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An intelligent EGWO-SCA-CS algorithm for PSS parameter tuning under system uncertainties

INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2020年第10期35卷 1520-1569页

作者： Devarapalli, Ramesh Bhattacharyya, Biplab Sinha, Nikhil K. Indian Inst Technol ISM Dept Elect Engn Dhanbad 826004 Jharkhand India BIT Sindri Dept Elect Engn Dhanbad Bihar India

This paper proposes a novel hybrid technique called enhanced grey wolf optimization-sine cosine algorithm-cuckoo search (EGWO-SCA-CS) algorithm to improve the electrical power system stability. The proposed method comprises of a popular grey wolf optimization (GWO) in an enhanced and hybrid form. It embraces the well-balanced exploration and exploitation using the cuckoo search (CS) algorithm and enhanced search capability through the sine cosine algorithm (SCA) to elude the stuck to the local optima. The proposed technique is validated with the 23 benchmark functions and compared with state-of-the-art methods. The benchmark functions consist of unimodal, multimodal function from which the best suitability of the proposed technique can be identified. The robustness analysis also presented with the proposed method through boxplot, and a detailed statistical analysis is performed for a set of 30 individual runs. From the inferences gathered from the benchmark functions, the proposed technique is applied to the stability problem of a power system, which is heavily stressed with the nonlinear variation of the load and thereby operating conditions. The dynamics of power system components have been considered for the mathematical model of a multimachine system, and multiobjective function has been framed in tuning the optimal controller parameters. The effectiveness of the proposed algorithm has been assessed by considering two case studies, namely, (i) the optimal controller parameter tuning, and (ii) the coordination of oscillation damping devices in the power system stability enhancement. In the first case study, the power system stabilizer (PSS) is considered as a controller, and a self-clearing three-phase fault is considered as the system uncertainty. In contrast, static synchronous compensator (STATCOM) and PSS are considered as controllers to be coordinated, and perturbation in the system states as uncertainty in the second case study.

关键词： cuckoo search algorithm enhanced grey wolf optimization hybrid optimization algorithm inter-area oscillations power system stabilizer STATCOM

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Prediction Model for Reference Crop Evapotranspiration Based on the Back-propagation algorithm with Limited Factors

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WATER RESOURCES MANAGEMENT 2023年第3期37卷 1207-1222页

作者： Zhao, Long Xing, Liwen Wang, Yuhang Cui, Ningbo Zhou, Hanmi Shi, Yi Chen, Sudan Zhao, Xinbo Li, Zhe Henan Univ Sci & Technol Coll Agr Equipment Engn Luoyang 471000 Henan Peoples R China Sichuan Univ Coll Water Resource & Hydropower State Key Lab Hydraul & Mt River Engn Chengdu 610065 Sichuan Peoples R China Henan Univ Sci & Technol Coll Hort & Plant Protect Luoyang 471000 Henan Peoples R China

The precise estimation of reference crop evapotranspiration (ETO) is vital for regional and irrigation water resource management. It is also beneficial to the rational allocation of regional water resources and alleviates the disparity between water supply and demand. This study accurately estimates the ETO of 14 meteorological stations in southern China. Five neural network models (extreme learning machine [ELM], back-propagation neural network [BP], ant colony optimization [ACO]-BP, bird swarm algorithm [BSA]-BP, and cat swarm optimization [CSO]-BP) were introduced to predict ETO with limited factors using different methods. The results demonstrated that models involving T (average, maximum, and minimum air temperature), sunshine duration (n), and relative humidity (RH) exhibited the highest accuracy of all studied combinations;the role of T, n, RH, wind speed (U-2) and average atmospheric pressure (AP) regarding ETO gradually decreased. These three biological heuristic algorithms (ACO, BSA, and CSO) each significantly enhanced the capability of the BP model. The accuracy and computational cost of the CSO-BP model are better than those built by other algorithms. Therefore, it is strongly recommended to use the CSO-BP model for ETO estimation in southern China. This result serves as a reference for a more accurate estimation of ETO for future irrigation decision-making and water resource management in southern China.

