Modified Particle Swarm Optimization Technique Research Articles

Techno-economic analysis of solar powered battery bank in a residential apartment

The electrical load is ever-increasing in the residential grid, there is a necessity to increase power generation to meet the demand (or) the centralized battery energy storage system can provide part of the generation. This paper describes the implementation of a centralized Solar Powered Battery Bank (SPBB) in residential apartments, which consists of solar panels and battery banks. To identify energy-saving recommendations, an energy audit is conducted using Fluke. As a result of performing quasi-dynamic load flow analysis in DIgSILENT for a realtime system with 64 residences and 47 inverter batteries, the study concludes that the centralized SPBB model reduces traditional grid power supply during peak periods. The proposed model's optimal sizing is determined using the Modified Particle Swarm Optimization technique. By using the SPBB model, the voltage stability of the fourth floor is improved from 0.8351 p.u. to 0.9566 p.u. In these models, daily energy consumption is reduced by up to 18%, with a minimum daily energy savings of 250 kWh, and total cost savings of 1491 rupees. Finally, an economic study is conducted to determine the proposed model's payback period, and comparing it with previous work shows that the proposed model is advantageous.

Speed Control of PMSM Using Modified Particle Swarm Optimization Technique Based on Inertia Weight Updating Mechanism

Speed Control of PMSM Using Modified Particle Swarm Optimization Technique Based on Inertia Weight Updating Mechanism

Triple-objective MPSO of zeotropic-fluid solar ejector cycle integrated with cold storage tank based on techno-economic criteria

The current study objective is to optimize the integration of a solar ejector cycle with cold storage by applying a multi-objective modified particle swarm optimization technique. Various collector and working fluid's effects are evaluated on dynamic system behavior, which zeotropic fluid and parabolic trough collector have been the most effective choices. Hot and cold storage tanks are applied to overcome the instability of solar energy and ensure sustainable access to produced cold. Design parameter's optimum values are estimated through sensitivity analysis and genetic algorithm optimization which are decreased up to 70% compared to initial guesses. An artificial neural network is employed to train exergo-economic-environmental data which fed into the multi-objective modified particle swarm optimization algorithm. With using of cold storage tank, the system's coefficient of performance is enhanced up to three times compared to the ejector cycle at optimum design variable. By employing of parabolic trough collector, the system has its maximum coefficient of performance and exergy efficiency, as well as the minimum size of the ejector cycle and cold storage tank. The parabolic trough collector, air handling unit, and cold storage tank waste over 80% of the overall system's exergy. The payback period of system is estimated about 3.5 years.

Reliability Enhancement on Distribution System Using Modified Multi-Objective Particle Swarm Optimization Technique

This research paper introduces an innovative approach for optimizing the placement of fault indicators (FIs) within electric distribution systems. The primary focus is on addressing how the presence of existing protection and control devices affects the time it takes to restore service to customers in the event of a fault. Unlike conventional FI placement methods, this extended approach considers the uncertainties associated with automatic switching and introduces a novel technical objective function known as the Customers' Average Restoration Time Index (CARTI). To tackle the complex task of multi-objective optimization, a solution approach has been devised, with the goal of simultaneously minimizing both economic and technical objectives. This methodology harnesses a Multi-Objective Particle Swarm Optimization (MOPSO) algorithm and incorporates a Modified Particle Swarm Optimization technique to select the most suitable solution from the set of Pareto optimal solutions generated. The proposed method's effectiveness is demonstrated through its application in two scenarios: firstly, in the context of bus number four within the Roy Billinton test system (RBTS4), and secondly, in a practical real-life distribution network. The study assesses technical objectives, specifically the System Average Interruption Duration Index (SAIDI) and CARTI, providing insights into the method's practical utility and its capacity to enhance FI placement for improved distribution system performance.

Image Reconstruction Algorithm for Electrical Capacitance Tomography Based on Constrained Particle Swarm Optimization

When the particle swarm is active, the robustness of the system is quite great, which is very useful for solving ill-conditioned problems like picture reconstruction. In the reconstructed image, however, the large number of pixels results in the large dimension of particles, making it harder for particles to attain the ideal solution during the optimization process. A constraint condition is applied to the position of particles to solve this problem. The image reconstruction algorithm regularized by Tikhonov is used as the particle position reference to constrain the particles to search within a certain range of the algorithm, and the penalty function is used to solve the problem to improve the particle search speed. For the inertia weight of particle swarm, this paper uses linear decreasing weight to realize its adaptive dynamic adjustment, which improves the flexibility of the algorithm. When chaos operator is added to the particle swarm location search process, if the particle is trapped in a local optimum, the chaos variable will fluctuate within a certain range, thus reducing the error rate of the optimal solution. The experimental comparison results illustrate that the modified particle swarm optimization technique for the image reconstruction of Electrical Capacitance Tomography outperforms the classic LBP and Tikhonov algorithms in terms of accuracy and efficiency.

