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The stabilisation and transient performance improvement of a non-linear direct current (DC) microgrid (MG) dynamic which is controlled by a power buffer is studied. The injecting current of the power buffer is designed based on a novel reset gain-scheduling controller. This controller is constructed via a polytopic linear parameter varying-based dynamic controller that is equipped with the resetting action. The dynamic controller theoretically assures the closed-loop stability of the DC MG through the continuous-time Lyapunov theory. Also, the resetting action enhances the transient performance. The sufficient conditions of the controller design are derived in terms of linear matrix inequality and are solved by convex numerical methods. Comparing with the existing results, the proposed hybrid approach provides a non-continuous current reference of the power buffer, which efficiently improves the transient performance. To show the merits of the proposed approach, it is applied to a non-linear DC MG feeding one constant power load (CPL). Results show the simplicity of designing the proposed controller and the settling time and overshoot of the DC bus voltage compared to the state-of-the-art methods. Also, software-in-the-loop simulations are presented to prove the practical applicability of the proposed controller.

The dc microgrid (MG) system has several advantages over the ac one. Therefore, it recently became a preferred architecture in numerous industrial applications. Many loads in dc MGs are electronically regulated and they challenge the stability of the system due to their constant power load (CPL) behavior. This letter proposes a systematic and simple approach to design an improved state feedback controller for the power buffer that can stabilize the dc MGs with multiple CPLs. Based on the so-called sector nonlinearity approach, the nonlinear dc MG with several CPLs is exactly represented in a Takagi–Sugeno fuzzy model. Then, by employing the quadratic D-stability theory, the sufficient conditions to guarantee the stability and transient performance of the closed-loop system are obtained in terms of linear matrix inequalities (LMIs), such that the decay rate and oscillatory behavior of the closed-loop dc MG system are guaranteed to lie inside a predefined region. The LMI conditions can be numerically solved by utilizing the YALMIP toolbox in the MATLAB. Finally, to illustrate the merits and implementation validity of the proposed approach, some hardware-in-the-loop real-time simulation (RTS) results on a dc MG, which feeds two CPLs, are presented. In comparison with the state-of-the-art techniques, the RTS results indicate the simplicity, validity, and better performance of the proposed approach. According to the results, one can conclude that the proposed approach not only theoretically assures the stability but also guarantees the fast convergence and less oscillatory response of the dc MGs with multiple CPLs.

The performance of de shipboard power systems (SPSs) may degrade due to the negative impedance of constant power loads (CPLs) connected to dc microgrids (MGs). To control the dcSPS effectively, estimation of the instantaneous power flow to the time-varying uncertain CPLs is necessary. Furthermore, fast adaptive control is needed to deal with changes in the CPL power flow and quick stabilization of the dc MGs. Such a controller typically uses injection current from an energy storage system for actuation. Since measuring the CPLs' powers require installing current sensors that are both costly and not optimal, an estimation of the CPLs' powers should be employed. In this paper, an extended Kalman filter (EKF) is developed to estimate a time-varying power of uncertain CPLs in a dc MG based on measuring capacitor voltages. The estimated power is then used in a Takagi-Sugeno fuzzy-based model predictive controller (MPC) to manipulate the energy storage unit. The proposed approach is tested experimentally on a dc MG that feeds a single CPL. The experimental results show that the proposed MPC controller alongside the developed EKF improves the transient performance and the stability margin of the dc MGs used in the SPSs.

Nowadays, due to the fact that motor efficiency is strictly related to the diminution of emissions, researchers pay much attention to find a robust and efficient motor control technique for hybrid electric vehicles (HEVs). In this study, an optimal type-2 fuzzy fractional proportional plus integral derivative (IT2FOFP+ID) controller is applied to solve the throttle position and speed control problem of the HEVs. It is undeniable that the performance and effectiveness of the fuzzy-based proportional-integral-derivative (PID) controllers are dependent on its gains’ value. Hence, a novel improved heuristic technique, called IJAYA algorithm, is employed for the online tuning of the coefficients embedded in the specific controller structure. In contrast with the classical control methodologies that suffer from the lack of the self-regulating feature, the established controller has been adjusted online automatically. As another advantage of this control strategy, it is a model-free scheme and does not need the mathematical computation to identify the system model. To appraise the supremacy of the optimal IT2FOFP+ID controller than the other prevalent methodologies, a highly nonlinear EV model is utilized as a case study. In addition, the usefulness and robustness of the proposed method are tested by the experimental data, the EPA New York City Cycle. In the end, the new time-varying proposed technique is validated and implemented in hardware-in-the-loop real-time simulation based on OPAL-RT to study the feasibility of the designed IT2FP+ID controller with check outcomes on a physical platform.

Switching sliding mode control (SSMC) can be utilized as a robust control technique, which is appropriate for the control of highly non-linear power systems like chaotic systems. The present study proposes a switching sliding mode control technique for control and chaos suppression of non-autonomous fractional-order (FO) nonlinear power systems with uncertainties and external disturbances. In the first step, a novel fractional switching sliding surface is introduced as well as its stability analysis to the origin is demonstrated. In the second step, based on the fractional version of the Lyapunov stability theory, a robust non-singular control law is designed to ensure the convergence of the system trajectories to the proposed sliding surface. Next, the proposed SSMC approach is utilized for designing a single input switching control technique for the stabilization of a class of 3D FO chaotic power systems. In order to evaluate the effectiveness and robustness of the suggested approach in practice, two examples including control and the stabilization of FO chaotic electric motors are illustrated.

