Surya Teja Kandukuri

• Chernobrova, Anna; Moloshnyi, Oleksandr; Koleva, Radmila; Nycz, Aneta; Szakal, Raul & Kandukuri, Surya Teja [Show all 10 contributors for this article] (2026). A comprehensive review of novel technologies for hydropower and strategic opportunities for Europe. Energy Reports. 15. doi: 10.1016/j.egyr.2025.109019. Full text in Research Archive Show summary

  This paper reviews recent developments in hydropower technologies, focusing on their role in improving operation, monitoring, performance, and sustainability within modern power systems. It examines how innovations in electro-mechanical equipment and digital tools, supported by the regulatory frameworks that drive the modernization and development of hydropower, can be exploited in the EU. The study explores the potential of small- and ultra-low-head installations, including gravity-based, hydrokinetic and impulse turbines, addressing their recent innovations and scalability potential. Environmental issues are addressed through fish-friendly designs, oil-free operation, and adaptive flow management. Pumps operating as turbines in reversible modes are addressed as key equipment for pumped storage hydropower, enabling flexibility and distribution. Variable-speed operation, enabled by Fully Fed Frequency Converters and Doubly Fed Induction Machines, is discussed to enhance performance and conversion efficiency under variable flow. Digitalization trends are analysed through digital twins, artificial intelligence, and machine learning, supporting predictive maintenance, real-time monitoring, and system analysis. Challenges are also discussed: implementation barriers include sensor reliability, cybersecurity, and integration complexity. The role of EU policies in guiding sustainable energy development and environmentally compliant hydropower deployment is examined, showing as new technological innovation, supported by a scientific assessment of its potential in the European context can support renewable energy integration, competitiveness and resilient power systems.

• Shanbhag, Vignesh Vishnudas; Kandukuri, Surya Teja; Olimstad, Grunde & Schlanbusch, Rune (2025). Predictive maintenance of critical components in hydroelectric turbines: A review. IEEE Sensors Journal. ISSN 1530-437X. 25(17), p. 31959–31979. doi: 10.1109/JSEN.2025.3577692. Full text in Research Archive Show summary

  Hydropower is a cost-effective, reliable, and programmable renewable energy source. Many hydropower plants worldwide have been in operation for decades and now need maintenance and refurbishment. While failures in hydroelectric plants are uncommon, they can be catastrophic and lead to prolonged downtimes. Thus, robust monitoring strategies are essential to track failure modes in critical components, preventing breakdowns and productivity losses. However, very few attempts are made to consolidate the new knowledge developed in this area in a state-of-the-art review paper. Therefore, in this article, we discussed the state-of-the-art sensing technologies that can be used to monitor critical components in hydropower turbines, their common failure causes and effects, identifying intermittent failures using various sensing technologies, and sensor locations to monitor these critical parts. In this state-of-the-art review, we identified that blades, nozzles and needles, bearings, and shafts are some of the critical parts in turbine that require continuous monitoring. For these critical components, vibration-based monitoring can be used to monitor the wear due to erosion, cavitation, wear, misalignment, or looseness. The information presented in this state-of-the-art review will be beneficial to new researchers getting accustomed to hydropower condition monitoring (CM) and will assist the industry in developing new turbine CM strategies and technologies.

• Kandukuri, Surya Teja; Tangrand, Kristoffer Meyer; Vognild, Lars Kristian & Olimstad, Grunde (2024). Automated Knowledge Graph Generation for Hydropower Plants using ISO 81346 and RAG-enhanced Large Language Models. In Gragger, Johannes; Colak, Ilhami & Kurokaw, Fujio (Ed.), ICRERA 2025. IEEE. ISSN 9798331599898. doi: 10.1109/icrera66237.2025.11284013. Full text in Research Archive Show summary

  Hydroelectric power plants present unique digitalization challenges as distributed, aging facilities with decades-old equipment configurations that vary significantly across installations. Conventional data analytics deployment requires custom adaptations to each facility's unique equipment setup, creating scalability barriers for fleet-wide condition monitoring and asset management even if the facilities are owned by a single utility company. Equipment modifications, replacements, and modernization over operational lifespans further complicate data standardization, as as-planned, as-built, and as-is configurations diverge substantially. This paper presents an automated methodology for constructing and maintaining knowledge graphs based on ISO 81346-10: 2022 Reference Designation System specifications using Large Language Models to address the data challenges. The approach enables rapid generation of machine-readable facility representations that capture hierarchical equipment relationships and semantic metadata essential for scalable industrial analytics. A case study using operational Norwegian hydropower data demonstrates > 95% accuracy in automated knowledge graph construction while ensuring 100% RDS compliance. The methodology provides a foundation for fleet-wide digital standardization, enabling consistent data integration and analytics deployment across heterogeneous hydropower installations.