关键词： Back-propagation neural network hybrid optimization algorithm Reference evapotranspiration K-fold Weather forecast

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Estimation of Geoacoustic Properties of Marine Sediment Using a hybrid Differential Evolution Inversion Method

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IEEE JOURNAL OF OCEANIC ENGINEERING 2010年第1期35卷 59-69页

作者： Jiang, Yong-Min Chapman, N. Ross Gerstoft, Peter Univ Victoria Sch Earth & Ocean Sci Victoria BC V8W 3V6 Canada Univ Calif San Diego Scripps Inst Oceanog Marine Phys Lab La Jolla CA 92093 USA

This paper reports the inversion of midfrequency (1500-4500 Hz) chirps from a short-range transmission experiment conducted on the New Jersey Continental Shelf during the 2006 Shallow Water Experiment (SW06). The source was held at different depths and the sound signals were recorded at a vertical line array to investigate the interactions with the sea bottom at various grazing angles. Strong reflections from the sediment layer were seen in the data for all of the sources. Due to the presence of complex microstructures in the thermocline of the oceanic sound-speed profile, fluctuations both in amplitude and arrival time of the direct path arrivals were observed. Time variation of the water-column environment was also evident during the source transmissions. To mitigate the effects of the ocean environment on the seabed property estimation, a multistage optimization inversion was employed. The sound speed and the experimental geometry were inverted first using only the travel times of the water-column arrivals. The bottom sound speed and the sediment layer thickness were then inverted by matching the travel times of the bottom and sub-bottom reflections. The average of the estimated values for the sediment sound speed is 1598 m/s, consistent with in situ measurements from other experiments in the vicinity.

关键词： Differential evolution (DE) downhill simplex (DHS) empirical orthogonal function (EOF) geoacoustic inversion hybrid optimization algorithm sediment property travel-time inversion

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hybrid artificial immune system in identification of room acoustic properties

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INVERSE PROBLEMS IN SCIENCE AND ENGINEERING 2013年第6期21卷 957-967页

作者： Poteralski, A. Szczepanik, M. Ptaszny, J. Kus, W. Burczynski, T. Silesian Tech Univ Inst Computat Mech & Engn Gliwice Poland Cracow Univ Technol Inst Comp Sci Krakow Poland

The paper deals with an application of a hybrid artificial immune system (HAIS) to the identification problems. The HAIS is applied to identify complex impedances of room walls. This approach is based on the mechanism discovered in biological immune systems. The numerical example demonstrates that the method based on immune computation is an effective technique for solving computer aided in identification problem.

关键词： artificial immune system acoustics identification method of fundamental solutions hybrid optimization algorithm

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Synergetic structure-control design via a hybrid gradient-evolutionary algorithm

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optimization AND ENGINEERING 2015年第3期16卷 511-539页

作者： Villarreal-Cervantes, Miguel G. Alberto Cruz-Villar, Carlos Alvarez-Gallegos, Jaime Inst Politecn Nacl CIDETEC Mechatron Sect Mexico City DF Mexico CINVESTAV Dept Elect Engn Mechatron Sect Mexico City 14000 DF Mexico

This paper proposes a synergetic approach to design a planar parallel robot with its control system. In this proposal, the design problem is stated as a dynamic optimization problem with dynamic and static constraints on both the robot parameters and the control input to the robot. Control parameterization via PID controllers is used to rewrite the dynamic optimization problem as a nonlinear programming problem, which is solved by using a hybrid gradient-evolutionary optimization technique. The dynamic optimization problem presents singularity regions in the design space requiring the use of the proposed hybrid gradient-evolutionary optimization technique. The rationale behind the proposed hybrid algorithm lies in using a exploratory search mechanism for finding the initial guess to the fine search mechanism, which is used to search in a local region of a solution. We discuss both the results of the proposed optimization technique and the experimental results of the robot designed with the proposed approach. In addition, the result provided by the proposed synergetic design approach is compared with a sequential design approach, showing the advantages of the synergetic approach.

关键词： hybrid optimization algorithm Structure-control design Parallel robot Differential evolution

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Interval inversion of multiwell logging data for estimating laterally varying petrophysical parameters and formation boundaries

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ACTA GEODAETICA ET GEOPHYSICA 2022年第2期57卷 373-396页

作者： Abdellatif, Mahmoud Szabo, Norbert P. Univ Miskolc Dept Geophys H-3515 Miskolc Hungary South Valley Univ Dept Geol Qena 83523 Egypt Univ Miskolc MTA ME Geoengn Res Grp H-3515 Miskolc Hungary

The present paper introduces a Legendre polynomials based interval inversion method for processing multi-borehole logging data. The method allows the determination of lateral changes of the layer-thicknesses together with the vertical and lateral variations of petrophysical parameters along a 2D cross-section of several boreholes. The method is assessed using noisy synthetic measurements of a petrophysical model made of two-layers structure related to hydrocarbon bearing formations. The numerical experiments aided to investigate the stability and convergence of the 2D interval inversion procedure. To ensure the accuracy and reliability of the inversion results, the misfit of data and model distance are tested, beside the calculation of estimation errors and correlation coefficients. A large amount of input data relative to the number of unknowns results in a high overdetermined ratio, consequently more precise estimates are obtained in stable and convergent procedure than in conventional local (1D) inversion schemes. The feasibility of the 2D interval inversion method is shown by analysing in-situ well logging data acquired in four wells situated in an Egyptian hydrocarbon field.

