Pallav Kumar Bera
@wku.edu
Assistant Professor
Western Kentucky University
RESEARCH, TEACHING, or OTHER INTERESTS
Electrical and Electronic Engineering
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Subodh Kumar Mohanty, Paresh Kumar Nayak, Pallav Kumar Bera, and Hassan Haes Alhelou
Institute of Electrical and Electronics Engineers (IEEE)
Pallav Kumar Bera, Vajendra Kumar, Samita Rani Pani, and Om P. Malik
Institute of Electrical and Electronics Engineers (IEEE)
Protective relays can mal-operate for transmission lines connected to doubly fed induction generator (DFIG) based large capacity wind farms (WFs). The performance of distance relays protecting such lines is investigated and a statistical model based intelligent protection of the area between the grid and the WF is proposed in this article. The suggested method employs an adaptive fuzzy inference system to detect faults based on autoregressive (AR) coefficients of the 3-phase currents selected using minimum redundancy maximum relevance algorithm. Deep learning networks are used to supervise the detection of faults, their subsequent localization, and classification. The effectiveness of the scheme is evaluated on IEEE 9-bus and IEEE 39-bus systems with varying fault resistances, fault inception times, locations, fault types, wind speeds, and transformer connections. Further, the impact of factors like the presence of type-4 WFs, double circuit lines, WF capacity, grid strength, FACTs devices, reclosing on permanent faults, power swings, fault during power swings, voltage instability, load encroachment, high impedance faults, evolving and cross-country faults, close-in and remote-end faults, CT saturation, sampling rate, data window size, synchronization error, noise, and semi-supervised learning are considered while validating the proposed scheme. The results show the efficacy of the suggested method in dealing with various system conditions and configurations while protecting the transmission lines that are connected to WFs.
Shashank Singh, Veena Anand, and Pallav Kumar Bera
Elsevier BV
Alejandro Malla and Pallav Kumar Bera
IEEE
In this article, the data set of diabetic patients that was used to test the Random Interaction Forest (RIF) algorithm is examined. An Artificial Neural Network (ANN) is designed which matches the accuracy obtained by the RIF algorithm earlier. The proposed ANN model has several regularization methods which are added to prevent over-fitting. The benefits of combining dropout layers with batch normalization layers are justified using the Central Limit Theorem. The model is tuned with TensorFlow’s Bayesian Optimization Tuner to explore a more extensive range of hyper-parameters at a faster pace.
Samita Rani Pani, Manoj Kumar Kar, and Pallav Kumar Bera
IEEE
As renewable energy sources become more widely available, distributed generation (DG) is expected to grow in distribution networks. This research presents a methodology for analysing the reliability of such distribution networks, which can be used in early planning studies. The IEEE 33-bus radial distribution system's reliability is tested first without DG and subsequently with DG connected at various load sites. The reliability indices are calculated using an analytical method. The influence of DG location, number, and availability on reliability indices is examined. The effect of random load variation on system reliability is studied. Because different DG locations provide different reliability indices, the location with the lowest value is picked.
Pallav Kumar Bera, Vajendra Kumar, Samita Rani Pani, and Vivek Bargate
IEEE
This article presents differential protection of the distribution line connecting a wind farm in a microgrid. Machine Learning (ML) based models are built using differential features extracted from currents at both ends of the line to assist in relaying decisions. Wavelet coefficients obtained after feature selection from an extensive list of features are used to train the classifiers. Internal faults are distinguished from external faults with CT saturation. The internal faults include the high impedance faults (HIFs) which have very low currents and test the dependability of the conventional relays. The faults are simulated in a 5-bus system in PSCAD/EMTDC. The results show that ML-based models can effectively distinguish faults and other transients and help maintain security and dependability of the microgrid operation.
Samita Rani Pani, Rajat Kanti Samal, and Pallav Kumar Bera
IEEE
The outages and power shortages are common occurrences in today's world and they have a significant economic impact. These failures can be minimized by making the power grid topologically robust. Therefore, the vulnerability assessment in power systems has become a major concern. This paper considers both pure and extended topological method to analyse the vulnerability of the power system to single line failures. The lines are ranked based on four spectral graph metrics: spectral radius, algebraic connectivity, natural connectivity, and effective graph resistance. A correlation is established between all the four metrics. The impact of load uncertainty on the component ranking has been investigated. The vulnerability assessment has been done on IEEE 9-bus system. It is observed that load variation has minor impact on the ranking.
