Regulation of Disturbance Magnitude for Locational Frequency Stability Using Machine Learning

Power systems must maintain the frequency within acceptable limits when subjected to a disturbance. To ensure this, the most significant credible disturbance in the system is normally used as a benchmark to allocate the Primary Frequency Response (PFR) resources. However, the overall reduction of system inertia due to increased integration of Converter Interfaced Generation (CIG) implies that systems with high penetration of CIG require more frequency control services, which are either costly or unavailable. In extreme cases of cost and scarcity, regulating the most significant disturbance magnitude can offer an efficient solution to this problem. This paper proposes a Machine Learning (ML) based technique to regulate the disturbance magnitude of the power system to comply with the frequency stability requirements i.e., Rate of Change of Frequency (RoCoF) and frequency nadir. Unlike traditional approaches which limit the disturbance magnitude by using the Centre Of Inertia (COI) because the locational frequency responses of the network are analytically hard to derive, the proposed method is able to capture such complexities using data-driven techniques. The method does not rely on the computationally intensive RMS-Time Domain Simulations (TDS), once trained offline. Consequently, by considering the locational frequency dynamics of the system, operators can identify operating conditions (OC) that fulfil frequency requirements at every monitored bus in the network, without the allocation of additional frequency control services such as inertia. The effectiveness of the proposed method is demonstrated on the modified IEEE 39 Bus network

Measurement based method for online characterization of generator dynamic behaviour in systems with renewable generation

This paper introduces a hybrid-methodology for online identification and clustering of generator oscillatory behavior, based on measured responses. The dominant modes in generator measured responses are initially identified using a mode identification technique and then introduced, in the next step, as input into a clustering algorithm. Critical groups of generators that exhibit poorly or negatively damped oscillations are identified, in order to enable corrective control actions and stabilize the system. The uncertainties associated with operation of modern power systems, including Renewable Energy Sources (RES) are investigated, with emphasis on the impact of the dynamic behavior of power electronic interfaced RES

Prediction of cascading failures and simultaneous learning of functional connectivity in power system

The prediction of power system cascading failures is a challenging task, especially with increasing uncertainty and complexity in power system dynamics due to integration of renewable energy sources (RES). Given the spatio-temporal and combinatorial nature of the problem, physics based approaches for characterizing cascading failures are often limited by their scope and/or speed, thereby prompting the use of a spatio-temporal learning technique. This paper proposes prediction of cascading failures using a spatio-temporal Graph Convolution Network (GCN) based machine learning (ML) framework. Additionally, the model also learns an importance matrix to reveal power system interconnections (graph nodes/edges) which are crucial to the prediction. The elements of learnt importance matrix are further projected as power system functional connectivities. Using these connectivities, insights on vulnerable power system interconnections may be derived for enhanced situational awareness. The proposed method has been tested on a modified IEEE 10 machine 39 bus test system, with RES and action of protection devices

Feasibility study of applicability of recurrence quantification analysis for clustering of power system dynamic responses

A methodology based on Recurrence Quantification Analysis (RQA) for the clustering of generator dynamic behavior is presented. RQA is a nonlinear data analysis method, which is used in this paper to extract features from measured generator rotor angle responses that can be used to cluster generators in groups with similar oscillatory behavior. The possibility of extracting features relevant to damping and frequency of oscillations present in power systems is studied. The k-Means clustering algorithm is further used to cluster the generator responses in groups exhibiting well or poorly damped oscillations, based on the extracted features from RQA. The effectiveness of RQA is investigated using simulated responses from a modified version of the IEEE 68 bus network, including renewable energy resources

Investigation of the impact of load tap changers and automatic generation control on cascading events

This paper presents an assessment of the impact of control mechanisms, specifically load tap changers (LTCs) and automatic generation control (AGC), on cascading events in power systems with renewable generation. In order to identify the impact of these voltage and frequency related mechanisms, a large number of dynamic RMS simulations for various operating conditions is performed taking into consideration renewable generation, system loading and the action of protection devices. The sequences in which the cascading events appear are analysed, and each cascading event is described by the component that trips, the time and the reason for tripping. The number and reason of cascading events, the average load loss and the time between consecutive events are used as metrics to quantify the impact of LTCs and AGC. The study is demonstrated on a modified version of the IEEE-39 bus model with renewable generation and protection devices

Using SHAP values and machine learning to understand trends in the transient stability limit

Machine learning (ML) for transient stability assessment has gained traction due to the significant increase in computational requirements as renewables connect to power systems. To achieve a high degree of accuracy; black-box ML models are often required - inhibiting interpretation of predictions and consequently reducing confidence in the use of such methods. This paper proposes the use of SHapley Additive exPlanations (SHAP) - a unifying interpretability framework based on Shapley values from cooperative game theory - to provide insights into ML models that are trained to predict critical clearing time (CCT). We use SHAP to obtain explanations of location-specific ML models trained to predict CCT at each busbar on the network. This can provide unique insights into power system variables influencing the entire stability boundary under increasing system complexity and uncertainty. Subsequently, the covariance between a variable of interest and the corresponding SHAP values from each location-specific ML model - can reveal how a change in that variable impacts the stability boundary throughout the network. Such insights can inform planning and/or operational decisions. The case study provided demonstrates the method using a highly accurate opaque ML algorithm in the IEEE 39-bus test network with Type IV wind generation

Efficient identification of transient instability states of uncertain power systems

This paper investigates the use of a game theoretic approach, namely the Monte Carlo Tree Search method, to identify critical scenarios considering transient stability of power systems. The method guides dynamic time domain simulations towards the cases that the system exhibits instability in order to explore efficiently the entire domain of possible operating conditions under uncertainty. Since the method focuses the search within the domain on the cases that are more probable to cause instability, information on stability boundaries and values of parameters critical for transient stability are also provided. Critical lines, penetration level of renewable energy sources and system loading can be defined this way

Dynamic performance of a low voltage microgrid with droop controlled distributed generation

Microgrids are small-scale highly controlled networks designed to supply electrical energy. From the operational point of view, microgrids are active distribution networks, facilitating the integration of distributed generation units. Major technical issues in this concept include system stability and protection coordination which are significantly influenced by the high penetration of inverter-interfaced distributed energy sources. These units often adopt the frequency-active power and voltage-reactive power droop control strategy to participate in the load sharing of an islanded microgrid. The scope of the paper is to investigate the dynamic performance of a low voltage laboratory-scale microgrid system, using experimental results and introduce the concept of Prony analysis for understanding the connected components. Several small disturbance test cases are conducted and the investigations focus on the influence of the droop controlled distributed generation sources