Artificial‐Intelligence‐Based Reduced Sensor Voltage Control Strategy for DC Microgrid Applications

Direct Current (DC) microgrids are expected to become larger due to the rapid growth of DC energy sources and power loads. As the scale of the system expends, the importance of voltage control will be increased to operate power systems stably. Many studies have been performed on voltage control methods in a DC microgrid, but most of them focused only on a small scale microgrid, such as a building microgrid. Therefore, a new control method is needed for a middle or large scale DC microgrid. This paper analyzes voltage drop problems in a large DC microgrid and proposes a cooperative voltage control scheme with a distributed generator (DG) and a grid connected converter (GCC). For the voltage control with DGs, their location and capacity should be considered for economic operation in the systems. Accordingly, an optimal DG allocation algorithm is proposed to minimize the capacity of a DG for voltage control in DC microgrids. The proposed methods are verified with typical load types by a simulation using MATLAB and PSCAD/EMTDC.

Nowadays, behaving as constant power loads (CPLs), rectifiers and voltage regulators are extensively used in microgrids (MGs). The MG dynamic behavior challenges both stability and control effectiveness in the presence of CPLs. CPLs characteristics such as negative incremental resistance, synchronization, and control loop dynamic with similar frequency range of the inverter disturb severely the MG stability. Additionally, the MG stability problem will be more sophisticated with a high penetration level of CPLs in MGs. The stability analysis becomes more essential especially with high-penetrated CPLs. In this paper, the dynamic stability performance of an MG involving a high penetration level of CPLs is analyzed and investigated. An autonomous MG engaging a number of CPLs and inverter distributed generations (DGs) is modeled and designed using MATLAB. Voltage, current, and power controllers are optimally designed, controlling the inverter DGs output. A power droop controller is implemented to share the output DGs powers. Meanwhile, the current and voltage controllers are employed to control the output voltage and current of all DGs. A phase-locked loop (PLL) is essentially utilized to synchronize the CPLs with the MG. The controller gains of the inverters, CPLs, power sharing control, and PLL are optimally devised using particle swarm optimization (PSO). As a weighted objective function, the error in the DC voltage of the CPL and active power of the DG is minimized in the optimal problem based on the time-domain simulation. Under the presence of high penetrated CPLs, all controllers are coordinately tuned to ensure an enhanced dynamic stability of the MG. The impact of the highly penetrated CPLs on the MG dynamic stability is investigated. To confirm the effectiveness of the proposed technique, different disturbances are applied. The analysis shows that the MG system experiences the instability challenges due to the high penetrated CPLs. The simulation results confirm the effectiveness of the proposed method to improve the MG dynamic stability performance.

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.

With increasing connection of distributed generations (DGs), the power flow in electric distribution network is no longer unidirectional and the network has become active distribution network. Thus, the connection of DGs in electric distribution networks has created a challenge for distribution network operators due to bidirectional power flow. One of the main technical challenges of an active distribution network is to maintain an acceptable voltage level in the system. This has initiated efforts in using various voltage control strategies to regulate the network voltage profile so that the voltage is maintained at its allowable voltage limits. A number of voltage control methods have been applied to solve voltage control problems associated with the connection of DGs in a distribution system. These voltage control strategies may be classified as decentralized or distributed and centralized or coordinated voltage control. From the literature, a number of coordinated voltage control strategies have been implemented to provide better and faster control to the system. This chapter presents an improved coordinated voltage control method in active electric distribution network by coordinating the three voltage control methods, namely, power factor control, on load tap changer control and generation curtailment control. These voltage control methods are coordinated using fuzzy logic by considering the load bus voltages and DG power as inputs and the voltage control actions as the outputs. The fuzzy logic if-then control rules which are generated to build the fuzzy logic control system are based on the simulations results as well as from the previous works. Results obtained using the fuzzy logic based coordinated voltage control has shown that the voltages are able to be kept within its permissible limits.

