Protection of multi-inverter based microgrid using phase angle trajectory

This thesis presents a simple, yet a clever way of using the current phase angle to develop low bandwidth communication-assisted line protection strategies for medium and low voltage AC microgrids, particularly those with multi-inverter interfaced distributed generators. It is now a trend in both AC transmission and distribution segments of power network that inverters interface renewable energy to the system. Unlike synchronous generators the fault feeding, and control characteristic of these generators are different and mostly influenced by the topology, switching, control deployed in the power electronics interface. The limited and controlled fault current challenges the existing conventional protection schemes. Offering higher power supply reliability and system resilience than conventional radial distribution systems, multi-inverter based microgrids, particularly those with loop and mesh typologies, are characterised by bidirectional power flow. This further constrains traditional protections such that communication-less protection schemes become ineffective for such systems. So unit protection types, such as differential protection, become more technically suitable for such microgrids despite the necessity for a communication system. In this thesis, two current direction based protection schemes for medium voltage islanded microgrids have been developed. The change in current flow direction in a line is detected using the cosine of the positive sequence current phase angle. Expressing the change and no-change of the flow directions as binary states, a low bandwidth communication based protection scheme is proposed comparing the binary states from local and remote ends of the line. To further enhance the scope and reliability of this scheme, a second protection scheme is proposed in Chapter 7 whereby the cosine function is combined with the rate of change of the slope of the phase angle (ROCOSP). This combination allows the detection and isolation of a fault even with the failure of the communication channel between relays protecting a faulted line. Furthermore, these scheme can work together and share the communication infrastructure as primary and backup protections. The performance of these schemes was assessed through simulations of microgrid models developed in Matlab/Simulink.Open Acces

Harmonics and Phasor Estimation for a Distorted Power System Signal

The controlling, operating and monitoring of electric devices has been possible because of the knowledge of power system parameters. The relay functionality in power systems is influenced by the two vital power system parameters which are frequency and harmonics. Hence in power systems, phasor estimation is of utmost importance. These computations not only facilitate realtime state estimation, but also improve protection schemes. However, in the presence of power frequency deviation, the phasor undergoes rotation in the complex plane. Interconnection of power grids and distributed generation systems becomes difficult because of this phenomenon. Hence, in this report different algorithms are studied and implemented for the estimation of phasor. The parameters estimated are limited to voltage amplitude and phase, change of frequency and rate of change of frequency. In this thesis, Singular Value Decomposition (SVD) technique and Recursive Least Square (RLS) algorithms are used to estimate the amplitude and phase for different harmonics present in a distorted power system signal. Simple DFT algorithm is used to estimate the phasor variation, change of frequency and rate of change of frequency when deviated from the nominal frequency

Distributed adaptive signal processing for frequency estimation

It is widely recognised that future smart grids will heavily rely upon intelligent communication and signal processing as enabling technologies for their operation. Traditional tools for power system analysis, which have been built from a circuit theory perspective, are a good match for balanced system conditions. However, the unprecedented changes that are imposed by smart grid requirements, are pushing the limits of these old paradigms. To this end, we provide new signal processing perspectives to address some fundamental operations in power systems such as frequency estimation, regulation and fault detection. Firstly, motivated by our finding that any excursion from nominal power system conditions results in a degree of non-circularity in the measured variables, we cast the frequency estimation problem into a distributed estimation framework for noncircular complex random variables. Next, we derive the required next generation widely linear, frequency estimators which incorporate the so-called augmented data statistics and cater for the noncircularity and a widely linear nature of system functions. Uniquely, we also show that by virtue of augmented complex statistics, it is possible to treat frequency tracking and fault detection in a unified way. To address the ever shortening time-scales in future frequency regulation tasks, the developed distributed widely linear frequency estimators are equipped with the ability to compensate for the fewer available temporal voltage data by exploiting spatial diversity in wide area measurements. This contribution is further supported by new physically meaningful theoretical results on the statistical behavior of distributed adaptive filters. Our approach avoids the current restrictive assumptions routinely employed to simplify the analysis by making use of the collaborative learning strategies of distributed agents. The efficacy of the proposed distributed frequency estimators over standard strictly linear and stand-alone algorithms is illustrated in case studies over synthetic and real-world three-phase measurements. An overarching theme in this thesis is the elucidation of underlying commonalities between different methodologies employed in classical power engineering and signal processing. By revisiting fundamental power system ideas within the framework of augmented complex statistics, we provide a physically meaningful signal processing perspective of three-phase transforms and reveal their intimate connections with spatial discrete Fourier transform (DFT), optimal dimensionality reduction and frequency demodulation techniques. Moreover, under the widely linear framework, we also show that the two most widely used frequency estimators in the power grid are in fact special cases of frequency demodulation techniques. Finally, revisiting classic estimation problems in power engineering through the lens of non-circular complex estimation has made it possible to develop a new self-stabilising adaptive three-phase transformation which enables algorithms designed for balanced operating conditions to be straightforwardly implemented in a variety of real-world unbalanced operating conditions. This thesis therefore aims to help bridge the gap between signal processing and power communities by providing power system designers with advanced estimation algorithms and modern physically meaningful interpretations of key power engineering paradigms in order to match the dynamic and decentralised nature of the smart grid.Open Acces

Gain Normalized Adaptive Observer For Three-Phase System

International audienceThis paper proposes the estimation of parameters and symmetrical components of unbalanced grid using adaptive observer framework. Recent adaptive observers proposed in the literature doesn’t employ any gain normalization in their frequency estimation loop. This can be problematic in the presence of large voltage dip. This paper proposes a solution to overcome this limitation using a novel gain normalized - frequency-locked loop (GN-FLL). Technical details of GN-FLL, stability analysis and tuning are provided in this paper. Comparative experimental results with adaptive Luenberger observer, second-order generalized integrator - phase-locked loop (SOGI-PLL) and enhanced PLL (EPLL) are provided to demonstrate the effectiveness the proposed technique in the single-phase case. Comparative experimental results with double SOGI-FLL (DSOGI-FLL) and adaptive notch filter (ANF) are provided to demonstrate the effectiveness the proposed technique in the three-phase case

Linear Kalman Filter-Based Grid Synchronization Technique: An Alternative Implementation

Grid synchronization techniques play a significant role in integrating renewable energy sources to the electric power grid. In this context, estimating the phase and frequency of the grid voltage signal is an interesting problem. Out of various techniques available in the literature, Linear Kalman Filter (LKF) is one of the most popular one. In this paper, we propose an alternative implementation of the LKF for grid synchronization application. The proposed implementation uses a linear parametric model of the grid voltage signal including DC offset. It does not involve any quadrature signal generation, rather it works by estimating the phase angle. This helps to estimate the unknown grid frequency directly from the phase angle. This clearly differentiates the proposed alternative implementation with respect to the existing implementations. Performance improvement by the proposed technique is verified extensively through comparative numerical simulation and experimental studies. Comparative results demonstrate the suitability of the proposed technique with respect to other state-of-the-art techniques namely SecondOrder Generalized Integrator Phase-Locked Loop (SOGI-PLL) and Enhanced Phase-Locked Loop (EPLL)