Pallav Kumar Bera, Can Isik, and Vajendra Kumar
Institute of Electrical and Electronics Engineers (IEEE)
This article solves the problem of accurate detection of internal faults and classification of transients in a five-bus interconnected system for phase angle regulators (PARs) and power transformers (PTs). The analysis prevents mal-operation of differential relays in case of transients other than faults, which include magnetizing inrush, sympathetic inrush, external faults with current transformer (CT) saturation, capacitor switching, nonlinear load switching, and ferroresonance. A gradient boosting classifier (GBC) is used to distinguish the internal faults from the transient disturbances based on 1.5 cycles of three-phase differential currents registered by a change detector. After the detection of an internal fault, GBCs are used to locate the faulty unit (PT, PAR series, or exciting unit) and identify the type of fault. In case, a transient disturbance is detected, another GBC classifies them into the six disturbances. Five most relevant frequency- and time-domain features obtained using information gain are used to train and test the classifiers. The proposed algorithm distinguishes the internal faults from the other transients with a balanced accuracy (<inline-formula><tex-math notation="LaTeX">$\\bar{\\eta }$</tex-math></inline-formula>) of 99.95%. The faulty transformer unit is located with <inline-formula><tex-math notation="LaTeX">$\\bar{\\eta }$</tex-math></inline-formula> of 99.5% and the different transient disturbances are identified with <inline-formula><tex-math notation="LaTeX">$\\bar{\\eta }$</tex-math></inline-formula> of 99.3%. Moreover, the reliability of the scheme is verified for different ratings and connections of the transformers involved, CT saturation, and noise levels in the signals. These GBC classifiers can work together with a conventional differential relay and offer a supervisory control over its operation. PSCAD/EMTDC software is used for simulation of the transients and to develop the two- and three-winding transformer models for creating the internal faults including interturn and interwinding faults.
Pallav Kumar Bera and Can Isik
IEEE
Faults during symmetrical power swings cause maloperation of distance relay. Undesired operation also occurs during unstable power swings causing uncontrolled islanding. Faster detection of faults during power swings and classification of power swings can assist the protection system in making reliable decisions on blocking or unblocking a relay's operation. This paper segregates the faults, faults during power swing from power swings in one-cycle with an accuracy of 99.3%. It then identifies the different power swings in 10 cycles that occur in a 9-bus WSCC power system. Support Vector Machines (SVM), Decision Tree (DT), and k-Nearest Neighbor (kNN) classifiers are trained and tested on six features obtained from 3-phase(ph) relay voltage and current to test the validity of the detection and classification scheme. The different faults, faults during swings, and power swings are simulated in PSCAD/EMTDC.
Pallav Kumar Bera, Vajendra Kumar, and Can Isik
IEEE
Distance relays mal-operate for transmission lines connected to type-3 Wind Farms (WFs). This paper proposes a waveshape property based protection of the intertie zone between wind farm and grid during 3-phase faults. It mitigates the challenges faced by the normally used distance relays and ensures the protection systems' security and dependability. The proposed scheme uses the auto-regressive coefficients of the 3-phase currents obtained from the Current Transformer at one end to distinguish the faults fed by the type-3 WFs and the primary grid and determines the fault zone accurately. PSCAD/EMTDC is used to verify the validity of the technique on three test systems. The results obtained with different wind speeds, crowbar resistance, fault resistance, inception time, and fault locations are encouraging and suggest the possible utilization of feature-based algorithms to improve the power system distance relaying system. In addition, the protection scheme can be utilized for lines compensated with series capacitors and phase shifting transformers.
