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Real-time optimal power flow solution for wind farm integrated power system using evolutionary programming algorithm

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INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY 2021年第7期18卷 1893-1910页

作者： Ida Evangeline, S. Rathika, P. Alagappa Chettiar Govt Coll Engn & Technol Dept Elect & Elect Engn Karaikkudi Tamil Nadu India VV Coll Engn Dept Elect & Elect Engn Tisaiyanvilai Tamil Nadu India

In order to satisfy the increasing demand for electricity, a huge burden is levied on the conventional coal-fired thermal power generation units, which affects the environment and human health adversely. To tackle this problem, the clean renewable sources of energy have gained more attention. With time, wind power is expected to contribute more significantly and should be used as much as possible. This has provided much focus to the wind farm integrated power system. But, the unsteady behavior of wind energy has made the operation of wind farm integrated power system more complex. Scenario generation methods are used for tackling the unsteady behavior of wind energy. A persistence method of scenario generation is proposed for solving real-time optimal power flow problem in wind farm integrated power system. The proposed persistence method performs better compared to other recent methods in the literature.

关键词： evolutionary programming algorithm Forecasting Optimal power flow Power system Wind farm

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Traffic Signal Light Control Model Based on evolutionary programming algorithm Optimization BP Neural Network 7

Traffic Signal Light Control Model Based on Evolutionary Pro...

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7th IEEE International Conference on Electronics Information and Emergency Communication (ICEIEC)

作者： Jiang, Linying Li, Yachen Liu, Yuanlong Chen, Chao Northeastern Univ Dept Software Engn Software Coll Shenyang Liaoning Peoples R China

ISBN: (纸本)9781509030255

In this paper, evolutionary programming algorithm through the mutation operator and the selection strategy to find weights and threshold value. Through this way, we can improve the training efficiency, speed up the convergence rate, increase the irregularity of the weights and threshold value, avoid BP neural network training into locally optimal solution. In this paper, the evolutionary programming algorithm optimization BP neural network is applied to the traffic signal light control, according to the historical traffic flow data of a crossroad to s traffic flow data, and then through the predicted traffic flow data to re-adjust the traffic signal light frequency, to improve traffic congestion and other traffic problems. The experimental results show that evolutionary programming algorithm optimization BP neural network has a good effect on traffic signal light optimization.

关键词： BP neural network evolutionary programming algorithm Intelligent traffic

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QoT aware evolutionary programming algorithm for transparent optical networks

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OPTIK 2013年第23期124卷 6391-6399页

作者： Bhanja, Urmila Mahapatra, Sudipta Roy, Rajarshi Indira Gandhi Inst Technol Dept Elect & Commun Engn Sarang 759146 Odisha India Indian Inst Technol Dept E & ECE West Midnapur 721302 W Bengal India

This paper presents an evolutionary programming algorithm to solve the quality of transmission (QoT) aware dynamic routing and wavelength assignment (DRWA) problem in transparent optical networks using wavelength division multiplexing (WDM). In this algorithm, the initial population is assumed to consist of a single individual and the algorithm uses only the mutation operator to explore the search space, both the features contributing to a reduction in algorithm execution time. A novel fitness function has been designed that takes into account the number of hops, the path cost, and the variance contributions due to the amplified spontaneous emission (ASE) noise and dynamic crosstalk at the nodes of each lightpath while evaluating a lightpath. This paper also proposes a new wavelength ordering assignment technique to reduce both the switch crosstalk and adjacent channel rejection crosstalk and hence, attempts to improve the mean blocking probability of the incoming call requests. This paper also, analyses the time complexity of the proposed algorithm.

关键词： evolutionary programming algorithm Fitness function Physical layer impairments Lightpath routing Wavelength ordering

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An evolutionary programming algorithm for survivable routing and wavelength assignment in transparent optical networks

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INFORMATION SCIENCES 2013年 222卷 634-647页

作者： Bhanja, Urmila Mahapatra, Sudipta Roy, Rajarshi Indira Gandhi Inst Technol Dept Elect & Telecommun Engn Sarang 759146 Odisha India Indian Inst Technol Dept Elect & Elect Commun Engn W Midnapur 721302 W Bengal India

The dynamic routing and wavelength assignment (DRWA) problem is a challenging optimisation problem in optical wavelength division multiplexed (WDM) networks. This paper proposes a novel evolutionary programming algorithm, namely, ERWA, to reduce the set-up time of lightpaths in a WDM network. It is shown that the proposed algorithm with simple extensions, referred to in this paper as ESRWA, is able to provide dedicated protection at the optical layer of the network, thereby addressing the survivable DRWA problem. In contrast to other bio-inspired algorithms available in the literature for the DRWA problem, ERWA facilitates the online discovery of a requested lightpath. The novelty of ERWA lies in the fact that it uses an initial population consisting of only one individual or chromosome and integrates a soft constraint with a set of hard constraints to accomplish proper load balancing. These features result in a lower mean blocking probability and a lower mean execution time compared to those exhibited by the existing bio-inspired algorithms for both the DRWA and the survivable DRWA problems. (C) 2012 Elsevier Inc. All rights reserved.

