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Article

Technological Advances on Fault Diagnosis in Wind Turbines: A Patent Analysis

by
Natasha Benjamim Barbosa
,
Danielle Devequi Gomes Nunes
,
Alex Álisson Bandeira Santos
and
Bruna Aparecida Souza Machado
*
Computational Modeling and Industrial Technology, SENAI CIMATEC University Center, Salvador 41650-010, Brazil
*
Author to whom correspondence should be addressed.
Appl. Sci. 2023, 13(3), 1721; https://doi.org/10.3390/app13031721
Submission received: 29 December 2022 / Revised: 23 January 2023 / Accepted: 24 January 2023 / Published: 29 January 2023
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Abstract

:
Given the urgency of mitigating the effects of global warming and the depletion of fossil energy sources, renewable sources of energy, such as wind power, are the focus of the future. However, due to the rapid growth of this technology, concerns about the security and reliability of wind turbines are increasing, especially because of associated hazards and financial costs. Hence, health monitoring and fault identification for wind turbine blades have become an important focus of research. Thus, the objective of this study was to generate data on the current scenario of the techniques used to identify failures and defects in wind turbines and their components. Through the results found, companies can find ways to make decisions and identify potential new technologies. In this way, a technology prospection was conducted that focused on patents to investigate the use of vibration analysis, thermography, and machine learning. A total of 635 patent documents were found, and the evolution in the number of patents over the years has demonstrated the current interest in developing new technologies in this research area. China, the world’s leading country in the area of wind energy, was the country with the highest number of filings, followed by the United States. In the patent documents analyzed, it was possible to identify that those innovative technologies for predicting and detecting failures are a topic of interest for the world’s largest economies. Additionally, it was clear from the results that the application of artificial intelligence to traditional techniques is a current trend and will continue in the future. Technological prospection studies can foster the development of new methods and devices, providing economic and environmental gains for the wind energy industry.

