Generation Control Parameters Research Articles

An enhanced distributed differential evolution algorithm for portfolio optimization problems

The population structure of differential evolution (DE) algorithm cannot maintain the diversity of the population to the greatest extent and help the population avoid to fall into the local optima in time. In this paper, a co-evolutionary multi-swarm adaptive differential evolution algorithm, namely ECMADE is proposed to solve the premature convergence and search stagnation. First of all, in terms of population structure, based on the parallel distributed framework, ECMADE randomly and evenly divides the population into exploration subpopulation, development subpopulation, and auxiliary subpopulation, and introduces an adaptive information exchange mechanism so that subpopulations can escape local optima in time. Then, a multi-operator parallel search strategy is proposed to keep population diversity and meet the optimization needs of different problems. Finally, an adaptive adjustment mechanism of control parameters is developed, through recent elite parameter archive and weight distribution to fully mine successful parameter information, and generate control parameters with a high success rate for the current evolutionary stage. In order to prove the effectiveness of the ECMADE, 10 test functions and portfolio optimization problem are selected in here. The experiment results show that the ECMADE can effectively solve these test functions, the accuracy and efficiency is superior to those of two classical DE algorithms. The actual application results show that the ECMADE can significantly improve the ability of portfolio to resist extreme losses, which proves the effectiveness and feasibility of the ECMADE once again. The ECMADE has better optimization performance by comparing with some well-known algorithms in term of the solution quality, robustness and space distribution. It provides a new algorithm for solving complex optimization problems.

Adaptive Tuning of PV Generator Control to Improve Stability Constrained Power Transfer Capability Limit

The stability of power systems can largely limit the power transfer capability limit of transmission lines. The PV generator control is different from synchronous generator control. The impact of PV generator dynamics on the power transfer limit constrained by stability is not well explored in the literature. This paper focuses on improving the stability constrained power transfer capability limit of transmission lines emanating from PV generators. The PV generator is provided with dc link voltage and reactive power control and is equipped with low voltage ride through capability, voltage and frequency support functionalities. In this paper, Lyapunov function analysis based adaptive tuning laws are proposed for PV generator control parameters to improve the power transfer capability limit (constrained by stability) of transmission lines connecting PV generators to grid. The tuning laws are proposed for Phase Locked Loop (PLL), outer and inner control loop parameters. The effectiveness of the proposed method is validated on a Single PV-Synchronous Machine system, modified IEEE-39 and IEEE-118 bus system. Comparison with existing method shows that the proposed tuning method can achieve higher power transfer capability limit considering stability.

A self-adaptive multi-population differential evolution algorithm

Differential evolution (DE) is an efficient population-based search algorithm for solving numerical optimization problems. However, the performance of DE is very sensitive to the choice of mutation strategies and their associated control parameters. In this paper, we propose a self-adaptive multi-population differential evolution algorithm, called SAMDE. The population is randomly divided into three equally sized sub-populations, each with different mutation strategies. At the end of each generation, all sub-populations are updated independently and recombined. Each sub-population uses an adaptive mechanism for selecting how current generation control parameters are generated. An improved mutation strategy, “rand assemble/1”, is proposed, its base vector is composed proportionally of three randomly selected individuals. The performance of SAMDE is evaluated on the suite of CEC 2005 benchmark functions. A comparative study is carried out with other state-of-the-art optimization techniques. The results show that SAMDE has a competitive performance compared to several other efficient DE variants.

Análisis de la contaminación ambiental producida por el plasma frío de baja presión en la limpieza de láminas metálicas

La presente investigación aborda un análisis del nivel de contaminación producido por los gases generados de monóxido de carbono (CO), dióxido de carbono (CO2) y los hidrocarburos (HC) en la limpieza de láminas metálicas de acero inoxidable AISI/SAE 304 aplicando plasma frío de oxígeno a baja presión para la remoción de los aceites ISO 32, ISO 68 e ISO 220, con diferentes parámetros de control del generador de acuerdo con el aceite lubricante removido de la superficie de la lámina metálica de acero inoxidable. La experimentación se realizó en un primer proceso con una descarga aplicada directamente a la superficie de la lámina impregnada con el aceite colocando con un volumen de 0,1 ml y en un segundo proceso donde la lámina con el aceite impregnado fue sumergida en un desengrasante para aceites y grasas con la finalidad de realizar una limpieza previa a la aplicación del plasma frío a baja presión en la superficie. Para el análisis de los resultados en el nivel de gases generados por cada aceite se aplica un análisis estadístico para determinar si existe una diferencia significativa en el nivel de los gases generados en las dos etapas.

