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The fault characteristics of large-capacity wind farms affect the sensitivity of transmission-line protection, resulting in protection devices refusing to operate or mal-operate. A phase-to-ground fault-locating method with single-end transient information for wind farm transmission lines was proposed in this study. The proposed method employs a time-domain transient quantity integration equation matrix with the transmission line equivalent resistance, inductance, and fault path resistance to solve the fault location. The effectiveness of the scheme was evaluated using Real-Time Lab (RTLAB). The impact of factors such as the fault resistance, location, and distributed capacitance is considered. Under different fault locations, the distance measurement error of the proposed algorithm is within 3%, and there is no rejection or mal-operation. The results demonstrate that the proposed algorithm is characterized by high speed and reliability.

Due to the distinctive fault characteristics of wind power converters in large-capacity wind farms (WFs) with power converter-based systems, protective relays for the connected transmission lines can malfunction, which poses a serious challenge to the stable and reliable power transmission. This article focuses on addressing this issue by proposing a phase-to-phase fault protection method that utilizes single-end transient information for wind farm transmission lines. Conventional solutions often encounter difficulties such as the negative influence of fault resistance and the uncertainty of remote parameters in single-ended data locations. The proposed method employs time-domain differential equations to accurately solve the fault location problem. It takes into account the equivalent resistance and inductance of the transmission line, as well as the fault path resistance. This helps minimize the adverse effects of fault resistance and parameter uncertainties, making the method insensitive to fault impedance and system parameters. The effectiveness of the proposed scheme is comprehensively evaluated using the Real-Time Lab (RTLAB). Various factors like fault resistances, locations, and distributed capacitance are considered. The results clearly demonstrate that the proposed algorithm not only operates at high speed but also exhibits remarkable reliability, offering a promising solution for enhancing the protection of wind farm transmission lines.

Given the continuous development of society and the escalating demand for clean energy, there is an imperative focus on wind farm control to overcome the primary obstacle hindering wind farm development: high operation and maintenance costs. This paper presents innovative solutions for intelligent fault-tolerant active power control design based on reinforcement learning, aiming to optimize the balance between grid load and wind farm active power. The proposed solutions effectively handle a range of fault scenarios, addressing both active power control and frequency regulation while safeguarding faulty wind turbines against further deterioration. Through comprehensive simulations conducted on a wind farm benchmark model, the efficacy of these solutions and strategies is demonstrated, showcasing their ability to achieve both passive and active fault-tolerant control across diverse load and fault scenarios.

In this paper, a novel adaptive neural fault-tolerant control scheme is proposed for uncertain large nonlinear systems with sensor, actuator faults and dead zone. Due to the fault of the sensor, the actual state and the fault parameters are coupled, and a fault parameter separation method is designed for decoupling. The radial basis function neural network (RBFNN) is used to approximate the unknown interconnection functions in nonlinear systems, and combining the RBFNN and backstepping technology, an adaptive neural fault-tolerant controller is designed for nonlinear large-scale systems through ordinary Lyapunov function. The stability of the closed-loop system is verified by Lyapunov analysis, and obtained satisfactory tracking performance under the comprehensive influence of sensor, actuator faults and dead zone. Finally, the effectiveness of the proposed adaptive neural fault-tolerant control is illustrated by simulation of large-scale wind farm system.

Grid-forming (GFM) control based high-voltage DC (HVDC) systems and renewable energy sources (RESs) provide support for enhancing the stability of power systems. However, the interaction and coordination of frequency support between the GFM-based modular multilevel converter based HVDC (MMC-HVDC) and grid-following (GFL) based RESs or GFM-based RESs have not been fully investigated, which are examined in this study. First, the detailed AC- and DC-side impedances of GFM-based MMC-HVDC are analyzed. The impedance characteristics of GFL- and GFM-based wind turbines are next analyzed. Then, the influences of GFL- and GFM-based wind farms (WFs) on the DC- and AC-side stabilities of WF-integrated MMC-HVDC systems are compared and evaluated. The results show that the GFM-based wind turbine performs better than the GFL-based wind turbine. Accordingly, to support a receiving-end AC system, the corresponding frequency supporting strategies are proposed based on the GFM control for WF-integrated MMC-HVDC systems. The GFM-based WF outperforms the GFL-based WF in terms of stability and response time. Simulations in PSCAD/EMTDC demonstrate the DC- and AC-side stability issues and seamless grid support from the RESs, i.e., WFs, to the receiving-end AC system.

With a higher wind power penetration level, the power system is facing more challenges in the aspects of active power balance and system frequency regulation. The power reserve control enables the wind farm (WF) to retain a certain available power as reserves, for participating in frequency regulation similarly to synchronous generators. In this paper, a coordinated power reserve control method of a WF for frequency regulation is proposed. By using the linear programming approach, the proposed method allocates the power reserve of each wind turbine generator (WTG) to maximize the kinetic energy of WTGs and minimize the blade pitch action of WTGs. A new coefficient to quantify the relation between the rotor speed variation and available power curtailment is defined for linear optimization. Based on the power reserve formed by the proposed method, the primary frequency control of a WF is achieved by determining the primary frequency control coefficients of WTGs. Finally, case studies based on EMTP-RV simulator are carried out to validate the proposed method. The results show that the proposed method can realize the power reserve control of a WF and improve the frequency regulation ability of a WF effectively.

The distributed capacitance of the line becomes larger as the scale of wind farms, the transmission voltage level, and the transmission distance increase. Hence, the error of the traditional time-domain distance protection scheme based on the R-L model, which ignores the distributed capacitance of the line, becomes unacceptable. Therefore, the error of the time-domain fault location method based on the R-L model, especially the maximum error range, is theoretically analyzed in this paper. On this basis, a novel fault location method based on the R-L and Bergeron models is proposed. Then, a fast time-domain distance protection scheme is designed. In the proposed scheme, the error in the fitting calculation is used to construct a weight matrix, and an algorithm for solving the time-domain differential equations is designed to improve the calculation speed and stability. Compared with the traditional frequency-domain distance protection scheme, the proposed scheme is independent of the power supply characteristics; thus, it is suitable for wind farm transmission lines. In addition, compared with the traditional method based on the R-L model, the proposed scheme effectively avoids the negative influence of the distributed capacitance of the line, which significantly improves the operating speed. Different types of faults are simulated by PSCAD/EMT-DC to verify the effectiveness and superiority of the proposed scheme.

