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8th International Conference on Information Technology and Electrical Engineering (ICITEE)

作者： Sarjiya Putra, Pradipta Hafiza Saputra, Toga Ardian Univ Gadjah Mada Dept Elect Engn & Informat Technol Yogyakarta Indonesia

ISBN: (纸本)9781509041398

Economic dispatch determines the optimal power generation output to meet the system load at the lowest possible cost. Characteristics of each generating unit may have an impact on the effectivity of the method used in solving economic dispatch problem. Conventional gradient-based methods are inapplicable in economic dispatch problem with non-smooth cost functions and multiple fuel options. In this paper, modified flower pollination algorithm (MFPA) with lambda iteration approach (LI) is proposed. The proposed method is tested on a 10-generator system with non-smooth and multiple fuel options. MFPA results in lower fuel cost compared to FPA. Furthermore, MFPA is tested against other methods used in similar economic dispatch problems. MFPA produces a better solution compared to some of the methods.

关键词： economic dispatch non-smooth multiple fuel options modified flower pollination algorithm lambda iteration

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Observer controller-based structure for a modified flower pollination algorithm for wind power generation

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INTERNATIONAL JOURNAL OF AUTOMATION AND CONTROL 2024年第1期18卷 53-86页

作者： Akwasi, Amoh Mensah Wei, Xie Duku, Otuo-Acheampong South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Peoples R China Wuhan Univ Sch Elect Engn Luojia Hill Wuhan 430072 Peoples R China

A proposed two-stage Luenberger observer controller-based structure is used by the pollinators in the flower pollination algorithm (FPA) to search for global and local pollination. The FPA moves in only one direction in search of the best solution and has a slow speed which is unable to properly find the best solution at longer distances. The modified FPA with the Luenberger method observes the best and optimal proliferation of specific flower species based on the combination of global and local pollination, which is used in the search for the best solution by the pollinators in both forward and backward movement, and with fast speed in the search for the local and global best solutions at both shorter and longer distances, which measure the input and output power generated by the wind turbine. The output MATLAB/Simulink simulation results show the effectiveness and high performance of the proposed method.

关键词： wind power generation Luenberger observer controller modified flower pollination algorithm global and local pollination forward and backward pollination

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Optimal design of Savonius wind turbine blade based on support vector regression surrogate model and modified flower pollination algorithm

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ENERGY CONVERSION AND MANAGEMENT 2022年 270卷

作者： Jia, Rongyuan Xia, Huaijie Zhang, Song Su, Weiguang Xu, Shuhui Qilu Univ Technol Shandong Acad Sci Sch Mech Engn Jinan 250353 Peoples R China Shandong Inst Mech Design & Res Jinan 250353 Peoples R China

Blade shape has a significant effect on the wind-capturing ability of the Savonius wind turbine. This paper proposes an intelligent optimization method for optimizing the shape of the blade of the Savonius wind turbine. This method firstly uses the cubic Bezier curve with four design parameters (x1, y1, x2, y2) to characterize the complex blade shape, and then uses the Latin hypercube sampling method to sample some design schemes throughout the design space, and uses computational fluid dynamics simulation to evaluate the response value, i. e. the moment coefficient, of each scheme, and then uses the support vector regression surrogate model to describe the relationship between the design parameters and their response values, finally uses modified flower pollination algorithm to solve the surrogate model to obtain the optimal blade shape. After comparing and analyzing the optimized blade and the classical semicircular blade, it is found that compared with the classical semicircular blade, the optimized blade has a better wind-capturing ability. When the wind speed is 7 m/s and the tip speed ratio is 1, its average power coefficient Cp is significantly increased from 0.260027 to 0.277902 (about 6.87% higher). In addition, the aerodynamic performance of the optimized blade is also better than the classical semicircular blade at other tip speed ratios (TSRs = 0.6-1.2). It is shown that the wind turbine with the optimized blade has great potential in a practical application environment.

