Adaptive Decentralized Fully-Actuated Two-Step Voltage Regulation Control for DC Microgrids With Nonlinear ZIP Loads Under Malicious Attacks

In dc microgrids, load power sharing and bus voltage regulation are two common control objectives. In this article, a consensus-based algorithm is presented to achieve proportional power sharing and regulation of weighted geometric mean of bus voltages in dc microgrids with ZIP (constant impedance, constant current, and constant power) loads simultaneously. By using the virtue of the Laplacian matrices of undirected connected graphs, a lemma is derived to assist the stability analysis of the dc microgrids. Thus, a sufficient condition that stabilizes the system with ZIP loads is established. In addition, with the help of a distributed voltage regulation error estimator, the tuning of the bus voltages can be realized without the requirement on initial voltage conditions. Through the Lyapunov synthesis, the large-signal stability of the closed-loop system is theoretically ensured for a wide range of load conditions. Finally, simulation studies are performed to validate the merits of the proposed consensus-based algorithm.

In this paper, a novel intelligent controller for grid connection of DC microgrids is presented. The proposed controller allows the bi-directional AC/DC converter, linking the DC microgrid to the main utility grid, to provide the main functionalities of an inverter. Such functions include bi-directional power flow control and independent control of both active power (P) and reactive power (Q). In addition, the developed controller acts as an intelligent active power filter (APF) to the main grid. This is because it autonomously detects any power quality problems, filters out load-created harmonics and compensates unbalances and/or DC offsets resulting from the local AC load connected at the point of common coupling (PCC). The objective of the controller is to force the AC grid currents at the PCC to be balanced three-phase currents with minor harmonics while maintaining its controllability of power flow regardless of the characteristics of the local AC load. The performance of the developed controller was examined under several extreme cases including: non-linear loads, distorted load currents, unbalanced and offset conditions. The results show that the developed controller succeeds to maintain bi-directional P/Q flow controllability, allows the inverter to provide ancillary services such as reactive power compensation or voltage control to the grid while simultaneously acting as an active power filter to assure improved power quality at the PCC under various loading conditions.

In this letter we propose a new distributed control scheme, achieving current sharing and average voltage regulation in direct current (dc) microgrids. The considered dc microgrid is composed of several distributed generation units (DGUs) interconnected through resistive-inductive power lines. Each DGU includes a generic energy source that supplies a local current load through a dc-dc buck converter. The proposed distributed control scheme achieves current sharing and average voltage regulation, independently of the initial condition of the controlled microgrid. Moreover, the proposed solution requires only measurements of the generated currents, and is independent of the microgrid parameters and the topology of the used communication network, facilitating plug-and-play capabilities. Global convergence to a desired steady state is proven and simulations indicate a good performance.

This article puts forward a robust model predictive control (MPC) strategy fortified by a learning model within the framework of fully actuated system (FAS) approach. The proposed strategy is crafted to tackle the control difficulties of robotic manipulators under the limitations of joint angles and angular velocities. By leveraging a radial basis function neural network (RBFNN) to offset the inaccuracies in the manipulator’s dynamic modelling, a FAS model with RBFNN is constructed. Based on this, the FAS approach is utilised to formulate the nominal controller, which can efficiently manage the nonlinear and coupled dynamics of the system. To deal with the disparities between the learning model and the actual system dynamics, the robust MPC is incorporated. This also ensures compliance with operating limits and improves the accuracy of the tracking control. Moreover, the iterative feasibility and stability analysis are completed. The effectiveness of the proposed method is validated through simulations with the Franka robotic numerical model.