In this article, a novel fuzzy proportional integral derivative (PID) controller with filtered derivative action and fractional order integrator (fuzzy PIλDF controller) is proposed to solve automatic generation control (AGC) problem in power system. The optimization task for fine-tuning parameters of the proposed controller structure is accomplished by cuckoo optimization algorithm (COA). To appraise the usefulness and practicability of proposed COA optimized fuzzy PIλDF controller, four extensively used interconnected test systems, that is, two-area non-reheat thermal, two-area multi-source, three-area thermal and three-area hydro-thermal power plants, are considered. Different nonlinearity such as generation rate constraint (GRC) and governor dead band (GDB) as a source of physical constraints are taken into account in the model of the three-area power systems to examine the ability of the proposed technique to handle practical challenges. The acceptability and novelty of COA-based fuzzy PIλDF controller to solve aforesaid test systems are evaluated in comparison with some recently reported approaches. The consequences of time domain simulation reveal that designed secondary controllers provide a desirable level of performance and stability compared with other existing strategies. Additionally, to explore the robustness of the proposed technique, sensitivity analysis is conducted by varying the operating loading conditions and system parameters within a specific tolerable range.

In this work, a novel nonlinear approach is proposed for the stabilization of microgrids with constant power loads (CPLs). The proposed method is constructed based on the incorporation of a pseudo-extended Kalman filter into stochastic nonlinear model predictive control (MPC). In order to achieve high-performance and optimal control in DC microgrids, estimating the instantaneous power flow of the uncertain constant power loads and the available power units is essential. Thus, by utilizing the advantages of the stochastic nonlinear model predictive control and the pseudo-extended Kalman filter, an effective control solution for the stabilization of DC islanded microgrids with CPLs is established. This technique develops a constrained controller for practical application to handle the states and control input constraints explicitly; furthermore, as it estimates the current by using the pseudo-EKF, it is a current-senseless approach. As noisy measurements are taken into account for the state estimation, it leads to a less conservative control action rather than the classical robust MPC, whereas it guarantees the global asymptotic stability in the presence of noisy measurements and parameter uncertainty. To validate the performance of the proposed controller, the attained results are compared to state-of-the-art controllers. Furthermore, the implementability of the proposed method is validated using real-time simulations on dSPACE hardware.

DC shipboard power systems are usually under the threat of instability due to the incremental negative impedance of the constant power loads connected to the DC bus. The instantaneous power flow, which moves along the time-varying uncertain constant power loads, is required to be estimated to enhance the control efficiency of the shipboard power grid. In this paper, at first, the estimation of the load power variation of uncertain constant power loads in a shipboard DC microgrid is conducted in the finite time by adopting the finite-time disturbance observer method. The estimated load power is then received by the fixed-time terminal sliding mode controller to stabilize the entire marine power grid as well as tracking the reference voltage of the DC bus in the fixed time independent of initial conditions. A Lyapunov-based stability analysis rigorously proves the fixed-time convergence of the proposed disturbance observer-based controller. Finally, Model-in-the-Loop real-time simulations using dSPACE emulator are carried out under various operating cases to evaluate the practical implementation of the presented disturbance observer-based scheme.

Cancer illness still is one of the most common illnesses in the world, which is constantly rising. Chemotherapy plays a crucial role in treating cancer patients. In this paper, we have presented a novel intelligent sensor for controlling and adjusting chemotherapy parameters which consist of an ultra-local (ULM) controller based on a deep deterministic policy gradient (DDPG). First, the feedback signal is provided using a sensor to calculate the population of cells. Then, a controller sends the proper control commands to the actuator (chemotherapy). In the suggested scheme, the ULM is applied to the dynamic model of cancer. In order to shrink tumor cells and rising immune and normal cells at the same time. Moreover, for improving the performance of the established ULM scheme, a DDPG algorithm with the actor-critic structure is used for tuning the parameters of ULM in an adaptive manner. To demonstrate the supremacy of the DDPG based ULM controller, the conventional ULM and proportional integrator (PI) are also designed for the cancer treatment. Simulation outcomes prove the improved cancer treatment compared to the ULM and PI schemes.

The power system of an all-electric ship (AES) establishes an independent microgrid using the distributed energy resources, energy storage devices, and power electronic converters. As a hybrid energy system (HES), the power system of an AES works as a unified system where each part can affect the reliability of the other parts. The systemic reliability centered maintenance (SRCM), which efficiently enhances the reliability and safety of the AES by identifying optimal maintenance tasks of the AES, is considered in this article to apply to the entire system. In order to calculate the reliability and optimal maintenance schedule, the Markov process and Enhanced JAYA (EJAYA) are utilized. A layer of protection analysis (LOPA), which is a risk management technique, is adopted to assess the safety of the system. A hybrid molten carbonate fuel cell, photovoltaic (PV), and lithium-ion battery are considered as energy sources of the AES. Based on two common standards, DNVGL-ST-0033 and DNVGL-ST-0373, the suggested maintenance planning method can be used in industrial applications. Eventually, in order to validate the proposed method, a model-in-the-loop real-time simulation using dSPACE is carried out. The obtained results show the applicability and efficiency of the proposed method for improving reliability and safety.

Due to the increasing impacts of ships pollutants on the environment and the preventive laws that are tightening every day, the utilization of all-electric ships is a recent emerging technology. Being a promising technology, the usage of fuel cells as the main energy resource of marine vessels is an interesting choice. In this article, an all-electric hybrid energy system with zero emission based on fuel cell, battery, and cold-ironing is proposed and analyzed. To this end, actual data of a ferry boat, including load profiles and paths, are considered to assess the feasibility of the proposed energy system. The configuration of the boat and energy resources as well as the problem constraints are modeled and analyzed. Finally, the boat's energy management in hourly form for a one-day period is implemented. The improved sine cosine algorithm is used for the power dispatch optimization, and all models are implemented in MATLAB software. Based on the analysis results, the proposed hybrid system and the energy management method have high performance as an applicable method for the marine vessels. In addition, to be a zero-emission ship, the proposed system has an acceptable energy cost.

