Determination of voltage stability boundary values in electrical power systems by using the Chaotic Particle Swarm Optimization algorithm

Sun

International Journal of Distributed Sensor Networks

et al. 2021

In this article, we propose a new particle swarm optimization–based satellite selection algorithm for BeiDou Navigation Satellite System/Global Positioning System receiver, which aims to reduce the computational complexity of receivers under the multi-constellation Global Navigation Satellite System. The influences of the key parameters of the algorithm—such as the inertia weighting factor, acceleration coefficient, and population size—on the performance of the particle swarm optimization satellite selection algorithm are discussed herein. In addition, the algorithm is improved using the adaptive simulated annealing particle swarm optimization (ASAPSO) approach to prevent converging to a local minimum. The new approach takes advantage of the adaptive adjustment of the evolutionary parameters and particle velocity; thus, it improves the ability of the approach to escape local extrema. The theoretical derivations are discussed. The experiments are validated using 3-h real Global Navigation Satellite System observation data. The results show that in terms of the accuracy of the geometric dilution of precision error of the algorithm, the ASAPSO satellite selection algorithm is about 86% smaller than the greedy satellite selection algorithm, and about 80% is less than the geometric dilution of precision error of the particle swarm optimization satellite selection algorithm. In addition, the speed of selecting the minimum geometric dilution of precision value of satellites based on the ASAPSO algorithm is better than that of the traditional traversal algorithm and particle swarm optimization algorithm. Therefore, the proposed ASAPSO algorithm reduces the satellite selection time and improves the geometric dilution of precision using the selected satellite algorithm.

Section: Adaptive Inertia Weightmentioning

confidence: 99%

Section: Asapso Satellite Selection Algorithmmentioning

confidence: 99%

A satellite selection algorithm based on adaptive simulated annealing particle swarm optimization for the BeiDou Navigation Satellite System/Global Positioning System receiver

Sun

International Journal of Distributed Sensor Networks

et al. 2021

“…Step 6: According to Formula (5) and (6), update the membership matrix ij u and clustering center 1 ,…”

Section: Flow Of Pso-fcmentioning

confidence: 99%

“…Compared with FC algorithm, PSO can seek the global optimal solution at a short time; it allows selecting initial value randomly and it can obtain the optimal solution; therefore, it can greatly reduce the pre-phase work [6]. By using such characteristics as ergodicity and 129 randomness of PSO and integrating the optimization characteristic of particle swarm, this paper proposes a PSO-FC algorithm, namely the fuzzy clustering algorithmbased on particle swarm optimization with global optimization ability by integrating PSO algorithm and FC algorithm.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Clustering Image Segmentation Based on Particle Swarm Optimization

Zhao¹,

Zhang²

2015

TELKOMNIKA

“…In recent years a lot of work has been done in the area of voltage stability. Voltage stability analysis was done by the researchers using nodal analysis [16], modified differential evolution [17], coupled single port circuit [18], circuit theory [19], catastrophe theory [20], chaotic particle swarm optimization algorithm [21], vector regression model [22] etc. Along with the other methods phasor measurement unit based schemes are also been used in power system to monitor voltage stability [23], [24].…”

Section: Introductionmentioning

confidence: 99%

Gauss-Seidel Method based Voltage Security Analysis of Distribution System

Deb¹,

Chakraborty²

2018

IJECE

Complexity of modern power network and Large disturbance results voltage collapse. So, voltage security analysis is important in power system. Indicators are helpful in voltage stability analysis, as they give information about the state of the system. In this paper a new indicator namely Distribution System Stability Indicator (DSSI) has been formulated using the information of Phasor Measurement Unit (PMU).The proposed indicator (DSSI) is tested on standard IEEE 33 bus radial distribution system. The suggested indicator is also applicable to the equivalent two bus system of a multi-bus power system. The proposed indicator is calculated for different contingent conditions at different system load configurations. The result of DSSI is verified with the standard indicator (VSI) which proves applicability of the proposed indicator. The bus voltages of all the buses at base loading and at maximum loading are evaluated for base data and for tripping of most critical line.