Effective Load Balancing and Security in Cloud using Modified Particle Swarm Optimization Technique and Enhanced Elliptic Curve Cryptography Algorithm

Effective Load Balancing and Security in Cloud using Modified Particle Swarm Optimization Technique and Enhanced Elliptic Curve Cryptography Algorithm

Removing Powerline Interference from EEG Signal using Optimized FIR Filters

The Electroencephalography (EEG) is a signal representing the electrical activity of the brain and is used in the diagnosis of brain diseases. The EEG signal is weak and highly prone to noise from the powerline which generates a sinusoidal signal with the main frequency of 5060 Hz. Therefore, three harmonics of powerline noise must be removed using notch filters for a perfect diagnosis which requires three series notch filters. This paper presents a new method to design a digital notch finite impulse response (FIR) filter using a modified particle swarm optimization technique. The proposed method provides a short length, maximum stopband attenuation, and small transition width compared to different algorithms which results in removing the noise in EEG signal efficiently.

Artificial Intelligence-oriented economic non-linear model predictive control applied to a pressure swing adsorption unit: Syngas purification as a case study

• An MPC controller is applied to the syngas purification by pressure swing adsorption. • The proposed Economic MPC (EMPC) makes use of a Deep Neural Network (DNN) model. • The EMPC control law is solved by a modified Particle Swarm Optimization technique. • The plant-model mismatch is evaluated by phenomenological and DNN surrogate models. • CO2 recovery and purity and H2/CO are controlled while productivity is maximized. A novel Artificial Intelligence-oriented Economic nonlinear model predictive control (EMPC) strategy is proposed herein. It was applied to a Pressure Swing Adsorption (PSA) unit where syngas is purified by porous amino-functionalized titanium terephthalate MIL-125. This study addresses the open research problem of simultaneous economic optimization and advanced control in PSA units. A surrogate model based on a Deep Learning Neural Network (DNN) architecture is built to capture the process dynamics with high fidelity. The deployment of these DNN models into the EMPC control law allows obtaining solutions at a low computational cost, making it feasible to be applied in practice, even if using a particle swarm optimization technique. The robustness of the proposed EMPC controller is evaluated under a realistic plant-model mismatch in a software-in-the-loop approach. An experimentally validated phenomenological nonlinear model of the PSA unit for syngas purification was used to represent the plant and to train the DNN models. While EMPC maximizes the unit productivity in all zone/output tracking scenarios for CO 2 recovery and CO 2 purity, it can successfully fulfill the key operating endpoint constraint related to the H 2 /CO ratio, including the unmeasured perturbation scenarios.

A novel swarm intelligence assisted Euclidean distance based single detection index approach in transmission line relaying for fast decision making

Abstract Intelligent threshold value improves the speed and accuracy of any fault detector unit. But in the literature, any such traditional threshold selection mechanism is not available to implant in the fault detector unit of protective relay. This paper introduces a swarm intelligence-based threshold setting mechanism for fault detector. In this context, the Euclidean distance approach (EDA) with a single detection index is used for transmission network protection. The detection index function is formulated with fault parameters on a priority base. Further, the modified particle swarm optimization technique with adaptive inertia strategy is used to identify the optimal threshold value of detection index function. With swarm intelligence based optimal threshold, numerous cases are investigated on a test power system and obtained results show the efficacy of the method.

Optimized scheduling algorithm for soft Real-Time System using particle swarm optimization technique

Scheduling of tasks in Real-Time Systems is based on static or dynamic priority like earliest deadline first (EDF) and rate monotonic, respectively. The static scheduler does not give assurance of scheduling all tasks during the underload scenario, whereas dynamic scheduler performs poorly during an overload scenario. This paper has proposed a swarm intelligence-based scheduling algorithm that can overcome both the situations. This paper has used particle swarm optimization (PSO) based swarm technique to design the new scheduling approach. It considers each task as a particle and applied modified PSO technique to identify the most critical task to execute. The efficiency of the newly proposed method has been compared with existing EDF and ACO based scheduling algorithms considering two significant parameters, the success ratio and the effective CPU utilization. All three algorithms have been tested on the simulator with a Soft Real-time periodic task set on 500 timelines. It has been observed that during the underload scenario, the proposed algorithm performs equally to EDF and ACO based algorithms. During overload and highly overload situations, the proposed algorithm performs batter compared to EDF and ACO based algorithms.