• Mathew, Manuel Sathyajith; Mansouri, Mo; Kandukuri, Surya Teja & Omlin, Christian Walter Peter (2024). A Systems Perspective on the Public Perception of Wind Power in Norway. INCOSE International Symposium. 34(1), p. 1429–1446. doi: 10.1002/iis2.13217. Full text in Research Archive Show summary

  The Sustainable Development Goals, a set of ambitious targets embraced by United Nations member states, are designed to meet global challenges head-on while shaping a sustainable future. Of these, Goal 7, in particular, focuses on the critical need for affordable, reliable, sustainable, and modern energy for all. Wind energy holds a significant potential in fulfilling Goal 7 of the sustainable development goals. In Norway, there exists a unique scenario, where nearly 98% of electricity is generated from renewable resources. However, without a subsequent increase in power production, a deficit in power is projected by 2027 according to a short-term market analysis by Statnett. To prevent this, it is im-perative to increase the production of electricity from wind. However, in recent years, there has been a significant rise in opposition towards wind power projects in Norway. Some of the wind power projects have even been put on hold because of the increase in protests. In light of this, we apply systems thinking methodologies to improve our understanding of this complex problem. Initially, we identify the stakeholders in our system of interest and categorize them through stakeholder salience analysis framework. Then, we developed a systemigram to graphically represent the system of interest. Finally, we carry out causal loop analysis to find causal loops in our system of interest. Our primary focus with this work is to better understand the factors shaping public perception of wind power projects in Norway. By gaining a deeper understanding about the factors influencing public perception of wind power projects in Norway, we aim to find better solutions to improve the social acceptance of these initiatives in Norway in future works.

• Mathew, Manuel Sathyajith; Kandukuri, Surya Teja & Omlin, Christian Walter Peter (2024). Soft Ordering 1-D CNN to Estimate the Capacity Factor of Windfarms for Identifying the Age-Related Performance Degradation. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). 8(1), p. 762–770. doi: 10.36001/phme.2024.v8i1.4028. Full text in Research Archive Show summary

  Wind energy plays a vital role in meeting the sustainable development goals set forth by the United Nations. Performance of wind energy farms degrades gradually with aging. For deriving maximum benefits from these capital-intensive projects, these degradation patten should be analyzed and understood. Variations in the capacity factor over the years could be an indication of the age-related degradation of the wind farms. In this study, we propose a novel data-driven model to estimate the capacity factor of wind farms, which could then be used to estimate its age-related performance decline. For this, a 1-dimensional convolutional neural network (1-D CNN) is developed with a soft ordering mechanism under this study. The model was optimized using Huber loss to counteract the effects of outliers in data. The developed model could perform very well in capturing the underlying dynamics in the data as evidenced by a normalized root mean squared error (NRMSE) of 0.102 and a mean absolute error (MAE) of 0.035 on the test dataset.

• Mathew, Manuel Sathyajith; Kandukuri, Surya Teja & Omlin, Christian Walter Peter (2024). Fully Automated Diagnostics of Induction Motor Drives in Offshore Wind Turbine Pitch Systems Using Extended Park Vector Transform and Convolutional Neural Network. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). 8(1), p. 488–498. doi: 10.36001/phme.2024.v8i1.4030. Full text in Research Archive Show summary

  Due to their location and related complexities, the offshore wind farms (OWF) have higher downtimes and operation and maintenance (O&M) costs compared to their onshore counterparts. Condition monitoring could help in bringing down the O&M costs of OWFs. The pitch system is one of the components most prone to failure. This paper details an approach for enhanced diagnosis of the electric pitch systems especially focusing on the induction motor drives (IMD) in wind turbines. The proposed method uses an extended Park vector approach (EPVA) in conjunction with a convolutional neural network (CNN) to accurately classify the condition of an IMD and localize the faults. The method is validated on data collected from a laboratory setup. The advantage of the proposed approach is that the condition of the IMD can accurately be classified, and faults localized in operating conditions with varying load and frequency without any additional information on the instantaneous operating speed, frequency, or load on the motor drives. This results in a non-invasive diagnostic approach incurring least additional expenses to implement.

