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Design of PSS for multi-machine system using extreme learning machine algorithm

MEASUREMENT 2025年 247卷

作者： Suman, M. Rao, M. Venu Gopala Veerendra, A. S. Mopidevi, Subbarao Marquez, Fausto Pedro Garcia Vignans Lara Inst Technol & Sci Dept Elect & Elect Engn Vadlamudi 522213 India Prasad V Potluri Siddhartha Inst Technol Dept Elect & Elect Engn Kanuru 520007 India Manipal Inst Technol Manipal Acad Higher Educ Dept Elect & Elect Engn Manipal 576104 India Vignans Fdn Sci Technol & Res Guntur Andhra Pradesh India Univ Castilla La Mancha Ingn Res Grp Albacete Spain

A novel extreme learning machine algorithm is used to train the neural network for Power System Stabilizer (PSS) to minimize low-frequency oscillations. The use of rapid-acting exciters, the interconnection of various power systems, and disturbances like faults and load changes all contribute to the generation of low-frequency oscillations. If sufficient damping is not provided, these oscillations generate and sustain, and eventually cause the power system to shut down entirely. The lead-lag power system stabilizer is a conventional device used but it is slow in operation and can apply to linear systems only. Artificial intelligence techniques like, fuzzy and neural networks are used to overcome the bottlenecks. The neural networks are trained using backpropagation and extreme learning algorithms. The operation of the designed power system stabilizers is verified on a 7-machine, 29-bus system, 4-machine, 11-bus system, and SMIB System. The proposed controller has been providing better damping performance compared to other controllers in terms of Integral Time Squared Error (ITSE) and Integral Squared Error (ISE). The proposed system is designed and validated through the R2023b MATLAB/Simulink Software environment.

关键词： Power System Stabilizer Power System Stability Fuzzy Controller Back Propagation algorithm extreme learning machine algorithm

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A reconstruction method for cross-cut shredded documents based on the extreme learning machine algorithm

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SOFT COMPUTING 2022年第22期26卷 12851-12862页

作者： Zhang, Zhenghui Zou, Juan Yang, Shengxiang Zheng, Jinhua Gong, Dunwei Pei, Tingrui Xiangtan Univ Key Lab Hunan Prov Internet Things & Informat Sec Xiangtan 411105 Hunan Peoples R China Xiangtan Univ Key Lab Intelligent Comp & Informat Proc Minist Educ Sch Comp Sci Xiangtan 411105 Hunan Peoples R China Xiangtan Univ Sch Cyberspace Sci Xiangtan 411105 Hunan Peoples R China Hunan Prov Key Lab Intelligent Informat Proc & Ap Hengyang 421002 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LE1 9BH Leics England China Univ Min & Technol Sch Informat & Elect Engn Xuzhou 221116 Jiangsu Peoples R China

Reconstruction of cross-cut shredded text documents (RCCSTD) has important applications for information security and judicial evidence collection. The traditional method of manual construction is a very time-consuming task, so the use of computer-assisted efficient reconstruction is a crucial research topic. Fragment consensus information extraction and fragment pair compatibility measurement are two fundamental processes in RCCSTD. Due to the limitations of the existing classical methods of these two steps, only documents with specific structures or characteristics can be spliced, and pairing error is larger when the cutting is more fine-grained. In order to reconstruct the fragments more effectively, this paper improves the extraction method for consensus information and constructs a new global pairwise compatibility measurement model based on the extreme learning machine algorithm. The purpose of the algorithm's design is to exploit all available information and computationally suggest matches to increase the algorithm's ability to discriminate between data in various complex situations, then find the best neighbor of each fragment for splicing according to pairwise compatibility. The overall performance of our approach in several practical experiments is illustrated. The results indicate that the matching accuracy of the proposed algorithm is better than that of the previously published classical algorithms and still ensures a higher matching accuracy in the noisy datasets, which can provide a feasible method for RCCSTD intelligent systems in real scenarios.