关键词： Savonius wind turbine Bezier curves Latin hypercube sampling CFD simulation SVR model modified flower pollination algorithm

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State of health estimation of lithium-ion batteries based on modified flower pollination algorithm-temporal convolutional network

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ENERGY 2023年第1期283卷

作者： Zhang, Hao Gao, Jingyi Kang, Le Zhang, Yi Wang, Licheng Wang, Kai Xian Univ Architecture & Technol Sch Mat Sci & Engn Xian 710061 Shaanxi Peoples R China Qingdao Univ Weihai Innovat Res Inst Coll Elect Engn Qingdao Shandong Peoples R China Xian Univ Sci & Technol Coll Mat Sci & Engn Xian 710054 Shaanxi Peoples R China Nanjing Tech Univ Sch Energy Sci & Engn Nanjing 211816 Jiangsu Peoples R China Zhejiang Univ Technol Sch Informat Engn Hangzhou 310014 Peoples R China

Lithium-ion batteries (LIBs) need to maintain high energy efficiency and power level in several application scenario. Accurate state of health (SOH) forecast is essential for designing a safe and reliable battery management systems (BMS). Temporal convolutional network (TCN) is a prevailing deep learning method for estimating the SOH of lithium-ion batteries. However, the hyperparameters in the network are usually difficult to predefine, which poses a challenge for the SOH estimation accuracy in real-world. To solve this problem, this paper pro-poses a data-driven estimation approach, where the TCN is combined with the modified flower pollination al-gorithm (MFPA) to determine the network topology. After hyperparameter optimization, the external sensor raw data and identified ohmic resistances trajectories in the equivalent circuits model (ECM) are both input to the TCN model to estimate SOH of LIBs. In contrast to prior approaches for feature extraction, this method is not only conductive to improve SOH estimation accuracy, but also can reduce on-board estimation computing burden. We carry out experiments on the same type of cells from NASA public data resources. The experimental results systematically validate the superiority of the proposed method, which covers high estimation accuracy, great robustness to varied training set and satisfied universality to different batteries.

关键词： Lithium -ion battery State of health estimation Temporal convolutional network modified flower pollination algorithm Hyperparameter optimization

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Reconfiguration and Capacitor Placement of Radial Distribution Systems by modified flower pollination algorithm

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ELECTRIC POWER COMPONENTS AND SYSTEMS 2016年第13期44卷 1492-1502页

作者： Namachivayam, Gnanasekaran Sankaralingam, Chandramohan Perumal, Sathish Kumar Devanathan, Sudhakar Thirumalaivasal Anna Univ Misrimal Navajee Munoth Jain Engn Coll Dept Elect & Elect Engn Madras 600097 Tamil Nadu India Anna Univ Elect & Elect Engn Dept Coll Engn Madras Tamil Nadu India Embedded Technol Solut Madras Tamil Nadu India St Josephs Coll Engn Madras Tamil Nadu India

This article proposes a combined methodology for network reconfiguration and optimal placement of shunt capacitors in radial distribution systems to reduce real power loss and enhance bus voltages. The power loss is reduced by network reconfiguration and capacitor placement, which in turn reduces a utility's loss of revenue. To ensure radial structure and avoid islanding of nodes, feasible ties-witch combinations are formed prior to the optimization process using a graph theory based method. The modified flower pollination algorithm uses a flower pollination process, an improved local neighborhood search method, and a dynamic switching probability approach to enhance the global search for optimization. The performance of this approach is tested on standard 33-bus, 69-bus, and 118-bus radial distribution test feeders. Results have been compared with previous methods reported in the literature, indicating the effectiveness of the combinatorial approach in terms of loss reduction, voltage enhancement, and cost saving.

关键词： radial distribution system real power loss modified flower pollination algorithm network reconfiguration optimal location sensitivity analysis shunt capacitor voltage deviation index simultaneous network reconfiguration and capacitor placement power loss minimization

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Optimal DGs Allocation in Distribution Networks Using modified flower pollination algorithm 19

Optimal DGs Allocation in Distribution Networks Using Modifi...

