Design of Intelligent PID Controller for AVR System Using an Adaptive Neuro Fuzzy Inference System

This paper study the model reduction procedures used for the reduction of large-scale dynamic models into a smaller one through some sort of differential and algebraic equations. A confirmed relevance between these two models exists, and it shows same characteristics under study. These reduction procedures are generally utilized for mitigating computational complexity, facilitating system analysis, and thence reducing time and costs. This paper comes out with a study showing the impact of the consolidation between the Bacterial-Foraging (BF) and Modified particle swarm optimization (MPSO) for the reduced order model (ROM). The proposed hybrid algorithm (BF-MPSO) is comprehensively compared with the BF and MPSO algorithms; a comparison is also made with selected existing techniques.

Section: Results and Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hybrid bacterial foraging and modified particle swarm optimization for model order reduction

Abdullah

2019

“…Also, some paper interests to forecast by focus in multi-area forecasts [44] and on the influence of weather factors [32]. While, some researches use AI for other work in power sytem [45][46].…”

Section: Introductionmentioning

confidence: 99%

Electricity Peak Load Demand using De-noising Wavelet Transform integrated with Neural Network Methods

Bunnoon

2016

One of most important elements in electric power system planning is load forecasts. So, in this paper proposes the load demand forecasts using de-noising wavelet transform (DNWT) integrated with neural network (NN) methods. This research, the case study uses peak load demand of Thailand (Electricity Generating Authority of Thailand: EGAT). The data of demand will be analyzed with many influencing variables for selecting and classifying factors. In the research, the de-noising wavelet transform uses for decomposing the peak load signal into 2 components these are detail and trend components. The forecasting method using the neural network algorithm is used. The work results are shown a good performance of the model proposed. The result may be taken to the one of decision in the power systems operation.

“…Traditional tuning methods such as Ziegler and Nichols are available but do not give optimum performance [1]. AI techniques such as fuzzy logic , neural networks neural-fuzzy logic have been widely used to proper tuning of PID controller parameters [2]- [4]. Tuning of PID controller parameters in off line made easy with the advent of heuristic optimization techniques [5]- [7].…”

Section: Introductionmentioning

confidence: 99%

Robustness Study of Fractional Order PID Controller Optimized by Particle Swarm Optimization in AVR System

Raju¹,

Reddy²

2016

In this paper a novel design method for determining fractional order PID (PI λ D µ ) controller parameters of an AVR system using particle swarm optimization algorithm is presented. This paper presents how to employ the particle swarm optimization to seek efficiently the optimal parameters of PI [8]. These techniques have very high probability to achieve global optimum solution.Since last decade the fractional order PID controllers are widely accepted in place of integer order PID controller [9], [10]. It has been proved that fractional order PID controller has superior performance compared to integer order PID controller [9]. The problem with fractional order PID controller is that it has five parameters need to be tuned,where as integer order PID controller has three parameters need to be tuned. This makes one step tuning of fractional order PID controller difficult compared to integer order PID controller. In this paper particle swarm optimization is used to tune the PI