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A hybrid MWOAL approach for fast and efficient maximum power point tracking in wind energy conversion systems

JOURNAL OF RENEWABLE AND SUSTAINABLE ENERGY 2019年第3期11卷 033302-033302页

作者： Bensam, Samraj Dhas Pandi, Perumal Maruthu Govt Coll Technol Dept Elect & Elect Engn Coimbatore Tamil Nadu India

This paper proposes an efficient hybrid approach for the fast and efficient MPPT in WECS. The system consists of an MPPT based control of IPMSG consisting of a hybrid Meta-heuristic algorithm. The hybrid Meta-heuristic algorithm presented in the proposed approach is the joined execution of both WOA and the ALO algorithm named as MWOAL. In the proposed approach, the WOA is considered to resolve the optimal gain parameters of the PI controller with a minimum error objective function based on the variation of direct and quadrature current parameters. Here, the searching behavior of the whales is modified by using the efficient ALO algorithm is known as modified WOAL MWOAL. The setpoint direct axis and the quadrature axis current parameters of the WECS are determined using the MPPT technique and the loss minimization approach based on the generator speed variation. Hence, the proposed scheme has improved the efficiency of the WECS. At that point, the performance of the proposed adaptive MPPT control of WECS is executed in the MATLAB/Simulink working platform and the execution is assessed using the existing techniques.

关键词： Maximum power point tracking optimization algorithms optimization problems algorithms and data structure Energy conversion Wind turbines Wind energy

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Load Frequency Control (LFC) Strategies in Renewable Energy-Based Hybrid Power Systems: A Review

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ENERGIES 2022年第10期15卷 3488页

作者： Gulzar, Muhammad Majid Iqbal, Muhammad Shahzad, Sulman Muqeet, Hafiz Abdul Shahzad, Muhammad Hussain, Muhammad Majid Univ Cent Punjab Dept Elect Engn Lahore 06375 Pakistan Islamia Univ Bahawalpur Dept Elect Engn Bahawalpur 63100 Pakistan Punjab Tianjin Univ Technol Dept Elect Engn Technol Lahore 54770 Pakistan Muhammad Nawaz Sharif Univ Engn & Technol Dept Elect Engn & Technol Multan 60650 Pakistan Univ South Wales Dept Elect & Elect Engn Pontypridd CF37 1DL M Glam Wales

The hybrid power system is a combination of renewable energy power plants and conventional energy power plants. This integration causes power quality issues including poor settling times and higher transient contents. The main issue of such interconnection is the frequency variations caused in the hybrid power system. Load Frequency Controller (LFC) design ensures the reliable and efficient operation of the power system. The main function of LFC is to maintain the system frequency within safe limits, hence keeping power at a specific range. An LFC should be supported with modern and intelligent control structures for providing the adequate power to the system. This paper presents a comprehensive review of several LFC structures in a diverse configuration of a power system. First of all, an overview of a renewable energy-based power system is provided with a need for the development of LFC. The basic operation was studied in single-area, multi-area and multi-stage power system configurations. Types of controllers developed on different techniques studied with an overview of different control techniques were utilized. The comparative analysis of various controllers and strategies was performed graphically. The future scope of work provided lists the potential areas for conducting further research. Finally, the paper concludes by emphasizing the need for better LFC design in complex power system environments.

关键词： load frequency control renewable energy systems single-area power system multi-area power system optimization algorithms multistage controllers artificial neural networks sliding mode controller

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USING EXCURSIONS TO ANALYZE SIMULATION OUTPUT

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PROBABILITY IN THE ENGINEERING AND INFORMATIONAL SCIENCES 2010年第2期24卷 201-218页

作者： Calvin, James M. New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA

We consider the steady-state simulation output analysis problem for a process that satisfies a functional central limit theorem. We construct an estimator for the time-average variance constant that is based on excursions of a process above the minimum. The resulting estimator does not require a fixed run length, and the memory requirement can be dynamically bounded. Standardized time series methods based on excursions are also described.

