Pallav Kumar Bera

@wku.edu

Assistant Professor
Western Kentucky University



                 

https://researchid.co/pallavbera

RESEARCH, TEACHING, or OTHER INTERESTS

Electrical and Electronic Engineering

17

Scopus Publications

147

Scholar Citations

8

Scholar h-index

6

Scholar i10-index

Scopus Publications

  • A hybrid intelligent system for protection of transmission lines connected to PV farms based on linear trends
    Pallav Kumar Bera, Samita Rani Pani, Can Isik, and Ramesh C. Bansal
    Elsevier BV

  • An Enhanced Protective Relaying Scheme for TCSC Compensated Line Connecting DFIG-Based Wind Farm
    Subodh Kumar Mohanty, Paresh Kumar Nayak, Pallav Kumar Bera, and Hassan Haes Alhelou
    Institute of Electrical and Electronics Engineers (IEEE)

  • Autoregressive Coefficients based Intelligent Protection of Transmission Lines Connected to Type-3 Wind Farms
    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.

  • Exploring Image Similarity through Generative Language Models: A Comparative Study of GPT-4 with Word Embeddings and Traditional Approaches
    Alejandro Malla, Maxwell M. Omwenga, and Pallav Kumar Bera
    IEEE
    In this article, we propose a novel approach for determining image similarity, leveraging advancements in generative artificial intelligence. At the heart of our method is the use of OpenAI’s GPT-4 large language model for generating image captions, combined with the Ada v2 word embedding model for semantic analysis. This technique involves creating textual descriptions of images via GPT-4 and subsequently computing cosine similarity of these descriptions using Ada v2 word embeddings. We compare this innovative approach with traditional image similarity methods, with a particular focus on the VGG16 neural network approach, employing the DISC21 dataset for our analysis. Preliminary results demonstrate the promising potential of this method in the field of image similarity assessment. The paper delves into both the advantages and current limitations of our approach, including constraints like rate limits in experimentation and the rapidly evolving capabilities of language models in vision tasks. Our findings indicate a trajectory towards improved outcomes as these models continue to advance, underscoring the growing intersection of language and vision models in artificial intelligence for applications like image similarity evaluation.

  • A Delay-Tolerant low-duty cycle scheme in wireless sensor networks for IoT applications
    Shashank Singh, Veena Anand, and Pallav Kumar Bera
    Elsevier BV

  • Artificial Neural Network with Dropout and Batch Normalization Applied on Diabetic Patient Data
    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.

  • Reliability Assessment of Distribution Power Network
    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.

  • Detection of High Impedance Faults in Microgrids using Machine Learning
    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.

  • A Graph-Theoretic Approach to Assess the Power Grid Vulnerabilities to Transmission Line Outages
    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.

  • Discrimination of Internal Faults and Other Transients in an Interconnected System With Power Transformers and Phase Angle Regulators
    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.

  • Identification of stable and unstable power swings using pattern recognition
    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.

  • Distance Protection of Transmission Lines Connected to Type-3 Wind Farms
    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.

  • A Data Mining Based Protection and Classification of Transients for Two-Core Symmetric Phase Angle Regulators
    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.

  • Detection and classification of internal faults in power transformers using tree based classifiers
    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%.

  • Identification of internal faults in indirect symmetrical phase shift transformers using ensemble learning
    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%).

  • Differential protection of indirect symmetrical phase shift transformer using wavelet transform
    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.

  • Differential protection of indirect symmetrical phase shift transformer and internal faults classification using wavelet and ANN
    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.