Utilization of renewable energies in association with energy storage is increased in different applications such as electrical vehicles (EVs), electric boats (EBs), and smart grids. A robust controller strategy plays a significant role to optimally utilize the energy resources available in a power system. In this paper, a suitable controller for the energy resources of an EB which consists of a 5 kW solar power plant, 5 kW fuel cell, and 2 kW battery package is designed based on the linear parameter varying (LPV) controller design approach. Initially, all component dynamics are augmented, and by exploiting the sector-nonlinearity approach, the LPV representation is derived. Then, the LPV control method determines the suitable gains of the states’ feedbacks to provide the required pulse commands of the boost converters of the energy resources to regulate the DC-link voltage and supply the power of EB loads. Comparing with the state-of-the-art nonlinear control methods, the developed control approach assures the stability of the overall system, as it considers all component dynamics in the design procedure. The real-time simulation results demonstrate the performance of the designed controller in the creation of a constant DC-link voltage.

Thinking-green consciousness is pushing international authorities to impose new regulations about the use of energy and CO2 emissions. Many hybrid energy solutions using renewable energies have been proposed and deployed. Designing real-time controllers that perform efficient and safe energy management is a challenging task due strong safety requirements, dynamic operation cost and real-time power demands. In this paper, we consider a hybrid combination of Fuel Cells (FC) and batteries sub-systems to aliment a marine vessel. Our contribution is to design a safety-driven efficient energy management control to dynamically schedule the energy sources to supply the real-time power requests so that 1) we maintain the system safety by not overloading or heating up an energy source; 2) reduce the operation cost by considering the cheapest energy source in a real-time manner. We use the symbolic model checker Uppaal to verify the safety properties. To demonstrate the efficiency of our energy scheduling, we analyze an actual case study.

This chapter presents novel structured single-input interval type-2 fuzzy logic controllers (SI-IT2-FLCs) for the frequency damping of multi-microgrids (MMGs), whereas the application of electric vehicles (EVs) is considered in this context. For this purpose, a new SI-IT2-fuzzy PD/fuzzy PI (SI-IT2-FPD/FPI) controller is designed on two levels. Initially, an improved whale optimization algorithm, called IWOA, is adopted to adjust the setting of the gains embedded in the FPD/FPI section effectively. Then, the impact of the footprint of uncertainty (FOU), to offer extra design freedom, on control surface generation of SI-IT2-FLC has been investigated. In this way, various control surfaces were generated by varying a single coefficient which forms the FOU. Lastly, by adopting hardware-in-the-loop (HIL) simulator, the feasibility and usefulness of the suggested framework are verified from a real-time perspective.

In this paper, a new structure is proposed for a boost dc–dc converter based on the voltage-lift (VL) technique. The main advantages of the proposed converter are its lack of transformer, simple structure, free and low input current ripple, high voltage gain capability by using an input source, suitable voltage stress on semiconductors and lower output capacitance. Herein, the analysis of the proposed converter operating and its elements voltage and current relations in continuous conduction mode (CCM) and discontinuous conduction mode (DCM) are presented, and the voltage gain of each operating mode is individually calculated. Additionally, the critical inductance, current stress of switches, calculation of passive components’ values and efficiency are analyzed. In addition, the proposed converter is compared with other studied boost converters in terms of ideal voltage gain in the CCM and the number of active and passive components, maximum voltage stress on semiconductors, and situation of input current ripples. The correctness of the theoretical concepts is examined from the experimental results using the laboratory prototype.

Compared to the previous communication networks, 5G networks provide safe and ultra-reliable connections and enable much more reliable communication with shorter response times, higher data speeds, and a much larger number of simultaneous users. With the growth of the utilization of 5G technology in several applications, the challenges of these power networks will also increase. The power plant in the 5G systems includes several converters feeding DC loads. Most of the loads in the 5G power plant behave as a constant power load (CPL). Due to the negative impedance specifications of CPLs, the stability of such a power electronic converter is threatened when feeding the non-ideal CPLs. In response to this challenge, eliminating the destructive effect of these CPLs in the 5G systems is a necessity. To stabilize the voltage of the dc-dc converter feeding CPLs in the 5G systems, this paper presents a model-free nonsingular terminal sliding-mode control (MFNTSMC) with a single input interval type-2 fuzzy logic control (SIT2-FLC) to solve the instability problem of the power electronic converters feeding CPLs in the 5G systems. The Hardware-in-the-Loop (HIL) tests are accomplished to verify the accuracy of the MFNTSMC based SIT2-FLC suggested controller against the instant changes of the CPL power.

Utilizing drones in many applications such as video and photography, farms, search and rescue operations, construction industry for mapping and site monitoring has increased. However, their operating time is limited according to the capacity of their batteries. Therefore, many researchers attempt to enhance the operating time of drones. In this study, to the best of the authors' knowledge for the first time, the feasibility assessment of a laser-based wireless power transfer (WPT) mechanism is theoretically investigated on drone applications to increase their operating time. In this method, drones can be charged during operation wirelessly at a considerable distance with acceptable efficiency. The Analytical code is developed in Engineering Equation Solver (EES) software. The acquired results for the main components of the system have a good agreement with published papers of other researchers. In this study, three commercial photovoltaic (PV) materials were evaluated. The results show that an equipped drone with laser-based WPT system is able to approximately receive net power of 73.5, 62.6, and 33.2 W at a 500 m distance by GaAs, CdTe, and c-Si PV materials, respectively, while the laser module consumes around 600 W electrical power. Moreover, since the optical power of the laser is concentrated on PV panel, a two-phase cooling system is suggested for PV panel to reduce the temperature of the under radiate area. Results demonstrate that the temperature of GaAs PV material without a two-phase cooling system is approximately three times more than the temperature of PV panels with a two-phase cooling system at high power transfer rates.

