Intelligent Control of the Regulators Adjustment of the Distributed Generation Installation

Kryukov, Andrey; Kargapol’cev, S. K.; Bulatov, Yuri; Skrypnik, Oleg Nicolaevich; Kuznetsov, B. F.

doi:10.17654/ec017051127

Cited by 31 publications

(23 citation statements)

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“…For this purpose, nonparametric identification is used, as a result of which, on the basis of a priori information about the process, the numerical values of the complex activation factor are determined as the ratio of the spectra of the output and input signals [16]. The diagram of 'turbine-generator' closed loop system used for generation of characteristic polynomial is shown in fig.…”

Section: Adaptive Neuro Fuzzy Control System For Distributed Generatimentioning

confidence: 99%

“…Characteristic polynomial of the system under consideration is determined using the expression: To obtain accurate activation function for identification, it is advisable to use test broadband signals. However, interference in the operation of power supply systems is undesirable, and therefore, it is proposed to use an approach in which the controllers' user-selected noise with the use of a wavelet transform is used [16].…”

Section: Adaptive Neuro Fuzzy Control System For Distributed Generatimentioning

confidence: 99%

“…The harmonized configuring module allows determining the optimum tuning factors for AER and ARRS using minimization of the quality criterion based on the GA [12,16] …”

Section: Adaptive Neuro Fuzzy Control System For Distributed Generatimentioning

confidence: 99%

“…At present, a number of studies have been carried out on the use of intelligent technologies in power supply systems [1][2][3][4][5][6][7][8][9], and in particular, for solving the problems of controllers parameterization [10][11][12], as well as on developing controllers working on the basis of artificial intelligence technologies [13][14][15][16]. A control system that adjusts AER and ARRS tuning and built on the basis of an adaptive neuro-fuzzy inference system is proposed in this work to improve the stability and reliable integration of the DG plants in EPS.…”

Section: Introductionmentioning

confidence: 99%

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Neuro Fuzzy Control system for Distributed Generation Plants

Bulatov¹,

Kryukov²

2018

Proceedings of the VTH International Workshop "Critical Infrastructures: Contingency Management, Intelligent, Agent-Based, Clou

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The transition of electric power industry to a new technological platform based on the concept of smart power grids with an active-and-adaptive network will allow to increase the efficiency of control, durability and reliability of power supply systems. The idea of applying the intelligent algorithms to control power grids becomes more pressing in case of wide using the distributed generation plants (DGP) and other grid active elements that allow to control power grid operation modes. The article describes the DGP consisting of synchronous generators that operate in various modes and often with poor power supply quality. To provide the parallel operation stability of synchronous turbo/ hydro generators of DGP, automatic excitation regulator (AER) and automatic regulator of rotor speed (ARRS) are used. Due to low inertia constant of distributed generation unit rotors, the problem of matched tuning of AER and ARRS becomes more topical. To increase stability and reliable integration of DGP in power grids, a control system built on a neurofuzzy match system is proposed. The system corrects AER/ ARRS settings. In this case, the following smart technologies were used: genetic algorithm to search optimal settings of AER and ARRS; a neuro-fuzzy network to identify an operation mode of a DGP and power grid; fuzzy interference to correct settings of AER/ ARRS in various modes of DGP operation. Based on modelling done in MATLAB system the efficiency of using the proposed neuro-fuzzy match system to identify DGP operation modes and to adaptively control matched tuning of AER and ARRS of synchronous generators is shown. If using the intellectual control system, one can decrease transition process time, generator voltage / frequency overcontrol, as well as provide reliability and durability of a power system in various operation modes of a DGP and power grids.

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Section: Adaptive Neuro Fuzzy Control System For Distributed Generatimentioning

confidence: 99%

Section: Adaptive Neuro Fuzzy Control System For Distributed Generatimentioning

confidence: 99%

“…The harmonized configuring module allows determining the optimum tuning factors for AER and ARRS using minimization of the quality criterion based on the GA [12,16] …”

Section: Adaptive Neuro Fuzzy Control System For Distributed Generatimentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Neuro Fuzzy Control system for Distributed Generation Plants

Bulatov¹,

Kryukov²

2018

Proceedings of the VTH International Workshop "Critical Infrastructures: Contingency Management, Intelligent, Agent-Based, Clou

View full text Add to dashboard Cite

show abstract

“…The development of electric power industry requires transition to the technological platform, encouraging implementation of smart grids. To set up these grids it is necessary to apply smart technologies of power supply system control modes [1][2][3][4][5][6][7], as well as interfaces, ensuring the connection of renewable energy sources. These interfaces may be represented by energy routers (ER) [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Application of energy routers in railway power supply systems

2018

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The development of electric power industry requires transition to the technological platform, encouraging implementation of smart grids. To set up these grids it is necessary to apply smart technologies of power supply system control modes, as well as interfaces, ensuring the connection of renewable energy sources. These interfaces may be represented by energy routers. The paper discusses the results of simulating emergency modes, caused by short circuits in 10 kV and 0.4 kV grids of non-traction users, connected via energy routers to the railway power supply system. The studies were performed in MATLAB system. Simulation results showed that the application of controlled energy routers for the non-traction user power supply completely removes electric power quality problems, allows for reliable integration of distributed generation plants into the power supply system, and limits short circuit currents and reduces the depth of the AC failure.

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