1. Introduction

For many decades, the global utilization of energy has increased remarkably, and significant efforts have been made by most developing countries to attenuate and minimize the impacts of climate change by maximizing energy use and minimizing greenhouse gas emissions [1]. Due to current economic and industrial growth, experts have estimated that by 2050, the global energy requirement will be around ~30 TW [2], exacerbating the negative impacts related to the use of fossil fuels [3]. The Intergovernmental Panel on Climate Change (IPCC) commented on the future risks emanating from climate change, stating: “Continued emission of greenhouse gases will cause further warming and long-lasting changes in all components of the climate system, increasing the likelihood of severe, pervasive, and irreversible impacts for people and ecosystems. Limiting climate change would require substantial and sustained reductions in greenhouse gas emissions which, together with adaptation, can limit climate change risks” [4].
Climate change is undoubtedly a major driver of wind power development, and the use of wind energy is an important factor in attenuating the negative effects of global warming. Along with the growing safety concerns surrounding the use of nuclear power, many countries have established important strategies targeting renewable energy with low greenhouse gas and pollutant emissions, including the use of wind energy [5,6].
Wind energy is a predominant and potentially low-cost renewable energy technology that plays a key role in the transition toward clean energy [7,8]. Among the current technologies for generating electricity based on renewable sources, the most widely implemented renewable energy in the world is wind power [9]. The energy is collected through conventional wind turbines, converting the kinetic energy of the wind into mechanical energy through the rotation of the blades and then into electrical energy through the use of generators. Depending on their location, wind turbines can be categorized as onshore or offshore wind turbines [10].
In recent decades, the global wind-power-generation capacity has increased from 7.5 GW in 1997 to 743 GW in 2020. Currently, the total world capacity for wind power is about 873 GW, helping the world avoid over 1.2 billion tons of CO2 annually. China and the U.S. are the two largest wind energy marketplaces in the world. In the United States, installed wind capacity at the end of 2020 was 122,426 MW of onshore wind power and 42 MW of offshore wind power, representing more than USD 20 billion of investment, while in China, the capacity was 272 GW onshore and 9 GW offshore [11]. Experts believe that the global wind energy market will reach over 557 GW of new capacity over the next five years due to current policies. That is more than 110 GW of new installations each year through 2026 [12].
WTs can be categorized according to their location, either onshore or offshore. Onshore wind turbines are located on land, and their systems suffer fewer failures, while offshore turbines, constructed in bodies of water where higher wind speeds are available, generate more electricity due to their larger size and the stronger winds present in the large open spaces where they are located, without restrictions caused by the environment, noise limits, and urban planning [13,14]. The rapid expansion of wind energy has created novel challenges in turbine control, plant operations, production planning, condition monitoring, and maintenance. Wind turbines (WTs) are exposed to highly variable and severe weather conditions, with rapidly changing ambient temperature, air pressure, and alternating load, including calm winds to gale force winds, tropical heat, lightning, arctic cold, hail, and snow [15]. Due to these external variances, WTs undergo constantly changing dynamics and local loads, resulting in a large variation in operating conditions that lead to intense mechanical stress [16,17]. Many of the already installed WTs are aging, driving the growing maintenance and repair market, along with the demand for the development of new maintenance and repair technologies. As the demand for wind power continues to grow at exponential rates, maintenance will be a permanent factor related to costs, and it can directly influence energy prices and the competitiveness of renewable energy [17,18]. Maintenance (O&M) activities are a critical aspect of reliability. As a matter of fact, the global O&M market is projected to grow to USD 27.4 billion by the year 2025. Maintenance is one of the leading costs in the total expenditure of a wind farm project and, if not effective, can cause drastic losses in energy production due to downtime [19,20,21].
Different faults can affect a WT with different degrees of severity and with different financial impacts (related to the shutdown time caused by the fault). The most common failures associated with WTs are electrical, mechanical, structural, and cyber incidents. When it comes to the components, the most frequent faults involve the electrical system, the blade and pitch system, the sensors, and the hydraulic system [22]. The different failures in the components of a WT can lead to damage directly linked to the electrical system, decreasing the power generated by the WT and increasing the overload of different mechanical parts. This overload can lead to an abnormal vibration spectrum, overheating, and shortening the life of the components of the wind turbine, directly affecting the quality of the energy generated [16,23]. These failures cause serious incidents, both to the workers involved in the operations and to the environment in which they are exposed; in addition to the loss of efficiency in energy production, one of the main consequences of the late diagnosis of these failures is electrical breakdown and fire in the structural components of the turbines. Due to the height of the turbines, these failures are extremely dangerous and difficult to extinguish while releasing toxic fumes and can cause secondary fires in their immediate environment; they can also lead to operator fatalities [24].
Taking all these factors into consideration, research into condition monitoring and failure diagnosis of WTs is increasing dramatically, and new fault-prediction techniques are needed to avoid downtime and increase plant reliability [25]. Monitoring and fault diagnosis based on vibration analysis is one of the most commonly applied technological tools in the industry for rotating equipment. Moreover, it is considered the method that covers the greatest possible number of failures. A challenge associated with monitoring using vibration analysis is the multiple frequency ranges, both low and high, of the different components of a WT. Usually, vibration techniques range from statistical techniques to techniques based on signal processing algorithms to extract diagnostic data [23,26].
De Azevedo and colleagues placed accelerometers on the main components of a WT and used a vibration-based condition monitoring methodology using signal processing techniques, such as the temporal variations in vibrating signals, spectrum analyses by fast Fourier transforms, and envelope analysis through Hilbert transformation to search for potential faults. The results of the analysis helped find a failure in the generator’s non-driving-end bearing, and the vibration characteristics were analyzed and reduced after the bearing was replaced [26]. Aihara et al. designed a blade-vibration monitoring system. The system used a strain gauge installed at the root of the blade to calculate and measure the deflection in real time according to the monitored stress. The results showed an acceptably high estimation accuracy and considered the system practical [27].
Another non-destructive evaluation technology used for detecting faults in wind turbines is thermography. Thermography technology is an advanced method based on thermal imaging that detects different temperatures on structural surfaces [28]. The thermography technique uses an infrared (IR) camera acting as a sensor, and the system makes it possible to detect surface damage on the different elements of wind turbines [29]. Structural defects cause friction between the constituent elements of wind rotors, which, in turn, generates heat. Heat flows along materials can be accurately assessed by capturing and analyzing thermal images. The benefits of using the thermography technique are visual damage description and full-field measurements. In addition, this method could be applied for massive inspections due to the short time consumption. Hwang et al. [30] utilized a thermography system based on a continuous line laser and an algorithm to measure internal delaminability in rotating blades. The thermal images demonstrated the explicit defect by color difference. Yang et al. [31] evaluated passive and active infrared thermographic techniques employed on wind turbine blades to detect multiple defects, such as delamination, air bubbles, and structural defects within glass-fiber-reinforced polymer composite blades.
Comparing the methods mentioned above, the vibration analysis technique, considered traditional, usually indicates failures linked to mechanical and electrical parts, and although it is widely used, it has limitations, such as difficulty in identifying the nature of the failure, many sensors are required to detect the location and severity of the damage, and environmental conditions can make damage detection difficult and inaccurate. In addition, it makes it difficult to differentiate between the vibrations caused by the environment or the operating conditions and the vibrations caused by blade damage [16]. On the other hand, the thermography technique, which can be used in various environmental conditions, presents some disadvantages, including the requirement for expensive thermal imaging cameras; the technique is also sensitive to temperature changes and is affected by air temperature and humidity [16,32,33]. Both techniques have advantages and limitations, meaning that scientific, technical development is essential for improving traditional techniques and developing new ones.
In recent years, research advances have offered numerous new solutions for various fault problems. Many researchers have focused on wind speed variability, improving power generation efficiency, and decreasing the cost of energy generation. A greater understanding of the evolved models and techniques could positively impact the future of wind energy research.
Patents play an important role in the development and innovation process, directly supporting three important areas, such as: providing back-up support for R&D; by providing state-of-the-art analysis in a given technological area; supporting the acquisition and protection of IP. In addition, the study of patents gives direct support to companies and the development of business and strategic transactions. Important data for analyzing competitors, understanding a particular technological area, determining patent applicability, and identifying potential alliance partners are made available [34]. In view of that, this study had the objective of performing a technological prospection focused on patents in order to verify the technological panorama of the different techniques used to identify faults and defects in wind turbines.

2. Materials and Methods

To analyze the technological information described in the patent documents related to fault diagnosis in WTs, an exploratory search was carried out using the database of Derwent Innovation Index (DWPI), Thomson Innovation©, licensed for use by SENAI CIMATEC University Center. The search was undertaken on 19 August 2022.
After the refinement, the keywords and codes used represented the strategy used in which the technology of interest could be identified in the documents: wind turbine AND failure detection AND (vibration analysis OR thermography).
Moreover, International Patent Classification (IPC) codes were also used to refine the exploratory search. In Table 1, the codes used in the prospection, as well as what each code is related to, are described.
It is worth mentioning that a secondary exploratory search was conducted by adding the keyword of machine learning and considering the same IPC codes in order to evaluate a more specific approach. The search browsed the title, abstract, and claims fields of the patent documents without restricting the data collection period. For the graphic construction, GraphPad Prism 9.2 (San Diego, CA, USA) software, licensed by SENAI CIMATEC University Center, was utilized to provide the time analysis of the patent documents (year of priority and year of expiration), main applicants, inventors, and the International Patent Classification (IPC) codes. The results for the geographical distribution of the main applicant countries/regions and the main technological areas were collected directly from the DWPI database (with adaptations).

