Power system parameter matching and particle swarm optimization of battery underground loader

2021 ◽  
Vol 13 (10) ◽  
pp. 168781402110414
Author(s):  
Sheng-Xian Yi ◽  
Zhong-Jiong Yang ◽  
Li-Qiang Zhou ◽  
Xiao-Yong Liu

As part of the ongoing research into new energy technology, battery-powered underground loaders have emerged. However, there have been few studies on power system optimization and matching for these battery underground loaders to date. This paper, which takes a 3-m3 battery underground loader as its research object, determines the loader’s optimal operating point through study of the power response characteristics of the loader’s motor under various working conditions. The effects of different power batteries on the working conditions are analyzed, and the loader’s component parameters are matched. Additionally, an optimization model of the driving system of the battery underground loader is constructed. On the basis of the driving operation characteristics of the loader, the particle swarm optimization algorithm is proposed to optimize the operating conditions of the loader’s driving motor. The results show that the transmission ratio is reduced after optimization. The single-cycle energy consumption is reduced by approximately 1.98% and the number of cycles in the health status of the power battery’s state-of-charge increases by approximately 1.91%, which verifies the feasibility of use of the particle swarm algorithm in the loader optimization problem. This work can serve as a reference for related theoretical research on underground loaders.

2013 ◽  
Vol 760-762 ◽  
pp. 2119-2122
Author(s):  
Peng Zheng ◽  
Wen Tan Jiao

Economic dispatch (ED) is a typical power system operation optimization problem. But it has non-smooth cost functions with equality and inequality constraints that make the problem of finding the global optimum difficult. According to the characteristics of economic dispatch problem, a improved algorithm based on particle swarm optimization for solving economic dispatch strategy is researched in this paper. Multi-objective economic\environmental dispatch demands that the pollutant emission of power plants should reach minimum while the condition of least generation cost should be satisfied. According to this demand, this multi-objective problem is solved by improved particle swarm optimization (PSO) algorithm. Using particle position and speed of change in the familiar update, the multi-objective particle swarm algorithm based on test function of this algorithm, and the simulation results of simulation optimization. The effectiveness of the proposed algorithm is verified by Simulation.


2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sreedivya Kondattu Mony ◽  
Aruna Jeyanthy Peter ◽  
Devaraj Durairaj

Purpose The extensive increase in power demand has challenged the ability of power systems to deal with small-signal oscillations such as inter-area oscillations, which occur under unseen operating conditions. A wide-area measurement system with a phasor measurement unit (PMU) in the power network enhances the observability of the power grid under a wide range of operating conditions. This paper aims to propose a wide-area power system stabilizer (WAPSS) based on Gaussian quantum particle swarm optimization (GQPSO) using the wide-area signals from a PMU to handle the inter-area oscillations in the system with a higher degree of controllability. Design/methodology/approach In the design of the wide-area stabilizer, a dead band is introduced to mitigate the influence of ambient signal frequency fluctuations. The location and the input signal of the wide-area stabilizer are selected using the participation factor and controllability index calculations. An improved particle swarm optimization (PSO) technique, namely, GQPSO, is used to optimize the variables of the WAPSS to move the unstable inter-area modes to a stable region in the s-plane, thereby improving the overall system stability. Findings The proposed GQPSO-based WAPSS is compared with the PSO-based WAPSS, genetic algorithm-based WAPSS and power system stabilizer. Eigenvalue analysis, time-domain simulation responses and performance index analysis are used to assess performance. The various evaluation techniques show that GQPSO WAPSS has a consistently good performance, with a higher damping ratio, faster convergence with fewer oscillations and a minimum error in the performance index analysis, indicating a more stable system with effective oscillation damping. Originality/value This paper proposes an optimally tuned design for the WAPSS with a wide-area input along with a dead-band structure for damping the inter-area oscillations. Tie line power is used as the input to the WAPSS and optimal tuning of the WAPSS is performed using an improved PSO algorithm, known as Gaussian quantum PSO.


