CERTS Microgrid Research Articles

Data-Mining Model-Based Enhanced Differential Relaying Scheme for Microgrids

The article presents a data-mining model-based enhanced differential relaying scheme for fault detection and classification task in the microgrids. The proposed research work aims to provide a fixed setting, fast, and reliable scheme for accurately detecting and classifying faults in the microgrid. The presented scheme fetches the three-phase current signals from both ends of the distribution lines and computes the differential current phasor, including the distributed generation (DG) in the fault model. The deep neural network (DNN)-based data-mining models for fault detection and classification are built using differential current phasor datasets generated by wide variations in fault and operating conditions of the microgrid, including different types of faults with the change in fault location, fault resistance, DG penetration in grid-connected and islanding mode of operation with types of DG units. The developed DNN models are validated for their ability to detect the faults in a low-voltage CERTS microgrid and subsequently for a medium-voltage IEEE-34 bus microgrid on a MATLAB/SIMULINK platform. Furthermore, the proposed intelligent scheme is also tested on the Typhoon hardware-in-loop platform for real-time performance evaluation. The accuracy and speed in detecting and classifying faults indicate that the proposed DNN-based intelligent differential relaying scheme can be a potential candidate for providing reliable protection measures for the microgrid.

Islanding detection in microgrid using deep learning based on 1D CNN and CNN-LSTM networks

Islanding detection is a critical task due to safety hazards and technical issues for the operation of microgrids. Deep learning (DL) has been applied for islanding detection and achieved good results due to the ability of automatic feature learning in recent years. Long short term memory (LSTM) and two dimensional (2D) convolutional neural networks (CNN) based DL techniques are implemented and demonstrated well performance on islanding detection. However, one dimensional (1D) CNN is more suitable for real-time implementations since it has relatively low complexity and cost-effective than 2D CNN. In this paper, for the first time, the 1D CNN and the combination of 1D CNN-LSTM are proposed for islanding detection to better exploit the global information of islanding data samples using the strengths of both networks. The proposed methods utilize only voltage and current harmonic measurements as input at the point of common coupling (PCC). About 4000 cases under the modified CERTS microgrid model are simulated to evaluate the performance of the proposed architectures. The simulation results and the presented analysis show that the proposed networks have achieved the maximum accuracy of 100% on the task of islanding detection; especially the proposed CNN-LSTM model outperforms the other approaches. Furthermore, the robustness of the proposed methods is demonstrated with unseen samples under low none detection zone and the expansion of microgrid topology.

Intelligent Fault Detection Scheme for Microgrids With Wavelet-Based Deep Neural Networks

Fault detection is essential in microgrid control and operation, as it enables the system to perform fast fault isolation and recovery. The adoption of inverter-interfaced distributed generation in microgrids makes traditional fault detection schemes inappropriate due to their dependence on significant fault currents. In this paper, we devise an intelligent fault detection scheme for microgrid based on wavelet transform and deep neural networks. The proposed scheme aims to provide fast fault type, phase, and location information for microgrid protection and service recovery. In the scheme, branch current measurements sampled by protective relays are pre-processed by discrete wavelet transform to extract statistical features. Then all available data is input into deep neural networks to develop fault information. Compared with previous work, the proposed scheme can provide significantly better fault type classification accuracy. Moreover, the scheme can also detect the locations of faults, which are unavailable in previous work. To evaluate the performance of the proposed fault detection scheme, we conduct a comprehensive evaluation study on the CERTS microgrid and IEEE 34-bus system. The simulation results demonstrate the efficacy of the proposed scheme in terms of detection accuracy, computation time, and robustness against measurement uncertainty.

A Novel Metric to Quantify and Enable Resilient Distribution System Using Graph Theory and Choquet Integral

Network operators and utilities are challenged with increasing extreme weather conditions, resulting in interrupted power supply to critical loads. Resiliency metrics, which can capture the level of preparedness to resist adverse impact of extreme conditions on a distribution system, can be leveraged in multiple ways to provide better operation of the network and design of the future systems. In this paper, a methodology to quantify resiliency and maintain power supply to critical loads (CLs) during extreme contingencies has been proposed. Resiliency evaluation of power distribution system has been defined as a multi-criteria decision making problem and quantified using graph theoretic approach and Choquet integral. The algorithm proposed in this paper to calculate the resiliency for all feasible network configurations supplying CLs in a network is useful in planning as well as operation of the distribution network. The application of the proposed algorithm is demonstrated through several case studies using two proximal CERTS microgrids and IEEE 123 node distribution system. Simulation studies are also provided for planning of resilient network, by placing additional switches in the considered distribution systems with microgrid.