The paradigm shift in electrical power grids and the increased interest towards decentralisation has opened a new window in the design, control and theoretical analysis of small scale power systems, i.e. microgrids, which aim at the integration and utilisation of renewable energy sources, energy storage systems and responsive loads at a local scale. Given their DC nature, DC microgrids have attracted significant interest as they provide a natural interface to the main grid by avoiding additional conversion steps. The aim of this thesis is to design and analyse novel hierarchical control schemes, both at the primary and secondary control level, that guarantee tight voltage regulation, accurate power sharing, current or voltage limitation, and present a straightforward approach towards deriving stability conditions for DC microgrids that incorporate nonlinear loads. As the microgrid configuration is paramount in the theoretical analysis, a rigorous method of computing the admittance matrix is developed that facilitates the stability analysis of DC microgrid systems supplying a constant impedance (Z), constant current (I) or constant power (P) load. This method is particularly useful as it permits the factorisation of the admittance matrix, while separating the singular matrices. In this way, the closed-loop asymptotic stability proof can be more easily approached by isolating the singularities and then, employing straightforward linear algebra tools, such as quadratic eigenvalue problem (QEP) theory, to derive the stability conditions. As stated in the technical specifications of every source, a crucial issue is represented by the ability to protect itself and its interface device (power converter) during faults, transients and unrealistic power demands. That is why current-limiting control is often combined with the primary control, i.e. often droop control, in DC microgrids to ensure the desired unit protection. In this thesis, novel current-limiting droop controllers areformulated for DC microgrids consisting initially of multiple unidirectional DC/DC boost converters and, later on, bidirectional DC/DC boost and three-phase AC/DC converters, that integrate different distributed generation units with the local grid. In contrast to the traditional approaches that use small-signal modelling, here, the accurate nonlinear models of the converter units are taken into account to prove the boundedness and the current-limiting property, by employing Lyapunov methods and the ultimate boundedness theory. Exponential stability at the desired equilibrium point is mathematically guaranteed, and further analysed from a graphical perspective, providing insights of the load’s effect onto the system performance and stability. To address the shortcomings that conventional droop control approaches introduce, i.e. inaccurate power sharing and significant load voltage drop, a DC microgrid architecture that takes into account the power converter dynamics of distributed generation units is deployed under decentralised primary and distributed secondary control scheme, in a hierarchical control framework. At the primary control layer, a novel current-limiting droop control scheme is implemented in a decentralised manner, whereas at the secondary control layer a fully distributed controller that performs a voltage restoration and improves the power sharing is deployed using a nearest-neighbour coupling communication network. By investigating for the first time both the dynamics of the converters with the nonlinear load and the two-layer control, singular perturbation theory is applied to analytically guarantee the stability of the entire DC microgrid. Finally, apart from the desired overcurrent protection, since DC capacitors are customary used at the output of each converter unit to stabilise the output voltage, they also introduce a maximum voltage limit. Hence, as the need for protection against overvoltages has emerged, droop controllers with inherent overvoltage protection are also proposed for parallel and meshed configuration networks. The upper limit of the voltage of each source is rigorously proven using ultimate boundedness theory. Asymptotic stability to the desired equilibrium for the closed-loop system is analytically guaranteed, and detailed conditions are derived to guide the control design. In order to validate the effectiveness of the different control methodologies developed in this thesis, both simulation and experimental testing are performed for each one of the methods, and are also compared with the conventional approaches to highlight their superiority.

Maximum span determination and optimal sizing of cable for improved performance of droop-controlled DC microgrid

An offline optimization approach based on energy storage management response in a microgrid was not fast and not reliable enough to control and adjust the system efficiently after the loss of the utility grid. Thus, it causes system inefficiency and collapse in the presence of violent changes of loads or outage of distributed generations. To solve such a problem, more real-time management is needed. Any changes in loads/generations should be compensated successfully by a battery energy storage system (BESS) in a short period of time. This paper presents a new method for the intelligent online management of both active and reactive power of a BESS based on a radial basis function neural network (RBFNN) incorporating particle swarm optimization (PSO) to prevent the stand-alone microgrid from instability and system collapse. BESS is centrally controlled by a controller developed by the proposed RBFNN. PSO is used to determine the optimized active and reactive power at every load/generation changing situation to monitor the effect of system frequency, voltage, and reference power regulation. These optimized power data are then employed as target data for the RBFNN generalization and training process. To enable the online updating of the operating parameters, the proposed RBFNN is implemented in the management process. With an appropriate RBFNN training, the optimum active and reactive power can directly be obtained without the necessity of performing the PSO optimization process at any change of load/generation. The results show that the predictive results of the proposed RBFNN model only slightly differed from the target results based on PSO and have a minimum statistical error compared to the predictive results based on the multilayer perceptron neural network (MLPNN) model. The proposed RBFNN is suitable for the online estimation of the active and reactive power of BESS and can be used for real-time energy storage management as an online controller.