关键词： Reconstruction of cross-cut shredded text documents (RCCSTD) extreme learning machine algorithm Consensus information Pairwise compatibility measurement model

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Reducing energy consumption of wireless sensor networks using rules and extreme learning machine algorithm

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JOURNAL OF ENGINEERING-JOE 2019年第9期2019卷 5443-5448页

作者： Duraisamy, Sathya Pugalendhi, Ganesh Kumar Balaji, Prasanalakshmi Kumaraguru Coll Technol Dept Comp Sci & Engn Coimbatore 641049 TN India Anna Univ Reg Campus Dept Informat Technol Coimbatore 641046 TN India King Khalid Univ Dept Comp Sci Ahad Rufida 61421 Asir Abha Saudi Arabia

Wireless sensor networks consist of a collection of sensors to monitor physical or environmental events. Nowadays, the sensor networks are used in important applications like military, health and civilian monitoring. Since it is a wireless medium, deployed in remote locations and resource-constrained nature, the sensor networks are easily vulnerable to attacks. The attack creates significant damages to the sensor networks. To avoid these problems, intrusion detection system (IDS) is implemented at the base station to filter any abnormal packets. In the proposed system, a survey is made on the attacks and rules to detect the attacks. Filtering the attacks using rule-based IDS at the sensor nodes would reduce the amount of packet transmission to the base station which, in turn, would reduce the energy consumption of the sensor network. extreme learning machine (ELM) algorithm is implemented at the base station to detect the abnormal packets. The experimental result shows the performance of different classification techniques and cross-layer rules over the NSL-KDD and real-time datasets. The detection rate of the ELM algorithm is higher compared to other systems.

关键词： security of data wireless sensor networks learning (artificial intelligence) telecommunication security telecommunication computing energy consumption power engineering computing energy consumption wireless sensor networks extreme learning machine algorithm sensor network health civilian monitoring base station rule-based IDS sensor nodes attack detection attack filtering intrusion detection system abnormal packet filtering classification techniques cross-layer rules

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Diagnosis of Diabetes using an extreme learning machine algorithm based Model 10

Diagnosis of Diabetes using an Extreme Learning Machine Algo...

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IEEE 10th International Conference on Awareness Science and Technology (iCAST)

作者： Shanthi, M. Marimuthu, Ramalatha Shivapriya, S. N. Navaneethakrishnan, R. Kumaraguru Coll Technol Dept Elect & Commun Engn Coimbatore 641049 Tamil Nadu India

ISBN: (纸本)9781728138213

Diabetes is a disorder caused by an increase in blood glucose levels due to insulin secretion deficiency (type 1 diabetes) or impaired insulin activity (type 2 diabetes). More than 90% of people with this condition are diagnosed with type 2 diabetes. Diabetes is now the leading cause of blindness, end-stage renal failure, non-traumatic limb amputations, heart disease, and stroke. Due to the high prevalence of type 2 diabetes in recent years, the prognosis and early diagnosis of the disease have gained much importance. In this paper a study on the types of diabetes is made and a model is proposed and developed for diagnosis of type 2 diabetes using extreme learning machine (ELM) method.

关键词： diabetes detection artificial intelligence neural networks extreme learning machine algorithm stress

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Numerical Solution of Passport Option Pricing Problem with Polynomial Neural Networks

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COMPUTATIONAL ECONOMICS 2025年 1-32页

作者： Badireddi, Satyadev Bansal, Saurabh Natesan, Srinivasan Indian Inst Technol Guwahati Dept Math Gauhati 781039 Assam India

In this article, we proposed a single layer feedforward polynomial neural network for numerically solving the passport option pricing problem. The passport option can be valued by solving a nonlinear backward pricing PDE and it is difficult to find analytical solution of this PDE. Laguerre, Hermite and Legendre polynomials are employed separately as the hidden layer neuron's activation function. The training points are uniformly chosen within the domain, the passport option PDE with the final condition and boundary conditions are defined as a penalty function. Network connection weights are optimized by means of the extreme learning machine technique. The proposed neural network approximates not only the passport option value but also some of its important Greeks (Delta, Gamma and Theta) without requiring any additional effort. Finally, exhaustive numerical experiments are carried out to verify the efficiency of the proposed technique.