3. Results and Discussion

The use of patents is an interesting and important tool in the assessment of innovative technologies and activities [35]. In the present investigation, a patent search was conducted to evaluate the technology of interest, the systems, methods, and devices for wind turbines. In total, 635 were found, with 342 DWPI families. Figure 1 details the annual distribution of patent applications regarding the described technology; the first applications were found in 1982 (Figure 1a). The number of patent document applications showed growth over the analyzed period, indicating a continued strong interest in the development of new technologies for wind energy. Significant growth in the publication year can be observed starting in 2014, followed by a peak in 2021, with 98 documents. The evolution of the number of patent applications may be related to the need to promote technologies aimed at combating extreme climate change caused by global warming, a consequence of exacerbated CO2 emissions. The first patent found was filed in 1982 by the General Electric Company (Boston, Massachusetts, United States) and refers to the diagnosis of undesirable internal changes in an operating turbine by monitoring the dynamic fluid flow pressure and the vibration of the stationary casing’s pump frame. The patent describes monitoring using temperature and static pressure data [36].
The consequences of exacerbated CO2 emissions are increasing in both frequency and intensity: floods, wildfires, melting polar ice caps, and extreme seasonal temperature changes—up and down—on all continents. Consequently, the search for strategies to reduce the dependence on fossil fuels for energy production is increasing. One important strategy is the mission of “net-zero emissions by 2050”, which emerged as a promising strategy to limit global warming and reduce the world temperature to 1.5 °C in order to significantly reduce the risks and impacts of climate change [37]. The accession of renewable energy and technological innovation provides a viable route to reduce fossil energy use and reduce CO2 emission levels [38,39].
Most of the technologies developed in the patents focused on new methods and/or systems for monitoring wind turbines and rotor blades. The patent filed by Qiao et al., 2021, refers to a wind generator failure detection method that collects and processes two different signals obtained from the wind turbine, the first signal coming from the wind turbine generator and the second coming from a vibration sensor attached to the wind turbine. Therefore, it is possible to detect and identify a characteristic signature from a wind turbine bearing failure [40]. Ernst et al., 2021 [41] described a blade-handling system and method for conducting tomography (X-ray) scans of wind turbine blades to detect various types of manufacturing defects before blade installation. Jiang et al., 2021 [42] described a method of fault diagnosis of the planetary gearbox of a wind turbine that comprised collecting a time domain vibration signal from the planetary gearbox as a sample to be diagnosed, these samples are normalized in amplitude, and the training results are validated and tested to obtain fault-diagnosis results. The present invention has the advantages of solving the problem of time-consuming and labor-intensive collection of real equipment data, reducing the reliance on traditional methods on a large amount of real equipment data, and improving diagnostic accuracy.
As the sectors of wind energy grow, business economics will demand increasingly careful management of costs, especially as the operation and maintenance (O&M) costs of WTs account for about 25–30% of the overall energy generation cost or 75–90% of the investment costs [16]. In view of that, condition monitoring (CM), fault diagnosis (FD), and nondestructive testing (NDT) are currently considered crucial means to increase the reliability and availability of wind turbines. With that, many researchers have focused on CM and FD for different components of wind turbines, especially for evaluating wind turbine blade failures [16,43].
One of the many ways to perform CM and FD is through vibration analysis. Vibration analysis can be used as an early damage-detection method in order to evaluate the health conditions of a structure [28]. The major types of sensors to capture vibration signals are displacement sensors, velocity sensors, and accelerometers [44,45]. The frequency ranges from low-frequency, middle-frequency, and high-frequency ranges and is used in the corresponding vibration sensors. Damage to a wind turbine’s structure changes its properties, e.g., mass, stiffness, and damping. In addition, it is also possible to observe changes in the mechanical properties by modifying the modal frequency, mode shape, and damping ratio. Blade damage detection can be mentioned as an example; when the structure is excited by external forces, such as wind flow or turbulence, the dynamic responses of the structure act as vibration signals. These signal responses can be extracted to identify defects using signal processing techniques [25].
Xu and colleagues evaluated a rotor imbalance fault in wind turbines by collecting the vibration signal from an accelerometer that monitored the wind turbine drive train. They proposed a vibration model using a complex Morlet wavelet transform applied to rotor-imbalance detection and a health indicator that quantified the degree of rotor imbalance by the ratio of the vibration amplitude of the rotational frequency to the third harmonics. The result showed a successful detection and qualification of rotor imbalance caused by a blade crack in an on-site wind turbine [46]. Strömbergsson et al. studied the relationship between the rotational speed recorded during a vibration measurement and the calculated condition indicator values of specific bearing failures in three wind turbine gearboxes. Using an artificial neural network (ANN), they demonstrated the differences between the predicted and true values, and the results showed the increased sensitivity of the detection in two cases of gearbox output-shaft bearing failures, indicating a planet bearing failure, which had gone undetected in the previous data [47]. Aihara et al. developed a vibration-monitoring system to be used in wind turbine blades with the objective of estimating the deflection on the blade tip in a wind turbine tower. The monitoring system developed in this research is considered easy to install and can measure in real time. The experiments were performed with a simple blade model of a 300 W rotating wind turbine, and the signals obtained from the strain gauges were acquired by a sensor network and sent to a computer via wireless communication. The results showed that the estimation accuracy was acceptably high, concluding that their proposed system was practical [27].
Thermography technology is an advanced method that uses thermal imaging to detect the temperature differences of structural surfaces [22]. The technique uses an infrared (IR) camera acting as a sensor and can inspect local damage or global damage, depending on the resolution of the camera, to access a single point or a full structure [48]. Material damages on the WT can be measured using temperature gradients in a non-destructive way, and the detected damaged part will present with a higher temperature than the normal part [49]. The use of thermography has some advantages, such as the availability for full-field measurements, suitability for detecting damages caused by fatigue and delamination, capability for visual interpretation, and a short inspection interval. Inspection techniques can be split into two categories: active and passive. In active thermography, different heating sources are employed to heat the object, which makes the technique less frequently used for operating wind turbines, while passive thermography utilizes solar radiation to heat a blade (usually around sunrise) or to cool it at sunset [15]. This method has been widely used to detect the subsurface defects of different materials, including metals, composites, and concrete [50,51,52].
Hwang and colleagues assessed a continuous line laser scanning thermography (CLLST) system to remotely inspect internal delamination defects in wind turbine blades. In order to evaluate the feasibility of the CLLST system, both laboratory and full-scale tests were conducted using a carbon-fiber-reinforced polymer (CFRP) plate, a glass-fiber-reinforced polymer (GFRP) 10 kW wind turbine blade, and a 3 MW GFRP wind turbine blade. The results demonstrated that the 10 mm diameter internal delamination located 1 mm below the blade surface was successfully detected even at a 10 m distance from the target blade with a laser scan speed of 2 mm/s. [53]. Doroshtnasir et al. used thermography to detect potential subsurface defects or damage to offshore wind farms. It was performed from remote distances by a data processing algorithm, which differs from the ordinary method of thermographic analysis using thermal photographic images. The results showed the detection of potential subsurface defects within rotating rotor blades from greater distances, such as from the ground, aircraft, or vessels [54]. Oehme et al. employed IR-thermography-based detection to assess turbulent flow separation in wind turbine rotor blades during operation. The measurement approaches measured the surface temperature response to unsteady flow conditions and enabled unambiguous detection of flow separation via temperature fluctuation maxima in flow transition regions, as well as increasing temperature fluctuations within the separated flow region [55].
