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Effective electricity load forecasting using enhanced double-reservoir echo state network

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2021年 99卷 104132-104132页

作者： Peng, Lu Lv, Sheng-Xiang Wang, Lin Wang, Zi-Yun Huazhong Univ Sci & Technol Sch Management Wuhan 430074 Peoples R China Guangdong Univ Finance & Econ Sch Business Adm Guangzhou 510320 Peoples R China

Accurate electricity load prediction is essential to ensure the efficient, reliable, and secure operation of the power system. In this study, a hybrid forecasting method called improved backtracking search optimization algorithm (IBSA)-double-reservoir echo state network (DRESN) (IBSA-DRESN) is proposed on the basis of IBSA and DRESN. Mutual information is utilized to eliminate low-significance input features and retain key input features. The DRESN structure aims to increase the diversity of the network. Roulette strategy, adaptive mutation operator, and niche operator is introduced to improve the standard BSA algorithm. The IBSA is applied to optimize several critical parameters in the DRESN neural network. The proposed IBSA-DRESN method is evaluated using two electricity load datasets, namely, North-America and PJM. Compared with eight popular benchmark models, prediction results show that IBSA-DRESN is more accurate for one-step ahead electricity load forecasting. In one-day ahead forecasting, IBSA-DRESN obtains better prediction performance in most cases.

关键词： Electricity load forecasting Echo state network Mutual information backtracking search optimization algorithm

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A plus Evolutionary search algorithm and QR decomposition based rotation invariant crossover operator

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EXPERT SYSTEMS WITH APPLICATIONS 2018年 103卷 49-62页

作者： Civicioglu, Pinar Besdok, Erkan Erciyes Univ Dept Aircraft Elect & Elect Fac Aeronaut & Astronaut Kayseri Turkey Erciyes Univ Fac Engn Dept Geomat Engn Kayseri Turkey

The recently proposed artificial cooperative search (ACS) algorithm is a population-based iterative evolutionary algorithm (EA) for solving real-valued numerical optimization problems. It uses a rotation-invariant line recombination-based mutation strategy and rule-based crossover operator. However, it performs poorly for problems that include closely-related variables because, in these cases, generating uncorrelated feasible trial solution vectors using stochastic crossover methods is extremely difficult, and its mutation and crossover operators are also less effective. This paper adds a new QR-decomposition-based rotation-invariant search strategy to the ACS algorithm to improve its ability to solve such problems. This new, advanced ACS algorithm, called A+, has only one control parameter, alpha, and experimental results have shown that its performance does not strongly depend on the initial value of alpha. This paper also examines A+'s performance for noisy point cloud filtering, which is a complex real-world problem. The results of numerical experiments demonstrate that A+'s performance when solving numerical and real-world problems with closely-related variables is better than those of the comparison algorithms. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Artificial cooperative search algorithm Artificial bee colony algorithm backtracking search optimization algorithm Cuckoo search algorithm CMAES Differential evolution algorithm

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Multi-objective optimal power flow using grasshopper optimization algorithm

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OPTIMAL CONTROL APPLICATIONS & METHODS 2024年第2期45卷 623-645页

作者： Mandal, Barun Roy, Provas Kumar Kalyani Govt Engn Coll Dept Elect Engn Kalyani India Kalyani Govt Engn Coll Dept Elect Engn Kalyani West Bengal India

This paper introduces grasshopper optimization algorithm to efficiently prove its superiority in the optimal power flow problem. To demonstrate the efficiency of the proposed algorithm, it is implemented on the standard IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus test systems with different objectives that reveal presentation indices of the power system. Twelve different cases, in single and multi-objective optimization space, are considered on different curves of fuel cost, environmental pollution emission, voltage profile, and active power loss. The simulation results obtained from grasshopper optimization algorithm techniques are compared to other new evolutionary optimization methods surfaced in the current state-of-the-art literature. It is revealed that the proposed approach secures better consequence over the other newly originated popular optimization techniques and reflects its improved quality solutions and faster convergence speed. The results obtained in this work demonstrate that the grasshopper optimization algorithm method can successfully be applied to solve the non-linear problems connected to power systems. 1. This paper introduces grasshopper optimization algorithm (GOA) to efficiently prove its superiority in the optimal power flow problem (OPF). 2. GOA is implemented on the standard IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus test systems with different objectives. 3. It is revealed that the proposed GOA secures better consequence over the other newly originated popular optimization techniques and reflects its improved quality solutions and faster convergence ***

关键词： backtracking search optimization algorithm emission grasshopper optimization algorithm multi-fuels multi-objective optimal power flow valve-point effect voltage deviation

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A novel modified BSA inspired by species evolution rule and simulated annealing principle for constrained engineering optimization problems

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NEURAL COMPUTING & APPLICATIONS 2019年第8期31卷 4157-4184页