• Lučin, Ivana; Čarija, Zoran; Kandukuri, Surya Teja & Schlanbusch, Rune (2024). Transnational experience exchange between countries towards speeding up EU renewable transition: example of Norway-Croatia. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 2875(1). doi: 10.1088/1742-6596/2875/1/012049. Full text in Research Archive Show summary

  The RePowerEU plan aims to increase wind energy capacity to 480 GW by 2030, with 300 GW coming from offshore wind farms, to achieve climate neutrality by 2050. Due to Croatia's substantial dependence on nautical and coastal tourism, along with significant concerns regarding negative environmental impacts, the installation of offshore wind farms has not been previously considered. However, due to increased demand for renewables, the offshore energy potential resurfaced. The recent Action Plan for the Uptake of Offshore Renewable Energy Sources in Croatia (May 2023) estimated offshore wind potential at 25 GW and identified more than 29 000 km2 of area available for offshore renewables. Norway, with its extensive experience in offshore engineering and pioneering developments in offshore wind, is a suitable partner in joint innovation and a resource for tried and tested best practices. In this collaborative research, a review of relevant documents, research articles, and interviews with industry experts from both from Croatia and Norway was conducted to obtain a broader perspective of the implications of offshore development in Croatia. As an outcome of this preliminary research, the paper outlines the required evaluations in the Croatian context, in terms of the social, environmental, legislative, and technical aspects that are to be considered for large-scale integration of wind energy into the Croatian grid. It also identifies the necessary grid improvements to achieve a reliable and sustainable grid of the future.

• Sakaris, Christos; Anastasiadis, Nikolas; Schlanbusch, Rune & Kandukuri, Surya Teja (2024). Sensitivity analysis for multi-measurement points based SHM in the mooring lines of floating offshore wind turbines. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 2875. doi: 10.1088/1742-6596/2875/1/012032. Full text in Research Archive Show summary

  Structural health monitoring in floating offshore wind turbines' mooring lines is vital for detecting early faults and preventing disruptions. Currently, sporadic and expensive monitoring is conducted via remote-operating vehicles. Efficient, automated, economical monitoring methods based on a continuous stream of data acquired via multiple measurement points in the wind turbine, are required. Such methods based on vibration signals have been investigated limitedly for steel chains. Recently, faulty synthetic mooring lines of a simulated 10MW semi-submersible wind turbine have been detected under varying environmental conditions and via the Functional Model Based Method (FMBM) equipped with functional models. The uncertainty due to the conditions' stochasticity has been addressed by training the models using ten signal realizations per condition (wind) under the healthy wind turbine and from each of two measurement points. The current study presents a preliminary sensitivity analysis of the FMBM's capability in detecting the simulated wind turbine's faulty mooring lines when the functional models are trained using one signal realization per varying condition (wind) from each measurement point. The method's effectiveness is evaluated with acceleration signals from 11 healthy/ 66 faulty (reduced stifness in one mooring line) cases. The FMBM detects all cases even when trained using one signal realization.

• Mathew, Manuel Sathyajith; Kolhe, Mohan Lal; Kandukuri, Surya Teja & Omlin, Christian Walter Peter (2023). Data driven approach for the management of wind and solar energy integrated electrical distribution network with high penetration of electric vehicles. Journal of Cleaner Production. ISSN 0959-6526. 421. doi: 10.1016/j.jclepro.2023.138467. Full text in Research Archive Show summary

