An On-Line PSO-Based Fuzzy Logic Tuning Approach

Abstract

Modern power systems require increased intelligence and flexibility in control and optimization. This issue is becoming more significant today due to the increasing size, changing structure, emerging renewable energy sources and Microgrids, environmental constraints, and the complexity of power systems. The control units and their associated tuning methods for modern power systems surely must be intelligent (based in flexible intelligent algorithms). This chapter addresses a new intelligent approach using a combination of fuzzy logic and Particle Swarm Optimization (PSO) techniques for optimal tuning of the existing most popular Proportional-Integral (PI) or Proportional-Integral-Derivative (PID) controllers in the power electric industry. In the proposed control strategy, the PI (PID) parameters are automatically tuned using fuzzy rules, according to the on-line measurements. In order to obtain an optimal performance, the PSO technique is used to determine the membership functions’ parameters. The proposed optimal tuning scheme offers many benefits for a new power system with numerous distributed generators and Renewable Energy Sources (RESs).In the developed tuning algorithm, the physical and engineering aspects have been fully considered. To demonstrate the effectiveness of the proposed control scheme, secondary frequency control problem in an islanded Microgrid (MG) system is considered a case study. The main source of power for a Microgrid is small generating units of tens of kW that are placed at the customer site. Simulation studies are performed to illustrate the capability of the proposed intelligent/optimal control approach.

Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids

“…The solution to this problem is to use another CI algorithm to tune the FLC parameters according to the system dynamics. For example, authors used GA in [75], online PSO [76], BCO [77] and chaotic PSO [78] to tune FLC in MG controls. Furthermore, the Adaptive neuro-fuzzy inference system is also one of the most efficient solutions to the stated problems, as discussed in the subsequent section [79].…”

Frequency control of the islanded microgrid based on optimised model predictive control by PSO

“…From controllers based on meta-heuristic algorithms such as genetic algorithm (GA) [22], particle swarm optimisation (PSO) algorithm [23], social spider optimization (SSO) algorithm [24], biogeography-based optimisation (BBO) algorithm for PID setting [25], quasi-oppositional harmony search algorithm (QOHSA) [26] has been used to control the load frequency in the microgrid. In references [27,28], a fuzzy con-troller whose coefficients have been determined using the PSO algorithm and type-two fuzzy logic has been used to control the load frequency. In references [29,30], they have used the secondary frequency control on the converter of renewable energy sources to control the load frequency.…”

Customer Profiling in Complex Analytical Environments Using Swarm Intelligence Algorithms

“…In case of predictive modeling, particle swarm optimization (PSO) algorithm is mostly used as an algorithm for learning optimization in neural networks (Clerck, 2013;Kurbatsky, 2014, Russel, 2001. Particle swarm optimization algorithm are used in many areas, mostly for solving problems in domain of optimization Anagnostopoulos, 2012;Babahajyani 2014 ;El-Shorbagy, 2013 ;Cheng, 2015).…”