Wind speed presents a potential seasonal pattern revealed by the self-similarity in wavelet periodogram with various scales. The corresponding seasonal pattern will promote the improvement of the short-term wind speed forecasting accuracy. In this study, a novel method for short-term wind speed forecasting using wavelet transformation (WT) and AdaBoost technique is proposed to analyse the wind speeds distribution features and promote the model configuration. Power spectrum and seasonal pattern analysis using the WT are presented to investigate the wind speeds feature distribution based on the scalogram percentage of energy distribution in different seasons. This procedure contributes to perfecting the investigation of wind speed seasonal pattern characteristics over time and promotes the sample division by computing the statistics measurement based on the estimated frequencies interval. The model order estimation based on the information criteria is processed to reflect the systems dynamical sustainability between the current outputs and historical data. Finally, the experiments based on the real data from Yunnan wind farm are given to verify the effectiveness of the proposed approach.

The ever increasing size of wind turbines and the move to build them offshore have accelerated the need for optimised maintenance strategies in order to reduce operating costs. Predictive maintenance requires detailed information on the condition of turbines. Due to the high costs of dedicated condition monitoring systems based on mainly vibration measurements, the use of data from the turbine supervisory control and data acquisition (SCADA) system is appealing. This review discusses recent research using SCADA data for failure detection and condition monitoring (CM), focussing on approaches which have already proved their ability to detect anomalies in data from real turbines. Approaches are categorised as (i) trending, (ii) clustering, (iii) normal behaviour modelling, (iv) damage modelling and (v) assessment of alarms and expert systems. Potential for future research on the use of SCADA data for advanced turbine CM is discussed.

The random fluctuation of wind is the basic factor causing the power fluctuation of wind turbines. On the basis of the relationship model between wind and power, and considering the influences of wind speed fluctuation and wind direction fluctuation on power fluctuation, a novel fluctuation coefficient of wind speed, fluctuation coefficient of wind direction and the comprehensive influence factor are defined. The one-dimensional (1D) evaluation models of wind speed fluctuation to power fluctuation, wind direction fluctuation to power fluctuation and the 2D evaluation model of wind speed and wind direction fluctuation to power fluctuation are presented. The operating process of wind turbines is divided into three regions: the constant region of power coefficient, the transition region and the constant power region. In the constant region of power coefficient, wind speed fluctuation is the main factor affecting power fluctuation; wind direction fluctuation has little effect. In the transition region and the constant power region, both wind speed fluctuation and wind direction fluctuation have little effect on power fluctuation; especially in the constant power region in which power capture is limited by the pitch control technology, power fluctuation is not affected by wind speed fluctuation and wind direction fluctuation.

Rapid development of wind energy requires effective wind turbine prognosis methods, which can give alarm before actual failure happens and hence enables condition-based maintenance. A hierarchical method based on GP (Gaussian Processes) and PCA (Principal Component Analysis) is proposed in this paper for turbine prognosis using SCADA data. The method includes two levels of prognosis: 1) detect which wind turbine behaves abnormally and has potential defect; 2) determine the defective components in the abnormal turbine. On turbine level, the relationship between selected parameters and power generation is trained based on GP. Then the model residual, which is calculated as the difference between the estimated output and the actually measured power, can indicate whether the turbine is defective. On component level, the contribution of each SCADA variable to turbine abnormality can be given based on PCA method, and can be used for indicating the defective components. Field dataset including 24 failed turbines is used to validate the proposed hierarchical method. The validation results show that the proposed method can achieve wind turbine prognosis with 79% detection rate on turbine level and 76% detection rate on component level. Moreover, the method can provide several months ahead alarm before severe failure happens.

Wind turbine (WT) blades are vulnerable to failure as they are exposed to direct harsh environment, suffering constantly varying loads by wind and cyclic fatigue load due to self-weight, experiencing extreme temperature and humidity changes, erosion and corrosion. As a consequence, blades show high failure rate and share significant downtime, which highlight the significance and essentiality of the research, development and application of blade structural health monitoring (SHM) techniques. To fulfil reliable SHM of WT blades, much effort has been spent in the past years as reported in the literature. However, to date, how to realise reliable WT blade SHM is still an open question. The previous reviews enumerate the non-destructive testing techniques that are potentially applicable to blade SHM, but they fail to indicate which technique is most suitable for blade SHM and how to implement reliable SHM of a WT blade. To fill this gap, this study is geared toward to investigate the pros and cons of existing blade SHM techniques and following which, a newly developed blade SHM technique that is effective in both damage detection and fault location is discussed.

