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Utility-Scale Bifacial Solar Photovoltaic System: Optimum Sizing and Techno-Economic Evaluation

Utility-Scale Bifacial Solar Photovoltaic System: Optimum Si...

引用

IEEE General Meeting Power& Energy Society

作者： Sharaf K. Magableh Caisheng Wang Feng Lin Department of Electrical and Computer Engineering Wayne State University Detroit United States

ISBN: (数字)9798350381832

ISBN: (纸本)9798350381849

Classical monofacial solar photovoltaic systems have gained prevalence and are widely reported in the literature because they have a lower initial cost compared with bifacial systems. However, limited investigation of both systems has been done on a utility scale with different performance indicators. This paper introduces a multifaceted comparative analysis including various aspects like energy generation, reliability, environmental effect, economic viability, and footprint area. Real measured data, including ambient temperature, solar irradiance, and a utility-scale load, were used for studying both systems in the City of Detroit. The optimal system sizing and energy management strategy are attained using the whale optimization algorithm. Minimizing the loss of power supply probability and sizing the number of photovoltaic panels (N PV ) are carried out for both cases. Results revealed that the bifacial solar system generates more power with a lower N PV , a smaller installation area, and hence a lower levelized cost of energy for the entire project lifetime compared to the monofacial system. Accordingly, the bifacial system outlined in this paper is recommended and can be implemented in various locations to establish a sustainable solar energy system that is economically feasible with clean energy production for the entire project's lifespan.

关键词： Photovoltaic systems Temperature measurement Solar irradiance Temperature distribution Costs Power supplies Urban areas whale optimization algorithms Solar system Reliability

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Multiple Fixed-wing UAV Cooperative Path Planning for a Mission in Complex Terrain

Multiple Fixed-wing UAV Cooperative Path Planning for a Miss...

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IEEE International Conference on Robotics and Biomimetics

作者： Umair Javaid Sami Shahid Robotic Institute Ningbo University of Technology Zhejiang China College of Automation Nanjing University of Aero-nautics and Astronautics Jiangsu China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Cooperative path planning (CoPP) is pivotal in the success of multiple fixed-wing UAV (multi-FW-UAV)-based missions. The CoPP for multi-FW-UAVs is challenging due to environment, UAV system, and mission constraints. In addition, the mission-specific constraints twofolds the CoPP problem difficulty. In this paper, we devise a CoPP problem for multi-FW-UAV systems considering change in heading to destination, speed, altitude, flight path slope, and turn angles. In addition, each FW-UAV must simultaneously reach the destination while avoiding collision with other FW-UAVs during the mission. To get an optimal feasible flying path for each FW-UAV in the system during the mission, we propose a two-step optimization algorithm using a hybrid improved whale optimization algorithm (HI-WOA). Initially, each FW-UAV computes its best feasible flying path, which is later used to develop a consensus on arrival time and collision avoidance mechanism in the system. Comparative simulation results show the effectiveness of the proposed HI-WOA for FW-UAVs CoPP.

关键词： Chaotic mapping Simulation Biomimetics Metaheuristics Autonomous aerial vehicles Cost function whale optimization algorithms Path planning Collision avoidance Robots

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Electricity theft detection model based on feature engineering and integrated classifier

Electricity theft detection model based on feature engineeri...

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2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE)

作者： Huijie Liu Qingsheng Zhao Dingkang Liang Shanxi Key Laboratory of Power System Operation and Control Taiyuan University of Technology Taiyuan Shanxi China

ISBN: (数字)9798331506230

ISBN: (纸本)9798331506247

Focused on tackling the issue because the electricity theft detection model has low applicability and the recognition rate is greatly affected by subjective factors like manual extraction features, a power-stealing user recognition model based on feature engineering and integrated classifier is proposed. The model uses Tsfresh and whale optimization Algorithm (WOA) as the main feature extraction and selection tool. Then we use an integrated classifier to identify the abnormal users. Firstly, we collected the electricity consumption data of 600 different users for 583 consecutive days. Secondly, the rough feature extraction is performed by Tsfresh and the variance-based method. Thirdly, the whale optimization Algorithm is used to finely select the features, getting the most suitable feature parameters. Finally, the integrated classifier is used to classify abnormal users. After evaluating the performance of different classifiers ensemble, an integrated classifier is composed of a Decision Tree (DT), Support Vector Machine (SVM), and Random Forest (RF). Meanwhile, in terms of feature extraction, we compared the results of three single classifiers which consist of the integrated classifier, also compared with other feature selection and classification methods. The experimental results show that the average accuracy of this method is 96.11%, which is higher than other methods.

关键词： Support vector machines Accuracy Electricity Computational modeling Manuals Feature extraction whale optimization algorithms Classification algorithms Random forests Classification tree analysis

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Open-Circuit Fault Diagnosis of NPC Three-Level Inverter Based on WOA-VMD

Open-Circuit Fault Diagnosis of NPC Three-Level Inverter Bas...