2014 ◽  
Vol 67 (3) ◽  
Author(s):  
J. Usman ◽  
M. W. Mustafa ◽  
G. Aliyu ◽  
B. U. Musa

This paper presents the coordination between the Automatic Voltage Regulator (AVR) and Power System Stabilizers (PSS) to increase the system damping over a wide range of systems’ operating conditions in order to improve the transient stability performance and steady state performance of the system. The coordinated design problem is formulated as an optimization problem which is solved using Iteration Particle Swarm Optimization (IPSO). The application of IPSO technique is proposed to optimize the parameters of the AVR and PSS to minimize the oscillations in power system during disturbances in a single machine infinite bus system (SMIB). The performance of the proposed IPSO technique is compared with the traditional PSO technique. The comparison considered is in terms of parameter accuracy and computational time. The results of the time domain simulations and eigenvalue analysis show that the proposed IPSO method provides a better optimization technique as compared to the traditional PSO technique.  


2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Hamza Yapıcı ◽  
Nurettin Çetinkaya

The power loss in electrical power systems is an important issue. Many techniques are used to reduce active power losses in a power system where the controlling of reactive power is one of the methods for decreasing the losses in any power system. In this paper, an improved particle swarm optimization algorithm using eagle strategy (ESPSO) is proposed for solving reactive power optimization problem to minimize the power losses. All simulations and numerical analysis have been performed on IEEE 30-bus power system, IEEE 118-bus power system, and a real power distribution subsystem. Moreover, the proposed method is tested on some benchmark functions. Results obtained in this study are compared with commonly used algorithms: particle swarm optimization (PSO) algorithm, genetic algorithm (GA), artificial bee colony (ABC) algorithm, firefly algorithm (FA), differential evolution (DE), and hybrid genetic algorithm with particle swarm optimization (hGAPSO). Results obtained in all simulations and analysis show that the proposed method is superior and more effective compared to the other methods.


2021 ◽  
pp. 15-27
Author(s):  
Mamdouh Kamaleldin AHMED ◽  
◽  
Mohamed Hassan OSMAN ◽  
Nikolay V. KOROVKIN ◽  
◽  
...  

The penetration of renewable distributed generations (RDGs) such as wind and solar energy into conventional power systems provides many technical and environmental benefits. These benefits include enhancing power system reliability, providing a clean solution to rapidly increasing load demands, reducing power losses, and improving the voltage profile. However, installing these distributed generation (DG) units can cause negative effects if their size and location are not properly determined. Therefore, the optimal location and size of these distributed generations may be obtained to avoid these negative effects. Several conventional and artificial algorithms have been used to find the location and size of RDGs in power systems. Particle swarm optimization (PSO) is one of the most important and widely used techniques. In this paper, a new variant of particle swarm algorithm with nonlinear time varying acceleration coefficients (PSO-NTVAC) is proposed to determine the optimal location and size of multiple DG units for meshed and radial networks. The main objective is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed methodology was tested using IEEE 14-bus, 30-bus, 57-bus, 33-bus, and 69- bus systems with the change in the number of DG units from 1 to 4 DG units. The result proves that the proposed PSO-NTVAC is more efficient to solve the optimal multiple DGs allocation with minimum power loss and a high convergence rate.


2012 ◽  
Vol 512-515 ◽  
pp. 719-722
Author(s):  
Yan Ren ◽  
Yuan Zheng ◽  
Chong Li ◽  
Bing Zhou ◽  
Zhi Hao Mao

The hybrid wind/PV/pumped-storage power system was the hybrid system which combined hybrid wind/PV system and pumped-storage power station. System optimization was very important in the system design process. Particle swarm optimization algorithm was a stochastic global optimization algorithm with good convergence and high accuracy, so it was used to optimize the hybrid system in this paper. First, the system reliability model was established. Second, the particle swarm optimization algorithm was used to optimize the system model in Nanjing. Finally, The results were analyzed and discussed. The optimization results showed that the optimal design method of wind/PV/pumped-storage system based on particle swarm optimization could take into account both the local optimization and the global optimization, which has good convergence high precision. The optimal system was that LPSP (loss of power supply probability) was zero.


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