In this study, a coordinated voltage control strategy based on model predictive control (MPC) is proposed for offshore radial DC-connected wind farms. Two control modes are designed in this strategy. In the economic operation mode, the wind farm controller generates optimal active power references as well as bus voltage references of medium-voltage collector for DC-connected wind turbine (DCWT) systems and high-voltage DC/DC converters, where the goal is to minimize power losses inside the wind farm and ensure that voltages are within a feasible range, all while tracking the power references. In the voltage control mode, the main control objective for the wind farm controller is to minimize voltage deviations from the rated voltage. With the MPC, the control objective and operation constraints can be explicitly represented in the optimization problem while considering the dynamic response of the DCWT system. In addition, a sensitivity coefficient calculation method for radial DC-connected wind farms is developed to improve computational efficiency. Finally, DC-connected wind farms with 20 wind turbines are used to demonstrate the performance of the proposed strategy.

With the continuous increase in the proportion, renewable energy sources have brought great challenges to the traditional protection based on fundamental frequency, due to their complex short-circuit current characteristics which are different from synchronous generators. As the main protection of the tie line (outgoing line) of the renewable energy station, the differential protection will also be seriously affected in terms of its operation performance, and even the operation failure or maloperation may occur in some cases. This paper proposes a new principle of pilot protection based on cosine similarity comparison by analyzing the fault characteristics of renewable energy sources. With the full current as the main criterion and the current fault component as the auxiliary criterion, this principle comprehensively utilizes the time domain information of the current on both sides of the line to improve the protection sensitivity. At the same time, a method of capacitive current compensation for the sampling value is proposed according to the algorithm characteristics of similarity comparison, to prevent the protection maloperation in the case of external faults. The simulation results show that, compared with the existing pilot protection based on similarity comparison, the new protection principle proposed in this paper has good engineering application value by identifying internal and external faults in a more reliable and sensitive manner.

High-quality datasets are of paramount importance for the operation and planning of wind farms. However, the datasets collected by the supervisory control and data acquisition (SCADA) system may contain missing data due to various factors such as sensor failure and communication congestion. In this paper, a data-driven approach is proposed to fill the missing data of wind farms based on a context encoder (CE), which consists of an encoder, a decoder, and a discriminator. Through deep convolutional neural networks, the proposed method is able to automatically explore the complex nonlinear characteristics of the datasets that are difficult to be modeled explicitly. The proposed method can not only fully use the surrounding context information by the reconstructed loss, but also make filling data look real by the adversarial loss. In addition, the correlation among multiple missing attributes is taken into account by adjusting the format of input data. The simulation results show that CE performs better than traditional methods for the attributes of wind farms with hallmark characteristics such as large peaks, large valleys, and fast ramps. Moreover, the CE shows stronger generalization ability than traditional methods such as auto-encoder, K-means, k-nearest neighbor, back propagation neural network, cubic interpolation, and conditional generative adversarial network for different missing data scales.

The reduction in system inertia under the penetration of power electronic-interfaced generation has fostered various proposals on wind synthetic inertia. Several works in the state of the art propose control strategies to optimize synthetic inertia performance; however, most of the performance metrics indirectly represent frequency control adequacy indicators such as rate of change of frequency or frequency nadir. Also, control definitions assume that there is one variable speed wind turbine and one controller, which limits the applicability of the proposals as power systems normally count with various wind turbines/farms over extended geographical areas with different wind regimes. This work presents a $H_{2}$ optimal control approach for a variable speed wind turbine synthetic inertia controller. First, the objective of the formulation is to explicitly minimize the rate of change of power system frequency. Second, a stability proof for the multi-wind turbine case is proposed, allowing optimal controllers to be independently implemented for an arbitrary number of wind turbines and wind regimes. The effectiveness of the proposed control scheme is demonstrated with a numerical example, considering an empirically-validated, reduced-order model of the Electric Reliability Council of Texas frequency dynamics.

PoolCo electricity trading is one of the most capable bidding practices for executing a centralized energy market model. In the PoolCo market model, each seller and buyer submit their bid price and bid quantity to the independent market operator, which they are ready to sell and buy from the market respectively. The market operator regulates the equilibrium market price and volume by considering the acquiesced bid price and bid quantity to settle the market. To maximize the social welfare of market participants, the optimal bidding strategy of a wind farm integrated system is represented as a centralized power market model. Initially, the bid price and bid quantity for consumers and suppliers have been calculated using the Monte-Carlo simulation (MCS) approach. Secondly, a wind farm is incorporated into the system with the help of locational marginal price (LMP). The market operator determines market clearing price (MCP) and market clearing volume (MCV) based on the submitted bid price and bid quantity of suppliers and buyers in order to find the eligible buyers and suppliers. After obtaining MCP and MCV, the market operator reschedules the supplier’s bid quantity with the help of an artificial gorilla troops optimizer (AGTO) algorithm to maximize social welfare by pleasing the system constraints. The AGTO algorithm is used here for the first time to solve the market-clearing power simulation (MCPS) problem with the integration of wind farm. To show the feasibility and effectiveness of the proposed bidding strategy, modified IEEE 14-bus and modified IEEE 30-bus test systems are used here along with a wind farm of 5 MW and 30 MW rated capacity, respectively. Results obtained by using the AGTO algorithm have been compared with those obtained by other optimization algorithms like honey badger algorithm (HBA), artificial bee colony (ABC), particle swarm optimizer (PSO), and slime mould optimizer (SMO) algorithms.

A distributed active and reactive power control (DARPC) strategy based on the alternating direction method of multipliers (ADMM) is proposed for regional AC transmission system (TS) with wind farms (WFs). The proposed DARPC strategy optimizes the power distribution among the WFs to minimize the power losses of the AC TS while tracking the active power reference from the transmission system operator (TSO), and minimizes the voltage deviation of the buses inside the WF from the rated voltage as well as the power losses of the WF collection system. The optimal power flow (OPF) of the TS is relaxed by using the semidefinite programming (SDP) relaxation while the branch flow model is used to model the WF collection system. In the DARPC strategy, the large-scale strongly-coupled optimization problem is decomposed by using the ADMM, which is solved in the regional TS controller and WF controllers in parallel without loss of the global optimality. The boundary information is exchanged between the regional TS controller and WF controllers. Compared with the conventional OPF method of the TS with WFs, the optimality and accuracy of the system operation can be improved. Moreover, the proposed strategy efficiently reduces the computation burden of the TS controller and eliminates the need of a central controller. The protection of the information privacy can be enhanced. A modified IEEE 9-bus system with two WFs consisting of 64 wind turbines (WTs) is used to validate the proposed DARPC strategy.