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Semiconductor and Electronic Technology (ISSET), International Symposium on

作者： Binbin Zhang Jiafan Wang Changhong Li Chaobo Chen School of Electronic Information Engineering Xi'an Technological University Xi'an China

ISBN: (数字)9798350390285

ISBN: (纸本)9798350390292

Due to the problems of poor feature extraction and low accuracy of fault diagnosis in traditional fault diagnosis methods for inverters, the whale optimization algorithm combined with variational modal decomposition is proposed as an open circuit fault diagnosis method for inverters. The VMD is performed on the output voltage to obtain multiple IMFs, and the energy entropy value of each mode is extracted, which is used as the input of SVM and verified in the simulation platform. The simulation results show that the proposed method can effectively extract fault features with high classification accuracy.

关键词： Fault diagnosis Support vector machines Accuracy Voltage Feature extraction Inverters Entropy whale optimization algorithms Vectors Circuit faults

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Low-Voltage Series Arc Fault Detection Method Based on VMD

Low-Voltage Series Arc Fault Detection Method Based on VMD

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International Conference on Electric Power Equipment - Switching Technology (ICEPE-ST)

作者： Qiaojia Tang Li'an Chen Wenxuan Li School of Electrical Engineering and Automation Xiamen University of Technology Xiamen China School of Electrical Engineering and Automation Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen University of Technology Xiamen China

ISBN: (数字)9798350388947

ISBN: (纸本)9798350388954

Aiming at the current situation that the traditional overcurrent protection device is difficult to detect the low-voltage series arc fault, a detection model for low-voltage series fault arc based on Variational Mode Decomposition (VMD) was proposed, utilizing a VMD method optimized by the whale optimization Algorithm (WOA). Firstly, WOA was used to optimize the VMD parameters of different loads to decompose the signal. Then, a multi-feature data set was constructed by combining Pearson correlation coefficient and random forest importance assessment. Finally, combined with support vector machine, a low-voltage series arc fault detection model was built. The results show that the accuracy of the proposed arc fault detection method is more than 99 %, and it is suitable for a variety of typical loads.

关键词： Support vector machines Low voltage Accuracy Fault detection Arc discharges Feature extraction Mathematical models whale optimization algorithms Random forests Load modeling

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Automatic Control of Microwave Heating System Based on PID and Evolutionary Computation

Automatic Control of Microwave Heating System Based on PID a...

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Emerging Smart Technologies and Applications (eSmarTA), International Conference on

作者： Mohaiyedin Idris Ahmad Zuhaily Anuar Zamani Muhammad Khusairi Osman Ahmad Asri Abd Samat Hasnain Abdullah Idris Mohd Mydin M. Abdul Kader Esa Apriaskar Mohd Fauzi Abu Hassan Centre for Electrical Engineering Studies Universiti Teknologi MARA Cawangan Pulau Pinang Pulau Pinang Malaysia Faculty of Electrical Engineering Studies Universiti Malaysia Perlis Arau Perlis Malaysia Department of Electrical Engineering Faculty of Engineering Universitas Negeri Semarang Semarang Indonesia Intelligent Automotive Systems Research Cluster Electrical Electronic and Automation Section Universiti Kuala Lumpur Kulim Kedah Malaysia

ISBN: (数字)9798350354133

ISBN: (纸本)9798350354140

The food and beverage industry has progressed in tandem with the evolution of communication technologies. Researchers are drawn to the developing stream of modern technology, particularly microwave heating technology, which offers good temperature control. In order to optimise system efficiency, it is important to adjust the controller parameters precisely. However, the system poses a bit of a challenge to control and stabilize the optimum temperature. This is due to the generated temperature is affected by several factors such as load variations, changes in material properties, and electromagnetic field distribution, making the conventional control process ineffective. This paper proposed the whale optimization algorithm (WOA) and particle swarm optimization (PSO) to determine the ideal tuning parameters for Proportional–Integral–Derivative (PID) controllers. An evaluation and comparison of the method's performance is conducted against genetic algorithm (GA). The results demonstrate that the proposed PID controller with WOA achieved superior performance compared to the GA and PSO. The WOA obtained the optimal values for the proportional gain (Kp), integral gain (Ki), and derivative gain (Kd) as 1000, 80, and 80, respectively, with the lowest integral absolute error (IAE) of 214.79 and fastest convergence rate compared to the GA and PSO.

关键词： Electromagnetic heating Temperature distribution Process control Microwave communication whale optimization algorithms Particle swarm optimization Tuning

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WOA based Optimal TIDF Controller for Time Delayed System

WOA based Optimal TIDF Controller for Time Delayed System

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IEEE Power India Conference

作者： Vineet Kumar Renu Sharma Department of Electrical and Electronics Engineering MITS Madanapalle Department of Electrical Engineering SOA University Odissa

ISBN: (数字)9798350367065

ISBN: (纸本)9798350367072

This manuscript presents a novel methodology to effectively control time-delayed systems. In this work, authors have proposed a whale optimization Algorithm (WOA) tuned Tilt Integral Derivative Controller with Derivative Filter (TIDF) specifically designed for regulating time-delayed systems characterized by 4th order dynamics. The efficacy of the proposed method has been thoroughly verified by comparing its transient response specifications with those of existing approaches documented in the literature. This comparative analysis demonstrates that the WOA-tuned TIDF controller offers superior performance in terms of transient response, highlighting its suitability for the intended application. Additionally, the robustness of the proposed algorithm has been rigorously tested under conditions involving variable time delays, which are a common and challenging aspect of real-world systems. The results of these tests indicate that the WOA-tuned TIDF controller not only achieves a better transient response but also maintains stability and robustness across different scenarios. Consequently, the proposed methodology provides a reliable and effective solution for controlling time-delayed systems, ensuring both enhanced performance and resilience in various industrial and technological applications.

