Wind Turbine Condition Monitoring Data Processing and Power Quality Requirements. Summary. Key findings. Recommendations

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1 Wind Turbine Condition Monitoring Data Processing and Power Quality Requirements Othmane El Mountassir January 2017 TLI-SP Summary Condition monitoring systems can improve the operation and efficiency of wind turbines especially offshore, where access can represent a great challenge. Due to the variable nature of wind and varying operational conditions of the wind turbines, more efficient condition monitoring systems are required to provide an accurate assessment of the health of the wind turbines and the quality of the power produced. Key findings Variable environmental factors and loads have made the interpretation of wind turbine conditions difficult. Due to the variable operation of the wind turbine, signal processing methods operating in the time-frequency domain are preferred because of their ability to process non-linear and nonstationary signals. The offshore wind industry acknowledges that there is a need for the implementation of more comprehensive remote condition monitoring systems to reduce the requirement for offshore access, increase reliability, and also reduce operational costs. Recommendations More efficient condition monitoring systems are required to ensure the provision of accurate information about the health of wind turbines. Power quality monitoring systems for individual wind turbines are required to ensure gird compliance and the safety and stability of the system. To improve the capabilities of wind turbine condition monitoring systems, the signal processing methods selected for implementation should be capable of adapting to existing integrated monitoring systems. 1

2 Wind turbine condition monitoring and signal processing techniques The safe operation of wind turbines and the delivery of reliable power quality is crucial in guaranteeing the security of electricity supply from offshore wind farms. The availability in recent years of high-performance sensors has led to the development of various wind turbine condition monitoring systems. These sensors are mainly focused on monitoring temperature and vibration; they aim to identify any substantial changes to the normal operation of the wind turbine and determine whether potential failures may develop. Wind turbine monitoring challenges Offshore wind turbines are usually deployed in remote locations and are subject to harsh operating environments. The challenging nature of these environments makes maintenance difficult and expensive, and may be responsible for equipment failures. As a result, the development and implementation of more comprehensive remote condition monitoring systems is critical, particularly in terms of cost reduction in operations and maintenance (O&M). According to industry guidelines [1], these will reduce the requirement for offshore access, increase reliability, and reduce operational costs. Some of the key issues encountered with current wind turbine condition monitoring systems are the variable environmental factors and the varying load, which have made the interpretation of wind turbine condition monitoring data difficult. Therefore, removal of the negative influences of varying load on the condition monitoring results has also become a matter of urgency for the offshore wind industry, in order to improve the reliability of these condition monitoring systems. Additionally, as we move towards the deployment of increased turbine ratings, power quality monitoring of individual turbines to ensure grid safety and stability presents another challenge. For example, poor-quality power generation by defective wind turbines is harmful not only to the grid, but also to other grid-connected assets such as substation peripheral equipment. Although various types of wind turbine condition monitoring systems are currently available, commercial power quality monitoring systems, which monitor the operation and control of individual wind turbines, are needed. 2

3 Current status of signal processing techniques in wind turbine monitoring To efficiently capture wind energy, most large, modern wind turbines operate at variable speed due to the variable nature of wind. As a result, the signals collected from a wind turbine s condition monitoring systems are characterised by their non-linear and non-stationary features. Nonstationary signals designate signals with changing spectral contents with respect to time. In order to achieve a reliable condition monitoring and prognostic system based on these signals, advanced signal processing techniques should be implemented to more efficiently interpret the condition monitoring signals collected from the turbines. Considerable effort has been made on the development and trialling of various signal processing techniques. However, these were considered to be unfit for commercial implementation due to their complexity, computation time, and sometimes their unsatisfactory presentation of the non-linear, non-stationary signals. For example, conventional Fourier transform analyses are deemed to be unable to provide a reliable diagnostic based on non-stationary wind turbine signals, due to the continuing change of time and frequency of the signals, although these are already being adopted in some commercial wind turbine condition monitoring systems. Hence, the use of non-stationary signal processing techniques has been strongly recommended by academia [2-4]. Regarding the wind turbine s power quality, different signal processing techniques and stochastic models have been developed by academics to classify power quality disturbances. However, none of these techniques have yet proved their suitability for integration into real-time wind turbine condition monitoring systems, due to their complex computation and response time. Therefore, advancing the development of existing wind turbine condition monitoring systems, to enable the integration of continuous monitoring of grid-compliant power quality, will offer significant benefit to the offshore wind industry. Wind turbine data processing Data acquired from the wind turbine may be represented in the time domain, the frequency domain, or the time-frequency domain. In an effort to address the challenges surrounding data processing requirements, signal processing methods operating in the time-frequency domain should be used because of their ability to process non-linear and non-stationary signals. 3

