scholarly journals Multi Objective for PMU Placement in Compressed Distribution Network Considering Cost and Accuracy of State Estimation

2019 ◽  
Vol 9 (7) ◽  
pp. 1515 ◽  
Author(s):  
Kong ◽  
Wang ◽  
Yuan ◽  
Yu
Keyword(s):  
State Estimation ◽  
Estimation Error ◽  
Distribution Network ◽  
Measurement Data ◽  
Optimal Solution ◽  
Pareto Set ◽  
Measurement Unit ◽  
Nsga Ii ◽  
Multi Objective ◽  
Pmu Placement

A phasor measurement unit (PMU) can provide phasor measurements to the distribution network to improve observability. Based on pre-configuration and existing measurements, a network compression method is proposed to reduce PMU candidate locations. Taking the minimum number of PMUs and the lowest state estimation error as the objective functions and taking full observability of distribution network as the constraint, a multi objective model of optimal PMU placement (OPP) is proposed. A hybrid state estimator based on supervisory control and data acquisition (SCADA) and PMU measurements is proposed. To reduce the number of PMUs required for full observability, SCADA measurement data are also considered into the constraint by update and equivalent. In addition, a non-dominated sorting genetic algorithm-II (NSGA-II) is applied to solve the model to get the Pareto set. Finally, the optimal solution is selected from the Pareto set by the technique for order preference by similarity to ideal solution (TOPSIS). The effectiveness of the proposed method is verified by IEEE standard bus systems.

Multi-Objective Trajectory Optimization of Free-Floating Space Manipulator Using NSGA-II

2015 ◽  
Vol 713-715 ◽  
pp. 800-804 ◽  
Author(s):  
Gang Chen ◽  
Cong Wei ◽  
Qing Xuan Jia ◽  
Han Xu Sun ◽  
Bo Yang Yu
Keyword(s):  
Trajectory Optimization ◽  
Interpolation Method ◽  
Optimal Solution ◽  
Optimization Method ◽  
Joint Space ◽  
Motion Path ◽  
Objective Functions ◽  
Nsga Ii ◽  
Space Manipulator ◽  
Multi Objective

In this paper, a kind of multi-objective trajectory optimization method based on non-dominated sorting genetic algorithm II (NSGA-II) is proposed for free-floating space manipulator. The aim is to optimize the motion path of the space manipulator with joint angle constraints and joint velocity constraints. Firstly, the kinematics and dynamics model are built. Secondly, the 3-5-3 piecewise polynomial is selected as interpolation method for trajectory planning of joint space. Thirdly, three objective functions are established to simultaneously minimize execution time, energy consumption and jerk of the joints. At last, the objective functions are combined with the NSGA-II algorithm to get the Pareto optimal solution set. The effectiveness of the mentioned method is verified by simulations.

scholarly journals Multi-objective optimization of solar thermal photovoltaic hybrid power generation system based on NSGA-II algorithm

2021 ◽  
Vol 336 ◽  
pp. 02022
Author(s):  
Liang Meng ◽  
Wen Zhou ◽  
Yang Li ◽  
Zhibin Liu ◽  
Yajing Liu
Keyword(s):  
Power Generation ◽  
Optimization Problems ◽  
Optimal Solution ◽  
Solar Thermal ◽  
Multi Objective Optimization ◽  
Generation System ◽  
Nsga Ii ◽  
Multi Objective ◽  
Hybrid Power ◽  
Power Generation System

In this paper, NSGA-Ⅱ is used to realize the dual-objective optimization and three-objective optimization of the solar-thermal photovoltaic hybrid power generation system; Compared with the optimal solution set of three-objective optimization, optimization based on technical and economic evaluation indicators belongs to the category of multi-objective optimization. It can be considered that NSGA-Ⅱ is very suitable for multi-objective optimization of solar-thermal photovoltaic hybrid power generation system and other similar multi-objective optimization problems.

