Cansin Yaman Evrenosoglu

Bio: Cansin Yaman Evrenosoglu is an academic researcher from Virginia Tech. The author has contributed to research in topics: Electric power system & Fault (power engineering). The author has an hindex of 13, co-authored 31 publications receiving 706 citations. Previous affiliations of Cansin Yaman Evrenosoglu include University of Nevada, Reno & Texas A&M University.

Travelling wave based fault location for teed circuits

Abstract: This paper describes a fault location algorithm for three terminal lines using wavelet transform of the fault initiated transients. The results presented in are extended to the case of three terminal configuration and a new single ended procedure is developed for teed circuits. The algorithm gives accurate results for the case of three terminal lines including series compensated branch, mutual coupled line section and different values of fault resistances. The performance of the algorithm is tested on different scenarios by using ATP/EMTP program and MATLAB Wavelet Toolbox.

A Fault Classification and Localization Method for Three-Terminal Circuits Using Machine Learning

Abstract: This paper presents a traveling-wave-based method for fault classification and localization for three-terminal power transmission systems. In the proposed method, the discrete wavelet transform is utilized to extract transient information from the recorded voltages. Support-vector-machine classifiers are then used to classify the fault type and faulty line/half in the transmission networks. Bewley diagrams are observed for the traveling-wave patterns and the wavelet coefficients of the aerial mode voltage are used to locate the fault. Alternate Transients Program software is used for transients simulations. The performance of the method is tested for different fault inception angles, different fault resistances, nonlinear high impedance faults, and nontypical faults with satisfactory results.

A Short-Term Nodal Voltage Phasor Forecasting Method Using Temporal and Spatial Correlation

Abstract: This paper presents a new method for short-term nodal voltage phasor forecasting in power systems to which a large number of renewable resources and microgrids are connected. Its motivation stems from the observation that such systems are characterized by an unconventional topology along with the presence of a number of buses with bursty power injection patterns, which in turn significantly impacts the temporal and spatial correlation of the nodal voltage phasors. Simulation results carried out on three IEEE test systems reveal that this new pattern affects the number of dominant buses, termed electrical hubs, and, subsequently, the time and spatial correlation among nodal voltage angles. They also reveal that the nodal voltage magnitudes exhibit time correlation, but no spatial correlation. In this paper, time series are represented by autoregressive (AR) processes while multivariate time series with spatial correlation are represented by vector autoregressive (VAR) processes. Statistical tests show that an order one for both models is adequate. The non-zero parameters of the VAR(1) models are identified using metrics on electrical connectivity, centrality, and node significance. The good performance of the proposed method is demonstrated on the IEEE 57-, 118-, and 300-bus test systems in presence of large-scale distributed wind farms and microgrids, and under loss of system observability.

Solar Power Prediction Using Interval Type-2 TSK Modeling

Abstract: The random nature of solar energy resources is one of the obstacles to their large-scale proliferation in power systems. The most practical way to predict this renewable source of energy is to use meteorological data. However, such data are usually provided in a qualitative form that cannot be exploited using traditional quantitative methods but which can be modeled using fuzzy logic. This paper proposes type-1 and interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy systems for the modeling and prediction of solar power plants. The paper considers TSK models with type-1 antecedents and crisp consequents, type-1 antecedents and consequents, and type-2 antecedents and crisp consequents. The design methodology for tuning the antecedents and consequents of each model is described. Finally, input-output data sets from a solar plant are used to obtain the three TSK models and their prediction results are compared to results from the literature. The results show that type-2 TSK models with type2 antecedents and crisp consequents provide the best performance based on the solar plant data.

A Survey on Cyber Security for Smart Grid Communications

Abstract: A smart grid is a new form of electricity network with high fidelity power-flow control, self-healing, and energy reliability and energy security using digital communications and control technology. To upgrade an existing power grid into a smart grid, it requires significant dependence on intelligent and secure communication infrastructures. It requires security frameworks for distributed communications, pervasive computing and sensing technologies in smart grid. However, as many of the communication technologies currently recommended to use by a smart grid is vulnerable in cyber security, it could lead to unreliable system operations, causing unnecessary expenditure, even consequential disaster to both utilities and consumers. In this paper, we summarize the cyber security requirements and the possible vulnerabilities in smart grid communications and survey the current solutions on cyber security for smart grid communications.

Online Short-term Solar Power Forecasting

Abstract: This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.