关键词： Dynamic routing evolutionary programming algorithm Lightpath Survivability Set up time Fitness function

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Evolution Performance of Symbolic Radial Basis Function Neural Network by Using evolutionary algorithms

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Computer Systems Science & Engineering 2023年第10期47卷 1163-1184页

作者： Shehab Abdulhabib Alzaeemi Kim Gaik Tay Audrey Huong Saratha Sathasivam Majid Khan bin Majahar Ali Faculty of Electrical and Electronics Engineering Universiti Tun Hussein Onn MalaysiaParit Raja86400JohorMalaysia School of Mathematical Sciences Universiti Sains Malaysia USM11800PenangMalaysia

Radial Basis Function Neural Network(RBFNN)ensembles have long suffered from non-efficient training,where incorrect parameter settings can be computationally *** paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network(SRBFNN)through the behavior’s integration of satisfiability *** by evolutionary algorithms,which can iteratively find the nearoptimal solution,different evolutionary algorithms(EAs)were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation(SRBFNN-2SAT).The SRBFNN’s objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms,including Genetic algorithm(GA),Evolution Strategy algorithm(ES),Differential Evolution algorithm(DE),and evolutionary programming algorithm(EP).Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming *** the use of SRBFNN-2SAT,a training method based on these algorithms has been presented,then training has been compared among algorithms,which were applied in Microsoft Visual C++software using multiple metrics of performance,including Mean Absolute Relative Error(MARE),Root Mean Square Error(RMSE),Mean Absolute Percentage Error(MAPE),Mean Bias Error(MBE),Systematic Error(SD),Schwarz Bayesian Criterion(SBC),and Central Process Unit time(CPU time).Based on the results,the EP algorithm achieved a higher training rate and simple structure compared with the rest of the *** has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight,accompanied by the slightest iteration error,which minimizes the objective function of SRBFNN-2SAT.

关键词： Satisfiability logic programming symbolic radial basis function neural network evolutionary programming algorithm genetic algorithm evolution strategy algorithm differential evolution algorithm

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FWM aware evolutionary programming algorithm for transparent optical networks

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PHOTONIC NETWORK COMMUNICATIONS 2012年第3期23卷 285-299页

作者： Bhanja, Urmila Mahapatra, Sudipta Roy, Rajarshi Indira Gandhi Inst Technol Dept Elect & Telecommun Engn Sarang 759146 Orissa India Indian Inst Technol Dept Elect & Elect Commun Engn Kharagpur 721302 W Bengal India

This paper proposes and evaluates a four-wave mixing (FWM) aware evolutionary programming algorithm for dynamically setting up lightpaths in an optical wavelength division multiplexed network (WDM network). The proposed algorithm also considers the effect of amplified spontaneous emission noise (ASE noise) on a lightpath during propagation of the optical signal from any source to the intended destination. As crosstalk due to FWM and ASE noise are two transmission impairments that degrade the quality of optical signal even at low to medium data rates, it is mandatory for an algorithm for dynamic routing and wavelength assignment in a WDM network to consider the effect of these two impairments on the lightpath to be established. The distinguishing feature of the proposed algorithm is that it is based on an initial population of a single individual and uses a fitness function that is expressed in terms of the number of hops, path cost, variance contributions due to FWM crosstalk, amplifier noise, and different beat noises at the receiver. The performance of a newly introduced FWM aware priority-based wavelength assignment technique is compared with few of the existing wavelength assignment techniques in the present work.

关键词： evolutionary programming algorithm DRWA algorithm Nonlinear impairments Four-wave mixing ASE noise FWM aware priority-based wavelength assignment techniques

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Chance constrained dynamic optimisation method for AGC units dispatch considering uncertainties of the offshore wind farm

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JOURNAL OF ENGINEERING-JOE 2019年第16期2019卷 2112-2119页

作者： Zhao, Xia Ye, Xiaobin Yang, Lun Zhang, Rongrong Yan, Wei Chongqing Univ State Key Lab Power Transmiss Equipment & Syst Se Chongqing Peoples R China State Grid Fujian Fuzhou Elect Power Supply Co Fuzhou Fujian Peoples R China State Grid Jiangxi Nanchang Elect Power Supply Co Nanchang Jiangxi Peoples R China

The continuing growth of an offshore wind farm integrated into a power grid via high-voltage DC has posed great challenges for automatic generation control (AGC) of the power system. To address these challenges, a new concept of dynamic dispatch of AGC units (DDA)' under control performance standards from the view of economic dispatch (ED) has been proposed in the previous work, and proved to be an effective technique to co-operate the AGC units with different ramping rates and to fill the gap between ED and AGC. However, the existing DDA model is deterministic in nature, which can hardly deal with the uncertain forecasting error of the offshore wind power output. A novel stochastic DDA model based on chance-constrained programming is proposed considering a random offshore wind power forecasting error, and a hybrid algorithm combining the evolutionary programming algorithm and point estimate method is then developed to solve the stochastic model. Numerical results from a two-area test system with additional offshore wind power generation demonstrate the accuracy and computation efficiency of the hybrid algorithm and the benefits offered by the stochastic AGC dispatch method.

