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This paper presents a modal analysis‐based impact analysis of the change in system inertia due to the integration of the large‐scale doubly fed induction generator (DFIG)‐based wind turbine system (WTS) on the power system damping in inter‐area oscillation modes. The impact of replacement of a synchronous generator by WTS of same MVA capacity on system dynamics such as emergence of new critical modes of inter‐area oscillations affecting system stability is studied. Further, a double‐channel H∞ scheme‐based wide‐area damping controller (WADC) is proposed for WTS, which provides sufficient damping of prominent oscillation modes and enhanced stability margin to alleviate the negative impact of reduction in system inertia. The comparative assessment of the proposed controller with a PSS‐based WADC on a modified New England 10‐machine system validates the performance and is found to be more effective in mitigating inter‐area oscillation in the system. The real‐time feasibility of the proposed double‐channel H∞ WADC is evaluated on the Real‐Time Digital Simulator (RTDS) platform. The simulation results show that the proposed WADC can successfully mitigate various inter‐area modes simultaneously. Furthermore, it provides robust performance for a variety of contingencies, time delays in feedback signals, and uncertainties associated with different operating scenarios of WTS. [ABSTRACT FROM AUTHOR]

Active vibration control is a critical issue of the wind turbine in the field of marine energy. First, based on a three-degree-of-freedom wind turbine, a state space model subject to wind and wave loads is obtained. Then, a delayed state feedback control scheme is illustrated to reduce the vibration of platform pitch angle and tower top foreaft displacement, where the control channel includes time-delay state signals. The designed controller's existence conditions are investigated. The simulation results show that the delayed feedback H ∞ controller can significantly suppress wind- and wave-induced vibration of the wind turbine. Furthermore, it presents potential advantages over the delay-free feedback H ∞ controller and the classic linear quadratic regulator in two aspects: vibration control performance and control cost. [ABSTRACT FROM AUTHOR]

Uncertainty is an important factor that needs to be considered while analyzing the performance of any engineering system. In order to quantify uncertainty, fuzzy set theory is frequently used by most of researchers, including energy system experts. According to the classical reliability theory, component lifetimes have crisp parameters, but due to uncertainty and inaccuracy in data, it is sometimes very difficult to determine the exact values of these parameters in real-world systems. To overcome this difficulty in the current research, failure and repair rates were taken as triangular fuzzy numbers to determine the fuzzy availability of a system undergoing calendar-based periodic inspection subject to multiple failure modes (FMs). It was assumed that each component in the system had an exponential failure rate and repair rate with fuzzy parameters. System FMs were explicitly taken into account when a functional state of the system was considered. Each FM had a random failure time. On the occurrence of any failure, a random time was selected for the relevant corrective repair work. The proposed research was studied for one of the major sources of green energy, namely a wind turbine system wherein all the derived propositions have been implemented on it. [ABSTRACT FROM AUTHOR]

With the increasing penetration of wind turbine (WT) systems with permanent magnet synchronous generators (PMSGs) into the power grids, the back-to-back converter (BTB) has become the key element interfacing wind sources and power grids. Compared to the grid-following voltage source converter (GFL-VSC), the grid-forming VSC (GFM-VSC) shows voltage and frequency support capabilities, which meets the requirement of grid codes for WT systems. Usually, the linear regulator is employed to realize the tracking of voltage and current of GFM-VSCs, but it has limitations of complex parameter design and dynamic performance. Recently, the model predictive control (MPC) is a promising alternative controller due to the easy adoption and fast control response. This paper proposes a novel MPC method for BTB to achieve grid-forming function. The model-based control concept of the MPC effectively overcomes the complex parameter-tuning process of the cascaded linear regulators. In addition, the overshoot in the step-response of the active power of GFM-VSCs during transient process is effectively improved by using a new multi-objective cost function. The reduced power overshoot is beneficial for fully utilizing the overload capacity of the converter, avoiding damage to semiconductor devices and causing system blocking. Finally, the simulation and experiments have confirmed the feasibility of the proposed MPC method.

