Efficient Database Generation for Data-driven Security Assessment of Power Systems

Power system security assessment methods require large datasets of operating points to train or test their performance. As historical data often contain limited number of abnormal situations, simulation data are necessary to accurately determine the security boundary. Generating such a database is an extremely demanding task, which becomes intractable even for small system sizes. This paper proposes a modular and highly scalable algorithm for computationally efficient database generation. Using convex relaxation techniques and complex network theory, we discard large infeasible regions and drastically reduce the search space. We explore the remaining space by a highly parallelizable algorithm and substantially decrease computation time. Our method accommodates numerous definitions of power system security. Here we focus on the combination of N-k security and small-signal stability. Demonstrating our algorithm on IEEE 14-bus and NESTA 162-bus systems, we show how it outperforms existing approaches requiring less than 10% of the time other methods require.Comment: Database publicly available at: https://github.com/johnnyDEDK/OPs_Nesta162Bus - Paper accepted for publication at IEEE Transactions on Power System

Adaptive Three-Stage Controlled Islanding to Prevent Imminent Wide-area Blackouts

Power blackouts are a recurring problem worldwide, and research in this area continues to focus on developing improved methods for their prediction and prevention. Controlled islanding has been proposed as a last resort action to save the network before imminent blackouts when the usual means fail in an unexpected manner. Successful controlled islanding has to deal with three important issues that are involved in the implementation of islanding: when to island, where to island and what to do after islanding is implemented in each island. This thesis presents a framework that combines all three issues to achieve successful islanding based on wide area measurement systems (WAMS). In addition, this thesis focuses on the question of when to island. This question is critical to the success of the three-stage controlled islanding scheme because the possible issues of false dismissal and false alarm have to be handled. In false dismissal, islanding is triggered too late. However, the potentially unstable system is still allowed to operate, and this unstable system, which could have survived, may cause uncontrolled cascading blackouts. In false alarm, islanding is triggered too early, and an originally stable system is forced to split into islands, resulting in unnecessary disruption and economic loss. Thus, the early recognition and identification of “the point of no return” before blackout is inevitable. The single machine equivalent (SIME) method is adopted online to predict transient stability during cascading outages that would shortly lead to blackouts, giving support in decisions about when to island in terms of transient instability. SIME also evaluates dynamic stability after islanding and ensures that the selected island candidates are stable before action is taken. Moreover, in this thesis, the power flow tracing-based method provides all possible islanding cutsets, and SIME helps to identify the one that has the best transient stability and minimal power flow disruption. If no possible island cut set exists, corrective actions through tripping critical generators or load shedding are undertaken in each island. The IEEE 10-generator, 39-busbar power system and 16-generator 68-busbar system are used to demonstrate the entire framework of the controlled islanding scheme. The performance of each methodology involved in each stage is then presented

Pattern Recognition of Power System Voltage Stability using Statistical and Algorithmic Methods

In recent years, power demands around the world and particularly in North America increased rapidly due to increase in customer’s demand, while the development in transmission system is rather slow. This stresses the present transmission system and voltage stability becomes an important issue in this regard. Pattern recognition in conjunction with voltage stability analysis could be an effective tool to solve this problem In this thesis, a methodology to detect the voltage stability ahead of time is presented. Dynamic simulation software PSS/E is used to simulate voltage stable and unstable cases, these cases are used to train and test the pattern recognition algorithms. Statistical and algorithmic pattern recognition methods are used. The proposed method is tested on IEEE 39 bus system. Finally, the pattern recognition models to predict the voltage stability of the system are developed

Pattern Recognition of Power System Voltage Stability using Statistical and Algorithmic Methods

In recent years, power demands around the world and particularly in North America increased rapidly due to increase in customer’s demand, while the development in transmission system is rather slow. This stresses the present transmission system and voltage stability becomes an important issue in this regard. Pattern recognition in conjunction with voltage stability analysis could be an effective tool to solve this problem In this thesis, a methodology to detect the voltage stability ahead of time is presented. Dynamic simulation software PSS/E is used to simulate voltage stable and unstable cases, these cases are used to train and test the pattern recognition algorithms. Statistical and algorithmic pattern recognition methods are used. The proposed method is tested on IEEE 39 bus system. Finally, the pattern recognition models to predict the voltage stability of the system are developed

A Logistic Regression and Markov Chain Model for the Prediction of Nation-state Violent Conflicts and Transitions

Using open source data, this research formulates and constructs a suite of statistical models that predict future transitions into and out of violent conflict and forecasts the regional and global incidences of violent conflict over a ten-year time horizon. A total of thirty predictor variables are tested and evaluated for inclusion in twelve conditional logistic regression models, which calculate the probability that a nation will transition from its current conflict state, either In Conflict or Not in Conflict, to a new state in the following year. These probabilities are then used to construct a series of nation-specific Markov chain models that forecast violent conflict, as well as yield insights into regional conflict trends out to year 2024 and beyond. The logistic regression models proposed in this study achieve training dataset accuracies of 88.76%, and validation dataset accuracies of 84.67%. Additionally, the Markov models achieve three year forecast accuracies of 85.16% during model validation. This study predicts that global violent conflict rates remain constant through year 2024, but are projected to increase beyond that timeframe with 95 of the 182 considered nations projected to be in a state of violent conflict from the current 84 nations in conflict