Comparison of Multi-Objective Evolutionary Algorithms to Prioritize Regression Test Cases

2021 ◽  
Vol 9 (12) ◽  
pp. 1445-1454
Keyword(s):  
Fault Detection ◽  
Evolutionary Algorithms ◽  
Fitness Function ◽  
Optimal Solution ◽  
Test Suite ◽  
Test Case ◽  
Test Cases ◽  
Nsga Ii ◽  
Multi Objective ◽  
Regression Test

Regression testing is one of the most critical testing activities among software product verification activities. Nevertheless, resources and time constraints could inhibit the execution of a full regression test suite, hence leaving us in confusion on what test cases to run to preserve the high quality of software products. Different techniques can be applied to prioritize test cases in resource-constrained environments, such as manual selection, automated selection, or hybrid approaches. Different Multi-Objective Evolutionary Algorithms (MOEAs) have been used in this domain to find an optimal solution to minimize the cost of executing a regression test suite while obtaining maximum fault detection coverage as if the entire test suite was executed. MOEAs achieve this by selecting set of test cases and determining the order of their execution. In this paper, three Multi Objective Evolutionary Algorithms, namely, NSGA-II, IBEA and MoCell are used to solve test case prioritization problems using the fault detection rate and branch coverage of each test case. The paper intends to find out what’s the most effective algorithm to be used in test cases prioritization problems, and which algorithm is the most efficient one, and finally we examined if changing the fitness function would impose a change in results. Our experiment revealed that NSGA-II is the most effective and efficient MOEA; moreover, we found that changing the fitness function caused a significant reduction in evolution time, although it did not affect the coverage metric.

Research on multi-objective optimization of switched flux motor based on improved NSGA-II algorithm

2019 ◽  
Vol 233 (6) ◽  
pp. 1268-1279 ◽  
Author(s):  
Liying Jin ◽  
Shengdun Zhao ◽  
Wei Du ◽  
Xuesong Yang ◽  
Wensheng Wang ◽  
...  
Keyword(s):  
Permanent Magnets ◽  
Optimal Solution ◽  
Torque Ripple ◽  
Maximum Output ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Tooth Width ◽  
The Individual ◽  
Rotor Tooth

In order to optimize the local search efficiency of multi-objective parameters of flux switching permanent motor based on traditional NSGA-II algorithm, an improved NSGA-II (iNSGA-II) algorithm is proposed, with an anti-redundant mutation operator and forward comparison operation designed for quick identification of non-dominated individuals. In the initial stage of the iNSGA-II algorithm, half of the individual populations were randomly generated, while the other half was generated according to feature distribution information. Taking the flux switching permanent motor stator/rotor gap, permanent magnets width, stator tooth width, rotor tooth width and other parameters as optimization variables, the flux switching permanent motor maximum output shaft torque and minimum torque ripple are taken as optimization objectives, thus a multi-objective optimization model is established. Real number coding was adopted for obtaining the Pareto optimal solution of flux switching permanent motor structure parameters. The results showed that the iNSGA-II algorithm is better than the traditional NSGA-II on convergence. A 1.8L TOYOTA PRIUS model was selected as the prototype vehicle. By using the optimized parameters, a joint optimization simulation model was established by calling ADVISOR’s back-office function. The simulation results showed that the entire vehicle’s 100-km acceleration time is under 8 s and the battery’s SOC value maintains at 0.5–0.7 in the entire cycle, implying that the iNSGA-II algorithm optimizes the flux switching permanent motor design and is suitable for the initial design and optimizing calculation of the flux switching permanent motor.

Optimization Scheme of Load Transferring Path for Distribution Network

2014 ◽  
Vol 494-495 ◽  
pp. 1656-1659
Author(s):  
Xin Lei Bai ◽  
Li Ping Guo ◽  
Liang Ze Liu
Keyword(s):  
Power Distribution ◽  
Optimization Problems ◽  
Fuzzy Theory ◽  
Distribution Network ◽  
Optimal Solution ◽  
Optimization Scheme ◽  
Entropy Weight ◽  
Power Distribution Network ◽  
Nsga Ii ◽  
Elitist Strategy

For the power distribution network load transferring path optimization problem, this paper focuses on optimization scheme of load transferring path based on the fast non-dominated storing genetic algorithm (NSGA-II) with elitist strategy, this proposed method can avoid the selection of weight and the preference of the solution, and the solution sets can highlight the essence of optimization problems, then the fuzzy theory and the entropy weight are employed to extract the comprehensive optimal solution. IEEE33 node system simulation results verify the effectiveness of the model and algorithm.

