Optimal design of power-system stabilizers using particle swarm optimization

In this paper, a novel evolutionary algorithm-based approach to optimal design of multimachine power-system stabilizers (PSSs) is proposed. The proposed approach employs a particle-swarm-optimization (PSO) technique to search for optimal settings of PSS parameters. Two eigenvalue-based objective functions to enhance system damping of electromechanical modes are considered. The robustness of the proposed approach to the initial guess is demonstrated. The performance of the proposed PSO-based PSS (PSOPSS) under different disturbances, loading conditions, and system configurations is tested and examined for different multimachine power systems. Eigenvalue analysis and nonlinear simulation results show the effectiveness of the proposed PSOPSSs to damp out the local and interarea modes of oscillations and work effectively over a wide range of loading conditions and system configurations. In addition, the potential and superiority of the proposed approach over the conventional approaches is demonstrated.     

A particle swarm optimization for wind energy control problem

The control problem of a wind turbine involves the determination of rotor speed and tip-speed ratio to maximize power and energy capture from the wind. The problem can be formulated as a nonlinear programming problem with the annual energy generation as the objective function. The wind speed distribution is modeled as the Weibull distribution. The Weibull shape and scale parameters are assigned to be stochastic in response to limited wind data and variability nature of the wind. It is proposed to apply particle swarm optimization to solve for optimum rotor speed under fixed-speed operation and optimum tip-speed ratio under variable-speed operation. The optimum rotor speed varies with the wind speed distribution, while the optimum tip-speed ratio does not depend on the wind speed distribution. It can be concluded from the simulation results that both the wind power and energy are more dependent of the Weibull scale parameter than the Weibull shape parameter. This implies that the wind power and energy are more dependent of the mean wind speed than the speed distribution.     

Adaptive particle swarm optimization approach for static and dynamic economic load dispatch

This paper presents a novel heuristic optimization approach to constrained economic load dispatch (ELD) problems using the adaptive–variable population – PSO technique. The proposed methodology easily takes care of different constraints like transmission losses, dynamic operation constraints (ramp rate limits) and prohibited operating zones and also accounts for non-smoothness of cost functions arising due to the use of multiple fuels. Simulations were performed over various systems with different numbers of generating units, and comparisons are performed with other existing relevant approaches. The findings affirmed the robustness, fast convergence and proficiency of the proposed methodology over other existing techniques.     

基于粒子群算法的储能电站经济优化调度策略

储能电站的功率分配方案直接影响其调度成本,合理的功率分配方案是保障电站运行经济性的基础。为了合理有效地进行储能电站功率分配,文章以单位周期内调度成本最低为优化目标,搭建了考虑电池容量损失的储能电站调度成本模型,并利用粒子群优化算法(PSO)寻求储能电站调度任务的最优分配方案。在保证完成储能电站调度任务的同时,最大限度地降低调度成本。案例仿真结果表明,文章所提出的PSO优化分配方案比传统等比例功率分配方案具有明显的优越性。当储能电站在传统调度方法下寿命终止(1 597次)时,PSO优化调度方法同比节约调度成本约19.7%,且在此基础上可继续工作370次,使储能电站的运行寿命延长23.2%。     

A new fuzzy adaptive particle swarm optimization for non-smooth economic dispatch

This paper proposes a novel method for solving the Non-convex Economic Dispatch (NED) problems, by the Fuzzy Adaptive Modified Particle Swarm Optimization (FAMPSO). Practical ED problems have non-smooth cost functions with equality and inequality constraints when generator valve-point loading effects are taken into account. Modern heuristic optimization techniques have been given much attention by many researchers due to their ability to find an almost global optimal solution for ED problems. PSO is one of modern heuristic algorithms, in which particles change place to get close to the best position and find the global minimum point. However, the classic PSO may converge to a local optimum solution and the performance of the PSO highly depends on the internal parameters. To overcome these drawbacks, in this paper, a new mutation is proposed to improve the global searching capability and prevent the convergence to local minima. Also, a fuzzy system is used to tune its parameters such as inertia weight and learning factors.     

