Susmita Kar

Bio: Susmita Kar is an academic researcher from Indian Institute of Technology Bhubaneswar. The author has contributed to research in topics: Microgrid & Decision tree. The author has an hindex of 7, co-authored 15 publications receiving 413 citations. Previous affiliations of Susmita Kar include Birla Institute of Technology, Mesra & C. V. Raman College of Engineering, Bhubaneshwar.

Data-Mining Model Based Intelligent Differential Microgrid Protection Scheme

Abstract: This paper presents a data-mining-based intelligent differential protection scheme for the microgrid. The proposed scheme preprocesses the faulted current and voltage signals using discrete Fourier transform and estimates the most affected sensitive features at both ends of the respective feeder. Furthermore, differential features are computed from the corresponding features at both ends of the feeder and are used to build the decision tree-based data-mining model for registering the final relaying decision. The proposed scheme is extensively validated for fault situations in the standard IEC microgrid model with wide variations in operating parameters for radial and mesh topology in grid-connected and islanded modes of operation. The extensive test results indicate that the proposed intelligent differential relaying scheme can be highly reliable in providing an effective protection measure for safe and secured microgrid operation.

Time-frequency transform-based differential scheme for microgrid protection

Abstract: The study presents a differential scheme for microgrid protection using time-frequency transform such as S-transform Initially, the current at the respective buses are retrieved and processed through S-transform to generate time-frequency contours Spectral energy content of the time-frequency contours of the fault current signals are calculated and differential energy is computed to register the fault patterns in the microgrid at grid-connected and islanded mode The proposed scheme is tested for different shunt faults (symmetrical and unsymmetrical) and high-impedance faults in the microgrid with radial and loop structure It is observed that a set threshold on the differential energy can issue the tripping signal for effective protection measure within four cycles from the fault inception The results based on extensive study indicate that the differential energy-based protection scheme can reliably protect the microgrid against different fault situations and thus, is a potential candidate for wide area protection

Data-mining-based intelligent anti-islanding protection relay for distributed generations

Abstract: A data-mining-based intelligent anti-islanding detection scheme for distributed generation (DG) protection has been presented. The process starts at deriving highly involved features using discrete Fourier transform-based pre-processor at the DG end. The features derived include both positive and negative sequence quantities and related features. Once the features are retrieved, the decision tree is trained to build a data-mining model for identifying the islanding events from non-islanding situations, including disturbances close to islanding conditions. The proposed anti-islanding relay is extensively tested on simulation model and provides encouraging results considering wide variations in operating conditions. Further, the validation is extended on real-time digital simulator module to test the efficacy of the proposed anti-islanding relay. The test results indicate that the proposed anti-islanding relay can reliably detect islanding conditions while meeting the speed criteria.

A comprehensive protection scheme for micro-grid using fuzzy rule base approach

Abstract: The paper presents a comprehensive primary and backup protection scheme for micro-grid using fuzzy rule base approach. The proposed scheme starts with pre-processing of the retrieved current and voltage signals at both ends of the faulted feeder and adjacent feeder to compute the differential features. The differential features are used to build two separate decision trees (DTs) for the primary and backup protection. The differential features between immediate buses of faulted feeder are considered for primary protection and differential features between far end buses of faulted and adjacent feeders are used to build decision tree for secondary protection. From the developed decision tree classification boundaries, the fuzzy membership functions are generated and the corresponding fuzzy rule base is formulated for final relaying decision. The proposed scheme is tested for numbers of fault and no fault events simulated in the studied micro-grid with wide variations in system parameters and fault parameters in different operating mode. The extensive test results show the efficacy of the proposed protection scheme to provide a reliable protection measure to both primary and backup protection of micro-grid.

A Fuzzy Rule Base Approach for Intelligent Protection of Microgrids

Abstract: The proposed work develops a decision tree-induced fuzzy rule base intelligent protection scheme for fault detection and classification in a microgrid with multiple distributed generation interfaces. The proposed protection scheme retrieves one cycle post-fault current signal samples of each phase from fault inception at bus ends of the respective feeder to derive some differential features. The retrieved current samples are pre-processed using S-transform to obtain a time–frequency contour. The statistical features, such as energy, mean, standard deviation, and entropy, are computed from the time–frequency contour, which is further used to calculate the differential features. The differential features are used to build the fault classification tree. From the decision tree classification boundaries, the fuzzy membership functions are drawn, and further, the corresponding fuzzy rule base is generated for the final relaying decision. The proposed scheme is developed on a MATLAB/SIMULINK (The MathWor...

