Application of energy-based power system features for dynamic security assessment

Abstract

To date, the potential of on-line Dynamic Security Assessment (DSA)-to monitor, alert, and enhance system security-is constrained by the longer computational cycle time. Traditional techniques requiring extensive numerical computations makes it challenging to complete the assessment within an acceptable time. Longer computational cycles produce obsolete security assessment results as the system operating point evolves continuously. This thesis presents a DSA algorithm, based on Transient Energy Function (TEF) method and machine learning, to enable frequent computational cycles in on-line DSA of power systems.The use of selected terms of the TEF as pre-processed input features for machine learning demonstrated the ability to successfully train a contingency-independent classifier that is capable of classifying stable and unstable operating points. The network is trained for current system topology and loading conditions. The classifier can be trained using a small dataset when the TEF terms are used as input features. The prediction accuracy of the proposed scheme was tested under the balanced and unbalanced faults with the presence of voltage sensitive and dynamic loads for different operating points. The test results demonstrate the potential of using the proposed technique for power system on-line DSA. Power system devices such as HVDC and FACTS can be included in the algorithm by incorporating the effective terms of a corresponding TEF.An on-line DSA system requires the integration of several functional components.Some of these components already exist and the others are newly introduced to the i power system. The practicality of the proposed technique in terms of a) critical data communications aspects b) computational hardware requirements; and c) capabilities and limitations of the tools in use was tested using an implementation of an on-line DSA system. The test power system model was simulated using a real-time digital simulator. The other functional units were distributed over the Local Area Network (LAN). The implementation indicated that an acceptable computational cycle time can be achieved using the proposed method.In addition, the work carried out during this thesis has produced two tools that can be used for a) web-based automated data generation for power system studies; and b) testing of on-line DSA algorithms.ii

Non‐parametric statistics‐based predictor enabling online transient stability assessment

“…Some promising results of response-based TSA by data mining have been reported in [10][11][12][13][14][15][16][17][18][19][20][21][22]. Decision trees (DTs) have the superiority of transparency [11] and do not rely on the backpropagation (BP) training process [12] in comparison with neural networks (NNs).…”

Real-time power system dynamic security assessment based on advanced feature selection for decision tree classifiers

“…However, the application of the online DSA is limited. It is not suitable for real-time protection and remedial control action [7][8][9] following a disturbance in the system. This is due to the fact that the power system is characterized by a large number of dynamic and nonlinear algebraic equations [10].…”

Power System Security Assessment and Remedial Measures based on the on-line Dynamic Security Assessment System

“…The off-line calculation manner is used for the traditional security analysis of power system, but there is difference between the selected typical mode and actual operation condition, which makes the results tended to conservation in most cases; thus, the on-line security analysis method gains extensive attention both domestic and abroad [5][6][7][8][9][10][11]. Aiming at the new demand for the development of power grid, on-line security and stability analysis technology is need urgently to overcome the insufficiency of traditional off-line calculation, adopting on-line dynamic security assessment to power grid and finding problems in all operation modes, putting forward prevention decision measures, thus to enhance the efficiency of the dispatchers, and effectively improve the capacity of reining operation of bulk power system.…”