关键词： optimization algorithms Computer simulation Random variables

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Distortion compensation of an omnidirectional camera by 3D reference objects

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TM-TECHNISCHES MESSEN 2022年第11期89卷 729-740页

作者： Sigel, Andre Heinrich, Andreas Hsch Aalen Zentrum Opt Technol Anton Huber Str 21 D-73430 Aalen Germany

To utilize images for metrology tasks distortion free images are required. If the optical setup doesn't allow to use low distortion optical elements, distorted images can subsequently be corrected by the means of software algorithms. In this publication such a procedure to correct radial distortion effects for a 3D light section sensor is presented. The calibration procedure was developed for a 3D metrology system but is based on processing of 2D image data. The selection of suitable calibration objects as well as the representation of the distortion effects by an appropriate correction function based on a Taylor series is explained in this publication. The correction procedure is executed by the means of an optimization algorithm and minimizes a merit function representing residual distortion errors. The presented procedure includes a distortion correction with an accuracy in the double-digit micrometer range for a wide angle lens.

关键词： Distortion correction optimization algorithms laser triangulation structured illumination 3D metrology

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Hybrid algorithm for MPPT tracking using a single current sensor for partially shaded PV systems

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2022年第PartA期53卷

作者： Balaji, V Fathima, A. Peer Vellore Inst Technol Sch Elect Engn Vandalur Kelambakkam Rd Chennai 600127 Tamil Nadu India

The power voltage characteristics of a photovoltaic array under partially shaded conditions is highly non linear and it exhibits multiple local peaks and a global peak. The maximum power is harvested from the photovoltaic panel only when the global peak is tracked. Many control algorithms were proposed in the past decades to track maximum power especially during partially shaded conditions. However, the control algorithms are still facing problems like slow convergence, more oscillations during tracking, and complexity in implementation. etc. In this work, a single sensor hybrid MPPT algorithm for battery charging application is proposed. The hybrid algorithm is a combination of salp swarm algorithm and conventional perturb and observe technique and it uses only battery charging current for tracking the maximum power. The performance of the algorithm has been tested under uniform and partially shaded conditions using MATLAB simulation and hardware prototype. The obtained results are compared against single sensor based grey wolf algorithm and sine cosine algorithm. The results show fast and accurate tracking of maximum power under uniform and dynamic shaded conditions.

关键词： Maximum power point tracking Salp swarm optimization Single senor algorithms optimization algorithms Grey wolf optimization Perturb and observe tracking

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PARAMETER ESTIMATION IN EPIDEMIC SPREAD NETWORKS USING LIMITED MEASUREMENTS

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SIAM JOURNAL ON CONTROL AND optimization 2022年第2期60卷 S49-S74页

作者： Ye, Lintao Pare, Philip E. Sundaram, Shreyas Univ Notre Dame Dept Elect Engn Notre Dame IN 46556 USA Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA

We study the problem of estimating the parameters (i.e., infection rate and recovery rate) governing the spread of epidemics in networks. Such parameters are typically estimated by measuring various characteristics (such as the number of infected and recovered individuals) of the infected populations over time. However, these measurements also incur certain costs, depending on the population being tested and the times at which the tests are administered. We thus formulate the epidemic parameter estimation problem as an optimization problem, where the goal is to either minimize the total cost spent on collecting measurements or to optimize the parameter estimates while remaining within a measurement budget. We show that these problems are NP-hard to solve in general and then propose approximation algorithms with performance guarantees. We validate our algorithms using numerical examples.

关键词： epidemic spread networks parameter estimation optimization algorithms

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Estimating pollution spread in water networks as a Schrödinger bridge problem with partial information

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EUROPEAN JOURNAL OF CONTROL 2023年 74卷

作者： Mascherpa, Michele Haasler, Isabel Ahlgren, Bengt Karlsson, Johan KTH Royal Inst Technol Dept Math Stockholm Sweden Ecole Polytech Fed Lausanne LTS4 Lausanne Switzerland RISE Res Inst Sweden Stockholm Sweden

Incidents where water networks are contaminated with microorganisms or pollutants can result in a large number of infected or ill persons, and it is therefore important to quickly detect, localize and estimate the spread and source of the contamination. In many of today's water networks only limited measurements are available, but with the current internet of things trend the number of sensors is increasing and there is a need for methods that can utilize this information. Motivated by this fact, we address the problem of estimating the spread of pollution in a water network given measurements from a set of sensors. We model the water flow as a Markov chain, representing the system as a set of states where each state represents the amount of water in a specific part of the network, e.g., a pipe or a part of a pipe. Then we seek the most likely flow of the pollution given the expected water flow and the sensors observations. This is a large-scale optimization problem that can be formulated as a Schrodinger bridge problem with partial information, and we address this by exploiting the connection with the entropy regularized multimarginal optimal transport problem. The software EPANET is used to simulate the spread of pollution in the water network and will be used for testing the performance of the methodology.(c) 2023 The Author(s). Published by Elsevier Ltd on behalf of European Control Association. This is an open access article under the CC BY license ( http://***/licenses/by/4.0/ )