This paper presents a dual-layer approach for managing and controlling an AC Microgrid (MG). The MG integrates a Photovoltaic System (PVS), Wind Turbine System (WTS), a Battery Storage System (BSS), all interconnected with the utility grid. The dual-layer is structured into a Control Layer (CL) and an Energy Management System Layer (EMS-L). The CL proposes an efficient model coupled with a system control, ensuring high power quality for the AC microgrid. This system employs a Particle Swarm Optimization (PSO) algorithm to optimize the set point tracking performance. Simultaneously, the EMS-L utilizes a Sparrow Search Algorithm (SSA) with the objective of minimizing the total operating costs of the AC microgrid. This is achieved by employing a 15-minutes step time over a 24-hours period, enhancing both precision and rapidity of the system’s operation. This enhanced temporal resolution effectively models and responds to fluctuations in energy demand, supply variability and environmental factors. This synergistic integration allows for nuanced and efficient energy flow management in order to optimise the MG performance. A significant aspect of the research is the comparative analysis of the proposed SSA-EMS with PSO and Genetic Algorithm (GA), alongside an unoptimized system. This analysis highlights the hybrid methodology’s superior efficiency and effectiveness. The robust framework, combining SSA at the EMS layer with PSO at the control layer, has been empirically tested using a real world data to confirm its effectiveness and resilience in practical scenarios. These tests provide solid evidence of the methodology’s potential in boosting the sustainable and reliable operation of microgrids. Significantly, the SSA-EMS showcases notable cost efficiency, achieving reductions of 33.34% and 41.18% compared to PSO and GA. Impressively, compared to an unoptimized system, the SSA-EMS demonstrates an even more remarkable cost reduction of 49.43% and 59.10% while considering the impact on battery health constraints.

This paper proposes a new spiking-free fixed-time disturbance observer (DO)-based sliding mode control (SMC) scheme of maximum power extraction for the wind turbine system. First, a fixed-time DO is developed by using uniform robust exact differentiator (URED). Then, an SMC scheme is proposed by using the estimation of fixed-time DO and designing a spiking-free function. The proposed spiking-free fixed-time DO-based SMC scheme exhibits three attractive features: first, compared with existing scheme, the proposed scheme can guarantee the fast convergent performance under different initial system conditions based on the design of fixed-time stability. Second, by designing a novel spiking-free function, the potential spiking problem of fixed-time DO that occurs when observer gain is high and initial estimate states and initial true states are not equal is avoided. Third, the control input of proposed SMC is continuous, thus the undesirable chatter of conventional SMC is avoided. Finally, the attractive features of proposed scheme are illustrated based on simulation. [ABSTRACT FROM AUTHOR]

A hybrid microgrid is an efficient technique to integrate renewable energy resources and allow energy exchange between ac and dc subgrids. Pf and QV droop controls are commonly employed in microgrid to deliver power demands while preserving voltage and frequency restrictions via interlinking converter (IC). When a microgrid is powered by renewable energy sources, due to the prevalence of impedance conflict traditional droop techniques offer an inappropriate response in power sharing. This study employs a predictive-based control scheme to ensure appropriate power sharing between both subgrids in the event of imbalancing on either side. Photovoltaic systems, battery storage, and a synchronous generator powered by wind turbines constitute a microgrid, which is connected via an interlinking converter. The MPC MPPT-based control approach dynamically dispatches power generated by wind and solar energy sources to the loads connected throughout the hybrid microgrid. The proposed control technique maintains power exchange between hybrid microgrids within an acceptable range, according to simulation and experimental results. [ABSTRACT FROM AUTHOR]

This article provides a systematic approach for synthesizing a novel robust synergetic control mechanism. This controller employs a dynamic global terminal synergetic surface with additional sinusoidal function. The overall macrovariable gives the system a rapid approaching rate, maintains the finite time stability, and provides a chattering-free control law. The stability analysis is validated for any uncertain second-order system via indirect approach of Lyapunov theorems. Furthermore, a hyperbolic tangent nonlinear disturbance observer is designed in order to estimate any lumped disturbance. The feasibility and the effectiveness of the proposed composite control law is tested and evaluated through simulations for simple model, wind turbine system and DC-DC buck converter. Finally, the simulation results show that the presented dynamic global terminal synergetic control–based hyperbolic tangent nonlinear disturbance observer can effectively reject the disturbances, enhance the robustness, and guarantee the tracking accuracy and rapidity. [ABSTRACT FROM AUTHOR]

In this study, an improved virtual space vector (VSV)-based two-stage model predictive control (MPC) scheme is presented for neutral point clamped (NPC) converters in high power-rated permanent magnet synchronous generator (PMSG)-based wind energy conversion system applications. The presented MPC scheme utilizes the VSVs and involves two-stage prediction for effectively reducing computation complexity. In stage-I prediction, a virtual space vector is identified based on the optimum cost function. Then, a set of sub-sector vectors are placed in stage II prediction based on the capacitor voltage levels. From this, the cost function predicts the optimum switching state. At the same time, the conditional selection of small voltage vectors in stage II prediction eliminates the neutral point voltage balancing constraint. Finally, the presented method is verified by the simulation of the back-to-back NPC converter of a high-power-rated PMSG-based wind energy conversion system. Further, the adaptability of the presented scheme for maximum power point tracking, reactive power control, and dc-link voltage control is also verified by simulation. • Model predictive current controller for power converter of wind energy conversion system. • Two-stage model predictive controller to minimize the computation and achieve voltage balancing. • Conditional selection of small voltage vectors eliminates voltage balancing constraints. • The results are verified through simulation and comparative study. • Compliance to maximum power extraction, reactive power injection, and dc-link fault are verified. [ABSTRACT FROM AUTHOR]

