Search

Abstract The article proposes a novel approach to assess rotor angle stability in microgrids by enhancing the Modified Galerkin Method (MGM), which is based on the Polynomial Approximation, using real-time RFID data acquisition. Due to their reliance on assumptions, traditional rotor angle stability methodologies frequently fail in online transient stability testing. MGM successfully captures the dynamic behavior of microgrids by approximating state variables using a sequence of polynomials and coefficients. Redundant data, like as vibrations or noise signals, can cause delays in defect diagnosis and decrease diagnostic accuracy. This problem is addressed by integrating RFID technology. RFID technology could potentially be used with a hybrid CNN-LSTM model to develop a sophisticated fault diagnostic system. This entails identifying fault characteristics through the use of signal processing techniques and feature extraction methods, such as the Fourier transform and time-domain statistical features. In addition, we use Total Harmonic Distortion (THD) to reduce superfluous data. The suggested techniques significantly increase fault detection efficiency and precision, outperforming existing techniques with a 0.94 classification accuracy. An extensive case study on an IEEE 3-machine 9-bus system is used to illustrate its efficacy, showing observable improvements in fault detection speed and accuracy that make microgrid operations safer and more dependable.

Abstract DC grid fault protection techniques have previously faced challenges such as fixed thresholds, insensitivity to high-resistance faults, and dependency on specific threshold settings. These limitations can lead to elevated fault currents in the grid, particularly affecting multi-modular converters (MMCs) vulnerability to large fault current transients. This paper proposes a novel approach that combines the disjoint-based Bootstrap Aggregating (Bagging) technique and Bayesian optimization (BO) for fault detection in DC grids. Disjoint partitions reduce variance and enhance Ensemble Artificial Neural Network (EANN) performance, while BO optimizes EANN architecture. The proposed approach uses multiple transient periods instead of a fixed time to train the model. Transient periods are segmented into multiple 1 ms intervals, and each interval trains a separate neural network. In this way, a robust local relay is created that does not require high-speed communication systems. Additionally, a discrete wavelet transform (DWT) is applied to select detailed coefficients of the transient fault current, measured at the DC line’s sending terminal for fault protection. EANN is trained in comprehensive offline data that considers noise impact. Simulation results demonstrate the scheme’s ability to detect faults as high as 400 Ω accurately. This makes it a robust, reliable, and effective solution for fault detection on high-voltage direct current (HVDC) transmission lines. Lastly, this research provides the first-ever scientometric analysis of HVDC transmission line fault protection using neural network algorithms, highlighting major research themes and trends. The scientometric analysis was based on a dataset of 136 available research articles from the Scopus database from the last ten years. Therefore, this research provides valuable insights into the use of ANN for HVDC transmission line fault protection.

Abstract As Europe integrates more renewable energy resources, notably offshore wind power, into its super meshed grid, the demand for reliable long-distance High Voltage Direct Current (HVDC) transmission systems has surged. This paper addresses the intricacies of HVDC systems built upon Modular Multi-Level Converters (MMCs), especially concerning the rapid rise of DC fault currents. We propose a novel fault identification and classification for DC transmission lines only by employing Long Short-Term Memory (LSTM) networks integrated with Discrete Wavelet Transform (DWT) for feature extraction. Our LSTM-based algorithm operates effectively under challenging environmental conditions, ensuring high fault resistance detection. A unique three-level relay system with multiple time windows (1 ms, 1.5 ms, and 2 ms) ensures accurate fault detection over large distances. Bayesian Optimization is employed for hyperparameter tuning, streamlining the model’s training process. The study shows that our proposed framework exhibits 100% resilience against external faults and disturbances, achieving an average recognition accuracy rate of 99.04% in diverse testing scenarios. Unlike traditional schemes that rely on multiple manual thresholds, our approach utilizes a single intelligently tuned model to detect faults up to 480 ohms, enhancing the efficiency and robustness of DC grid protection.

Power generation in the global power sector has recently seen a paradigm shift from centralized bulk generation to distributed generation. In this regard, there are two transmission technologies available: high-voltage alternating current (HVAC) and high-voltage direct current (HVDC). The technical and economic benefits of HVDC transmission technology have enabled many countries to implement it widely for long-distance bulk power transmission, particularly between remote locations and densely populated areas. HVDC technology, however, is still relatively new in Pakistan. Despite this, HVDC technology holds great promise in Pakistan, specifically in utilizing and integrating distributed renewable energy sources with the national grid, as well as enabling cross-border energy trade. This study aims to provide a first-ever scientometric analysis of the HVDC technology field, highlighting key research themes and trends. A correlation is established between HVDC research and its adoption in Pakistan by extracting articles from the Scopus database and analyzing them with the VOS viewer, a modern data analysis tool. The scientometric analysis indicates a minimal contribution from Pakistani authors in the field of HVDC technology as compared to authors of the neighboring nations, highlighting a significant gap in knowledge transfer and technology adoption. Moreover, an overview of HVDC technology is provided, along with a comparison between HVAC and HVDC technologies. Last but not least, Pakistan’s existing and proposed HVDC projects, prospects, and challenges associated with HVDC adoption are discussed.

With the ever-increasing economic development and population growth in Pakistan, there is an unprecedented increase in demand for electricity. On the other hand, using fossil fuels for the generation of electricity is leading to severe environmental pollution. To solve the above-mentioned problems, the use of renewable energy resources (RES) is gaining considerable attention recently. Ample solar energy resources and vast wastelands make Pakistan an ideal country for the development of solar thermal power. Amongst the available solar power technologies, the concentrated solar power (CSP) system is potentially low cost and has no environmental impact. This paper aims to analyze the prospect of electricity generation using various technologies for concentrated solar power systems in Pakistan.

One of the technical challenges that needs to be addressed for the future of the multi-terminal high voltage direct current (M-HVDC) grid is DC fault isolation. In this regard, HVDC circuit breakers (DCCBs), particularly hybrid circuit breakers (H-DCCBs), are paramount. The H-DCCB, proposed by the ABB, has the potential to ensure a reliable and safer grid operation, mainly due to its millisecond-level current interruption capability and lower on-state losses as compared to electromechanical and solid-state based DCCBs. This paper aims to study and evaluate the operational parameters, e.g., electrical, and thermal stresses on the IGBT valves and energy absorbed by the surge arrestors within H-DCCB during different DC fault scenarios. A comprehensive set of modeling requirements matching with operational conditions are developed. A meshed four-terminal HVDC test bench consisting of twelve H-DCCBs is designed in PSCAD/EMTDC to study the impacts of the M-HVDC grid on the operational parameters of H-DCCB. Thus, the system under study is tested for different current interruption scenarios under a (i) low impedance fault current and (ii) high impedance fault current. Both grid-level and self-level protection strategies are implemented for each type of DC fault.