Real-time Prediction of Cascading Failures in Power Systems

Blackouts in power systems cause major financial and societal losses, which necessitate devising better prediction techniques that are specifically tailored to detecting and preventing them. Since blackouts begin as a cascading failure (CF), an early detection of these CFs gives the operators ample time to stop the cascade from propagating into a large-scale blackout. In this thesis, a real-time load-based prediction model for CFs using phasor measurement units (PMUs) is proposed. The proposed model provides load-based predictions; therefore, it has the advantages of being applicable as a controller input and providing the operators with better information about the affected regions. In addition, it can aid in visualizing the effects of the CF on the grid. To extend the functionality and robustness of the proposed model, prediction intervals are incorporated based on the convergence width criterion (CWC) to allow the model to account for the uncertainties of the network, which was not available in previous works. Although this model addresses many issues in previous works, it has limitations in both scalability and capturing of transient behaviours. Hence, a second model based on recurrent neural network (RNN) long short-term memory (LSTM) ensemble is proposed. The RNN-LSTM is added to better capture the dynamics of the power system while also giving faster responses. To accommodate for the scalability of the model, a novel selection criterion for inputs is introduced to minimize the inputs while maintaining a high information entropy. The criteria include distance between buses as per graph theory, centrality of the buses with respect to fault location, and the information entropy of the bus. These criteria are merged using higher statistical moments to reflect the importance of each bus and generate indices that describe the grid with a smaller set of inputs. The results indicate that this model has the potential to provide more meaningful and accurate results than what is available in the previous literature and can be used as part of the integrated remedial action scheme (RAS) system either as a warning tool or a controller input as the accuracy of detecting affected regions reached 99.9% with a maximum delay of 400 ms. Finally, a validation loop extension is introduced to allow the model to self-update in real-time using importance sampling and case-based reasoning to extend the practicality of the model by allowing it to learn from historical data as time progresses

Situational Intelligence for Improving Power System Operations Under High Penetration of Photovoltaics

Nowadays, power grid operators are experiencing challenges and pressures to balance the interconnected grid frequency with rapidly increasing photovoltaic (PV) power penetration levels. PV sources are variable and intermittent. To mitigate the effect of this intermittency, power system frequency is regulated towards its security limits. Under aforementioned stressed regimes, frequency oscillations are inevitable, especially during disturbances and may lead to costly consequences as brownout or blackout. Hence, the power system operations need to be improved to make the appropriate decision in time. Specifically, concurrent or beforehand power system precise frequencies simplified straightforward-to-comprehend power system visualizations and cooperated well-performed automatic generation controls (AGC) for multiple areas are needed for operation centers to enhance. The first study in this dissertation focuses on developing frequency prediction general structures for PV and phasor measurement units integrated electric grids to improve the situational awareness (SA) of the power system operation center in making normal and emergency decisions ahead of time. Thus, in this dissertation, a frequency situational intelligence (FSI) methodology capable of multi-bus type and multi-timescale prediction is presented based on the cellular computational network (CCN) structure with a multi-layer proception (MLP) and a generalized neuron (GN) algorithms. The results present that both CCMLPN and CCGNN can provide precise multi-timescale frequency predictions. Moreover, the CCGNN has a superior performance than the CCMLPN. The second study of this dissertation is to improve the SA of the operation centers by developing the online visualization tool based on the synchronous generator vulnerability index (GVI) and the corresponding power system vulnerability index (SVI) considering dynamic PV penetration. The GVI and SVI are developed by the coherency grouping results of synchronous generator using K-Harmonic Means Clustering (KHMC) algorithm. Furthermore, the CCGNN based FSI method has been implemented for the online coherency grouping procedure to achieve a faster-than-real-time grouping performance. Last but not the least, the multi-area AGCs under different PV integrated power system operating conditions are investigated on the multi-area multi-source interconnected testbed, especially with severe load disturbances. Furthermore, an onward asynchronous tuning method and a two-step (synchronous) tuning method utilizing particle swarm optimization algorithm are developed to refine the multi-area AGCs, which provide more opportunities for power system balancing authorities to interconnect freely and to utilize more PV power. In summary, a number of methods for improving the interconnected power system situational intelligence for a high level of PV power penetration have been presented in this dissertation

Machine Learning-Incorporated Transient Stability Prediction and Preventive Dispatch for Power Systems with High Wind Power Penetration

Historically, transient instability has been the most severe stability challenge for most systems. Transient stability prediction and preventive dispatch are two important measures against instability. The former measure refers to the rapid prediction of impending system stability issues in case of a contingency using real-time measurements, and the latter enhances the system stability against preconceived contingencies leveraging power dispatch. Over the last decade, large-scale renewable energy generation has been integrated into power systems, with wind power being the largest single source of increased renewable energy globally. The continuous evolution of the power system poses more challenges to transient stability. Specifically, the integration of wind power can decrease system inertia, affect system dynamics, and change the dispatch and power flow pattern frequently. As a result, the effectiveness of conventional stability prediction and preventive dispatch approaches is challenged. In response, a novel transient stability prediction method is proposed. First, a stability index (SI) that calculates the stability margin of a wind power-integrated power system is developed. In this method, wind power plants (WPPs) are represented as variable admittances to be integrated into an equivalent network during transients, whereby all WPP nodes are eliminated from the system, while their transient effects on each synchronous generator are retained. Next, the calculation of the kinetic and potential energies of a system is derived, and accordingly, a novel SI is put forward. The novel approach is then proposed taking advantage of the machine learning (ML) technique and the newly defined SI. In case of a contingency, the developed SI is calculated in parallel for all possible instability modes (IMs). The SIs are then formed as a vector and applied to an ensemble learning-trained model for transient stability prediction. Compared with the features used in other studies, the SI vector is more informative and discriminative, thus lead to a more accurate and reliable prediction. The proposed approach is validated on two IEEE test systems with various wind power penetration levels and compared to the existing methods, followed by a discussion of results. In addition, to address the issues existing in preventive dispatch for high wind power-integrated electrical systems, an hour-ahead probabilistic transient stability-constrained power dispatching method is proposed. First, to avoid massive transient stability simulations in each dispatching operation, an ML-based model is trained to predict the critical clearing time (CCT) and IM for all preconceived fault scenarios. Next, a set of IM-categorized probabilistic transient stability constraints (PTSCs) are constructed. Based on the predictions, the system operation plan is assessed with respect to the PTSCs. Then, the sensitivity of the probabilistic level of CCT is calculated with respect to the active power generated from the critical generators for each IM category. Accordingly, the implicit PTSCs are converted into explicit dispatching constraints, and the dispatch is rescheduled to ensure the probabilistic stability requirements of the system are met at an economical operating cost. The proposed approach is validated on modified IEEE 68- and 300-bus test systems, wherein the wind power installed capacity accounts for 40% and 50% of the total load, respectively, reporting high computational efficiency and high-quality solutions. The ML-incorporated transient stability prediction and preventive dispatch methods proposed in this research work can help to maintain the transient stability of the system and avoid the widespread blackouts

Intentional Controlled Islanding in Wide Area Power Systems with Large Scale Renewable Power Generation to Prevent Blackout

Intentional controlled islanding is a solution to prevent blackouts following a large disturbance. This study focuses on determining island boundaries while maintaining the stability of formed islands and minimising load shedding. A new generator coherency identification framework based on the dynamic coupling of generators and Support Vector Clustering method is proposed to address this challenge. A Mixed Integer Linear Programming model is formulated to minimize power flow disruption and load shedding, and ensure the stability of islanding. The proposed algorithm was validated in 39-bus and 118-bus test systems