Paper Particle Swarm Optimization Research Articles

IMPROVED PSO ALGORITHM FOR A PV SYSTEM UNDER PARTIAL SHADING CONDITION

Maximum power point tracking is essential for utilization of more amount of solar irradiation. Partial Shading condition is one of the reason behind not getting maximum power point. Partial shading condition may cause due to trees, chimney, clouds and so on. Due to partial shading, multiple peaks occurs in P-V characteristics of PV array. Conventional maximum power point algorithms are not able to track the maximum power point. To overcome this drawback, Particle swarm optimization algorithm is used for maximum power point tracking. Improved version of PSO also used in given paper for better results. In this paper comparative study of particle swarm optimization is done.In this paper particle swarm optimization with time varying inertia weight(PSO-TVIW) is applied for maximum power point tracking(MPPT). Results of classical PSO(particle swarm optimization) and POS-TVIW have analyzed. Effectiveness of both the algorithm have studied. Simulation of the PV system is done using MATLAB/Simulink.

Particle swarm optimization based metamaterial inspired circularly polarized patch antenna for S band applications

In this paper Particle Swarm Optimization technique (PSO) has been used to design a metamaterial inspired circularly polarised patch antenna with truncated corners and embedded square slots. The circular polarization has been achieved by trimming the corners of the square patch along one of its diagonal and square slots have been embedded along these truncated corners to improve the 3 dB axial ratio bandwidth of this proposed antenna. To achieve the maximum axial ratio bandwidth, the dimensions of this square slot have been optimized using PSO algorithm. Without any square slot, the axial ratio bandwidth of the patch antenna is 700 MHz. However, when square slots are embedded along the trimmed corners, with slot length w = 2.3 mm, axial ratio bandwidth of 1150 MHz is achieved. But, when PSO is applied to obtain the optimized dimension of this square slot so as to maximize this axial ratio bandwidth, the optimised w is determined to be 2.8 mm and the axial ratio bandwidth with this optimized slot length is determine to be 1600 MHz. Thus it is observed that by using the optimized value of w, obtained by using PSO technique, improvement in axial ratio bandwidth by 450 MHz is observed. To improve its gain, an array of splitted circular metamaterial rings is etched on the ground plane of this proposed patch antenna. The circularly polarised patch antenna with truncated corners resonates at 2.4 GHz with gain of 4.8 dBic and impedance bandwidth of 600 MHz. However, when square shaped slots of optimized length are embedded in the truncated corners and metamaterial substrate is loaded on the ground plane of this antenna, its gain reaches to 5.2 dBic. All simulations are done in an electronic simulator, HFSS.

LUCID PARTICLE SWARM OPTIMIZATION ALGORITHM FOR SOLVING OPTIMAL REACTIVE POWER PROBLEM

This paper presents, Lucid Particle Swarm Optimization (LPSO) algorithm for Solving Optimal Reactive Power Problem. Particle swarm method is typically made up of a population of simple agents intermingle locally with one another and with their surroundings, with the aim of locating the optima within the operational environment. In this paper, a robust and Lucid particle swarm optimization framework based on multi-agent system is presented, where learning capabilities are integrated into the particle agents to dynamically fiddle with their optimality behaviours. Self-Sufficiency is achieved by the use of communicators that separate an agent’s individual operation from that of the swarm, thereby making the system more robust. The proposed Lucid Particle Swarm Optimization (LPSO) algorithm has been tested on standard IEEE 118 & practical 191 bus test systems and simulation results show clearly about the premium performance of the proposed algorithm in reducing the real power loss.