  With the increased penetration of fluctuating renewables and growing population of contemporary loads such as electric vehicles, the uncertainties in the generation and demand in the electric power grids are increasing. This makes the efficient operation and management of these systems challenging. Objective of this study is to propose a real-time management system for EV charging, which maximises the renewable energy utilization. An electric power distribution network with an average and peak demands of 1.51 MW, and 3.6 MW respectively, was chosen for the study. The real time power flow through the network components were analyzed using the OpenDSS model. With a wind power density of 574.51 W/m2 and a solar insolation of 4.14 kWh/m2/day, an optimized renewable energy system consisting of a 2.3 MW wind turbine and 2.61 MWp photovoltaic power plant are proposed for the network. Models based on k-Nearest Neighbors algorithms were developed for predicting the performances of these renewable energy systems at the network area. Based on the load profile, power flow analysis, and the predicted generation from solar and wind systems, a demand side management algorithm has been developed for the charge/discharge scheduling of the electric vehicles connected within the network. The basic objective of the algorithm is to maximize the renewable energy utilization by triggering the charging cycle during the periods of excess renewable energy generation. With an annual contribution of renewables is estimated as 12.61 GWh out of which 9.33 GWh is from wind and 3.29 GWh from solar. Wind from wind and solar energy systems, the proposed scheduling algorithm could contribute 71.56 percent of the charging load demand by the EVs.

• Mathew, Manuel Sathyajith; Kandukuri, Surya Teja & Omlin, Christian Walter Peter (2022). Estimation of Wind Turbine Performance Degradation with Deep Neural Networks. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). 7(1), p. 351–359. doi: 10.36001/phme.2022.v7i1.3328. Full text in Research Archive Show summary

  In this paper, we estimate the age-related performance degradation of a wind turbine working under Norwegian environment, based on a deep neural network model. Ten years of high-resolution operational data from a 2 MW wind turbine were used for the analysis. Operational data of the turbine, between cut-in and rated wind velocities, were considered, which were pre-processed to eliminate outliers and noises. Based on the SHapley Additive exPlanations of a preliminary performance model, a benchmark performance model for the turbine was developed with deep neural networks. An efficiency index is proposed to gauge the agerelated performance degradation of the turbine, which compares measured performances of the turbine over the years with corresponding bench marked performance. On an average, the efficiency index of the turbine is found to decline by 0.64 percent annually, which is comparable with the degradation patterns reported under similar studies from the UK and the US.

• Noori, Nadia; Shanbhag, Vignesh Vishnudas; Kandukuri, Surya Teja & Schlanbusch, Rune (2022). Data Driven Seal Wear Classifications using Acoustic Emissions and Artificial Neural Networks. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). 7(1), p. 368–375. doi: 10.36001/phme.2022.v7i1.3327. Full text in Research Archive Show summary

  The work presented in this paper is built on a series of experiments aiming to develop a data-driven and automated method for seal diagnostics using Acoustic Emission (AE) features. Seals in machineries operate in harsh conditions, and seal wear in hydraulic cylinders results in fluid leakage, and instability of the piston rod movement. Therefore, regular inspection of seals is required using automated approaches to improve productivity and to reduce unscheduled maintenance. In this study, we implemented a data-driven diagnostics approach which utilizes AE measurements along with light weight Artificial Neural Networks (ANN) as a classifier to investigate the performance and resources (hardware & software) required for implementing a real-time soft sensor unit for monitoring seal wear condition. We used a feedforward multilayer perceptron ANN (Scaled Conjugate Gradient- SCG algorithm) that is trained with the back propagation algorithm, which is a popular network architecture for a multitude of applications (automotive, oil and gas, electronics). We benchmark the developed method against previous work conducted based on Support Vector Machine (SVM), and we compare ANN performance in classifying the running condition of seals in hydraulic cylinders by applying it to both raw (full frequency spectrum) and down sampled frequency measurements. The experiments were performed at varying pressure conditions on a hydraulic test rig that can simulate fluid leakage conditions like that of hydraulic cylinders. The test cases were generated with seals of three different conditions (unworn, semi-worn, worn). From the AE spectrum, the frequency bands were identified with peak power and by heterodyning the signal. This technique results in 10X down sampling without losing the information of interest. Further, the signal was divided into smaller “snapshots” to facilitate rapid diagnosis. In these tests, the diagnosis was made on short-time windows, as low as 0.3 seconds in length. A general set of 16 time and frequency domain features were designed. Then a training set was developed using relevant set of features (4, 5, and 16 features). The data was used to train the ANN (70% training – 30% test & validation) and SVM (60 % training - 40% test and validation). Classification of down sampled measurements, both ANN and SVM were able to accurately classify the status irrespective of the pressure conditions, with an accuracy of ~99% with execution time less than seconds. Therefore, the proposed approach can be applied as part of an automated seal wear classification technique based on AE and ANN/SVM and can be used for real-time monitoring of seal wear in hydraulic cylinders.

