Low order harmonic cancellation in a grid connected multiple inverter system via current control parameter randomization

In grid connected multiple inverter systems, it is normal to synchronize the output current of each inverter to the common network voltage. Any current controller deficiencies, which result in low order harmonics, are also synchronized to the common network voltage. As a result the harmonics produced by individual converters show a high degree of correlation and tend to be additive. Each controller can be tuned to achieve a different harmonic profile so that harmonic cancellation can take place in the overall system, thus reducing the net current total harmonic distortion level. However, inter-inverter communication is required. This paper presents experimental results demonstrating an alternative approach, which is to arrange for the tuning within each inverter to be adjusted automatically with a random component. This results in a harmonic output spectrum that varies with time, but is uncorrelated with the harmonic spectrum of any other inverter in the system. The net harmonics from all the inverters undergo a degree of cancellation and the overall system yields a net improvement in power quality

Power Quality Enhancement in Electricity Grids with Wind Energy Using Multicell Converters and Energy Storage

In recent years, the wind power industry is experiencing a rapid growth and more wind farms with larger size wind turbines are being connected to the power system. While this contributes to the overall security of electricity supply, large-scale deployment of wind energy into the grid also presents many technical challenges. Most of these challenges are one way or another, related to the variability and intermittent nature of wind and affect the power quality of the distribution grid. Power quality relates to factors that cause variations in the voltage level and frequency as well as distortion in the voltage and current waveforms due to wind variability which produces both harmonics and inter-harmonics. The main motivation behind work is to propose a new topology of the static AC/DC/AC multicell converter to improve the power quality in grid-connected wind energy conversion systems. Serial switching cells have the ability to achieve a high power with lower-size components and improve the voltage waveforms at the input and output of the converter by increasing the number of cells. Furthermore, a battery energy storage system is included and a power management strategy is designed to ensure the continuity of power supply and consequently the autonomy of the proposed system. The simulation results are presented for a 149.2 kW wind turbine induction generator system and the results obtained demonstrate the reduced harmonics, improved transient response, and reference tracking of the voltage output of the wind energy conversion system.Peer reviewedFinal Accepted Versio

Three-phase phase-locked loop synchronization algorithms for grid-connected renewable energy systems:A review

The increasing penetration of distributed renewable energy sources (RES) requires appropriate control techniques in order to remain interconnected and contribute in a proper way to the overall grid stability, whenever disturbances occur. In addition, the disconnection of RES due to synchronization problems must be avoided as this may result in penalties and loss of energy generation to RES operators. The control of RES mainly depends on the synchronization algorithm, which should be fast and accurately detect the grid voltage status (e.g., phase, amplitude, and frequency). Typically, phase-locked loop (PLL) synchronization techniques are used for the grid voltage monitoring. The design and performance of PLL directly affect the dynamics of the RES grid side converter (GSC). This paper presents the characteristics, design guidelines and features of advanced state-of-the-art PLL-based synchronization algorithms under normal, abnormal and harmonically-distorted grid conditions. Experimental tests on the selected PLL methods under different grid conditions are presented, followed by a comparative benchmarking and selection guide. Finally, corresponding PLL tuning procedures are discussed.This work was supported by the supported by the Research Promotion Foundation (RPF) of Cyprus under Project KOINA/SOLAR-ERA.NET/1215/06