Residential Microgrids and Rural Electrifications contains an overview of microgrids' architecture, load assessments, designing of microgrids for residential systems, and rural electrifications to help readers understand the fundamentals. Including many new topics in the field of home automation and the application of IoT for microgrids monitoring and control, the book includes sections on the infrastructure necessary for charging Electric Vehicles in residential systems and rural electrifications and how to estimate the energy and cost of various combinations of energy resources. Many examples and practical case studies are included to enhance and reinforce learning objective goals. Those in engineering research and technical professions will be able to perform energy and cost analyses of various combinations of energy sources by using advanced, real simulation tools.

Since isolated microgrid systems are prone to unwanted frequency oscillations against load fluctuations and randomness of renewable sources, Load frequency control (LFC) plays a vital role in restoring the frequency stability of such systems. In the microgrid with virtual inertias, the existing dynamic analysis of LFC assessment is not enough to meet the grid stability due to the virtual inertia control. Confronted with this problem, a non-integer (or fractional order) type of proportional-integral (PI) controller design is developed in this work for LFC synthesis of microgrid with the application of virtual inertia. In particular, a new analytical tuning scheme is adopted for the proper setting of the established non-integer controller to effectively damp the frequency oscillations of the concerned system under the virtual inertia. In the suggested analytical scheme, the stability boundary locus (SBL) and geometric centroid methods are used to tune the coefficients of the non-integer LFC controller. The analysis of stability region curves has been conducted to study the dynamic effects of the virtual inertia and damping over the microgrid performance. Finally, the efficiency of the suggested non-integer controller design and accuracy of the stability analysis in regulating a microgrid with virtual inertia and damping are proved.

In this paper, optimal energy resources scheduling for a hybrid diesel/battery ship that sails in shallow water is studied. Sailing in shallow waters increases the impact of water depth on the ship’s resistance. Changing the ship’s resistance affects the speed, consumed fuel and consequently the operation cost. This paper proposes a non-linear model to find the optimal operation of diesel generator and battery bank for supplying the ships’ engines considering the impact of variable water depth of the traveling route on the ship’s operation cost. To this end, first, different types of resistance are calculated for the understudy real case ship. Then the impact of the water depth on the resistance is investigated, and finally, the optimization problem is formulated and solved using a Genetic Algorithm. Simulation results show the efficiency of the proposed scheduling method in decreasing the total operation cost while considering the different operational constraints.

The time-varying nonlinearity and uncertain nature of the wind turbine (WT) systems lead to changes of an operating point, which necessitates a powerful control methodology to regulate such complex systems. The challenges of regulation of WT systems have been intensified in the presence of time delay. In this research, the pitch angle regulation of a 2-mass WT model with time delay is addressed by the ultralocal model (ULM) control scheme, by means of Smith predictor (SP). In particular, to deal with the difficulties in adjusting the ULM coefficients, we adopt a deep deterministic policy gradient (DDPG) as the tuner mechanism of these coefficients. Firstly, we formulate the ULM scheme in a model-independent manner for the pitch angle control of WT to ameliorate the plant dynamic performance. Secondly, we incorporate the SP into the control structure to compensate for the effects of time delay in the WT plant. Thirdly, we employ the DDPG with the actor-critic framework for adaptive autotuning of the ULM controller coefficients. The analysis and tuning are of tutorial value for practitioners and engineers, and the usefulness and applicability are verified by a comparative simulation.

This brief proposes a dynamic consensus algorithm-based nonlinear I-V droop control for the balancing state of charge of energy storage systems (ESSs) in DC Nanogrids (DCNGs). The dynamic consensus algorithm (DCA) provides a coordinated secondary control with sharing information between Distributed Generation (DG) units to regulate the output power of each DG based on the capacity and state of charge (SoC) of the ESSs. Furthermore, a novel high bandwidth fractional order general type-2 fuzzy logic proportional-integral-derivative (FOGT2FPID) controller is applied in the secondary control level to ensure fast and accurate voltage regulation, and SoC balancing in the DCNG. In the primary control level, a nonlinear I-V droop control approach provides fast dynamic and accurate power-sharing among DGs. Moreover, the proposed control method can provide reliability, modularity, and flexibility. Compared with conventional methods, over-current failures and abruptly disconnection of DGs are prevented with the proposed controller. Experimental results are shown to verify the effectiveness of the proposed control scheme under different scenarios using facilities at the Microgrid Laboratory, Aalborg University.

