MIMO Grid Impedance Identification of Three-Phase Power Systems: Parametric vs. Nonparametric Approaches

A fast and accurate grid impedance measurement of three-phase power systems is crucial for online assessment of power system stability and adaptive control of grid-connected converters. Existing grid impedance measurement approaches typically rely on pointwise sinusoidal injections or sequential wideband perturbations to identify a nonparametric grid impedance curve via fast Fourier computations in the frequency domain. This is not only time-consuming, but also inaccurate during time-varying grid conditions, while on top of that, the identified nonparametric model cannot be immediately used for stability analysis or control design. To tackle these problems, we propose to use parametric system identification techniques (e.g., prediction error or subspace methods) to obtain a parametric impedance model directly from time-domain current and voltage data. Our approach relies on injecting wideband excitation signals in the converter's controller and allows to accurately identify the grid impedance in closed loop within one injection and measurement cycle. Even though the underlying parametric system identification techniques are well-studied in general, their utilization in a grid impedance identification setup poses specific challenges, is vastly underexplored, and has not gained adequate attention in urgent and timely power systems applications. To this end, we demonstrate in numerical experiments how the proposed parametric approach can accomplish a significant improvement compared to prevalent nonparametric methods.Comment: 7 pages, 7 figure

Aspects of Network Harmonic Impedance Modelling in High Voltage Distribution Networks

This paper evaluates the performance of five leading power system analysis softwares in terms of network harmonic impedance calculation for High Voltage distribution grids. Based on a set of systematic case studies the first part of the work presents a comparative analysis of the software packages in calculation of first resonance point. The different network element models and load models and their impact on the resonance parameters are discussed in detail. The second part of the research work assesses the sensitivity of the resonance parameters (impedance magnitude and frequency) depending on the change of certain network model parameters and compares the results amongst the different software packages. This gives an idea about the robustness of frequency and magnitude response at the resonance point and points out the most sensitive parameter in a HV network

Grid impedance estimation for islanding detection and adaptive control of converters

The grid impedance is time varying due to the changing structure of the power system configuration and it can have a considerable influence on the control and stability of grid connected converters. This paper presents an online grid impedance estimation method using the output switching current ripple of a SVPWM based grid connected converter. The proposed impedance estimation method is derived from the discretised system model using two consecutive samples within the switching period. The estimated impedance is used for islanding detection and online current controller parameter adaptation. Theoretical analysis and MATLAB simulation results are presented to verify the proposed method. The effectiveness of the grid impedance estimator is validated using experimental results

Estimation of frequency-dependent impedances in power grids by deep lstm autoencoder and random forest

This paper proposes a deep-learning-based method for frequency-dependent grid impedance estimation. Through measurement of voltages and currents at a specific system bus, the estimate of the grid impedance was obtained by first extracting the sequences of the time-dependent features for the measured data using a long short-term memory autoencoder (LSTM-AE) followed by a random forest (RF) regression method to find the nonlinear map function between extracted features and the corresponding grid impedance for a wide range of frequencies. The method was trained via simulation by using time-series measurements (i.e., voltage and current) for different system parameters and verified through several case studies. The obtained results show that: (1) extracting the time-dependent features of the voltage/current data improves the performance of the RF regression method; (2) the RF regression method is robust and allows grid impedance estimation within 1.5 grid cycles; (3) the proposed method can effectively estimate the grid impedance both in steady state and in case of large transients like electrical faults

Optimal H2 control design of active front-end integrating grid model identification