As WTs are exposed to harsh environmental conditions, such as rain, snow, and airborne particles, such as snow, rain, and ice, multiple faults can occur at the same time. Acknowledging that each failure detection method has its own limitations, new investigations are seeking to combine different failure detection techniques to provide more accurate and detailed information. As an example, Li et al. [56] and Ma et al. [57] united the techniques that use acoustic and vibration signals for gearbox fault diagnosis, and the results proved the superior performance of multi-sensor fusion compared with single-sensor-based methods.
In recent decades, artificial intelligence, such as machine learning, has been investigated for optimal control in industrial applications and is considered to be a promising approach to increasing system efficiency by including probability functions and nonlinear modeling into relevant signal processing methods. Wang et al. proposed a deep-learning-based model called Multi-Resolution and Multi-Sensor Fusion Network (MRSFN) to evaluate motor fault diagnosis through the multi-scale analysis of motor vibrations and stator current signals. The applied method’s advantage is that it automatically learns discriminating characteristics through the training process of the network without requiring manual feature selection. The results of the two cases studied demonstrated the advantages of the developed method [58]. Dao et al. presented a novel approach for condition monitoring and fault diagnosis in wind turbines based on structural break detection in SCADA data. The technique was employed for the DC of a wind turbine with 2 MW rated power using SCADA data related to the temperature. To detect structural changes, they used a method based on control charts, where the sequences of the calculated probability (p-values) were plotted together with the critical line and defined by the significance level. The method was validated using two known fault events on the WTs, and the results demonstrated the effectiveness of the method in monitoring the WT and confidently detecting abnormal faults [59]. Mian et al. studied multiple combinations of bearing defects, including both dual- and multiple-defect conditions. They selected two predominant methods of fault diagnosis: vibration monitoring utilizing time–frequency scalograms extracted using a Continuous Waveform Transect (CWT) and a Non-Invasive Infrared Thermography (IRT). The group found adequate accuracy in both the dual- and multiple-fault conditions for vibration-based fault diagnosis, with a range of 99.39% to 99.97%. Meanwhile, for the IRT-based failure diagnosis, a 100% classification accuracy was reached for double and multiple failures under all conditions [60]. Table 2 presents some articles in the scientific literature that approach the use of the prospected technologies.
Figure 2 shows the main applicants (a) and inventors (b) of the prospected patent documents. The Vestas Wind Systems A/S (Aarhus N, Arhus, Denmark) appears in first place with 26 documents. The company is a Danish company and the global leader in sustainable energy solutions, with more than 40 years of experience in wind energy, and currently has more than 160 GW of wind turbines installed in 88 countries, preventing 1.5 billion tonnes3 of CO2 from being emitted into the atmosphere. The group is specialized in the manufacturing, installation, and servicing of wind turbines and is the market leader in the North American wind industry, with 42,000 MW installed and more than 38,000 MW under service in the U.S. and Canada [66]. Compared to the top ten competitors in this set, Vestas Wind Systems A/S has approximately 13% of those records. In second place is the State Grid Corporation of China, with 18 documents. The State Grid Corporation of China (Beijing, China), founded on 29 December 2002, is a large corporation and is considered crucial to China’s energy and economic security. The company supplies power to a population of more than 1.1 billion across 26 provinces, autonomous regions, and municipalities, covering 88% of China’s national territory [67]. General Electric, in third place with 16 documents, is an American multinational company with 125 years of experience; it is known for its efforts in the power, renewable energy, aviation, and healthcare industries. In 2021, the company had 52k wind turbines installed in more than 35 countries, the first position in the United States in terms of wind installations [68].
It can be seen from Figure 2a that not only do companies appear between the main applicants but also research institutes and universities. This reflects the possibility of patent applications not only by companies but also by research and development institutions. Among the patents that can be cited is the Inner Mongolia University of Technology (Inner Mongolia–Hohhot, China), which is the publication applied for in 2021. The invention concerns a process related to a new device and method for measuring the dynamic noise of wind turbines. Figure 2b shows the top ten inventors of the prospected technologies, most of whom are associated with the companies and universities found.
The main countries and regions that produce this type of technology are demonstrated in Figure 3. By analyzing the main countries that deposited those technologies, it is possible to observe that China and the United States are the main countries, with 373 and 45 documents, respectively. China plays an important role in the development and export of technologies involving renewable energies [69,70]. The country has made a commitment to achieving peak carbon by 2030 and carbon neutrality by 2060, and wind power has become a practical alternative [71]. The electricity created in China by wind power was 467 billion kWh in 2020, accounting for more than 20% of the total renewable energy generated [72]. In order to encourage the sustainable development of the wind power industry, China’s Government has provided support through political measures [73]. Such policies might include clarifying the goal of large-scale wind power development, raising the amount of wind power in the electricity supply, and sharing the operating costs of wind power generation through some political actions such as taxation, financial subsidies, and feed-in tariff adjustments [74,75].
In 2010, China became the leading wind power market in the world, both annually and cumulatively, in terms of market size. The country usually establishes a Five-Year Plan (FYP), which takes five years as a stage to undertake the development planning of social economy and energy. The most current FYP is the 14th Five-Year Plan (FYP) on Renewable Energy Development (2021–2025), released in June of 2022 [76]. The plan aims for a 50% increase in renewable energy generation (from 2.2 trillion kWh in 2020 to 3.3 trillion kWh in 2025), establishes a 33% share of renewable electricity consumption by 2025, and mandates that 50% of the increase in electricity and energy consumption nationally must come from renewable sources in the period 2021–2025. Achieving the objectives, China will reduce up to 2.6 gigatons of carbon emissions annually [77].
The development history of wind power in China can be divided into four main stages: the initial pilot stage (1986–1995), the experimental development stage (1995–2006), the comprehensive expansion stage (2006–2010), and the stable development stage (since 2010) [78]. In the initial stage, the Chinese government imported small off-grid wind turbines to be used in remote areas, and the country initiated the application and construction of wind farms [79,80]. During the experimental stage, China built small wind farms with foreign grants and loans, and the government provided support to companies to license and start the production of wind turbines and all relevant technology [81,82]. The comprehensive stage defined the development targets of the wind power industry and established a stable cost-sharing system by publishing the Renewable Energy Law of the People’s Republic of China in 2006 [83]. In the stable development stage (since 2010), China has become one of the largest technology-developing countries in the world, and, in light of this, companies have explored the development of raw materials, component manufacturing, wind turbine manufacturing, and wind-farm management in order to decrease costs and increase the competitiveness of the wind energy market in the world. By the end of 2010, four Chinese companies were among the world’s top 10 wind turbine manufacturers [78].
It is important to mention that one of the reasons for the great technological advance achieved by China is the growing collaboration between research centers and private companies. An example of this collaboration was the creation of the Offshore Wind Power Research Institute by the Shanghai Electric Wind Power Group and Zhejiang University and also the Shanghai Donghai Wind Power Co. Ltd., who cooperated with the Shanghai Electric Power University to develop floating wind power [78]. This collaboration can also be seen in the results found in the patents. The patent refers to an infrared thermal image detection method for detecting micro-defects in the blades of large wind turbines, and the Shenyang University of Technology (Tiexi, Shenyang, China) is the applicant: deposited by Nanjing Ruigong Engineering Testing Co. Ltd. (Nanjing, China), the patent is related to a nondestructive testing device for wind turbine blades. Considering all these important efforts, and under the guidance of wind power policies, China has grown significantly in the wind energy industry and market. However, there are still some problems in the industry, such as the difficulty of accommodation, inadequate financial subsidies, and imperfect market systems, which can occur due to imperfect policies, inadequate implementation of such policies, etc. [84]. Therefore, China still needs to improve policies and continue to develop wind power technology in order to solve the problems that might arise along the way.
