Improved differential evolution‐based Elman neural network controller for squirrel‐cage induction generator system

Lin, Faa Jeng; Tan, Kean Ming; Tsai, Chia-Hung Dylan

doi:10.1049/iet-rpg.2015.0329

Cited by 14 publications

(9 citation statements)

References 22 publications

(28 reference statements)

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“…Hence, the DFIG is one of the most significant types of generators being installed in wind turbines. In DFIG, both stator and rotor are connected to the main power grid directly and by power electronic converters, respectively [24][25][26][27][28]. As it can be seen in Figure 1, the typical circuit of a doubly fed induction generator DFIG is specified, by taking into consideration the several important parts including maximum power point tracking (MPPT), rotor and grid side controllers and power electronic converters, and pulse wide modulation PWM.…”

Section: Active and Reactive Power Control In The Ocean Wind Energy Smentioning

confidence: 99%

Ocean Wind Energy Technologies in Modern Electric Networks: Opportunity and Challenges

Gandoman¹,

Ahmadi²,

Aleem³

et al. 2020

Advances in Modelling and Control of Wind and Hydrogenerators

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Wind energy is one of the most important sources of energy in the world. In recent decades, wind as one of the massive marine energy resources in the ocean to produce electricity has been used. This chapter introduces a comprehensive overview of the efficient ocean wind energy technologies, and the global wind energies in both offshore and onshore sides are discussed. Also, the classification of global ocean wind energy resources is presented. Moreover, different components of a wind farm offshore as well as the technologies used in them are investigated. Possible layouts regarding the foundation of an offshore wind turbine, floating offshore, as well as the operation of wind farms in the shallow and deep location of the ocean are studied. Finally, the offshore wind power plant challenges are described.

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Section: Active and Reactive Power Control In The Ocean Wind Energy Smentioning

confidence: 99%

Ocean Wind Energy Technologies in Modern Electric Networks: Opportunity and Challenges

Gandoman¹,

Ahmadi²,

Aleem³

et al. 2020

Advances in Modelling and Control of Wind and Hydrogenerators

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show abstract

“…and input these identification results into a Bayesian filter. This algorithm included the forward propagation of signals and backpropagation of errors; that is, the actual output was calculated from input to output, while the weight and threshold were corrected from output to input [28,29]. In Figure 7, represents the input at the th node of the input layer (where = 1, 2, .…”

Section: State Division and Determination Of Characterizationmentioning

confidence: 99%

Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data

Peng

Shao

2018

Complexity

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Risky driving behavior is a major cause of traffic conflicts, which can develop into road traffic accidents, making the timely and accurate identification of such behavior essential to road safety. A platform was therefore established for analyzing the driving behavior of 20 professional drivers in field tests, in which overclose car following and lane departure were used as typical risky driving behaviors. Characterization parameters for identification were screened and used to determine threshold values and an appropriate time window for identification. A neural network-Bayesian filter identification model was established and data samples were selected to identify risky driving behavior and evaluate the identification efficiency of the model. The results obtained indicated a successful identification rate of 83.6% when the neural network model was solely used to identify risky driving behavior, but this could be increased to 92.46% once corrected by the Bayesian filter. This has important theoretical and practical significance in relation to evaluating the efficiency of existing driver assist systems, as well as the development of future intelligent driving systems.

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“…Some of the DE applications in power systems are in topics such as voltage stability assesstment [37], automatic generation control (AGC) [38], induction generation [39], PSS tuning [34], [40], voltage power (source) converters [41], optimal power flow [42], electricity forecast [43], etc.…”

Section: Introductionmentioning

confidence: 99%

Synthetic inertia control based on fuzzy adaptive differential evolution

Chamorro

Riano

Gerndt

et al. 2019

International Journal of Electrical Power & Energy Systems

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The transformation of the traditional transmission power systems due to the current rise of non-synchronous generation on it presents new engineering challenges. One of the challenges is the degradation of the inertial response due to the large penetration of high power converters used for the interconnection of renewables energy sources. The addition of a supplementary synthetic inertia control loop can contribute to the improvement of the inertial response. This paper proposes the application of a novel Fuzzy Adaptive Differential Evolution (FADE) algorithm for the tuning of a fuzzy controller for the improvement of the synthetic inertia control in power systems. The method is validated with two test power systems: (i) an aggregated power system and its purpose is to understand the controller-system behavior, and (ii) a two-area test power system where one of the synchronous machine has been replaced by a a full aggregated model of a Wind Turbine Generator (WTG), whereby different limits in the tuning process can be analyzed. Results demonstrate the evolution of the membership functions and the inertial response enhancement in the respective test cases. Moreover, the appropriate tuning of the controller shows that it is possible to substantially reduce the instantaneous frequency deviation.

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Improved differential evolution‐based Elman neural network controller for squirrel‐cage induction generator system

Cited by 14 publications

References 22 publications

Ocean Wind Energy Technologies in Modern Electric Networks: Opportunity and Challenges

Ocean Wind Energy Technologies in Modern Electric Networks: Opportunity and Challenges

Intelligent Method for Identifying Driving Risk Based on V2V Multisource Big Data

Synthetic inertia control based on fuzzy adaptive differential evolution

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