Comparative performance of wind turbine driven PMSG with PI-controllers tuned using heuristic optimization algorithms

Optimization of solutions on expansion of electric power systems (EPS) and their control plays a crucial part in ensuring efficiency of the power industry, reliability of electric power supply to consumers and power quality. Until recently, this goal was accomplished by applying classical and modern methods of linear and nonlinear programming. In some complicated cases, however, these methods turn out to be rather inefficient. Meta-heuristic optimization algorithms often make it possible to successfully cope with arising difficulties. State estimation (SE) is used to calculate current operating conditions of EPS using the SCADA measurements of state variables (voltages, currents etc.). To solve the SE problem, the Energy Systems Institute of Siberian Branch of Russian Academy of Sciences (ESI of SB RAS) has devised a method based on test equations (TE), i.e. on the steady state equations that contain only measured parameters. Here, a technique for EPS SE using genetic algorithms (GA) is suggested. SE is the main tool for EPS monitoring. The quality of SE results determines largely the EPS control efficiency. An algorithm for exclusion of wrong SE calculations is described. The algorithm using artificial neural networks (ANN) is based on the analysis of results of the calculation performed solving the SE problem with different combinations of constants. The proposed procedure is checked on real data.

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The Application of Meta-Heuristic Algorithms to Improve the Performance of Software Development Effort Estimation Models

International Journal of Applied Evolutionary Computation ◽

10.4018/ijaec.2015100104 ◽

2015 ◽

Vol 6 (4) ◽

pp. 39-68 ◽

Cited By ~ 1

Author(s):

Maryam Hassani Saadi ◽

Vahid Khatibi Bardsiri ◽

Fahimeh Ziaaddini

Keyword(s):

Software Development ◽

Optimization Algorithms ◽

Heuristic Optimization ◽

Development Effort ◽

Special Role ◽

Effort Estimation ◽

Software Projects ◽

Software Development Effort ◽

Estimation Models ◽

Software Development Effort Estimation

One of the major activities in effective and efficient production of software projects is the precise estimation of software development effort. Estimation of the effort in primary steps of software development is one of the most important challenges in managing software projects. Some reasons for these challenges such as: discordant software projects, the complexity of the manufacturing process, special role of human and high level of obscure and unusual features of software projects can be noted. Predicting the necessary efforts to develop software using meta-heuristic optimization algorithms has made significant progressions in this field. These algorithms have the potent to be used in estimation of the effort of the software. The necessity to increase estimation precision urged the authors to survey the efficiency of some meta-heuristic optimization algorithms and their effects on the software projects. To do so, in this paper, they investigated the effect of combining various optimization algorithms such as genetic algorithm, particle swarm optimization algorithm and ant colony algorithm on different models such as COCOMO, estimation based on analogy, machine learning methods and standard estimation models. These models have employed various data sets to evaluate the results such as COCOMO, Desharnais, NASA, Kemerer, CF, DPS, ISBSG and Koten & Gary. The results of this survey can be used by researchers as a primary reference.

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Nature-inspired and hybrid optimization algorithms on interval Type-2 fuzzy controller for servo processes: a comparative performance study

SN Applied Sciences ◽

10.1007/s42452-020-3024-5 ◽

2020 ◽

Vol 2 (7) ◽

Author(s):

Ritu Rani De (Maity) ◽

Rajani K. Mudi ◽

Chanchal Dey

Keyword(s):

Fuzzy Controller ◽

Optimization Algorithms ◽

Hybrid Optimization ◽

Performance Study ◽

Comparative Performance ◽

Hybrid Optimization Algorithms ◽

Interval Type

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Meta-heuristic optimization algorithms for simultaneous optimization of sidelobe level and directivity of uniformly excited concentric ring array antennas

International Journal of Microwave and Wireless Technologies ◽

10.1017/s1759078719001120 ◽

2019 ◽

Vol 12 (2) ◽

pp. 183-192

Author(s):

Kailash Pati Dutta ◽

G. K. Mahanti

Keyword(s):

Search Algorithm ◽

Optimization Algorithms ◽

Beam Width ◽

Simultaneous Optimization ◽

Heuristic Optimization ◽

The Novel ◽

Sidelobe Level ◽

Concentric Ring ◽

Array Antennas ◽

Symbiotic Organism Search

AbstractThis paper proposes the novel application of three meta-heuristic optimization algorithms namely crow search algorithm, moth flame optimization, and symbiotic organism search algorithm for the synthesis of uniformly excited multiple concentric ring array antennas. The objective of this work is to minimize the sidelobe level (SLL) and maximize the peak directivity simultaneously. Three different cases are demonstrated separately with various constraints such as optimal inter-element spacing and/or optimal ring radii. Comparative study of the algorithms using common parameters such as SLL, directivity, first null beam width, best cost, and run time has been reported. Investigation results prove the superiority of case 3 over other cases in terms of directivity and SLL. This work demonstrates the potential of these algorithms.

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Optimal Transient Search Algorithm-Based PI Controllers for Enhancing Low Voltage Ride-Through Ability of Grid-Linked PMSG-Based Wind Turbine

Electronics ◽

10.3390/electronics9111807 ◽

2020 ◽

Vol 9 (11) ◽

pp. 1807

Author(s):

Mohammed H. Qais ◽

Hany M. Hasanien ◽

Saad Alghuwainem

Keyword(s):

Wind Turbine ◽

Low Voltage ◽

Search Algorithm ◽

Fitness Function ◽

Synchronous Generator ◽

Pi Control ◽

Low Voltage Ride Through ◽

Control Scheme ◽

Pi Controllers ◽

Grid Side

This paper depicts a new attempt to apply a novel transient search optimization (TSO) algorithm to optimally design the proportional-integral (PI) controllers. Optimal PI controllers are utilized in all converters of a grid-linked permanent magnet synchronous generator (PMSG) powered by a variable-speed wind turbine. The converters of such wind energy systems contain a generator-side converter (GSC) and a grid-side inverter (GSI). Both of these converters are optimally controlled by the proposed TSO-based PI controllers using a vector control scheme. The GSC is responsible for regulating the maximum power point, the reactive generator power, and the generator currents. In addition, the GSI is essentially controlled to control the point of common coupling (PCC) voltage, DC link voltage, and the grid currents. The TSO is applied to minimize the fitness function, which has the sum of these variables’ squared error. The optimization problem’s constraints include the range of the proportional and integral gains of the PI controllers. All the simulation studies, including the TSO code, are implemented using PSCAD software. This represents a salient and new contribution of this study, where the TSO is coded using Fortran language within PSCAD software. The TSO-PI control scheme’s effectiveness is compared with that achieved by using a recent grey wolf optimization (GWO) algorithm–PI control scheme. The validity of the proposed TSO–PI controllers is tested under several network disturbances, such as subjecting the system to balanced and unbalanced faults. With the optimal TSO–PI controller, the low voltage ride-through ability of the grid-linked PMSG can be further improved.

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