Intelligent Detection of High Impedance Faults in Microgrid Distribution Lines Using Optimized Machine Learning Models Imen Ben Hamida, Pallav Kumar Bera, Taha Al-Saadi, Samita Rani Pani, Majdi Mansouri IEEE Access, 2026 This paper presents a data-driven protection scheme for high impedance fault (HIF) classification in microgrids. Conventional overcurrent protection often lacks the sensitivity to detect HIFs because of their low current magnitude and nonlinear characteristics. To address these limitations, we investigate an approach based on multiple machine learning (ML) classifier models for distribution line fault diagnosis. The proposed technique trains ML classifiers directly on raw three-phase differential current signals, allowing the models to autonomously learn patterns without manual feature extraction. The classifiers are optimized through a grid-search procedure and evaluated using a PSCAD/EMTDC model of a 5-bus AC microgrid test system with distributed generations (DGs) operating in both grid-connected and islanded modes. The generated dataset includes healthy conditions, internal faults (with/without HIF), and external faults with current transformer (CT) saturation. Nine ML models are assessed using accuracy, precision, recall, F1-score, receiver operating characteristic (ROC) analysis, computational cost, and noise sensitivity tests. The results indicate that the feedforward neural network (FFNN), random forest (RF), and stochastic gradient descent (SGD) classifiers exhibit robust and consistent classification performance on the test set. Significantly, even when using raw input data, the proposed methodology effectively discriminates subtle HIF signatures from other transient events and CT saturation conditions. These findings highlight the potential of optimized ML models, particularly FFNN and RF classifiers, to provide accurate and robust support for real-time microgrid protection.
Dimensionality Reduction for Embeddings: A Pattern-Based Approach with Comparative Benchmarks Alejandro Malla, Maxwell M. Omwenga, Pallav Kumar Bera 2025 6th International Conference on Artificial Intelligence Robotics and Control Airc 2025, 2025 This article suggests a new and innovative method of embedding dimensionality reduction using variance-based selection methods to achieve maximum storage with optimal accuracy. By leveraging the central limit theorem, we identify specific dimensions with significantly low variance when the embedding engine processes a limited range of semantic meanings. Using diverse datasets, we demonstrate that a small trade-off in accuracy can yield substantial memory savings by mapping and omitting dimensions with minimal impact. This technique is particularly beneficial when embeddings are stored in data repositories. Experiments are conducted across various embedding models to validate the robustness of the method. Additionally, the approach is compared with OpenAI's dimensionality reduction feature in their embedding-large-3 model, highlighting the respective advantages and limitations of each method.
Identification of High Impedance Faults Utilizing Recurrence Plots Pallav Kumar Bera, Samita Rani Pani, Rajesh Kumar Proceedings of the IEEE Power India International Conference Piicon, 2025 This paper presents a systematic approach to detecting High Impedance Faults (HIFs) in medium voltage distribution networks using recurrence plots and machine learning. We first simulate 1150 internal faults, including 300 HIFs, 1000 external faults, and 40 normal conditions using the PSCAD/EMTDC software. Key features are extracted from the 3-phase differential currents using wavelet coefficients, which are then converted into recurrence matrices. A multi-stage classification framework is employed, where the first classification stage identifies internal faults, and the second stage distinguishes HIFs from other internal faults. The framework is evaluated using accuracy, precision, recall, and F1 score. Tree-based classifiers, particularly Random Forest and Decision Tree, achieve superior performance, with 99.24% accuracy in the first stage and 98.26% in the second. The results demonstrate the effectiveness of integrating recurrence analysis with machine learning for fault detection in power distribution networks.
Predicting Cascading Failures in Power Systems using Machine Learning Samita Rani Pani, Pallav Kumar Bera, Rajat Kanti Samal Proceedings of the IEEE Power India International Conference Piicon, 2025 Cascading failure studies help assess and enhance the robustness of power systems against severe power outages. Onset time is a critical parameter in the analysis and management of power system stability and reliability, representing the timeframe within which initial disturbances may lead to subsequent cascading failures. In this paper, different traditional machine learning algorithms are used to predict the onset time of cascading failures. The prediction task is articulated as a multi-class classification problem, employing machine learning algorithms. The results on the UIUC 150-Bus power system data available publicly show high classification accuracy with Random Forest. The hyperparameters of the Random Forest classifier are tuned using Bayesian Optimization. This study highlights the potential of machine learning models in predicting cascading failures, providing a foundation for the development of more resilient power systems.
