Diesel Engine performance improvement in a 1-D engine model using Particle Swarm Optimization

2015 ◽  
Vol 4 (4) ◽  
Author(s):  
Prashanth Karra
Keyword(s):  
Particle Swarm Optimization ◽  
Diesel Engine ◽  
Particle Swarm ◽  
Merit Function ◽  
Operating Conditions ◽  
Pilot Injection ◽  
Swarm Optimization ◽  
Operating Condition ◽  
Engine Model ◽  
Pilot Fuel

AbstractA particle swarm optimization (PSO) technique was implemented to improve the engine development and optimization process to simultaneously reduce emissions and improve the fuel efficiency. The optimization was performed on a 4-stroke 4-cylinder GT-Power based 1-D diesel engine model. To achieve the multi-objective optimization, a merit function was defined which included the parameters to be optimized: Nitrogen Oxides (NOx), Nonmethyl hydro carbons (NMHC), Carbon Monoxide (CO), Brake Specific Fuel Consumption (BSFC). EPA Tier 3 emissions standards for non-road diesel engines between 37 and 75 kW of output were chosen as targets for the optimization. The combustion parameters analyzed in this study include: Start of main Injection, Start of Pilot Injection, Pilot fuel quantity, Swirl, and Tumble. The PSO was found to be very effective in quickly arriving at a solution that met the target criteria as defined in the merit function. The optimization took around 40-50 runs to find the most favourable engine operating condition under the constraints specified in the optimization. In a favourable case with a high merit function values, the NOx+NMHC and CO values were reduced to as low as 2.9 and 0.014 g/kWh, respectively. The operating conditions at this point were: 10 ATDC Main SOI, -25 ATDC Pilot SOI, 0.25 mg of pilot fuel, 0.45 Swirl and 0.85 tumble. These results indicate that late main injections preceded by a close, small pilot injection are most favourable conditions at the operating condition tested.

scholarly journals The Optimization of Marine Diesel Engine Rotational Speed Control Process by Fuzzy Logic Control Based on Particle Swarm Optimization Algorithm

2018 ◽  
Vol 10 (10) ◽  
pp. 99 ◽  
Author(s):  
Tien Tran
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Diesel Engine ◽  
Fuzzy Logic Control ◽  
Engine Speed ◽  
Fuzzy Logic Controller ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Logic Control

The marine main diesel engine rotational speed automatic control plays a significant role in determining the optimal main diesel engine speed under impacting on navigation environment conditions. In this article, the application of fuzzy logic control theory for main diesel engine speed control has been associated with Particle Swarm Optimization (PSO). Firstly, the controller is designed according to fuzzy logic control theory. Secondly, the fuzzy logic controller will be optimized by Particle Swarm Optimization (PSO) in order to obtain the optimal adjustment of the membership functions only. Finally, the fuzzy logic controller has been completely innovated by Particle Swarm Optimization algorithm. The study results will be represented under digital simulation form, as well as comparison between traditional fuzzy logic controller with fuzzy logic control–particle swarm optimization speed controller being obtained.

A reinforced combinatorial particle swarm optimization based multimodel identification of nonlinear systems

2016 ◽  
Vol 31 (3) ◽  
pp. 327-358 ◽  
Author(s):  
Ahmed A. Adeniran ◽  
Sami El Ferik
Keyword(s):  
Particle Swarm Optimization ◽  
Nonlinear Systems ◽  
Particle Swarm ◽  
Operating Conditions ◽  
Large Set ◽  
Continuous Stirred Tank Reactor ◽  
Swarm Optimization ◽  
Industrial Systems ◽  
Space Partition ◽  
Operating Space

AbstractSeveral industrial systems are characterized by high nonlinearities with wide operating ranges and large set point changes. Identification and representation of these systems represent a challenge, especially for control engineers. Multimodel technique is one effective approach that can be used to describe nonlinear systems through the combination of several submodels, where each is contributing to the output with a certain degree of validity. One major concern in this technique, especially for systems with unknown operating conditions, is the partitioning of the system's operating space and thus the identification of different submodels. This paper proposes a three-stage approach to obtain a multimodel representation of a nonlinear system. A reinforced combinatorial particle swarm optimization and hybrid K-means are used to determine the number of submodels and their respective parameters. The proposed method automatically optimizes the number of submodels with respect to the submodel complexity. This allows operating space partition and generation of a parsimonious number of submodels without prior knowledge. The application of this approach on several examples, including a continuous stirred tank reactor, demonstrates its effectiveness.

