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More and more system operators are interested in calculating transfer capability in real time using real-time power flow models generated from the Energy Management System (EMS). However, in comparing these models with offline study models, the EMS models usually include only a limited portion of the interconnected system. It is not practical to extend the EMS model in order to capture the impact of the external systems and as such using an equivalent network becomes necessary for this purpose. Equivalent circuits derived from the offline models for certain predetermined operating conditions would suffer from invalid results under various real-time operating conditions. Hence, it is necessary to derive an equivalent circuit model that is adaptive to the real-time system conditions. This paper presents a general method of creating an equivalent circuit model based on the measured boundary station parameters such as voltages and power flows. Simulation studies show that such derived equivalent circuit models provide solutions with adequate accuracy. [ABSTRACT FROM AUTHOR]

Part I of this paper describes the design and implementation of an islanding detection method based on transient signals. The proposed method utilizes discrete wavelet transform to extract features from transient current and voltage signals. A decision-tree classifier uses the energy content in the wavelet coefficients to distinguish islanding events from other transient generating events. The verification tests performed in Part I, for a two generator test system having a synchronous generator and a wind farm, showed more than 98% classification accuracy with 95% confidence and a response time of less than two cycles. In Part II, the proposed methodology is applied to an extended test system with a voltage-source converter-based dc source. The proposed relay's performance is compared with the existing passive islanding detection methods under different scenarios. Furthermore, the effect of noise on the performance of the proposed method is studied. The transient-based islanding detection methodology exhibits very high reliability and fast response compared to all other passive islanding detection methods and shows that the relay can be designed with a zero nondetection zone for a particular system. [ABSTRACT FROM AUTHOR]

In this paper, an artificial neural network (ANN) based method is developed for quickly estimating the long-term voltage stability margin. The investigation presented in the paper showed that node voltage magnitudes and the phase angles are the best predictors of voltage stability margin. Further, the paper shows that the proposed ANN based method can successfully estimate the voltage stability margin not only under normal operation but also under N-1 contingency situations. If the voltage magnitudes and phase angles are obtained in real-time from phasor measurement units (PMUs) using the proposed method, the voltage stability margin can be estimated in real time and used for initiating stability control actions. Finally, a suboptimal approach to determine the best locations for PMUs is presented. Numerical examples of the proposed techniques are presented using the New England 39-bus test system and a practical power system which consists of 1844 buses, 746 load buses, and 302 generator buses. [ABSTRACT FROM AUTHOR]

A new method for predicting the rotor angle stability status of a power system immediately after a large disturbance is presented. The proposed two-stage method involves estimation of the similarity of post-fault voltage trajectories of the generator buses after the disturbance to some pre-identified templates and then prediction of the stability status using a classifier which takes the similarity values calculated at the different generator buses as inputs. The typical bus voltage variation patterns after a disturbance for both stable and unstable situations are identified from a database of simulations using fuzzy C-means clustering algorithm. The same database is used to train a support vector machine classifier which takes proximity of the actual voltage variations to the identified templates as features. Development of the system and its performance were demonstrated using a case study carried out on the IEEE 39-bus system. Investigations showed that the proposed method can accurately predict the stability status six cycles after the clearance of a fault. Further, the robustness of the proposed method was examined by analyzing its performance in predicting the instability when the network configuration is altered. [ABSTRACT FROM AUTHOR]