关键词： Interval inversion Legendre polynomial Global optimization hybrid optimization algorithm Petrophysical parameter Komombo basin Egypt

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A novel dynamic planning mechanism for allocating electric vehicle charging stations considering distributed generation and electronic units

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ENERGY REPORTS 2022年 8卷 14658-14672页

作者： Adetunji, Kayode E. Hofsajer, Ivan W. Abu-Mahfouz, Adnan M. Cheng, Ling Univ Witwatersrand Sch Elect & Informat Engn 1 Jan Smuts Ave ZA-2000 Johannesburg South Africa Council Sci & Ind Res CSIR ZA-0184 Pretoria South Africa

Achieving a sustainable and efficient power systems network and decarbonized environment involves the optimal allocation of multiple distributed energy resource (DERs) unit types and flexible alternating current transmission systems (FACTS) to distribution networks. However, while the most focus is on optimization algorithms and multi-objective techniques, little to no attention is paid to the underlying mechanisms in planning frameworks. This paper goes beyond existing literature by investigating the impact of planning mechanisms in smart grid planning frameworks when considering the allocation of PV distributed generation units, battery energy storage systems, capacitor banks, and electric vehicle charging station facilities. First, a single- and multi-objective planning problem is formulated. Then, we propose a novel adaptive-dynamic planning mechanism that uses a recombination technique to find optimal allocation variables of multiple DER and FACTS types. To cope with the additional complexity resulting from the expanded solution space, we develop a hybrid stochastic optimizer, named cooperative spiral genetic algorithm with differential evolution (CoSGADE) optimization scheme, to produce optimal allocation solution variables. Through numerical simulations, it is seen that the proposed adaptive planning mechanism improves achieves a 12% and 14% improvement to the conventional sequential (multi-stage) and simultaneous mechanisms, on small to large scale distribution networks. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by- nc- nd/4.0/).

关键词： Battery energy storage systems Computational intelligence Distributed generation Electric vehicles Electric vehicle charging station hybrid optimization algorithm Planning mechanism Reinforcement learning

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Enhanced security for IoT networks: a hybrid optimized learning model for intrusion classification

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SADHANA-ACADEMY PROCEEDINGS IN ENGINEERING SCIENCES 2024年第2期49卷 1-12页

作者： Rajarajan, S. Kavitha, M. G. Kings Coll Engn Dept Comp Sci & Engn Punalkulam 613303 Tamil Nadu India Univ Coll Engn Dept Comp Sci & Engn Pattukkottai Tamil Nadu India

The Internet of Things (IoT) features multiple device connectivity and breaks the conventional network connectivity limitations like limited wireless range, scalability specific communication protocol dependency, etc. Multiple devices can be connected in an IoT network without significant infrastructure changes and the devices can communicate with each other through variety of protocols, which could be more beneficial in many organizations, consumers, and governments. However, the rapid development of IoT technology requires a secure network as it must access different devices and communication methods. This diversity and heterogeneity make network intrusions more convenient for intruders. IoT network complexity and security flaws increase when a large volume of data is transferred through a network. Intrusion detection systems (IDS) are used to monitor the network behavior for detecting unusual behaviors or intrusions. Numerous machine learning models are used in IDS for classifying network traffic. However, these methods lag in detection performances due to limited feature handling abilities. Thus, in selecting optimal features that correctly indicate the intrusions in the network, optimization models are used in IDSs. However, due to the limited exploration and exploitation ability of conventional optimization algorithms, this research presents a hybrid optimization algorithm using Salp Swarm optimization and Bee Foraging (SSA-BF) optimization approaches for optimal feature selection. The optimal features are classified using a multiplicative Long Short-Term Memory (MLSTM) network. To check the robustness of the proposed IDS, accuracy, recall, f1-score, and precision metrics are considered for analysis. Simulation results of the proposed IDS exhibited a maximum accuracy of 95.8%, better than conventional Auto Encoder, Convolutional Neural Network, Gaussian mixture model with Generative adversarial Network, Multi-CNN, and DeepNet-based IDSs.