The United States (U.S.) was the second country found in terms of research conducted and started seriously harnessing wind power after the oil crisis. In 2021, wind energy accounted for 32% of the country’s total energy capacity, with 13.4 GW of wind capacity taking the cumulative total to almost 136 GW by the end of the year [85,86]. The country’s investments in wind energy were about USD 20 billion, with the aim of implementing new wind power projects by 2021, accumulating to about USD 270 billion since the early 1980s [87]. One of the ways that the U.S. used to support the development of wind power was the establishment of the National Offshore Wind Power Strategy: creating the U.S. Offshore Wind Power Industry released by the United States Department of Energy and the Department of the Interior. This strategy planned to invest in research and innovation, wind turbine manufacturing, and grid technology [88]. In terms of policies, the government provides support from federal funds and wind energy technology foundations, such as credit loans and financing R&D designs and equipment manufacturing, as well as wind farm construction. These policies prioritized the development and promotion of wind energy technology and invested substantial R&D technical subsidies in wind energy technology [89].
It is worth mentioning that two international organizations were identified, these being the World Intellectual Property Organization (WIPO) and the European Patent Office (EPO), with 60 and 49 applications, respectively. These organizations make it possible to apply for several international applications through a single application, such as the WIPO Patent Cooperation Treaty (PCT). Most technology manufacturers are looking to secure their inventions through international applications via EPO and WIPO with the aim of reducing costs and simplifying the individual filing process in each country while protecting their technology in several countries.
In regard to the technological areas of the prospective inventions (Figure 4), 24 technology classifications were discovered. According to the DWPI database, the number of technologies points to recent innovations and can provide an important overview of the “state of the market” and how it is segmented. The number of technology area assignments to patent applications can report a diversified portfolio or a specific technical focus. As it can be noticed, the three leading companies in development in these technological areas are Vestas Wind, State Grid Corp, and Gen Electric, and they count for 41% of all records in the resulting set.
In regard to the technological area (a), pink refers to “wind turbine, rotor blade”. Here, Gen Electric has 47% of their patent applications classified in this area, while Vestas Wind has 38% and State Grid Corp has 28%. From this result, we can observe that practically all the applicants have patents that fit in this technological area.
The technological area (b) in blue refers to “additively, three-dimensional printing, additive, composite, build, thermoplastic”, and (c) purple refers to “testing, leakage, abnormality, monitoring, sensor, detecting, inspection”. The identified patent applications in these technological areas most often seek improvements in wind turbines, especially in individual turbine parts, such as motors, blades, and gearboxes. In addition, many of the patents describe a new device or even a novel method for fault diagnosis based on non-destructive testing. Zhang et al., 2022 [90] described a utility model using an infrared non-destructive device related to a wind turbine blade infrared flaw-detection robot. The robot can realize omni-directional scanning of blades with complex shapes and automatic quantitative identification of internal damage in blades. Jiang and colleagues, 2022 [91] showed a wind power generation blade defect-detection system comprised of a working device, where the working device comprises a main bracket, a walking mechanism, and a flaw-detection mechanism. Table 3 shows other patents that have been prospected that are related to the use of different techniques to identify faults in the wind turbine and its components.
The last indicator of the patent search performed: Figure 5 shows the main International Patent Classification (IPC) codes attributed to the patent documents found. The IPC code was declared in 1971 and is based on a hierarchical language of independent symbols for the classification of patents and utility models in accordance with the different areas of technology to which they belong. Of all the documents found, 241 were classified with the code F03D 17/00, which is related to the monitoring or testing of wind motors. The second code exhibited in the patent documents was the G01M, which refers to the testing of machine parts, which corroborates with the key words chosen in this work for the patent search.
Most of the documentation was classified in technology area F, which relates to “mechanical”. However, some of the patents were classified by area G, which is related to “physics”. It is worth pointing out that the same document can be assigned more than one code. Table 4 shows the descriptions of the top 10 codes.
One of the challenges of the 21st century is the great need to change the way energy systems generate around the world. Energy transition is a reality for all nations because of the targets made in the Paris agreement. Decarbonization plans are under constant evolution by the global community with the aim of reducing greenhouse gas emissions in a sustainable manner. This is due to the global warming effect and its drastic consequences for society. There are several strategies, measurements, and technologies that can be applied to improve sustainability, and wind energy is one of them [3,9].
Wind power is a renewable energy source with great potential for reducing greenhouse gas emissions from the use of fossil fuel and can mitigate climate change and improve air quality. The rapid and great advances in the use of wind energy have raised important concerns about the costs associated with this technology, mainly the operational and security cost impacts and also reliability [96]. Consequently, new technologies are constantly being developed with the main objective of mitigating the potential failures and defects found in wind turbines. Several researchers have employed structural health monitoring (SHM) and nondestructive testing (NDT) techniques to further develop effective damage detection tools for WTBs. These techniques can play a key role in improving reliability, optimizing production yield, and managing maintenance strategies for wind turbines.
The main techniques used for fault detection are strain measurements, acoustic emission methods, ultrasonic-based methods, and thermography and, more recently, researchers are trying the combination of at least two NDTs to be applied to WTB monitoring. In addition, as wind turbines are usually sited in remote areas, health monitoring with long-distance applications is extremely necessary for instantaneous service. In light of this, signal processing techniques involving data extraction, analysis, and normalization are increasingly being employed to extract features from the signals and attempt to determine potential damage and their location and quantify it.
Signal-processing algorithms can provide useful and accurate results in order to mitigate the shortcomings of existing and currently employed techniques. Examples of recently developed and applied algorithms for fault diagnosis in wind turbines are the fast S-transformation, empirical WT, full EMD set, and others.
Machine learning is a system that can modify its behavior autonomously based on its own experience. Improvements were observed in the integrated application of computational techniques such as machine learning, SCADA, and neural networks (ANNs) with the more traditional techniques [97,98]. One of the main technological advances in the field of wind energy is not only the identification of faults but also their prediction using the Internet of Things (IoT) on Deep Learning (DL) based on Artificial intelligence (AI). These are considered future directions and have potential benefits, such as high-performance predictive and low false-positive rates via numerical simulations of collapsing phases and the diagnostic troubleshooting of wind energy production equipment [99].
The estimation of patent documents could reflect the newness that is brought by this work, and this may help companies and/or organizations in making decisions about technological developments in this particular area. Based on the information presented, companies can target opportunities as well as review the risks associated with the development of new inventions in the area of renewable energies, especially wind power energy. By leveraging the information extracted from patent research, together with marketing research, consumer analysis, and the assessment of internal production capacity, the process of research and development of new products can be guided to help the industry. Overall, the analysis and extraction of patent data can support the acquisition of information to analyze and predict possible future trends in technological development, providing assistance in corporate decision-making.