In this paper, optimal energy management of a zero-emission ferry ship is studied. Fuel cells are considered as the main energy resources of the ship. Solar energy, batteries, and cold-ironing are also used as auxiliary energy resources. Non-linearity in the efficiency of the fuel cells is estimated using piecewise linear models. A mixed-integer linear programming method is applied to model the optimal energy management problem of the ship. Moreover, model predictive control approach is applied to update control parameters in the model and provide the ability of adapting the energy management system with forecast updates. The effectiveness of the proposed method is investigated through an actual case study. The analysis results demonstrate that the proposed method is able to consider different operation aspects of the zero-emission ferry ship effectively. GAMS software is used to solve the optimization problem. In this paper, optimal energy management of a zero-emission ferry ship is studied. Fuel cells are considered as the main energy resources of the ship. Solar energy, batteries, and cold-ironing are also used as auxiliary energy resources. Non-linearity in the efficiency of the fuel cells is estimated using piecewise linear models. A mixed-integer linear programming method is applied to model the optimal energy management problem of the ship. Moreover, model predictive control approach is applied to update control parameters in the model and provide the ability of adapting the energy management system with forecast updates. The effectiveness of the proposed method is investigated through an actual case study. The analysis results demonstrate that the proposed method is able to consider different operation aspects of the zero-emission ferry ship effectively. GAMS software is used to solve the optimization problem.

Today, regarding the increase in using renewable energy resources, DC/DC boost converters have become more popular than in the past, in particular, the multilevel DC/DC boost converter due to its high voltage gain and simple components to implement. However, the constant power loads (CPLs) are contributed to instability in these converters because of the intense non-linear nature of CPLs. This paper intends to address this issue by applying a new method based on a combination of the Nonlinear Integral-Backstepping technique (NIB) and Model-Free Control (MFC) to a DC/DC multilevel boost converter (MBC) in presence of a non-ideal CPL. To validate the effectiveness of the proposed controller theoretical analysis and simulation results are represented.

In this paper, we propose a safety-driven cost effective scheduling controller to arbitrate and operate the energy resources of a maritime hybrid energy application. The proposed control algorithm enables efficient energy management to dynamically schedule the energy sources to supply the realtime power requests so that 1) we maintain the system safety by not overloading or heating up an energy source; 2) reduce the operation cost by considering the cheapest energy source in a real-time manner. The efficiency and safety of our scheduling algorithm have been examined using Uppaal model checker. The experiment outputs show that our controller maintains the system safety and guarantees the lowest operation cost.

Appearing faults in a practical system is dispensable, and if it is not compensated, it results in poor system performance or even dysfunction of the system. The fault detection has become a promising challenging issue to guarantee the safety and reliability of systems. In this paper, a novel fuzzy-based sliding mode observer for the simultaneous actuator and sensor fault reconstruction of nonlinear systems subjected to external disturbance is proposed. The proposed approach employs the Takagi-Sugeno fuzzy model, sliding mode observer and non-quadratic Lyapunov function. First, by filtering the system output, a fictitious system whose actuator faults are the original actuator and sensor faults is constructed. Then, by considering the [Formula: see text] performance criteria, the effect of disturbance on the state estimations is minimized. It is proved that the estimations asymptotically converge to their actual values for non-perturbed systems. In the process of designing the observer gains, some transformation matrices are obtained by solving linear matrix inequalities. The proposed approach has some superiority over the existing methods. First, considering the non-quadratic Lyapunov function leads to relaxed results and good estimation performance. Second, using the sliding mode observer makes the proposed approach insensitive to the uncertainties and unknown inputs and determines the shape and size of the fault. Third, assuming the premise variables are immeasurable makes the presented approach more applicable. In conclusion, two practical systems are considered and simulation results illustrate the merits of the proposed approach in comparison with the recent methods from the fast and precise fault detection performance viewpoints.

In this paper, a novel energy management system (EMS) with two operating horizons is proposed for a residential microgrid application. The microgrid utilizes the energies of a photovoltaic, a fuel cell (FC), and a battery bank to supply the local loads through a combination of electric and magnetic buses. The proposed microgrid operates in a large number of grid-connected and off-grid operation modes. The EMS includes a long-Term data prediction unit based on a 2-D dynamic programing and a short-Term fuzzy controller. The long-Term prediction unit is designed to determine the appropriate variation range of the battery state of charge and FC state of hydrogen. The efficiency performance of the microgrid components, predicted energy generation and demand, energy cost, and the system constraints are taken into account. The resultant data then are sent to the short-Term fuzzy controller which determines the operation mode of the microgrid based on the real-Time condition of the microgrid elements. A prototype of the proposed microgrid including the EMS is developed, and experimental tests are conducted for three different energy management scenarios. The proposed management technique is validated through energy distribution and cost analysis.

Environmental pollutants, as a global concern, have led to a general increase in the utilization of renewable energy resources instead of fossil fuels. Accordingly, the penetration of these resources in all-electric ships, as well as power grids, has increased in recent years. In this article, in order to have a zero-emission and cost-effective energy management in an all-electric ferry boat, a new reliable and optimal power scheduling is presented that uses fuel cell and battery energy storage systems. Furthermore, the real information including load profile and paths is considered for the case study to assess the feasibility and superiority of the proposed approach. In addition to the cost of energy management, to have a reliable combination of the proposed resources, the loss of load expectation (LOLE) as a reliability index is considered in the energy management context and the problem is solved by the deep reinforcement learning in a multiobjective manner. The results of the consideration of two common standards, including DNVGL-ST-0033 and DNVGL-ST-0373, demonstrate that the proposed energy management method is applicable in industrial applications. Finally, the real-time simulation-based hardware-in-the-loop (HIL) is conducted to validate the performance and efficacy of the suggested power scheduling for the emission-free ships.