Distributed Power-Generation Systems and Protection

Abstract: Continuously expanding deployments of distributed power-generation systems (DPGSs) are transforming the conventional centralized power grid into a mixed distributed electrical network. The modern power grid requires flexible energy utilization but presents challenges in the case of a high penetration degree of renewable energy, among which wind and solar photovoltaics are typical sources. The integration level of the DPGS into the grid plays a critical role in developing sustainable and resilient power systems, especially with highly intermittent renewable energy resources. To address the challenging issues and, more importantly, to leverage the energy generation, stringent demands from both utility operators and consumers have been imposed on the DPGS. Furthermore, as the core of energy conversion, numerous power electronic converters employing advanced control techniques have been developed for the DPGS to consolidate the integration. In light of the above, this paper reviews the power-conversion and control technologies used for DPGSs. The impacts of the DPGS on the distributed grid are also examined, and more importantly, strategies for enhancing the connection and protection of the DPGS are discussed.

Intelligent Fault Detection Scheme for Microgrids With Wavelet-Based Deep Neural Networks

Abstract: Fault detection is essential in microgrid control and operation, as it enables the system to perform fast fault isolation and recovery. The adoption of inverter-interfaced distributed generation in microgrids makes traditional fault detection schemes inappropriate due to their dependence on significant fault currents. In this paper, we devise an intelligent fault detection scheme for microgrid based on wavelet transform and deep neural networks. The proposed scheme aims to provide fast fault type, phase, and location information for microgrid protection and service recovery. In the scheme, branch current measurements sampled by protective relays are pre-processed by discrete wavelet transform to extract statistical features. Then all available data is input into deep neural networks to develop fault information. Compared with previous work, the proposed scheme can provide significantly better fault type classification accuracy. Moreover, the scheme can also detect the locations of faults, which are unavailable in previous work. To evaluate the performance of the proposed fault detection scheme, we conduct a comprehensive evaluation study on the CERTS microgrid and IEEE 34-bus system. The simulation results demonstrate the efficacy of the proposed scheme in terms of detection accuracy, computation time, and robustness against measurement uncertainty.

Data-Mining Model Based Intelligent Differential Microgrid Protection Scheme

Abstract: This paper presents a data-mining-based intelligent differential protection scheme for the microgrid. The proposed scheme preprocesses the faulted current and voltage signals using discrete Fourier transform and estimates the most affected sensitive features at both ends of the respective feeder. Furthermore, differential features are computed from the corresponding features at both ends of the feeder and are used to build the decision tree-based data-mining model for registering the final relaying decision. The proposed scheme is extensively validated for fault situations in the standard IEC microgrid model with wide variations in operating parameters for radial and mesh topology in grid-connected and islanded modes of operation. The extensive test results indicate that the proposed intelligent differential relaying scheme can be highly reliable in providing an effective protection measure for safe and secured microgrid operation.

Big data analytics in smart grids: a review

Abstract: Data analytics are now playing a more important role in the modern industrial systems. Driven by the development of information and communication technology, an information layer is now added to the conventional electricity transmission and distribution network for data collection, storage and analysis with the help of wide installation of smart meters and sensors. This paper introduces the big data analytics and corresponding applications in smart grids. The characterizations of big data, smart grids as well as huge amount of data collection are firstly discussed as a prelude to illustrating the motivation and potential advantages of implementing advanced data analytics in smart grids. Basic concepts and the procedures of the typical data analytics for general problems are also discussed. The advanced applications of different data analytics in smart grids are addressed as the main part of this paper. By dealing with huge amount of data from electricity network, meteorological information system, geographical information system etc., many benefits can be brought to the existing power system and improve the customer service as well as the social welfare in the era of big data. However, to advance the applications of the big data analytics in real smart grids, many issues such as techniques, awareness, synergies, etc., have to be overcome.