A significant electromotive force is induced in the rotor circuit of a doubly fed induction generator (DFIG) due to its high vulnerability to grid faults. Therefore, the system performance must be increased with appropriate control actions that can successfully offset such abnormalities in order to provide consistent and stable operations during grid disturbances. In this regard, this paper presents a solution based on a combination of an energy storage-based crowbar and a rotor-side crowbar that makes the effective transient current and voltage suppression for wind-driven DFIG possible. The core of the solution is its ability to restrict the transient rotor and stator overcurrents and DC-link overvoltages within their prescribed limits, thereby protecting the DFIG and power converters and improving the system's ability to ride through faults. Further, the capacity of an energy storage device for transient suppression is estimated. The results confirmed that the proposed approach not only kept the transient rotor and stator currents within ±50% of their respective rated values in severe system faults but also limited the DC-link voltage variations under ±15% of its rated value, achieving transient control objectives precisely and maintaining a stable grid connection during the faults. [ABSTRACT FROM AUTHOR]

A significant electromotive force is induced in the rotor circuit of a doubly fed induction generator (DFIG) due to its high vulnerability to grid faults. Therefore, the system performance must be increased with appropriate control actions that can successfully offset such abnormalities in order to provide consistent and stable operations during grid disturbances. In this regard, this paper presents a solution based on a combination of an energy storage-based crowbar and a rotor-side crowbar that makes the effective transient current and voltage suppression for wind-driven DFIG possible. The core of the solution is its ability to restrict the transient rotor and stator overcurrents and DC-link overvoltages within their prescribed limits, thereby protecting the DFIG and power converters and improving the system’s ability to ride through faults. Further, the capacity of an energy storage device for transient suppression is estimated. The results confirmed that the proposed approach not only kept the transient rotor and stator currents within ±50% of their respective rated values in severe system faults but also limited the DC-link voltage variations under ±15% of its rated value, achieving transient control objectives precisely and maintaining a stable grid connection during the faults.

Active vibration control is a critical issue of the wind turbine in the field of marine energy. First, based on a three-degree-of-freedom wind turbine, a state space model subject to wind and wave loads is obtained. Then, a delayed state feedback control scheme is illustrated to reduce the vibration of platform pitch angle and tower top foreaft displacement, where the control channel includes time-delay state signals. The designed controller’s existence conditions are investigated. The simulation results show that the delayed feedback H∞ controller can significantly suppress wind- and wave-induced vibration of the wind turbine. Furthermore, it presents potential advantages over the delay-free feedback H∞ controller and the classic linear quadratic regulator in two aspects: vibration control performance and control cost.

Integration of renewable energy sources (RESs) in modern power system has put a great challenge on researchers and system engineers to satisfactorily operate the power system when it is subjected to various disturbances. Automatic generation control (AGC) plays a very essential role in modern power system to control the tie‐line power flows and system frequencies. This paper illustrates about optimal design and implementation of various controllers for AGC of a two‐area non‐reheat thermal power system with some supplementary generation from dish stirling solar thermal system and wind‐turbine system. In addition to the generating sources, some energy storage systems like capacitive energy storage and redox flow battery are integrated to improve the dynamic stability of the system. Various controllers like fuzzy logic based PID controller, PID controller with filter (PIDF) and fuzzy PIDF (1 + PI) controller are used as supplementary controllers of the AGC system. The proposed controllers are optimally designed by FBI algorithm and QO‐FBI algorithm. A step load of 1% (0.01 pu) is applied in area‐1 to analyze the performances of the proposed controllers. Investigation of the study includes undershoot, overshoot and settling time of frequency and tie‐line power deviations. The percentage improvement of QO‐FBI based FPIDF (1 + PI) controller with respect to ICA based FPIDF (1 + PI) controller is examined. It is noticed that, AGC system equipped with QO‐FBI based FPIDF (1 + PI) controller outperforms ICA‐FPIDF (1 + PI) controller in all aspects. The superiority of the QO‐FBI based FPIDF (1 + PI) controller is validated under parametric variation in system parameters. Sensitivity analysis is also carried out by varying the inputs to the intermittent renewable sources. Comparative performance analysis reveals that QO‐FBI based FPIDF (1 + PI) controller performs better in comparison with other proposed controllers and a recently proposed ICA based FPIDF (1 + PI) controller. [ABSTRACT FROM AUTHOR]

