Maveeya Baba

The rapid growth of renewable energy, especially solar photovoltaic (PV) power, is expanding worldwide and supporting the main load in DC power systems and microgrid (μG) networks. However, the long-term performance of PV systems can degrade due to their intermittent nature, direct exposure to weather conditions, high voltage operations, aging wires, and improper connections, all of which increase the risk of arc faults (AFs). These faults pose serious safety risks, including, fire hazards, equipment failure, and even threats to human lives. Detecting PV-based DC AFs remains challenging due to varying electrical and physical behavior of arcs and the absence of natural current zero-crossing in DC system. This review paper presents a comprehensive analysis of AF detection methods, covering traditional and advanced signal processing and multi-domain feature engineering techniques as well as state-of-the-art artificial intelligence (AI)-based approaches for DC series AFs detection, particularly in PV systems. Various detection schemes are compared and their limitations are critically discussed. Additionally, the paper reviews DC series AF detection under uncertain real-world conditions, mitigation and limitation strategies, and industry-related case studies. Furthermore, it emphasizes the significance of conventional empirical, physical-based AFs modeling to advanced and adaptive multi-physics arc model, and simulation studies, highlighting their essential role in developing accurate and reliable detection algorithms.

The increasing penetration of distributed renewable energy sources (DRES) in hybrid AC/DC microgrids (HAC/DC μG) introduces significant safety concerns. Among these, arc faults (AFs) on the DC side pose a particular risk due to the absence of natural current zero-crossing, making detection and protection more challenging compared to AC systems. AFs are unavoidable during normal operating conditions due to dielectric breakdown. They can cause fire hazards, system malfunctions, and even fatalities. To address this issue, this study proposes an AF detection framework that captures AF V-I characteristics using both linear ( L A F ) and non-linear ( N A F ) AF modeling. A feature extraction approach is applied to arc voltage and current signals, where statistical time-domain features are combined with frequency-domain features obtained using the Fast Fourier Transform (FFT). These features are then used to train a Deep Fully Connected Neural Network (DFCNN) with three hidden layers, enhancing the detection capability. To further enhance detection accuracy and develop a highly precise AF detection model, the DFCNN is optimized using the Pelican Optimization Algorithm, which fine-tunes its parameters for superior performance. The mathematical models of L A F and N A F models are developed and implemented in MATLAB Simulink. Additionally, an AF generator setup is designed and operated on the Electron A0240 HAC/DC platform to emulate real-time AF tests. The findings indicate that the proposed approach improves classification performance compared to other classifiers. The framework integrates the modeling of both L A F and N A F models, time-frequency domain features, and optimized DFCNN classification to evaluate AF detection in HAC/DCμG, achieving a detection accuracy of 98.97% with an average detection time of 45 ms.

Microgrids (MG), considered the future of power systems, has gained significant attention in the present time. MG’s inherent capability of combining both AC and DC operations in a single frame. Its function is crucial in achieving a sustainable electricity supply and advances a decentralized power system approach. MG not only combines the benefits of both AC and DC but also incorporates distributed generations (DGs) and distributed renewable energy sources (DREs). However, the advancement of DGs and DREs with power electronics interfaces (PEIs), and smart loads (SLs) poses the dynamic nature of DGs and DREs such as bi-directional power flow and varying output, which has introduced certain protection challenges in MG maneuver. Therefore, this paper reviews the protection challenges in MG and critically addresses the assessment of existing protection schemes developed so far. It also categorizes the MG protection issues including its components and faults separately for both AC and DC MGs and analyzes the performance of measurement and protection techniques for MG protection strategy on the basis of operational modes AC and DC MGs.

The growing interest in wind power and its expanding integration into the electrical power grid necessitate significant efforts to enhance the efficiency and performance of wind power generation Systems (WPGS) through optimized design and operation. This paper investigates the implementation of five optimization algorithms:sine cosine algorithm (SCA), grey wolf optimizer (GWO), particle swarm optimizer (PSO), transient search optimization (TSO), and a hybrid sine cosine algorithm-transient search optimizer (HSCATSO), to optimally design control schemes (ODCSs) of frequency converters within grid-connected WPGS. These schemes are crucial for improving conversion efficiency and performance. The optimal design procedure involves all optimization algorithms to minimize the total control scheme error by identifying the best settings for six controllers in the proposed system. The feasibility of ODCSs is validated through extensive simulation analysis in the MATLAB/Simulink environment, considering the challenges posed by the low wind speed profiles of Malaysia and severe grid disturbance conditions. The findings from the comparative analysis show that the ODCS developed with HSCATSO achieved the highest conversion efficiency, reaching 98.57%, and exhibited superior system stability, followed by the ODCS utilizing SCA. In contrast, the ODCSs designed using PSO demonstrated the lowest conversion efficiency, at 93.79%, along with the least system stability. This outcome confirms the significant role of ODCSs in improving the efficiency and performance of WPGS under the mentioned challenges, while also revealing that their effectiveness varies depending on the precision of design established by the utilized optimization algorithm.