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Designing optimal architecture of recurrent neural network (LSTM) with particle swarm optimization technique specifically for educational dataset

Designing an optimal neural network architecture plays an important role in the performance of a neural network model. In the past few years, various bio-inspired optimization techniques have been applied to find the optimal architecture of a neural network model. In this paper particle swarm optimization (PSO) technique has been applied with recurrent neural network long short-term memory (LSTM) algorithm to find an optimal architecture for feed forward neural network. To optimize the architecture of neural network model Parameters considered are hidden neurons, learning rate and activation function. Fitness function applied for the selection of the optimal combination of the parameters is root mean square error (RMSE). Due to privatization of education number of private institutes and universities are increasing rapidly every year. This increase has resulted in huge number of data (NAAC reports) regarding the assessment and accreditation of higher education institutions. Dataset of 500 educational institutes has been collected from the official site of National Assessment and Accreditation Council (NAAC). Hybrid model of PSO with LSTM algorithm has been applied to the educational dataset. Selection of the optimal architecture is done on the basis of RMSE, Accuracy and other performance parameters.

Particle swarm optimization based PID controller tuning for level control of two tank system

Automatic control plays a vital role in industrial operation. In process industries, in order to have an improved and stable control system, we need a robust tuning method. In this paper Particle Swarm Optimization (PSO) based algorithm is proposed for the optimization of a PID controller for level control process. A two tank system is considered. Initially a PID controller is designed using an Internal Model Control (IMC). The results are compared with the PSO based controller setting. The performance of the controller is compared and analyzed by time domain specification. In order to validate the robustness of PID controller, disturbance is imposed. The system is simulated using MATLAB. The results show that the proposed method provides better controller performance.

Shape and size determination of plasmonic nano particles using particle swarm optimization algorithm based absorption coefficient

Plasmonic nano particles can be greatly enhancing the optical absorption coefficient spectrum. Since optical properties of these particles strongly depends on the size and shape of the nano particles, in this paper particle swarm optimization algorithm (PSO) is used to optimize the nano particles shape and size in order to amplification of the absorption coefficient. In PSO a swarm consists of a matrix with decimal numbers, controls the particles shape and size in order to increase the absorption coefficient in the visible part of light spectrum. It is found that significant plasmonic enhancement of above 100000 can be obtained by optimize selection of particle shapes and sizes.

Optimum design of a hybrid PV–CSP–LPG microgrid with Particle Swarm Optimization technique

Designing an energy system using multiple energy sources including renewables and providing multiple energy services (e.g. electricity, heating) can enhance the reliability and efficiency of the system while mitigating the environmental footprint. However, interaction among various components, variation of the energy demand profile, and local ambient conditions make design optimization a complex task, and suggesting that efficient simulation tools and optimization techniques can help designers to determine the best solutions within a reasonable timeframe and budget.Previous work on a dynamic microgrid simulation tool called “u-Grid” used an exhaustive search technique to find optimum configurations. However, the high computational cost of the exhaustive search was a motivation to explore alternative optimization methods to improve the optimization process and also to enhance search speed. In this paper Particle Swarm Optimization (PSO) has been presented as a global optimizer and incorporated within the problem context. Results from the exhaustive search have been used as a benchmark for testing and validation of the newly introduced optimization technique. The result shows that the PSO method is an efficient technique which has the ability to determine a high quality design solution for an optimized microgrid with a relatively low computational cost. Applying this PSO-based algorithm to the case study has reduced the total computation time a factor of about 6 in a significantly smaller computational platform.

Estimation of the particle size distribution of colloids from multiangle dynamic light scattering measurements with particle swarm optimization

In this paper particle Swarm Optimization (PSO) algorithms are applied to estimate the particle size distribution (PSD) of a colloidal system from the average PSD diameters, which are measured by multi-angle dynamic light scattering. The system is considered a nonlinear inverse problem, and for this reason the estimation procedure requires a Tikhonov regularization method. The inverse problem is solved through several PSO strategies. The evaluated PSOs are tested through three simulated examples corresponding to polysty-rene (PS) latexes with different PSDs, and two experimental examples obtained by simply mixing 2 PS standards. In general, the evalu-ation results of the PSOs are excellent; and particularly, the PSO with the Trelea’s parameter set shows a better performance than other implemented PSOs.