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.

In this paper, a novel distributed control algorithm for current sharing and voltage regulation in DC microgrids is proposed. The DC microgrid is composed of several distributed generation units, including buck converters and current loads. The considered model permits an arbitrary network topology and is affected by an unknown load demand and modeling uncertainties. The proposed control strategy exploits a communication network to achieve proportional current sharing using a consensus-like algorithm. Voltage regulation is achieved by constraining the system to a suitable manifold. Two robust control strategies of sliding mode type are developed to reach the desired manifold in a finite time. The proposed control scheme is formally analyzed, proving the achievement of proportional current sharing, while guaranteeing that the weighted average voltage of the microgrid is identical to the weighted average of the voltage references.

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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.

This paper deals with the model predictive current control of a three‐level bidirectional buck‐boost converter for a battery energy storage system in a bi‐polar direct current (DC) microgrid. It comprises a solar photovoltaic system, a wind energy system, a battery and DC loads. The proposed control scheme provides voltage balance and voltage regulation at the bi‐polar DC link during dynamic operating conditions, such as variations in solar irradiance, wind velocity and load. Predictive current control utilises the converter's discrete behaviour to identify the suitable switching states that minimise the cost function. Furthermore, bidirectional power flow capabilities allow the connection of energy storage devices, such as batteries and ultra‐capacitors, to the bi‐polar DC microgrid. The proposed system is verified through simulations and validated on a lab‐scale prototype. The results have been found satisfactory with respect to steady‐state error, peak overshoot, and settling time.

A new passivity-based voltage tracker for islanded Direct Current (DC) microgrids is presented in this paper. The proposed design develops a new sufficient condition for passivity-based state feedback with proportional and integral control using the attracting ellipsoid method. In this paper, we consider the time behavior of the extended vector, which completely describes the principle properties of the closed-loop system such as the boundedness of the trajectories within some ellipsoid and the dependence of its “size” on the feedback gains. The next step, which we are realizing in this paper, is the minimization of the attractive ellipsoid by selecting the “best” admissible feedback parameters. Here, it is important to note that the applied feedback is of PD-type (proportional differential) on the system state and I-type (integral) on the output. This is a new construction of the suggested feedback which gives several advantages for a designer. The suggested control is decentralized and uses only the local states; it is cost-effective and avoids the time delays in the communication networks which are needed if centralized control is used. The suggested control is carried out in the bilinear matrix inequality (BMI) framework. Extensive simulation is performed on a test system composed of renewable energy sources, under plug and play (PnP) operations, and uncertainties in distribution lines and loads. The performance of the proposed decentralized voltage controller is compared with that of a voltage tracker present in the literature. The comparison shows the improvements introduced by the proposed control ensure the stability of the dc bus voltage and a quick response under different scenarios of operating conditions.

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.

This work is associated with the renewable energy based microgrid for a standalone operation. The places where the renewable energy sources such as wind, solar, hydro etc. are in abundance can be used to generate electricity by developing microgrid (MG) such as wind- hydro MG. A synchronous reluctance generator (SyRG) is used as a constant speed hydro turbine and a permanent magnet brushless DC generator (PMBLDC) as a variable speed wind turbine. The wind power is converted into electricity and converted into DC power using three phase diode bridge with a boost converter, a maximum power point tracking (MPPT) based perturb and observe (P&O) technique is applied. This power is fed to a battery bank and the consumer loads. The battery bank is attached at DC bus of VSI (Voltage Source Inverter). The SyRG is directly feeding to the AC loads and VSI is connected at the PCC (Point Of Common Coupling). This VSI is also used for the power quality improvements through harmonic suppression of nonlinear loads, voltage regulation during contingencies like load unbalance, reactive power compensation according to the system requirement etc. at PCC. It is capable of providing power balance with various changes. For the appropriate functioning of VSI, a reweighted zero attractor least mean square (RZALMS) control algorithm is applied to generate PWM switching pulses. A MG model is developed in MATLAB/ Simulink environment to simulate the MG performance in normal and dynamic conditions at linear and nonlinear loads.