The electrical network is a man-made complex network that makes it difficult to monitor and control the power system with traditional monitoring devices. Traditional devices have some limitations in real-time synchronization monitoring which leads to unwanted behavior and causes new challenges in the operation and control of the power systems. A Phasor measurement unit (PMU) is an advanced metering device that provides an accurate real-time and synchronized measurement of the voltage and current waveforms of the buses in which the PMU devices are directly connected in the grid station. The device is connected to the busbars of the power grid in the electrical distribution and transmission systems and provides time-synchronized measurement with the help of the Global Positioning System (GPS). However, the implementation and maintenance cost of the device is not bearable for the electrical utilities. Therefore, in recent work, many optimization approaches have been developed to overcome optimal placement of PMU problems to reduce the overall cost by providing complete electrical network observability with a minimal number of PMUs. This research paper reviews the importance of PMU for the modern electrical power system, the architecture of PMU, the differences between PMU, micro-PMU, SCADA, and smart grid (SG) relation with PMU, the sinusoidal waveform, and its phasor representation, and finally a list of PMU applications. The applications of PMU are widely involved in the operation of power systems ranging from power system control and monitor, distribution grid control, load shedding control and analyses, and state estimation which shows the importance of PMU for the modern world.

The application of the Phasor Measurement Unit (PMU) in the power system is expanding day by day since it provides a higher reliability through fast symmetrically monitoring and protection and assists in controlling power systems. For power systems, islanding is a significant event due to its hazardous consequences. To detect islanding events, several schemes have been previously proposed but inappropriate threshold setting, higher computational time, and false tripping are the main limitations. In addition, differentiating between real island events and transient faults is another limitation. However, appropriate threshold setting plays a considerable part in detecting the island event, which is also important to differentiate between real and non-island events. Phasor Measurement Unit can assist in islanding detection, but it can generate 30 samples/s, so there is always the possibility of making particular period data disappear. The principal contribution of this review article is its detailed discussion of real-time symmetrical PMU data and it further presents different PMU data analytic techniques and the proposed schemes for the islanding detection system. An appropriate methodology tried to understand how to incorporate missing PMU data techniques along with the islanding detection system to ensure the higher reliability of the network.

With the increasing trend in the energy demand, power networks are transitioning from conventional generation systems to renewable energy sources (RESs). The energy is harvested from these RESs and fed to grid-connected inverters (GCIs), as the output power of major sources (e.g., solar and fuel cell) is mainly DC. However, owing to the lower output voltage of renewable RESs, power converters play a vital role in two-stage power systems for enhancing its lower value to a higher value. The basic requirement for the GCI is to maintain the constant output voltage for which it is essential to have a constant input voltage. Therefore, high gain and efficient power boost converters are required for a robust and reliable two-stage power system. This paper investigates the performance of an efficient model of a high step-up switched Z-source DC-DC converter (HS-SZSC) for grid-connected 3-phase H-bridge inverter applications. The proposed design achieves high voltage gain and eliminates the problems of circuit complexity by utilizing a smaller number of components, which makes it cost effective and highly efficient. The working principle is discussed in detail. To validate the proposed model, the performance of the conventional Z-source converter (ZSC) and proposed HS-SZSC employed with GCI is analyzed and compared for both normal and transient states through MATLAB simulations. The HS-SZSC with an open- and closed-loop system is tested at different loads (AC), representing varying power factor conditions, and results verify the suitability of the proposed design for grid-connected inverters. Lastly, another model is presented to resolve the issue of grid islanding in GCIs.

Recent developments in electrical power systems are concerned not only with static power flow control but also with their control during dynamic processes. Smart Grids came into being when it was noticed that the traditional electrical power system structure was lacking in reliability, power flow control, and consistency in the monitoring of phasor quantities. The Phasor Measurement Unit (PMU) is one of the main critical factors for Smart Grid (SG) operation. It has the ability to provide real-time synchronized measurement of phasor quantities with the help of a Global Positioning System (GPS). However, when considering the installation costs of a PMU device, it is far too expensive to equip on every busbar in all grid stations. Therefore, this paper proposes a new approach for the Optimum Placement of the PMU problem (OPP problem) to minimize the installed number of PMUs and maximize the measurement redundancy of the network. Exclusion of the unwanted nodes technique is used in the proposed approach, in which only the most desirable buses consisting of generator bus and load bus are selected, without considering Pure Transit Nodes (PTNs) in the optimum PMU placement sets. The focal point of the proposed work considers, most importantly, the case factor of the exclusion technique of PTNs from the optimum PMU locations, as prior approaches concerning almost every algorithm have taken PTNs as their optimal PMU placement sets. Furthermore, other case factors of the proposed approach, namely, PMU channel limits, radial bus, and single PMU outage, are also considered for the OPP problem. The proposed work is tested on standard Institute of Electrical and Electronics Engineering (IEEE)-case studies from MATPOWER on the MATLAB software. To show the success of the proposed work, the outputs are compared with the existing techniques.