Date of Graduation
Statler College of Engineering and Mineral Resources
Lane Department of Computer Science and Electrical Engineering
Sarika Khushalani Solanki
Muhammad A. Choudhry
Power grids are large cyber-physical systems with physical operation controlled and integrated through communications. The technological breakthroughs made by the availability of low-cost, high speed communications, larger storage spaces, greater internet bandwidths have led to increased attentions towards advantages of these systems. The use of Phasor Measurement Unit (PMU) in complex interconnected power system for monitoring and control has increased significantly. This has resulted in huge amounts of data and growing databases, thus gearing towards an era where utilities might encounter an enormous amount of data daily from their measurement units including sensitive information useful for daily operations. With increasing energy demand, more energy resources are continuously being added in the existing network. This complex network thus operates comparatively closer to its stability limits with minimal flexibility and reliability. Such conditions may lead to low amplitude oscillations causing power fluctuations and discontinuity of supply in some cases. Therefore, recently, the attention of utilities has shifted towards tools and methods, which help in locating the source of these electromechanical oscillations. We propose a novel data driven Credibility Search Ensemble Learning (CSEL) technique to identify the source location of these oscillations using synchrophasor measurements, offline credibility estimation and data mining based classification models. The proposed framework was tested and validated with promising results using western interconnection power system in North America (WECC-179). The reliability and robustness of the proposed framework was checked against measurement errors in PMUs as well as for practical topology change scenarios. Such oscillation source identification methods were mostly developed and tested in transmission system, where PMU measurements at almost each bus are readily available. In addition, the presence of inertia in the form of rotating synchronous machines is also extremely helpful against these oscillations. On the other hand, with increased penetration of DERs, the analysis of these oscillations in islanded microgrid, which is not connected or supported through large interconnected transmission system, is very crucial. We demonstrate and validate the applicability of the proposed model free source identification approach for 13-node and 34-node distribution networks operating in islanded mode. We also analyze the performance with multiple causes and also with the cause being a fluctuating load.
In events, where finding the oscillation source of disturbance does not provide adequate information to the system operator to take countermeasures, controlled islanding can still be applied as a last countermeasure to prevent system-wide instabilities and blackouts. It splits the system into self-sustained islands to maintain transient stability at the expense of possible loss of load. However, the stability of each newly formed island depends on the coherency of the generating units. Generator coherence identification is critical to controlled islanding scheme as it helps identify the optimal cut-set to maintain the transient stability of the post-islanding systems. Therefore, correct and adaptive identification of generator’s coherency is essential. Moreover,the coherency between groups of generators varies over time, due to changing network topology and operating conditions, necessitating real-time coherency determination. We propose a novel approach for online generator coherency identification using phasor measurement unit (PMU) data and dynamic time warping (DTW). In addition, we also propose a unique data driven approach for coherence identification of generators using Phasor Measurement Unit (PMU) data and its structural characteristic measures like Kurtosis, entropy etc. Results from the coherence identification are used to further cluster nongenerator buses using spectral clustering with the objective of minimizing power flow disruptions. The proposed approach is validated and compared to existing methods on the IEEE 39-bus system and WECC 179-bus system, through which its advantages are demonstrated.
Ul Banna, Hasan, "Data Driven Intelligent Grid Stability Monitoring and Adaptive Emergency Response" (2019). Graduate Theses, Dissertations, and Problem Reports. 7389.