Hybrid Approach for Detecting and Classifying Power Quality Disturbances Based on the Variational Mode Decomposition and Deep Stochastic Configuration Network

This paper proposes a novel, two-stage and hybrid approach based on variational mode decomposition (VMD) and the deep stochastic configuration network (DSCN) for power quality (PQ) disturbances detection and classification in power systems. Firstly, a VMD technique is applied to discriminate between stationary and non-stationary PQ events. Secondly, the key parameters of VMD are determined as per different types of disturbance. Three statistical features (mean, variance, and kurtosis) are extracted from the instantaneous amplitude (IA) of the decomposed modes. The DSCN model is then developed to classify PQ disturbances based on these features. The proposed approach is validated by analytical results and actual measurements. Moreover, it is also compared with existing methods including wavelet network, fuzzy and S-transform (ST), adaptive linear neuron (ADALINE) and feedforward neural network (FFNN). Test results have proved that the proposed method is capable of providing necessary and accurate information for PQ disturbances in order to plan PQ remedy actions accordingly

Neural Network Fault Recognition in Power Systems with High Penetrations of Inverter-Based Resources

The growing demand for renewable energy resources (RER) has led to increased integration of inverter-based resources (IBRs), into existing power distribution and transmission networks. However, RER locations are often not ideally suited for direct integration, necessitating a restructuring of the grid from a traditional radial network to a more complex mesh network topology. This transition presents challenges in terms of protection and coordination, as IBRs exhibit atypical responses to power system anomalies compared to conventional synchronous generation. To address these challenges and support existing power system protection infrastructure, this work explores the incorporation of machine learning algorithms. Specifically, an optimized convolutional neural network (CNN) is developed for real-time application in power system protection schemes. The focus is on prioritizing key performance metrics such as recall, specificity, speed, and the reduction of computational resources required for effective protection. The machine learning model is trained to differentiate between healthy system dynamics and hazardous conditions, such as faults, in the presence of IBRs. By analyzing data retrieved from an IEEE 34-bus 24kV distribution network, the model's application is demonstrated and its performance is evaluated. A photovoltaic (PV) source was incorporated into the IEEE 34-bus distribution feeder model at the end of the feeder. By adding a PV source at the end of the feeder, IBR characteristics, such as its response to system anomalies can be monitored through the model. Once the modified IEEE 34-bus distribution feeder model with the PV source was set up, various system anomalies were simulated to create a diverse dataset for training the machine learning (ML) model. These anomalies included; load rejection - a sudden and complete removal of load from the distribution network, simulating a scenario where a significant portion of the load disconnects from the grid, load addition - a sudden and significant increase in load demand, representing a scenario where new loads are connected to the grid, islanding - a scenario where the distribution feeder becomes electrically isolated from the main grid, with the PV source acting as a microgrid and supplying power to the local loads, and various types of faults, such as short-circuits or ground faults, occurring at different locations along the distribution line. To create a diverse dataset, model parameters were varied through 50 different iterations of each simulated anomaly scenario. These parameters included the PV system's capacity, the location of the anomaly on the feeder, the severity and duration of the anomaly, and other relevant grid parameters. For each iteration and anomaly scenario, the responses of the system were recorded, including voltage levels, current flows, and other relevant synchorphasors at the PV source's point of common coupling (PCC). These responses formed the dataset for training the ML model. The accumulated dataset was then used to train the various ML models, including the optimized convolutional neural network (CNN), to identify patterns and hidden characteristics in the data corresponding to different system anomalies. The training process involved feeding the model with input data from the various iterations and scenarios, along with corresponding labels indicating the type of anomaly present. By exposing the ML model to diverse scenarios and varying parameters, the model learns to generalize its understanding of system dynamics and accurately distinguish between healthy system states and hazardous conditions. The models in this work were specifically trained to recognize the various fault characteristics on the system. The trained model's ability to process time-series data and recognize anomalies from the accumulated dataset enhances power system protection infrastructure's capability to respond rapidly and accurately to various grid disturbances, ensuring the reliable and stable operation of the distribution network, especially in the presence of PV and other IBRs. The results show that the optimized CNN outperforms traditional machine learning models used in time-series data analysis. The model's speed and reliability make it an effective tool for identifying hidden characteristics in power system data without the need for extensive manual analysis or rigid programming of existing protection relays. This capability is particularly valuable as power grids integrate a higher penetration of IBRs, where traditional protection infrastructure may not fully account for their unique responses. The successful integration of the optimized CNN into power system protection infrastructure enhances the grid's ability to detect and respond to anomalies, such as faults, in a more efficient and accurate manner. By leveraging machine learning techniques, power system operators can better adapt to the challenges posed by the increasing presence of IBRs and ensure the continued stability and reliability of the distribution network

K-Means and Alternative Clustering Methods in Modern Power Systems

As power systems evolve by integrating renewable energy sources, distributed generation, and electric vehicles, the complexity of managing these systems increases. With the increase in data accessibility and advancements in computational capabilities, clustering algorithms, including K-means, are becoming essential tools for researchers in analyzing, optimizing, and modernizing power systems. This paper presents a comprehensive review of over 440 articles published through 2022, emphasizing the application of K-means clustering, a widely recognized and frequently used algorithm, along with its alternative clustering methods within modern power systems. The main contributions of this study include a bibliometric analysis to understand the historical development and wide-ranging applications of K-means clustering in power systems. This research also thoroughly examines K-means, its various variants, potential limitations, and advantages. Furthermore, the study explores alternative clustering algorithms that can complete or substitute K-means. Some prominent examples include K-medoids, Time-series K-means, BIRCH, Bayesian clustering, HDBSCAN, CLIQUE, SPECTRAL, SOMs, TICC, and swarm-based methods, broadening the understanding and applications of clustering methodologies in modern power systems. The paper highlights the wide-ranging applications of these techniques, from load forecasting and fault detection to power quality analysis and system security assessment. Throughout the examination, it has been observed that the number of publications employing clustering algorithms within modern power systems is following an exponential upward trend. This emphasizes the necessity for professionals to understand various clustering methods, including their benefits and potential challenges, to incorporate the most suitable ones into their studies