scholarly journals Experimental Verification of Self-Adapting Data-Driven Controllers in Active Distribution Grids

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2837
Author(s):  
Stavros Karagiannopoulos ◽  
Athanasios Vasilakis ◽  
Panos Kotsampopoulos ◽  
Nikos Hatziargyriou ◽  
Petros Aristidou ◽  
...  
Keyword(s):  
Power Quality ◽  
Experimental Verification ◽  
Low Voltage ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Training Data ◽  
Data Driven ◽  
Local Power ◽  
Quality Issues ◽  
Optimal Behaviour

Lately, data-driven algorithms have been proposed to design local controls for Distributed Generators (DGs) that can emulate the optimal behaviour without any need for communication or centralised control. The design is based on historical data, advanced off-line optimization techniques and machine learning methods, and has shown great potential when the operating conditions are similar to the training data. However, safety issues arise when the real-time conditions start to drift away from the training set, leading to the need for online self-adapting algorithms and experimental verification of data-driven controllers. In this paper, we propose an online self-adapting algorithm that adjusts the DG controls to tackle local power quality issues. Furthermore, we provide experimental verification of the data-driven controllers through power Hardware-in-the-Loop experiments using an industrial inverter. The results presented for a low-voltage distribution network show that data-driven schemes can emulate the optimal behaviour and the online modification scheme can mitigate local power quality issues.

scholarly journals Enhancing the LVRT Capability and Mitigation of Power Quality Issues Using UPQC of a Grid Connected Wind Conversion System

2017 ◽  
Vol 7 (3) ◽  
pp. 643
Author(s):  
C.S Boopathi ◽  
Kuppusamy Selvakumar ◽  
Avisek Dutta
Keyword(s):  
Power Quality ◽  
Reactive Power ◽  
Low Voltage ◽  
Active Filter ◽  
Shunt Active Filter ◽  
Unified Power Quality Conditioner ◽  
Conversion System ◽  
Quality Issues ◽  
Power Quality Conditioner ◽  
Doubly Fed

In this paper unified power quality conditioner has been used to enhance low voltage ride through capability of grid connected wind conversion system taking Doubly fed induction generator (DFIG). Unified Power quality conditioner (UPQC) device is a combination of series active filter and shunt active filter. This custom power device is mainly used to mitigate power quality issues which is an essential factor today because of wide application of power electronics devices. UPQC is capable to deal with voltage and current imperfection simultaneously. It is installed in the system mainly to improve the power quality i.e. Voltage sag/swell, Harmonics, reactive power compensation etc. at point of common coupling. System is modeled in MATLAB/SIMULINK and results shows utilization of UPQC for the enhancement of LVRT of a DFIG wind system according to Grid code. when fault occurs in the system, it will create voltage dip and series compensator of UPQC injects during this time to prevent disconnection from grid and stay connected to contribute during fault. UPQC is also used for fast restoration of system steady state, power factor improvement, prevent rotor over current.

scholarly journals Computational Intelligence-Based Optimization Methods for Power Quality and Dynamic Response Enhancement of ac Microgrids

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4063 ◽  
Author(s):  
Touqeer Ahmed Jumani ◽  
Mohd Wazir Mustafa ◽  
Nawaf N. Hamadneh ◽  
Samer H. Atawneh ◽  
Madihah Md. Rasid ◽  
...  
Keyword(s):  
Dynamic Response ◽  
Power Quality ◽  
Computational Intelligence ◽  
Research Work ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Operating Conditions ◽  
Inference System ◽  
Study Objective ◽  
Comprehensive Review

The penetration of distributed generators (DGs) in the existing power system has brought some real challenges regarding the power quality and dynamic response of the power systems. To overcome the above-mentioned issues, the researchers around the world have tried and tested different control methods among which the computational intelligence (CI) based methods have been found as most effective in mitigating the power quality and transient response problems intuitively. The significance of the mentioned optimization approaches in contemporary ac Microgrid (MG) controls can be observed from the increasing number of published articles and book chapters in the recent past. However, literature related to this important subject is scattered with no comprehensive review that provides detailed insight information on this substantial development. As such, this research work provides a detailed overview of four of the most extensively used CI-based optimization techniques, namely, artificial neural network (ANN), fuzzy logic (FL), adaptive neuro-fuzzy inference system (ANFIS) and genetic algorithm (GA) as applied in ac MG controls from 42 research articles along with their basic working mechanism, merits, and limitations. Due to space and scope constraints, this study excludes the applications of swarm intelligence-based optimization methods in the studied field of research. Each of the mentioned CI algorithms is explored for three major MG control applications i.e., reactive power compensation and power quality, MPPT and MG’s voltage, frequency, and power regulation. In addition, this work provides a classification of the mentioned CI-based optimization studies based on various categories such as key study objective, optimization method applied, DGs utilized, studied MG operating mode, and considered operating conditions in order to ease the searchability and selectivity of the articles for the readers. Hence, it is envisaged that this comprehensive review will provide a valuable one-stop source of knowledge to the researchers working in the field of CI-based ac MG control architectures.