Solar photovoltaic and wind power systems are very much dependent on climate variations. Wind and solar photovoltaic systems are unreliable without storage units like batteries and diesel generators as a backup. The addition of storage devices increases the reliability of the hybrid system consisting of solar photovoltaic and wind turbines. During cloudy and slow windy days, sufficient battery bank capacity is required to meet the load demand. This review paper gives new ways of hybrid energy generation. It discusses several optimization approaches and ideas for hybrid networks. Hybrid systems are gaining more popularity and fame in the current energy crisis scenario and environmental pollution. This research has provided a comprehensive assessment of existing optimization strategies, particularly those associated with the isolated microgrid in the literature. Artificial intelligence offers noteworthy optimization for microgrid operation without long-term weather data, as evidenced by the current optimization pattern for hybrid renewable sources. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to propose an efficient model and a robust control that ensures good power quality for the AC microgrid (MG) connected to the utility grid with the integration of an electric vehicle (EV). The MG consists of two renewable energy sources: a photovoltaic system (PVS) and a wind turbine system (WTS) based on a permanent magnet synchronous generator (PMSG), with the integration of an EV. These sources are used to supply active and reactive power to the AC bus and the utility grid. Maximum power point tracking (MPPT) based on the perturb-and-observe (PO) method is used to increase the efficiency of the photovoltaic modules and improve overall performance. The MG system includes a 2-MW WTS, a 100-kW PVS and 12 kW provided by the EV. To validate the performance of the proposed system, a series of simulations were conducted using the MATLAB®/Simulink® environment. The results demonstrate that the proposed system ensures high performance in terms of power quality, system stability, power tracking and safe integration of the EV. [ABSTRACT FROM AUTHOR]

Modern complex engineering systems oftentimes possess hierarchical structures which can be physically divided into several levels. Traditional reliability assessment methods were conducted using field data or time‐to‐failure data. For a hierarchical system, different levels' reliability‐related data from monitoring or observations can provide additional information to update the system's reliability. However, with the limitation of sensing and monitoring technologies and vague judgments from experts, the reliability data inevitably contain epistemic uncertainty. Properly fusing the imprecise data can still improve the accuracy of system reliability assessment. In this article, we propose a reliability assessment approach based on the evidential network (EN) model to update the hierarchical systems' reliability by aggregating the system‐level imprecise knowledge. First, the evidence theory is applied to quantify the epistemic uncertainty associated with the imprecise system‐level reliability data. Then, an EN model is created to realize the uncertainty propagation and fusion of imprecise data. Finally, at any particular time instant, the system reliability can be updated with the aid of imprecise data based on the proposed EN model. The effectiveness of the proposed method is validated by a real engineering case of a wind turbine system. Overall, the results shown from the case study support that the proposed method can update the reliability of hierarchical systems by using system‐level imprecise data. [ABSTRACT FROM AUTHOR]

Summary: Due to the stochastic and unpredictable nature of wind speed, extraction of maximum power from the wind turbine system becomes an attractive control objective. Therefore, to capture more wind power, various conventional maximum power point tracking (MPPT) schemes are developed for standalone small‐scale variable speed wind power generation system (VSWPGS). This manuscript proposes novel adaptive step size (ASS) and drift‐free ASS (DF‐ASS) MPPT methods for a permanent magnet synchronous generator (PMSG)‐based VSWPGS. The control schemes achieve maximum power point (MPP) without using mechanical sensors like speed encoder and anemometer. The proposed MPPT controllers are implemented on a laboratory‐scale DC motor‐PMSG‐based wind power generation system (WPGS) using OPAL‐RT real‐time digital platform. The ASS and DF‐ASS methods capture more power, reduce the steady‐state power oscillation around MPP, and improve the tracking speed. Also, the proposed DF‐ASS control scheme avoids drift phenomenon during wind speed increase condition. The comparative experimental results of proposed ASS and DF‐ASS methods with respect to the fixed step size MPPT schemes show better dynamic and steady‐state performance, making it pertinent for small‐scale WPGS. [ABSTRACT FROM AUTHOR]

Wind and solar photovoltaic (PV) based hybrid renewable energy generation are environmentally friendly and reasonable. This study describes a hybrid distributed energy generations (DEGs)-based microgrid framework where DC-DC converter, three-stage inverter with fuzzy logics control (FLC), and LC filter channel are coupled with the PV and wind energy. In India, wind and PV energies are affordable for hybrid power frameworks since they are ecological well-disposed and broadly accessible. Generally, these sources produce a fluctuating yield voltage that prompts harm to the working grid on a steady inventory. The proposed model of the hybrid-based framework is executed utilizing MATLAB/Simulink. A boost converter being associated with PV cluster is associated with the basic DC bus also a battery is used by a two-way DC to DC converter, and connected into the utility framework by a typical DC to AC inverter. Wind cluster and PV is linked with maximum power point tracking (MPPT) to produce the higher capacity to the grid, and the charging and discharging of the battery energy can be done to adjust the energy between DEGs generation and utility network. In this paper, various cases of harmonic analysis are executed on MATLAB-simulation and feasibility of proposed grid models and FLC-based intelligent control. [ABSTRACT FROM AUTHOR]

A robust control scheme of a wind turbine system (WTS) based on a dual star induction generator (DSIG) in normal and open-phase fault (OPF) mode is presented in this paper. The strong point of this generator that recently attracted the attention of several researchers especially in wind energy field is that it combines the advantages of the squirrel cage induction machine and those of multiphase ones. This research is carried out to reach two main objectives. Firstly, in order to ensure a robust control of the DSIG-based WTS with a low effect of chattering phenomenon, an improved control scheme based on super-twisting continuous sliding mode controllers (STCSMC) is proposed. The second objective is to verify, by an OPF test, the ability of the DSIG to operate in degraded conditions. Finally, simulated results show that more robust control is achieved and that the DSIG-based WTS continues to provide suitable electrical energy to the grid without remarkable effects.