Economic dispatch using particle swarm optimization: A review

Electrical power industry restructuring has created highly vibrant and competitive market that altered many aspects of the power industry. In this changed scenario, scarcity of energy resources, increasing power generation cost, environment concern, ever growing demand for electrical energy necessitate optimal economic dispatch. Practical economic dispatch (ED) problems have nonlinear, non-convex type objective function with intense equality and inequality constraints. The conventional optimization methods are not able to solve such problems as due to local optimum solution convergence. Meta-heuristic optimization techniques especially particle swarm optimization (PSO) has gained an incredible recognition as the solution algorithm for such type of ED problems in last decade. The application of PSO in ED problem, which is considered as one of the most complex optimization problem has been summarized in present paper.     

Prediction of natural gas hydrate inhibitor vaporization rate using particle swarm optimization approach

Natural gas is a very important source of energy. In natural gas processing, accurate prediction of methanol loss to the vapor phase during natural gas hydrate inhibition is necessary to compute the total methanol injection rate required to effectively prevent the formation of natural gas hydrate. A reliable prediction tool that has the capability to accurately predict methanol losses to the vapor phase is thus needed. In order to address this matter, the current research was aimed at assessing the ability and feasibility of a robust computational intelligence paradigm. Based on a total of 326 dataset collected from the reliable literature, methanol loss to the vapor phase was predicted using artificial neural network (ANN) linked with particle swarm optimization (PSO) which is employed to determine the optimal values of the ANN weights. Success of the introduced hybrid intelligence model (or PSO-ANN) was confirmed with overall mean squared error (MSE), mean absolute error (MAE), and coefficient of determination (R²) values of 0.16421, 0.33210, and 0.99696, respectively.     

A passivity approach to controller design for a TCSC

A passivity-based controller is designed for a thyristor controlled series capacitor (TCSC) aimed to enhance power system stability. A Hamiltonian representation of the power system is employed, revealing some structural properties instrumental for the controller design. Results show the capability of the proposed controller when the power system is subject to major disturbances     

Compressor speed control design using PID controller in hydrogen compression and transfer system

Hydrogen is an energy carrier which can be processed by high pressure compressor and they can be transported, stored and converted to electricity for later use. This paper proposes a hydrogen compression model development and modeling of hydrogen transportation between two tanks using MATLAB software version 22. The proposed model provides amount of hydrogen required in volumes (m³) and compressor power required in (KW) for compressor speed of 500 rad/s, 1000 rad/s and 1500 rad/s. This model provides hydrogen volume of 1 m³ and 10 KW compressor power requirement at 500 rad/s compressor speed. For compressor speed of 1000 rad/s, the proposed model provides hydrogen volume of 10 m³and 20 KW compressor power requirements and for 1500 rad/s this model provides volume of 30 m³and 30 KW compressor power requirements which indicates that the increase in compressor speed increases hydrogen volume generated and increase the power requirement also. For maintaining compressor speed at desired value, a PID (Proportional + Integral + Derivative) controller has been designed and the results were compared with Proportional (P), PI (Proportional + Integral), and PD (Proportional + Derivative) controllers. PID controller performance can be measured using the parameters delay time and settling time. Low values of settling time and delay time indicate the better performance of PID controller. P controller achieves the desired compressor speed with delay time of 228 ms; settling time of 1250 s. PI controller achieves the desired compressor speed with delay time of 210 ms, settling time of 1210 s. PD controller achieves the desired compressor speed with delay time of 185 ms, settling time of 1280 s. PID controller provides better speed regulation with low delay time of 110 ms and settling time of 1120 s when compared with P, PI, PD controllers. From the simulation results it is observed that PID controller can be a good option for slow process like hydrogen gas flow through pipeline for effective speed regulation.     

A multi-objective fuzzy optimization for optimum dimensions design of a convective spine

A systematic multi-objective fuzzy optimization procedure to design the optimum dimensions of a conical convective spine is presented in this paper. Conflicting fuzzy design objectives, such as the weight and the length of spine as well as ‘soft’ constraints, are considered simultaneously in this study. The conical convective spine design based on the proposed procedure leads to a practical spine shape for production. The proposed design procedure cal also is applied to other design problems with complicated constraints.     

