Wind Farm Generation Research Articles

An asynchronous distributed optimal wake control scheme for suppressing fatigue load and increasing power extraction in wind farms

The fatigue damage and power generation of wind farms (WFs) is related to their service lifetime and economic benefits. In this paper, an asynchronous distributed optimal wake control (OWC) scheme is proposed to suppress fatigue load and increase power extraction in doubly-fed induction generator (DFIG)-based wind farm (WF). To improve control flexibility, the generator torque and pitch angle of a wind turbine (WT) is simultaneously controlled. Then, considering the influence of wake effect, the wind speed sensitivity is used to combine with the states of WTs for formulating the model predictive control (MPC)-based optimal control objectives. To deal with the higher computational complexity of the aerodynamic interactions efficiently, an asynchronous distributed alternating direction method of multipliers (AD-ADMM) is designed for the control system. A layout of a 3 × 5 wake WF is considered in the case studies to validate the outstanding performance of the proposed scheme.

Ensemble machine learning model for forecasting wind farm generation

Ensemble machine learning model for forecasting wind farm generation

Research on the Technical and Economic Development of Large Megawatt Wind Turbines Based on Medium-Voltage Electrical System

INTRODUCTION: With the advent of the "bidding era" and "parity era" in the wind power market, the competition of the whole machine factory is becoming more and more fierce, and the capacity of the single fan is getting larger and larger, which becomes the key to the design of the fan electrical system of the large-capacity unit (5.XMW or more). At present, the low-voltage wind power system (690V,1140V) is the common solution for wind turbines. However, due to the limitation of the cable section of low-voltage electrical system and the increase of the rated current of the generator, the increase of the capacity of a single machine makes more cables from the generator side to the grid, and the cost also increases.
 OBJECTIVES: Aiming at the future large megawatt wind power market, the medium voltage Doubly-Fed electrical system solution is proposed to increase the higher generation and electricity income of wind farms and reduce the manufacturing cost of wind farms.
 METHODS: The technology and economy of medium voltage and low voltage electrical system are compared.
 RESULTS: With the gradual increase of single capacity, the economy of medium pressure wind power generation system is getting better and better, and the higher the height of the tower, the better the economy. At the same time, the reduction of the rated current of the generator brings about the reduction of line loss and the increase of power generation. The number of cables is greatly reduced, and the construction cost and difficulty of cable laying will be greatly reduced.
 CONCLUSION: In response to the technical trend of large-capacity wind turbines in the future, the medium-voltage wind power generation system has a good application prospect, both from the economic and technical point of view.

Time-domain fatigue damage assessment for wind turbine tower bolts under yaw optimization control at offshore wind farm

Yaw optimization control is recognized as the most effective active wake control strategy for enhancing the overall power generation of wind farms. However, the potential adverse effects of yaw optimization control on the fatigue life of wind turbines remain unclear. This study examined the effect of yaw optimization control on the fatigue life of offshore wind turbines using tower bolts. Initially, the yaw misalignment of the wind turbines was optimized using the open-source FLORIS package to maximize wind farm power generation. Subsequently, the time-varying load of the wind turbines was obtained through the OpenFAST software, using the optimal yaw misalignment, wind speed, and turbulence intensity at the hub height as inputs. The fatigue life of the wind turbines was then calculated by integrating the Schmidt–Neuper engineering stress algorithm, rain flow counting method, and Miner-Palmgren fatigue damage accumulation theory. Finally, a case study of the Horns Rev I large-scale offshore wind farm, comprising 80 wind turbines, revealed that yaw optimization control could significantly enhance the annual power generation of the wind farm by approximately 1.818%. However, optimization comes at the cost of reducing the fatigue life of wind turbine tower bolts by approximately 3–9 years.

Robust Constrained Model Predictive Voltage Control Scheme and its Classification

In this study, a prospective power system is suggested (PPS), predictive current control (PCC), enhanced voltage control (EVC), model predictive control (MPC), induction motor (IM) circuit. Instead of monitoring the torque and speed as in conventional MP DTC, the stator voltage is directly regulated in the suggested PVC model thanks to the predictive control theory. A voltage source inverter's use of a model predictive control approach. For all potential voltage vectors produced by the inverter, this method forecasting value of the load using a discrete model of the system. "Voltage-source converter based high voltage direct current (VSC-HVDC)" connected offshore wind farms using a "model predictive control (MPC)" based Enhanced Voltage Control Strategy (EVCS) (OWFs). All wind farm generator (WTGs) and wind farm side VSCs are effectively synchronized in the proposed Midi controller EVCS to maintain voltages within a practical range and reduce system power loss. Review of induction motor defects, including mechanical and insulating issues. The summary of static current signature of machine failures. Numerous useful feature extraction techniques have been introduced for induction motor problem detection.

