Improved Impedance Stability Analysis of Power Electronic System Considering Harmonic Source

The Nyquist stability criterion is a graphical technique for determining the stability of a dynamical system. It relates the stability of a closed‐loop system to the open‐loop frequency response and open‐loop pole location. The Nyquist technique is limited to linear, time‐invariant (LTI) systems and has been widely used for designing and analyzing systems with feedback. A practical advantage of the technique is that it can be applied directly to measured frequency response data, due to which the technique is widely used in power electronics systems, where the complexity of a mathematical model would make its widespread use unfeasible. In its first part, this article guides through the fundamentals of the Nyquist stability criterion applied to feedback systems with SISO and MIMO representations. The generalized Nyquist criterion for MIMO systems is then discussed to illustrate its application to the stability assessment of three‐phase power electronics systems. In the second part, four representative cases are introduced to illustrate the applicability of Nyquist criterion–based stability analysis for power electronics systems: a dc‐dc converter, an HVDC system, a three‐phase converter, and a wind farm–HVDC grid integrated system. In the last section of this article, some advanced topics related to large‐scale power electronics systems are introduced. Inherent aspects that are essential for the proper use of these stability techniques are elaborated and discussed in detail, for instance, impedance transformation and modified sequence impedance or the system‐level analysis.

Control and Stability of the Multi-Robots System

Harmonic source identification is one of the most important power quality problems in the modern power systems. With the widespread application of the nonlinear power electronic devices, quantifying the individual responsibility of every distributed harmonic source becomes an urgent task. In this paper, a new responsibility identification method for the power system harmonic problems has been proposed. The harmonic impact index is defined and theorized, and the data selection method is also studied. The operational voltage and current data for the harmonic sources are used to calculate the harmonic impact index. Case study results show that, the proposed method is valid and accurate for identifying the main contributing harmonic sources.

Due to the internal dynamics of the modular multilevel converter (MMC), the coupling between the positive and negative sequences in impedance, which is defined as frequency coupling, inherently exists in MMC. Ignoring the frequency coupling of the MMC impedance model may lead to inaccurate stability assessment, and thus the multi-input multi-output (MIMO) impedance model has been developed to consider the frequency coupling effect. However, the generalized Nyquist criterion (GNC), which is used for the stability analysis of an MIMO model, is more complicated than the stability analysis method applied on single-input-single-output (SISO) models. Meanwhile, it is not always the case that the SISO model fails in the stability assessment. Therefore, the conditions when the MIMO impedance model needs to be considered in the stability analysis of an MMC system should be analyzed. This paper quantitatively analyzes the effect of frequency coupling on the stability analysis of grid-connected MMC, and clarifies the frequency range and grid conditions that the coupling effect required to be considered in the stability analysis. Based on the quantitative relations between the frequency coupling and the stability analysis of the grid-connected MMC system, a simple and accurate stability analysis method for the grid-connected MMC system is proposed, where the MIMO impedance model is applied when the frequency coupling has a significant effect and the SISO impedance model is used if the frequency coupling is insignificant.

In analysis and design of a power electronic system, it is essentially important to check its stability for a given system's parameters. This paper presents a computer-aided method for analyzing stability of a general class of power electronic systems. In usual operating modes of power electronic systems, the solution orbits are periodic due to the periodic switching operations of power semiconductor devices. We derive an efficient computer algorithm to determine the stability of periodic solutions of power electronic systems. The proposed method is based on the use of a transient simulator for power electronics systems. The method can check stability of any power electronics system with considering detailed switching behaviors which the transient simulator can treat.

294 fault patterns of 12-pulse waveform controlled rectifier circuit (CRC) are studied firstly; a special fault classification method according to the rectifier aberrant voltage waveforms is put forward then. 12-dimension fault patterns and the corresponding fault codes are obtained through logic pre-processing of the fault rectifier voltage waveforms. A recurrent neural network (RNN) with deviation error units is used to construct a fault mapping. The fault patterns are used as the input of the network, after being calculated by the neural network the 12-dimension fault codes are obtained to indicate the fault state of the system. Simulation and experiment study demonstrate that the proposed technique is low time consuming with high fault identification rate, it improves the performance of the existing neural network based on-line fault diagnosis methods effectively. The proposed method is suitable for the fault diagnosis of complex power electronic devices or systems.

