Fault Detection and Localization in a Ring Bus DC Microgrid Using Current Derivatives

In the low-voltage (LV) ac microgrids (MGs), with a relatively high R/X ratio, virtual impedance is usually adopted to improve the performance of droop control applied to distributed generators (DGs). At the same time, LV dc MG using virtual impedance as droop control is emerging without adequate power flow studies. In this paper, power flow analyses for both ac and dc MGs are formulated and implemented. The mathematical models for both types of MGs considering the concept of virtual impedance are used to be in conformity with the practical control of the DGs. As a result, calculation accuracy is improved for both ac and dc MG power flow analyses, comparing with previous methods without considering virtual impedance. Case studies are conducted to verify the proposed power flow analyses in terms of convergence and accuracy. Investigation of the impact to the system of internal control parameters adopted by DGs is also conducted by using proposed method.

Photovoltaic (PV) and energy storage(ES) hybrid systems are widely used in microgrids due to their high efficiency, environmental friendliness, and flexible regulation capabilities. As platforms for integrating multiple types of distributed power sources, energy storage devices, and loads, AC/DC hybrid microgrids have attracted extensive attention. Droop control is widely used in AC/DC hybrid microgrid. By equivalent conversion of AC frequency droop and DC voltage droop coefficients, the static power can be reasonably distributed between the AC and DC microgrids, and the steady-state stability of DC voltage and AC frequency can be maintained. However, drop method cannot realize the effective allocation of dynamic power. Transient overloads of partial ES units and instantaneous overlimit of AC frequency or DC voltage may occur when system is subjected to power steps. The essence of the above problem is how to reasonably allocate instantaneous power according to the dynamic changes of DC voltage and AC frequency, or the cooperative inertia response between DC and AC microgrid. Through power and energy balance analysis, this paper establishes intrinsic equivalence relationships between AC torque and DC current, AC frequency and DC voltage, AC inertia and DC inertia, AC inductance and DC inductance. According to the equivalent relationship, an inertial cooperative control strategy for ILC is proposed, which realizes the complementary support of AC and DC side dynamic and static power, as well as the stability of AC frequency and DC voltage. Simulation results verify the correctness and effectiveness of the proposed strategy.

This study is dedicated to establish an adaptive primary droop control of interlinking converter (IC) for the islanded operation of hybrid AC–DC microgrids (MGs). In this practise, the amount of transferred power through the IC is controlled such that all of the resources in both AC and DC MGs are in equal loading per cents. To do so, two different notions are contemplated. In the first one, the IC is controlled such that the aforementioned control objective is satisfied for all possible values of transmission lines impedance angles. To this end, a new algorithm is developed within which the IC estimates the impedance angle based on the local measurements in AC MG and then adapts itself. In the second outlook, the uncertainties in association with the renewable energy resources are considered and their effects are explored on IC performance. To lessen the negative impacts of uncertainties and fluctuating power generation of renewable resources, the control of IC is updated based on the instantaneous power generation in AC and DC MGs. Accordingly, the main control objective is effectively pursued. Performance of the proposed adaptive droop control is evaluated on a test MG. Results are discussed in depth.

Detection and classification of DC and feeder faults in DC microgrid using new morphological operators with multi class AdaBoost algorithm

This paper proposes a bidirectional virtual inertial control (BVIC) strategy for an islanded hybrid AC/DC microgrid (MG) including two renewable-energy power-generation systems and an energy-storage system (ESS). The studied hybrid AC/DC MG consists of a wind farm, a PV farm, a bidirectional DC-DC converter, and a bidirectional interlinking converter (BIC). The vanadium redox flow battery (VRFB)-based ESS is connected to the DC MG through a bidirectional DC-DC converter, while the DC MG is connected to the AC MG through the BIC. On the basis of the virtual synchronous generator, this paper uses the BVIC strategy based on BIC to achieve power distribution between the DC and AC MGs, suppress the deviations of both DC-link voltage and frequency of the AC side, and compare the differences for the studied system with and without the BVIC. The steady-state frequency-domain analysis of the studied system under different operating conditions is performed. Both dynamic and transient time-domain simulations of the studied system under various disturbance conditions are also carried out.

A Solid-State Transformer as Enabling Technology for Microgrids for Integration into the Power Distribution System: An Overview

The integration of information and communication technologies (ICTs) has provided modern smart grids, such as direct current (DC) microgrids (MGs), with various advantages, e.g., the possibility of large-scale integration of renewable energy sources and complex control schemes. This extensive deployment of ICTs, however, makes DC MGs prone to a variety of cyber threats, e.g., false data injection (FDI) attacks. On this basis, this paper develops online detection and mitigation schemes to counter FDI attacks against DC MGs. Initially, in the offline training of the detection scheme, the density-based spatial clustering of applications with noise (DBSCAN) method is used for extracting prescriptive features of DC MG measurements and clustering them in healthy and under-attack operations under several uncertainties in DC MG operations. These features are used to train an adaptive network-based fuzzy inference system (ANFIS) architecture for the online identification of FDI attacks. Afterward, the trained ANFIS is used to detect FDI attacks and obtain the attack vectors benefiting from the real-time measurements estimated by an optimal state observer. Finally, an adaptive model predictive controller (AMPC) is developed to update the power-sharing commands—and mitigate the FDI attack—using the ANFIS’s obtained attack vectors. The effectiveness of the proposed framework is evaluated for several realistic FDI attack scenarios and different uncertainties. To show the scalability of the proposed detection and mitigation methods, a DC MG with eight converters is also studied in the presence of FDI attacks and uncertainties using real-time simulations.