Defining and Enabling Resiliency of Electric Distribution Systems With Multiple Microgrids

This paper presents a method for quantifying and enabling the resiliency of a power distribution system using analytical hierarchical process and percolation theory. Using this metric, quantitative analysis can be done to analyze the impact of possible control decisions to pro-actively enable the resilient operation of distribution system with multiple microgrids and other resources. Developed resiliency metric can also be used in short term distribution system planning. The benefits of being able to quantify resiliency can help distribution system planning engineers and operators to justify control actions, compare different reconfiguration algorithms, and develop proactive control actions to avert power system outage due to impending catastrophic weather situations or other adverse events. Validation of the proposed method is done using modified CERTS microgrids and a modified industrial distribution system. Simulation results show topological and composite metric considering power system characteristics to quantify the resiliency of a distribution system with the proposed methodology, and improvements in resiliency using two-stage reconfiguration algorithm and multiple microgrids.

Vulnerability assessment and reconfiguration of microgrid through search vector artificial physics optimization algorithm

Taking the mathematical model and reliability parameters of the microgrid into account, this paper has proposed two vulnerability assessment indices (VAI) – weighted complex network structure parameters and comprehensive operational sensitivity to set up a vulnerability assessment system. The microgrid reconfiguration (MR) model is built with consideration of the grid-connected/island mode and the VAI. A novel artificial physics optimization algorithm with a searching vector (SVAPO) is presented and applied to minimize the system vulnerability. The technique has been successfully implemented on CERTS and 38-bus microgrids to demonstrate the performance and effectiveness of the proposed method. The results obtained are encouraging.

CERTS Microgrid Laboratory Test Bed

The CERTS Microgrid concept captures the emerging potential of distributed generation using a system approach. CERTS views generation and associated loads as a subsystem or a “microgrid.” The sources can operate in parallel to the grid or can operate in island, providing uninterruptible power-supply services. The system can disconnect from the utility during large events (i.e., faults, voltage collapses), but may also intentionally disconnect when the quality of power from the grid falls below certain standards. CERTS Microgrid concepts were demonstrated at a full-scale test bed built near Columbus, OH, and operated by American Electric Power. The testing fully confirmed earlier research that had been conducted initially through analytical simulations, then through laboratory emulations, and finally through factory acceptance testing of individual microgrid components. The islanding and resynchronization method met all Institute of Electrical and Electronics Engineers Standard 1547 and power-quality requirements. The electrical protection system was able to distinguish between normal and faulted operation. The controls were found to be robust under all conditions, including difficult motor starts and high impedance faults.

Distributed Generation Interface to the CERTS Microgrid

This paper focuses on the energy storage system and the power electronic interface included in microsources of the CERTS microgrid. CERTS stands for the Consortium for Electric Reliability Technology Solutions. The consortium was formed in 1999 to research, develop, and disseminate new methods, tools, and technologies to protect and enhance the reliability of the U.S. electric power system and efficiency of competitive electricity markets. To provide the plug-and-play feature and the power quality requirements of the CERTS microgrid, all microsources regardless of their prime mover type must have a unified dynamic performance. This necessitates attaching an energy storage module to some or all of the microsources. The storage module is attached to the prime mover through a power electronic interface that couples the microsource to the microgrid. Details of the energy storage module, the power electronic interface and the corresponding controls are described. Performance of an example microsource, which includes a synchronous generator, a storage module and an electronic interface, is studied. Dynamic performance of the example microsource when operating in the CERTS microgrid is evaluated based on digital time-domain simulations in the EMTP-RV software environment. Effectiveness of the storage module, the electronic interface and the corresponding controls in enhancing the microsource performance is verified.