Research and application of a Model selection forecasting system for wind speed and theoretical power generation in wind farms based on classification and wind conversion

This study focuses on ensuring the stable operation of the power grid by accurately forecasting the theoretical power generation capacity of wind turbine units, especially in scenarios integrating significant amounts of renewable energy into the grid. The forecasting process involves two key steps: initially forecasting wind speeds and then estimating theoretical power generation using wind turbine power conversion curves. This article proposes a wind speed forecasting system based on deep learning, integrating multiple hybrid models and employing deep learning algorithms to select the optimal wind speed hybrid forecasting model, optimized by the multi-objective mayfly optimization algorithm. Additionally, a wind energy conversion simulation system for wind turbines has been developed, precisely simulating the physical process of converting wind energy into electrical energy. This system, in conjunction with wind speed forecasting, estimates the theoretical power generation of wind farms. The results of this research hold significant practical implications for enhancing the operational efficiency of wind power, strengthening the grid's supply-demand balance, and increasing the economic and environmental value of wind power projects.

Assessment of the Risk of the Loss of Supply, the Recycling Rate and the Degree of Substitutability of Elements in the NdFeB Magnets of a Small Wind Farm Generator

Existing shortages on the market of critical and strategic raw materials and problems with the supply chain of selected raw materials, mainly those from China, indicate the need to develop recycling technologies for devices containing the above-mentioned materials. The aim of this work should be to ensure a sustainable supply of raw materials for industry in the EU. One potential source of raw materials may be small-power wind turbine generators equipped with permanent magnets (NdFeB). This research allowed for the development of a methodology for disassembling a generator containing NdFeB magnets in order to determine the shares of individual elements. Due to the significant shares of critical and strategic elements in permanent magnets, a detailed economic analysis was carried out. Based on the research results, economic benefits resulting from the recovery of valuable elements were indicated. The significant market value of critical and strategic raw materials contained in neodymium magnets was demonstrated. The risk of the loss of supplies, the recycling rate and the possibility of replacing the elements in permanent magnets were assessed.

Optimization Study of Carbon Emissions in Wind Power Integrated Systems Based on Optimal Dispatch Algorithm

Abstract With the integration of wind power into the power system, dispatch becomes more complex and existing algorithms are no longer applicable. This paper focuses on optimizing carbon emissions in wind farm generation while considering issues related to wind power integration and carbon trading. An optimal dispatch algorithm was designed with the objective of minimizing total costs, which was then solved using the cuckoo search (CS) algorithm. Additionally, an adaptive improvement was made to the CS algorithm to obtain the improved cuckoo search (ICS) algorithm. An analysis was conducted on a case study with 10 units. The ICS algorithm obtained higher quality solutions, with a total cost of $ 632 719 and a calculation time of 0.51 minutes, which was superior to the solutions obtained by the particle swarm optimization and CS algorithms. Fluctuations in the confidence level of system rotation reserve capacity could lead to variations in the final system cost, which needs to be adjusted according to actual conditions. The dispatch scheme obtained by the ICS algorithm showed reduced carbon emissions, total costs, and better performance when compared with the optimal dispatch algorithm in different scenarios. The results show that the proposed methods are reliable and practical.

An adaptive distance protection back‐up scheme for transmission lines connected to doubly fed induction generator wind farms based on setting groups procedure

AbstractGenerally, wind speed alters continuously during a day and leads to fluctuating output power of a wind farm (WF). Connecting such a WF to the transmission line of grid causes a malfunction in the performance of the backup zone of the distance relays due to the random nature of generation and the number of connected wind turbines (WF penetration). In this study, a new adaptive setting groups‐based method has been proposed to set the second zone of the distance relay in the grid with a WF equipped with a doubly fed induction generator. In this method, the authors have also taken into account the uncertainty of wind fluctuations and the availability of WT. A Markov model is developed to implement these features. To determine the impedance setting of each of the set groups of the second zone in the distance relays of the remote bus, the obtained impedances are clustered in four clusters by using the combination of K‐means and particle swarm optimization algorithms. Finally, the details of the analysis results of this issue have been compared with other methods available in the literature. The validity and accuracy of the proposed approach also have been assayed and confirmed.