Wind turbines (WT) are complex multidisciplinary systems containing a large number of mechanical, control, and electrical components. Model-Based Systems Engineering (MBSE) provides an approach for cross-discipline development to address the system complexity and focuses on creating and utilizing domain models as the primary means of information exchange. The domain models predict system behaviors and can support system validation through virtual testing at an early stage of system development. However, the further the WT development proceeds, the more system parameters are set, and the more domain models and virtual tests are involved. Therefore, it is necessary to design a framework of virtual testing workflows of WTs to support virtual validation processes as well as to automate those workflows. To achieve this goal, this contribution shows how standardized virtual testing workflows can be designed and linked to hierarchical and functional system architectures modeled in the Systems Modeling Language (SysML). The virtual testing workflows enable to trigger simulations of domain models and handle system parameters participating in the simulations, thus ensuring data consistency. Furthermore, to facilitate modular management and reuse of domain models, the domain models are classified according to model purposes, model fidelities, and system scopes. The virtual testing workflows are structured corresponding to the classification of the domain model, thereby forming a nested framework. To verify the feasibility of the proposed workflows, a virtual testing process of WT components (i.e., bearings) inside the system context with different model purposes and different model fidelities is demonstrated. It is shown that virtual testing workflows are systematically organized so that engineers can easily virtually (re-)validate the systems.

The wind turbine system is the core equipment of wind power generation. Scientific formulation of an extended warranty (EW) scheme to optimize the cost-effectiveness ratio per unit time for a wind turbine system is one of the key concerns both for users and manufacturers. Based on the failure dependence analysis of the multi-component system, the EW cost model and availability model of the multi-component system are established. Based on the EW cost model and availability model, the cost-effectiveness ratio model per unit time is constructed. Through the case study, the optimal EW scheme of the wind turbine system is obtained via genetic algorithm, so as to minimize the cost-effectiveness ratio per unit time. The results of fitting prediction analysis and flexible decision analysis show that the model can excellently predict the minimum warranty cost and maximum availability of failure dependence wind turbine systems and can supply different EW schemes for users and manufacturers to choose from under a specific cost–effectiveness ratio. Through sensitivity analysis, reasonable suggestions for optimizing the EW scheme of the wind turbine system are proposed.

The power system network grows yearly with a large number of nonlinear power generation systems. In this scenario, accurate modeling, control, and monitoring of interface systems and energy conversion systems are critical to the reliability and performance of the overall power system. In this trend, the permanent magnet synchronous generator (PMSG)-based wind turbine systems (WTS) equipped with a full-rated converter significantly contribute to the development of new and renewable energy generation. The various components and control systems involved in operating these systems introduce higher complexity, uncertainty, and highly nonlinear control challenges. To deal with this, state estimation remains an ideal and reliable procedure in the relevant control of the entire WTS. In essence, state estimation can be useful in control procedures, such as low-voltage ride-through operation, active power regulation, stator fault diagnosis, maximum power point tracking, and sensor faults, as it reduces the effects of noise and reveals all hidden variables. However, many advanced studies on state estimation of PMSG-based WTS deal with real-time information of operating variables through filters and observers, analysis, and summary of these strategies are still lacking. Therefore, this article aims to present a review of state-of-the-art estimation methods that facilitate advances in wind energy technology, recent power generation trends, and challenges in nonlinear modeling. This review article enables readers to understand the current trends in state estimation methods and related issues of designing control, filtering, and state observers. Finally, the conclusion of the review demonstrates the direction of future research. [ABSTRACT FROM AUTHOR]

Radial distribution systems (RDSs) can work better from a technical, economic, and environmental point of view if they have the right mix of renewable energy-based distribution generation (DG) and reactive power sources like and distribution-static compensator (DSTATCOM). In this paper, the Northern Goshawk Optimization (NGO), a new meta-heuristic that mimics the hunting behaviour of the northern goshawk, is introduced as a way to figure out where and how big different types of active and reactive power DGs in RDSs should be. For the suggested multi-objective function, NGO is used to find the best places and sizes for photovoltaic systems (PVs), wind turbine systems (WTs), and DSTATCOMs. The main goals of the suggested method are to reduce distribution losses, improve the voltage profile, increase the voltage stability margin, and reduce greenhouse gas (GHG) emissions. Five different case studies are simulated to figure out how well the suggested method works with computers. Simulations use IEEE 33-bus feeder. When compared to the literature and other heuristic algorithms, the results show that NGO works. The computational efficiency of NGO is compared for four cases: (i) only PV ((ii) only WT (iii) only DSTATCOM and (vi) simultaneous PV, WT and DSTATCOM allocation. The results also show that the proposed NGO much efficient than other algorithms and also improves all techno-environmental factors and RDS performance in a big way. [ABSTRACT FROM AUTHOR]