• Kandukuri, Surya Teja; Shanbhag, Vignesh Vishnudas; Meyer, Thomas; Caspers, Leo; Noori, Nadia & Schlanbusch, Rune (2021). Automated and Rapid Seal Wear Classification Based on Acoustic Emission and Support Vector Machine. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). 6(1), p. 203–210. doi: 10.36001/phme.2021.v6i1.2942. Full text in Research Archive Show summary

  Seal wear in hydraulic cylinders results in fluid leakage, and instability of the piston rod movement. Therefore, regular inspection of seals is required using automated approaches to improve productivity and to reduce unscheduled maintenance. In literature, successful attempts have been made using acoustic emission-based condition monitoring to classify the seal wear. However, limited attempts have been made to implement automated approaches to classify seal wear using acoustic emission features. Therefore, this article presents an automated approach for rapid and computationally economical diagnosis of seal wear using acoustic emission. The experiments were performed at varying pressure conditions on a hydraulic test rig that can simulate fluid leakage conditions similar to that of hydraulic cylinders. From the acoustic emission spectrum, the frequency bands were identified with peak power and by heterodyning the signal. This technique results in 10X downsampling without losing the information of interest. Further, the signal was divided into smaller “snapshots” to facilitate rapid diagnosis. In these tests, the diagnosis was made on short-time windows, as low as 0.3 seconds in length. A set of time and frequency domain features were designed, and the Support Vector Machine (SVM) was trained on 60 % of the test cases generated with seals of three different conditions. The SVM was able to accurately classify the status irrespective of the pressure conditions, with an accuracy of ~99 %. Therefore, the proposed automated seal wear classification technique based on acoustic emission and SVM can be used for real-time monitoring of seal wear in hydraulic cylinders.

• Golzar, Milad; Kandukuri, Surya Teja; Huynh, Khang; Choux, Martin Marie Hubert & Versland, Alf Magne (2020). A Novel Fault Indicator for Local Demagnetization in Fractional-Slot Permanent Magnet Synchronous Motor using Winding Function Theory. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). Full text in Research Archive Show summary

  Local demagnetization is an irreparable failure mode in permanent magnet synchronous motors (PMSMs), resulting in reduced motor efficiency and high cogging torque. These effects are particularly disadvantageous to fractional-slot wound PMSMs used in industrial applications, such as robotics and automations, that require high power density and precise operation. In this article, a diagnostic indicator is developed for detecting local demagnetization fault in fractional-slot wound PMSMs based on winding function theory, utilizing back-electromotive force (back-EMF) as a medium for fault detection. Further, the developed indicator is validated in a commercial fractional-slot wound PMSM of an in-house experimental setup.

• Husebø, Arild Bergesen; Kandukuri, Surya Teja; Klausen, Andreas; Huynh, Khang & Robbersmyr, Kjell Gunnar (2020). Rapid Diagnosis of Induction Motor Electrical Faults using Convolutional Autoencoder Feature Extraction. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). 5(1). Full text in Research Archive
• Klausen, Andreas; Kalaoja, Johannes; Kandukuri, Surya Teja & Robbersmyr, Kjell Gunnar (2020). Sensitivity Analysis Of Online Oil Quality Monitoring For Early Detection Of Water Ingress In Marine Propulsion Systems. Proceedings of the European Conference of the Prognostics and Health Management Society (PHME). 5(1). doi: 10.36001/phme.2020.v5i1.1268. Full text in Research Archive Show summary

  Gearboxes are critical equipment in the vessel propulsion system. Lubrication contamination caused by water ingress or condensation is one of the major failure modes in marine gearboxes leading to accelerated aging of lubricant, resulting in accelerated wear of gearbox components such as bearing and gears. This article presents a systematic evaluation of moisture ingress sensitivity of three commercial online oil quality monitoring sensors. A laboratory setup consisting of an industrial multi-stage planetary gearbox is utilised for the experimental studies. Furthermore, the gearbox vibration signatures are analysed to investigate the sensitivity of vibration measurements to water ingress to determine the need for oil quality monitoring when considering this specific failure mode.

