A Novel Power Quality Monitor Placement Method Using Adaptive Quantum-Inspired Binary Particle Swarm Optimization
نویسندگان
چکیده
منابع مشابه
A Novel Power Quality Monitor Placement Method Using Adaptive Quantum- Inspired Binary Particle Swarm Optimization
This paper presents a novel method for solving optimal power quality monitor placement problem in monitoring voltage sags in power systems using the adaptive quantum-inspired particle swarm optimization (PSO). The optimization considers multi objective functions and handles observability constraint determined by the concept of the topological monitor reach area. The overall objective function c...
متن کاملPower Quality Monitor Placement Using a Tri-level Approach
Finding minimum number of connecting lines is as important as locating power quality monitors (PQMs) for full observability of power system. Therefore, a PQM placement method should determine both optimum buses and lines, since utilities can make better decisions for monitoring of power system with this information. This paper attempted to propose a new method to locate the PQMs at various unob...
متن کاملApplication of Quantum-Inspired Binary Gravitational Search Algorithm for Optimal Power Quality Monitor Placement
This paper presents a combinational quantum-inspired binary gravitational search algorithm (QBGSA) for solving the optimal power quality monitor (PQM) placement problem in power systems for voltage sag assessment. In this algorithm, the standard binary gravitational search algorithm is modified by applying the concept and principles of quantum behaviour as to improve the search capability with ...
متن کاملRELIABILITY-BASED DESIGN OPTIMIZATION OF COMPLEX FUNCTIONS USING SELF-ADAPTIVE PARTICLE SWARM OPTIMIZATION METHOD
A Reliability-Based Design Optimization (RBDO) framework is presented that accounts for stochastic variations in structural parameters and operating conditions. The reliability index calculation is itself an iterative process, potentially employing an optimization technique to find the shortest distance from the origin to the limit-state boundary in a standard normal space. Monte Carlo simulati...
متن کاملAdaptive feature selection using v-shaped binary particle swarm optimization
Feature selection is an important preprocessing method in machine learning and data mining. This process can be used not only to reduce the amount of data to be analyzed but also to build models with stronger interpretability based on fewer features. Traditional feature selection methods evaluate the dependency and redundancy of features separately, which leads to a lack of measurement of their...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Renewable Energy and Power Quality Journal
سال: 2012
ISSN: 2172-038X,2172-038X
DOI: 10.24084/repqj10.212