Design of hybrid Nano-Grid system (HNGS) necessitates a careful thought to benchmark reliable and cost effective solutions for remote areas. Optimal sizing of HNGS using Honey Badger Algorithm (HBA) for futuristic residential area in Mahendragarh, Haryana, India is presented in this paper. The HNGS comprise combination of solar panels, wind turbines, storage batteries, diesel generator, inverter and residential load components. The primary objective functions considered in this paper are Cost of Electricity (COE) and Net Present Cost (NPC), in continuation these problems are stated as multi objective optimization issues. Numerous permutations of diverse resources were examined to assess parameters such as NPC, COE, battery storage, land utilization, emission and potential employment opportunities. HBA is almost independent of traps in the local minimum, therefore the results obtained are superior as compared with Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Biogeography Basic Optimization (BBO), Improved Artificial Ecosystem Optimization (IAEO) and Grey Wolf Optimization (GWO) as well as HOMER software, in order to determine the quality of solution the proposed HBA algorithm has obtained. HNGS system can be readily and efficiently optimized using the suggested HBA algorithm a detailed sensitivity analysis is done to better understand the system behavior for a wider region use. • A well optimized HNGS design is cost-effective, highly reliable and simple system for remote areas. • An optimization problem is formulated and solved by means of Honey Badger algorithm. • The optimization aim is to find HNGS size in terms of number of individual sources which supplies demand successfully. • Outcomes show that HBA effectively optimizes HNGS by lowering the NPC and CoE, ensuring the electricity supply. • The HBA's results has been compared to GA, PSO, BBO, IAEO, and GWO, as well as HOMER software. • Sensitivity analysis has been used to explore the impact of weather and component parameters on COE and NPC. [ABSTRACT FROM AUTHOR]

This paper concerns the issue of finite-time dissipative asynchronous output feedback control for a wind turbine system that can be modelled as Markov jump Lur'e systems. Due to quantisation, time delays and ubiquitous data dropouts, the actual controller modes in practical application cannot always operate synchronously with the plant modes. To conquer this difficulty, a hidden Markov model is employed to provide some estimated modes information for controlling. In addition, by the Lyapunov function approach and linear matrix inequality technology, sufficient conditions are developed to guarantee finite-time boundedness of the continuous-time closed-loop system subject to strictly (U , G , V) -α-dissipative performance. Finally, a simulation example of a wind turbine system is given to verify the correctness and applicability of the proposed controller. [ABSTRACT FROM AUTHOR]

To guarantee the safe and stable operation of wind farms, this article establishes a backstepping sliding mode fault-tolerant controller for the wind turbine system to surmount uncertain problems, including actuator gain–bias faults, system modeling errors, and external stochastic disturbances. The nonlinear disturbance observer is employed for the stochastic disturbances, which can online estimate and compensate the external disturbance term. In addition, the backstepping control strategy is introduced to reduce the complexity of fault-tolerant controller design. Subsequently, combining the backstepping control algorithm and nonlinear disturbance observer, a disturbance observer-based backstepping sliding mode fault-tolerant control approach is applied for the wind turbine system. Thereinto, the terminal attractor is employed, which is mainly utilized to improve the convergence rate of the sliding surface and reduce the chattering phenomenon. The stability of the wind power closed-loop control system is rigorously verified via Lyapunov stability theory, which can obtain satisfactory control performance. Finally, numerical simulation results demonstrate that the proposed control approach can guarantee that the system state quickly reaches stability within 6–8 s, and the steady-state adjustment time is greatly reduced to 50%–62% when compared with the proportional–integral–derivative control and sliding mode control. [ABSTRACT FROM AUTHOR]

Wind turbines (WT) are complex multidisciplinary systems containing a large number of mechanical, control, and electrical components. Model-Based Systems Engineering (MBSE) provides an approach for cross-discipline development to address the system complexity and focuses on creating and utilizing domain models as the primary means of information exchange. The domain models predict system behaviors and can support system validation through virtual testing at an early stage of system development. However, the further the WT development proceeds, the more system parameters are set, and the more domain models and virtual tests are involved. Therefore, it is necessary to design a framework of virtual testing workflows of WTs to support virtual validation processes as well as to automate those workflows. To achieve this goal, this contribution shows how standardized virtual testing workflows can be designed and linked to hierarchical and functional system architectures modeled in the Systems Modeling Language (SysML). The virtual testing workflows enable to trigger simulations of domain models and handle system parameters participating in the simulations, thus ensuring data consistency. Furthermore, to facilitate modular management and reuse of domain models, the domain models are classified according to model purposes, model fidelities, and system scopes. The virtual testing workflows are structured corresponding to the classification of the domain model, thereby forming a nested framework. To verify the feasibility of the proposed workflows, a virtual testing process of WT components (i.e., bearings) inside the system context with different model purposes and different model fidelities is demonstrated. It is shown that virtual testing workflows are systematically organized so that engineers can easily virtually (re-)validate the systems. [ABSTRACT FROM AUTHOR]