Bouc-Wen Model Parameter Identification for a MR Fluid Damper Using Particle Swarm Optimization

2014 ◽  
Vol 903 ◽  
pp. 279-284 ◽  
Author(s):  
Mohd Azraai Razman ◽  
Gigih Priyandoko ◽  
Ahmad Razlan Yusoff
Keyword(s):  
Particle Swarm Optimization ◽  
Parameter Identification ◽  
Particle Swarm ◽  
Operating Conditions ◽  
Model Parameters ◽  
Swarm Optimization ◽  
Mr Fluid ◽  
Dynamic Representation ◽  
Fluid Damper ◽  
Mr Fluid Damper

This paper present parameter identification fitting which are employed into a current model. Irregularity hysteresis of Bouc-Wen model is colloquial with magneto-rheological (MR) fluid damper. The model parameters are identified with a Particle Swarm Optimization (PSO) which involves complex dynamic representation. The PSO algorithm specifically determines the best fit value and decrease marginal error which compare to the experimental data from various operating conditions in a given boundary.

Gaussian quantum particle swarm optimization-based wide-area power system stabilizer for damping inter-area oscillations

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sreedivya Kondattu Mony ◽  
Aruna Jeyanthy Peter ◽  
Devaraj Durairaj
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Particle Swarm ◽  
Operating Conditions ◽  
Wide Area ◽  
Power System Stabilizer ◽  
Swarm Optimization ◽  
Content Type ◽  
Index Analysis ◽  
System Stabilizer

Purpose The extensive increase in power demand has challenged the ability of power systems to deal with small-signal oscillations such as inter-area oscillations, which occur under unseen operating conditions. A wide-area measurement system with a phasor measurement unit (PMU) in the power network enhances the observability of the power grid under a wide range of operating conditions. This paper aims to propose a wide-area power system stabilizer (WAPSS) based on Gaussian quantum particle swarm optimization (GQPSO) using the wide-area signals from a PMU to handle the inter-area oscillations in the system with a higher degree of controllability. Design/methodology/approach In the design of the wide-area stabilizer, a dead band is introduced to mitigate the influence of ambient signal frequency fluctuations. The location and the input signal of the wide-area stabilizer are selected using the participation factor and controllability index calculations. An improved particle swarm optimization (PSO) technique, namely, GQPSO, is used to optimize the variables of the WAPSS to move the unstable inter-area modes to a stable region in the s-plane, thereby improving the overall system stability. Findings The proposed GQPSO-based WAPSS is compared with the PSO-based WAPSS, genetic algorithm-based WAPSS and power system stabilizer. Eigenvalue analysis, time-domain simulation responses and performance index analysis are used to assess performance. The various evaluation techniques show that GQPSO WAPSS has a consistently good performance, with a higher damping ratio, faster convergence with fewer oscillations and a minimum error in the performance index analysis, indicating a more stable system with effective oscillation damping. Originality/value This paper proposes an optimally tuned design for the WAPSS with a wide-area input along with a dead-band structure for damping the inter-area oscillations. Tie line power is used as the input to the WAPSS and optimal tuning of the WAPSS is performed using an improved PSO algorithm, known as Gaussian quantum PSO.

DESIGN OF PWMSC CONTROLLER USING AUGMENTED LAGRANGIAN PARTICLE SWARM OPTIMIZATION ALGORITHM

2014 ◽  
Vol 23 (08) ◽  
pp. 1450110 ◽  
Author(s):  
AMIN SAFARI ◽  
HEIDAR ALI SHAYANFAR ◽  
HOSSEIN SHAYEGHI ◽  
AMIR AMELI
Keyword(s):  
Particle Swarm Optimization ◽  
Augmented Lagrangian ◽  
Particle Swarm ◽  
Operating Conditions ◽  
Performance Criteria ◽  
Lagrangian Particle ◽  
Nonlinear Simulation ◽  
Swarm Optimization ◽  
Pulse Width Modulated ◽  
The Time Domain

This paper presents a systematic process for designing a damping controller for pulse width modulated series compensator (PWMSC) to improve the angular stability of a multi-machine power system. The proposed controller is robust and tuned by satisfying the multiple H∞ performance criteria to stabilize the system at multiple operating conditions. The design problem has been converted into a constrained nonlinear optimization problem with the time domain-based objective function which is solved by an augmented Lagrangian particle swarm optimization (ALPSO) algorithm. The proposed control scheme has been implemented on two nonlinear test systems. The nonlinear simulation results clearly verify that the designed controller with proposed model improves the dynamic stability of the case studies, particularly when the operating loadings changes.