• Kandukuri, Surya Teja; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Diagnostics of stator winding failures in wind turbine pitch motors using Vold-Kalman filter, Proceeding 45th Annual Conference of the IEEE Industrial Electronics Society - IECON 2019. IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728148786. p. 5992–5997. doi: 10.1109/IECON.2019.8926983. Full text in Research Archive Show summary

  Pitch systems are among the most failure-prone components in wind turbines. Winding failures in pitch motors are common due to high start-up loads and poor ventilation. This article presents a diagnostics scheme that can detect the stator winding failures in the pitch motors under time-varying speed and load conditions. The proposed approach based on three-phase motor currents can be directly integrated into the motor drive and can be used for induction as well as permanent magnet synchronous machines. The extended Park's vector calculated on the motor currents is order tracked based on the supply frequency from the drive using Vold-Kalman filter. The approach is shown to be robust under arbitrary load and speed variations in a laboratory setup of a pitch drive.

• Kandukuri, Surya Teja; Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). A Two-Stage Fault Detection and Classification Scheme for Electrical Pitch Drives in Offshore Wind Farms Using Support Vector Machine. IEEE transactions on industry applications. ISSN 0093-9994. 55(5), p. 5109–5118. doi: 10.1109/TIA.2019.2924866. Full text in Research Archive Show summary

  Pitch systems are one of the components with the most frequent failure in wind turbines. This article presents a two-stage fault detection and classification scheme, for electric motor drives in wind turbine pitch systems. The presented approach is suitable for application in offshore wind farms, with electric pitch systems driven by induction motors as well as permanent magnet synchronous motors. The adopted strategy utilizes three-phase motor current sensing at the pitch drives for fault detection and only when a fault condition is detected at this stage, features extracted from the current signals are transmitted to a support vector machine classifier located centrally to the wind farm. The proposed method is validated in a laboratory setup of the pitch drive.

• Kandukuri, Surya Teja; Huynh, Khang & Robbersmyr, Kjell Gunnar (2019). Diagnosis of inverter-fed induction motors in short time windows using physics-assisted deep learning framework, 2019 IEEE 12th International Symposium on Diagnostics for Electrical Machines, Power Electronics and Drives (SDEMPED). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781728118321. p. 264–270. doi: 10.1109/DEMPED.2019.8864889. Full text in Research Archive
• Kandukuri, Surya Teja; Robbersmyr, Kjell Gunnar & Karimi, Hamid Reza (2018). Toward farm-level health management of wind turbine systems: status and scope for improvements. In Karimi, Hamid Reza (Eds.), Structural Control and Fault Detection of Wind Turbine Systems. IET Digital Library. ISSN 9781785613944. p. 149–168. doi: 10.1049/PBPO117E_ch6. Full text in Research Archive
• Kandukuri, Surya Teja; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Multi-Component Fault Detection in Wind Turbine Pitch Systems Using Extended Park's Vector and Deep Autoencoder Feature Learning. In Kim, Chang-Eob (Eds.), 2018 21st International Conference on Electrical Machines and Systems (ICEMS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9788986510201. p. 1002–1007. doi: 10.23919/ICEMS.2018.8549293. Full text in Research Archive
• Huynh, Khang; Kandukuri, Surya Teja; Pawlus, Witold & Robbersmyr, Kjell Gunnar (2018). Parameter Identification of a Winding Function Based Model for Fault Detection of Induction Machines, 2018 Eighth International Conference on Information Science and Technology (ICIST). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538637821. p. 200–205. doi: 10.1109/ICIST.2018.8426188. Full text in Research Archive
• Kandukuri, Surya Teja; Klausen, Andreas; Huynh, Khang & Robbersmyr, Kjell Gunnar (2018). Fault diagnostics of wind turbine electric pitch systems using sensor fusion approach. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 1037(3). doi: 10.1088/1742-6596/1037/3/032036. Full text in Research Archive
• Kandukuri, Surya Teja; Huynh, Khang; Karimi, Hamid Reza & Robbersmyr, Kjell Gunnar (2018). Fault diagnostics for electrically operated pitch systems in offshore wind turbines. In Karimi, Hamid Reza (Eds.), Structural Control and Fault Detection of Wind Turbine Systems. IET Digital Library. ISSN 9781785613944. p. 193–211. doi: 10.1049/pbpo117e_ch8. Full text in Research Archive
• Håland, David; Kandukuri, Surya Teja & Huynh, Khang (2017). Sensorless control of induction motors using an extended Kalman filter and linear quadratic tracking, 2017 20th International Conference on Electrical Machines and Systems (ICEMS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538632468. doi: 10.1109/ICEMS.2017.8056264. Full text in Research Archive
• Kandukuri, Surya Teja; Senanayaka, Jagath Sri Lal; Huynh, Khang & Robbersmyr, Kjell Gunnar (2017). A two-stage fault detection and classification for electric pitch drives in offshore wind farms using support vector machine, 2017 20th International Conference on Electrical Machines and Systems (ICEMS). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781538632468. doi: 10.1109/ICEMS.2017.8056262. Full text in Research Archive
• Senanayaka, Jagath Sri Lal; Kandukuri, Surya Teja; Huynh, Khang & Robbersmyr, Kjell Gunnar (2017). Early detection and classification of bearing faults using support vector machine algorithm. In Gerada, Chris (Eds.), Proceedings- 2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781509058532. p. 250–255. doi: 10.1109/WEMDCD.2017.7947755. Full text in Research Archive
• Kandukuri, Surya Teja; Senanayaka, Jagath Sri Lal; Huynh, Khang; Karimi, Hamid Reza & Robbersmyr, Kjell Gunnar (2017). Current signature based fault diagnosis of field-oriented and direct torque–controlled induction motor drives. Proceedings of the Institution of mechanical engineers. Part I, journal of systems and control engineering. ISSN 0959-6518. 231(10), p. 849–866. doi: 10.1177/0959651817731259. Full text in Research Archive Show summary