Transmission congestion identification and expansion planning methods for power systems with wind power are proposed in this paper. First, wind farm model is established based on Copula theory considering the uncertainty and correlation of wind speed and wind turbine’s failure. Next, generation rescheduling model with wind farm and TCSC is established and a transmission congestion identification method based on reliability evaluation is proposed. Then, alternative schemes of expansion planning have been proposed according to the results of congestion identification, and the final scheme is determined by probabilistic economic analysis method. Finally, case studies have been carried out on modified RBTS test system. For the mRBTS system, when wind farms are integrated into system from bus 1 and 2, the best expansion solution is to build new transmission line; while when wind farms are integrated from bus 3 and 5, the best expansion solution is to install TCSC. Results of case studies verify the effectiveness of the proposed congestion identification and expansion planning methods of transmission system, and reveal that the location of wind power affects the decision making of transmission system expansion planning scheme.

Accurate wind speed prediction can help the power department to perceive the change rule of wind power in advance, reduce the impact of wind power grid connection, and then improve the wind power consumption rate. Therefore, an optimized variational modal decomposition (OVMD) method combined with optimized depth belief neural network (ODBN) is proposed to predict wind speed. First, the original wind speed data are processed by OVMD method, then the decomposed data are predicted by ODBN method, and the predicted component values are superimposed to obtain the wind speed prediction results. Taking the actual wind speed data of a certain area in Northwest China as an example, the proposed combined model is compared with common prediction methods such as DBN, long short term memory (LSTM), extreme learning machine (ELM), BP neural network, etc. The experimental results show that its RMSE decreases by 0.4494, 0.4778, 0.6217 and 0.6587, and its MAPE decreases by 10.3554%, 11.5484%, 14.6226% and 15.9493% respectively. The results verify the effectiveness of the prediction model.

Sufficient fault ride-through (FRT) of large wind power plants (WPPs) is essential for the operation security of transmission system. The majority of studies on FRT do not include all disturbances originating in the transmission system or the disturbances irrelevant to the operation security. Based on the knowledge of power quality, this paper provides a guide to stakeholders in different aspects of FRT for wind turbines (WTs) and WPPs. This paper details the characteristics of the most common disturbances originated in the transmission system, how they propagate to the WT terminals, and how they impact the dynamic behavior of a large WPP. This paper shows that the details of the voltage disturbances, not only in the transmission system, but also at the WT terminals, should be taken into consideration. Moreover, a detailed representation or characterization of voltage dips is important for FRT studies, despite that the simplified models used in the literature are in-sufficient. This paper strongly recommends that distinct events and additional characteristics such as the phase-angle jump and oscillations in the transition segments should be considered in FRT analysis.

One of the major challenges in grid-connected wind farms is the flicker emission caused by the reactive power extremely short time variations. A general used solution is installing a static volt-ampere reactive (VAR) compensator (SVC) as the most cost-saving device, which still holds the risk of incomplete reactive power compensation subject to the operating delay time. To this end, an accurate extreme short-term reactive power forecasting structure is essential. Although there are many studies related to the forecasting of wind power in long, medium, and short terms, however, the extremely short-term reactive power prediction of the wind farms is only addressed in few studies. The main aim in this article is to develop a method based on the convolutional neural network (CNN) to directly learn nonstationary and complex features from the raw wind farm reactive power time series and to contribute a predictive controller to mitigate the voltage flicker through an SVC connected to the wind farm. However, high sensitivity of typical mean-squared-based loss functions might lead to considerable errors in the forecasting. To resolve this issue, a modified loss function is proposed to enhance the performance and, consequently, an improved CNN is presented. The evaluation of the proposed method is based on the actual recorded data of a wind farm in Manjil, Iran. The data are directly used in the modeling of the wind farm. A current source, which is based on the actual records its amplitude and phase angle change every 0.01 s, is utilized to model the wind farm. The numerical results in terms of flicker sensation and short-term flicker perceptibility (Pst) measurement are used to verify the performance of the proposed method through comparison with the wind farm performance without SVC and SVC with a common control system. [ABSTRACT FROM AUTHOR]

This paper proposes a gradient correction-based decentralized optimal Var/Volt adaptive fault-tolerant control (GC-AFTC) method for wind farms (WFs). The aim is to ensure the decentralized optimal Var/Volt operation of wind turbines (WTs) inside a radial topology WF even under the fault condition due to several WTs being tripped. When some faults occur and several WTs are tripped, the decentralized optimal Var/Volt operation of WF still can be maintained without the reconstruction of the incidence matrix and any quantitative fault information of the fault WTs. To achieve the fault-tolerant optimal Var/Volt control, the impact of random off-grid events (ROEs) on the WF collection system is analyzed at the farm level. Subsequently, a unified GC method is derived based on the gradient projection (GP) method. The transient Var related to the ROE is incorporated into the GC method to enhance the capability of decentralized online applications of WTs. Additionally, to improve the dynamic performance of WTs, a personalized step coefficient is designed within a stable range, which is utilized in each local WT controller during the GC-AFTC period. Case studies show that the proposed method can drive the WF system towards a better operating point only with local measurements in normal and fault conditions. [ABSTRACT FROM AUTHOR]

Advanced controllers have been widely introduced in power system-related research to alleviate the uncertainties of renewable sources. The performance of the controllers should be verified in a realistic environment before the controllers are used in practical applications. This study focuses on voltage regulation of wind farms in voltage drop cases where communication failures occur and discusses modifications done on the online wind farm management system. The modified management system is designed to respond to a voltage drop when the outer reference signal from the upper-level controller is absent due to technical problems, especially communication errors. Such a situation has not been considered in most of the management systems in the literature. The predictable situations in real fields have been further verified under the hardware-in-the-loop simulation environment RTDSTM. During communication failure, the proposed method can respond automatically to voltage recovery, along with a designed objective function. A comparative analysis of actual control modes was also conducted to prove the relevance and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In this paper, an approximate aggregated impedance model (AAIM) of a grid-connected wind farm with multiple wind turbine generators (WTGs) for the small-signal stability study is derived. The derivation is conducted under the condition that the WTGs inside the wind farm are of the same type and from the same manufacturer. The AAIM derived can be seen as the impedance model (IM) of a grid-connected aggregated WTG. The IM of the aggregated WTG is the weighted average of the IMs of the individual WTGs in the wind farm and hence is simple to be built. The reactance of the line connecting the aggregated WTG to the external AC grid is the maximum eigenvalue of the network reactance matrix of the grid-connected wind farm. In addition, the derivation justifies the representation of a wind farm by an aggregated WTG in the IM-based small-signal stability analysis. Finally, two example grid-connected wind farms are used to evaluate and demonstrate the effectiveness of the AAIM proposed in the small-signal stability analysis. It is also shown that the computational burden of the stability analysis of a large-scale wind farm could be effectively reduced when the AAIM is used. [ABSTRACT FROM AUTHOR]