Performance Analysis of Permanent Magnet Synchronous Motor Drive using Particle Swarm Optimization Technique

2017 ◽  
Vol 1 (2) ◽  
pp. 19
Author(s):  
Deepti Yadav ◽  
Dr. Arunima Verma
Keyword(s):  
Particle Swarm Optimization ◽  
Permanent Magnet ◽  
Permanent Magnet Synchronous Motor ◽  
Particle Swarm ◽  
Speed Control ◽  
Synchronous Motor ◽  
Operating Conditions ◽  
Peak Time ◽  
Swarm Optimization ◽  
Speed Controller

Permanent magnet synchronous motor (PMSM), a nonlinear speed controller design based on Particle Swarm Optimization (PSO) technique is presented in this paper. For tuning the PID controller, the speed control for PMSM was analyzed using PSO algorithm to optimize the parameters in terms of proportional gain (Kp), integral gain (Ki), and derivative gain (Kd). Moreover, the overall system is evaluated under various operating conditions such as starting, braking, load application and load removal. Besides this, comparison between speed control of PMSM using Ziegler-Nichols (Z-N) method and speed control of PMSM with PSO technique has been carried out. These analyses are estimated in terms of dynamic and static response. The transient response are examined in terms of settling time (ts), rise time (tr), peak time (tp), and peak overshoot (Mp). Overall, PID speed controller with PSO technique shows that the proposed method has improved the performance under the various operating conditions.

Parameter Optimization for AP1000 Steam Generator Feedwater Control System Using Particle Swarm Optimization Algorithm

2016 ◽  
Author(s):  
Shifa Wu ◽  
Pengfei Wang ◽  
Jiashuang Wan ◽  
Xinyu Wei ◽  
Fuyu Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Water Level ◽  
Steam Generator ◽  
Parameter Optimization ◽  
Particle Swarm ◽  
Operating Conditions ◽  
Control Performance ◽  
Level Control ◽  
Swarm Optimization

The U-tube Steam Generator (UTSG) of AP1000 Nuclear Power Plant (NPP) is the crucial component transferring heat from the primary loop to the secondary loop to make steam. The UTSG of AP1000 NPP is a highly complex, nonlinear and time-varying system and its parameters vary with operating conditions. Therefore, it is difficult and challenging to well control the water level of AP1000 UTSG by tuning the PID controller parameter in a traditional way, especially when the system is undergoing a sharp transient. To achieve better control performance, the Particle Swarm Optimization (PSO) algorithm was applied for the parameter optimization of the AP1000 UTSG feedwater control system in this study. First, the mathematical model of AP1000 UTSG was established and the objective function was developed with the system constraints considered. Second, the simulation platform was built and then the simulation was conducted in MATLAB/Simulink environment. Finally, the optimized parameters were obtained and the feedwater control system with optimized parameters was simulated against that without optimized. The simulation results demonstrate that optimized parameters of AP1000 UTSG feedwater control system can significantly improve the water level control performance with smaller overshoot and faster response. Therefore, the PSO based optimization method can be applied to optimizing AP1000 UTSG feedwater control system parameters to provide much better control capabilities.

Coordinated AVR-PSS for Transient Stability Using Modified Particle Swarm Optimization

2014 ◽  
Vol 67 (3) ◽  
Author(s):  
J. Usman ◽  
M. W. Mustafa ◽  
G. Aliyu ◽  
B. U. Musa
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Transient Stability ◽  
Optimization Technique ◽  
Particle Swarm ◽  
Operating Conditions ◽  
Computational Time ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization ◽  
Wide Range

This paper presents the coordination between the Automatic Voltage Regulator (AVR) and Power System Stabilizers (PSS) to increase the system damping over a wide range of systems’ operating conditions in order to improve the transient stability performance and steady state performance of the system. The coordinated design problem is formulated as an optimization problem which is solved using Iteration Particle Swarm Optimization (IPSO). The application of IPSO technique is proposed to optimize the parameters of the AVR and PSS to minimize the oscillations in power system during disturbances in a single machine infinite bus system (SMIB). The performance of the proposed IPSO technique is compared with the traditional PSO technique. The comparison considered is in terms of parameter accuracy and computational time. The results of the time domain simulations and eigenvalue analysis show that the proposed IPSO method provides a better optimization technique as compared to the traditional PSO technique.  

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