Improving the dynamic performance of grid connected wind farms using modern UPFC

Reliable power from Wind energy is one of the promising non-conventional energy sources for the future. But the fluctuating nature of wind introduces power quality problems affects the stability of the entire grid. Another issue is that power system grid is non-linear dynamic system which needs plenty of real and reactive power to provide its load and power loss is unreliable. Nowadays Flexible AC Transmission Systems (FACTS) controllers are handling a transmission line power surges which magnify the entire grid operation. This manuscript aims at reducing the power quality issues using Unified Power Flow Controller (UPFC) which is interconnected at the Point of Common Coupling (PCC) to improves the transmission ability of the system and enhance the dynamic response of ac grids. The distortions in the waveform are reduced by the proper switching of the semiconductor devices of the UPFC. The switching is controlled using a PI controller with current controlled Pulse Width Modulation (PWM). The gains of the PI controller are tuned using Modified Particle Swarm Optimization Technique (MPSO). The power quality is then analyzed with Total Harmonics Distortion (THD) and the performance is simulated in SIMULINK/MATLAB. These results are acquired by means of modified Particle Swarm Optimization (MPSO), to demonstrate the superiority of the proposed method, the simulation conceded out on grid connected wind power system with Proportional Integral Controller, Fuzzy Logic Controller, Artificial Bee Colony Algorithm and Modified Particle swarm Optimization.

3D brain tumor segmentation in MRI images based on a modified PSO technique

AbstractThree‐dimensional (3D) brain tumor segmentation is a clinical requirement for brain tumor diagnosis and radiotherapy planning. This is a challenging task due to variation in type, size, location, and shape of tumors. Several methods such as particle swarm optimization (PSO) algorithm formed a topological relationship for the slices that converts 2D images into 3D magnetic resonance imaging (MRI) images which does not provide accurate results and they depend on the number of input sections, positions, and the shape of the MRI images. In this article, we propose an efficient 3D brain tumor segmentation technique called modified particle swarm optimization. Also, segmentation results are compared with Darwinian particle swarm optimization (DPSO) and fractional‐order Darwinian particle swarm optimization (FODPSO) approaches. The experimental results show that our method succeeded 3D segmentation with 97.6% of accuracy rate more efficient if compared with the DPSO and FODPSO methods with 78.1% and 70.21% for the case of T1‐C modality.

Hybrid task scheduling strategy for cloud computing by modified particle swarm optimization and fuzzy theory

As the world is progressing towards more efficient computing and faster approaches, cloud computing is a popular computing model to such increasing requirements. In order to provide cost-effective executions in cloud environment, appropriate task scheduling strategy is necessary. This paper proposes a hybrid task scheduling algorithm named FMPSO that is based on Fuzzy system and Modified Particle Swarm Optimization technique to enhance load balancing and cloud throughput. FMPSO strategy at first considers four modified velocity updating methods and roulette wheel selection technique to enhance the global search capability. Then, it uses crossover and mutation operators to overcome some drawbacks of PSO such as local optima. Finally, this schema applies fuzzy inference system for fitness calculations. The input parameters for the proposed fuzzy system are length of tasks, speed of CPU, size of RAM, and total execution time. By adding these fuzzy systems, FMPSO strategy achieves the goal of minimizing the execution time and resource usage. We evaluate FMPSO algorithm using the CloudSim toolkit and simulation results demonstrate that the proposed strategy has a better performance in terms of makespan, improvement ratio, imbalance degree, efficiency, and total execution time comparing to other approaches.

Trajectory planning of an autonomous mobile robot

The latest moves in trajectory planning for autonomous mobile robots are directed towards a popular investigation work. This paper introduces modified particle swarm optimisation technique called as adaptive particle swarm optimisation (APSO) and particle swarm optimisation (PSO) for trajectory length optimisation. For estimating the trajectory length of the robot, nine numbers of obstacles is selected between start and goal point in a static environment. Lastly a comparison is established between these two approaches, to identify the approach that affords shortest trajectory length in a least computation time and shortest possible travel time. Simulation result shows that APSO contributes towards curtail trajectory length at a lesser computational and travel time as compared to PSO.

Solar cell parameters identification using hybrid Nelder-Mead and modified particle swarm optimization

Solar cell modelling primarily involves the formulation of the non-linear current versus voltage (I-V) curve. Determination of parameters plays an important role in solar cell accurate modelling. This paper presents an application of the hybrid Nelder-Mead simplex search method and modified Particle Swarm Optimization technique for identifying the parameters of solar cell and photovoltaic module models. The proposed technique is used to identify the unknown model parameters, namely, the generated photocurrent, saturation current, series resistance, shunt resistance, and ideality factor that govern the current-voltage relationship of a solar cell. A measurement data of 57 mm diameter commercial (R.T.C. France) silicon solar cell and a module consisting of 36 polycrystalline silicon cells (Photowatt-PWP 201) are used to test and verify the consistency of accurately estimating various parameters. Comparative study among different parameter estimation techniques is presented to demonstrate the effectiveness of the proposed approach. Furthermore, to verify the accurateness of the proposed technique, an experimental test bench unit is built using 72 mono-crystalline silicon cells (HIP-210NKHB5-2 module) on different environment conditions.

