An on-line training radial basis function neural network for optimum operation of the UPFC

The concept of Flexible A.C. Transmission Systems (FACTS) technology was developed to enhance the performance of electric power networks (both in steady-state and transient-state) and to make better utilization of existing power transmission facilities. The continuous improvement in power ratings and switching performance of power electronic devices together with advances in circuit design and control techniques are making this concept and devices employed in FACTS more commercially attractive. The Unified Power Flow Controller (UPFC) is one of the main FACTS devices that have a wide implication on the power transmission systems and distribution. The purpose of this paper is to explore the use of Radial Basis Function Neural Network (RBFNN) to control the operation of the UPFC in order to improve its dynamic performance. The performance of the proposed controller compares favourably with the conventional PI and the off-line trained controller. The simple structure of the proposed controller reduces the computational requirements and emphasizes its appropriateness for on-line operation. Real-time implementation of the controller is achieved through using dSPACE ds1103 control and data acquisition board. Simulation and experimental results are presented to demonstrate the robustness of the proposed controller against changes in the transmission system operating conditions

Measurements, Models, Systems and Design

531 s.

Neural Networks for Modeling and Control of Particle Accelerators

We describe some of the challenges of particle accelerator control, highlight recent advances in neural network techniques, discuss some promising avenues for incorporating neural networks into particle accelerator control systems, and describe a neural network-based control system that is being developed for resonance control of an RF electron gun at the Fermilab Accelerator Science and Technology (FAST) facility, including initial experimental results from a benchmark controller.Comment: 21 p

Architecture of a network-in-the-Loop environment for characterizing AC power system behavior

This paper describes the method by which a large hardware-in-the-loop environment has been realized for three-phase ac power systems. The environment allows an entire laboratory power-network topology (generators, loads, controls, protection devices, and switches) to be placed in the loop of a large power-network simulation. The system is realized by using a realtime power-network simulator, which interacts with the hardware via the indirect control of a large synchronous generator and by measuring currents flowing from its terminals. These measured currents are injected into the simulation via current sources to close the loop. This paper describes the system architecture and, most importantly, the calibration methodologies which have been developed to overcome measurement and loop latencies. In particular, a new "phase advance" calibration removes the requirement to add unwanted components into the simulated network to compensate for loop delay. The results of early commissioning experiments are demonstrated. The present system performance limits under transient conditions (approximately 0.25 Hz/s and 30 V/s to contain peak phase-and voltage-tracking errors within 5. and 1%) are defined mainly by the controllability of the synchronous generator

The Modeling and Advanced Controller Design of Wind, PV and Battery Inverters

Renewable energies such as wind power and solar energy have become alternatives to fossil energy due to the improved energy security and sustainability. This trend leads to the rapid growth of wind and Photovoltaic (PV) farm installations worldwide. Power electronic equipments are commonly employed to interface the renewable energy generation with the grid. The intermittent nature of renewable and the large scale utilization of power electronic devices bring forth numerous challenges to system operation and design. Methods for studying and improving the operation of the interconnection of renewable energy such as wind and PV are proposed in this Ph.D. dissertation.;A multi-objective controller including is proposed for PV inverter to perform voltage flicker suppression, harmonic reduction and unbalance compensation. A novel supervisory control scheme is designed to coordinate PV and battery inverters to provide high quality power to the grid. This proposed control scheme provides a comprehensive solution to both active and reactive power issues caused by the intermittency of PV energy. A novel real-time experimental method for connecting physical PV panel and battery storage is proposed, and the proposed coordinated controller is tested in a Hardware in the Loop (HIL) experimental platform based on Real Time Digital Simulator (RTDS).;This work also explores the operation and controller design of a microgrid consisting of a direct drive wind generator and a battery storage system. A Model Predictive Control (MPC) strategy for the AC-DC-AC converter of wind system is derived and implemented to capture the maximum wind energy as well as provide desired reactive power. The MPC increases the accuracy of maximum wind energy capture as well as minimizes the power oscillations caused by varying wind speed. An advanced supervisory controller is presented and employed to ensure the power balance while regulating the PCC bus voltage within acceptable range in both grid-connected and islanded operation.;The high variability and uncertainty of renewable energies introduces unexpected fast power variation and hence the operation conditions continuously change in distribution networks. A three-layers advanced optimization and intelligent control algorithm for a microgrid with multiple renewable resources is proposed. A Dual Heuristic Programming (DHP) based system control layer is used to ensure the dynamic reliability and voltage stability of the entire microgrid as the system operation condition changes. A local layer maximizes the capability of the Photovoltaic (PV), wind power generators and battery systems, and a Model Predictive Control (MPC) based device layer increases the tracking accuracy of the converter control. The detail design of the proposed SWAPSC scheme are presented and tested on an IEEE 13 node feeder with a PV farm, a wind farm and two battery-based energy storage systems