A multi-role based differential evolution

Differential evolution (DE) is an efficient and powerful stochastic optimization algorithm. Extensive studies in recent years have verified that different trial vector generation strategies and associated control parameters offer distinct characteristics on different problems. To take full advantages of them, different ensemble methods of trial vector generation strategies and control parameters based on various adaptive strategies have been proposed during the last decade. Aiming to organically integrate merits of some popular generation strategies and control parameters, and then utilize distinct advantages of them, a multi-role based DE (MRDE) is proposed in this paper. In MRDE, the entire population is divided into multiple small-sized groups, and individuals in each group are assigned with different roles in each generation according to their fitness. Based on the assigned role, an individual selects its own trial vector generation strategies and control parameters from a pool to breed offspring. Moreover, an adaptive strategy for population size is used to rationally distribute the computational resources, which is beneficial for speeding up the convergence. Furthermore, a regroup strategy enables individuals to play different roles in different generations, which is favorable for diversifying the search behaviors. The performance of MRDE is compared with that of ten state-of-the-art DE variants on CEC2017 test suite with three dimension cases, and the experimental results demonstrate the competitive and reliable performance of MRDE. In addition, the effectiveness of the newly proposed strategies is also verified through comparison experiments.

Planning trigonometric frequency central pattern generator trajectory for cyclic tasks of robot manipulators

Planning efficient trajectories is an essential task in most automated robotic applications. The execution time and smoothness are usually important considerations for economic and safety reasons. A novel method to generate a trigonometric frequency central pattern generator trajectory is presented in this paper for cyclic point-to-point tasks of industrial robotic manipulators. The proposed method is biologically motivated by the concept of central pattern generator, which is a special neural circuit underlying most rhythmic activities in living beings. A modified central pattern generator model with simple network structure is developed for yielding the desired joint trajectories of robots. An important property of this technique lies in the fact that stable online trajectory transition between different paths is enabled by simply adjusting the central pattern generator control parameters. Moreover, kinematic constraints of the robot can be taken into account for optimizing the robot motion instead of setting a priori the execution time. Two examples of the pick-and-place operation, which is a typical cyclic point-to-point task, are used to illustrate the validity of the method. The results of simulation indicate that the proposed method is capable of producing smooth and time-optimal trajectories, which have also been compared with those yielded by other trajectory planning approaches found in the scientific literature.

Ensemble of differential evolution variants

Differential evolution (DE) is one of the most popular and efficient evolutionary algorithms for numerical optimization and it has gained much success in a series of academic benchmark competitions as well as real applications. Recently, ensemble methods receive an increasing attention in designing high-quality DE algorithms. However, previous efforts are mainly devoted to the low-level ensemble of mutation strategies of DE. This study investigates the high-level ensemble of multiple existing efficient DE variants. A multi-population based framework (MPF) is proposed to realize the ensemble of multiple DE variants to derive a new algorithm named EDEV for short. EDEV consists of three highly popular and efficient DE variants, namely JADE (adaptive differential evolution with optional external archive), CoDE (differential evolution with composite trial vector generation strategies and control parameters) and EPSDE (differential evolution algorithm with ensemble of parameters and mutation strategies). The whole population of EDEV is partitioned into four subpopulations, including three indicator subpopulations with smaller size and one reward subpopulation with much larger size. Each constituent DE variant in EDEV owns an indicator subpopulation. After every predefined generations, the most efficient constituent DE variant is determined and the reward subpopulation is assigned to that best performed DE variant as an extra reward. Through this manner, the most efficient DE variant is expected to obtain the most computational resources during the optimization process. In addition, the population partition operator is triggered at every generation, which results in timely information sharing and tight cooperation among the component DE variants. Extensive experiments and comparisons have been done based on the CEC2005 and CEC2014 benchmark suit, which shows that the overall performance of EDEV is superior to several state-of-the-art peer DE variants. The success of EDEV reveals that, through an appropriate ensemble framework, different DE variants of different merits can support one another to cooperatively solve optimization problems.

Adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism

The performance of differential evolution (DE) has been significantly influenced by trial vector generation strategies and control parameters. Various powerful trial vector generation strategies with adaptive parameter adjustment methods such that the population generation is guided by the elites have been proposed. This paper aims to strengthen the performance of DE by compositing these powerful trial vector generation strategies, making it possible to obtain the guidance of each individual from multiple elites concurrently and independently. In this manner, the deleterious behavior in which an individual is misguided by various local optimal solutions into unpromising areas could be restrained to a certain extent. An adaptive multiple-elites-guided composite differential evolution algorithm with a shift mechanism (abbreviated as AMECoDEs) has been proposed in this paper. This algorithm concurrently employs two elites-guided trial vector generation strategies for each individual to generate two candidate solutions accordingly, and the best one is adopted to participate in the selection. Moreover, a novel shift mechanism is established to handle stagnation and premature convergence issues. AMECoDEs has been tested on the CEC2014 benchmark functions. Experimental results show that AMECoDEs outperforms various classic state-of-the-art DE variants and is better than or at least comparable to various recently proposed DE methods.

