scholarly journals Multi-Objective Optimization for Determining Trade-Off between Output Power and Power Fluctuations in Wind Farm System

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4242 ◽  
Author(s):  
Van-Hai Bui ◽  
Akhtar Hussain ◽  
Woon-Gyu Lee ◽  
Hak-Man Kim
Keyword(s):  
Output Power ◽  
Wind Farm ◽  
Optimal Operation ◽  
Multi Objective Optimization ◽  
Power Fluctuation ◽  
Trade Off ◽  
Multi Objective ◽  
Optimal Output ◽  
Power Fluctuations ◽  
Optimal Set

In this paper, a multi-objective optimization method is proposed to determine trade-off between conflicting operation objectives of wind farm (WF) systems, i.e., maximizing the output power and minimizing the output power fluctuation of the WF system. A detailed analysis of the effects of different objective’s weight values and battery size on the operation of the WF system is also carried out. This helps the WF operator to decide on an optimal operation point for the whole system to increase its profit and improve output power quality. In order to find out the optimal solution, a two-stage optimization is also developed to determine the optimal output power of the entire system as well as the optimal set-points of wind turbine generators (WTGs). In stage 1, the WF operator performs multi-objective optimization to determine the optimal output power of the WF system based on the relevant information from WTGs’ and battery’s controllers. In stage 2, the WF operator performs optimization to determine the optimal set-points of WTGs for minimizing the power deviation and fulfilling the required output power from the previous stage. The minimization of the power deviation for the set-points of WTGs helps the output power of WTGs much smoother and therefore avoids unnecessary internal power fluctuations. Finally, different case studies are also analyzed to show the effectiveness of the proposed method.

Characteristics of Wind Power Fluctuations

2014 ◽  
Vol 926-930 ◽  
pp. 919-922
Author(s):  
Jia Yu Xu
Keyword(s):  
Wind Turbine ◽  
Wind Power ◽  
Output Power ◽  
Wind Turbines ◽  
Wind Farm ◽  
Power Fluctuation ◽  
Distribution Fitting ◽  
Power Fluctuations ◽  
Wake Effects ◽  
Northeast Wind

Wind power is also known as junk. This is because wind power fluctuations affect the security and stability operation. Wind power wind turbines created is mainly concerned with the speed of wind. Because of the wind direction uncertain, intermittent, and wake effects between each unit wind farm, wind turbines cannot make that kind of power according to the demand for energy as conventional generators. Due to the lack of experimental data, assess the volatility of wind power is still a lack of effective methods. This article studies the sample in a northeast wind farm power, and based on a sliding differential algorithm, distribution fitting and quantitative calculations describe the characteristics of wind power fluctuations. This article studies the sample in a northeast wind farm power, and based on a sliding difference algorithm, through the analysis showed that wind power fluctuations obey t location scale distribution. And it is affected by factors such as spatial and temporal distribution, there is a big difference between the output power fluctuation characteristics of wind farm output power and single wind turbine. This is due to the wind turbine suffered varying differences, and wake effects between field units, making the distribution of frequent power fluctuations; relative to a single unit, the fluctuation of the whole wind farm is more gentle, that is to say with the spatial distribution increased scale, wind power fluctuations presents certain "gentle effect."

A Review of Multi-Objective Evolutionary Based Fuzzy Classifiers

2020 ◽  
Vol 13 (1) ◽  
pp. 77-85
Author(s):  
Praveen Kumar Dwivedi ◽  
Surya Prakash Tripathi
Keyword(s):  
Fuzzy Systems ◽  
Evolutionary Process ◽  
Fuzzy Rule ◽  
Fuzzy Classification ◽  
Multi Objective Optimization ◽  
Fuzzy Classifier ◽  
Rule Based ◽  
Trade Off ◽  
Multi Objective ◽  
Rule Based Systems

Background: Fuzzy systems are employed in several fields like data processing, regression, pattern recognition, classification and management as a result of their characteristic of handling uncertainty and explaining the feature of the advanced system while not involving a particular mathematical model. Fuzzy rule-based systems (FRBS) or fuzzy rule-based classifiers (mainly designed for classification purpose) are primarily the fuzzy systems that consist of a group of fuzzy logical rules and these FRBS are unit annexes of ancient rule-based systems, containing the "If-then" rules. During the design of any fuzzy systems, there are two main objectives, interpretability and accuracy, which are conflicting with each another, i.e., improvement in any of those two options causes the decrement in another. This condition is termed as Interpretability –Accuracy Trade-off. To handle this condition, Multi-Objective Evolutionary Algorithms (MOEA) are often applied within the design of fuzzy systems. This paper reviews the approaches to the problem of developing fuzzy systems victimization evolutionary process Multi-Objective Optimization (EMO) algorithms considering ‘Interpretability-Accuracy Trade-off, current research trends and improvement in the design of fuzzy classifier using MOEA in the future scope of authors. Methods: The state-of-the-art review has been conducted for various fuzzy classifier designs, and their optimization is reviewed in terms of multi-objective. Results: This article reviews the different Multi-Objective Optimization (EMO) algorithms in the context of Interpretability -Accuracy tradeoff during fuzzy classification. Conclusion: The evolutionary multi-objective algorithms are being deployed in the development of fuzzy systems. Improvement in the design using these algorithms include issues like higher spatiality, exponentially inhabited solution, I-A tradeoff, interpretability quantification, and describing the ability of the system of the fuzzy domain, etc. The focus of the authors in future is to find out the best evolutionary algorithm of multi-objective nature with efficiency and robustness, which will be applicable for developing the optimized fuzzy system with more accuracy and higher interpretability. More concentration will be on the creation of new metrics or parameters for the measurement of interpretability of fuzzy systems and new processes or methods of EMO for handling I-A tradeoff.