关键词： Transient response Heuristic algorithms Delay effects Filtering algorithms Control systems whale optimization algorithms Robustness Stability analysis Transient analysis Testing

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Intelligent Baijiu Blending Model Based on Meta-Goal Programming

Intelligent Baijiu Blending Model Based on Meta-Goal Program...

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Asian Control Conference

作者： Ding Wang Zhiyao Zhao Jiabing Yu Xin Zhang School of Computer and Artificial Intelligence Beijing Technology and Business University Beijing China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

This paper proposes a new Baijiu blending optimization method, combining Meta-Goal Programming (MGP) and whale optimization Algorithm (WOA). The proposed method takes into account the complexity of Baijiu's composition and the significant influence of trace components on its flavor profile. To address this, a mathematical modeling framework that surpasses traditional linear and goal programming methods is introduced, which often neglects the nuances of liquor flavor in the pursuit of cost reduction. The study shows that the combination of MGP and WOA improves the accuracy of Baijiu blending by systematically balancing compound concentrations to achieve desired flavor profiles. The experimental results demonstrate the advantages of this approach over traditional methods, partic-ularly in reducing deviations in compound concentrations and better meeting the qualitative goals set by the blenders. This study contributes to the scientific and precise method for Baijiu blending and the intelligent automation of Baijiu production. It lays a theoretical foundation for automated blending.

关键词： Intelligent automation Process control optimization methods Production Programming Mathematical models whale optimization algorithms

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PMSM Inter-Turn Short Circuit Fault Diagnosis Based on WOA-VMD

PMSM Inter-Turn Short Circuit Fault Diagnosis Based on WOA-V...

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Electrical Energy Conversion Systems and Control（IEECSC), IEEE International Conference on

作者： Jinyong Zhang Dingguo Shao Yitong Wei School of Electromechanical Engineering and Automation Shanghai University Shanghai China

ISBN: (数字)9798350389357

ISBN: (纸本)9798350389364

The safety and reliability of the motor is the basis and guarantee of the stable operation of the motor, so it is of great significance to be able to quickly and accurately diagnose the early motor faults to avoid unnecessary downtime or even more serious consequences due to motor faults. In this paper, we diagnose the early inter-turn short-circuit fault of the motor from the two aspects of motor fault modeling and early fault signal processing. As the early fault signal of the motor is not obvious, we use the Variable Modal Decomposition (VMD) algorithm to process the fault signal, and use the whale optimization algorithm (WOA) to optimize the parameters of the VMD algorithm. After simulation verification results show that the best combination of parameters obtained after the whale optimization algorithm is applied to the VMD algorithm to decompose the fault signal, which can effectively eliminate the noise interference to extract the fault characteristics.

关键词： Fault diagnosis Torque Velocity control Signal processing algorithms Motors Feature extraction whale optimization algorithms Safety Circuit faults Spectrogram

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Rolling Bearing Fault Diagnosis Based on Improved Multi-Scale Dispersion Entropy and Deep Extreme Learning Machine

Rolling Bearing Fault Diagnosis Based on Improved Multi-Scal...

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Automation in Manufacturing, Transportation and Logistics (ICaMaL), International Conference on

作者： Xu Chengjun Shu Sheng Wang Kun School of Transportation and Logistics Engineering Wuhan University of Technology Wuhan China Hainan Institute Wuhan University of Technology Wuhan China

ISBN: (数字)9798350378658

ISBN: (纸本)9798350378665

In response to the challenges associated with the incomplete extraction of fault features in rolling bearings and the resultant low recognition accuracy, this study proposes a fault diagnosis model that integrates local mean decomposition, enhanced multi-scale dispersion entropy, and a whale optimization algorithm to refine the deep extreme learning machine. Initially, the model employs local mean decomposition to break down the original vibration signal into several product function components. Subsequently, spurious product function signal components are eliminated through the application of mutual information and kurtosis screening criteria. The multi-scale fault features of the signal components are then extracted using the improved multi-scale dispersion entropy method. Finally, the extracted fault features are fed into a deep extreme learning machine, which has been optimized via the whale optimization algorithm, for training and testing purposes. Experimental investigations conducted on rolling bearings, alongside comparisons with a control group, demonstrate that the proposed model effectively extracts fault features and enhances the identification accuracy of rolling bearing faults.

关键词： Fault diagnosis Accuracy Extreme learning machines Rolling bearings Feature extraction Entropy whale optimization algorithms Dispersion Monitoring Mutual information

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