The open-circuit fault in converters is one of the wind turbine faults, which degrades the power quality and can even cause potential secondary faults in other components. So, an effective fault diagnostic technique for wind turbine converters is needed. This study presents a new open-circuit fault diagnostic approach for back-to-back converters of doubly fed (DF) wind turbines. The proposed fault diagnostic method has fault detection and fault localisation. The fault detection is based on the absolute normalised Park's vector approach; this method can detect multiple open-circuit switch faults and guarantee immunity to false alarms when the DF induction generator operates around the synchronous speed. For fault localisation, this approach applies the normalised current average values, which can be used to identify single and double open-circuit switch faults. The simulation and experimental results indicate that the diagnostic method can not only diagnose multiple open-circuit faults, but that false alarms due to transients can be avoided.

Frequency components of complicated asymmetric modulation sidebands, existing in the vibration of the healthy planetary gear train, are prone to be erratically diagnosed as fault characteristics, which leads to difficulties in fault diagnosis of wind turbine planetary gearbox. The factors affecting the modulation sideband, i.e. the periodical time-varying transmission path and meshing force direction, are analysed. Considering both the meshing vibrations of the planet–ring and planet–sun gear pairs, a mathematical model was developed to analyse the planetary gear train's vibration response. Simulation and experiments were conducted, and the mechanism of vibration modulation sidebands was revealed. The modulation sideband is not caused by the meshing vibration itself, but by the testing method that sensors are fixed on the ring gear or gearbox casing. The frequency components and amplitudes of the sidebands are determined by the tooth number of the ring gear and sun gear, the number of planet gears and their initial assembling phases. The asymmetric modulation sideband is mainly caused by the phase difference of the initial planets' assembling phase.

Dynamic analysis of the gearbox system plays a significant role in wind turbine fault diagnosis. The dynamic behaviour of the gearbox system may be affected by the unavoidable uncertainties of system parameters and external excitations. The dynamic characteristics of the wind turbine gearbox system with uncertainties subjected to random wind excitation are studied. A lumped-parameter model of a multistage gearbox system under transmission errors, time-varying mesh stiffness and random wind excitation is established. By using the Chebyshev inclusion function and the Welch method, an interval method for predicting the interval bounds of power spectral density for system responses is proposed. The accuracy of the presented method is verified by comparing with the scanning method. As an example, the effects of different uncertain parameters on the stochastic responses of system are investigated. The result illustrates the effectiveness of the proposed method. It is also shown that the dynamic responses of the system are sensitive to the uncertain stiffness parameters at the intermediate and high-speed stages. The result is of significant importance for the fault diagnosis in the wind turbine drive train.

Due to constantly varying wind speed, wind turbine (WT) components often operate at variable speeds in order to capture more energy from wind. As a consequence, WT condition monitoring (CM) signals always contain intra-wave features, which are difficult to extract through performing conventional time–frequency analysis (TFA) because none of which is locally adaptive. So far, only empirical mode decomposition (EMD) and its extension forms can extract intra-wave features. However, the EMD and those EMD-based techniques also suffer a number of defects in TFA (e.g. weak robustness of against noise, unidentified ripples, inefficiency in detecting side-band frequencies etc.). The existence of these issues has significantly limited the extensive application of the EMD family techniques to WT CM. Recently, an alternative TFA method, namely variational mode decomposition (VMD), was proposed to overcome all these issues. The purpose of this study is to verify the superiorities of the VMD over the EMD and investigate its potential application to the future WT CM. Experiment has shown that the VMD outperforms the EMD not only in noise robustness but also in multi-component signal decomposition, side-band detection, and intra-wave feature extraction. Thus, it has potential as a promising technique for WT CM.