Real-Time Implementation of an Optimal Transient Neurocontroller for a GCSC

This paper presents the design of an optimal Auxiliary Transient Neurocontroller (ATNC) for the Gate Controlled Series Capacitor (GCSC) in a multi-machine power system. GCSC is a recent advancement in the family of series FACTS devices. While GCSC regulates power flow through a transmission line, an auxiliary control signal can provide damping to the power oscillations during disturbances. The ATNC has been designed using heuristic dynamic programming to develop an optimal neurocontroller with fixed weights. The ATNC is implemented in real-time using the TMS3206701 DSP and the Real-Time Digital Simulator (RTDS). Results are presented to show the effectiveness of the ATNC in damping transient oscillations during disturbances

Improvement of voltage stability for grid connected solar photovoltaic systems using static synchronous compensator with recurrent neural network

Purpose. This article proposes a new control strategy for static synchronous compensator in utility grid system. The proposed photovoltaic fed static synchronous compensator is utilized along with recurrent neural network based reference voltage generation is presented in grid system network. The novelty of the proposed work consists in presenting a Landsman converter enhanced photovoltaic fed static synchronous compensator with recurrent neural network algorithm, to generate voltage and maintain the voltage-gain ratio. Methods. The proposed algorithm which provides sophisticated and cost-effective solution for utilization of adaptive neuro-fuzzy inference system as maximum power point tracking assures controlled output and supports the extraction of complete power from the photovoltaic panel. Grid is interconnected with solar power, voltage phase angle mismatch, harmonic and voltage instability may occur in the distribution grid. The proposed control technique strategy is validated using MATLAB/Simulink software and hardware model to analysis the working performances. Results. The results obtained show that the power quality issue, the proposed system to overcome through elimination of harmonics, reference current generation is necessary, which is accomplished by recurrent neural network. By recurrent neural network, the reference signal is generated more accurately and accordingly the pulses are generated for controlling the inverter. Originality. Compensation of power quality issues, grid stability and harmonic reduction in distribution network by using photovoltaic fed static synchronous compensator is utilized along with recurrent neural network controller. Practical value. The work concerns the comparative study and the application of static synchronous compensator with recurrent neural network controller to achieve a good performance control system of the distribution network system. This article presents a comparative study between the conventional static synchronous compensator, static synchronous compensator with recurrent neural network and hardware implementation with different load. The strategy based on the use of a static synchronous compensator with recurrent neural network algorithm for the control of the continuous voltage stability and harmonic for the distribution network-linear as well as non-linear loads in efficient manner. The study is validated by the simulation results based on MATLAB/Simulink software and hardware model.Мета. У статті пропонується нова стратегія управління статичним синхронним компенсатором в енергосистемі. Запропонований статичний синхронний компенсатор з живленням від фотоелектричних елементів використовується разом з генератором опорної напруги на основі нейронної рекурентної мережі, представленим в мережі енергосистеми. Новизна запропонованої роботи полягає у поданні статичного синхронного компенсатора з покращеним фотоелектричним перетворювачем Ландсмана з алгоритмом рекурентної нейронної мережі для генерації напруги та підтримки коефіцієнта посилення за напругою. Методи. Запропонований алгоритм, який забезпечує ефективне та економічне рішення для використання адаптивної нейро-нечіткої системи логічного виведення як відстеження точки максимальної потужності, забезпечує контрольований вихід та підтримує вилучення повної потужності з фотогальванічної панелі. Мережа взаємопов’язана із сонячною енергією, у розподільній мережі можуть виникати невідповідність фазового кута напруги, гармоніки та нестабільність напруги. Запропонована стратегія методу управління перевіряється з використанням моделей програмного забезпечення MATLAB/Simulink та апаратного забезпечення для аналізу робочих характеристик. Результати. Отримані результати показують, що проблема якості електроенергії, яку запропонована система долає за допомогою усунення гармонік,потребує генерації еталонного струму, що здійснюється рекурентною нейронної мережею. За допомогою рекурентної нейронної мережі більш точно формується еталонний сигнал і відповідно генеруються імпульси для керування інвертором. Оригінальність. Компенсація проблем з якістю електроенергії, стабільністю мережі та зниженням гармонік у розподільній мережі за допомогою статичного синхронного компенсатора з фотоелектричним живленням використовується разом із контролером рекурентної нейронної мережі. Практична цінність. Робота стосується порівняльного дослідження та застосування статичного синхронного компенсатора з рекурентним нейромережевим контролером для досягнення хорошої продуктивності системи управління системою розподільної мережі. У цій статті представлено порівняльне дослідження традиційного статичного синхронного компенсатора, статичного синхронного компенсатора з рекурентною нейронною мережею та апаратною реалізацією з різним навантаженням. Стратегія, що ґрунтується на використанні статичного синхронного компенсатора з рекурентним алгоритмом нейронної мережі для ефективного контролю стабільності постійної напруги та гармонік для лінійних та нелінійних навантажень розподільної мережі. Дослідження підтверджується результатами моделювання з урахуванням програмно-апаратної моделі MATLAB/Simulink