This article proposes a coordinated management of electrical energy in a steelworks and a wind farm that are connected to the same distribution network. The suggested solution seeks to improve the efficiency of the hot rolling mill, to reduce greenhouse gas emissions, to minimize the cost of the electrical energy utilized in the manufacture of steel coils, to increase the power system chargeability, and to guarantee power quality. The proposal consists of constituting a virtual plant (comprising the wind farm and the rolling mill) to be managed by a single operator. The approach is mainly focused on the management of the virtual plant reactive power. The algorithm proposed to optimize this reactive power is based on the so-called particle swarm optimization. Three optimization strategies are analyzed: minimization of losses in the distribution network, minimization of the voltage deviation at two of its nodes, and maximization of the displacement factor in both the rolling mill and the wind farm. Energy losses are reduced by up to 20% when adopting the first strategy in comparison to the least efficient case. Voltage variations are kept at less than 1% at both nodes when using the second strategy, whereas deviations between 1% and 5% are obtained when implementing the other two strategies. The study is based on actual measurements and simulation tests. [ABSTRACT FROM AUTHOR]

This article proposes a generalized neural continual learning-based cybertwin (GNC) modeling framework to realize developing one wind turbine (WT) cybertwin serving multiple modeling tasks in the wind farm operations and maintenance (O&M). A generalized WT cybertwin modeling problem, which considers modeling one cybertwin for multiple tasks without additional computational burden, is studied for the first time. Fully connected neural networks are adopted as the backbone for developing the GNC model. The online elastic weight consolidation method is incorporated to mitigate the catastrophic forgetting phenomenon among different modeling tasks. Computational experiments are conducted to validate the effectiveness of the proposed GNC framework based on the supervisory control and data acquisition data. Modeling tasks in three important problems of the wind farm O&M, the WT gearbox failure detection, WT blade breakage detection, and wind power prediction, are considered in the experiment. Compared with other benchmarking models, such as the multiple neural cybertwins, neural cybertwin, and regularized neural cybertwin, the proposed GNC can achieve high accuracies on both new tasks and existing tasks, which further verifies the WT cybertwin generalization via the proposed GNC. [ABSTRACT FROM AUTHOR]

This paper proposes a hierarchical event-triggered model predictive control (HEMPC)-based voltage control strategy to coordinate the reactive power (Var) outputs among all WTs for improving the high-voltage ride-through (HVRT) and post-fault voltage restoration capability of the large-scale wind farms (WFs), while reducing the heavy computational task of the WF controller. The event-triggering condition and optimal controller are designed to activate the proposed HEMPC strategy based on the system state deviation and input-to-state stability (ISS) mechanism, which can substantially decrease data communication in a non-periodic sampling fashion. A hierarchical solution method based on the alternating direction method of multipliers (ADMM) is developed, aiming to further improve the computing efficiency of the voltage optimization problem of the large-scale WF. Two time-domain WF simulation cases in Matlab/Simulink corroborate that the proposed HEMPC strategy is more efficient in minimizing the WT terminal voltage deviations and reducing the computational burdens of the WF controller compared with traditional control strategies. [ABSTRACT FROM AUTHOR]

The uncertainty of wind power and electricityprice restrict the profitability of wind-storage integrated system (WSS) participating in real-time market (RTM). This paper presents a self-dispatch model for WSS based on deep reinforcement learning (DRL). The designed model is able to learn the integrated bidding and charging policy of WSS from the historical data. Besides, the maximum entropy and distributed prioritized experience replay frame, known as Ape-X, is used in this model. The Ape-X decouples the acting and learning in training by a central shared replay memory to enhance the efficiency and performance of the DRL procedures. Besides, the maximum entropy framework enables the designed agent to explore various optimal possibilities, thus the learned policy is more stable considering the uncertainty of wind power and electricity price. Compared with traditional methods, this model brings more benefits to wind farms while ensuring robustness. [ABSTRACT FROM AUTHOR]

This paper proposes a decentralized Volt/Var control method based on variable gradient projection (VGP) for the permanent magnet synchronous generator (PMSG)-based wind farm (WF). The aim is to achieve a fast Var response of each wind turbine (WT) to depress voltage fluctuations inside the WF caused by the stochastic wind and voltage disturbance of external girds. The gradient projection (GP) method is used in the PMSG-based WF to regulate terminal bus voltage within a feasible range in a fully decentralized manner while the dynamic characteristics of the PMSGs and grid side converters (GSCs) are taken into consideration. Moreover, according to the difference of the available power distribution among the WTs inside the WF, the adaptive and variable gain method is designed for the GP method, which can achieve maximum Var margin of each local WTs during the real-time operation of the WF. With the proposed control method, the PMSG-based WTs are coordinated to achieve near-global optimal performance only with local measurements. The voltage fluctuations of buses are reduced while the Var margin of WT converters is maximized. The WF with $24 \times 5$ MW WTs is tested in Matlab/Simulink with the real-time fully nonlinear AC power flow environment. [ABSTRACT FROM AUTHOR]

As the wind power penetration increases, wind farms are required by the grid codes to provide frequency regulation services. This article develops a fully data-driven model predictive control (DMPC) scheme for the wind farm to provide temporal frequency support. The main technical challenge is the complexity and the nonlinearity of wind turbine dynamics that make the DMPC intractable. Based on Koopman operator (KO) theory, a specialized dynamic mode decomposition (SDMD) algorithm is proposed, which fits a global linear dynamic model of the wind turbines. The performance of learning dynamics is powered through integrating the physical knowledge of the wind turbine into the specialized observables of KO. To stabilize the rotor speeds in frequency regulation, the active power contribution is optimally dispatched in a moving horizon fashion. Simulation results show that the DMPC can efficiently learn and predict the wind turbine dynamics. During the frequency response process, the proposed method can effectively track the frequency support order specified by the utility grid operator while significantly stabilizing the rotor speeds. [ABSTRACT FROM AUTHOR]

Wind turbines play an increasingly important role in renewable power generation. To ensure the efficient production and financial viability of wind power, it is crucial to maintain wind turbines’ reliability and availability (uptime) through advanced real-time condition monitoring technologies. Given their plurality and evolution, this article provides an updated comprehensive review of the state-of-the-art condition monitoring technologies used for fault diagnosis and lifetime prognosis in wind turbines. Specifically, this article presents the major fault and failure modes observed in wind turbines along with their root causes, and thoroughly reviews the techniques and strategies available for wind turbine condition monitoring from signal-based to model-based perspectives. In total, more than 390 references, mostly selected from recent journal articles, theses, and reports in the open literature, are compiled to assess as exhaustively as possible the past, current, and future research and development trends in this substantial and active investigation area. [ABSTRACT FROM AUTHOR]