关键词： power generation control evolutionary computation power generation dispatch power generation economics power generation scheduling power generation reliability stochastic processes wind power offshore installations wind power plants power grids optimisation additional offshore wind power generation hybrid algorithm stochastic AGC dispatch method dynamic optimisation method AGC units offshore wind farm continuing growth power grid high-voltage DC great challenges automatic generation control power system dynamic dispatch control performance standards economic dispatch existing DDA model uncertain forecasting error offshore wind power output novel stochastic DDA model chance-constrained programming random offshore wind power forecasting error evolutionary programming algorithm point estimate method

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A metaheuristic approach for optical network optimization problems

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APPLIED SOFT COMPUTING 2013年第2期13卷 981-997页

作者： Bhanja, Urmila Mahapatra, Sudipta Indira Gandhi Inst Technol Dept Elect & Commun Engn Sarang 759146 Odisha India Indian Inst Technol Dept E & ECE W Midnapur 721302 W Bengal India

Two of the most complex optimization problems encountered in the design of third generation optical networks are the dynamic routing and wavelength assignment (DRWA) problem under the assumptions of ideal and non-ideal physical layers. Both these problems are NP-complete in nature. These are challenging due to the presence of multiple local optima in the search space. Even heuristics-based algorithms fail to solve these problems efficiently as the search space is non-convex. This paper reports the performance of a metaheuristic, that is, an evolutionary programming algorithm in solving different optical network optimization problems. The primary motivation behind adopting this approach is to reduce the algorithm execution time. It is demonstrated that the same basic approach can be used to solve different optimization problems by designing problem-specific fitness functions. Also, it is shown how the algorithm performance can be improved by integrating suitable soft constraints with the original constraints. Exhaustive simulation studies are carried out assuming the presence of different levels of linear impairments such as switch and demultiplexer crosstalk and non-linear impairments like four wave mixing to illustrate the superiority of the proposed algorithms. (C) 2012 Elsevier B.V. All rights reserved.

关键词： QoS routing DRWA problem Linear impairments Non-linear impairments evolutionary programming algorithm Convergence rate Fitness function

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Comparison of Metaheuristic Approaches for Impairment Aware Transparent Optical Networks

Comparison of Metaheuristic Approaches for Impairment Aware ...

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International Conference on Wireless and Optical Communications Networks (WOCN 2012)

作者： Urmila Bhanja Debajyoti Mishra Dept. of Electronics and Communication Engineering Indira Gandhi Institute of Technology Sarang Orissa 759146 India

ISBN: (纸本)9781467319881

In this paper two metaheuristic approaches are compared while solving the dynamic routing and wavelength assignment (DRWA) problem in wavelength division multiplexing (WDM) optical network. In this problem, the effect of Four Wave Mixing (FWM) and amplifier spontaneous emission (ASE) noise are incorporated while solving the problem. The two metaheuristic approaches used in this work are evolutionary programming algorithm (EP) and simulated annealing (SA). These metaheuristic techniques use a common objective function that is designed to reduce the effect of FWM noise and ASE noise on a requested lightpath. The FWM crosstalk products and the static FWM noise power per link are pre computed in order to reduce the set up time of a requested lightpath, and stored in an offline database. These are retrieved during the setting up of a lightpath and evaluated online taking the dynamic parameters like cost of the links into consideration.

关键词： evolutionary programming algorithm Simulated annealing Impairment aware DRWA problem Four wave mixing ASE noise

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An improved evolutionary programming algorithm for fuzzy programming problems and its application

An improved evolutionary programming algorithm for fuzzy pro...

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5th International Conference on Machine Learning and Cybernetics

作者： Qian, Wei-Yi Zhang, Jin Bohai Univ Dept Math Jinzhou 121000 Peoples R China

ISBN: (纸本)1424400600

An improved evolutionary programming algorithm is proposed for fuzzy programming problems. The idea is to mimic the physics of electromagnetism by considering each individual as an electrical charge. Charge of each individual is defined using membership functions. After calculating these charges, like the electromagnetic force, we calculate a combination force exerted on each individual. This combination force is regarded as the search direction of mutation operator. The fitness function is given by membership functions. A fuzzy model is developed for designing 3D trajectory of horizontal wells. The fuzzy model can be transformed to fuzzy programming problem and solved by our proposed method. The model is illustrated numerically and the results are efficiently.

关键词： fuzzy programming membership function horizontal wells evolutionary programming algorithm

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