Over the past decade, the high penetration of renewable-based distributed generation (DG) units has witnessed a considerable rise in electrical networks. In this context, direct current (DC) microgrids based on DGs are being preferred due to having less complexity for the establishment and control. At the same time, they offer higher efficiency and reliability compared to their alternating current (AC) counterparts. This paper proposes a new model predictive control (MPC)-trained artificial neural network (ANN) control strategy being an ANN-MPC instead of conventional cascaded-proportional-integral (PI)-trained ANN control for dynamic damping of photovoltaic (PV)-battery-based grid-connected DC microgrids. Unlike traditional controllers, the proposed control approach more rapidly attains generation-load power balancing under variable climate input (meteorological sensor data) and output (load demand), hence achieving quick DC-bus voltage damping. The proposed ANN-MPC scheme is examined under different operating conditions, and the results are compared with the ANN-based conventional PI controller. The results show the proposed control strategy's efficacy to lessen the instability issues and achieve effective attenuation of oscillations in DC microgrids.

The hierarchical control strategy enables seamless integration of power electronics-based distributed generators (DGs) into microgrids (MGs). This work presents a distributed coordinated secondary voltage and frequency control strategy of islanded MGs, utilizing distributed model predictive control (DMPC) approach. The Proposed approach is entirely distributed in nature, with plug and play features and requires minimal communication network with information exchange between immediate neighbors. This technique facilitates efficient information exchange and obviates the need for centralized control. Droop control is the primary controller for all the DGs, which leads to the deviations in DG voltage and operating frequency from standard nominal values. Secondary control enables the restoration of DG voltage and frequency to their nominal values. Input–output feedback linearization (IOFL) is used to transform nonlinear dynamics of inverter-interfaced DGs into first-order linear dynamics between input and output. Compared to some published works, the single-integrator dynamics eliminates the need to compute the derivative of the output voltage. Furthermore, DMPC has been used for all DG units as secondary controller, to achieve voltage and frequency regulation. This approach takes advantage of distributed architecture with optimal control actions and enhances reliability and flexibility of MG operations. Moreover, the proposed approach ensures that voltage and frequency of overall system are restored to its reference value while maintaining accuracy in real power sharing. Effectiveness of proposed control scheme is validated with simulations conducted on islanded MG test system using MATLAB/Simulink toolbox.

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.

The concept of microgrid (MG) in the field of power engineering rapidly adapted for the rapid improvement in power electronics technology. Due to the benefit of such a distribution system with direct current (DC) minimum losses and simple to integrate with an electrical energy storage system DC MG have attracted more attention these days. DC MG is a hot research topic consists of various sources due to the expansion of distributed generation. The critical issue is to dispense regulated voltage support and accurate power-sharing capability in such a multisource DC MGs. Since the control scheme plays an essential role in establishing premier power quality and high efficiency of MG, a thorough review of trending control methodology in DC MGs is required. This paper explains the primary control methodology within the hierarchical control architecture for DC MGs.

Due to improved efficiency, reduced conversion stages, integration of distributed generators (DGs), and increased use of DC loads, DC microgrids (MGs) are earning extensive approval in modern power grids. Islanding detection is a critical issue for both AC and DC MGs. If the detection technique fails, it may lead to instability in the system, affecting the load-side devices and employees. Therefore, this article aims for the development of a novel passive islanding detection scheme (IDS) based on the cumulative sum of superimposed impedance ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSOSI</i> ) for DC MGs. In the proposed IDS, voltage and current samples are collected at DGs terminals to analyze a DC MG response for an islanding condition. Superimposed impedance ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SI</i> ) and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSOSI</i> are calculated, and the variations in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CSOSI</i> are measured to detect islanding phenomena. The proposed passive IDS is simulated using MATLAB/Simulink environment, and the experimental validation is accomplished on a DC MG hardware setup. The obtained results depict that the proposed technique effectively discriminates islanding events from other nonislanding scenarios and acts effectively during islanding scenarios for radial and ring-type systems irrespective of the type and number of DGs present in the DC MGs. The effectiveness of the proposed scheme is also tested for both constant resistance and constant power loads.

