order PID Controllers for Solid Oxide Fuel Cell System Employing PSO Algorithm

Solid Oxide Fuel Cells (SOFCs) are gaining attraction in order to facilitate various applications owing to portability, low pollution and high efficiency. However, due to strong nonlinearity, fast variations in loading conditions and sluggish dynamics, regulation of the output voltage of SOFCs is disturbed. This paper aims to enhance the dynamic performance of SOFC by employing PI, PI Fast, PIDF, 2-DOF PID and PSO optimized FO-PID controllers under uncertain input conditions. The PID tuner is used for tuning the PI, PI fast, PIDF and 2-DOF PID controller parameters. The PSO technique is utilized for optimizing of FO-PID controller. The SOFC output with various controllers is compared in terms of performance specifications such as peak overshoot, settling time, steady state error and rise time. The comparison of computer simulation results manifests that the proposed PSO-FOPID controller scheme yields in far better performance with SOFC subjected to uncertain input.

The Optimal Placement of Electric Vehicle Fast Charging Stations in the Electrical Distribution System with Randomly Placed Solar Power Distributed Generations

The growing use of electric vehicles (EVs) in today’s transport sector isgradually reducing the use of petroleum-based vehicles. However, as EVpenetration grows, the EV’s demand influences distribution network parameterssuch as power loss, voltage profile. Therefore, an improved bald eaglesearch (IBES) algorithm is suggested for the optimal placement of FCSsinto the distribution network with high penetration of randomly distributedsolar power generation (SPDG). This study suggests a two-stage approachfor placing FCSs. The charging station investor decision index (CSIDI) was introduced in the first stage, taking into account the land cost index (LCI)and the electric vehicle population index (EVPI). The CSIDI was developedto decrease land costs while increasing EV population for FCS installation.In the next one, an optimization problem is constructed to minimize totalactive power loss while taking distribution system operator (DSO) constraintsinto consideration. The IEEE-34 bus distribution system is used as theproposed network. The simulation is carried out in MATLAB to integratethe EVCSs in three cases in the distribution network with SPDGs randomlyplaced. Therefore, The IBES found the best optimal positions with a powerloss of 198.43 kW. When compared to the PSO technique, the IBES techniquehas a reduced average power loss of 2.02%.