Short-Circuit Analysis of DER-Based Microgrids in Connected and Islanded Modes of Operation

Since microgrids should be able to smoothly operate in two distinct modes—grid-connected and islanded, their fault currents can widely fluctuate depending on the operational mode. When the microgrid is connected to the grid, the highest fault current, by far, is supplied by the utility grid. In this mode, the fault current contribution from distributed energy resources (DERs) is less than 20%. However, when the microgrid switches to the islanded mode, the fault current contribution from the utility grid is lost and DERs are the sole fault current sources. Thus, the overall fault current in the islanded mode is multiple times lower when compared to the grid connected mode. Moreover, most of the DERs are inverter-based, with limited fault currents, which further reduces the overall fault current in the islanded mode. With the rapid rise of the microgrid penetration around the globe, this phenomenon can adversely influence the relay protection, and thus the microgrid fault current needs to be precisely analyzed. Therefore, the main purpose of this paper is to thoroughly analyze the fault current differences in two distinct operation modes of a microgrid, and to consequently derive conclusions regarding the required improvements in fault calculations and relay protection analysis in emerging microgrids. A representative microgrid test bed is developed and modelled using the in-house developed software as well as in a state-of-the-art hardware-in-the-loop environment. Several different short-circuit faults were simulated and analyzed in both grid-connected and islanded modes. The results show that the fault currents significantly differ depending on the operating mode, and thus highly influence the protection system. Moreover, test results show that the fault calculation algorithms aimed at radial distribution grids, mostly used for microgrid fault calculations in the available literature, need to be further improved to provide precise and time-efficient results when the emerging microgrids are considered. These results provide a valuable insight into the current state of the microgrids’ fault calculation and protection and reveal several important directions for future research.

Equivalent model of multi-type distributed generators under faults with fast-iterative calculation method based on improved PSO algorithm

There are various types of distributed generators (DGs) with different grid integration strategies. The transient characteristics of the fault currents provided by the DGs are different to those of conventional synchronous generators. In this paper, a distribution network with multi-type DGs is investigated, including consideration of DG low-voltage ride through (LVRT). The fault current characteristics of two typical DGs, i.e. an inverter-interfaced distributed generator (IIDG) and a doubly-fed induction generator (DFIG), are analyzed, considering the specific operation modes. Based on analysis of the fault characteristics, an equivalent model of the multi-type DGs under symmetrical/asymmetrical fault conditions is established. A fast-iterative fault calculation method for enhancing the calculation efficiency while avoiding local convergence is then proposed using an improved particle swarm optimization (PSO) algorithm. A simulation system of the distribution network with multi-type DGs is established in PSCAD/EMTDC. The simulation results validate the high accuracy and calculation efficiency of the proposed calculation method of the fault components. This can assist in the settings of the protection threshold.

Fault Calculation Method of Distribution Network Based on Deep Learning

Under the low voltage ride through (LVRT) control strategy, the inverter interfaced distributed generation (IIDG) needs to change the output mode of the inverter according to the voltage of the connected nodes. The short-circuit current is related to the system rated capacity, network short-circuit impedance, and distributed power output. So, based on the deep learning algorithm, a predicting method of the voltage drop is proposed. By predicting the voltage of connected nodes, the output mode of IIDG can be determined based on the LVRT control. Thus, the fault calculation model of IIDG is accurately established. Compared with the three-phase asymmetric Gaussian fault calculation method, the proposed method can achieve fault calculation accurately. Finally, a case study is built to verify the effectiveness of the proposed method. The results indicate that the proposed method can make accurate voltage prediction and improve the computation speed of the fault calculation.

A Comprehensive Health Indicator Integrated by the Dynamic Risk Profile from Condition Monitoring Data and the Function of Financial Losses

Large rotating machinery, such as centrifugal gas compressors and pumps, have been widely applied and acted as crucial components in the oil and gas industries. Breakdowns or deteriorated performance of these rotating machines can bring significant economic loss to the companies. In order to conduct effective maintenance and avoid unplanned downtime, a system-wide health indicator is proposed in this paper. The health indicator not only uses a dynamic risk profile, but also considers financial loss and the fault probability based on condition monitoring data. This methodology is carried out by four steps: fault detection, probability of fault calculation, consequence of fault calculation and dynamic risk assessment. In our methodology, the fault probability is calculated by robust Mahalanobis distance, presenting as a system-wide feature from a sparse autoencoder fault detection model enabled early fault detection. The value of the health indicator is presented in financial loss, which assists in effective operational decision-making in a process system. To evaluate the performance of the proposed indicator, two case studies were carried out—one case tested on multivariate industrial data obtained from a pump, and another one tested on an industrial data set from a compressor. Results prove that the integrated health indicator can detect the faults at their incipient stages, indicate the degradation of the system with dynamically updated process risk at each sampling instant, and suggest an appropriate shutdown time before the system suffers severe damage. In addition, this methodology can be adapted to other machines’ health assessments, such as those of turbines and motors. The presented method of processing the industrial data set can benefit relevant readers.