Vibration signal of wind turbine has the non-linear and non-stationary characteristic, thus it is difficult to extract the fault feature. In this study, a novel method based on variational mode decomposition (VMD) and Teager energy operator (TEO) is proposed to diagnose the bearing faults of wind turbine. First, vibration signal is decomposed into several intrinsic mode function (IMF) components by means of VMD, which is a recently proposed signal decomposition method. Then, the most sensitive IMF component is selected according to kurtosis criterion. Moreover, TEO is applied to the most sensitive IMF in order to highlight impact signal. Finally, spectrum is obtained by applying Fourier transform to Teager energy of the selected IMF, thus extracting the fault feature to diagnose bearing fault. The effectiveness of the proposed method for fault diagnosis is validated by simulation and experimental signal analysis results, and comparison studies show its advantage over empirical mode decomposition and conventional spectrum analysis for wind turbine bearing fault diagnosis.

Global energy challenges have driven the adoption of renewable energy sources. Usually, an intelligent energy and battery management system is deployed to harness the renewable energy sources efficiently, whilst maintaining the reliability and robustness of the power system. In recent years, the battery-supercapacitor based hybrid energy storage system (HESS) has been proposed to mitigate the impact of dynamic power exchanges on battery's lifespan. This study reviews and discusses the technological advancements and developments of battery-supercapacitor based HESS in standalone micro-grid system. The system topology and the energy management and control strategies are compared. The study also discusses the technical complexity and economic sustainability of a standalone micro-grid system. A case study of a standalone photovoltaic-based micro-grid with HESS is presented.

Grid-interactive converters with primary frequency control and inertia emulation have emerged and are promising for future renewable generation plants because of the contribution in power system stabilisation. This study gives a synchronous active power control solution for grid-interactive converters, as a way to emulate synchronous generators for inerita characteristics and load sharing. As design considerations, the virtual angle stability and transient response are both analysed, and the detailed implementation structure is also given without entailing any difficulty in practice. The analytical and experimental validation of frequency support characteristics differentiates the work from other publications on generator emulation control. The 10 kW simulation and experimental frequency sweep tests on a regenerative source test bed present good performance of the proposed control in showing inertia and droop characteristics, as well as the controllable transient response.

This study introduces a mathematical trajectory based ‘Weibull Pareto sine–cosine optimisation (WPSCO)’ algorithm for the maximum power point tracking (MPPT) in the dynamics as well as in steady-state conditions of a partially shaded (PS) solar photovoltaic (PV) system. This ‘WPSCO’ technique is used for quick and oscillation-free tracking of the global best peak position in a very less number of steps, which is necessary to work in real-time atmospheric conditions. The unique advantage of this algorithm for MPPT problem in PS condition is as it is free from common and generalised problem of the other evolutionary techniques such as longer convergence duration, a large number of search particles, steady-state oscillation, unnecessary computational burden etc., which create power loss and oscillations in output. This hybrid algorithm is tested on different types of PV characteristics of the PS solar PV array by using MATLAB simulator and verified on a developed hardware of the solar PV system. Moreover, the tracking ability is compared with the state-of-the-art methods. The satisfactory steady-state and dynamic performances of the WPSCO algorithm under variable irradiance and temperature levels show the superiority over the state-of-the-art control methods.

This study proposes an incentive-based demand response (IDR) unit commitment model considering different types of demand response (DR) resources. In the proposed IDR dispatch model, (i) different load characteristic patterns of DR users can be included, such as transfer-type, shift-type and clip-type electricity users, and (ii) the uncertainty of DR participation behaviour is considered in the system reserve electricity using chance constrained programming. Simulation results for the Pennsylvania–New Jersey–Maryland (PJM) 5-bus system and the Institute of Electrical and Electronics Engineers (IEEE) 118-bus system indicate that the proposed model can achieve optimal DR scheduling while considering both economics and system reliability when high-quality DR resources are limited. Moreover, the unique scheduling features of DR must be considered in addition to economics and flexibility when dispatching DR resources; the uncertainty of DR can affect the highest confidence level of system operation. In addition, some significant coefficients of the special dispatch constraints of IDR clearly influence the performance of IDR resources.