作者： Wang, Hailong Hu, Zhongbo Sun, Yuqiu Su, Qinghua Xia, Xuewen Yangtze Univ Sch Informat & Math Jingzhou Hubei Peoples R China East China Jiao Tong Univ Sch Software Nanchang Jiangxi Peoples R China

The backtracking search optimization algorithm (BSA) is one of the recently proposed evolutionary algorithms (EAs) for solving numerical optimization problems. In this study, a nature-inspired modified BSA (called SSBSA) is proposed and investigated to improve the exploitation and convergence performance of BSA. Inspired by the species evolution rule and the simulated annealing principle, this paper proposes two modified strategies through introducing a specified retain mechanism and an acceptance probability into BSA. In SSBSA, the specified previous individuals of historical population (oldP) and their corresponding amplitude control factors (F) are retained according to the fitness feedback for the next iteration, and a new adaptive F that could decrease as the number of iterations increases is redesigned by learning the acceptance probability. SSBSA has two main advantages: (1) The way to retain the specified previous information improves BSA's exploitation capability. (2) This new F adaptively controls the diversity of population which makes convergence faster. Simulation experiments are carried on fourteen constrained benchmarks and engineering design problems to test the performance of SSBSA. To fully evaluate the performance of SSBSA, several comparisons between SSBSA and other well-known algorithms are implemented. The experimental results show that SSBSA improves the performance of BSA and its performance is more competitive than that of the other algorithms.

关键词： backtracking search optimization algorithm Evolutionary algorithms Simulated annealing Constrained optimization problems

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An adaptive dynamic short-term wind speed forecasting model using secondary decomposition and an improved regularized extreme learning machine

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ENERGY 2018年第PartB期165卷 939-957页

作者： Sun, Na Zhou, Jianzhong Chen, Lu Jia, Benjun Tayyab, Muhammad Peng, Tian Huazhong Univ Sci & Technol Sch Hydropower & Informat Engn Wuhan 430074 Hubei Peoples R China Hubei Key Lab Digital Valley Sci & Technol Wuhan 430074 Hubei Peoples R China China Three Gorges Univ Coll Hydraul & Environm Engn Yichang 443002 Peoples R China

Accurate and reliable multi-step wind speed forecasting is extremely crucial for the economic and safe operation of power systems. A novel dynamic hybrid model, which combines an adaptive secondary decomposition (ASD), a leave-one-out cross-validation-based regularized extreme learning machine (LRELM) and the backtracking search algorithm (BSA), is proposed to mitigate the practical difficulties of the traditional decomposition-ensemble forecasting models (DEFMs) through adaptive dynamic decomposing and modeling when new data is added. The new ASD method, which fuses ensemble empirical mode decomposition (EEMD), adaptive variational mode decomposition (AVMD) with sample entropy (SE), is developed for smoothing the raw series to reduce computational time as well as enhance generalization and stability of forecasting models. BSA is employed to optimize LRELM to overcome the drawback of instability. To validate its efficacy, the proposed model and thirteen benchmark models are compared by diverse lead-time forecasting of several real cases. Comprehensive comparisons with a coherent set of indices suggest that the proposed model is an effective and powerful tool for short-term wind speed forecasting not only from the perspective of reliability and sharpness but also from the view of overall skills. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Short-term wind speed forecasting Decomposition-ensemble forecasting model Secondary decomposition backtracking search optimization algorithm Regularized extreme learning machine Adaptive forecasting

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Simultaneous simulation of the three components of seismic accelerograms at locations around seismological stations

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JOURNAL OF SEISMOLOGY 2021年第6期25卷 1361-1384页

作者： Dalla Chiesa, Daniela Fadel Miguel, Leticia Fleck Riera, Jorge Daniel Fed Univ Rio Grande Sul Do UFRGS Postgrad Program Mech Engn PROMEC Av Sarmento Leite 4252 Andar BR-90050170 Porto Alegre RS Brazil Fed Univ Rio Grande do Sul UFRGS Dept Mech Engn DEMEC Postgrad Program Mech Engn PROMEC Postgrad Program Civil Engn PPGEC Av Sarmento Leite 4252 Andar BR-90050170 Porto Alegre RS Brazil Fed Univ Rio Grande do Sul UFRGS Dept Civil Engn DECIV Postgrad Program Civil Engn PPGEC Av Osvaldo Aranha 993 Andar BR-90035190 Porto Alegre RS Brazil

This paper proposes a new numerical procedure, based on an evolutionary optimization algorithm, for the simultaneous generation of the three components of the seismic ground acceleration at sites in the vicinity of recording stations. Different from other studies proposed to simulate only one component of the seismic excitation, in this paper, the three components are generated simultaneously in a single stage. The methodology allows the determination of a train of seismic waves, modeled as Morlet's wavelets, that are next used for the generation of ground acceleration components. The parameters of each wave, i.e., amplitude, frequency, duration, time of arrival, and direction, are determined using an evolutionary optimization algorithm. The seismic wave train is validated by comparison with the recorded accelerograms and respective response spectra. Next, these same seismic waves are used for the simultaneous simulation of the three seismic acceleration time histories at locations on the surface close to the seismic station. The results suggest that the proposed methodology can be used for engineering applications in which the ground motion must be specified at various points, for instance in the case of electric power transmission lines or at bridge supports. Highlights: A complete methodology to simultaneously generate the 3 components of seismic accelerograms is proposed in this work. A train of seismic waves is obtained, via Morlet's wavelets, which allows simulating accelerograms at different points. The results showed that the proposed method is a simple and effective tool that can be used in practical situations.

