POWER OSCILLATIONS AND CONTROLLED ISLANDING IN INTERCONNECTED POWER SYSTEMS

Abstract

Power oscillations are transient conditions that electrical power systems may encounter continuously. According to IEEE, transient stability refers to the system's ability to restore and maintain synchronization between the generator and the network, or between the internal areas of the network, after disturbances such as line disconnection, generator disconnection, or load fluctuations. These power flow oscillations, known as power swings, cause frequency deviations that oscillate around the nominal value at a frequency between 0.3-7 Hz. Power swings can be categorized into stable and unstable oscillations, where stable swings have positive damping with descending peaks over time, while unstable swings exhibit negative damping with continuous high-amplitude oscillations, potentially leading to loss of synchronization known as Out of step (OOS). OOS conditions occur when portions of an electrical power system, such as generators or interconnected regions, lose synchronization due to disturbances or severe oscillations. These conditions are typically identified when the bus voltage angle difference between areas or between generators and power system exceeds a critical threshold of 180 degrees, indicating a loss of synchronism. Uncontrolled OOS conditions can result in significant deviations in power flow, voltage, and frequency, potentially causing cascading failures and system-wide blackouts. Asynchronous regions within the network may lose synchronization, leading to uncontrolled islanding where parts of the system become electrically isolated from each other. This isolation can exacerbate the problem, leading to further instability and cascading tripping of system elements. The literature provides various approaches to detecting and mitigating OOS conditions. Traditional methods, such as impedance-based relays and power swing blocking schemes, have been used to identify and manage these conditions. However, these methods often rely on steady-state assumptions and may not effectively predict or react to OOS conditions in real time. Detecting OOS conditions in real time is challenging for power system operators, as conventional measuring instruments struggle to identify when the bus voltage angle between two areas connected by a tie line falls out of synchronism. However, the advent of synchrophasor-based measurement units enables real-time measurement of the bus voltage angle, facilitating direct detection of OOS conditions when voltage angle difference exceeds 180-degree. By measuring the phase angle differences between system buses at high speed, these technologies improve the ability to predict OOS conditions and implement controlled islanding strategies to maintain system stability. Despite these advancements, challenges remain in fully addressing OOS conditions, particularly in large, interconnected networks with complex dynamic behavior. vi In maintaining system stability, Remedial Action Schemes (RAS) are critical tools in power system operation designed to automatically take corrective actions in response to detected disturbances or anomalies, thereby maintaining system stability and preventing widespread failures. RAS can include a variety of actions such as automatic load shedding, generator tripping, or controlled islanding to restore equilibrium within the network. The effectiveness of a RAS, however, hinges on the ability to predict potential problems before they escalate into severe conditions. Accurate prediction is essential because RAS actions need to be timely and precise to prevent system instability. Due to potential scope of application of the PMUs in controlled islanding and RAS, this thesis work has identified the research gaps available in literature and novel approaches have been proposed in this work for application of PMUs of the purpose. The major objective of this thesis is to ● Determine the coherency areas in power system with PMU measurements ● Prediction of OOS condition with PMU measurements for Remedial action schemes ● Controlled islanding for safe operation. Coherency detection in power systems is a fundamental process for effective controlled islanding, a strategy essential for maintaining system stability after disturbances. Coherency refers to the dynamic relationship between different parts of the power system that oscillate in unison or maintain a consistent phase angle relative to one another. Identifying coherent areas within the network is crucial because it allows operators to make informed decisions about where and how to separate the system to prevent large-scale failures. Traditionally, the model-based slow-coherency method has been employed for this purpose, which involves identifying coherent groups based on their dynamic behavior and often relies on extensive computational resources and steady-state assumptions. This method, while effective, can be computationally intensive and slow, limiting its applicability in real-time scenarios. The proposed synchrophasor-based method innovates by leveraging the dynamics of bus voltage phase angles captured through WAMS. In formulating the method, the rate of change of the bus voltage angle (first derivative) provides an initial estimate of phase angle trends across buses, while the second derivative indicates acceleration or deceleration, capturing more complex dynamic behavior. The clustering algorithm takes these derivatives as inputs, creating a multidimensional space where each bus’s response to a disturbance is a vector, and coherence is determined by the proximity of these vectors using hierarchical clustering of measured phase angles. The key steps in this method are: 1- Data Acquisition and Phase Angle Differentiation: Using PMUs, bus voltage angle measurements are continuously streamed. By calculating the first and second derivatives of these angles, the proposed method assesses the rate of change in phase angle, which reflects generator dynamics and helps in distinguishing coherent groups. 