Differential evolution dynamics analysis by complex networks

Differential evolution is a simple yet efficient heuristic originally designed for global optimization over continuous spaces that has been used in many research areas. The question how to improve its performance is still popular and during the years, many successful methods dealing with optimal setting or hybridization of the control parameters were proposed. In this paper, we propose a novel approach based on modeling of the differential evolution dynamics by complex networks. In each generation, the individuals are mapped to the nodes and the relationships between them are modeled by the edges of the graph. Thanks to this simple visualization, the interconnection between the differential evolution convergence speed and the weighted clustering coefficients has been revealed. As a consequence, we have focused on the parents selection in the mutation step where the individuals are not selected randomly as usual but on the basis of their weighted clustering coefficients. Our enhancement has been incorporated in the classical differential evolution, self-adaptive differential evolution (jDE) and differential evolution with composite trial vector generation strategies and control parameters. Finally, a set of well-known benchmark functions (including 21 functions) has been used to test and evaluate the performance of the proposed enhancement of the differential evolution. The experimental results and statistical analysis indicate that the enhanced algorithms perform better or at least comparable to their original versions and the analysis of the differential evolution dynamics with the aim of the complex network might be an effective tool to improve the differential evolution convergence in the future.

Load Frequency Control of Multi Area SSSC and CES Based System Under Deregulation Using Particle Swarm Optimization

This paper deals with the application of various evolutionary optimization techniques for designing the gain of integral controller in a multi area hydrothermal system under deregulation. The various techniques which have been used are Simulated Annealing (SA), Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). A two area hydrothermal system in the presence of Static Synchronous Series Compensator (SSSC) and Capacitive Energy Storage (CES) under deregulation is considered to exemplify the optimum parameter search. The integral of square of error (performance) index is considered in the search of optimal Automatic Generation Control (AGC) parameters. The results presented in this paper demonstrate the superior working of PSO over others in the tuning of AGC.

New color image encryption algorithm based on compound chaos mapping and hyperchaotic cellular neural network

We propose an image encryption/decryption algorithm based on chaotic control parameter and hyperchaotic system with the composite permutation-diffusion structure. Compound chaos mapping is used to generate control parameters in the permutation stage. The high correlation between pixels is shuffled. In the diffusion stage, compound chaos mapping of different initial condition and control parameter generates the diffusion parameters, which are applied to hyperchaotic cellular neural networks. The diffusion key stream is obtained by this process and implements the pixels’ diffusion. Compared with the existing methods, both simulation and statistical analysis of our proposed algorithm show that the algorithm has a good performance against attacks and meets the corresponding security level.

Tuning of Integral Controller for Load Following of SMES and SSSC based Multi Area System under Deregulated Scenario

This paper deals with the application of various evolutionary optimization techniques for optimizing the gain of integral controller in a SMES and SSSC based load following hydrothermal system under deregulated scenario. The various techniques which have been used are Simulated Annealing (SA), Genetic Algorithm (GA), Particle Swarm Optimization (PSO). A two area load following based hydrothermal system under deregulated scenario is considered to exemplify the optimum parameter search. The integral of square of error performance index is considered in the search of optimal Automatic Generation Control (AGC) parameters. The results presented in this paper demonstrate the superior working of various techniques over one another in the tuning of AGC parameters.

Self-Adaptive Evolutionary Extreme Learning Machine

In this paper, we propose an improved learning algorithm named self-adaptive evolutionary extreme learning machine (SaE-ELM) for single hidden layer feedforward networks (SLFNs). In SaE-ELM, the network hidden node parameters are optimized by the self-adaptive differential evolution algorithm, whose trial vector generation strategies and their associated control parameters are self-adapted in a strategy pool by learning from their previous experiences in generating promising solutions, and the network output weights are calculated using the Moore–Penrose generalized inverse. SaE-ELM outperforms the evolutionary extreme learning machine (E-ELM) and the different evolutionary Levenberg–Marquardt method in general as it could self-adaptively determine the suitable control parameters and generation strategies involved in DE. Simulations have shown that SaE-ELM not only performs better than E-ELM with several manually choosing generation strategies and control parameters but also obtains better generalization performances than several related methods.