This paper proposes a novel adaptive controller based on digital twin (DT) by integrating software-in-loop (SIL) and hardware-in-loop (HIL). This work aims to reduce the difference between the SIL controller and its physical controller counterpart using the DT concept. To highlight the applicability of the suggested methodology, the regulation control of a horizontal variable speed wind turbine (WT) is considered for the design and assessment purposes. In the presented digital twin framework, the active disturbance rejection controller (ADRC) is implemented for the pitch angle control of the WT plant in both SIL and HIL environments. The design of the ADRC controllers in the DT framework is accomplished by adopting deep deterministic policy gradient (DDPG) in two stages: ( i ) by employing a fitness evaluation of wind speed error, the internal coefficients of HIL controller are adjusted based on DDPG for the regulation of WT plant, and ( ii ) the difference between the rotor speed waveforms in HIL and SIL are reduced by DDPG to obtain a similar output behavior of the system in these environments. Some examinations based on DT are conducted to validate the effectiveness, high dynamic performance, robustness and adaptability of the suggested method in comparison to the prevalent state-of-the-art techniques. The suggested controller is seen to be significantly more efficient especially in the compensation of high aerodynamic variations, unknown uncertainties and also mechanical stresses on the plant drive train.

Robotic manipulator control is a challenging task due to its nonlinear, interacting multi-input–multi-output dynamics. In this paper, we proposed an optimal robust fractional-order fuzzy PID controller based on multi-objective particle swarm optimization (MOPSO) algorithm for a two-link robotic manipulator. In order to minimize position and trajectory-tracking error, MOPSO finds the optimal parameters of fuzzy membership functions and order of the fractional operators. To show the effectiveness of the proposed controller, the software-in-the-loop real-time simulation is executed, and the results are compared to conventional fuzzy PID, fractional-order PID, and linear PID controllers.

This paper presents the applicability of the future fifth-generation network technology for a marine vessel power system with sea wave energy, photovoltaic, and energy storage systems. In this paper, a new optimal structured interval type-2 fractional order fuzzy proportional derivative/fuzzy proportional integral controller is proposed for the secondary load frequency control (LFC) of a networked shipboard multimicrogrid. The effect of the various degradation factors associated with the communication infrastructure such as the time delay and packet loss is modeled and addressed to assess the system performance in the networked control system (NCS) operation. The parameters embedded in the established structure are decisive factors, which significantly affect the quality of control output actions. Accordingly, by employing the concepts of the black-hole optimization algorithm and Lévy flight, an enhanced JAYA algorithm is proposed to adjust the setting of the established structured controller. Finally, comprehensive studies and hardware-in-the-loop real-time simulations are conducted to appraise the acceptability of the suggested controller for a secondary LFC problem in the face of the uncertain NCS.

This paper proposes a novel structure for industrial demand response aggregators (IDRAs) to provide operational flexibility for the power system. A robust self-scheduling approach is formulated for the first time to optimize different sub-processes of the whole production line of heavy industries. The new approach satisfies the customer order with the lowest energy cost. Numerical studies are implemented on eight integrated cement factories, from Khorasan Regional Electric Company, in the east of Iran. The results show that the integrated model of heavy industries provides guaranteed flexibility to the system when a power shortage occurs or system reliability is jeopardized.

In recent years, deep brain stimulation (DBS) has been one of the most effective methods for treating movement disorders, including hand tremor in advanced Parkinson’s disease (PD). In order to decrees the Parkinson’s tremor, a model of basal ganglia (BG) is often used to design a closed-loop control scheme for DBS. First, a feedback signal is provided using a sensor mounted to the patient’s finger to measure the tremor values. Then, a controller sends the appropriate control commands to the actuator, and finally, the BG areas of the brain actuator are simulated by the actuator. In the present paper, to control Parkinson’s tremor and reduce the value of stimulation intensity efficiently, two areas of BG such as subthalamic nucleus (STN) and globus pallidus internal (GPi) were controlled simultaneously. This strategy provides a reduction in the applied field and side-effects (e.g. muscle contraction and speech disorder) resulting from stimulation intensity. In particular, a new model-free scheme, called intelligent single input interval type-2 fuzzy logic (iSIT2-FL) combined with non-integer sliding mode control (SMC), is proposed to control two areas of BG. In the suggested strategy, an extended state observer (ESO) is established to approximate the unknown BG dynamics, whereas the non-integer SMC is applied to remove the ESO estimation error. Comparative simulation explorations are performed subsequently to ascertain the superior performance of the suggested intelligent control scheme over that of the state-of-the-art approaches.

This study proposes a new decentralised non-linear I-V droop control to achieve current sharing and voltage restoration for paralleled DC nanogrids (DCNGs) in a cluster. The performance of the conventional droop methods in current sharing among parallel converters degrades due to line impedances. The proposed method improves current sharing among the DCNGs with parallel-connected converters by considering the effect of line impedance, which is an important issue in practical applications. By the proposed method, desired current sharing from the light-load to heavy-load condition is achieved. Since only local information (the output voltage and output current of the DCNGs) is used, the proposed method is fully decentralised, and no communication infrastructure is required, which improves the reliability and stability of the overall system. Furthermore, Lyapunov stability theory is applied to investigate the stability of the proposed method. Additionally, the plug-andplay feature is achieved as an important desired functionality in the DCNGs as well as the proposed method provides scalability and modularity for the DCNGs in a cluster. In the end, theoretical analysis and experimental results validate the effectiveness of the proposed control framework for different scenarios.

In this paper, a new interleaved AC-DC converter with a reduced number of semiconductor components for high power energy storage applications is proposed. The interleaved structure reduces the AC side current ripple as well as increases the overall current rating. Moreover, the proposed switching pattern reduces switching and the developed controller provides reactive power control during both power flow directions. The proposed structure is simulated and analyzed using MATLAB/Simulink software, and a 3.5kW prototype of the system has been implemented in the Lab. The results confirm the performance of the proposed topology. There is a seamless transition between operation with different power factors and the current ripple is significantly reduced.