Application and Comparison of a Modified Protection Scheme Utilizing a Proportional–Integral Controller with a Conventional Design to Enhance Doubly Fed Induction Generator Wind Farm Operations during a Balanced Voltage Dip

The doubly fed induction generator (DFIG) is vulnerable to grid faults due to its direct stator connection, causing issues like excess stator current during voltage dips. Consequently, sensitive inverters suffer from increased currents, and the DC-link capacitor undergoes overcharging. This document examines two protection strategies employing a proportional–integral (PI) controller to manage the transient rotor current and mitigate DC-link overcharging, thereby optimizing DFIG behavior during network faults. One option combines a classic crowbar circuit with a DC-chopper, while the other is a modified protection scheme (MPS) that includes an impedance with passive elements and a crowbar. The impedance forms a resistance Rp parallel with an inductance Lp. Both configurations, situated between the rotor coils and the rotor-side converter (RSC), augment the capacity for low-voltage ride-through (LVRT). MATLAB/SIMULINK simulations of the two schemes demonstrate successful rotor current reduction at 2.9 kA and 3.4 kA, and DC-link tension reduction below and at 1.4 KV. In addition, the conventional crowbar and MPS configurations efficiently restrict the RSC current to levels below 0.21 kA and 2.94 kA, while absorbing up to 2.52 kA and 1.52 kA, respectively. The key difference lies in the fact that fine-tuning the parameters in the MPS design prevents rotor disconnection when faced with a balanced fault. This enhancement enhances machine performance and enables full stator power control via the RSC.

Targeted adversarial attacks on wind power forecasts

In recent years, researchers proposed a variety of deep learning models for wind power forecasting. These models predict the wind power generation of wind farms or entire regions more accurately than traditional machine learning algorithms or physical models. However, latest research has shown that deep learning models can often be manipulated by adversarial attacks. Since wind power forecasts are essential for the stability of modern power systems, it is important to protect them from this threat. In this work, we investigate the vulnerability of two different forecasting models to targeted, semi-targeted, and untargeted adversarial attacks. We consider a long short-term memory (LSTM) network for predicting the power generation of individual wind farms and a convolutional neural network (CNN) for forecasting the wind power generation throughout Germany. Moreover, we propose the Total Adversarial Robustness Score (TARS), an evaluation metric for quantifying the robustness of regression models to targeted and semi-targeted adversarial attacks. It assesses the impact of attacks on the model’s performance, as well as the extent to which the attacker’s goal was achieved, by assigning a score between 0 (very vulnerable) and 1 (very robust). In our experiments, the LSTM forecasting model was fairly robust and achieved a TARS value of over 0.78 for all adversarial attacks investigated. The CNN forecasting model only achieved TARS values below 0.10 when trained ordinarily, and was thus very vulnerable. Yet, its robustness could be significantly improved by adversarial training, which always resulted in a TARS above 0.46.

Improving power swing detection in the presence of doubly-fed induction generator wind farms based on setting adaptation

This article proposes a new approach to improve the security of transmission line protection by correctly detecting power swing when large-scale doubly-fed induction generator wind farms (DFIGWF) exist in the proximity of the relay. In the presence of large-scale DFIGWFs, following large disturbances, power swing may probably cause the distance protection to malfunction. This issue can also lead to widespread transmission line outage and cause instability in the power system. Therefore, a new approach is proposed to solve the challenge by adapting the power swing blocking (PSB) function in the distance protection relays and utilizing the dominant power system oscillation modes. Thus, related calculations are performed in a central module remotely connected to intelligent electronic devices (IEDs) for distance protection and stabilized against power swing. The calculation to adjust the PSB setting of the IEDs in the central module is based on the oscillation modes of the power system and the rate of change of impedance, which is influenced by the penetration rate of the DFIGWFs. Simulation studies have been done on two test systems and the results reveal that the proposed scheme significantly improves the security of distance protection against power swing under different DFIGWF intrusion conditions.