Design optimization of shell-and-tube heat exchanger using particle swarm optimization technique

Shell-and-tube heat exchangers (STHEs) are the most common type of heat exchangers that find widespread use in numerous industrial applications. Cost minimization of these heat exchangers is a key objective for both designer and users. Heat exchanger design involves complex processes, including selection of geometrical parameters and operating parameters. The traditional design approach for shell-and-tube heat exchangers involves rating a large number of different exchanger geometries to identify those that satisfy a given heat duty and a set of geometric and operational constraints. However, this approach is time-consuming and does not assure an optimal solution. Hence the present study explores the use of a non-traditional optimization technique; called particle swarm optimization (PSO), for design optimization of shell-and-tube heat exchangers from economic view point. Minimization of total annual cost is considered as an objective function. Three design variables such as shell internal diameter, outer tube diameter and baffle spacing are considered for optimization. Two tube layouts viz. triangle and square are also considered for optimization. Four different case studies are presented to demonstrate the effectiveness and accuracy of the proposed algorithm. The results of optimization using PSO technique are compared with those obtained by using genetic algorithm (GA).     

Combined modeling for electric load forecasting with adaptive particle swarm optimization

Electric load forecasting is crucial for managing electric power systems economically and safely. This paper presents a new combined model for electric load forecasting based on the seasonal ARIMA forecasting model, the seasonal exponential smoothing model and the weighted support vector machines. The combined model can effectively count for the seasonality and nonlinearity shown in the electric load data and give more accurate forecasting results. The adaptive particle swarm optimization is employed to optimize the weight coefficients in the combined forecasting model. The proposed combined model has been compared with the individual models and the other combined model reported in the literature and its results are promising.     

An approach for the optimum design of heat exchangers

In this paper, an approach for the optimum design of heat exchangers has been presented. Traditional design method of heat exchangers involves many trials in order to meet design specifications. This can be avoided through the present design method, which takes the minimization of annual total cost as a design objective. In alternative optimum design methods, such as Lagrange multiplier method, by changing one variable at a time and using a trial–error or a graphical method, optimum results are obtained in a long time. In the present design optimization problem, the total annual cost has been taken as the objective function and heat balance, and rate equation have been taken as equal constraint. The method using the penalty function transforms the constrained problem into a single unconstrained problem. To solve the optimal problem, the method of steepest descent has been used. Initial design variables include the tube‐inside coefficient of heat transfer, tube‐outside coefficient of heat transfer, temperature difference and outside tube area of heat transfer. The changes in variables are considered simultaneously to reach an optimum solution. The results show that the present approach is a powerful tool for optimum design of heat exchangers and is expected to be beneficial to energy industry. Copyright © 2004 John Wiley & Sons, Ltd.     

Optimization for ice-storage air-conditioning system using particle swarm algorithm

Ice-storage air-conditioning system, while known for its advantage of shifting power consumption at peak hours during the day to the nighttime, can increase both energy consumption and CO₂ emission. The study adopts particle swarm algorithm to facilitate optimization of ice-storage air-conditioning systems and to develop optimal operating strategies, using minimal life cycle cost as the objective function. Increase in power consumption and CO₂ emission triggered by the use of ice-storage air-conditioning system is also examined and analyzed. Case study is based on a typical air-conditioning system in an office building. Results indicate that, with proper parameters, particle swarm algorithm can be effectively applied to the optimization of ice-storage air-conditioning system. In addition, optimal capacity of the ice-storage tank can be obtained. However, the volume of power consumption and CO₂ emission rises with the increase in ice-storage tank capacity. Consideration of additional costs of power consumption like carbon tax can therefore lead to changes in the optimal system configuration.     