关键词： Optimal feature selection network traffic Internet of Things hybrid optimization algorithm deep learning multiplicative LSTM

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Amended GWO approach based multi-machine power system stability enhancement

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ISA TRANSACTIONS 2021年 109卷 152-174页

作者： Devarapalli, Ramesh Bhattacharyya, Biplab Sinha, Nikhil Kumar Dey, Bishwajit Indian Inst Technol ISM Dept Elect Engn Dhanbad Jharkhand India BIT Sindri Dept Elect Engn Dhanbad Jharkhand India

The conception of electromechanical oscillations initiates in the power network when there is an installation of the generator in parallel with the existent one. Further, the interconnection of multiple areas, extension in transmission, capricious load characteristics, etc. causes low-frequency oscillations in the consolidated power network. This paper proposes variants of a booming population-based grey wolf optimization (GWO) algorithm in the tuning of power system stabilizer parameters of a multi-machine system in damping low-frequency oscillations. The parameters have been tuned by framing an objective function considering the improving damping ratios for the system states with lesser damping ratios and shifting the system eigenvalues towards the left-hand side of s-plane for the improved settling characteristics for the oscillations in the system. The requisites of stabilizer strategy are mapped with the hallmarks of prevalent algorithms and designed hybrid versions of GWO for the enhancement of the multi-machine power system stability. Four variants of GWO technique are nominated based on the competent stabilizer performance namely, modified grey wolf optimization (MGWO), hybrid MGWO particle swarm optimization (MGWOPSO), hybrid MGWO sine cosine algorithm (MGWOSCA) and hybrid MGWO crow search algorithm (MGWOCSA) for the designed multi-machine power network. The proposed methods have been realized with the statistical analysis on the 23 benchmark functions. Nonparametric statistical tests, namely, Feidman test, Anova test and Quade tests, have been performed on the test system, further analysed in detail. A detailed comparative analysis under the self-clearing fault is presented to illustrate the suitability of the proposed techniques. For the analysis purpose, the location of system eigenvalues has been observed along with their oscillating frequencies and corresponding damping ratios. Further, the damping nature offered with considered system uncertainty for

关键词： hybrid optimization algorithm Grey wolf optimization Power system stabilizer Low-frequency oscillation damping Inter-area oscillations Power system oscillations damping

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Long-term optimal planning for renewable based distributed generators and battery energy storage systems toward enhancement of green energy penetration

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JOURNAL OF ENERGY STORAGE 2024年 90卷

作者： Alahmad, Ahmad K. Verayiah, Renuga Shareef, Hussain Univ Tenaga Nasl UNITEN Inst Power Engn Putrajaya CampusJalan IKRAM UNITEN Kajang 43000 Selangor Malaysia United Arab Emirates Univ Coll Engn Dept Elect Engn POB 15551 Al Ain U Arab Emirates

In this paper, we formulate a stochastic long-term optimization planning problem that addresses the cooperative optimal location and sizing of renewable energy sources (RESs), specifically wind and photovoltaic (PV) sources and battery energy storage systems (BESSs) for a project life span of 10-years. The aim is to enhance the integrated capacity of green energy in the electric power distribution system (DS) while adhering to topological, technical, and economic constraints and considering the annual load growth. Moreover, to account for uncertainties related to various input random variables such as wind speed, solar irradiation, load power, and energy prices, Monte Carlo Simulation (MCS) is employed to generate multiple scenarios. The backward reduction method (BRM) is then applied to streamline the number of generated scenarios, reducing computational efforts. To solve the optimization planning model, a hybrid optimization algorithm is proposed, combining the non-dominating sorting genetic algorithm (NSGAII) and multi-objective particle swarm optimization (MOPSO). This hybrid approach aims to simultaneously minimize three long term objective functions from the economic, environmental, and technical point of view: total expected investment, operational, and carbon emission cost, power loss, and voltage deviation. The effectiveness of the planning model and the performance of the solver method are validated using the 69-bus benchmark test system. The adopted system is configured into three cases, including basic DS, DS with RESs, and DS with a combination of RESs and BESSs. Simulation results demonstrate the capability of the proposed planning model in achieving the following improvements: RESs without ESS achieved 3.35 MVA penetration while reducing DS dependency by 31.44 %. Moreover, the technical objectives improved: power loss by 39.14 % and voltage deviation by 45.45 %. Post-BESS deployment, green energy capacity reached 3.65 MVA, enhancing technical objectiv

关键词： Long term planning Renewable energy sources Battery energy storage systems Uncertainties hybrid optimization algorithm Charging and discharging control strategy

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