关键词： optimization algorithms Markov processes Sensor and signal fusion Schrodinger bridge

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A memristor-based transient chaotic neural network model and its application

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JOURNAL OF APPLIED PHYSICS 2019年第11期126卷 114901-114901页

作者： Liu, Y. A. Yu, Q. Hu, S. G. Qiao, G. C. Liu, Y. Univ Elect Sci & Technol China State Key Lab Elect Thin Films & Integrated Devic Chengdu 610054 Sichuan Peoples R China

Transient chaotic neural networks (TCNNs) have shown promise in solving optimization problems but still suffer from slow convergence and being difficult to implement in hardware. In this paper, the HP memristor is introduced to a TCNN to develop a memristor-based transient chaotic neural network (MTCNN) model that is highly efficient, converges quickly, and has significant prospects for physical implementation. The proposed MTCNN makes full use of the nonlinearity and memory-related characteristics of memristors, and their conductance values are used as self-feedback connection weights that can be adjusted dynamically according to the annealing algorithm. The MTCNN model was applied to solve combinatorial optimization problems, including the channel assignment problem (CAP) of four cells and the traveling salesman problem (TSP) of 10 cities. In 500 runs, the MTCNN algorithm delivered a 5% higher optimal solution rate than the TCNN algorithm while using only 70% of its number of iterations in the CAP, and achieved a shorter average distance and a 40% higher convergence speed than the TCNN algorithm in the TSP.

关键词： optimization algorithms optimization problems Neuron model Artificial neural networks Memristor Chaos computing

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A machine learning approach for automated fine-tuning of semiconductor spin qubits

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APPLIED PHYSICS LETTERS 2019年第13期114卷 133102-133102页

作者： Teske, Julian D. Humpohl, Simon Sebastian Otten, Rene Bethke, Patrick Cerfontaine, Pascal Dedden, Jonas Ludwig, Arne Wieck, Andreas D. Bluhm, Hendrik Forschungszentrum Julich JARA FIT Inst Quantum Informat D-52074 Aachen Germany Rhein Westfal TH Aachen D-52074 Aachen Germany Ruhr Univ Lehrstuhl Angew Festkorperphys Bochum Germany

While spin qubits based on gate-defined quantum dots have demonstrated very favorable properties for quantum computing, one remaining hurdle is the need to tune each of them into a good operating regime by adjusting the voltages applied to electrostatic gates. The automation of these tuning procedures is a necessary requirement for the operation of a quantum processor based on gate-defined quantum dots, which is yet to be fully addressed. We present an algorithm for the automated fine-tuning of quantum dots and demonstrate its performance on a semiconductor singlet-triplet qubit in GaAs. The algorithm employs a Kalman filter based on Bayesian statistics to estimate the gradients of the target parameters as a function of gate voltages, thus learning the system response. The algorithm's design is focused on the reduction of the number of required measurements. We experimentally demonstrate the ability to change the operation regime of the qubit within 3-5 iterations, corresponding to 10-15 min of lab-time. Published under license by AIP Publishing.

关键词： Bayesian statistics Semiconductors Machine learning Kalman filter Quantum computing Quasi-Newton methods optimization algorithms Finite difference methods Quantum dots

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The Recent Development of Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

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ENERGIES 2019年第16期12卷 3108-3108页

作者： Lytras, Miltiadis D. Chui, Kwok Tai Amer Coll Greece Deree Coll Sch Business Athens 15342 Greece Effat Univ Effat Coll Engn POB 34689 Jeddah 21478 Saudi Arabia Open Univ Hong Kong Sch Sci & Technol Dept Technol Ho Man TinKowloon Hong Kong Peoples R China

Human beings share the same community in which the usage of energy by fossil fuels leads to deterioration in the environment, typically global warming. When the temperature rises to the critical point and triggers the continual melting of permafrost, it can wreak havoc on the life of animals and humans. Solutions could include optimizing existing devices, systems, and platforms, as well as utilizing green energy as a replacement of non-renewable energy. In this special issue Artificial Intelligence for Smart and Sustainable Energy Systems and Applications, eleven (11) papers, including one review article, have been published as examples of recent developments. Guest editors also highlight other hot topics beyond the coverage of the published articles.

关键词： artificial intelligence computational intelligence energy management machine learning optimization algorithms sensor network smart city smart grid sustainable development

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