2- Hierarchical Clustering: Hierarchical clustering is applied to organize buses into clusters based on their phase angle behavior over time. This method builds a tree-like structure, where similar buses (in terms of phase angle evolution) are grouped together iteratively. Hierarchical clustering, as opposed to flat clustering techniques like k- vii means, is particularly advantageous here since it doesn’t require a predefined number of clusters, allowing coherent groups to be dynamically detected based on real-time data. 3- Real-Time Coherency Detection: the proposed algorithm’s reliance on only three consecutive PMU data points per bus allows for prompt coherency identification. This minimal data requirement accelerates detection, which is crucial for initiating timely controlled islanding during severe disturbances. Validation in OPAL-RT with the HYPERSIM platform supports the algorithm’s application to different benchmark systems, such as the IEEE 39-bus system. Simulation results highlight the method’s robustness across a range of disturbances and confirm the ability to detect coherent areas accurately, aiding in controlled islanding decisions that contain oscillations and prevent cascading failures. The synchrophasor-based approach provides several benefits over traditional model- based coherency detection: 1- Reduced Computational Burden: By avoiding complex state-space models and using only bus voltage phase angle derivatives, the method remains computationally light, enhancing real-time feasibility. 2- Robustness to Network Latency: Unlike traditional methods that may struggle with delayed or incomplete data, the proposed method's reliance on phase angle clustering is more resilient to latency, as shown in your real-time simulation studies. 3- Automatic and Adaptive Grouping: Hierarchical clustering allows the system to adaptively form clusters based on current dynamics, enabling it to respond to varying system conditions and new disturbance patterns. Following coherency detection, another crucial aspect of maintaining power system stability is early and accurate out-of-step (OOS) prediction. The OOS condition represents a significant threat in power systems, as it can lead to uncontrolled islanding—a scenario where sections of the grid become desynchronized, causing cascading failures and widespread tripping. To prevent this, it is essential to detect the onset of OOS conditions early, before the synchronization between generators and system areas is lost. Controlled islanding, where the system is intentionally split into coherent islands, serves as a last-resort strategy to contain instability. Traditional OOS detection methods, which rely on impedance-based measurements, often face limitations in speed and accuracy. However, the integration of wide-area measurement systems (WAMS) with synchrophasor measurement units (PMUs) offers a new solution by enabling rapid, real-time measurements of bus voltage angles at a much faster rate than conventional SCADA systems. In this research, it has been proposed a synchrophasor-based approach that utilizes the bus voltage angle measurements for early prediction of OOS conditions. The core of this method is a novel algorithm designed to track the trajectory of the first and second derivatives of the bus voltage phase angle, which reflects the system's response to disturbances and helps detect OOS conditions across both generator units and tie lines. This predictive method has been found to estimate OOS conditions effectively, with prediction speeds ranging between 5% and 70% of the time to OOS after disturbance clearance, depending on system conditions. This speed and precision viii mark a significant improvement over traditional techniques, supporting faster and more reliable intervention. To validate the effectiveness of this algorithm, it has been thoroughly tested on three benchmark systems within a real-time simulation environment, using an OPAL-RT simulator integrated with the HYPERSIM platform. Results from these simulations demonstrate the method's robustness and adaptability across various system configurations, confirming its potential as a reliable solution for early OOS detection. This research contributes a key component for controlled islanding strategies, enhancing the grid’s ability to respond proactively to instabilities and maintain synchronization in complex interconnected systems. Controlled islanding is a critical strategy for managing severe power system disturbances, particularly in interconnected networks with weak tie lines that can induce low-frequency oscillations. Such disturbances can excite the system's low- frequency modes, leading to oscillations that, if left unchecked, may destabilize the system. In the case of unstable oscillations, portions of the network may begin to oscillate against one another, resulting in out-of-step (OOS) conditions. Accurately detecting these conditions in real time is challenging for operators. The proposed research introduces a synchrophasor-based approach to controlled islanding, which leverages real-time bus voltage angle measurements from wide-area measurement systems (WAMS) to directly detect OOS conditions and facilitate intentional islanding. This method marks a shift from conventional techniques by focusing on the voltage angle differences across the system, using synchronized data from phasor measurement units (PMUs). Through systematic analytical studies and electromagnetic transient (EMT) time-domain simulations, this work models OOS conditions, allowing operators to make informed islanding decisions that minimize cascading failures and stabilize isolated sections of the grid. The research has been implemented on various test systems, including a single- machine infinite bus (SMIB) system to monitor generator OOS, Kundur’s two-area system for interarea OOS detection, and the IEEE 39-bus system for multi-area analysis. Each system has been simulated using OPAL-RT with HYPERSIM, where the proposed algorithm has been tested under various fault conditions to evaluate its accuracy and response time. The results demonstrate the algorithm’s capability to identify OOS conditions swiftly, aligning closely with analytical predictions and confirming its potential to support controlled islanding decisions in real time. This method thus provides a robust solution for maintaining stability in interconnected power systems by enabling proactive, precision-based controlled islanding. In conclusion, this research advances stability management in interconnected power systems through novel methods for coherency detection, out-of- step prediction, and controlled islanding. The synchrophasor-based approaches proposed here enable real-time, accurate detection and response to disturbances, significantly enhancing resilience. Validated across multiple test systems, these methods offer practical solutions for modern, dynamically complex power grids.