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19th International Middle East Power Systems Conference (MEPCON)

作者： Oda, Eyad S. Abdelsalam, Abdelazeem A. Suez Canal Univ Fac Engn Dept Elect Engn Ismailia 41522 Egypt

ISBN: (纸本)9781538609903

In the last decade, the integration of distributed generators (DGs) in radial distribution systems has reached high penetration levels. Impacts of DGs on the active power loss and system voltage profile has become significant. Optimization algorithms are tools which are used to optimally find the site and size of DGs within specific limits and constraints. This paper presents an optimization algorithm that employs modified flower pollination algorithm (MFPA) to determine the optimal DGs allocation in order to minimize the system power losses. MFPA is a new metaheuristic population-based optimization algorithm, and has few tuning parameters. Therefore, the updating of these tuning parameters towards the most effective values has a higher reliability of success than in other competing metaheuristic methods. The performance of the proposed MFPA is investigated on three standard test systems;IEEE 33-bus, IEEE 69-bus and IEEE 136-bus. The results show that the effectiveness and validity of MFPA in determining the optimal allocation of DER units with minimum power losses. The superiority of the proposed MFPA is evaluated by comparing it with other metaheuristic optimization algorithms such as the discrete artificial bee colony algorithm (DABC) and standard flower pollination algorithm (FPA).

关键词： Distributed Generations Optimization Power Loss Reduction modified flower pollination algorithm

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An improved back propagation neural network based on complexity decomposition technology and modified flower pollination optimization for short-term load forecasting

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NEURAL COMPUTING & APPLICATIONS 2019年第7期31卷 2679-2697页

作者： Pan, Lina Feng, Xiaosu Sang, Fawen Li, Longjie Leng, Mingwei Chen, Xiaoyun Lanzhou Univ Sch Informat Sci & Engn Lanzhou 730000 Gansu Peoples R China Northwest Univ Nationalities Sch Educ Sci & Technol Lanzhou 730000 Gansu Peoples R China

Accurate short-term load forecasting (STLF) is crucial for reliable operation of a power system. Back propagation neural network (BPNN) is widely used in the forecasting field because of its powerful approximation capability. However, due to a variety of unstable factors, electrical time series often exhibit highly noisy and nonlinear characteristics. Usually, a large deviation will be produced when employing single BPNN to capture the complex data pattern. To solve this problem, this paper proposes a new hybrid forecasting approach that combines ensemble empirical mode decomposition (EEMD), chaotic self-adaptive flower pollination algorithm (CSFPA) and BPNN. EEMD is employed to decompose the original load series with the purpose of reducing the forecasting complexity. Developed CSFPA uses logistic equation to produce the chaotic initial population. In addition, aiming at providing a better optimization capability, CSFPA calculates the self-adaptive switch probability at each iteration. The best initial weights and biases of BPNN are provided by the optimization result of CSFPA. The performance of the proposed method is validated by two real-world load data sets from different electricity markets. The numerical results demonstrate that the proposed method outperforms three advanced methods;it is an effective and promising method for STLF.

关键词： Short-term load forecasting Hybrid forecasting method Complexity decomposition Back propagation neural network modified flower pollination algorithm

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modified bio-inspired optimisation algorithm with a centroid decision making approach for solving a multi-objective optimal power flow problem

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IET GENERATION TRANSMISSION & DISTRIBUTION 2017年第4期11卷 1012-1022页

作者： Barocio, Emilio Regalado, Jose Cuevas, Erick Uribe, Felipe Zuniga, Pavel Ramirez Torres, Pedro J. Univ Guadalajara Grad Program Elect Engn Av Revoluc 1500 Guadalajara Jalisco Mexico Univ Guadalajara Dept Computat Sci Av Revoluc 1500 Guadalajara Jalisco Mexico Imperial Coll London Dept Elect & Elect Engn London England

A method to solve a multi-objective optimal power flow (MOOPF) problem with multiple and competing objective functions (OF) is presented. The modified flower pollination algorithm and the normal boundary intersection method are used in a complementary way to determine the Pareto front solution of the MOOPF problem. To help in the decision making process, an intuitive criterion based on the centroid concept is proposed to select the best compromise solution from the Pareto frontier. To demonstrate the capabilities of the proposed method, different OFs are combined to calculate the Pareto front solution on the IEEE 30 bus test system. Finally, a comparison of the proposed centroid based method against the well-known fuzzy membership and entropy criterions is provided in the results section.