4. Conclusions

Wind power is a renewable energy widely used worldwide, and its rapid advances raise concerns, especially relating to safety and reliability. Based on the results of this work, it was possible to observe that fault diagnosis for wind turbines is very important, and the development of new technologies is still necessary. This can be seen by taking into account the increasing evolution in patent filings over the years. Furthermore, it can be observed that of the 636 patent documents found, most reported the development of new methods or new devices for identifying potential faults and defects in wind turbines and their components, especially in the blade components. In addition, it was possible to retrieve patents for a used model, improving an already used technique.
This technological prospection focused on patent documents has highlighted the important growth in the development of inventions of new methods and techniques used for the accurate detection of faults in wind turbines through non-destructive methods. The findings pointed out the main applicants of the technologies, as well as the main inventors, highlighting Vestas Wind SYS As and Wang, Rui-ming, respectively. In evidence, the world leader in the wind power market is China, which has a total of 373 patent documents and is also the main market for these applications. The results found in this study make clear that artificial intelligence and database analysis are the future strategies for improving the methods already used, especially in view of the peculiarities involved in the maintenance and viability of wind turbines.
All the results observed in this prospection can directly contribute to possible investments in research and development on fault diagnosis techniques, identifying potential saturated fields and also the ones that present possible technological trends. Furthermore, by identifying the main areas and countries, companies can use this technological research to help in their decision-making when needed. They could also use the findings to target new possible technologies of interest and focus their efforts on the development of their own inventions while identifying gaps that can be converted into new discoveries.
Additionally, this work could directly support the identification of market needs, which may be the target for research and development of novel inventions based on the application of different fault diagnosis techniques, combining traditional techniques with advanced mathematical models, such as artificial intelligence and deep learning, especially considering the increased importance for the worldwide development of renewable and sustainable energy. This research may present some limitations, which may be similar to any other technological prospection using patent analysis. The limitations may be related to the search strategy, which limited access to documents related to the selected keywords. However, it is important to point out that the keywords proposed in this analysis allowed the presentation and discussion of a cohesive and robust form regarding the diagnosis of wind turbine failures, exposing, especially, the challenges of this sector. Continued research is needed to contribute to clarifying the issues within this area.