RECENT SCHOLAR PUBLICATIONS

  • Identification of High Impedance Faults Utilizing Recurrence Plots
    PK Bera, SR Pani, R Kumar
    arXiv preprint arXiv:2503.02995 2025

  • Predicting Cascading Failures in Power Systems using Machine Learning
    SR Pani, PK Bera, RK Samal
    arXiv preprint arXiv:2503.00567 2025

  • A hybrid intelligent system for protection of transmission lines connected to PV farms based on linear trends
    PK Bera, SR Pani, C Isik, RC Bansal
    Electric Power Systems Research 237, 110991 2024

  • Exploring Image Similarity through Generative Language Models: A Comparative Study of GPT-4 with Word Embeddings and Traditional Approaches
    A Malla, MM Omwenga, PK Bera
    2024 IEEE International Conference on Electro Information Technology (eIT 2024

  • Autoregressive coefficients based intelligent protection of transmission lines connected to type-3 wind farms
    PK Bera, V Kumar, SR Pani, OP Malik
    IEEE Transactions on Power Delivery 39 (1), 71-82 2023

  • An enhanced protective relaying scheme for TCSC compensated line connecting DFIG-Based wind farm
    SK Mohanty, PK Nayak, PK Bera, HH Alhelou
    IEEE Transactions on Industrial Informatics 20 (3), 3425-3435 2023

  • Transients in transmission lines connected to Photovoltaic Farms (Dataset)
    P Bera, S Pani, C Isik, R Bansal
    IEEE Dataport 2023

  • Artificial Neural Network with Dropout and Batch Normalization Applied on Diabetic Patient Data
    A Malla, PK Bera
    2023 IEEE ICECCME, Canary Islands, Spain, 1-4 2023

  • A Delay-Tolerant low-duty cycle scheme in wireless sensor networks for IoT applications
    S Singh, V Anand, PK Bera
    International Journal of Cognitive Computing in Engineering 4, 194-204 2023

  • Reliability assessment of distribution power network
    SR Pani, MK Kar, PK Bera
    2022 2nd Odisha International Conference on Electrical Power Engineering 2022

  • Detection of High Impedance Faults in Microgrids using Machine Learning
    PK Bera, V Kumar, SR Pani, V Bargate
    2022 IEEE Green Energy and Smart System Systems Conference(IGESSC), CA, USA, 1-5 2022

  • Transients in transmission lines connected with DFIG based Wind Farms (Dataset)
    P Bera, V Kumar, S Pani, OP Malik
    IEEE Dataport 2022

  • A graph-theoretic approach to assess the power grid vulnerabilities to transmission line outages
    SR Pani, RK Samal, PK Bera
    2022 International Conference on Intelligent Controller and Computing for 2022

  • A data mining based protection and classification of transients for two-core symmetric phase angle regulators
    PK Bera, C Isik
    IEEE Access 9, 72937-72948 2021

  • Identification of stable and unstable power swings using pattern recognition
    PK Bera, C Isik
    2021 IEEE Green Technologies Conference (GreenTech), CO, USA, 286-291 2021

  • Distance protection of transmission lines connected to type-3 wind farms
    PK Bera, V Kumar, C Isik
    2021 IEEE Power and Energy Conference at Illinois (PECI), IL, USA, 1-7 2021

  • Data-driven protection of transformers, phase angle regulators, and transmission lines in interconnected power systems
    PK Bera
    Syracuse University 2021

  • Detection and Classification of Internal Faults in Power Transformers using Tree-based Classifiers
    SR Pani, PK Bera, V Kumar
    2020 IEEE PEDES, Jaipur, India 2020

  • Discrimination of Internal Faults and Other Transients in an Interconnected System With Power Transformers and Phase Angle Regulators
    PK Bera, C Isik, V Kumar
    IEEE Systems Journal 2020

  • Data: Transients in Indirect Symmetrical Phase Shift Transformers
    P Bera, C Isik
    IEEE Dataport 2020

MOST CITED SCHOLAR PUBLICATIONS

  • Discrimination of Internal Faults and Other Transients in an Interconnected System With Power Transformers and Phase Angle Regulators
    PK Bera, C Isik, V Kumar
    IEEE Systems Journal 2020
    Citations: 22