Small-signal stability and dynamic performance are of great concern for AC power grids with high penetration of power converters. The interactions between these converters may lead to performance degradation or even system instability and failure at certain conditions. To deal with such problems, global modelling and integrated control are proposed. However, because of the highly integration of power grids in commercial industry environment, the lack of information about parameters and methods of the embedded converters, impedes the development of global models for system analysis and control design. To fill the gap, this research investigated the utility of system identification techniques to estimate a state space model of the unknown power grid, and proposed an approach to incorporate it into the design of local converters. Hence interactions between the grid and the to be designed converters could be taken into account and issues mitigated. Specifically in this research, the proposed method is applied to the control design of a grid-connected active front end (AFE). In a notional system, a voltage source inverter (VSI) is included to emulate the unknown grid and supplies power to an AFE feeding a constant power load (CPL). Firstly, a state space model of the grid is identified through perturbation and response test at the point of common coupling (PCC) in a specially designed experiment. It is then combined with the open loop model of the AFE to build a global model of the grid-AFE system. The plant for the control design will then be not only represented by the AFE's dynamics, but will also include that of the identified grid at the PCC. Implementation of the identification experiment involved and mathematical manipulations used to merge the two subsystem models are presented in detail. The global model is utilized to synthesize a state feedback controller, denoted as `optimal $H_2$ controller' in this thesis for the AFE by the use of a structured $H_2$ algorithm, which optimizes the dynamic performance of AFE while intrinsically ensuring stability of the grid-AFE system. Effectiveness and advantages of the proposed control design method is validated by simulations and experiments. The grid-AFE system performance when the AFE adopts the optimal $H_2$ controller or best-tuned proportional-integral (PI) controllers is compared. The use of optimal $H_2$ controller outperforms with faster dynamic response and greater stability margin the PI based solution. Scalability of the proposed method in more complex power grids and its robustness against system parameters drifting are also discussed

Soft-start procedure for a three-stage smart transformer based on dual-active bridge and cascaded H-bridge converters

Power electronics based three-stage smart transformers (STs) can be seriously damaged by inrush currents and overvoltages during the start-up phase if the control of the stages is not correctly coordinated. Hence, it is crucial to design properly the start-up procedure, especially in case of modular architectures with distributed dc-links. The design of the start-up procedure depends on the ST power stages topologies, their control systems, and the operation modes. This article proposes a soft-shift start modulation technique that allows to limit the inrush current in the dc/dc isolation stage during the dc-link capacitors precharging. A fast voltage-balancing control, performed by the dc/dc isolation stage, is introduced to avoid overvoltages and unbalanced voltage conditions among the different power cells. Under the proposed method, fast control dynamics is guaranteed thanks to the high frequency bandwidth of the dc/dc isolation stage converters. Theoretical analysis, based on a detailed small signal model of the ST, and simulations are used to demonstrate the principle of the operation. Experimental results, carried out in an ST prototype, confirm the performances of proposed solution in realizing a smooth start-up without voltage/current overshoots

New Control Algorithms for the Robust Operation and Stabilization of Active Distribution Networks