DC microgrid is a compact framework comprising interconnected nearby sources and loads. The renewable energy source used in DC microgrids being intermittent leads to the change in the power availability as well as the fault current levels. In such situations, detecting and clearing the faults is very important to protect the DC microgrid without compromising on fault clearing time and interruption of the load. This paper proposes a hybrid Cumulative Sum (CumSum) and Wavelet transform-based approach to detect the fault. The CumSum value raises the amplitude by averaging the fault current. Wavelet transforms obtain important fault current features by decomposing the current signal. The hybrid method of CumSum and Wavelet analysis proposed here enables the detection of the fault and differentiates the fault condition from sudden load variation. Additionally, it helps to recognize the location of the fault by the wavelet energy difference. The proposed scheme is tested with a developed ring-type low voltage DC (LVDC) microgrid hardware model under various fault conditions. The scheme is implemented using TMS320F28069 digital signal processors (DSP) of Texas Instruments. The hardware results are validated using MATLAB simulation. The proposed method performance is also compared with the existing methods used for DC microgrid protection. The outcome shows that the proposed method has a high accuracy of 98.72%, selectivity of 96.08%, and reliability of 99.01%. The execution time required by the proposed method is also less.

A Novel Adaptive Protection Technique Based on Rate-of-Rise of Fault Current in DC Microgrid

This comprehensive review critically analyses the complex correlation between DC microgrids and the incorporation of Distributed Generation (DG). It offers a full evaluation of fundamental principles, advanced control strategies, technology advancements, and practical implementations in real-world scenarios. This research examines the ability of Direct Current (DC) microgrids to effectively integrate and optimize various DG sources within the context of a paradigm shift towards decentralized energy solutions. The paper critically examines the obstacles and opportunities associated with renewable energy systems and different DG technologies. It provides essential insights for researchers, engineers, and politicians who are involved in the complex process of integrating distributed energy into existing systems. This review serves as a crucial resource that contributes to the understanding of DG in DC microgrids. It plays a significant role in developing conversations surrounding energy solutions that are both resilient and sustainable. This paper not only discusses the difficulties related to the integration of DG, but also emphasizes practical examples that demonstrate the flexibility and effectiveness of DC microgrid designs. This study serves as a crucial resource for stakeholders who aim to use the potential of DC microgrids, since it encompasses a comprehensive analysis of power electronics, energy storage, and sophisticated control systems. By combining critical evaluation with practical implementation, this review offers valuable insights and guidance in the field. The complete investigation plays a significant role in advancing the integration of DG, which is essential for creating a resilient and sustainable energy environment. It provides basic information that is needed for achieving effective integration. This review paper provides a comprehensive examination of the integration of DG within DC microgrids. From laying the groundwork with fundamental principles to scrutinizing challenges, technological advancements, and practical applications, this review aims to offer a holistic understanding of the dynamic landscape where DC microgrids intersect with distributed energy generation.

This report presents a novel fault detection, characterization, and fault current control algorithm for a standalone solar-photovoltaic (PV) based dc microgrids. The protection scheme is based on the current derivative algorithm. The overcurrent and current directional/differential comparison based protection schemes are incorporated for the dc microgrid fault characterization. For a low impedance fault, the fault current is controlled based on the current/voltage thresholds and current direction. Generally, the droop method is used to control the power-sharing between the converters by controlling the reference voltage. In this article, an adaptive droop scheme is also proposed to control the fault current by calculating a virtual resistance R droop , and to control the converter output reference voltage. For a high impedance fault, differential comparison method is used to characterize the fault. These algorithms effectively control the converter pulsewidth and reduce the flow of source current from a particular converter, which helps to increase the fault clearing time. Additionally, a trip signal is sent to the corresponding dc circuit breaker (DCCB), to isolate the faulted converter, feeder or a dc bus. The dc microgrid protection design procedure is detailed, and the performance of the proposed method is verified by simulation analysis.