Author Contributions

Conceptualization, N.B.B., D.D.G.N., A.Á.B.S. and B.A.S.M.; Data Curation, D.D.G.N.; Formal Analysis, N.B.B., D.D.G.N., A.Á.B.S. and B.A.S.M.; Investigation, N.B.B., D.D.G.N. and B.A.S.M.; Methodology, N.B.B., D.D.G.N., A.Á.B.S. and B.A.S.M.; Project Administration, A.Á.B.S. and B.A.S.M.; Software, D.D.G.N.; Supervision, B.A.S.M.; Validation, A.Á.B.S.; Visualization, N.B.B.; Writing—Original Draft, N.B.B. and D.D.G.N.; Writing—Review and Editing, A.Á.B.S. and B.A.S.M. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All research data were reported in the manuscript.

Acknowledgments

The authors thank the SENAI CIMATEC University Center for their support in the development of this research, CNPq (Conselho Nacional de Desenvolvimento Científico e Tecnológico) (B.A.S.M. is a Technological fellow from CNPq 306041/2021 and A.Á.B.S. is a Technological fellow from CNPq 313213/2019), and also Aneel (Agência Nacional de Energia Elétrica) and CHESF (Companhia Hidro Elétrica do São Francisco).

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Time analysis of patent documents. First year of priority.
Figure 1. Time analysis of patent documents. First year of priority.
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Figure 2. Analysis of the main (a) applicants and (b) inventors of the prospected technologies.
Figure 2. Analysis of the main (a) applicants and (b) inventors of the prospected technologies.
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Figure 3. Geographical distribution of the analysis of the main countries/regions of the depositors of the prospected technologies with their number of patent documents. EP—European Patent Office; WO—World Intellectual Property Organization.
Figure 3. Geographical distribution of the analysis of the main countries/regions of the depositors of the prospected technologies with their number of patent documents. EP—European Patent Office; WO—World Intellectual Property Organization.
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Figure 4. Analysis of the three main technological areas related to the inventions. Comparisons between the top filing company, Vestas Wind Sys As, and other companies, wherein ((a), pink) refers to wind turbine, rotor, and blade; ((b), blue) refers to additively, three-dimensional printing, additive, composite, build, and thermoplastic; ((c), purple) refers to testing, leakage, abnormality, monitoring, sensor, detecting, and inspection. In the center of the larger donut chart is the number of patent documents of the main applicant for a given technology and, in the smaller donut charts, the percentage of companies involved in the production of inventions related to this technology.
Figure 4. Analysis of the three main technological areas related to the inventions. Comparisons between the top filing company, Vestas Wind Sys As, and other companies, wherein ((a), pink) refers to wind turbine, rotor, and blade; ((b), blue) refers to additively, three-dimensional printing, additive, composite, build, and thermoplastic; ((c), purple) refers to testing, leakage, abnormality, monitoring, sensor, detecting, and inspection. In the center of the larger donut chart is the number of patent documents of the main applicant for a given technology and, in the smaller donut charts, the percentage of companies involved in the production of inventions related to this technology.
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Figure 5. Most-used classification codes in area of development of technologies applied to wind turbines.
Figure 5. Most-used classification codes in area of development of technologies applied to wind turbines.
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Table
Table 1. IPC codes used in the prospecting of current patent technology.
Table 1. IPC codes used in the prospecting of current patent technology.
IPC CodeRegarding
F03DWind Motors
G01HMeasurement of Mechanical Vibrations or Ultrasonic, Sonic, or Infrasonic Waves
G01MTesting Static or Dynamic Balance of Machines or Structures; Testing of Structures or Apparatus, Not Otherwise Provided For
G01RMeasuring Electric Variables; Measuring Magnetic Variables (Indicating Correct Tuning of Resonant Circuits H03j3/12)
G01JMeasurement of Intensity, Velocity, Spectral Content, Polarization, Phase, or Pulse Characteristics of Infra-Red, Visible, or Ultra-Violet Light; Colorimetry, Radiation Pyrometry (Light Sources F21, H01J, H01K, H05B; Investigating Properties of Materials by Optical Means G01N)
G01KMeasuring Temperature; Measuring Quantity of Heat; Thermally Sensitive Elements Not Otherwise Provided For (Radiation Pyrometry G01j5/00)
Table
Table 2. Articles that focus on the use of condition monitoring (CD) and nondestructive testing (NDT) for fault diagnosis on Wind Turbines.
Table 2. Articles that focus on the use of condition monitoring (CD) and nondestructive testing (NDT) for fault diagnosis on Wind Turbines.
TitleTechnologyMain Findings and/or ConclusionsReference
Multiview enhanced fault diagnosis for wind turbine gearbox bearings with fusion of vibration and current signalsVibration Analysis + Canonical correlation analysis (CCA)The paper undertook a novel evaluation of a multiview fault diagnosis framework enhanced to comprehend the correlated and complementary features between current and vibration signals, which were considered as two different but related views. They used an unsupervised multiview learning method based on canonical correlation analysis (CCA) to evaluate this correlation. The results have shown balanced fault characteristics and achieved higher performance in fault diagnosis, especially in composite faults, compared to methods based on unimodal signals. [61]