关键词： Three components of ground acceleration Seismic waves Evolutionary optimization backtracking search optimization algorithm

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Coordinated Control of DFIG Wind Turbine and SVC for Robust Power System Stabilization 12

Coordinated Control of DFIG Wind Turbine and SVC for Robust ...

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International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON)

作者： Uong, Sothy Ngamroo, Issarachai King Mongkuts Inst Technol Ladkrabang Int Coll Bangkok 10520 Thailand King Mongkuts Inst Technol Ladkrabang Dept Elect Engn Fac Engn Bangkok 10520 Thailand

ISBN: (纸本)9781479979615

In this paper, a coordinated control of doubly fed induction generator (DFIG) wind turbine and static var compensator (SVC) is proposed for the robust stabilization of interconnected power system. By modulating the reactive power output of DFIG and SVC, the power oscillation can be damped. To implement this control, the power oscillation damper (POD) with the 2nd-order lead-lag compensator is added to the voltage controller of DFIG and SVC. The mixed H-2/H-infinity control method is adopted to formulate the parameters optimization problem of POD. Solving the problem by an improved backtracking search algorithm (BSA), the optimal parameters of POD can be achieved. Simulation results in the two-area four-machine power system confirm the robustness and damping effect of the proposed coordinated control.

关键词： power oscillation damper coordinated control doubly fed induction generator wind turbine static var compensator backtracking search optimization algorithm

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BackIP: Mutation Based Test Data Generation Using Hybrid Approach 3

BackIP: Mutation Based Test Data Generation Using Hybrid App...

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3rd International Conference on Information and Communication Technology for Development for Africa (ICT4DA)

作者： Bejo, Seifu Detso Assefa, Beakal Gizachew Mohapatra, Sudhir Kumar AASTU Dept Software Engn HPC & Big Data Anlyt CoE Addis Ababa Ethiopia Addis Ababa Univ Sch Informat Technol & Engn AAiT Addis Ababa Ethiopia Sri Sri Univ Fac Emerging Technol Cuttack Odisha India

ISBN: (纸本)9781665436663

Fault-based testing is a powerful technique to ensure the quality of software by evaluating the efficacy of the test suits and also used to check the thoroughness of testing performed by other software testing techniques. However, it is very complicated and computationally expensive testing method. Literature shows that there is a tremendous effort to give formal solutions and heuristics methods. Recently, state-of-the-art approaches based on hybrid optimization techniques have been proven to be suitable for cost effective results. This work implements and presents a multi-objective novel hybrid method by combining backtracking search optimization algorithm and Integer programming approach(BackIP). Unlike, some other approaches, BackIP is a test input data generation method which includes test data generation, mutation analysis, and test suite reduction simultaneously. Experimental comparison is conducted on a widely used benchmark java programs and results show that the proposed approach achieves test data generation with mutation score up to 94% and improved test suite reduction between 70% to 94% as compared to the state-of-the-art techniques.

关键词： Software testing Software security Test data Test suite backtracking search optimization algorithm Integer programming Different operator algorithm

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Crack Detect Of Carbide Anvil Based On BSA Optimized SVM

Crack Detect Of Carbide Anvil Based On BSA Optimized SVM

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The 29th International Congress on Condition Monitoring and Diagnostic Engineering Management

作者： WenliaoDu ChunhuaQian KunLi XiaoyunGong Ansheng Li Liangwen Wang Zhiyang Wang Yanming Li School of Mechanical and Electronic Engineering Zhengzhou University of Light Industry School of Mechanical and Power Engineering Henan Polytechnic University School of Mechanical Engineering Shanghai Jiao Tong University

Under the high temperature and pressure,the carbide anvil of artificial diamond suffers from *** the cracked one is not checked out and replaced in time,more anvils would break up simultaneously and result in serious economic *** at detecting the crack as early as possible,a new method based on backtracking search optimization algorithm and support vector machine is put *** the acoustic emission is produced when the brittle materials cracks,the acoustic emission signals is used to detect the ***,the BSA algorithm is used to optimize the parameters of SVM,and then the SVM is trained to perform the crack *** last,the proposed method is verified by using the data under normal and two burst acoustic emission *** experiment results show that this method can accurately detect the cracks of the anvil,and we can enjoy its promising classification performance and online monitoring.

关键词： backtracking search optimization algorithm Support vector machine Carbide anvil Crack detect

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