Demand response resources (DRRs) have been recently introduced as one of the most economic tools of congestion alleviation in power systems. Nevertheless, the severe uncertainty of multiple DRRs constituting a demand response firm (DRF) is an indispensable issue to be considered for the resilient operation of future power systems. Consequently, the information gap decision theory (IGDT) technique is utilized for addressing the uncertainty of consumers’ participation in demand response programs. This article presents a novel hybrid scenario-IGDT-based framework, designated as SIGDT, for corrective transmission congestion management (CM) in the presence of large-scale uncertain wind farms and DRFs as well as the uncertainty of conventional generating units. A reliability network modeling of a repairable N-component wind turbine (WT) is presented, considering failure and repair rates of turbines’ components, and then the uncertainty of wind farms’ generation is handled using the scenario-based approach. The proposed framework is applied to the IEEE-reliability test system (RTS) system to demonstrate its accuracy and capability. The results discuss the impact of failure and repair rates of WTs components on the proposed SIGDT-based CM and emphasize the application of the proposed framework for decision-makers to ensure the optimal operation of power systems under uncertainties of DRFs, wind farms, and conventional units. [ABSTRACT FROM AUTHOR]

By increasing the penetration of wind power in the modern power systems, to investigate transient stability is of special importance. This article presents an innovative index based on the concept of the potential energy boundary surface along with kinetic energy, which aims to detect the transient instability of a power system comprising doubly fed induction generator (DFIG)-based wind farms. Accordingly, transient instability detection (TID) is performed without directly calculating the unstable equilibrium point and thus the computational load is decreased. Since the proposed approach requires only the postfault data that can be easily measured by the phasor measurement units, it is suitable for real-time applications. Therefore, the proposed approach can be applied as a general tool to any power system with any change in topology and operating conditions. Moreover, considering that the current-balance form is the preferred industry model for the implementation of transient stability simulation, the network equations in the current-balance form are extracted for grid-connected DFIG and synchronous generators. Different scenarios are simulated in the Western System Coordinating Council 3-machine, 9-bus system, and the 10-generator New England system. To validate the new index for TID, simulation results are compared with the transient stability index and out-of-step distance relay. The obtained results validate the correctness and effectiveness of the presented new index. [ABSTRACT FROM AUTHOR]

This article presents a new adaptive and optimal overcurrent protection for large-scale wind farms using hybrid grey wolf optimizer and rule-based fuzzy logic controller (GWO-FLC) scheme. The proposed GWO-FLC based adaptive protection scheme overcomes the limitation of conventional protection scheme that suffers from maloperation of relays that damages the power components in the system during fault. Furthermore, due to the intermittent nature of wind, the generated current feeding the grid during extreme high and low wind speeds creates a significant difference in magnitudes of the generated current. Thus, the existence of only one group of settings for the overcurrent relays (OCRs) would cause drastic miscoordination and false tripping during fault. In order to test the performance of the proposed GWO-FLC scheme, several OCRs based on standard Schweitzer Engineering Laboratories industrial protection relay (SEL-751) are developed in MATLAB/Simulink. A large-scale wind farm model is also developed to perform fault analysis, relay setting, and coordination calculation. The GWO is implemented to attain optimal OCR coordination for the defined quadruple group settings for all relays, and the FLC is developed to provide adaptive feature for the relays so that the OCR group settings for each relay are intelligently updated according to the variation of wind speed. Finally, in a laboratory environment, the proposed protection scheme is implemented in real time using field-programmable gate array DE2-115 board equipped with Cyclone IV-E device (EP4CE115F29C7). It is found from both simulation and experimental results that the performance of the proposed hybrid GWO-FLC is robust, reliable, efficient and extremely satisfactory. [ABSTRACT FROM AUTHOR]

With the integration of the large-scale wind farms, the conventional pilot protection is facing new challenges. It suffers from the performance degradation can even refuse operation for some extreme fault scenarios. To address this problem, this article continues the research of the novel pilot protection based on cosine similarity. The protection pickup setting and industrial application testing is investigated. The functional structure is described and the logic of the cosine similarity based protection is evaluated under different fault scenarios. Compared with the conventional differential protection, application of the proposed protection can greatly improve the relay performance. The proposed protection is embedded into an industry relay device, and the standard hardware-in-loop tests are carried out using the real time digital simulator before the industrial application. Now, the protection device based on cosine similarity is installed in the wind farms and put into operation in Inner Mongolia. [ABSTRACT FROM AUTHOR]

Forced Oscillations can occur even for a power system with good damping performance. With increasing high penetration of renewable energy generation towards 100% renewable generation in the power grid, exploitation of future control potential from wind turbines becomes inevitable. This paper develops an isolation and suppression method of Forced Oscillations by using wind farms (WFs) under both grid-following and grid-forming control principles. The proposed method controls a WF to absorb or release active and reactive power in a timely manner and as a result Forced Oscillations are prevented from spreading to the rest of the power grid and hence they are isolated. The Forced Oscillations of the disturbed area, which is bounded by the location of WF installation, are also reduced and suppressed. The proposed method is easy to implement and economically effective as there is no requirement of extra energy storage and power electronic devices, and a prior knowledge of the frequency of Forced Oscillations is not required. With the proposed control method, the loss of wind power capture is negligible. Furthermore, the control method is also able to damp natural oscillations. Simulations of both doubly fed induction generator (DFIG)-, and permanent magnetic synchronous generator (PMSG)-based WFs under either grid-following or grid-forming control principle, with constant or varying wind speeds and different WF locations with respect to the source of Forced Oscillations, on the modified two-area and IEEE 39-bus power systems, using Real-time Digital Simulator (RTDS) and Dymola, are used to verify the effectiveness of the proposed method.