Design of Fractional Order PID Controller Based Particle Swarm

Fractional order PID (FOPID) controller is a special kind of PID controller whose derivative and integral order are fractional rather than integer which has five parameters to be tuned. This paper presents study of the implementation of tuning method and performance enhancement of the closed loop system by use of the fractional order PID (PIλDμ) controller utilizing a MATLAB/Simulink. The tuning methods for these type controllers have many mixed tools of the available optimization methods and update artificial optimization methods in the design. In this paper particle swarm optimization has been implemented to design FOPID controller in which the unknown parameters are determined minimizing a given integral of time weighted absolute error (ITAE). The main specification of this paper is that the all five parameters of (PIλDμ) have been found directly without spreading the steps. It has been shown that the response and performance of the closed loop system with FOPID controller is much better than integer order PID controller for the same system and with better robustness.

Relieving Transmission Congestion by Optimal Rescheduling of Generators Using PSO

Congestion management is one of the most important issues for secure and reliable system operations. One of the most practiced techniques for congestion management is rescheduling the real power output of generators in the system. In this paper Particle Swarm Optimization (PSO) is used to determine the optimal generation levels to alleviate transmission congestion. Numerical results on IEEE 30 Bus test system is presented and the experimental outcomes demonstrate that PSO is one among the demanding optimization methods which are certainly capable of obtaining higher quality solutions for the proposed problem.

PSO based Cross Layer Optimization for Primary User selection in Cognitive Radios

In Cognitive Radio Networks, selection of primary users (PUs) is an important process for achieving optimal performance during a session. The selection process of PU is limited due to the strict boundaries between layers that are enforced in Open System Interconnection (OSI) model which prevent the coordination, interaction and data transfer between the layers. To overcome such limitations, cross layer optimization is proposed where different operating parameters such as transmission power, packet length, bandwidth etc. across the OSI layers of a device are optimized. In this paper Particle Swarm Optimization (PSO) algorithm is proposed to optimize different operating parameters with the objective to optimize throughput, power consumption, interference, Bit Error Rate and spectral efficiency for a set of PUs across the physical, network and Media Access Control (MAC) layer in OSI model. The fitness values of these objective functions in different modes and channels are investigated using MATLAB and the results shows that PSO is 70% faster than the Genetic Algorithm in terms of convergence rate. Finally paper proposes that PSO based algorithm is an efficient, reliable and fast technique for primary user selection in cognitive radios.

Hybrid Evolutionary Particle Swarm Optimization for the Coordination of Wind and Thermal Generation Dispatch

Development of better wind and thermal coordination dispatch is necessary to determine the optimal dispatch scheme that can integrate wind power reliably and efficiently. In this paper hybrid Evolutionary Programming (EP) and Particle Swarm Optimization (PSO) approach is utilized to coordinate the wind and thermal generation dispatch and to minimize the total production cost considering wind power generation and valve effect of thermal units. Numerical studies have been performed for three different test systems, i.e., six, thirteen and forty generating unit systems. The simulation results demonstrate the effectiveness of the proposed approach and shows the effect of wind power generation in reducing the total fuel.

Particle Swarm Optimization (PSO) based optimum Distributed Generation (DG) location and sizing for Voltage Stability and Loadability Enhancement in Radial Distribution System

This paper presents a new approach for optimum Distributed Generation (DG) location and sizing based on voltage stability maximization and minimization of real power losses. In this paper a new index is proposed which combines both voltage stability index (VSI) and line voltage stability index (LVSI) for identifying the weakest bus and branch in a radial distribution network is considered in the fitness function. Minimization of real power losses is also considered in the fitness function. In this paper Particle Swarm Optimization (PSO) is used to solve this fitness function. The effectiveness of the proposed method has been successfully tested on 12-bus, 30-bus, 33- bus and 69-bus radial distribution networks and the results are found to be in very good agreement.