On Deep Machine Learning Based Techniques for Electric Power Systems

This thesis provides deep machine learning-based solutions to real-time mitigation of power quality disturbances such as flicker, voltage dips, frequency deviations, harmonics, and interharmonics using active power filters (APF). In an APF the processing delays reduce the performance when the disturbance to be mitigated is tima varying. The the delays originate from software (response time delay) and hardware (reaction time delay). To reduce the response time delays of APFs, this thesis propose and investigate several different techniques. First a technique based on multiple synchronous reference frame (MSRF) and order-optimized exponential smoothing (ES) to decrease the settling time delay of lowpass filtering steps. To reduce the computational time, this method is implemented in a parallel processing using a graphics processing unit (GPU) to estimate the time-varying harmonics and interharmonics of currents. Furthermore, the MSRF and three machine learning-based solutions are developed to predict future values of voltage and current in electric power systems which can mitigate the effects of the response and reaction time delays of the APFs. In the first and second solutions, a Butterworth filter is used to lowpass filter the\ua0 dq\ua0 components, and linear prediction and long short-term memory (LSTM) are used to predict the filtered\ua0 dq\ua0 components. The third solution is an end-to-end ML-based method developed based on a combination of convolutional neural networks (CNN) and LSTM. The Simulink implementation of the proposed ML-based APF is carried out to compensate for the current waveform harmonics, voltage dips, and flicker in Simulink environment embedded AI computing system Jetson TX2.\ua0In another study, we propose Deep Deterministic Policy Gradient (DDPG), a reinforcement learning (RL) method to replace the controller loops and estimation blocks such as PID, MSRF, and lowpass filters in grid-forming inverters. In a conventional approach it is well recognized that the controller tuning in the differen loops are difficult as the tuning of one loop influence the performance in other parts due to interdependencies.In DDPG the control policy is derived by optimizing a reward function which measure the performance in a data-driven fashion based on extensive experiments of the inverter in a simulation environment.\ua0Compared to a PID-based control architecture, the DDPG derived control policy leads to a solution where the response and reaction time delays are decreased by a factor of five in the investigated example.\ua0Classification of voltage dips originating from cable faults is another topic addressed in this thesis work. The Root Mean Square (RMS) of the voltage dips is proposed as preprocessing step to ease the feature learning for the developed\ua0 LSTM based classifier. Once a cable faults occur, it need to be located and repaired/replaced in order to restore the grid operation. Due to the high importance of stability in the power generation of renewable energy sources, we aim to locate high impedance cable faults in DC microgrid clusters which is a challenging case among different types of faults. The developed Support Vector Machine (SVM) algorithm process the maximum amplitude and\ua0 di/dt\ua0 of the current waveform of the fault as features, and the localization task is carried out with\ua0 95 %\ua0 accuracy.\ua0Two ML-based solutions together with a two-step feature engineering method are proposed to classify Partial Discharges (PD) originating from pulse width modulation (PWM) excitation in high voltage power electronic devices. As a first step, maximum amplitude, time of occurrence, area under PD curve, and time distance of each PD are extracted as features of interest. The extracted features are concatenated to form patterns for the ML algorithms as a second step. The suggested feature classification using the proposed ML algorithms resulted in\ua0 95.5 %\ua0 and\ua0 98.3 %\ua0\ua0 accuracy on a test data set using ensemble bagged decision trees and LSTM networks

Methods for improving stability and power quality in networks with high levels of power electronics

Advanced power electronics are essential to the development of fully active electric power systems. There are, however, potential problems that can arise when high levels of power-electronic systems are distributed throughout a network. Most importantly, power electronics can degrade the quality of the power that is delivered by utility companies; furthermore, they can cause instabilities that lead to complete failures. New "smart" power systems are highly dynamic, meaning that a regulated converter thought to be stable under ideal conditions could easily become unstable for reasons well outside of the designer's control. This thesis addresses the issue of improving power quality in networks with high levels of power electronics. The core concept presented here is an effective on-line approach for the estimation of network impedance, a time-varying quantity that plays a key role in reducing power quality. Real-time information about the network impedance at the Point of Common Coupling (PCC) can produce more stable power converters and pave the way for new measurement techniques that help to monitor power quality. This thesis also examines the application of network impedance measurements for producing model-based adaptive controllers that allow power-electronic systems to remain stable when connected to "non-stiff" networks. This work can be applied in any system that is heavily dependent on power electronics, including terrestrial "Smart Grids," all-electric ships, aircraft, and spacecraft

Stability Analysis and Design of a Tracking Filter for Variable Frequency Applications

The work presented in this thesis is a frequency adaptive tracking filter that can be used in exact tracking of power frequencies and rejection of unwanted harmonics introduced during power disturbances. The power synchronization process includes power converters and other equipment that have many non-linear components that introduce unwanted harmonics. This new design is motivated by the requirement of a filter that can filter all the harmonics and exactly track a rapidly varying fundamental frequency with little time delay and phase error. This thesis analyzes the proposed filter mathematically based on Lyapunov theory and simulations are presented to show the performance of the design in rapid frequency variations

Application of Power Electronics Converters in Smart Grids and Renewable Energy Systems

This book focuses on the applications of Power Electronics Converters in smart grids and renewable energy systems. The topics covered include methods to CO2 emission control, schemes for electric vehicle charging, reliable renewable energy forecasting methods, and various power electronics converters. The converters include the quasi neutral point clamped inverter, MPPT algorithms, the bidirectional DC-DC converter, and the push–pull converter with a fuzzy logic controller