As renewable energy sources such as wind are connected to the grid on a large scale, the safe and stable operation of the power system is facing challenges and the demand for flexibility is becoming increasingly prominent. In recent years, with the advancement of Vehicle-to-Grid (V2G) technology, electric vehicles (EVs) have become a non-negligible flexibility resource for the power system and an emerging path to solve the renewable energy consumption problem. To address the problem of wind farms’ difficulty in making profits in the power market, this paper considers the cooperation between wind farms and EV aggregators and uses the levelable characteristics of EVs charging load to ease the anti-peak characteristics of wind power. Given this, this paper proposes a cooperation mode between the wind farm and the Electric Vehicle (EV) aggregator, constructs a cooperation income and income distribution model, and solves the model using the Asynchronous Advantage Actor-Critic (A3C) reinforcement learning algorithm. Finally, based on the simulation analysis of historical data, the following conclusions are drawn: (1) the cooperation between the wind farm and the EV aggregator can effectively mitigate the negative impact of the anti-peak characteristics of wind power on profitability and achieve an increase in overall economic benefits; (2) the income distribution based on the Shapley value method ensures that the respective income of the wind farm and the EV aggregator increase after cooperation, which is conducive to the promotion of the willingness of both parties to cooperate; (3) the A3C reinforcement learning algorithm is applied to solve the model with good convergence to achieve fast and continuous intelligent pricing decisions for EV aggregators, thus optimizing the charging schedule of EVs promptly.

Large-scale renewable energy integration decreases the system inertia and restricts frequency regulation. To maintain the frequency stability, allocating adequate frequency-sup-port sources poses a critical challenge to planners. In this context, we propose a frequency-constrained coordination planning model of thermal units, wind farms, and battery energy storage systems (BESSs) to provide satisfactory frequency supports. Firstly, a modified multi-machine system frequency response (MSFR) model that accounts for the dynamic responses from both synchronous generators and grid-connected inverters is constructed with preset power-headroom. Secondly, the rate-of-change-of-frequency (ROCOF) and frequency response power are deduced to construct frequency constraints. A data-driven piecewise linearization (DDPWL) method based on hyperplane fitting and data classification is applied to linearize the highly nonlinear frequency response power. Thirdly, frequency constraints are inserted into our planning model, while the unit commitment based on the coordinated operation of the thermal-hydro-wind-BESS hybrid system is implemented. At last, the proposed model is applied to the IEEE RTS-79 test system. The results demonstrate the effectiveness of our co-planning model to keep the frequency stability.

Scenario forecasting methods have been widely studied in recent years to cope with the wind power uncertainty problem. The main difficulty of this problem is to accurately and comprehensively reflect the time-series characteristics and spatial-temporal correlation of wind power generation. In this paper, the marginal distribution model and the dependence structure are combined to describe these complex characteristics. On this basis, a scenario generation method for multiple wind farms is proposed. For the marginal distribution model, the autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity-t (ARIMA-GARCH-t) model is proposed to capture the time-series characteristics of wind power generation. For the dependence structure, a time-varying regular vine mixed Copula (TRVMC) model is established to capture the spatial-temporal correlation of multiple wind farms. Based on the data from 8 wind farms in Northwest China, sufficient scenarios are generated. The effectiveness of the scenarios is evaluated in 3 aspects. The results show that the generated scenarios have similar fluctuation characteristics, autocorrelation, and crosscorrelation with the actual wind power sequences.

Line-commutated converter (LCC) based high-voltage direct current (HVDC) technology is a common technology for long-distance transmission of electricity. Commutation failure is a common fault in the LCC-HVDC transmission system, which brings new challenge to the stable operation of wind turbines on the sending AC grid. It is necessary to suppress the overvoltage of the sending AC grid to improve the fault ride-through (FRT) capability of the LCC-HVDC system under commutation failure. The voltage amplitude of the sending AC grid is affected by the power characteristics of the wind farms on the sending AC grid. Based on the analysis of the mathematical relationship between the reactive power of the wind farms and the overvoltage of sending AC grid and the mathematical relationship between the reactive power absorption capacity of the wind farms and the active power of the wind farms, the overvoltage of the sending AC grid can be suppressed by redistributing the power of the wind farms. An optimized power distribution method for wind farms is proposed to suppress the overvoltage of the sending AC grid and improve the FRT capability of the LCC-HVDC system under commutation failure. The effectiveness of the proposed optimal method is verified through two cases by building the simulation model based on Matlab/Simulink.

This paper is dedicated to presenting control-tuning methodologies for the rotor-side converter (RSC) and grid-side converter (GSC) of series-compensated DFIG-based wind farms (WF) and to determining the origin of subsynchronous resonance (SSR). Unlike conventional approaches to re-tuning controller gains to achieve desirable performance, these methodologies only consider the mathematical models of the states variables to be controlled. A PI cascade topology is used to control the RSC and GSC. An inner loop is required for current control and an outer loop for voltage control. Special consideration is taken for tuning the RSC because this converter is coupled with the mechanical variables as mechanical rotor speed of the DFIG. Compared with standard tuning, a better system behavior during a resonance condition is achieved with the proposed tuning methodology. Regarding the analysis of the SSR origin, the modal impedance (MI) technique is used as a tool to study SSR issues; its advantages compared to the driving point impedance technique are highlighted. In this context, the small-signal-stability (SSS) analysis is performed to evaluate the overall system. With the combination of MI and SSS, the phenomena of induction generator effect, torque interaction, and torque amplification are analyzed. As a result, the modes involved in the SSR phenomenon are identified and a discrimination procedure is described to determine the origin of the SSR.

Recently, small-signal stability of the modular multilevel converter (MMC) based high-voltage direct current (HVDC) transmission for wind farm integration has attracted great attentions. The impedance-based frequency-domain method is an effective tool for such studies, in which the accuracy of the impedance model is of significant importance. Currently, the decoupled single-input single-output (SISO) sequence impedance of the MMC-HVDC system with wind farm is commonly used due to the simplicity in stability analysis. However, since both the MMC and wind farm exhibit frequency coupling behaviors, the decoupled SISO sequence impedance model may lead to inaccurate stability conclusion under certain conditions. In order to improve the model accuracy, based on the harmonic state-space (HSS) modeling approach, this paper proposes a generalized multi-input multi-output (MIMO) sequence impedance model of the MMC-wind farm system. The proposed MIMO impedance model can effectively capture the frequency couplings in the interconnected system, based on which the coupling mechanism of multi-frequency components within the MMC is then revealed. To facilitate the interconnection analysis, the model truncation criteria is proposed and validated by comparing the accuracy of the MIMO model with different truncated dimensions. In addition, this paper considers the internal impedance network of the wind farm when developing the aggregated wind farm impedance model for interaction stability analysis. Finally, the proposed MIMO sequence impedance model is applied to accurately predict the instability of the MMC-HVDC connected wind farm system.