关键词： Option pricing Passport options Nonlinear PDE Polynomial neural network Numerical solution extreme learning machine algorithm Greeks

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Forecasting China's Renewable Energy Terminal Power Consumption Based on Empirical Mode Decomposition and an Improved extreme learning machine Optimized by a Bacterial Foraging algorithm

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ENERGIES 2019年第7期12卷 1331页

作者： Jiang, Peng Dong, Jun Huang, Hui North China Elect Power Univ Sch Econ & Management Beijing 102206 Peoples R China

The energy consumption pattern dominated by traditional fossil energy has led to global energy resource constraints and the deterioration of the ecological environment. These challenges have become a major issue all over the world. At present, the Chinese government aims to significantly reduce the fossil energy consumption contribution in the terminal energy consumption. The development of renewable energy in the terminal energy and energy conversion links has significantly increased the proportion of clean low-carbon energy. In order to accurately get the proportion of renewable energy terminal power consumption, firstly, this paper selects a primary influencing-factors set including the gross GDP, fixed investment in renewable energy industry, total length of cross-provincial and cross-regional high-voltage transmission lines, etc. as influencing factors of China's electricity consumption fraction produced by renewable energy based on a multitude of papers. Secondly, from the perspective of signal decomposition, the data inevitably has a lot of interference and noise. This paper uses the empirical mode decomposition (EMD) algorithm to reduce the degree of signal distortion and decomposes the signal into natural modes including several intrinsic mode functions (IMFs) and a residual term (Res);afterwards, a new extreme learning machine (ELM) forecasting model optimized by an Inverse Square Root Linear Units (ISRLU) activation function is proposed, and the ISRLU function is used to replace the implicit layer activation function in the original ELM algorithm. Then, a new bacterial foraging algorithm (BFOA) is applied to optimize the parameters of the optimized ELM forecasting model. After multiple learning and training operations, the optimal parameters are obtained. Finally, we superimpose the output of each IMF and Res training task to get the amount of China's power consumption produced by renewable energy. Some statistical indicators including root mean squard er

关键词： renewable energy terminal power consumption energy forecasting grey theory empirical mode decomposition bacterial foraging algorithm extreme learning machine algorithm

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Orthogonal polynomial-based neural network solution for differential equations with implicit boundary conditions

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International Journal of Numerical Methods for Heat and Fluid Flow 2025年

作者： Dey, Shilpa Dubey, Shruti Department of Mathematics Indian Institute of Technology Madras Chennai India

Purpose: This paper aims to provide the approximate solution to variety of differential equations with implicit boundary conditions. Specifically, the focus is on solving higher-order differential equations and system of differential equations, fluid mechanics problem using an orthogonal polynomial-based neural network with extreme learning machine (ELM) algorithm. Design/methodology/approach: The authors use neural network constructed with four different types of orthogonal polynomials: Legendre polynomial, Laguerre polynomial, Chebyshev polynomial and Hermite polynomial and train it using ELM algorithm. The neural network consists of single hidden layer replaced by functional expansion block which uses orthogonal polynomial to extract its features. The result of neural network provides approximate solutions to the problems. To show the capability and efficacy of the approach, obtained solutions are compared with the exact solutions and those derived from traditional numerical technique. Findings: Numerical and comparative studies show that the neural network solutions are obtained with better accuracy. Moreover, the used approach is simple to implement and offer robust framework for solving differential equations with complex boundary conditions. Originality/value: Implicit boundary value problems are successfully addressed, yielding closed form solutions that are highly valuable for real-world applications. Further, the proposed approach for solving implicit problems enhances the applicability of orthogonal polynomial-based neural network with ELM. © 2025, Emerald Publishing Limited.