  In this article, the operation of three-phase squirrel-cage induction motors is analysed under faulty conditions in closed loop with state-of-the-art controllers, namely, the field-oriented control and the direct torque control. The motivation behind this study is to examine the effectiveness of current signature–based fault detection schemes under closed-loop operation, in the presence of inverter harmonics. Various commonly occurring induction motor fault conditions are modelled based on the modified winding function theory, and each fault case is further simulated in a closed-loop framework to verify the fault detectability. The effectiveness of current signature–based diagnostics in varying fault severity, loads and speeds is studied. Furthermore, the faults are artificially seeded in a laboratory test set-up of an induction motor, and the effectiveness of current signature analysis is verified with commercially available field-oriented and direct torque control drives in the closed-loop framework

• Pawlus, Witold; Kandukuri, Surya Teja; Hovland, Geir; Choux, Martin Marie Hubert & Hansen, Michael Rygaard (2016). EKF-based estimation and control of electric drivetrain in offshore pipe racking machine, 2016 IEEE International Conference on Industrial Technology (ICIT). IEEE (Institute of Electrical and Electronics Engineers). ISSN 9781467380751. p. 153–158. doi: 10.1109/ICIT.2016.7474742. Full text in Research Archive
• Kandukuri, Surya Teja; Huynh, Khang; Karimi, Hamid Reza & Robbersmyr, Kjell G (2016). Fault Diagnostics for Electrically Operated Pitch Systems in Offshore Wind Turbines. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 753(5). doi: 10.1088/1742-6596/753/5/052005. Full text in Research Archive
• Kandukuri, Surya Teja; Klausen, Andreas; Karimi, Hamid Reza & Robbersmyr, Kjell G (2016). A review of diagnostics and prognostics of low-speed machinery towards wind turbine farm-level health management. Renewable and Sustainable Energy Reviews. ISSN 1364-0321. 53, p. 697–708. doi: 10.1016/j.rser.2015.08.061. Full text in Research Archive
• Chougule, Abhijit; Kandukuri, Surya Teja & Beyer, Hans-Georg (2016). Assessment of synthetic winds through spectral modeling and validation using FAST. Journal of Physics: Conference Series (JPCS). ISSN 1742-6588. 753(4). doi: 10.1088/1742-6596/753/4/042011. Full text in Research Archive Show summary

  In this paper, we analyse the simulated and measured wind data with respect to their spectral characteristics and their effect on wind turbine loads. The synthetic data is generated from a stochastic full-field turbulent wind simulator - TurbSim for neutral stability conditions. We first investigate a model for velocity spectra and, a coherence model, by comparing the model results with the measurements. In the second part we analyse the synthetic data via spectra and coherence for two cases; without and with adding coherent events. Finally, we compare wind turbine loads calculated by using FAST simulation of 5 MW reference wind turbine on the basis of simulated and measured data for the given mean wind speed.