Motivated by recent years' resonance incidents in wind-integrated power systems, this paper investigates the resonance-induced harmonic distortion and stability issues in doubly fed induction generator (DFIG)-based offshore wind farm (OWF) with high-voltage direct current (HVDC) grid connection. To accurately capture the dynamic characteristics of DFIG-based wind farm, a comprehensive impedance modelling considering the detailed PI control loop and DC dynamics of wind turbine as well as the cable connections of the medium-voltage (MV) collector system is conducted. Through stepwise simulation verifications, aggregated modelling of MV collector system is proved to be suitable for wideband resonance analysis. On this basis, the resonance analysis regarding grid topology change and controller parameter variation is conducted and the impact of frequency coupling on subsynchronous resonance (SSR), middle- and high-frequency resonances is analyzed using the aggregated models derived from a practical HVDC connected DFIG-based OWF. The strength or degree of the frequency coupling between the sequence impedances of wind farm, which is induced by the asymmetrical converter control of wind turbines, is found to be dependent on the impedances of all the components of the system. Moreover, case studies are conducted to demonstrate the importance of including the frequency coupling in SSR stability assessment. Simulations in MATLAB / Simulink validate the modelling and resonance analysis.

An effective model to represent real-world wind power production scenarios is essential for an accurate assessment of the impact of wind power generation on power systems. Such a model should capture the spatial and temporal correlations between different wind turbines that are part of a wind farm. Accurate modeling of these correlations will ensure that the wind power uncertainty can be properly quantified. This is especially critical when analyzing systems with high penetration of wind power productions. This paper addresses this critical need by developing the Hourly Mixed Copula Model, a new probabilistic model, based on Gaussian and Archimedean copulas, to model the uncertainty and variability of wind power generation. This model enables the simulation of realistic scenarios of a wind farm power production. Metrics such as the Euclidean distance and Kullback-Leibler divergence are used to demonstrate the effectiveness of the developed model in incorporating the spatio-temporal dependence among the turbine power productions. The impact of accurate characterization of uncertainty in wind power modeling on power system steady-state operational planning is also demonstrated by case studies. These studies indicate that, in order to correctly evaluate the effect of wind power variation on steady-state operational planning, the dependence among wind power resources should not be neglected for power flow studies.

There is a growing interest to increase the grid integration of large-scale wind power farms (WPF). As most WPFs are located in remote areas where abundant wind resources are available, these sites are lacking communication infrastructures and network coverage which present major obstacles in enabling reliable data transmission between WPFs and their control centers. With the absence of unified communication network architecture, different vendors and manufacturers are developing their own monitoring and control solutions according to their needs. There is a knowledge gap related to the design of WPF communication networks, where the assumptions of available articles do not represent the complete monitoring data from WPF subsystems including wind turbines, meteorological towers and substations. This work aims to design a wireless network architecture for the grid integration of cyber physical wind energy system based on the IEC 61400-25 standard. The proposed architecture consists of four layers: a wind farm layer, a data acquisition layer, a communication network layer and an application layer. Wireless communication technologies outperform conventional wired-based solutions by offering lower costs, greater flexibility and easier deployment. Based on IEC 61400-25 standard, a wireless turbine area network is proposed for collecting sensing data from wind turbine parts, and connected to a wireless farm area network developed for communication between the remote control center and wind turbines. The network performance of the proposed wireless wind turbine internal network (includes the number of sensor nodes, data types and data size) is evaluated considering different wireless technologies (ZigBee, WiFi and WiMAX) in view of end-to-end delay, wireless channel capacity, and data loss. The simulation results show that wireless-based solutions can meet the delay requirements of the IEEE 1646 standard. This work contributes for building a redundant wireless communication infrastructure for remote monitoring of WPFs with scalable coverage and capacity.

Wind farms are developed and implemented in many places around the globe. Designing a wind farm is becoming more and more complex especially with the recent trend towards large farms. Finding the optimal locations of wind turbines inside a wind farm to reduce energy cost is a highly challenging task, as it requires the handling of conflicting criteria and depending on the number of turbines considered it can turn to a large scale-optimization problem. Therefore, the aim of this paper is to place efficiently wind turbines inside a given area considering all constraints. This problem formulated as an optimization problem is referred to as the wind farm layout optimization (WFLO) problem. This real-world problem is nonlinear and difficult to solve using classical optimization algorithms and it has to take into consideration wind scenarios, power curve and wake effects. For this purpose, a binary version of the most valuable player algorithm (MVPA) called BMVPA is developed and implemented. Furthermore, ten scenarios were investigated using different wind speeds, terrain sizes with and without obstacles. For the same terrain but including obstacles, it was found that the energy cost increased due to the presence of obstacles that could limit the search space and consequently reduces the number of available options. The empirical results obtained using BMVPA were compared with those obtained using other well-known algorithms like the binary particle swarm optimization and genetic algorithm. BMVPA showed better results in solving the WFLO problem than the comparative algorithms. The optimum design of the wind farm obtained will allow an efficient and economic exploitation of wind resource.

Coordinated droop control strategies can provide frequency support for AC area grids from wind farms integrated through multi-terminal high-voltage DC (MTDC) transmission. However, such a strategy inevitably causes DC voltage deviation for transmitting AC area frequency information, thereby deteriorating the stability and security of the MTDC system operation. This paper reports an improved adaptive virtual capacitor control strategy that can provide inertial support for the system. Instead of increasing the capacitance of the physical capacitor, a virtual capacitor is generated by utilizing the rest energy of the deloaded wind farm. Furthermore, an S-shape function is designed to adaptively adjust the capacitance of the virtual capacitor based on the operating points of the system, in order to provide a better inertial support than with the fixed virtual capacitor control strategy. The proposed strategy not only enhances the DC voltage nadir but also improves the frequency nadir of the AC area by releasing additional power from the deloaded wind farm., a MTDC system of a four-terminal voltage source converter with two wind farms is simulated using PSCAD/EMTDC. Case studies are conducted to compare and demonstrate the effectiveness of the proposed strategy under sudden load variations.

The injection of a significant amount of wind power tends to increase the difficulty of the grid frequency control. Therefore, wind farm (WF) has to have the ability to participate in system frequency support. However, during the frequency control process in a large WF, individual wind generators may prone to instability due to possible over-deceleration. In addition, the deeply-intertwined cyber physical power system (CPPS) has gradually replaced the traditional grid due to the development of information and communication technology. However, CPPS also introduces cyber uncertainties that have a significant negative impact on the real-time control of power system. To address above issues, this paper considering possible cyber uncertainty, and proposes a grouping-based hierarchical frequency support scheme for WF. In the proposed scheme, the variable speed wind turbines (VSWTs) are clustered into several groups, according to their wind profiles at first, by dispatching the same control commands to the VSWTs belonging to the same group. As a result, the number of the VSWTs and control variables reduces significantly. Then, a hierarchical control scheme based on virtual inertial control is proposed with consideration of possible cyber uncertainty. The top-layer controller determines whether cyber uncertainty exists, and calculates power increment based on frequency deviation and cyber uncertainty. The bottom-layer controller coordinates the power increment distribution on the basis of ensuring the safe operation of all VSWTs. By do this, it is expected to achieve not only optimal frequency response but also wind generator stability. The effectiveness of proposed scheme is verified on a simulation platform built using MATLAB.