关键词： extreme learning machine algorithm Orthogonal polynomial-based neural network Robin’s boundary conditions

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extreme learning machine-based alleviation for overloaded power system

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IET GENERATION TRANSMISSION & DISTRIBUTION 2019年第22期13卷 5058-5070页

作者： Labed, Imen Labed, Djamel Univ Constantine 1 Dept Elect Engn Lab Elect Engn Constantine Algeria

The present study proposes a corrective method of electric system overload provided that the wind farm is integrated into the distribution system, taking into account the congestion cost. The authors attempted to mitigate the overload and to monitor flow over transmission lines. Unified power flow controller device was the first suggestion utilised to solve this problem, then due to its extremely fast training and the excellent generalisation performance, extreme learning machine algorithm is employed. The fundamental point is the transmission line alleviation. In addition, other targets are realised: the load shedding avoidance, minimisation of losses and congestion cost. This study is also designed to utilise PowerWorld Simulator and MATLAB software to demonstrate methods for relieving transmission overloads. The accuracy of the proposed approach has been tested for Algerian (Adrar) 22-bus system. Obtained results showed an improvement in power system behaviour. Simulation results are exposed, discussed and compared at the end of this study.

关键词： power transmission control learning (artificial intelligence) wind power plants power system simulation load flow load shedding load flow control power system harmonics flexible AC transmission systems least squares approximations extreme learning machine-based alleviation overloaded power system corrective method electric system overload wind farm distribution system congestion cost transmission lines unified power flow controller device extremely fast training excellent generalisation performance extreme learning machine algorithm fundamental point transmission line alleviation load shedding avoidance transmission overloads 22-bus system power system behaviour

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Optimization free neural network approach for solving ordinary and partial differential equations

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ENGINEERING WITH COMPUTERS 2021年第4期37卷 2989-3002页

作者： Panghal, Shagun Kumar, Manoj Motilal Nehru Natl Inst Technol Allahabad Prayagraj 211004 Uttar Pradesh India

Current work introduces a fast converging neural network-based approach for solution of ordinary and partial differential equations. Proposed technique eliminates the need of time-consuming optimization procedure for training of neural network. Rather, it uses the extreme learning machine algorithm for calculating the neural network parameters so as to make it satisfy the differential equation and associated boundary conditions. Various ordinary and partial differential equations are treated using this technique, and accuracy and convergence aspects of the procedure are discussed.

关键词： Feed-forward neural network extreme learning machine algorithm Partial differential equations

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No-reference video quality assessment method based on spatio-temporal features using the ELM algorithm

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IET IMAGE PROCESSING 2020年第7期14卷 1316-1326页

作者： da Silva, Wyllian Bezerra Mikowski, Alexandre Casali, Rafael Machado Univ Fed Santa Catarina Lab Modelling & Data Anal LabMAD Florianopolis SC Brazil

This work presents an application of the extreme learning machine (ELM) algorithm based on a single-hidden layer feedforward neural network for no-reference video quality assessment. The present research introduces an augmented version of ELM through simple stop criteria, which proved the effectiveness of the video quality assessment method. The authors present empirical studies using LIVE video data base show that the proposed method delivers accuracy (Pearson's correlation coefficient) and monotonicity (Spearman's correlation coefficient) with subjective scores against no-reference, Joint Photographic Experts Group No-Reference, metric and full-reference metrics, for instance, peak signal-to-noise ratio, structural similarity (SSIM) and multi-scale-SSIM indexes, and the proposed method is suitable for quality monitoring of video transmission and reception system.

关键词： data compression feedforward neural nets feature extraction learning (artificial intelligence) video coding Pearson correlation coefficient Spearman correlation coefficient full-reference metrics quality monitoring video transmission reception system reference video quality assessment method spatio-temporal features ELM algorithm extreme learning machine algorithm single-hidden layer feedforward neural network no-reference video quality assessment stop criteria joint photographic experts group no-reference LIVE video data base

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