Transmission Loss Minimization Using Optimization Technique Based On Pso

Power is generated in generating station, transmitted through transmission line and then distributed to consumers.Power system consists of three types of buses. They are generator bus, load bus and slack bus. Each bus is characterized by four parameters. They are bus voltage, phase angle, active power and reactive power. Thesebuses are classified according to known parameters. Unknown parameters are found using load flow studies. In this paper load flow studies are done using Newton-Raphson method. Transmission line is characterized by resistance, inductance and capacitance. This will result in losses. These losses cannot be eliminated but can be reduced.Optimization is a mathematical tool to find the maximum or the minimum of a function subject to some constrains. Using lose function as objective functionsubjected togenerator MW, transformer tapping, reactive power injection and controlled voltage as constrains, optimization technique can be used to minimize transmission losses. Using this we get optimal value for bus parameters such that transmission losses are minimum. In this paper Particle Swarm Optimization (PSO) is employed to solve optimal power flow problems. IEEE 30-bus power systems are used for testing the objective of this paper.

Comparative Study on Multiobjective Power Unit Coordinated Control Problem via Differential Evolution and Particle Swarm Optimization Algorithms

Power unit coordinated control (PUCC) problem was being focused by many optimizers as a serious task to be tackled in several aspects. Through this paper differential evolution (DE) and particle swarm optimization (PSO) algorithms are implemented to solve the PUCC problem. With considering four objectives to be optimized: minimization of tracking error in load power demand, fuel consumption, throttling losses due to steam control valve, and feeding water. Simulation experiments are made through case-study by aid of MATLAB programs to develop the required comparison through the two algorithms.

DTC torque ripple minimization based on PSO-PID controller

In conventional direct torque controlled (DTC) induction motor drive, there is usually undesired torque and flux ripple. The given torque is the speed output regulator; therefore, it necessary continues tuning for adjusting parameters Kp, Ki. In conventional proportional- integral (PI) speed controller, the performance of motor may differ over time that may cause unexpected torque disturbances, causing sluggishness of a control system. Tuning PID parameters are essential to DTC system to improve the performance of the system at a low speeds. In this paper particle swarm optimization (PSO) proposed to correct the parameters (Kp, Ki) of a speed controller in order to minimize torque ripple, flux ripple, and stator current distortion. Firstly, introduce brief description for conventional DTC design and PSO. Secondly, close loop speed controller in DTC for induction motor using PSO technique to provide high performance of accuracy. Finally, simulation results demonstrate that a reduction of torque and flux ripples is achieved in a whole speed range. Key words: Particle swarm optimization, PID controller, direct torque control.

Https://www.kau.edu.sa/Files/320/Researches/61836_32803.pdf

Unit commitment (UC) in power systems typically involves properly scheduling the on/off states as well as the real power outputs (economic dispatch) of all resources (generators/units) in the system. In addition to fulfill a large number of system constraints, the UC meets forecasted load demand calculated in advance, plus the spinning reserve requirement at each time interval, such that the total cost (mainly fuel cost) is minimum. In this paper particle swarm optimization (PSO) is applied to minimize cost, emission or costemission of the Unit Commitment problem. For unit commitment both cost and emission are considered. Actually cost and emission are trade-off. If cost increases, emission decreases and vice-versa. Real power is forecasted and used in the Unit Commitment of the Western Area of Saudi Arabia. The total cost for generation for 24 h is 4332905$ and the emission is 193479 ton.

Optimal Location and Design of TCSC controller For Improvement of Stability

Power system stability improvement by a coordinate Design ofThyristor Controlled Series Compensator (TCSC) controller is addressed in this paper.Particle Swarm Optimization (PSO) technique is employed for optimization of the parameterconstrained nonlinear optimization problem implemented in a simulation environment. The proposed controllers are tested on a weakly connected power system. The non-linear simulation results are presented. The eigenvalue analysis and simulation results show the effectiveness and robustness of proposed controllers to improve the stability performance of power system by efficient damping of low frequency oscillations under various disturbances.