scholarly journals An Open Hardware Design for Internet of Things Power Quality and Energy Saving Solutions

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 627 ◽  
Author(s):  
Eduardo Viciana ◽  
Alfredo Alcayde ◽  
Francisco Montoya ◽  
Raul Baños ◽  
Francisco Arrabal-Campos ◽  
...  
Keyword(s):  
Power Systems ◽  
Open Source ◽  
Power Quality ◽  
Low Voltage ◽  
Renewable Energy Sources ◽  
Hardware Design ◽  
Operating Conditions ◽  
Monitoring And Control ◽  
Open Hardware ◽  
Wide Range

An important challenge for our society is the transformation of traditional power systems to a decentralized model based on renewable energy sources. In this new scenario, advanced devices are needed for real-time monitoring and control of the energy flow and power quality (PQ). Ideally, the data collected by Internet of Thing (IoT) sensors should be shared to central cloud systems for online and off-line analysis. In this paper openZmeter (oZm) is presented as an advanced low-cost and open-source hardware device for high-precision energy and power quality measurement in low-voltage power systems. An analog front end (AFE) stage is designed and developed for the acquisition, conditioning, and processing of power signals. This AFE can be stacked on available quadcore embedded ARM boards. The proposed hardware is capable of adapting voltage signals up to 800 V AC/DC and currents up to thousands of amperes using different probes. The oZm device is described as a fully autonomous open-source system for the computation and visualization of PQ events and consumed/generated energy, along with full details of its hardware implementation. It also has the ability to send data to central cloud management systems. Given the small size of the hardware design and considering that it allows measurements under a wide range of operating conditions, oZm can be used both as bulk metering or as metering/submetering device for individual appliances. The design is released as open hardware and therefore is presented to the community as a powerful tool for general usage.

Power quality issues into a Danish low-voltage grid with electric vehicles

2011 ◽  
Author(s):  
Francesco Marra ◽  
Morten Moller Jensen ◽  
Rodrigo Garcia-Valle ◽  
Chresten Traholt ◽  
Esben Larsen
Keyword(s):  
Power Quality ◽  
Electric Vehicles ◽  
Low Voltage ◽  
Quality Issues

scholarly journals A physically interpretable statistical wake steering model

2021 ◽  
Author(s):  
Balthazar Sengers ◽  
Matthias Zech ◽  
Pim Jacobs ◽  
Gerald Steinfeld ◽  
Martin Kühn
Keyword(s):  
Cross Sections ◽  
Numerical Data ◽  
Operating Conditions ◽  
Training Data ◽  
Data Driven ◽  
Eddy Simulation ◽  
Stable Conditions ◽  
Large Eddy ◽  
High Degree ◽  
Far Wake

Abstract. Wake steering models for control purposes are typically based on analytical wake descriptions tuned to match experimental or numerical data. This study explores the potential of a data-driven statistical wake steering model with a high degree of physical interpretation. A linear model trained with large eddy simulation data estimates wake parameters such as deficit, center location and curliness from measurable inflow and turbine variables. These wake parameters are then used to generate vertical cross sections of the wake at desired downstream locations. In a validation against eight boundary layers ranging from neutral to stable conditions, the trajectory, shape and available power of the far wake are accurately estimated. The approach allows the choice of different input parameters, while the accuracy of the power estimates remains largely unchanged. A significant improvement in accuracy is shown in a benchmark study against two analytical wake models, especially under derated operating conditions and stable atmospheric stratifications. While results are encouraging, the model’s sensitivity to training data needs further investigation.

scholarly journals Signatures of Transient Overvoltages in Low Voltage Power Systems in Tea Factories and Their Implications on Insulation Deterioration and Allied Power Quality Issues

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Earl A. R. L. Pannila ◽  
Mahesh Edirisinghe
Keyword(s):  
Power Systems ◽  
Power Quality ◽  
Low Voltage ◽  
Electrical Equipment ◽  
High Rate ◽  
Electrical Stress ◽  
Transient Nature ◽  
Electrical Resonance ◽  
Quality Issues ◽  
The Impact