This study focuses on the development of optimal torque (OT) control, which is a commonly used method for maximum power point tracking (MPPT). Due to the sluggish response of wind turbines with high inertia, conventional OT control was improved to increase MPPT efficiency by dynamically modifying the generator torque versus rotor speed curve. An idea that tracking a local interval of wind speed where the wind energy is primarily distributed rather than the total range of wind speed variation is applied in this study. On this basis, an effective tracking range (ETR) that corresponds to the local interval of wind speed with concentrated wind energy distribution is proposed and an improved OT control based on ETR is developed. In this method, based on a direct relationship between ETR and wind conditions, the torque curve can be quickly optimised so that higher and more stable MPPT efficiency can be achieved under varying wind conditions. Meanwhile, MPPT efficiency enhancement by reducing tracking range without increasing torque discrepancy leads to a low cost of generator torque fluctuation and drive train load. Finally, simulations based on fatigue, aerodynamics, structures, and turbulence (FAST) code and experiments conducted on a wind turbine simulator are presented to verify the proposed method.

These days, extensive efforts are being carried out to improve energy efficiency and energy recovery in various energy systems, such as extraction of energy from the high-pressure natural gas (NG) in pressure reduction stations (PRSs) employing a turbo-expander (TE). In conventional PRSs, NG pressure reduction is carried out by pressure regulators, leading to the loss of mechanical exergy available in the pressurised NG as heat. Thus, replacing a pressure regulator with TE allows the extraction of the mechanical exergy to change into electrical form of energy by coupling with a generator. So, a novel configuration is presented here for grid-connected microgrid based on TE. Furthermore, a new procedure is developed for optimal capacity sizing of energy resources in the proposed TE-based microgrid. Finally, optimisation results (with and without TE) and economical considerations are discussed.

This study proposes the detailed modelling of a novel automatic centralised micro-grid controller (ACMC)-based hybrid AC/low-voltage DC (LVDC) micro-grid network, capable of off-grid and on-grid operation of the system with a coordinated control. The micro-grid is designed to work majorly with renewable power sources. This hybrid micro-grid is capable of interconnecting very large AC and LVDC networks, using a bi-directional AC/DC/AC converter. The AC and the LVDC networks consist of different feeders with loads connected at various voltages. The ACMC design proposed is responsible for controlling the real(P) and reactive(Q) power from the sources based on load requirement and voltage control of the LVDC network. It enables the system to have a plug and play feature. The proposed ACMC has been implemented on a test system consisting of AC and LVDC radial distribution networks designed, with a bi-directional converter. A doubly fed induction generator-based wind turbine and solar photovoltaic array with maximum power point tracking have been used as the sources. The system has been simulated in Simulink. The results show the ACMC successfully performs the four quadrant operation of P,Q in the system for various system conditions.

Wind turbine simulators (WTSs), devised for pre-validation of control strategies for wind energy conversion system, commonly employ the inertia compensation scheme for reproducing mechanical behaviours similar to real wind turbines (WTs). However, it is found in this study that when a WT with large inertia is simulated, the time delay in command communication from control unit to motor driver, usually neglected in the existing inertia compensation scheme, results in the oscillating acceleration response and consequently leads to instability of the WTS system. As a result, the existing WTS is unable to stably simulate large-inertia WTs, which significantly limits its applicability. Hence, in this study, a linear discrete model of the inertia compensation part that considers time delay of acceleration observation and communication is developed. On the basis of this model, an improved inertia compensation scheme in which a high-order filter is introduced to eliminate the deviation of acceleration response caused by the two types of time delay is proposed. Finally, the improved inertia compensation scheme and its applicability to simulating a 600 kW WT developed by National Renewable Energy Laboratory are experimentally verified.

With the increase of power generated by wind power plants, network operators have placed steady-state and dynamic reactive power requirements at their grid connection point in several EU member states. This has forced wind turbine manufacturers to invest in upgrade of their technology and power plant owners and developers to invest in external compensation devices, adding to overall project costs. With the introduction of EU regulation Network Code Requirements for Generators (NC RfG) in 2016, it is expected that many countries (national implementation) will implement higher than currently required reactive power requirements accordingly to the new EU framework. The purpose of this study is to discuss the reactive power requirements and to investigate the reactive power support actually provided by wind power plants to date. SCADA data from a number of operational sites are analysed with regards to their actual reactive production utilising different control modes and to compare the given reactive power support with the requirements defined by the limits within the NC RfG. At the end of this study, a derived proposal for steady-state reactive power needs and dynamic power needs are discussed, therefore a minimum technical reactive power provision required is derived based on the current utilisation.