• Kandukuri, Surya Teja; Robbersmyr, Kjell G & Karimi, Hamid Reza (2015). Towards farm-level health management of offshore wind farms for maintenance improvements. The International Journal of Advanced Manufacturing Technology. ISSN 0268-3768. p. 1–11. doi: 10.1007/s00170-015-7616-y. Full text in Research Archive

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• Sakaris, Christos; Schlanbusch, Rune; Anastasiadis, Nikolaos & Kandukuri, Surya Teja (2024). Structural Health Monitoring of synthetic mooring lines for semi-submersible wind floaters under varying environmental conditions. Full text in Research Archive
• Kandukuri, Surya Teja; Vognild, Lars Kristian; Schlanbusch, Rune & Olimstad, Grunde (2024). An investigation into the use of RDS-PS (ISO81346-10) for hydropower condition monitoring and performance analytics. Full text in Research Archive Show summary

  The green transition targets worldwide are driving increased penetration of non-programmable renewable energy sources, warranting more flexible operations from hydropower to stabilize the grid. Such flexible off-design operations can result in failures and unwanted downtimes. Continuous monitoring of machine performance and health through data analytics and condition monitoring is crucial to ensure high reliability. However, these technologies can only succeed at scale if the underpinning data is appropriately captured, contextualized and structured. The article presents how the reference designation system for power systems (RDS-PS) can enable organizing the measured data from hydropower plants in a structured, coherent, and unified manner to fully exploit it through advanced analytics. In this context, the article analyses the use of the novel reference designation system for power systems (RDS-PS) defined in ISO-81346-10:2022 for use in the Norwegian hydropower sector.

• Mathew, Manuel Sathyajith; Kandukuri, Surya Teja & Omlin, Christian Walter Peter (2023). Predicting the capacity factor of wind farms using deep neural networks for estimating age related performance degradation. Full text in Research Archive Show summary

  Performance of wind energy conversion systems gradually decline over its life cycle. Understanding this age-related performance degradation is essential in efficiently maintaining and managing these systems. In case of wind farms where sufficient time series performance data are available through SCADA, the decline in the wind farm level performance can be modelled though data driven methods. In this work, we propose such a deep neural network (DNN) model for predicting the capacity factor, which can further be used in analysing the age-related performance degradation of wind farms.

• Mathew, Manuel Sathyajith; Kandukuri, Surya Teja & Omlin, Christian Walter Peter (2023). Predicting the capacity factor of wind farms using deep neural networks for estimating age related performance degradation. Full text in Research Archive Show summary

  Performance of wind energy conversion systems gradually decline over its life cycle. Understanding this age-related performance degradation is essential in efficiently maintaining and managing these systems. In case of wind farms where sufficient time series performance data are available through SCADA, the decline in the wind farm level performance can be modelled though data driven methods. In this work, we propose such a deep neural network (DNN) model for predicting the capacity factor, which can further be used in analysing the age-related performance degradation of wind farms.

• Kandukuri, Surya Teja & Robbersmyr, Kjell Gunnar (2021). Analytics for asset integrity management in wind farms (AIMWind). Full text in Research Archive
• Mathew, Manuel Sathyajith; Kandukuri, Surya Teja & Omlin, Christian Walter Peter (2021). Analytics for Wind Farm Asset Management. Full text in Research Archive
• Kandukuri, Surya Teja; Rasmussen, Helge; Karimi, Hamid Reza & Robbersmyr, Kjell G (2015). Diagnostics and Prognostics of Pitch and Yaw Systems in Offshore Wind Turbines. Full text in Research Archive
• Kandukuri, Surya Teja; Robbersmyr, Kjell Gunnar & Huynh, Khang (2018). Health Monitoring of Pitch and Yaw Systems in Offshore Wind Farms. Universitetet i Agder. ISSN 9788271178918. Full text in Research Archive

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