Power quality and electromagnetic compatibility: special report, session 2

The scope of Session 2 (S2) has been defined as follows by the Session Advisory Group and the Technical Committee: Power Quality (PQ), with the more general concept of electromagnetic compatibility (EMC) and with some related safety problems in electricity distribution systems. Special focus is put on voltage continuity (supply reliability, problem of outages) and voltage quality (voltage level, flicker, unbalance, harmonics). This session will also look at electromagnetic compatibility (mains frequency to 150 kHz), electromagnetic interferences and electric and magnetic fields issues. Also addressed in this session are electrical safety and immunity concerns (lightning issues, step, touch and transferred voltages). The aim of this special report is to present a synthesis of the present concerns in PQ&EMC, based on all selected papers of session 2 and related papers from other sessions, (152 papers in total). The report is divided in the following 4 blocks: Block 1: Electric and Magnetic Fields, EMC, Earthing systems Block 2: Harmonics Block 3: Voltage Variation Block 4: Power Quality Monitoring Two Round Tables will be organised: - Power quality and EMC in the Future Grid (CIGRE/CIRED WG C4.24, RT 13) - Reliability Benchmarking - why we should do it? What should be done in future? (RT 15

International White Book on DER Protection : Review and Testing Procedures

This white book provides an insight into the issues surrounding the impact of increasing levels of DER on the generator and network protection and the resulting necessary improvements in protection testing practices. Particular focus is placed on ever increasing inverter-interfaced DER installations and the challenges of utility network integration. This white book should also serve as a starting point for specifying DER protection testing requirements and procedures. A comprehensive review of international DER protection practices, standards and recommendations is presented. This is accompanied by the identifi cation of the main performance challenges related to these protection schemes under varied network operational conditions and the nature of DER generator and interface technologies. Emphasis is placed on the importance of dynamic testing that can only be delivered through laboratory-based platforms such as real-time simulators, integrated substation automation infrastructure and fl exible, inverter-equipped testing microgrids. To this end, the combination of fl exible network operation and new DER technologies underlines the importance of utilising the laboratory testing facilities available within the DERlab Network of Excellence. This not only informs the shaping of new protection testing and network integration practices by end users but also enables the process of de-risking new DER protection technologies. In order to support the issues discussed in the white paper, a comparative case study between UK and German DER protection and scheme testing practices is presented. This also highlights the level of complexity associated with standardisation and approval mechanisms adopted by different countries

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

A Review on Artificial Intelligence Applications for Grid-Connected Solar Photovoltaic Systems

The use of artificial intelligence (AI) is increasing in various sectors of photovoltaic (PV) systems, due to the increasing computational power, tools and data generation. The currently employed methods for various functions of the solar PV industry related to design, forecasting, control, and maintenance have been found to deliver relatively inaccurate results. Further, the use of AI to perform these tasks achieved a higher degree of accuracy and precision and is now a highly interesting topic. In this context, this paper aims to investigate how AI techniques impact the PV value chain. The investigation consists of mapping the currently available AI technologies, identifying possible future uses of AI, and also quantifying their advantages and disadvantages in regard to the conventional mechanisms

A survey of islanding detection methods for microgrids and assessment of non-detection zones in comparison with grid codes

Detection of unintentional islanding is critical in microgrids in order to guarantee personal safety and avoid equipment damage. Most islanding detection techniques are based on monitoring and detecting abnormalities in magnitudes such as frequency, voltage, current and power. However, in normal operation, the utility grid has fluctuations in voltage and frequency, and grid codes establish that local generators must remain connected if deviations from the nominal values do not exceed the defined thresholds and ramps. This means that islanding detection methods could not detect islanding if there are fluctuations that do not exceed the grid code requirements, known as the non-detection zone (NDZ). A survey on the benefits of islanding detection techniques is provided, showing the advantages and disadvantages of each one. NDZs size of the most common passive islanding detection methods are calculated and obtained by simulation and compared with the limits obtained by ENTSO-E and islanding standards in the function of grid codes requirements in order to compare the effectiveness of different techniques and the suitability of each one

Metering and adaptive protection for a microgrid with distributed generation

The main objective of this project is to develop an adaptive relaying system that will protect the microgrid both in connected and isolated modes. Therefore the settings for the different relays will be observed for the two modes of operation. This will determine whether they are correctly coordinated in order to operate as an adaptive relaying system. A secondary but also important objective is to identify load management techniques through smart metering that could facilitate power system operation and in turn power system protection. To achieve the goal of this project the proposed relaying system will have to prove appropriate in all the test cases. Based on the results obtained in the simulations, conclusions about the relaying scheme were drawn. Based on cases where the scheme seemed inappropriate or could be improved, recommendations were made. The relaying scheme proposed in this project proved highly successful in detecting abnormalities and protecting the power system when necessary