The power electronic devices (PEDs) have small signal stability issues in weak grids. as high-controllability devices, the STATCOMs can be a potential choice to enhance the small-signal stability. However, how the location of STATCOMs influences the stability of a multi-infeed power electronic system (MIPES) has not been investigated. To quantify the STATCOMs’ location on the small signal stability for a structure-preserving MIPES, an eigenvalue-sensitivity-based method is proposed in this paper. The theoretical analysis shows that for a homogeneous MIPES, the STATCOMs’ location on the stability mainly depends on the network structure, PEDs’ capacities and STATCOMs’ location. Based on this, a metric is proposed for the optimal placement of STATCOMs in a MIPES to enhance the small signal stability. Simulation results verify the validity of this metric.

Most transient stability programs can solve very large systems but the representation of individual components of the system, in particular power electronic devices and control systems associated with FACTS and HVDC transmission systems cannot be represented in detail. Electromagnetic transients simulators, on the other hand, can represent system components and control systems in great detail, but are impracticable for the simulation of very large systems, and dynamic equivalents are required. It is apparent that the future of simulation lies in the development of hybrid simulators, which would allow practicable simulation of large-scale power systems with embedded modeling on power electronic devices in devices level. In this paper, an attempt has been made to incorporate an electromagnetic transient model of a static VAr compensator (SVC) into the traditional transient stability simulation program.

Chapter 31 - Power Electronics Applications in Smart Grid

The direct measurement of losses in power electronic systems is possible through the use of a calorimeter. An improved and fully automated version of a "mini" double-jacketed, closed type calorimeter is described in this paper. The accuracy of the calorimeter has been confirmed by calibration tests and is better than 90 mW in 25 W. The losses in the ballast circuit of compact fluorescent lamps (CFL) and mobile (cellphone) chargers for domestic use have been measured in this calorimeter and compared to conventional measuring methods.

Renewable energy and electric vehicles have become involved in power systems, which has attracted researchers to stochastic continuous disturbances (SDEs). This paper addresses stochastic analysis issues for the stability of a power system with losses under SDEs. Firstly, the quasi-Hamiltonian models of power systems with losses under SDEs are given. Secondly, a novel analytical method is proposed to analyze the stability of the power system with losses under SDEs based on the stochastic averaging method. Thirdly, comparisons of stability probability under different parameters are performed, from which insights to improve the stability probability of power systems with losses under SDEs can be obtained. Even though it is challenging to assess the stability of a power system with losses under SDEs, the proposed method in this paper could serve well in this regard.

Asymmetric Lévy noise changed stability in a gene transcriptional regulatory system

This paper presents a method for evaluating the harmonic contributions of multiple harmonic sources to voltage distortion at the point of common coupling (PCC). The proposed method quantitatively represents harmonic contributions based on the equivalent voltage models of harmonic sources. The equivalent voltage models can be estimated using the recursive least square (RLS) algorithm and measured voltage and current at the PCC. The assessment of harmonic contribution heavily depends on the accuracy of equivalent model estimation. Many existing studies have assumed that the equivalent model parameters of harmonic sources are not changed. However, because harmonic source parameters and system operating conditions are not constant in real power systems, this assumption can lead large errors in harmonic contribution assessment. In this paper, we propose a new assessment method based on variable forgetting factor RLS and parameter change detection to solve the problem. The proposed method is also effective for evaluating comprehensive harmonic contributions as well as individual harmonic contributions. The case studies show that the proposed method is superior to the previous one even under parameter changing conditions.

This paper proposes a method for power system stability monitoring and analysis through Phasor Measurement Units' (PMUs) measurements (i.e., bus voltage magnitudes and angles). Real-time stability monitoring can be achieved by observing the behavior of the measurement data's frequency spectra. In addition, a modified Thevenin equivalent-based model is proposed to analyze the system small-signal stability and transient stability. The model's parameters are estimated based on the phasor measurements. We verify our proposed method through simulations on a RTDS/PMU/PDC testbed and perform analyses on real PMU data.

Power systems are being operated close to their voltage security limit which makes their reliable operation more challenging. Voltage stability has been a major problem faced by utilities now a days. Many blackout have been reported around the world related to voltage instability problem. Many voltage stability analysis methods have been presented up till now. Here artificial neural network based approach for voltage stability analysis of power system is presented. Voltage stability analysis of power system has been done in terms of voltage stability margin. Voltage stability margin is calculated by estimating distance from current operating point to maximum voltage stability point according to loading parameter. Principal Component Analysis is an efficient feature selection method used here to find few input variables required to estimate voltage stability margin with sufficient accuracy and execution speed. Major drawback of previously published work is they need to train new neural network whenever change in system topology occur which is very time consuming process. Therefor in this paper single ANN has been prepared for estimating VSM for several system configuration. Feed forward neural network and radial basis function neural network have been used for voltage stability analysis of power system. The final result indicate that proposed method provide an efficient and compact ANN model for different system configuration as well as base case operating condition with lowest possible input required.