Multi-Objective Optimization Analysis of Motor Cooling System in Articulated Dump Truck

2011 ◽  
Vol 383-390 ◽  
pp. 4715-4720
Author(s):  
Yan Zhang ◽  
Yan Hua Shen ◽  
Wen Ming Zhang
Keyword(s):  
Field Distribution ◽  
Cooling System ◽  
Dump Truck ◽  
Water Cooling ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Temperature And Pressure ◽  
Water Cooling System ◽  
Optimal Set

In order to ensure the reliable and safe operation of the electric driving motor of the articulated dump truck, water cooling system is installed for each motor. For the best performance of the water cooling system, not only the heat transfer should be enhanced to maintain the motor in relatively low temperature, but also the pressure drop in the water cooling system should be reduced to save energy by reducing the power consumption of the pump. In this paper, the numerical simulation of the cooling progress is completed and the temperature and pressure field distribution are obtained. The multi-objective optimization model is established which involves the cooling system structure, temperature field distribution and pressure field distribution. To improve the computational efficiency, the surrogate model of the simulation about the cooling process is established based on the Response Surface Methodology (RSM). After the multi-objective optimization, the Pareto optimal set is obtained. The proper design point, which could make the average temperature and pressure drop of the cooling system relative desirable, is chosen from the Pareto optimal set.

scholarly journals Optimal Operation of Interprovincial Hydropower System Including Xiluodu and Local Plants in Multiple Recipient Regions

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 144 ◽  
Author(s):  
Jianjian Shen ◽  
Xiufei Zhang ◽  
Jian Wang ◽  
Rui Cao ◽  
Sen Wang ◽  
...  
Keyword(s):  
Power Grid ◽  
Optimal Operation ◽  
Dry Season ◽  
Power Grids ◽  
Mixed Integer ◽  
Multi Objective Optimization ◽  
Hydropower Generation ◽  
Multi Objective ◽  
Hydropower System ◽  
Local Plants

This paper focuses on the monthly operations of an interprovincial hydropower system (IHS) connected by ultrahigh voltage direct current lines. The IHS consists of the Xiluodu Hydropower Project, which ranks second in China, and local plants in multiple recipient regions. It simultaneously provides electricity for Zhejiang and Guangdong provinces and thus meets their complex operation requirements. This paper develops a multi-objective optimization model of maximizing the minimum of total hydropower generation for each provincial power grid while considering network security constraints, electricity contracts, and plant constraints. The purpose is to enhance the minimum power in dry season by using the differences in hydrology and regulating storage of multiple rivers. The TOPSIS method is utilized to handle this multi-objective optimization, where the complex minimax objective function is transformed into a group of easily solved linear formulations. Nonlinearities of the hydropower system are approximatively described as polynomial formulations. The model was used to solve the problem using mixed integer nonlinear programming that is based on the branch-and-bound technique. The proposed method was applied to the monthly generation scheduling of the IHS. Compared to the conventional method, both the total electricity for Guangdong Power Grid and Zhejiang Power Grid during dry season increased by 6% and 4%, respectively. The minimum monthly power also showed a significant increase of 40% and 31%. It was demonstrated that the hydrological differences between Xiluodu Plant and local hydropower plants in receiving power grids can be fully used to improve monthly hydropower generation.

scholarly journals Multi-Objective Optimization of Impedance Parameters in a Prosthesis Test Robot

2015 ◽  
Author(s):  
Poya Khalaf ◽  
Hanz Richter ◽  
Antonie J. van den Bogert ◽  
Dan Simon
Keyword(s):  
Control System ◽  
Motion Tracking ◽  
Optimization Problem ◽  
Peak Load ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Pareto Optimal Set ◽  
Rms Error ◽  
Optimal Set

We design a control system for a prosthesis test robot that was previously developed for transfemoral prosthesis design and test. The robot’s control system aims to mimic human walking in the sagittal plane. It has been seen in previous work that trajectory control alone fails to produce human-like forces. Therefore, we utilize an impedance controller to achieve reasonable tracking of motion and force simultaneously. However, these objectives conflict. Impedance control design can therefore be viewed as a multi-objective optimization problem. We use an evolutionary multi-objective strategy called Multi-Objective Invasive Weed Optimization (MOIWO) to design the impedance controller. The multi-objective optimization problem admits a set of equally valid alternative solutions known as the Pareto optimal set. We use a pseudo weight vector approach to select a single solution from the Pareto optimal set. Simulation results show that a solution that is selected for pure motion tracking performs very accurate motion tracking (RMS error of 0.06 cm) but fails to produce the desired forces (RMS error of 70% peak load). On the other hand, a solution that is selected for pure force tracking successfully tracks the desired force (RMS error of 12.7% peak load) at the expense of motion trajectory errors (RMS error of 4.5 cm).

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