An Enhanced Protective Relaying Scheme for TCSC Compensated Line Connecting DFIG-Based Wind Farm Subodh Kumar Mohanty, Paresh Kumar Nayak, Pallav Kumar Bera, Hassan Haes Alhelou IEEE Transactions on Industrial Informatics, 2024 The electricity generated from the present-day large capacity doubly fed induction generator (DFIG) installed wind farm is generally transmitted to utility grid via medium or high voltage transmission line (TL). Due to the restriction of building new TLs, series compensated TLs are some cases preferred for such applications. But, the nonlinear output power versus wind speed relation, control strategies of power electronic interfaced DFIG-wind turbine generators and the nonlinear operation of the thyristor-controlled series capacitor (TCSC) during fault impose adverse impact on the performance of the conventionally used distance relaying-based TL protection schemes. In this article, an improved fault detection and classification technique is proposed to assist distance relay in ensuring fast and reliable protection to TCSC compensated TL linked to DFIG-installed wind farm. In this method, a feature called transient monitoring indexed (TMI) is derived from the measured three-phase currents at the relay location for fault detection and TMI-assisted support vector machine is employed further for fault classification. Performance of the proposed scheme is validated on various fault and nonfault transients simulated on a test power system through MATLAB/Simulink. This protective scheme is farther validated throughout real-time assembled dSPACE DS 1104 control prototype hardware. The superiority of the proposed method is also demonstrated through comparative assessment results with few existing techniques. The overall results justify the merits of the proposed method for fast and accurate detection and classification of faults in such crucial TLs.
Autoregressive Coefficients Based Intelligent Protection of Transmission Lines Connected to Type-3 Wind Farms Pallav Kumar Bera, Vajendra Kumar, Samita Rani Pani, Om P. Malik IEEE Transactions on Power Delivery, 2024 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, Pallav Kumar Bera IEEE International Conference on Electro Information Technology, 2024 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.
Reliability Assessment of Distribution Power Network Samita Rani Pani, Manoj Kumar Kar, Pallav Kumar Bera 2nd Odisha International Conference on Electrical Power Engineering Communication and Computing Technology Odicon 2022, 2022
Intelligent Detection of High Impedance Faults in Microgrid Distribution Lines Using Optimized Machine Learning Models IB Hamida, PK Bera, T Al-Saadi, SR Pani, M Mansouri IEEE Access , 2026 2026
Recurrence plot and change quantile-based deep supervised and semi-supervised protection for transmission lines connected to photovoltaic plants PK Bera, SR Pani Engineering Applications of Artificial Intelligence 163, 113034 , 2026 2026 Citations: 1
Simplified P&O based MPPT control for power transfer and voltage stability in IIG systems driven by ungoverned micro-hydro turbine HS Chatterjee, PK Bera, SN Mahato Measurement 256, 117943 , 2025 2025 Citations: 1
Dimensionality Reduction for Embeddings: A Pattern-Based Approach with Comparative Benchmarks A Malla, MM Omwenga, PK Bera 2025 6th International Conference on Artificial Intelligence, Robotics and … , 2025 2025
Identification of High Impedance Faults Utilizing Recurrence Plots PK Bera, SR Pani, R Kumar 2024 IEEE 11th Power India International Conference (PIICON), 1-6 , 2024 2024
Predicting Cascading Failures in Power Systems using Machine Learning SR Pani, PK Bera, RK Samal 2024 IEEE 11th Power India International Conference (PIICON), 1-5 , 2024 2024 Citations: 4
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 2024 Citations: 12
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 2024 Citations: 7
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 2023 Citations: 28
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 2023 Citations: 32
Transients in transmission lines connected to Photovoltaic Farms (Dataset) P Bera, S Pani, C Isik, R Bansal IEEE Dataport , 2023 2023 Citations: 4
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 2023 Citations: 2
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 2023 Citations: 23
Reliability assessment of distribution power network SR Pani, MK Kar, PK Bera 2022 2nd Odisha International Conference on Electrical Power Engineering … , 2022 2022 Citations: 7
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 … , 2022 2022 Citations: 7
Transients in transmission lines connected with DFIG based Wind Farms (Dataset) P Bera, V Kumar, S Pani, OP Malik IEEE Dataport , 2022 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 2022 Citations: 9
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 2021 Citations: 13
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 2021 Citations: 10
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 2021 Citations: 7
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 2020 Citations: 35
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 2023 Citations: 32
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 2023 Citations: 28
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 2020 Citations: 26
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 2023 Citations: 23
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), 1-6 , 2015 2015 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 2021 Citations: 13
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 2024 Citations: 12
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 2021 Citations: 10
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 2022 Citations: 9
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 2018 Citations: 9
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 2024 Citations: 7
Reliability assessment of distribution power network SR Pani, MK Kar, PK Bera 2022 2nd Odisha International Conference on Electrical Power Engineering … , 2022 2022 Citations: 7
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 … , 2022 2022 Citations: 7
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 2021 Citations: 7
Transients and Faults in Power Transformers and Phase Angle Regulators (DATASET) PK Bera, C Isik, V Kumar IEEE DataPort , 2020 2020 Citations: 5
Predicting Cascading Failures in Power Systems using Machine Learning SR Pani, PK Bera, RK Samal 2024 IEEE 11th Power India International Conference (PIICON), 1-5 , 2024 2024 Citations: 4
Transients in transmission lines connected to Photovoltaic Farms (Dataset) P Bera, S Pani, C Isik, R Bansal IEEE Dataport , 2023 2023 Citations: 4
Data-driven protection of transformers, phase angle regulators, and transmission lines in interconnected power systems PK Bera Syracuse University , 2021 2021 Citations: 4
Data: Transients in Indirect Symmetrical Phase Shift Transformers P Bera, C Isik IEEE Dataport , 2020 2020 Citations: 3