  • Differential protection of indirect symmetrical phase shift transformer using wavelet transform
    SK Bhasker, PK Bera, V Kumar, M Tripathy
    2015 Annual IEEE India Conference (INDICON), New Delhi, India, 1-6 2015
    Citations: 17

  • Detection and Classification of Internal Faults in Power Transformers using Tree-based Classifiers
    SR Pani, PK Bera, V Kumar
    2020 IEEE PEDES, Jaipur, India 2020
    Citations: 16

  • A data mining based protection and classification of transients for two-core symmetric phase angle regulators
    PK Bera, C Isik
    IEEE Access 9, 72937-72948 2021
    Citations: 13

  • Autoregressive coefficients based intelligent protection of transmission lines connected to type-3 wind farms
    PK Bera, V Kumar, SR Pani, OP Malik
    IEEE Transactions on Power Delivery 39 (1), 71-82 2023
    Citations: 12

  • A Delay-Tolerant low-duty cycle scheme in wireless sensor networks for IoT applications
    S Singh, V Anand, PK Bera
    International Journal of Cognitive Computing in Engineering 4, 194-204 2023
    Citations: 12

  • An enhanced protective relaying scheme for TCSC compensated line connecting DFIG-Based wind farm
    SK Mohanty, PK Nayak, PK Bera, HH Alhelou
    IEEE Transactions on Industrial Informatics 20 (3), 3425-3435 2023
    Citations: 9

  • Identification of stable and unstable power swings using pattern recognition
    PK Bera, C Isik
    2021 IEEE Green Technologies Conference (GreenTech), CO, USA, 286-291 2021
    Citations: 8

  • Identification of internal faults in indirect symmetrical phase shift transformers using ensemble learning
    PK Bera, R Kumar, C Isik
    2018 IEEE ISSPIT, KY, USA, 1-6 2018
    Citations: 8

  • Distance protection of transmission lines connected to type-3 wind farms
    PK Bera, V Kumar, C Isik
    2021 IEEE Power and Energy Conference at Illinois (PECI), IL, USA, 1-7 2021
    Citations: 6

  • Reliability assessment of distribution power network
    SR Pani, MK Kar, PK Bera
    2022 2nd Odisha International Conference on Electrical Power Engineering 2022
    Citations: 5

  • A graph-theoretic approach to assess the power grid vulnerabilities to transmission line outages
    SR Pani, RK Samal, PK Bera
    2022 International Conference on Intelligent Controller and Computing for 2022
    Citations: 4

  • Transients and Faults in Power Transformers and Phase Angle Regulators (DATASET)
    PK Bera, C Isik, V Kumar
    IEEE DataPort 2020
    Citations: 4

  • Detection of High Impedance Faults in Microgrids using Machine Learning
    PK Bera, V Kumar, SR Pani, V Bargate
    2022 IEEE Green Energy and Smart System Systems Conference(IGESSC), CA, USA, 1-5 2022
    Citations: 3

  • Data: Transients in Indirect Symmetrical Phase Shift Transformers
    P Bera, C Isik
    IEEE Dataport 2020
    Citations: 3

  • A hybrid intelligent system for protection of transmission lines connected to PV farms based on linear trends
    PK Bera, SR Pani, C Isik, RC Bansal
    Electric Power Systems Research 237, 110991 2024
    Citations: 2

  • Exploring Image Similarity through Generative Language Models: A Comparative Study of GPT-4 with Word Embeddings and Traditional Approaches
    A Malla, MM Omwenga, PK Bera
    2024 IEEE International Conference on Electro Information Technology (eIT 2024
    Citations: 1

  • Artificial Neural Network with Dropout and Batch Normalization Applied on Diabetic Patient Data
    A Malla, PK Bera
    2023 IEEE ICECCME, Canary Islands, Spain, 1-4 2023
    Citations: 1

  • Data-driven protection of transformers, phase angle regulators, and transmission lines in interconnected power systems
    PK Bera
    Syracuse University 2021
    Citations: 1