Adaptive Machine-Learning-Based Transmission Line Fault Detection and Classification Connected to Inverter-Based Generators

Adaptive protection schemes have been developed to address the problem of behavior-changing power systems integrated with inverter-based generation (IBG). This paper proposes a machine-learning-based fault detection and classification technique using a setting-group-based adaptation approach. Multigroup settings were designed depending on the types of power generation (synchronous generator, PV plant, and type-3 wind farm) connected to a transmission line in the 39-Bus New England System. For each system topology, an optimized pretrained ensemble tree classifier was used. The adaptation process has two phases: an offline learning phase to tune the classifiers and select the optimum subset of features, and an online phase where the circuit breaker (CB) status and the active output power of the generators are continuously monitored to identify the current system topology and to select the appropriate setting group. The proposed system achieved an average accuracy of 99.4%, a 99.5% average precision, a 99.9% average specificity, and a 99.4% average sensitivity of classification. The robustness analysis was conducted by applying several fault scenarios not considered during training, which include different transmission network configurations and different penetration levels of IBGs. The case of incorrect selection of the appropriate setting group resulting from selecting the wrong topology is also considered. It was noticed that the performance of developed classifiers deteriorates when the transmission network is reconfigured and the incorrect setting group is selected.

Performance analysis of modeling scale on multiband oscillations in grid-connected wind farm

Grid-connected permanent magnet synchronous generator (PMSG) wind farms may be susceptible to oscillation when connected to weak grids. This paper explores the root causes of the oscillations from two perspectives: the model and the oscillation frequency band. First, the small signal model of PMSG wind farm grid-side system is established, and the small disturbance comparison with the PMSGA full-order electromagnetic transient model in MATLAB/Simulink is carried out. Then, we calculatue the eigenvalues of the small signal model and use model analysis techniques such as participation factors calculation and root locus method to identify the critical factors that cause the oscillation modes to be dominant. Finally, time-domain simulation is used to verify the theoretical analysis. Two dominant modes are identified: the subsynchronous oscillation mode and the low-frequency oscillation mode. The subsynchronous oscillation mode is closely related to the direct current (DC) voltage control and the dynamics of DC link. The low-frequency oscillation is significantly weakened with the decrease in grid strength, and it is closely related to the phase-locked loop (PLL) control. The conclusions can provide a reference for tuning the control parameters of PMSG converter.

An adaptive charge control strategy for participation of neighbourhood battery energy storage systems in frequency stability

Neighbourhood Battery Energy Storage System (N-BESS) is a new scale of energy storage that is expected to have a potential role in modern power systems stability. In the literature, there is a lack of studies that proposed a smart engagement of N-BESS in the frequency stability. In this paper, an adaptive charge control strategy for the N-BESS has been proposed to enhance the frequency stability while maintaining a fair State-of-Charge (SOC) levels for N-BESS. Also, a collaborative framework for large-scale Battery Energy Storage System (L-BESS) with N-BESS has been introduced, in which the N-BESS play an auxiliary role in frequency stability. In this study, a number of system uncertainties, including N-BESS SOC, load consumption, wind farm generation, and contingency size and location, have been considered. The simulation has been carried out in the Simplified 14-Generator Model of the South East Australian Power System over one-year simulation using DIgSILENT/PowerFactory software and Python interface. The results have demonstrated that a higher SOC levels of N-BESS under the proposed strategy have been maintained compared to that of the conventional strategy. Moreover, when the L-BESS collaborated with the N-BESS under the proposed framework, the best frequency stability with highest SOC levels of N-BESS have been obtained; at the same time less discharged power by the L-BESS was noted compared to that when the L-BESS participated individually which indicated that a smaller L-BESS size would be required if it collaborated with N-BESS in frequency stability service.

Study of aerodynamic performance and wake effects for offshore wind farm cluster

The large-scale development of wind farms has led to an increase in the concentration of wind farms, due to factors such as concentrated wind resources, operational and maintenance costs. This has resulted in the formation of wind farm clusters, especially offshore wind farms. The wake and cluster effects of upstream wind farms have a significant impact on the power generation of downstream wind farms. In this study, the Large Eddy Simulation (LES) combined with actuator line model is used to simulate the flow field of a wind farm cluster under turbulence inflow conditions. The numerical methodology is first validated using data from the Nibe wind turbine. The wind farm cluster with NREL 5 MW wind turbine is then analyzed to examine the aerodynamic performance and wake effect for different wind farm cluster configurations. The results show that there is a clear wake and cluster effect for wind farm clusters, and that this effect reduces as the distance between wind farms increases, with 30D being a reasonable separation distance. The power of the wind farm still decreases when three wind farms are clustered, which is further reduced compared to two wind farms, indicating that the cluster effect becomes more severe as the number of wind farms increases. The study also found that staggering wind farms in a wind farm cluster can improve the overall power of the wind farm cluster.