Pallav Kumar Bera and Can Isik
Institute of Electrical and Electronics Engineers (IEEE)
Several conventional and non-conventional transient conditions cause differential relays associated with Phase Angle Regulators to malfunction. For Two-core Symmetric Phase Angle Regulators, this article investigates the suitability of time and time-frequency feature-based estimators to differentiate internal faults from other transient conditions such as overexcitation, external faults with current transformer (CT) saturation, and magnetizing inrush. Subsequently, the faulty core unit (series or exciting) is located, and the transients are identified. Six well-known classifiers are trained on features extracted from one-cycle of post transient 3-phase differential currents filtered by an event detector. Maximum Relevance Minimum Redundancy, Random Forest, and exhaustive search with Decision Trees are used to select the relevant wavelet energy, time-domain, and wavelet coefficient features respectively. The fault detection scheme trained on XGBoost classifier with hyperparameters obtained from Bayesian Optimization gives an accuracy of 99.8%. The reliability of the proposed scheme is verified with varying tap positions, noise levels, and transformer ratings; and under different conditions like CT saturation, fault during magnetizing inrush, series core saturation, low current faults, and integration of wind energy. As a potential application, the methodology can be deployed to supervise microprocessor-based differential relays to improve the security and dependability of the protection system.
Samita Rani Pani, Pallav Kumar Bera, and Vajendra Kumar
IEEE
This article discriminates the internal faults from magnetizing inrush for a 3-phase transformer using a Decision Tree (DT). Afterwards, the internal faults are classified with DT, Random Forest (RF), and Gradient Boost (GB) classifiers. An array of time, frequency, and time-frequency features are extracted from the 3-phase differential currents. Sample entropy is chosen to distinguish the faults; and change quantile and absolute energy are used to classify the internal faults. The internal faults and magnetizing inrush cases are created by altering the system parameters in PSCAD/EMTDC software. The DT performs well with 100% accuracy for fault detection, and the GB based fault classifier performed the best among the three classifiers with an accuracy of 95.4%.
Pallav Kumar Bera, Rajesh Kumar, and Can Isik
IEEE
This paper proposes methods to identify 40 different types of internal faults in an Indirect Symmetrical Phase Shift Transformer (ISPST). The ISPST was modeled using Power System Computer Aided Design (PSCAD)/ Electromagnetic Transients including DC (EMTDC). The internal faults were simulated by varying the transformer tapping, backward and forward phase shifts, loading, and percentage of winding faulted. Data for 960 cases of each type of fault was recorded. A series of features were extracted for a, b, and c phases from time, frequency, time-frequency, and information theory domains. The importance of the extracted features was evaluated through univariate tests which helped to reduce the number of features. The selected features were then used for training five state-of-the-art machine learning classifiers. Extremely Random Trees and Random Forest, the ensemble-based learners, achieved the accuracy of 98.76% and 97.54% respectively outperforming Multilayer Perceptron (96.13%), Logistic Regression (93.54%), and Support Vector Machines (92.60%).
Shailendra Kumar Bhasker, Pallav Kumar Bera, Vishal Kumar, and Manoj Tripathy
IEEE
This paper illustrates a differential protection technique for indirect symmetrical phase shifting transformer (ISPST) using wavelet transform (WT). Discrimination between internal fault and magnetizing inrush is developed based on time duration between time of disturbance and the first maximum peak, called Td in D4 frequency component of WT. The merit of the proposed algorithm is demonstrated for different internal fault and inrush conditions data generated by simulation using PSCAD/EMTDC and RSCAD/RTDS. It distinguishes internal fault with in quarter cycle.
Shailendra Kumar Bhasker, Pallav Kumar Bera, Vishal Kumar, and Manoj Tripathy
IEEE
This paper illustrates a differential protection algorithm for indirect symmetrical phase shifting transformer (ISPST) using wavelet transform (WT). Further, a Multi-Layer Feed Forward Neural Network (MLFFNN) based algorithm has been developed for classification of internal fault in ISPST. Detailed coefficient at level four (D4) of phase current is used as input vector for MLFFN network. Principle component analysis (PCA) at input reduces the burden and makes the detection and classification algorithm fast. Genetic Algorithm (GA) is used to obtain the optimal structure of MLFFNN. The discrimination between internal fault and magnetizing inrush is developed based on the time elapsed between the instant of inception of disturbance and the instant of the maximum peak in frequency component D4 of WT. It distinguishes magnetizing inrush and internal fault within quarter cycle after disturbance. An ISPST is simulated using PSCAD/EMTDC and RSCAD/RTDS platform to obtain the differential current signal.