The reliable operation of electrical power transmission systems is crucial for ensuring consumer’s stable and uninterrupted electricity supply. Faults in electrical power transmission systems can lead to significant disruptions, economic losses, and potential safety hazards. A protective approach is essential for transmission lines to guard against faults caused by natural disturbances, short circuits, and open circuit issues. This study employs an advanced artificial neural network methodology for fault detection and classification, specifically distinguishing between single-phase fault and fault between all three phases and three-phase symmetrical fault. For fault data creation and analysis, we utilized a collection of line currents and voltages for different fault conditions, modelled in the MATLAB environment. Different fault scenarios with varied parameters are simulated to assess the applied method’s detection ability. We analyzed the signal data time series analysis based on phase line current and phase line voltage. We employed SMOTE-based data oversampling to balance the dataset. Subsequently, we developed four advanced machine-learning models and one deep-learning model using signal data from line currents and voltage faults. We have proposed an optimized novel glassbox Explainable Boosting (EB) approach for fault detection. The proposed EB method incorporates the strengths of boosting and interpretable tree models. Simulation results affirm the high-efficiency scores of 99% in detecting and categorizing faults on transmission lines compared to traditional fault detection state-of-the-art methods. We conducted hyperparameter optimization and k-fold validations to enhance fault detection performance and validate our approach. We evaluated the computational complexity of fault detection models and augmented it with eXplainable Artificial Intelligence (XAI) analysis to illuminate the decision-making process of the proposed model for fault detection. Our proposed research presents a scalable and adaptable method for advancing smart grid technology, paving the way for more secure and efficient electrical power transmission systems.

Improvements in control of renewable energy-based microgrids are a growing area of interest. A hierarchical control structure is popularly implemented to regulate key parameters such as power sharing between generation sources, system frequency and node voltages. A distributed control infrastructure is realized by means of a communication network that spans the micro-distribution grid. Measured and estimated values, as well as corrective signals are transmitted across this network to effect required system regulation. However, intermittent latencies and failures of component communication links may result in power imbalances between generation sources, deviations in node voltages and system frequency. This paper proposes a hierarchical control structure to regulate the operation of an islanded AC microgrid experiencing communication link failures. The proposed strategy aims to virtually sub-divide the microgrid into controllable “islands”. Thereafter, active power sharing, frequency and voltage restoration is achieved by competing converter systems through multi-agent consensus. The effectiveness of the proposed methodology has been verified through stability analyses using system wide mathematical small signal models and case study simulations in MATLAB, Simpower systems.

Communication-based distributed secondary control is extensively used in DC microgrids. Compared to centralized control, it can provide better voltage regulation and load sharing in microgrids. A conventional secondary control technique that converges the system to a common operating point is improved by using the control methodology to detect the communication link failure and stabilize the system operation during communication islanding. Recently, more robust control schemes have been proposed to improve resilience, but communication islanding has not been addressed at the secondary level control for which the system requires additional tertiary control. However, link failure is a possibility in the microgrid, so this paper proposes a control scheme at the secondary level to detect communication islanding. Communication islanding may lead the system to unpredictable behavior, which may cause the system to become unstable and may further lead to a cascading failure. The proposed control scheme sustains the stability and operation of a DC microgrid. Voltage and current observer works in a parallel manner with the proposed secondary control to achieve a correction term for global operating points. The proposed control scheme has been verified through analysis and simulation.

Self-governed micro power networks are a promising solution for meeting the energy needs of isolated communities not having access to regular transmission networks. The control of such isolated networks requires regulation and “fair” sharing of several power generation and storage resources as well as efficient peer-to-peer coordination between power converters operating in the network. The regulation of key parameters as voltage, frequency and power sharing is to be ensured for the system to operate optimally. This paper proposes a new, de-centralized, and hierarchical control approach for power inverters in isolated micro networks with multi-layered controls, each addressing the regulation of key system parameters. The proposed scheme uses distributed quasi-averaging estimators at each participating node to achieve resilience towards disturbances caused by delayed transmission of measurement and control signals in the data acquisition and information exchange layer. Detailed system models are developed using MATLAB and Sim-power systems to test the effectiveness of the proposed scheme under varying control and network scenarios. The results of these studies are presented as pole zero evolutions, stability margins and case study wise simulations. The studies carried out verify the validity of the proposed control strategy for micro-distribution networks.

The monitoring and control of renewable energy sources (RESs) based on DC (Direct Current) microgrids (DC MG) are gaining much consideration at this time. In comparison with the isolated individual control of converters in a microgrid, DC microgrids provide better voltage regulation and harmonized energy generation/consumption. To address the inherent vulnerability of communication links, robust methods have been proposed that improve the resilience of communication-based control. However, the failure of the communication links in microgrid control layers remains a considerable issue that may lead to one or more nodes being disconnected and operating as a communication island. Such types of communication islanding may cause the unpredictable behavior of the system and further destabilization may lead to a cascaded failure. This paper proposes a fast algorithm to detect and evaluate network connectivity based on the information stored at every node in the form of a look-up table. The control structure has been modified under communication islanding, and a communication connectivity observer is used at every node to detect and address power flow issues under communication islanding. The proposed method has been verified through mathematical analysis, simulation, and experimental results.