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

The high penetration levels of distributed generators (DGs), on the one hand, make the operational state of distribution grids increasingly complex; on the other hand, they enrich voltage regulation measures. Consequently, a voltage control strategy of the distribution grid for coordinating DGs and other voltage regulation devices is needed. This paper applies the security region methodology to coordinated voltage control. To meet the demand of online voltage security assessment and control in the distribution grids, a hyperplane expression for the boundary of the static voltage security region (SVSR) in complex power injection spaces is derived, and a corresponding fast generation method is proposed. Additionally, based on SVSR, a two-stage voltage control strategy is proposed. In the first stage, the regulation devices are automatically selected according to the coefficients in the hyperplane expressions, and the regulation effect can be estimated by SVSR. In the second stage, the outputs of the devices are determined by utilizing an optimization technique. The proposed voltage control strategy is tested by using a modified PG&E69-bus distribution grid, and the results verify the effectiveness.

Microgrid (MG), considered the future of power systems, has gained significant attention in the present time. MG's inherent capability of combining both AC and DC operations in a single frame. Its function is crucial in achieving a sustainable electricity supply and advances a decentralized power system approach. MG not only combines the benefits of both AC and DC but also incorporates distributed generations (DGs) and distributed renewable energy sources (DREs). However, the advancement of DGs and DREs with power electronics interfaces (PEIs), and smart loads (SLs) poses the dynamic nature of DGs and DREs such as bi-directional power flow and varying output, which has introduced certain protection challenges in MG maneuver. Therefore, this paper reviews the protection challenges in MG and critically addresses the assessment of existing protection schemes developed so far. It also categorizes the MG protection issues, including its components and faults, separately for both AC and DC MGs and analyzes the performance of measurement and protection techniques for MG protection strategy on the basis of operational modes AC and DC MGs.

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.

In this article, a new procedure is proposed on the basis of Hilbert-Huang Transform and deep learning for cyber-attacks detection in direct current (DC) micro-grids (MGs) as well as detection of the attacks in distributed generation (DG) units and its sensors. An advanced elective group deep learning method with Krill Herd Optimization (KHO) algorithm is proposed. At first, Hilbert-Huang Transform is used with the aim of extracting the signals feature and next these features are applied as the multiple deep input basis models are made with the aim of capturing automatically sentient traits from raw fluctuation signals. At third, to make sure the variety of the basis patterns, linear decoder, denoising autoencoder and sparse autoencoder are applied to make various deep autoencoders, respectively. Further, Bootstrap is applied with the aim of designing separate educational data subsets for any base model. Fourth, for implementing selective ensemble learning, a combination strategy of enhanced weighted voting (EWV) with class-particular thresholds is studied. Eventually, KHO algorithm is applied with the aim of adaptive selecting the optimal class-specific thresholds. In the offered tactic, firstly, a DC micro-grid is functioned and controlled with the lack of any false data injection attacks (FDIAs) to collect adequate information within the usual operation needed for the educating of deep learning networks. It is noteworthy that, in the procedure of datum production, load variable is also determined with the aim of having distinctive datasets for cyber-attack scenarios and load variables. Also, to provide more realistic method, the smart plug-in electric vehicle is also considered in the model. Outcomes of Simulation in various scenarios are applied with the aim of verifying the benefit of the offered procedure. The outcomes propose that the offered procedure is able to more accurate and robust know various type of false data injection attack over than 93.76% accuracy detection of true rate.

Electric spring (ES) is an innovative design that can regulate voltage abnormalities developed by intermittent nonconventional power sources (NCPS) connected with smart grid. Renewable power sources are employed nowadays in contemporary power systems worldwide to overcome the fast depletion of fossil fuels. Nonetheless, the infiltration of these NCPS into the power system produces an adverse effect on the power system stability and quality. This research recommends various control schemes and offers a potent approach called as ES which regulates the mains voltage in spite of changes caused by the intermittent NCPS in the DC microgrid (MG) environment. This investigation analyses a Model Predictive Controller (MPC) which is enforced to a DC-DC converter to help in the examination the power quality issues like harmonics reduction, voltage regulation and voltage ripple. In addition, the paper also addresses the battery’s lifetime by lessening its storage capacity. The suggested system is simulated using Matlab/Simulink software. The performance specifications of the system are achieved by the MPC controller and the parameters are studied and correlated with the artificial neural network (ANN) based intelligent controller and the conventional PI controller results. The suitability and cogency of the control scheme are affirmed, simulation studies are carried out on the sample test systems, and the simulation results are reported.