Traditional fault recognition algorithms can only identify the known classes of fault in wind turbine systems(WTs). These faults have already appearedin the WTs, thus, the fault recognition algorithm can identify the classesof them. However, if a new classfault didnot happen in the WTs before, the traditional fault recognition algorithm can only identify it as known class of fault, which results in an incorrect identification. Toaddress this problem, a new class fault recognition method based on semi-supervised deep learning(SDL-NCFR)is proposed. Firstly, multiple WTs signals are used as input and features are extracted by convolutional autoencoder network; secondly, the initialization model is built with compressed features as input to the classifier and error feature map as input to the detector; finally, the detector will put new class fault instances into the buffer. When the buffer overflows, the algorithm starts to update, thus achieving the purpose of identifying new class faults. The experimental results show that the average accuracy of the initialized model could reach more than 98%. The accuracy of updated model could still reach 89.39%, and the detection rate could reach 99.50%, the recall reached 88.76%, the precision reached 92.03%, and the F1 score reached 90.36% respectively. The experimental results show that the proposed algorithm can effectively solve the problem of identifying new class faults in WTs, and the accuracy is much higher than that of traditional detection methods. [ABSTRACT FROM AUTHOR]

This article introduces an innovative approach for online detection and localization of forced oscillation (FO) in power systems with significant integration of wind turbine generation (WTG). The synergistic combination of Multichannel Prony analysis with Periodogram and dissipating energy flow (DEF) approach is proposed in this paper to overcome the challenges inherent in detecting FO amidst the complexities introduced by WTG integration. An adaptive version of multichannel Prony analysis is proposed in this paper that dynamically estimates Prony's system order via the recursive process. It estimates the frequency and damping ratio of all oscillatory modes, which is used to identify low damping modes that are potentially attributed to FO. Leveraging this information, the proposed method enhances both FO detection and source localization capabilities. The proposed method is verified on measurements taken from IEEE benchmark 4-machine, 11-bus system and 10-machine, 39-bus system with high wind energy penetration. Comprehensive testing demonstrates the proposed approach's effectiveness across various FO scenarios, including governor valve malfunction, exciter malfunction, and cyclic loads. Furthermore, the methodology is evaluated under resonant conditions and scenarios involving the simultaneous identification of multiple disturbances. • Detection & source location in WTG integrated power system. • MDMMO for dynamic estimation of Prony model order. • OFDM using Periodogram technique. • OFSLM using dissipating energy flow method. [ABSTRACT FROM AUTHOR]

With the advent of high-speed and parallel computing, the applicability of computational optimization in engineering problems has increased, with greater validation than conventional methods. Pitch angle is an effective variable in extracting maximum wind power in a wind turbine system (WTS). The pitch angle controller contributes to improve the output power at different wind speeds. In this paper, the pitch angle controller with proportional (P) and proportional-integral (PI) controllers is used. The parameters of the controllers are tuned by computational optimization techniques for a doubly-fed induction generator (DFIG)-based WTS. The study is carried out on a 9 MW DFIG based WTS model in MATLAB/SIMULINK. Two computational optimization techniques: particle swarm optimization (PSO), a swarm intelligence algorithm, and a genetic algorithm (GA), an evolutionary algorithm, are applied. A multi-objective, multi-dimensional error function is defined and minimized by selecting an appropriate error criterion for each objective of the function which depicts the relative magnitude of each objective in the error function. The results of the output power flow and the dynamic response of the optimized P and PI controllers are compared with the conventional P and PI controller in three different cases. It is revealed that the PSO-based controllers performed better in comparison with both the conventional controllers and the GA-based controllers. [ABSTRACT FROM AUTHOR]

In this paper, we propose a direct power control (DPC) using a second order sliding mode control (SOSMC) strategy and artificial neural network (ANN) for the doubly-fed induction generator (DFIG), which is applied to wind turbine system. The main objective of this intelligent technique is to replace the switching table and the hysteresis comparators with a NSOSMC control to reduce the ripple to the level of current and power. The best performances offered by the control technique proposed in the context of this work are verified by simulation using the Matlab / Simulink software. The proposed conventional direct power control (C-DPC) strategy produces a fast and robust power response. However, it suffers from the major problem which resides in the lack of control of the switching frequency which has a large variation which generates ripples at the level of the various quantities characterizing the DFIG. In this context and in order to overcome this problem, the C-DPC has been replaced by the intelligent NSOSMC control, which have a better response to variations in machine parameters.