关键词： decision making optimisation load flow modified bio-inspired optimisation algorithm centroid decision making approach multiobjective optimal power flow problem modified flower pollination algorithm objective functions normal boundary intersection method Pareto front solution MOOPF problem IEEE 30 bus test system Pareto frontier fuzzy membership entropy criterions

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MOFPA-BASED PIDA CONTROLLER DESIGN OPTIMIZATION FOR ELECTRIC FURNACE TEMPERATURE CONTROL SYSTEM

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INTERNATIONAL JOURNAL OF INNOVATIVE COMPUTING INFORMATION AND CONTROL 2020年第6期16卷 1863-1876页

作者： Pringsakul, Noppadol Puangdownreong, Deacha Southeast Asia Univ Dept Elect Engn 19-1 Petchkasem Rd Bangkok 10160 Thailand

The flower pollination algorithm (FPA) was developed and proposed as one of the nature-inspired population-based metaheuristic optimization techniques for solving optimization problems. In this paper, the enhanced version of the original FPA called the modified flower pollination algorithm (MoFPA) is presented to improve the search performance. The switching probability of the original FPA used for selection between local and global pollination is changed from the fixed manner to the random manner. The proposed MoFPA is tested against five benchmark optimization problems compared with the original FPA. Then, the proposed MoFPA is applied to the proportional-integral-derivative-accelerated (PIDA) controller design optimization for the electric furnace temperature control system. System responses obtained by the PIDA controller designed by the MoFPA will be compared with those obtained by the PID controller designed by the MoFPA. As results, the proposed MoFPA performs more efficient and more robust in global optimum finding of five selected benchmark optimization problems with higher success rates than the original FPA. In addition, the PIDA controller designed by the proposed MoFPA can provide the very satisfactory tracking and regulating responses of the electric furnace temperature control system superior to the PID controller.

关键词： modified flower pollination algorithm PIDA controller Electric furnace temperature control system Constrained optimization problem

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Integration of hybrid controller for power quality improvement in photo-voltaic/wind/battery sources

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JOURNAL OF CLEANER PRODUCTION 2022年 330卷

作者： Naidu, R. S. R. Krishnam Palavalasa, Mahesh Chatterjee, Shamik NS Raju Inst Technol Dept Elect & Elect Engn Vishakhapatnam India Lovely Profess Univ Sch Elect & Elect Engn Phagwara Punjab India

Renewable Energy Sources (RES) are currently being used on a larger scale to support and satisfy the higher energy demands caused by industrialization and population growth. Therefore, the power generation has to be increased at a greater speed to meet the daily user needs for improvement in lifestyle. The electric utilities and system operators face a tough challenge due to the rise in the number of consumers of power systems and the unpredictable nature of the electric load. Therefore, an effective Energy Management System (EMS) is developed using a combination of modified flower pollination algorithm (MFPA) and modified Perturb and Observe (MP&O) method. Three different systems namely: RES Photovoltaic (PV) module, wind turbine, and battery are used to create an effective EMS. MFPA extracts more power from the PV panel by providing less scaling factor. Then it is utilized to increase the initialized population of particles to enhance step size and reset the position of particles. Furthermore, MFPA is used to effectively control the switching between the wind turbine and battery storage system. Furthermore, the MP&O method is used to trigger the switch of the DC-DC converter to achieve stable power from the PV/wind/battery system. The MFPA-MP&O controller is compared with three standard controller combinations FPA-P&O, FPA-MP&O and MFPA-P&O and is also compared with existing RES designs that are: MPCP-MPVP, Fuzzy Logic Control - Proportional Integral (FLC-PI), and MPPVC. Total Harmonic Distortion (THD) of MFPA-MP&O controller is 0.40%, which is less when compared with the MPCP-MPVP, FLCPI and MPPVC RES designs.

关键词： Energy management system modified flower pollination algorithm modified perturb and observe Renewable energy source Total harmonic distortion

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