Frequency and fundamental signal measurement algorithms for distributed control and protection applications

Increasing penetration of distributed generation within electricity networks leads to the requirement for cheap, integrated, protection and control systems. To minimise cost, algorithms for the measurement of AC voltage and current waveforms can be implemented on a single microcontroller, which also carries out other protection and control tasks, including communication and data logging. This limits the frame rate of the major algorithms, although analogue to digital converters (ADCs) can be oversampled using peripheral control processors on suitable microcontrollers. Measurement algorithms also have to be tolerant of poor power quality, which may arise within grid-connected or islanded (e.g. emergency, battlefield or marine) power system scenarios. This study presents a 'Clarke-FLL hybrid' architecture, which combines a three-phase Clarke transformation measurement with a frequency-locked loop (FLL). This hybrid contains suitable algorithms for the measurement of frequency, amplitude and phase within dynamic three-phase AC power systems. The Clarke-FLL hybrid is shown to be robust and accurate, with harmonic content up to and above 28% total harmonic distortion (THD), and with the major algorithms executing at only 500 samples per second. This is achieved by careful optimisation and cascaded use of exact-time averaging techniques, which prove to be useful at all stages of the measurements: from DC bias removal through low-sample-rate Fourier analysis to sub-harmonic ripple removal. Platform-independent algorithms for three-phase nodal power flow analysis are benchmarked on three processors, including the Infineon TC1796 microcontroller, on which only 10% of the 2000 mus frame time is required, leaving the remainder free for other algorithms

Analysis and Mitigation of Power Quality Issues in Distributed Generation Systems Using Custom Power Devices

This paper discusses the power quality issues for distributed generation systems based on renewable energy sources, such as solar and wind energy. A thorough discussion about the power quality issues is conducted here. This paper starts with the power quality issues, followed by discussions of basic standards. A comprehensive study of power quality in power systems, including the systems with dc and renewable sources is done in this paper. Power quality monitoring techniques and possible solutions of the power quality issues for the power systems are elaborately studied. Then, we analyze the methods of mitigation of these problems using custom power devices, such as D-STATCOM, UPQC, UPS, TVSS, DVR, etc., for micro grid systems. For renewable energy systems, STATCOM can be a potential choice due to its several advantages, whereas spinning reserve can enhance the power quality in traditional systems. At Last, we study the power quality in dc systems. Simpler arrangement and higher reliability are two main advantages of the dc systems though it faces other power quality issues, such as instability and poor detection of faults

Measurement, control and protection of microgrids at low frame rates supporting security of supply

Increasing penetrations of distributed generation at low power levels within electricity networks leads to the requirement for cheap, integrated, protection and control systems. To minimise unit cost, algorithms for the measurement of AC voltage and current waveforms should be implemented on a single microcontroller, which also carries out all other protection and control tasks, including communication and data logging. This limits the frame rate of the major algorithms, although ADCs can be over-sampled using peripheral control processors on suitable microcontrollers. Measurement algorithms also have to be tolerant of poor power quality which may arise, even transiently, within a microgrid, battlefield, or disaster-relief scenario. This thesis analyses the potential magnitude of these interfering signals, and presents suitably tolerant architectures and algorithms for measurements of AC waveforms (amplitude, phase and frequency). These algorithms are shown to be robust and accurate, with harmonic content up to the level of 53% THD, and with the major algorithms executing at only 500 samples per second. This is achieved by the careful optimisation and cascaded use of exact-time averaging techniques, which prove to be useful at all stages of the measurements: from DC bias removal to low-sample-rate Fourier analysis to sub-harmonic ripple removal. Algorithms for three-phase nodal power flow analysis are benchmarked on the Infineon TC1796 microcontroller and require less than 8% of the 2000μs frame time, leaving the remainder free for other algorithms. Furthermore, to optimise security of supply in a microgrid scenario, loss-of-mains must be detected quickly even when there is an accidental or deliberate balance between local active power generation and demand. The measurement techniques are extended to the detection of loss-of-mains using a new Phase Offset relay, in combination with a novel reactive power control technique to avoid the non-detection-zone. These techniques are tested using simulation, captured network transient events, and a real hardware microgrid including a synchronous generator and inverter