The DC microgrid is connected to the AC utility by parallel bidirectional power converters (BPCs) to import/export large power, whose control directly affects the performance of the grid-connected DC microgrid. Much work has focused on the hierarchical control of the DC, AC, and hybrid microgrids, but little has considered the hierarchical control of multiple parallel BPCs that directly connect the DC microgrid to the AC utility. In this paper, we propose a hierarchical control for parallel BPCs of a grid-connected DC microgrid. To suppress the potential zero-sequence circulating current in the AC side among the parallel BPCs and realize feedback linearization of the voltage control, a d-q-0 control scheme instead of a conventional d-q control scheme is proposed in the inner current loop, and the square of the DC voltage is adopted in the inner voltage loop. DC side droop control is applied to realize DC current sharing among multiple BPCs at the primary control level, and this induces DC bus voltage deviation. The quantified relationship between the current sharing error and DC voltage deviation is derived, indicating that there is a trade-off between the DC voltage deviation and current sharing error. To eliminate the current sharing error and DC voltage deviation simultaneously, slope-adjusting and voltage-shifting approaches are adopted at the secondary control level. The proposed tertiary control realizes precise active and reactive power exchange through parallel BPCs for economical operation. The proposed hierarchical control is applied for parallel BPCs of a grid-connected DC microgrid and can operate coordinately with the control for controllable/uncontrollable distributional generation. The effectiveness of the proposed control method is verified by corresponding simulation tests based on Matlab/Simulink, and the performance of the hierarchical control is evaluated for practical applications.

In a smart grid, various kinds of distributed generation (DG) sources could be connected into the main power grid in order to enhance the reliability of the power system. The combination of ac and dc distribution grid are also considered for the efficient connection of renewable power resources. In this case, one of the critical problems due to these integrations is the excessive increase in the fault current because of the presence of DG within the smart grid. In order to protect the smart grid from increasing fault current, a superconducting fault current limiter (SFCL) could be applied, which has negligible power loss and capability to limit initial fault currents effectively. This paper presents feasibility analysis results of the positioning of the SFCL and its effects on reducing fault current in a smart grid having ac and dc microgrid. The detailed power system was implemented with a microgrid having wind farm and low voltage dc grid connected with a photovoltaic farm. Transient analyses were performed for the worst case faults with the different SFCL arrangements. The strategic location of SFCL in the power grid, which could limit fault currents and has no negative effect on the DG sources, was found to be the connection point of integration of the each DG sources in the ac and dc microgrid.

• Introduces a novel method using Hilbert-Huang Transform (HHT) with LSTM networks for fault detection in DC microgrids. • Targets marine and satellite DC microgrids, addressing challenges with no zero-crossing points in traditional methods. • Demonstrates superior performance, outperforming SVM and Decision Tree techniques in fault detection time and accuracy. • Validates the method using MATLAB/Simulink, effectively detecting faults with resistance up to 1.3 ohms. • Ensures compliance with IEEE 8570 standards, enhancing reliability and applicability in real-world microgrid systems. Fault detection in DC microgrids is a major challenge because traditional protection methods like wavelet and Fourier transforms rely on zero-crossing points, which the DC signal lacks. This paper introduces a new approach combining the Hilbert-Huang Transform (HHT) and a long short-term memory (LSTM) neural network to improve fault detection and classification in DC microgrids. The proposed method first uses Empirical Mode Decomposition (EMD) to break down the fault current signal into multiple intrinsic mode function (IMFs). These IMFs capture important fault-related information at different frequency levels. Next, the Hilbert Transform is applied to these IMFs to obtain amplitude and frequency features over time. These features are used to create a set of numerical descriptors that summarize the fault characteristics. These extracted features are then fed into a two-layer LSTM neural network that can learn patterns in time-series data. The LSTM model is trained to classify the input into three categories: no fault, Pole-Ground (P-G) Fault, or Pole-Pole (P-P) Fault. Simulation tests were carried out using a MATLAB Simulink model of a 20kWatt DC microgrid under different fault conditions, noise levels, and load transients. Results show that the proposed HHT-LSTM model detects faults faster and more accurately than conventional techniques like Support Vector Machines (SVM) and Decision Trees (DT). It also meets the fault detection time requirements set by IEEE8570 and aligns with IEC standards. The model remains robust even under noisy conditions, making it reliable for real-time fault protection in marine, satellite, and an effective solution for enhancing fault detection in modern DC microgrids.

A significant amount of interest from the scientific community has been focused on Direct Current (DC) microgrids in recent years, as a direct result of the proliferation of appliances that run on DC power. Nevertheless, the acceptability of DC microgrids by power utilities is still restricted owing to the challenges connected with the construction of a dependable protection system. This is because obtaining dependable protection for DC microgrids might be difficult due to the large size of DC fault current, its quick rate of rise, and the lack of zero crossing. In addition, the intermittent nature of the power produced by non-traditional distributed generators necessitates adaptability in response to a wide range of climatic circumstances. The method used in this study to solve the problems outlined above is developing an Ensemble of Decision Tree-based protection solutions for a multi-terminal DC microgrid. The plan developed accommodates the intermittent nature of renewable energy sources. It only uses information that is local to the current and voltage signals, which allows it to execute the duties of fault detection and classification, and prevents the disadvantages associated with communication networks, such as data loss and delay.