Differential Evolution With Composite Trial Vector Generation Strategies and Control Parameters

Trial vector generation strategies and control parameters have a significant influence on the performance of differential evolution (DE). This paper studies whether the performance of DE can be improved by combining several effective trial vector generation strategies with some suitable control parameter settings. A novel method, called composite DE (CoDE), has been proposed in this paper. This method uses three trial vector generation strategies and three control parameter settings. It randomly combines them to generate trial vectors. CoDE has been tested on all the CEC2005 contest test instances. Experimental results show that CoDE is very competitive.

Energy Management and Control System for Smart Renewable Energy Remote Power Generation

Abstract This paper presents the application energy management system and control system for smart renewable energy power generation. The development of communication platform is use LAB-View technology as a basis design for overall system. The proposed prototype is construct PV 1.8 kWp, Battery 18 kWh, 5 kW Generator. The main central control system will acquire data from the remote renewable energy system. All necessary monitor data are including power generation, load consumption, protection system, and other control parameters will be store at a control unit. All monitoring data are monitored as real-time data therefore the operator can also evaluate the system situation in the current states and make decisions to take an immediate action if needed. The system can be improved by learning from monitored data recorded. Moreover the system itself can forecast and make a decision for future power analysis.

A differential evolution algorithm with self-adapting strategy and control parameters

This paper presents a Differential Evolution algorithm with self-adaptive trial vector generation strategy and control parameters (SspDE) for global numerical optimization over continuous space. In the SspDE algorithm, each target individual has an associated strategy list (SL), a mutation scaling factor F list (FL), and a crossover rate CR list (CRL). During the evolution, a trial individual is generated by using a strategy, F, and CR taken from the lists associated with the target vector. If the obtained trial individual is better than the target vector, the used strategy, F, and CR will enter a winning strategy list ( wSL), a winning F list ( wFL), and a winning CR list ( wCRL), respectively. After a given number of iterations, the FL, CRL or SL will be refilled at a high probability by selecting elements from wFL, wCRL and wSL or randomly generated values. In this way, both the trial vector generation strategy and its associated parameters can be gradually self-adapted to match different phases of evolution by learning from their previous successful experience. Extensive computational simulations and comparisons are carried out by employing a set of 19 benchmark problems from the literature. The computational results show that overall the SspDE algorithm performs better than the state-of-the-art differential evolution variants.

Static VAR compensation of a fixed speed stall control wind turbine during start-up

An installed 900 kW fixed speed stall controlled soft-started wind turbine connected to a weak distribution grid was modeled under start-up conditions. Generator and soft-start control and design parameters were not available, so a modeling process independent of this information was developed. Field measured transients were closely reproduced in simulation using a full-order generator model with generic parameters and a thyristor-based graduated interconnection to the grid. A Static VAR Compensator model was then added to the wind turbine model to explore a method of reducing start-up transients. It was found that for the specific transients modeled, a 300 kVAR SVC supplied sufficient reactive power to limit line voltage variations to within 3% of steady state values. Model details and simulation output are presented in this paper.

Laser droplet welding of zinc coated steel sheets

The weldability of zinc coated steel sheets is often compromised by weld seam defects caused by rapid zinc vaporisation and burned-off zinc. Owing to this, welded seams usually remain unprotected from corrosion and are accompanied by undesirable porosity. In this paper, the laser droplet generation process and its application to laser droplet welding of zinc coated steel sheets are described. The influences of laser droplet generation and welding process control parameters on the properties of joints of zinc coated steel sheets are presented. Results show that laser droplet welding offers a promising technology for spot and continuous welding of zinc coated steel sheets in different joint configurations with good mechanical properties and corrosion resistance.

A genetic approach to the optimization of automatic generation control parameters for power systems

AbstractThis paper presents a method based on genetic algorithm for the automatic generation control of power systems. The technique is applied to control a system, which includes two areas tied together through a power line. As a consequence of continuously load variation, the frequency of the power system changes with time. In conventional studies, frequency transients are minimized by using integral controllers and thus zero steady‐state error is obtained. In this paper, integral controller gains and frequency bias factors are determined by using genetic algorithm. The results of simulation reveal the application of the genetic algorithm having easy implementation to find the global optimum values of the control parameters.

Automatic design of fuzzy controllers

In this note, we propose to use adaptive fuzzy controller as a mean to generate the rule base of fuzzy controller automatically. Specifically, we run an adaptive fuzzy controller from a number of initial conditions such that the trajectories of these runs cover the domain of interest. Since the adaptive fuzzy controller can automatically generate control parameters along the trajectories, we store these parameters after each run. The final fuzzy rule base is constructed from these stored parameters. Since the trajectories cover the whole domain, the rule base generated is complete. The main advantage of the method is that a complete fuzzy rule base can be generated automatically without domain experts and without mathematical model.