The influence of uncertainties and parameter structural dependencies in distribution system state estimation

This paper evaluates a number of uncertain parameters that affect the accuracy of distribution system state estimation, and ranks their importance using an efficient sensitivity analysis technique, Morris screening method. The influence of the uncertain parameters on state estimation performance is analysed globally and zonally. Furthermore the dependence structure between the critical variable and state estimation accuracy is analysed using copula to establish their relationship at different section of the bivariate space. The sensitivity of the critical parameter at different ranges is also studied and ranked using Morris screening methods to present the variation of state estimation performance when the critical variable is allocated at different sections within the feasible range. Accurate assessment of the importance of various uncertain parameters and the analysis of the dependence structure can inform power system operators which parameters will require the greatest levels of mitigation or increased monitoring accuracy in order to have satisfactory performance of distribution system state estimation

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

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

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

Statistical assessment of the impact of renewable energy sources on transient stability

This paper studies the impact of penetration of Renewable Energy Sources (RES) on the dynamic behavior of large power systems. Statistical analysis of the number of instabilities and the time of occurrence of loss of synchronism in the network was performed following large number of Monte Carlo simulations and considering high degrees of uncertainty. A criterion for instability detection is introduced. Conclusions are drawn with respect to the influence of RES on transient stability, the identification of critical generators and how these generators and their behavior may change for a wide range of RES penetration levels