اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدOnline Static Security Assessment of Power Systems Based on Lasso Algorithm
90
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
As one important means of ensuring secure operation in a power system, the contingency selection and ranking methods need to be more rapid and accurate. A novel method-based least absolute shrinkage and selection operator (Lasso) algorithm is proposed in this paper to apply to online static security assessment (OSSA). The assessment is based on a security index, which is applied to select and screen contingencies. Firstly, the multi-step adaptive Lasso (MSA-Lasso) regression algorithm is introduced based on the regression algorithm, whose predictive performance has an advantage. Then, an OSSA module is proposed to evaluate and select contingencies in different load conditions. In addition, the Lasso algorithm is employed to predict the security index of each power system operation state with the consideration of bus voltages and power flows, according to Newton-Raphson load flow (NRLF) analysis in post-contingency states. Finally, the numerical results of applying the proposed approach to the IEEE 14-bus, 118-bus, and 300-bus test systems demonstrate the accuracy and rapidity of OSSA.
قيم البحث
اقرأ أيضاً
We consider a class of malicious attacks against remote state estimation. A sensor with limited resources adopts an acknowledgement (ACK)-based online power schedule to improve the remote state estimation performance. A malicious attacker can modify
the ACKs from the remote estimator and convey fake information to the sensor. When the capability of the attacker is limited, we propose an attack strategy for the attacker and analyze the corresponding effect on the estimation performance. The possible responses of the sensor are studied and a condition for the sensor to discard ACKs and switch from online schedule to offline schedule is provided.
With the recent interest in net-zero sustainability for commercial buildings, integration of photovoltaic (PV) assets becomes even more important. This integration remains a challenge due to high solar variability and uncertainty in the prediction of
PV output. Most existing methods predict PV output using either local power/weather history or global weather forecasts, thereby ignoring either the impending global phenomena or the relevant local characteristics, respectively. This work proposes to leverage weather data from both local weather history and global forecasts based on time series modeling with exogenous inputs. The proposed model results in eighteen hour ahead forecasts with a mean accuracy of $approx$ 80% and uses data from the National Ocean and Atmospheric Administrations (NOAA) High-Resolution Rapid Refresh (HRRR) model.
A multivariate density forecast model based on deep learning is designed in this paper to forecast the joint cumulative distribution functions (JCDFs) of multiple security margins in power systems. Differing from existing multivariate density forecas
t models, the proposed method requires no a priori hypotheses on the distribution of forecasting targets. In addition, based on the universal approximation capability of neural networks, the value domain of the proposed approach has been proven to include all continuous JCDFs. The forecasted JCDF is further employed to calculate the deterministic security assessment index evaluating the security level of future power system operations. Numerical tests verify the superiority of the proposed method over current multivariate density forecast models. The deterministic security assessment index is demonstrated to be more informative for operators than security margins as well.
One of the fundamental concerns in the operation of modern power systems is the assessment of their frequency stability in case of inertia-reduction induced by the large share of power electronic interfaced resources. Within this context, the paper p
roposes a framework that, by making use of linear models of the frequency response of different types of power plants, including also grid--forming and grid-following converters, is capable to infer a numerically tractable dynamical model to be used in frequency stability assessment. Furthermore, the proposed framework makes use of models defined in a way such that their parameters can be inferred from real-time measurements feeding a classical least squares estimator. The paper validates the proposed framework using a full-replica of the dynamical model of the IEEE 39 bus system simulated in a real-time platform.
This paper proposes a fully distributed robust state-estimation (D-RBSE) method that is applicable to multi-area power systems with nonlinear measurements. We extend the recently introduced bilinear formulation of state estimation problems to a robus
t model. A distributed bilinear state-estimation procedure is developed. In both linear stages, the state estimation problem in each area is solved locally, with minimal data exchange with its neighbors. The intermediate nonlinear transformation can be performed by all areas in parallel without any need of inter-regional communication. This algorithm does not require a central coordinator and can compress bad measurements by introducing a robust state estimation model. Numerical tests on IEEE 14-bus and 118-bus benchmark systems demonstrate the validity of the method.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