4 Moreover, the extraction of the instantaneous amplitude at the corresponding instantaneous frequency is also important, as any variation of the signal amplitude at fault-related frequencies may assist in identifying potential damage. In addition to the frequency and amplitude features, it is also very important that any signal processing method selected to process wind turbine condition monitoring data is also capable of processing lengthy data in a reasonable time. Wind turbine power quality Monitoring and assessment of wind turbine power quality parameters and their compliance with the grid code is an essential feature for offshore wind farm operators. Based on the IEC standard, the assessment of power quality of wind turbines should be tested during certification. In practice and for safety reasons, assessment of the quality of the generated power is often conducted at the wind farm substation. However, this does not guarantee the identification of individual defective wind turbines which produce poor-quality power. One potential solution is to monitor the power quality at each wind turbine and disconnect the specific turbine found to generate poor-quality power from the grid. This can only be achieved by integrating power quality monitoring into existing wind turbine condition monitoring systems. To implement this solution, it is important to determine suitable signal processing techniques capable of carrying out both power quality monitoring and the other existing integrated wind turbine health monitoring systems. Monitoring power quality should include key parameters such as flicker, total harmonic distortion, phase unbalance and frequency shift as described in the relevant standards [5-6]. These parameters can be assessed by analysing the readily-accessible electrical signals of the wind turbine (i.e. voltage and current). The electrical signals are also used to help detect mechanical faults. Therefore, this makes them the most suitable signals to accomplish both integrated power quality monitoring and wind turbine health monitoring. In an attempt to investigate the capabilities of different signal processing methods to analyse both mechanical faults and power quality parameters, ORE Catapult led a project [7] to demonstrate different methods and tools for the optimisation of operational reliability of large-scale, industrial wind turbines. 4

5 Findings of the project regarding the ability of different signal processing methods have shown that the power quality parameters are affected differently, depending on the fault type. For example, it was found that a mechanical shaft imbalance has more effect on the frequency shift and total harmonic distortion, while winding faults have more effect on the total harmonic distortion and phase imbalance. This implies that there is a requirement for a holistic assessment, whereby both mechanical and electrical conditions are assessed simultaneously and an accurate diagnostic is carried out. ORE Catapult s approach In an attempt to address the industry s need to accelerte the development of more integrated wind turbine condition monitoring systems, and encourage further development of sensor technology into market-ready products, ORE Catapult has launched a number of initiatives. These include the Catapult s Innovation Challenges, which identify specific technology areas in which novel solutions are required to lower the cost of producing offshore renewable energy, and the digital Clone of the Levenmouth Offshore Wind Turbine programme, which will use sensor instrumentation to identify cost reduction opportunities through design optimisation. Both initiatives received numerous proposals, and ORE Catapult is working closely with relevant partners to advance the development of their technologies to help drive down costs and improve efficiency. Summary Modern wind turbines require reliable condition monitoring techniques to assure their long-term reliability, availability and economic return. To improve the capabilities of wind turbine condition monitoring systems, the proposed signal processing methods should be capable of: Extracting the relevant amplitudes and frequencies of non-linear and non-stationary signals. Processing large amounts of data in real time. Integrating existing integrated wind turbine condition monitoring systems. Providing early warnings as part of a predictive maintenance regime. Furthermore, there is a pressing need to monitor the power quality characteristics of individual wind turbines. Such functionality will result in improved turbine efficiency and system stability, as the turbine can be operated in accordance with grid code requirements. 5

6 Innovation in integrated wind turbine condition monitoring systems has lagged behind the comparable development of wind turbines themselves since their initial deployment. Technical innovation is therefore required to enable the integration of additional condition monitoring systems, such as power electronics and power quality systems. This can only improve the optimisation of O&M strategies, contributing indirectly to cost reductions. Appendices Recommended Reading [1] DNV GL, Guideline for the certification of condition monitoring, Edition [2] C. C. Ciang, J. R. Lee and H. J. Bang, Structural health monitoring for a wind turbine system, Measurement Science and Technology, vol. 19, no. 12, pp. 1-20, [3] Y. Amirat, M. E. Benbouzid, E. Al Ahmar, B. Bensaker and S. Turri, A brief status on condition monitoring and fault diagnosis in wind energy conversion systems, Renewable and Sustainable Energy Reviews, vol. 13, no. 9, pp , [4] Y. Qiu, Y. Feng, P. Tavner, P. Richardson, G. Erdos and B. Chen, Wind turbine SCADA alarm analysis for improving reliability, Wind Energy, vol. 15, no. 8, pp , [5] IEC , Testing and measurement techniques - Flickermeter - Functional and design specifications, IEC, [6] IEC , Measurement and assessment of power quality characteristics of grid connected wind turbines, IEC, [7] Demonstration of methods and tools for the optimisation of operational reliability of large-scale industrial wind turbines, Optimus Project, available online at Author Profile Othmane El Mountassir is an Electrical Project Engineer at ORE Catapult. Othmane is responsible for the technical delivery of ORE Catapult projects and programmes within the electrical infrastructure knowledge area. Othmane has expertise in power transmission, power cables, condition monitoring systems and the detection and location of faults in HV environments, having worked in this field in both academia and industry over the past five years. Othmane is also ORE Catapult s technical lead on a number of projects. 6

7 Inovo 121 George Street Glasgow G1 1RD T +44 (0) ORE Catapult National Renewable Energy Centre Offshore House Albert Street Blyth Northumberland NE24 1LZ T +44 (0) Fife Renewables Innovation Centre (FRIC) Ajax Way Leven KY8 3RS T +44 (0) info@ore.catapult.org.uk Web: Disclaimer While the information contained in this report has been prepared and collated in good faith, ORE Catapult makes no representation or warranty (express or implied) as to the accuracy or completeness of the information contained herein nor shall be liable for any loss or damage resultant from reliance on same. 7