Case-by-case study in the literature has found from the results of numerical computation and simulation that a grid-connected virtual synchronous generator (VSG) can maintain the stability under the condition of extremely weak grid connection. This paper presents the theoretical proof to the finding and thus, generally concludes that the grid-connected VSG is immune to the instability risk under the condition of weak grid connection. In addition, the paper extends the theoretical proof to the case of a grid-connected PMSG wind farm with multiple VSGs. An example grid-connected PMSG wind farm with twenty VSGs is presented to demonstrate and evaluate the theoretical analysis and conclusion made in the paper. [ABSTRACT FROM AUTHOR]

This paper analytically derives oscillatory stability criteria of a grid-connected PMSG based on the block diagram model. The derivation reveals the general mechanism about how the condition of grid connection, loading and converter control parameters setting of the PMSG jointly affect the oscillatory stability of the PMSG. It logically explains why the condition of weak grid connection and heavy loading may cause destabilization. In addition, computationally simple and modal-computation free indices are proposed to identify the instability risk caused by the “improper parameters setting” of the control system of grid side converter (GSC) and the phase locked loop (PLL). Following analytical conclusions are obtained: (1) When the PLL is of high-frequency bandwidth, high-frequency oscillations are mainly caused by the PLL or the open-loop modal resonance between the PLL and current control of the GSC. In addition, the low-frequency oscillations may also occur as caused by the power control of the GSC. (2) When the PLL is of low-frequency bandwidth, low-frequency oscillations may be caused jointly by the power control of GSC and the PLL. An example grid-connected wind farm with eighteen similar PMSGs is presented to demonstrate and evaluate the stability criteria derived and analytical conclusions obtained in the paper. [ABSTRACT FROM AUTHOR]

Identifying sources of subsynchronous resonance (SSR) is crucial for understanding and mitigating incidents. Recent studies have shown that SSR impedance and power are effective indicators that can identify an SSR source. However, the calculation of two indexes requires SSR voltage and current phasors as the inputs, which are not directly available from existing online monitoring systems. To address this issue, this paper presents a novel method to calculate SSR impedance/power based on synchrophasors provided by a wide-area monitoring system. As a result, the online detection of SSR sources is enabled. Through mathematical derivation, it is revealed that a synchrophasor under SSR is dominated by four modes: two from the fundamental frequency and two from the SSR frequency. The problem thus can be transformed to a modal parameter extraction (MPE). Two classic MPE algorithms, Prony analysis and the matrix pencil method, are employed in this paper to extract the SSR parameters and calculate the SSR impedance/power. Their performances are verified using synthetic signals and electromagnetic transient simulations. Furthermore, the method is applied to an actual SSR incident involving 23 wind farms. The findings suggest that 17 of 23 wind farms did not contribute negative damping to the oscillation. [ABSTRACT FROM AUTHOR]

This paper proposes a new and comprehensive approach for optimal sizing and placement of a wind farm (WF) in a radial distribution network (RDN) while considering several factors associated with its reliability, operation, economics and environment impact. The consideration of the reliability aspect is essential to correctly assess the impacts of forced outages of wind turbines in the field. In this context, a sequential Monte Carlo simulation-based technique has been formulated to evaluate the performance of the WF with varying capacities at different locations in the RDN. Additionally, the capability of the WF to perform Volt-Var and Volt-Watt control has been capitalized on to provide voltage support to the grid in compliance with international standards (IEEE 1547-2018). Finally, a combination of Analytic Hierarchy Process and Technique for Order Preference by Similarity to Ideal Solution has been used to assess and determine the optimal size and location of the WF, based on a selected set of performance criteria, namely cumulative violations of the steady-state voltage range, cumulative losses in RDN, cumulative violations of the line thermal rating, levelized cost of energy, and reduction in carbon emission. The proposed approach has been validated considering different modes of voltage control on the standard 33-bus RDN system. [ABSTRACT FROM AUTHOR]

The volatile nature of wind power generation creates challenges in achieving secure power grid operations. It is, therefore, necessary to accurately predict wind power and its uncertainty quantification. Wind power forecasting usually depends on wind speed prediction and the wind-to-power conversion process. However, most current wind power prediction models only consider portions of the uncertainty. This paper develops an integrative framework for predicting wind power density, considering uncertainties arising from both wind speed prediction and the wind-to-power conversion process. Specifically, we model wind speed using the inhomogeneous Geometric Brownian Motion and convert the wind speed prediction density into the wind power density in a closed-form. The resulting wind power density allows quantifying prediction uncertainties through prediction intervals. To forecast the power output, we minimize the expected prediction cost with (unequal) penalties on the overestimation and underestimation. We show the predictive power of the proposed approach using data from multiple operating wind farms located at different sites. [ABSTRACT FROM AUTHOR]

The increasingly complex AC/DC network as a result of the massive integration of wind farms manifests the significance of a comprehensive transient study. In this work, the wind turbine (WT) and the DC grid are modeled in detail for the electromagnetic transient (EMT) simulation to maximize its fidelity, whilst the AC grid transient stability is analyzed by dynamic simulation (DS). An interactive EMT-DS interface is thus introduced to enable their concurrency and subsequently form a co-simulation. The CPU which is dominant in system study faces a tremendous challenge in handling a great number of components albeit they exhibit homogeneity. The many-core graphics processing unit (GPU) featuring massive parallelism is therefore exploited and following the definition of an adaptive computing boundary, a flexible heterogeneous sequential-parallel processing architecture is proposed for efficient analysis of the wind-farm-integrated AC/DC grid. Topological reconfiguration of WTs is specifically carried out to reduce the numerical order whilst enhancing system homogeneity that enables the GPU to thoroughly utilize its peculiar property of single-instruction multiple-thread (SIMT) compute paradigm. Consequently, significant speedups can be attained by the proposed computing framework over pure CPU computation, while its accuracy is validated by the commercial EMT and dynamic security analysis tools PSCAD/EMTDC and DSATools, respectively. [ABSTRACT FROM AUTHOR]