This brief proposes a novel approach to apply reliability centered maintenance (RCM) on a distance protection system in a smart grid. In the proposed approach, instead of focusing on individual equipment, the RCM is implemented for the whole protection system. The approach helps to calculate the reliability of protection systems by overcoming the limitation of their hidden function and also considering the effect of all system equipment on reliability. Markov process and improved Jaya algorithm (IJA) are used to calculate the reliability and find the optimal maintenance plan. The proposed approach has been applied to real data of smart grid distance protection systems, which results in a higher level of reliability and less cost. Furthermore, in order to validate the suggested technique and its efficiency, the simulation is carried out using a real-time digital simulators and hardware-in-the-loop testing equipment known as opal-RT technology.

This paper proposes a novel hybrid algorithm based on a combination of the simple quadratic interpolation and the symbiosis organisms search algorithm (SQI-SOS) for finding the optimal location and size of capacitors in radial distribution networks. The objective of the problem is to minimize the system operating cost so that the net yearly savings of the system are increased. The effectiveness of the SQI-SOS has been tested on 33-, 69-, and 119-bus radial distribution networks with different load models. The obtained results from the test system by the proposed SQI-SOS are compared with those from the conventional SOS and other mature optimization methods in the literature. The result comparison has shown that the proposed SQI-SOS algorithm can provide a better solution than the other methods. Accordingly, the proposed SQI-SOS can be a very effective and efficient method for dealing with the optimal capacitor placement problem in distribution networks.

This paper proposes a deep deterministic policy gradient (DDPG) based nonlinear integral backstepping (NIB) in combination with model free control (MFC) for pitch angle control of variable speed wind turbine. In particular, the controller has been presented as a digital twin (DT) concept, which is an increasingly growing method in a variety of applications. In DDPG-NIB-MFC, the pitch angle is considered as the control input that depends on the optimal rotor speed, which is usually derived from effective wind speed. The system stability according to the Lyapunov theory can be achieved by the recursive nature of the backstepping theory and the integral action has been used to compensate for the steady-state error. Moreover, due to the nonlinear characteristics of wind turbines, the MFC aims to handle the un-modeled system dynamics and disturbances. The DDPG algorithm with actor-critic structure has been added in the proposed control structure to efficiently and adaptively tune the controller parameters embedded in the NIB controller. Under this effort, a digital twin of a presented controller is defined as a real-time and probabilistic model which is implemented on the digital signal processor (DSP) computing device. To ensure the performance of the proposed approach and output behavior of the system, software-in-loop (SIL) and hardware-in-loop (HIL) testing procedures have been considered. From the simulation and implementation outcomes, it can be concluded that the proposed backstepping controller based DDPG is more effective, robust, and adaptive than the backstepping and proportional-integral (PI) controllers optimized by particle swarm optimization (PSO) in the presence of uncertainties and disturbances.

Due to environmental and economic issues as well as the high performance of marine vessels, efficient energy using has been becoming more demanding. Also, in order to have a zero-emission ship, the utilization of a fuel cell combined with energy storage such as batteries gets more and more attention. In this work, a zero-emission hybrid energy system, including fuel cells, batteries, and cold-ironing, is employed to have an environmentally friendly vessel, and to create condition in which ship operates with high performance, both energy management and components sizing of fuel cells and batteries using real data of ferry boat and intelligent optimization method are done simultaneously. In addition, all constraints related to energy management and component sizing with the topography of the boat and electric power sources are represented and analyzed thoroughly. Ultimately, hourly energy management and component sizing for one specific day are considered in this work, and to optimize this problem, the Improved Sine Cosine Algorithm (ISCA) is utilized. According to obtained results, the proposed energy management and component sizing result in the high-performance ship which could be utilized in the marine industry.

This paper proposes a new fuzzy model predictive control approach for continuous-time nonlinear systems in terms of linear matrix inequalities (LMIs). The proposed approach is based on the Takagi–Sugeno fuzzy modeling, a quadratic Lyapunov function, and a sampled-data parallel distributed compensation controller with constant sampling time. The goal is designing the sampled-data controller such that at each sampling time, the stability of the closed-loop system is guaranteed and an infinite horizon cost function is minimised. The main advantage of the proposed approach is to eliminate the approximations induced from discretizing the original system and cost function upper bound minimisation. Consequently, a lower bound of the cost function is obtained and the performance of the proposed model predictive controller is improved compared to the recently published papers in the same field of interest. In addition, the Euclidean norm constraint of the control input vector is derived in terms of LMIs. To illustrate the merits of the proposed approach, the proposed technique is applied to a continuous stirred tank reactor system.

This study utilizes a new optimal model-free controller based on a sliding mode technique to make a balance between load demands and power generations in the time-delay stand-alone microgrid system with electric vehicles. Forasmuch as the performance of the proposed controller is related to its parameter values, a new modified optimization algorithm, entitled the Black Hole optimization search algorithm, is applied to tune the coefficients of the proposed model-free technique. Finally, in order to validate the feasibility of the suggested approach under load disturbances, detailed simulation, and hardware-in-the-loop experimental results are shown.