The integration of renewable distributed generation units (DGs) alters distribution systems so that rather than having passive structures, with unidirectional power flow, they become active distribution networks (ADNs), with multi-directional power flow. While numerous technical, economic, and environmental benefits are associated with the shift toward ADNs, this transition also represents important control challenges from the perspective of both the supervisory and primary control of DGs. Voltage regulation is considered one of the main operational control challenges that accompany a high penetration of renewable DGs. The intermittent nature of renewable energy sources, such as wind and solar energy, can significantly change the voltage profile of the system and can interact negatively with conventional schemes for controlling on-load tap changers (OLTCs). Another factor is the growing penetration of plug-in electric vehicles (PEVs), which creates additional stress on voltage control devices due to their stochastic and concentrated power profiles. These combined generation and load power profiles can lead to overvoltages, undervoltages, increases in system losses, excessive tap operation, infeasible solutions (hunting) with respect to OLTCs, and/or limits on the penetration of either PEVs or DGs. With regard to the dynamic control level, DG interfaces are typically applied using power electronic converters, which lack physical inertia and are thus sensitive to variations and uncertainties in the system parameters. Grid impedance (or admittance), which has a substantial effect on the performance and stability of primary DG controllers, is nonlinear, time-varying, and not passive in nature. In addition, constant-power loads (CPLs), such as those interfaced through power electronic converters, are also characterized by inherited negative impedance that results in destabilizing effects, creating instability and damping issues. Motivated by these challenges, the research presented in this thesis was conducted with the primary goal of proposing new control algorithms for both the supervisory and primary control of DGs, and ultimately of developing robust and stable ADNs. Achieve this objective entailed the completion of four studies: Study#1: Development of a coordinated fuzzy-based voltage regulation scheme with reduced communication requirements Study#2: Integration of PEVs into the voltage regulation scheme through the implementation of a vehicle-to-grid reactive power (V2GQ) support strategy Study#3: Creation of an estimation tool for multivariable grid admittance that can be used to develop adaptive controllers for DGs Study#4: Development of self-tuning primary DG controllers based on the estimated grid admittance so that stable performance is guaranteed under time-varying DG operating points (dispatched by the schemes developed in Study#1 and Study#2) and under changing grid impedance (created by network reconfiguration and load variations). As the first research component, a coordinated fuzzy-based voltage regulation scheme for OLTCs and DGs has been proposed. The primary reason for applying fuzzy logic is that it provides the ability to address the challenges associated with imperfect information environments, and can thus reduce communication requirements. The proposed regulation scheme consists of three fuzzy-based control algorithms. The first control algorithm was designed to enable the OLTC to mitigate the effects of DGs on the voltage profile. The second algorithm was created to provide reactive power sharing among DGs, which will relax OLTC tap operation. The third algorithm is aimed at partially curtailing active power levels in DGs so as to restore a feasible solution that will satisfy OLTC requirements. The proposed fuzzy algorithms offer the advantage of effective voltage regulation with relaxed tap operation and with utilization of only the estimated minimum and maximum system voltages. Because no optimization algorithm is required, it also avoids the numerical instability and convergence problems associated with centralized approaches. OPAL real-time simulators (RTS) were employed to run test simulations in order to demonstrate the success of the proposed fuzzy algorithms in a typical distribution network. The second element, a V2GQ strategy, has been developed as a means of offering optimal coordinated voltage regulation in distribution networks with high DG and PEV penetration. The proposed algorithm employs PEVs, DGs, and OLTCs in order to satisfy the PEV charging demand and grid voltage requirements while maintaining relaxed tap operation and minimum curtailment of DG active power. The voltage regulation problem is formulated as nonlinear programming and consists of three consecutive stages, with each successive stage applying the output from the preceding stage as constraints. The task of the first stage is to maximize the energy delivered to PEVs in order to ensure PEV owner satisfaction. The second stage maximizes the active power extracted from the DGs, and the third stage minimizes any deviation of the voltage from its nominal value through the use of available PEV and DG reactive power. The primary implicit objective of the third stage problem is the relaxation of OLTC tap operation. This objective is addressed by replacing conventional OLTC control with a proposed centralized controller that utilizes the output of the third stage to set its tap position. The effectiveness of the proposed algorithm in a typical distribution network has been validated in real time using an OPAL RTS in a hardware-in-the loop (HiL) application. The third part of the research has resulted in the proposal of a new multivariable grid admittance identification algorithm with adaptive model order selection as an ancillary function to be applied in inverter-based DG controllers. Cross-coupling between the and grid admittance necessitates multivariable estimation. To ensure persistence of excitation (PE) for the grid admittance, sensitivity analysis is first employed as a means of determining the injection of controlled voltage pulses by the DG. Grid admittance is then estimated based on the processing of the extracted grid dynamics by the refined instrumental variable for continuous-time identification (RIVC) algorithm. Unlike nonparametric identification algorithms, the proposed RIVC algorithm provides a parametric multivariable model of grid admittance, which is essential for designing adaptive controllers for DGs. HiL applications using OPAL RTS have been utilized for validating the proposed algorithm for both grid-connected and isolated ADNs. The final section of the research is a proposed adaptive control algorithm for optimally reshaping DG output impedance so that system damping and bandwidth are maximized. Such adaptation is essential for managing variations in grid impedance and changes in DG operating conditions. The proposed algorithm is generic so that it can be applied for both grid-connected and islanded DGs. It involves three design stages. First, the multivariable DG output impedance is derived mathematically and verified using a frequency sweep identification method. The grid impedance is also estimated so that the impedance stability criteria can be formulated. In the second stage, multi-objective programming is formulated using the -constraint method in order to maximize system damping and bandwidth. As a final stage, the solutions provided by the optimization stage are employed for training an adaptation scheme based on a neural network (NN) that tunes the DG control parameters online. The proposed algorithm has been validated in both grid-connected and isolated distribution networks, with the use of OPAL RTS and HiL applications.1 yea