scholarly journals ESTABLISHMENT AND PLACEMENT OF A MULTI-PURPOSE PHASOR MEASUREMENT UNIT TO IMPROVE PARALLEL STATE ESTIMATION IN DISTRIBUTION NETWORKS

2021 ◽  
Vol 3 (1) ◽  
pp. 1-17
Author(s):  
Zeyad Khashroum ◽  
Ali Dehghan Chaharabi ◽  
Lorena Palmero ◽  
Keiichiro Yasukawa
Keyword(s):  
State Estimation ◽  
Distribution Systems ◽  
Distribution Network ◽  
Distribution Networks ◽  
Phasor Measurement Unit ◽  
Measurement Unit ◽  
Optimal Method ◽  
Control Approach ◽  
Phasor Measurement ◽  
Neural Fuzzy

Today, microgrids in distribution networks are in dire need of improvement to cope with economic challenges, human losses, and equipment placement issues. Today, there is the issue of scattered resources in distribution systems, which has created many problems in the areas of environment, economy, and human and animal losses. The most important challenge in this section is the existence of voltage and frequency fluctuations during the occurrence of possible events such as severe load changes or errors in distribution networks. Having such a big problem can call a distribution network into question and destroy it. Therefore, it is necessary to provide an optimal method that can meet and cover these challenges. For this purpose, the present research deals with the problem of establishing and placing a multifunctional phasor measurement unit to improve the parallel state estimation in distribution networks, which offers a control approach. This approach determines the time of occurrence of internal and external disturbances after using the phasor unit. The approach of this research is to use a neural-fuzzy method because there is uncertainty in the distribution network due to the mentioned challenges, and training in the system is needed to accurately deploy and place possible errors. Do not occur. When setting up and placing the phasor measuring unit, the most important issue is the proper distribution of the load in the distribution network. The simulation results in the MATLAB / Simulink environment show the improvement of the results according to the proposed approach.Keywords: Distribution Network, Neural-Fuzzy Network, Optimal Load Distribution, Parallel State Estimation, Phasor Measurement Unit.

scholarly journals Copula-Based Research on the Multi-Objective Competition Mechanism in Cascade Reservoirs Optimal Operation

Water ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 995 ◽  
Author(s):  
Menglong Zhao ◽  
Shengzhi Huang ◽  
Qiang Huang ◽  
Hao Wang ◽  
Guoyong Leng ◽  
...  
Keyword(s):  
Power Generation ◽  
Irrigation Water ◽  
Optimal Solution ◽  
Water Shortage ◽  
Pareto Set ◽  
The Other ◽  
Competition Mechanism ◽  
Multi Objective ◽  
Cascade Hydropower Stations ◽  
Hydropower Stations

Water resources systems are often characterized by multiple objectives. Typically, there is no single optimal solution which can simultaneously satisfy all the objectives but rather a set of technologically efficient non-inferior or Pareto optimal solutions exists. Another point regarding multi-objective optimization is that interdependence and contradictions are common among one or more objectives. Therefore, understanding the competition mechanism of the multiple objectives plays a significant role in achieving an optimal solution. This study examines cascade reservoirs in the Heihe River Basin of China, with a focus on exploring the multi-objective competition mechanism among irrigation water shortage, ecological water shortage and the power generation of cascade hydropower stations. Our results can be summarized as follows: (1) the three-dimensional and two-dimensional spatial distributions of a Pareto set reveal that these three objectives, that is, irrigation water shortage, ecological water shortage and power generation of cascade hydropower stations cannot reach the theoretical optimal solution at the same time, implying the existence of mutual restrictions; (2) to avoid subjectivity in choosing limited representative solutions from the Pareto set, the long series of non-inferior solutions are adopted to study the competition mechanism. The premise of sufficient optimization suggests a macro-rule of ‘one falls and another rises,’ that is, when one objective value is inferior, the other two objectives show stronger and superior correlation; (3) the joint copula function of two variables is firstly employed to explore the multi-objective competition mechanism in this study. It is found that the competition between power generation and the other objectives is minimal. Furthermore, the recommended annual average water shortage are 1492 × 104 m3 for irrigation and 4951 × 104 m3 for ecological, respectively. This study is expected to provide a foundation for selective preference of a Pareto set and insights for other multi-objective research.

Sign in / Sign up

Export Citation Format

Share Document