© 2007-2012 IEEE. It is undeniable that marine vessel systems play an important role to transfer huge loads and weapons with low cost. However, ship power systems produce a lot of greenhouse gases, which in turn lead to serious environmental pollution. Hence, the utilizing of wind turbines (WTs), solar generation, sea wave energy (SWE), and energy storage systems (ESSs) in marine vessel power systems have been attracting a lot of attention in recent years. In this paper, it is assumed that a marine vessel power system with photovoltaic (PV), WT, SWE, and ESS can be regarded as a mobile-islanded MG. Then, a novel topology for hybrid shipboard microgrids (MGs) is presented. Next, in order to make a balance between consumption and power generation in shipboard MGs, an optimal modified model-free nonlinear sliding mode controller is introduced for the secondary load frequency control. Since the quality of the control actions of the proposed model-free approach depends on its parameters, a hybrid version of the sine-cosine algorithm (SCA) and wavelet-mutation (WM), called SCAWM, is employed to find the best value of these coefficients. Comparisons are conducted with other existing methodologies, such as model predictive control, interval type-2 fuzzy logic controller, and conventional PI (PI) to establish the supremacy of the newly suggested control strategy. Finally, a real-time hardware-in-the-loop (HIL) simulation based on OPAL-RT is accomplished to affirm the applicability of the suggested controller, from a systemic perspective, for the load frequency control problem in the shipboard MG.

This paper presents a new approach for solving the rearrangement of the unbalanced distribution network. Objectives such as reducing power losses, improving the voltage profile and increasing the voltage stability index of the system are also pursued in this article. In the other part of this paper, we have rearranged the distribution network by using the optimal location of dispersed production units. Harmony search algorithm is used to achieve these goals simultaneously, as well as the optimal location of distribution generation units in the distribution network. Performing a rearrangement in the presence of optimal distribution generation units positions leads to far better results. A fast load distribution technique called the bus injection to branch current and branch current to bus voltage has been used to perform online rearrangement. In solving the stated target functions, the voltage constraints, line flow capacity and the radial limitation of the distribution network are also considered. Then, separately, we explain the target functions described in the article. The proposed method on the standard system is IEEE 25-Bass test which is done in two scenarios including: (1) rearrangement of unbalanced distribution network without the presence of distribution generation units, (2) rearrangement of unbalanced distribution in the presence of distribution generation units. The results obtained at each scenario of simulation were compared with each other and with the results obtained before the rearrangement. Finally, the best and most effective results are presented. Simulation is done using MATLAB software.

Owing to the severe limitations imposed by the Intergovernmental panel on climate change and the rapid development of the automobile industry, the utilize of energy storage units in vehicle systems has been increasingly attracting attention. Hence, this study proposes a new fuzzy Proportional Derivative + Integral (PD+I) controller based on a non-integer system for the robust speed control of highly nonlinear hybrid electric vehicles. In order to have an optimal and adaptive controller, the controller coefficients are tuned online by a novel optimization algorithm, which is called Adaptive Black Hole. In addition, the performance and robustness of the proposed method are tested by the experimental data, the Supplemental Federal Test Procedure (SFTP - US06). In order to prove the superiority and effectiveness of the suggested novel smart controller, a valid comparison is conducted between the results of the proposed method and recent studies on the same topic like the Model Predictive Control and the conventional online fuzzy PD+I (OFPD+I) controllers. Finally, extensive studies and hardware-in-the-loop simulations are presented to prove that the proposed controller can track a desired reference signal with lower deviation and show that the performance of suggested method is more robust in comparison with the prior-art controllers for all the case studies.

The extensive use of electric vehicles (EVs) can reduce concerns about climate change and fossil fuel shortages. One of the main obstacles to accepting EVs is the limitation of charging stations, which consists of high-charge batteries and high-energy charging infrastructure. A new transformer-less topology for boost dc-dc converters with higher power density and lower switch stress is proposed in this paper, which may be a suitable candidate for high-power fast-charging battery chargers of EVs. Throughout this paper, two operating modes of the proposed converter, continuous current mode (CCM) and discontinuous current mode (DCM), are analyzed in detail. Additionally, critical inductances and design considerations for the proposed converter are calculated. Finally, real-time verifications based on hardware-in-loop (HiL) simulation are carried out to assess the correctness of the proposed theoretical concepts.

Extensive use of wind turbine (WT) systems brings remarkable challenges to the stability and safety of the power systems. Due to the difficulty and complexity of modeling such large plants, the model-independent strategies are preferred for the control of the WT plants which eliminates the need to model identification. This current work proposes a novel model-independent control methodology in the rotor side converter (RSC) part to ameliorate low voltage ride through (LVRT) ability especially for the doubly-fed induction generator (DFIG) WT. A novel model-independent nonsingular terminal sliding mode control (MINTSMC) was developed based on the principle of the ultra-local pattern. In the suggested controller, the MINTSMC scheme was designed to stabilize the RSC of the DFIG, and a sliding-mode supervisor was adopted to determine the unknown dynamics of the proposed system. An auxiliary dual input interval type 2 fuzzy logic control (DIT2-FLC) was established in a model-independent control structure to remove the estimation error of the sliding mode observer. Real-time examinations have been carried out using a Real-Time Model in Loop (RT-MiL) for validating the applicability of the proposed model-independent control in a real-time platform. To evaluate the usefulness and supremacy of the MINTSMC based DIT2-FLC, the real-time outcomes are compared with outcomes of RSC regulated conventional PI controller and MINTSMC controller.

The wind and hydro technologies express a significant part of the electricity generation section. This study presents an optimal coordinated bidding strategy of wind, cascaded hydro generation, and pumped-storage (PS) units. One of the chief purposes of this study is maximisation the profit of the wind and hydro plants by participating in the day-ahead energy and ancillary service markets. The regulation and spinning reserve markets are regarded as ancillary services. Thanks to the inherent variability and uncertainty of wind power, it does not participate in the ancillary service market. Hydro company is constructed of several cascaded hydro units which design alongside a river basin as well as a PS unit. In this study, the risk is modelled by using conditional value at risk. To reach the optimum solution, a new improved clonal selection algorithm is applied which shows the effectiveness of the proposed method for optimising a generation companies (GENCOs) profit.