Electrical equipment and supply cables demand a better quality of supply, with the recent advancements in integrated sensitive solid-state controls. Divergently, proliferated heavy inductive motors and some performance additions based on power electronics have introduced power quality issues to the network. Thus, this study mainly investigates the impact of switching transients generated by electromechanical machines in industrial power systems on insulation deterioration while taking transient overvoltages due to capacitor bank switching also to support. Transients with a high rate of rise are likely to catalyze the degradation of the insulation quality and break down the insulating material through ionization. These steeply passing overvoltage stresses let partial discharges ensue, which can attack the insulation over long service. To unveil this danger, 314 common-mode transient waveforms were measured in the electrical machines of five tea factories in Sri Lanka, in a 50 ms measurement window, taken in 55 measuring attempts. Most of the transients observed are in the form of a damped oscillatory waveform tailed by fast exponential collapse. That correlates to insulation degradation having a very steep rise as 30.04 V/ns, the highest at the withering section. When machines are heavily loaded, situations tend to generate transients with high amplitudes. There were transient bursts that spread as 426.3 ms, while 14 ns fast rise times were recorded from withering motors. Unlike electrical resonance and power-frequency overvoltages, electromagnetic switching transients last even less than 100 ms. To underline this, an analysis of the frequency domain of transients was also presented, which proves high density of high-frequency components reaching 107 kHz range. Accepting the fact that frequency and amplitude are always under the influences of innumerable dynamics, the observational evidence of the study endorses that electrical stress built by the transient nature of the factories reduces the life expectancy of electrical insulation.

scholarly journals Power Quality Issues and Mitigation for Electric Grids with Wind Power Penetration

2020 ◽  
Vol 10 (24) ◽  
pp. 8852
Author(s):  
Sulaiman A. Almohaimeed ◽  
Mamdouh Abdel-Akher
Keyword(s):  
Wind Energy ◽  
Case Studies ◽  
Wind Power ◽  
Power Quality ◽  
Reactive Power ◽  
Test System ◽  
Operating Conditions ◽  
Voltage Variation ◽  
Electric Grids ◽  
Quality Issues

Large penetration of wind energy systems into electric-grids results in many power quality problems. This paper presents a classification of power quality issues, namely harmonics and short-duration voltage variation observed due to the integration of wind power. Additionally, different techniques and technologies to mitigate the effect of such issues are discussed. The paper highlights the current trends and future scopes in the improvement of the interconnection of wind energy conversion systems (WECSs) into the grid. As the voltage variation is the most severe power quality issue, case studies have been presented to investigate this problem using steady-state time-series simulations. The standard IEEE test system namely IEEE 123-node test feeder and IEEE 30-node grid are solved under different operating conditions with wind power penetration. Typical daily load profiles of a substation in Riyadh, Saudi Arabia, and an intermittent wind power generation profile are used in all case studies. Mitigation of voltage variations due to wind intermittency is achieved using reactive power compensation of the interface inverter. The results show the effectiveness of these approaches to avoid voltage variation and excessive tap setting movements of regulators and keep the voltage within the desired operating conditions.

scholarly journals Data-driven remaining useful life prediction based on domain adaptation

2021 ◽  
Vol 7 ◽  
pp. e690
Author(s):  
Bin cheng Wen ◽  
Ming qing Xiao ◽  
Xue qi Wang ◽  
Xin Zhao ◽  
Jian feng Li ◽  
...  
Keyword(s):  
Test Data ◽  
Health Management ◽  
Domain Adaptation ◽  
Current Data ◽  
Remaining Useful Life ◽  
Operating Conditions ◽  
Training Data ◽  
Data Driven ◽  
Target Domain ◽  
Useful Life

As an important part of prognostics and health management, remaining useful life (RUL) prediction can provide users and managers with system life information and improve the reliability of maintenance systems. Data-driven methods are powerful tools for RUL prediction because of their great modeling abilities. However, most current data-driven studies require large amounts of labeled training data and assume that the training data and test data follow similar distributions. In fact, the collected data are often variable due to different equipment operating conditions, fault modes, and noise distributions. As a result, the assumption that the training data and the test data obey the same distribution may not be valid. In response to the above problems, this paper proposes a data-driven framework with domain adaptability using a bidirectional gated recurrent unit (BGRU). The framework uses a domain-adversarial neural network (DANN) to implement transfer learning (TL) from the source domain to the target domain, which contains only sensor information. To verify the effectiveness of the proposed method, we analyze the IEEE PHM 2012 Challenge datasets and use them for verification. The experimental results show that the generalization ability of the model is effectively improved through the domain adaptation approach.

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