Design of an Intelligent Controller Based on Wavelet Neural Network to Improve the Stability of Power Systems

This study proposes a new intelligent controller based on self-constructing wavelet neural network (SCWNN) to suppress the subsynchronous resonance (SSR) in power systems compensated by series capacitors. In power systems, the use of intelligent technique is inevitable, because of the uncertainties such as operating condition variations, different kinds of disturbances, etc. Accordingly, an intelligent control system that is an on-line trained SCWNN controller with adaptive learning rates is used to mitigate the SSR. The Lyapunov stability method is used to extract the adaptive learning rates. Hence, the convergence of the proposed controller can be guaranteed. At first, there is no wavelet in the structure of controller. They are automatically generated and begin to grow during the control process. In the whole design process, the identification of the controlled plant dynamic is not necessary according to the ability of the proposed controller. The effectiveness and robustness of the proposed controller are demonstrated by using the simulation results.

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Synthesizing a Power System Stabilizer Using the Fuzzy Logic and Neural Network Methods

Elektrichestvo ◽

10.24160/0013-5380-2021-10-22-30 ◽

2021 ◽

pp. 22-30

Author(s):

Kahramon R. ALLAEV ◽

◽

Tokhir F. MAKHMUDOV ◽

Keyword(s):

Neural Network ◽

Fuzzy Logic ◽

Power Systems ◽

Power System ◽

Fuzzy Logic Controller ◽

Power Transmission ◽

Low Frequency ◽

Synchronous Generator ◽

Electromechanical Oscillations ◽

The Stability

Power systems are large non-linear systems that are often subject to low frequency electromechanical oscillations with a frequency of 0.5–2.5 Hz. Power system stabilizers (PSS) are commonly used as effective and economically efficient means to dampen electromechanical oscillations of generators and increase the stability of power systems. PSS can increase the power transmission stability limits by adding a stabilizing signal through the channels of the automatic excitation control system. The article presents the results of training a neural network based on which a fuzzy logic PSS is obtained for increasing the stability of electric power systems. The synchronous generator rotor speed deviation and acceleration were taken as input data for the fuzzy logic controller. These variables have a significant effect on damping the rotor's electromechanical oscillations. The characteristics of the power system equipped with the proposed fuzzy logic based PSS are compared with its characteristics with a PSS with non-optimized parameters and without a PSS.

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Wavelet neural network–based wind speed forecasting and application of shuffled frog leap algorithm for economic dispatch with prohibited zones incorporating wind power

Wind Engineering ◽

10.1177/0309524x17723208 ◽

2017 ◽

Vol 42 (1) ◽

pp. 3-15 ◽

Cited By ~ 4

Author(s):

Ulagammai Meyyappan

Keyword(s):

Neural Network ◽

Power Generation ◽

Wind Speed ◽

Power Systems ◽

Wind Power ◽

Economic Dispatch ◽

Wavelet Neural Network ◽

Wind Power Generation ◽

Smooth Functions ◽

Wind Speed Forecasting

Wind speed and wind power generation are characterized by their inherent variability and uncertainty. To overcome this drawback, an accurate prediction of wind speed is essential. The purpose of this article is to develop a hybrid wavelet neural network model for wind speed forecasting and thus, in turn, for wind power generation. The combined optimal economic scheduling of the wind generators and conventional generators has also been investigated in this article. This article proposes shuffled frog leap algorithm for solving economic dispatch problem in power systems. The non-linear characteristics of the generator such as prohibited operating zone and non-smooth functions are considered. The feasibility of the proposed algorithm is demonstrated for 5 units, 6 units and 15 units systems and it is compared with the existing solution techniques. The results show that the proposed algorithm is indeed capable of handling economic dispatch problems.

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Short-Term Power Load Forecasting Based on Improved Wavelet Neural Network

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.816-817.766 ◽

2013 ◽

Vol 816-817 ◽

pp. 766-769

Author(s):

Dong Xiao Niu ◽

Lei Lei Fan ◽

Chun Xiang Liu

Keyword(s):

Neural Network ◽

Power Systems ◽

Load Forecasting ◽

Weighted Average ◽

Wavelet Neural Network ◽

Generalization Capability ◽

Short Term ◽

Nonlinear Fitting ◽

Power Load ◽

Short Term Load Forecasting

Accurate short-term load forecasting contributes to safe and economic operation of power systems. Due to the shortcomings of traditional wavelet neural network (WNN), which usually has low convergence rate and easily falls into local minimum, an improved wavelet neural network (IWNN) is proposed to modify the algorithm by introducing momentum. Together with the weighted average method (WA) and WNN, these three methods are applied to an example of short-term load forecasting. The results show that compared with the WA method, WNN has obvious advantages of nonlinear fitting and forecasting, and the IWNN method is superior to the others in terms of prediction accuracy and generalization capability, which is helpful to further improve the accuracy of short-term load forecasting.

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Production Logistics Management of Industrial Enterprises Based on Wavelet Neural Network

Journal Européen des Systèmes Automatisés ◽

10.18280/jesa.530418 ◽

2020 ◽

Vol 53 (4) ◽

pp. 581-588

Author(s):

Qian Liang

Keyword(s):

Neural Network ◽

Production Scheduling ◽

Time Window ◽

Wavelet Neural Network ◽

Logistics Management ◽

Efficient Production ◽

Logistics System ◽

Production Logistics ◽

Industrial Enterprises ◽

The Stability

With an efficient production logistics system, intelligent manufacturers can reduce the investment in production, improve the stability and self-repair ability of production logistics, and strike a perfect balance between production scheduling and production logistics. This paper probes deep into the production logistics management (PLM) of industrial enterprises, and proposes a PLM model for such enterprises based on wavelet neural network (WNN). Firstly, the PLM system architecture of industrial enterprises was established, and the scheduling and task allocation principles were proposed for the collaboration of various subjects in the system. Based on curved time window, a multi-objective path planning and optimization model was established, under influencing factors like the dynamics of station demand and the maximum driving range of handling equipment. Simulation results show that the proposed model is effective in optimizing the path for industrial production logistics. The research results provide theoretical supports to the real-time optimization of PLM and rationalization of production scheduling in industrial enterprises.

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