Alternative fault detection and diagnostic using information theory quantifiers based on vibration time-waveforms from condition monitoring systems: Application to operational wind turbinesVibration + Probability mass functionThe paper analyzed information on two theory quantifiers used to monitor and detect changes in the vibration signals of two operational wind turbines of 750 kW and 2 MW. The authors evaluated the signals by power spectrum (frequency domain method), wavelet transform (time-frequency domain method), and Bandt–Pompe (time-domain method). The results demonstrated that the proposed method could distinguish (cluster) well between the states of fault.[62]
Wind turbine fault detection based on deep residual networksSCADAIn this article, researchers proposed a new depth network called deep residual network (DRN) to further analyze the raw data generated by WTs. In the method, the raw data gathered by the SCADA system are applied directly as inputs to the DRN network. Then, a convolutional residual building block (CRBB) was established by using convolutional layers and squeeze and excitation units. The results indicate that the proposed DRN achieved better performance and outperforms some published fault-detection methods.[63]
Deep learning strategies for automatic fault diagnosis in photovoltaic systems by thermographic imagesDeep Learning + ThermographyThe authors proposed a system for the automatic classification of thermographic images using a convolutional neural network developed via open-source libraries. The results showed a 99% accuracy for a dataset of 1000 images using a multi-layer perceptron architecture and 100% accuracy for a convolutional neural network. [64]
Attention-guided joint learning CNN with noise robustness for bearing fault diagnosis and vibration signal denoisingDeep Learning + VibrationThe paper reports a novel attention-driven joint learning convolutional neural network (JL-CNN) for monitoring conditions. The fault diagnosis task (FD-Task) and the signal denoising task (SD-Task) are integrated into an end-to-end CNN architecture, reaching good noise robustness through dual-task joint learning. This method allowed FD-Task and SD-Task to achieve deep cooperation and mutual learning, and the results showed outstanding fault diagnosis capacity and signal denoising ability.[65]
Table
Table 3. Patents documents involving technologies related to fault diagnosis for wind energy.
Table 3. Patents documents involving technologies related to fault diagnosis for wind energy.
Priority NumberTitleRefers toReference
US20210108988A1Detecting Faults in Wind TurbinesA wind turbine monitoring system for detecting faults produced by wind turbine generators and comprises a shaft rotation frequency signal that is determined from the first signal, and the first signal that is obtained from the generator of the wind turbine.[40]
CN108957315AFault diagnosis method and equipment of wind turbine generator systemA wind turbine generator system fault diagnosing method that involves determining the testing point of fault detection of a system, detecting the testing signal of a testing point for fault detection, and determining the fault diagnosis function system.[92]
CN104374575AWind turbine main bearing fault diagnosis method based on blind source separationBlind source separating wind turbine main bearing fault diagnosis method involves receiving sound transmission signals and acoustic emission signals, adopting a reconstitution algorithm, and determining turbine test normal operation conditions.[93]
CN107560849AWind turbine generator bearing fault diagnosis method for multi-channel deep convolutional neural networkNeural-network-based multi-channel depth convolution wind turbine bearing fault diagnosing method involves collecting test bearing under each state drive end and evaluating diagnosis model for obtaining application bearing to be monitored.[94]
CN113323823AFan blade icing fault detection method and system based on AWKELMMethod for detecting fan blade icing fault that involves inputting supervisory control and data acquisition (SCADA) data of wind generating set to be tested and performing maintenance decisions according to the detection results.[95]
Table
Table 4. IPC codes in technological prospecting.
Table 4. IPC codes in technological prospecting.
IPC CodeRelated to
F03D 17/00Monitoring or testing of wind motors, e.g., diagnostics (testing during the commissioning of wind motors F03D13/30)
G01M 13/00Testing of machine parts
F03D 11/00Details, parts, and accessories not included in or pertinent to the other groups of this subclass
G01R 31/34Testing dynamo-electric machines
F03D 1/06Rotors
F03D 80/50Maintenance or repair
F03D 80/00Details, components, or accessories not provided for in groups F03D1/00—F03D17/00
F03D 7/00Controlling wind motors
G01R 31/00Arrangements for testing electric properties; arrangements for locating electrical faults; arrangements for electrical testing characterized by what is being tested not provided for elsewhere
F03D 7/02The wind motors have a rotation axis substantially parallel to the air flow entering the rotor
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MDPI and ACS Style

Barbosa, N.B.; Nunes, D.D.G.; Santos, A.Á.B.; Machado, B.A.S. Technological Advances on Fault Diagnosis in Wind Turbines: A Patent Analysis. Appl. Sci. 2023, 13, 1721. https://doi.org/10.3390/app13031721

AMA Style

Barbosa NB, Nunes DDG, Santos AÁB, Machado BAS. Technological Advances on Fault Diagnosis in Wind Turbines: A Patent Analysis. Applied Sciences. 2023; 13(3):1721. https://doi.org/10.3390/app13031721

Chicago/Turabian Style

Barbosa, Natasha Benjamim, Danielle Devequi Gomes Nunes, Alex Álisson Bandeira Santos, and Bruna Aparecida Souza Machado. 2023. "Technological Advances on Fault Diagnosis in Wind Turbines: A Patent Analysis" Applied Sciences 13, no. 3: 1721. https://doi.org/10.3390/app13031721

APA Style

Barbosa, N. B., Nunes, D. D. G., Santos, A. Á. B., & Machado, B. A. S. (2023). Technological Advances on Fault Diagnosis in Wind Turbines: A Patent Analysis. Applied Sciences, 13(3), 1721. https://doi.org/10.3390/app13031721

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