Wind Power Scenario Synthesis With Smoothing Effect Through Spectral Decomposition and Its Application to Flexible Resource Adequacy

We synthesize future scenarios of total wind power generation (WPG) of future wind farms by considering the smoothing effect, in which mid-term variability fades out, but periodicity and short-term variability remain as more geographically distributed wind farms are aggregated. First, we decompose the WPG into a periodic component (PC) and non-periodic component (NC). We design the future PC by modifying the standard deviation and mean of the current PC. Then, we assume that the NC can be decomposed into a variable component (VC), which is a source of mid-term variability, and an uncertain component (UC), which is a source of short-term variability. We separate the VC and UC in the frequency domain. We design the VC by modifying the slope of power spectral density (PSD) to represent the smoothing effect and the UC by synthesizing the PSD distribution to represent randomness. Based on our scenarios, we develop a new flexibility index and a novel recursive algorithm to calculate the required amount of future flexible resources and the value of the smoothing effect to maintain the flexible resource adequacy. Finally, we verify the smoothing effect and the amount of flexible resources by simulating the IEEE test network based on actual WPG in Texas and Australia.

Improving dynamic stability of wind integrated system by incorporating magnetic energy storage

AbstractThe need to incorporate renewable energy sources in the present grid due to energy demand and environmental issues has led the power industry to face new challenges and unexpected transformations. This new epitome to build a flexible power system with coordination of increasing intermittent renewable energy resources and delivering power efficiently with reliability demand new technologies. This work investigates the coupling of static synchronous compensator (STATCOM) with the most exclusive superconducting magnetic energy storage (SMES) device designed for the improved functioning of a grid network that is incorporated with a wind farm. The widespread integration of intermittent wind generators in the grid is disturbing its stability and reliability. The SMES is interlinked with the grid system via a power electronic interface and chopper for the energy exchange. This work suggests STATCOM as a power electronic interface and a three‐level chopper configuration with novel control scheme for enhancing the functioning of the test system. This integrated controller (STAT‐SMES) has been analyzed for grid‐connected doubly fed induction generator wind farm under different fault conditions and time durations. The results have been compared without any controller, with STATCOM only, and with the proposed STAT‐SMES controller using MATLAB. The simulation outcomes prove that coupled control scheme is effective and better in handling wind farm integration issues.

High resolution wind resource assessment method based on mesoscale atmospheric model and CFD technology

The evaluation results of wind energy resources directly affect the economic benefits and healthy development of wind farms. Therefore, a high-resolution wind resource assessment method for wind farms based on mesoscale atmospheric model and CFD technology is studied to accurately simulate relevant data of wind resources and improve the assessment effect. The mesoscale WRF numerical model is used to solve the regional data of wind farms and obtain the mesoscale meteorological analysis data. According to the solution results of the mesoscale atmospheric model, the wind speed profile is established, the boundary conditions and initial conditions are extracted, and the CFD micro scale model is input to obtain the wind speed and wind speed frequency at the height of the fan impeller. In order to improve the accuracy of numerical simulation of micro scale CFD model, the large eddy simulation method is used to simulate the operation of wind farms. Complete theoretical power generation evaluation based on wind speed, wind speed frequency and generator power. The experimental results show that this method can accurately simulate wind resources and accurately evaluate the theoretical power generation of wind farms. The wind farm is rated as Level 3, and the wind frequency is mainly between 4 and 10 m/s. This method can ensure that the wind farm can generate electricity all year round without damaging the wind speed.

Power Losses of Asynchronous Generators Based on Renewable Energy Sources

This article discusses the possibility of using asynchronous generators in wind turbines, in particular asynchronous generators with phase rotors, in parallel with the network. Currently, due to the decrease in non-renewable energy sources around the world, the use of renewable energy sources, especially wind energy, is being considered. Also in this article, the power losses and energy balance of asynchronous generators with a phase rotor, all losses in an asynchronous generator, its efficiency, and torque are considered. In addition, a functional diagram of an asynchronous generator in a wind turbine has been developed, as have methods for simplifying calculations due to the generator substitution scheme. Due to this, methods for calculating the impedance of an asynchronous generator are given. With the help of an experimental research stand, the state of a number of parameters was analyzed during the parallel operation of an asynchronous generator with a phase rotor and a network. Taking into account that the indicators of the wind potential of land plots identified for the construction of wind turbines in Uzbekistan are higher than the average indicators of existing wind turbines in the world, it is assumed that the use of asynchronous generators in wind farms in the near future is considered promising.