Due to the influence of wind speed disturbance, there are some uncertain phenomena in the parameters of the nonlinear wind turbine model with time in an actual working environment. In order to mitigate the side effects of uncertainties in speed models of wind turbines, researchers have designed a variety of controllers in recent years. However, traditional control methods require more knowledge of dynamics. Therefore, based on reinforcement learning and system state data, a robust wind turbine controller that adopts adaptive dynamic programming (ADP) is proposed. The ADP algorithm is a combination of Temporal-Difference (TD) algorithm and actor-critic structure, which can guarantee the rotor speed is stable around the rated value to indirectly adjust the wind energy utilization coefficient by changing the pitch angle in the area of high wind speed and achieve online learning in real-time. In addition, the variation of the pitch angle command of the proposed controller is relatively gradual, which can reduce the energy consumption of the variable pitch actuator, and extend the service life of the equipment. Finally, the wind speed model is simulated by combined wind speed based on Weibull distribution, the comprehensive simulation results show that the proposed controller has better control effect than some existing ones.

The recent power cut incident in the UK on 9th August 2019 indicated that frequency control to raise frequency nadir and eliminate frequency second dip is highly desirable for power grids with high penetration of wind energy. This paper proposes a fast frequency support scheme for wind turbine systems (WTSs) that can enable frequency nadir to be significantly raised and close to the settling frequency and eliminate frequency second dip. In the proposed frequency support scheme, in order to achieve similar frequency support performance and ensure stability of WTSs under varying wind speeds, different levels of wind power penetration and system conditions, an adaptive gain, which is a function of real-time rotor speed and wind power penetration level, is proposed. In the proposed scheme, rotor speeds of WTSs are proposed not to be recovered to the optimal operating points during the primary frequency control, but recovered during the secondary frequency control. Simulation results on the IEEE two-area power system with a doubly fed induction generator (DFIG)-based wind farm and the IEEE 39-bus power system with permanent magnetic synchronous generator (PMSG)-based wind farms using real-time digital simulator (RTDS) and Dymola are presented to verify the effectiveness of the proposed scheme.

The power system network grows yearly with a large number of nonlinear power generation systems. In this scenario, accurate modeling, control, and monitoring of interface systems and energy conversion systems are critical to the reliability and performance of the overall power system. In this trend, the permanent magnet synchronous generator (PMSG)-based wind turbine systems (WTS) equipped with a full-rated converter significantly contribute to the development of new and renewable energy generation. The various components and control systems involved in operating these systems introduce higher complexity, uncertainty, and highly nonlinear control challenges. To deal with this, state estimation remains an ideal and reliable procedure in the relevant control of the entire WTS. In essence, state estimation can be useful in control procedures, such as low-voltage ride-through operation, active power regulation, stator fault diagnosis, maximum power point tracking, and sensor faults, as it reduces the effects of noise and reveals all hidden variables. However, many advanced studies on state estimation of PMSG-based WTS deal with real-time information of operating variables through filters and observers, analysis, and summary of these strategies are still lacking. Therefore, this article aims to present a review of state-of-the-art estimation methods that facilitate advances in wind energy technology, recent power generation trends, and challenges in nonlinear modeling. This review article enables readers to understand the current trends in state estimation methods and related issues of designing control, filtering, and state observers. Finally, the conclusion of the review demonstrates the direction of future research.

This paper proposes an advanced fault ride-through strategy using feedback linearization (FL) and sliding mode control (SMC) for squirrel-cage induction generator (SCIG)-based wind energy conversion systems (WECSs). Based on FL theory, a nonlinear dynamic model of a SCIG wind turbine system is linearized with only two decoupled state variables: d-axis stator current and DC-link voltage. Thus, d-axis stator current and DC-link voltage controllers can be simply designed with the linear control theory. Moreover, with the proposed FL control law, the DC-link voltage control can be performed by directly regulating the q-axis stator voltage without a q-axis current controller. During grid voltage sags, the output power of the SCIG is reduced to maintain the DC-link voltage. By applying SMC theory to the design of d-axis stator current and DC-link voltage controllers, robust performance of control systems can be achieved even under parameter variations. The feasibility of the proposed method has been demonstrated via simulation and experimental results. [ABSTRACT FROM AUTHOR]

This paper is a state-of-the-art that aims to highlight all the new developments for wind renewable energy sources, wind turbine system, vertical axis wind turbine and the included components to find the best solution for an offshore vertical axis wind turbine that supplies ships with energy in the outer harbor. This review could help to understand the potential future choices in the design of vertical-axes wind turbine in order to reduce pollution in marine environment. [ABSTRACT FROM AUTHOR]