Surya Teja Kandukuri

• 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. ISSN 2334-5837. Vis sammendrag

  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). ISSN 2325-016X. 8(1), s. 762–770. doi: 10.36001/phme.2024.v8i1.4028. Vis sammendrag

  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). ISSN 2325-016X. 8(1), s. 488–498. doi: 10.36001/phme.2024.v8i1.4030. Vis sammendrag

  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.

• 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. Fulltekst i vitenarkiv Vis sammendrag

  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). ISSN 2325-016X. 7(1), s. 351–359. doi: 10.36001/phme.2022.v7i1.3328. Fulltekst i vitenarkiv Vis sammendrag

  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). ISSN 2325-016X. 7(1), s. 368–375. doi: 10.36001/phme.2022.v7i1.3327. Fulltekst i vitenarkiv Vis sammendrag

  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). ISSN 2325-016X. 6(1), s. 203–210. doi: 10.36001/phme.2021.v6i1.2942. Fulltekst i vitenarkiv Vis sammendrag

  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). ISSN 2325-016X. Fulltekst i vitenarkiv
• 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). ISSN 2325-016X. 5(1). Fulltekst i vitenarkiv
• 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). ISSN 2325-016X. 5(1). Fulltekst i vitenarkiv
• 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 conference proceedings. ISSN 978-1-7281-1832-1. s. 264–270. doi: 10.1109/DEMPED.2019.8864889.
• 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 conference proceedings. ISSN 978-1-7281-4878-6. s. 5992–5997. doi: 10.1109/IECON.2019.8926983. Vis sammendrag

  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), s. 5109–5118. doi: 10.1109/TIA.2019.2924866. Vis sammendrag

  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; Robbersmyr, Kjell Gunnar & Karimi, Hamid Reza (2018). Toward farm-level health management of wind turbine systems: status and scope for improvements. I Karimi, Hamid Reza (Red.), Structural Control and Fault Detection of Wind Turbine Systems. IET Digital Library. ISSN 9781785613944. s. 149–168. doi: 10.1049/PBPO117E_ch6.
• Kandukuri, Surya Teja; Huynh, Khang; Karimi, Hamid Reza & Robbersmyr, Kjell Gunnar (2018). Fault diagnostics for electrically operated pitch systems in offshore wind turbines. I Karimi, Hamid Reza (Red.), Structural Control and Fault Detection of Wind Turbine Systems. IET Digital Library. ISSN 9781785613944. s. 193–211. doi: 10.1049/pbpo117e_ch8.
• 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 conference proceedings. ISSN 978-1-5386-3782-1. s. 200–205. doi: 10.1109/ICIST.2018.8426188.
• 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. I Kim, Chang-Eob (Red.), 2018 21st International Conference on Electrical Machines and Systems (ICEMS). IEEE conference proceedings. ISSN 978-89-86510-20-1. s. 1002–1007. doi: 10.23919/ICEMS.2018.8549293.
• 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.
• 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 conference proceedings. ISSN 978-1-5386-3246-8. doi: 10.1109/ICEMS.2017.8056262.
• 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 conference proceedings. ISSN 978-1-5386-3246-8. doi: 10.1109/ICEMS.2017.8056264.
• 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), s. 849–866. doi: 10.1177/0959651817731259. Vis sammendrag

  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

• 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. I Gerada, Chris (Red.), Proceedings- 2017 IEEE Workshop on Electrical Machines Design, Control and Diagnosis (WEMDCD). IEEE conference proceedings. ISSN 978-1-5090-5853-2. s. 250–255. doi: 10.1109/WEMDCD.2017.7947755.
• 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 978-1-4673-8075-1. s. 153–158. doi: 10.1109/ICIT.2016.7474742.
• 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.
• 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. Fulltekst i vitenarkiv Vis sammendrag

  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; 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, s. 697–708. doi: 10.1016/j.rser.2015.08.061.
• 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. s. 1–11. doi: 10.1007/s00170-015-7616-y. Fulltekst i vitenarkiv

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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.
• 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. . Vis sammendrag

  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. . Vis sammendrag

  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).
• Mathew, Manuel Sathyajith; Kandukuri, Surya Teja & Omlin, Christian Walter Peter (2021). Analytics for Wind Farm Asset Management.
• Kandukuri, Surya Teja; Rasmussen, Helge; Karimi, Hamid Reza & Robbersmyr, Kjell G (2015). Diagnostics and Prognostics of Pitch and Yaw Systems in Offshore Wind Turbines.
• Kandukuri, Surya Teja; Robbersmyr, Kjell Gunnar & Huynh, Khang (2018). Health Monitoring of Pitch and Yaw Systems in Offshore Wind Farms. Universitetet i Agder. ISSN 978-82-7117-891-8.

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