Modified particle swarm optimisation technique for optimal design of small renewable energy system supplying a specific load at Mansoura University

Recently, a special attention has been attributed to the renewable energy in Egypt. Optimal sizing of small renewable energy system has a very important role in the use of renewable energy effectively and economically. Particle swarm optimisation (PSO) is a popular stochastic optimisation method that has found in wide applications. Conventional PSO suffers from high computational complexity and slow convergence speed. This study presents a modified PSO (MPSO) technique to optimise the capacity sizes of different components of hybrid PV/wind/battery power generation system for supplying communication and information technology centre in Mansoura University-Egypt. A feasibility study for two options is investigated; stand-alone system composed of PV/wind/battery combination and a grid connected PV/wind system. The proposed MPSO technique proves faster convergence speed and shorter computational time as compared with conventional techniques.

Performance of smart maximum power point tracker under partial shading conditions of photovoltaic systems

Partial shading on photovoltaic (PV) modules reduces the generated power of the PV system than the maximum power generated from each module separately. The shaded PV module acts as a load to unshaded ones which can lead to hot-spot. To alleviate the effect of partial shading, bypass diodes should be connected across each PV modules. Connecting several PV modules together produces multiple peaks (one global peak (GP) and multiple local peaks (LPs)) on partial shading conditions. Maximum power point tracker conventional techniques are designed to follow the GP but they stuck around LPs such as fuzzy logic controller (FLC). In this paper, modified particle swarm optimization (MPSO) using genetic algorism has been used to follow the GP under any operating conditions. MPSO has been studied and compared with the FLC technique to show the superiority of this technique under all operating conditions. Co-simulation between Matlab/Simulink and PSIM has been used to model the PV system under partial shading conditions. The simulation results show that the MPSO technique is more effective than FLC in following the GP. The generated power increases considerably with the MPSO than the FLC technique in shading conditions.

A Modified Particle Swarm Optimization Technique for Finding Optimal Designs for Mixture Models.

Particle Swarm Optimization (PSO) is a meta-heuristic algorithm that has been shown to be successful in solving a wide variety of real and complicated optimization problems in engineering and computer science. This paper introduces a projection based PSO technique, named ProjPSO, to efficiently find different types of optimal designs, or nearly optimal designs, for mixture models with and without constraints on the components, and also for related models, like the log contrast models. We also compare the modified PSO performance with Fedorov's algorithm, a popular algorithm used to generate optimal designs, Cocktail algorithm, and the recent algorithm proposed by [1].

Modified Particle Swarm Optimization technique based Maximum Power Point Tracking for uniform and under partial shading condition

Generation of electricity from solar energy has gained worldwide acceptance due to its abundant availability and eco-friendly nature. Even though the power generated from solar looks to be attractive; its availability is subjected to variation owing to many factors such as change in irradiation, temperature, shadow etc. Hence, extraction of maximum power from solar PV using Maximum Power Point Tracking (MPPT) method was the subject of study in the recent past. Among many methods proposed, Hill Climbing and Incremental Conductance MPPT methods were popular in reaching Maximum Power under constant irradiation. However, these methods show large steady state oscillations around MPP and poor dynamic performance when subjected to change in environmental conditions. On the other hand, bio-inspired algorithms showed excellent characteristics when dealing with non-linear, non-differentiable and stochastic optimization problems without involving excessive mathematical computations. Hence, in this paper an attempt is made by applying modifications to Particle Swarm Optimization technique, with emphasis on initial value selection, for Maximum Power Point Tracking. The key features of this method include ability to track the global peak power accurately under change in environmental condition with almost zero steady state oscillations, faster dynamic response and easy implementation. Systematic evaluation has been carried out for different partial shading conditions and finally the results obtained are compared with existing methods. In addition, simulations results are validated via built-in hardware prototype.

Modeling and optimization of a hybrid power system supplying RO water desalination plant considering CO2emissions

AbstractSeawater desalination is an attractive choice especially for remote areas where freshwater is rare. Egypt is moving toward the desalination of water as an alternative solution to the decrease in freshwater. The main objective of this study was to design an optimal, economic, and efficient hybrid system that feeds the electric needs of a small-scale brackish reverse osmosis desalination unit and a tourism motel located in Hurghada, Egypt. The optimization problem of sizing different components of hybrid system is a complicated one; it needs a special tool capable of solving it rapidly and effectively. Three different hybrid system scenarios are discussed to select the most optimum combination of them. These scenarios are the following: wind/PV/battery, wind/PV/diesel, and wind/PV/battery/diesel. In this study, a modified particle swarm optimization technique is applied for optimum sizing of the proposed hybrid system scenarios. The optimization problem is solved to minimize the annual total investm...