Design and real-time implementation of data-driven adaptive wide-area damping controller for back-to-back VSC-HVDC

This paper proposes a data-driven adaptive wide-area damping controller (D-WADC) for back-to-back VSC-HVDC to suppress the low frequency oscillation in a large-scale interconnected power system. The proposed D-WADC adopts a dual-loop control structure to make full use of the active and reactive power control of VSC-HVDC to improve the damping of the power system. A data-driven algorithm named the goal representation heuristic dynamic programming is employed to design the proposed D-WADC, which means the design procedure only requires the input and output data rather than the mathematic model of the concerned power system. Thus, the D-WADC can adapt to the change of operating condition through online weight modification. Besides, the adaptive delay compensator (ADC) is added to effectively compensate the stochastic delay involved in the wide-area feedback signal. Case studies are conducted based on the simplified model of a practical power system and the 16-machine system with a back-to-back VSC-HVDC. Both the simulation and hardware-in-loop experiment results verify that the proposed D-WADC can effectively suppress the low-frequency oscillation under a wide range of operating conditions, disturbances, and stochastic communication delays

Regulatori struje aktivnih filtara snage za poboljšanje kvalitete snage: Tehnička analiza

Non-linear load deteriorates the quality of current waveforms at the point of common coupling of various consumers. Active power filter (APFs) is used to mitigate the most concern harmonic pollution in an electrical network. The controller part is the nucleus of an active power filter configuration. Active power filter performance is affected significantly by the selection of current control techniques. The active filter and its current control must have the capability to track sudden slope variations in the current reference to compensate the distorted current drawn by the voltage source inverter. Therefore, the choice and implementation of the current regulator is more important for the achievement of a satisfactory performance level. In this survey, technical reviews of various types of controllers covering a wide range have been presented. This work also reveals the advantages and disadvantages of the practiced control strategies. The effectiveness of the study will help the researchers to choose the proper control methods for various applicationsof active power filter.Nelinearni tereti pogoršavaju kvalitetu strujnih valova u točki u kojoj se spaja više potrošača. Aktivni filtar snage se koristi za ublažavanje najvažnijeg harmoničkog onečišćenja strujne mreže. Jezgra aktivnog filtra snage je regulator. Na performanse aktivnog filtra snage značajno utječe odabir upravljačke tehnike. Aktivni filtar i njegova tehnika upravljanja strujom moraju imati mogućnost pratiti nagle skokove u referentnoj vrijednosti struje kako bi mogli kompenzirati izobličenja struje koju vuče inverter naponskog izvora. Zato su izbor i implementacija regulatora struje iznimno važni za postizanje zadovoljavajuće razine performansi. U ovom pregledu su predstavljene tehničke recenzije koje pokrivaju širok raspon regulatora. Ovaj rad također otkriva prednosti i mane korištenih strategija upravljanja. Efektivnost ovog pregleda pomoći će istraživačima da izaberu ispravnu metodu upravljanja za različite aplikacije aktivnog filtra snage