Optimal sizing of a stand-alone hybrid power system via particle swarm optimization for Kahnouj area in south-east of Iran

In this paper a novel intelligent method is applied to the problem of sizing in a hybrid power system such that the demand of residential area is met. This study is performed for Kahnouj area in south-east Iran. It is to mention that there are many similar regions around the world with this typical situation that can be expanded. The system consists of fuel cells, some wind units, some electrolyzers, a reformer, an anaerobic reactor and some hydrogen tanks. The system is assumed to be stand-alone and uses the biomass as an available energy resource. In this system, the hydrogen produced by the reformer is delivered to the fuel cell directly. When the power produced by the wind turbine plus power produced by the fuel cell (fed by the reformer) are more than the demand, the remainder is delivered to the electrolyzer. In contrast, when the power produced by the wind turbine plus that produced by the fuel cell (fed by the reformer) are less than the demand, some more fuel cells are employed and they are fed by the stored hydrogen. Our aim is to minimize the total costs of the system such that the demand is met. PSO algorithm is used for optimal sizing of system's components.     

Improved particle swarm optimization for photovoltaic system connected to the grid with low voltage ride through capability

Grid connected photovoltaic (PV) system encounters different types of abnormalities during grid faults; the grid side inverter is subjected to three serious problems which are excessive DC link voltage, high AC currents and loss of grid-voltage synchronization. This high DC link voltage may damage the inverter. Also, the voltage sags will force the PV system to be disconnected from the grid according to grid code. This paper presents a novel control strategy of the two-stage three-phase PV system to improve the Low-Voltage Ride-Through (LVRT) capability according to the grid connection requirement. The non-linear control technique using Improved Particle Swarm Optimization (IPSO) of a PV system connected to the grid through an isolated high frequency DC–DC full bridge converter and a three-phase three level neutral point clamped DC-AC converter (3LNPC²) with output power control under severe faults of grid voltage. The paper, also discusses the transient behavior and the performance limit for LVRT by using a DC-Chopper circuit. The model has been implemented in MATLAB/SIMULINK. The proposed control succeeded to track MPP, achieved LVRT requirements and improving the quality of DC link voltage. The paper shows superiority of IPSO than Incremental Conductance (IC) method during MPPT mode of PV system.     

Development of hybrid energy system with cycle charging strategy using particle swarm optimization for a remote area in India

In recent years, renewable energy can be seen as one of the important prospect of today's research, as it is likely to enlighten the lives of millions of people by fulfilling demand of electricity in their daily life. The present work focuses on the development of optimal hybrid energy system sizing model based on comparative analysis of particle swarm optimization, genetic algorithm and Homer software for energy index ratio of 1. The model also incorporates renewable fraction, emissions of carbon di oxide from diesel generator, net present cost and cost of energy. The system is developed to supply the demand of 7 un-electrified villages of Dhauladevi block of Almora district in Uttarakhand, India with the help of the available resources of solar, hydro, biomass and biogas energy along with the addition of diesel generator, for meeting out the energy deficit. From the optimization results, minimum cost of energy and maximum renewable fraction are obtained as 5.77 Rs/kWh and 92.6% respectively.     

Grey Predictor reference model for assisting particle swarm optimization for wind turbine control

This paper proposes an approach of forming the average performance by Grey Modeling, and use an average performance as reference model for performing evolutionary computation with error type control performance index. The idea of the approach is to construct the reference model based on the performance of unknown systems when users apply evolutionary computation to fine-tune the control systems with error type performance index. We apply this approach to particle swarm optimization for searching the optimal gains of baseline PI controller of wind turbines operating at the certain set point in Region 3. In the numerical simulation part, the corresponding results demonstrate the effectiveness of Grey Modeling.     

New design of robust PID controller based on meta‐heuristic algorithms for wind energy conversion system

This paper proposes a new robust control method for a wind energy conversion system. The suggested method can damp the deviations in the generator speed because of the penetration of wind speed and load demand fluctuations in the electrical grid. Furthermore, it can overcome the uncertainties of the plant parameters because of load demand fluctuations and the errors of the implementation. The new method has been built based on new simple frequency‐domain conditions and the whale optimization algorithm (WOA). This method is utilized to design a robust proportional‐integral‐derivative (PID) controller based on the WOA in order to enhance the damping characteristics of the wind energy conversion system. Simulation results confirm the superiority and robustness of the proposed technique against the wind speed fluctuations and the plant parameters uncertainties compared with other meta‐heuristic algorithms.