Towards Concise Models of Grid Stability

Arzamasov, Vadim; Böhm, Klemens; Jochem, Patrick

doi:10.1109/smartgridcomm.2018.8587498

Cited by 99 publications

(71 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, in this paper, we only consider binary classification problems. We also employed a synthetic dataset and several real datasets [38], [41]- [45] for the performance evaluation. In particular for the parameter studies, we use a synthetic dataset and a real scene dataset [38] (available at http://mulan.sourceforge.net /datasets-mlc.html), which can be categorized as the following 4 scenarios: S1.…”

Section: Numerical Resultsmentioning

confidence: 99%

A Stochastic Quasi-Newton Method for Large-Scale Nonconvex Optimization With Applications

Chen

Chan

et al. 2020

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

Ensuring the positive definiteness and avoiding illconditioning of the Hessian update in the stochastic Broyden-Fletcher-Goldfarb-Shanno (BFGS) method are significant in solving nonconvex problems. This paper proposes a novel stochastic version of damped and regularized BFGS method for addressing the above problems. While the proposed regularized strategy helps to prevent the BFGS matrix from being close to singularity, the new damped parameter further ensures positivity of the product of correction pairs. To alleviate the computational cost of the stochastic LBFGS updates, and to improve its robustness, the curvature information is updated using the averaged iterate at spaced intervals. The effectiveness of the proposed method is evaluated through the logistic regression and Bayesian logistic regression problems in machine learning. Numerical experiments are conducted by using both synthetic dataset and several real datasets. The results show that the proposed method generally outperforms the stochastic damped limited memory BFGS (SdLBFGS) method. In particular, for problems with small sample sizes, our method has shown superior performance and is capable of mitigating ill-conditioned problems. Furthermore, our method is more robust to the variations of the batch size and memory size than the SdLBFGS method.Index Terms-nonconvex optimization, stochastic quasi-Newton method, LBFGS, damped parameter, nonconjugate exponential models, variational inference.

show abstract

Section: Numerical Resultsmentioning

confidence: 99%

A Stochastic Quasi-Newton Method for Large-Scale Nonconvex Optimization With Applications

Chen

Chan

et al. 2020

IEEE Trans. Neural Netw. Learning Syst.

View full text Add to dashboard Cite

show abstract

“…The physical dynamics of electric power generation and its connection with consumption loads is expressed for the DSGC model as Eq. (1) [6,22,24] taking into account power grid synchronization [38,39]):…”

Section: Decentralised and Idealised Power Grid Defined And Quantifiedmentioning

confidence: 99%

“…Arzamasov et al [24] used Eq. (4) to conduct a simulation to evaluate DSGC stability under a range of feasible values for ranges of input assumptions applied to a simplistic four node start motif DSGC network as illustrated in Fig.…”

Section: Decentralised and Idealised Power Grid Defined And Quantifiedmentioning

confidence: 99%

“…DSGC also can be configured to limit non-gaussian power grid frequency fluctuations [23]. Arzamasov et al [24] conducted a simulation sampling the key variables over a broad feasible solution space to further assess grid stability with DSGC applied to a simple decentralized grid configuration. Their dataset is registered in the UCI machine-learning repository [25] and is the dataset further evaluated by this study.…”

Section: Introductionmentioning

confidence: 99%

“…Various machine-learning and data-mining algorithms have been applied to the decentralized management and control of microgrids and to aid reinforcement learning [29]. These include fuzzy Q-learning [30]; artificial neural networks for conducting virtual field tests [31]; decision trees [24,32]; support vector machines [33] for predicting transient grid instability; and, deep learning [34,35] algorithms applied to large-scale smart-grid databases. Several studies focus machine learning studies on the detection of smart grid data compromised by cyberattacks (e.g.., [36,37]).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting Stability of a Decentralized Power Grid Linking Electricity Price Formulation to Grid Frequency Applying an Optimized Data-Matching Learning Network to Simulated Data

Wood

2020

Technol Econ Smart Grids Sustain Energy

View full text Add to dashboard Cite

The stability of decentralized electricity grids is influenced by real-time electricity prices and the cost sensitivity and reaction times of power producers and consumers. The decentral smart grid control (DSGC) system is designed to provide demand-side control of decentralized electricity grids by linking real-time electricity prices to changes in grid frequency over the time scale of a few seconds. This stimulates electricity demand-side consumption / production on similar time scales. Grid stability of DSGC systems can be simulated by considering a wide range of assumptions for the electricity volumes consumed / produced (P) by each grid participant, their cost-sensitivity (G) and reaction times (Tau) to changing grid conditions. Such a simulation (10,000 cases) published for a simple four-node star decentralized grid configuration with randomized values for P, G and Tau quantifies dynamic grid stability (Stb in) in terms of grid mechanical and pricing influences. This study applies an optimized data-matching machine-learning algorithm, the, transparent open box (TOB) learning network to predict Stb in (ranging from −0.0808 to +0.1094 s −2) for this published simulation from its independent variables. TOB manages to predict Stb in to a high degree of accuracy (RMSE~0.016 s −2 ; R 2~0 .85) for this grid configuration in which independent variables P, G and Tau are poorly correlated with Stb in. By involving average G and Tau values for the three consumers as input variables TOB prediction accuracy is further improved (RMSE~0.0075 s −2 ; R 2~0 .90). The study highlights the importance of compound feature selection when predicting grid stability of decentralized electricity grids.

show abstract

Comparative analysis of machine learning algorithms for prediction of smart grid stability ^†

Khan

Prabadevi

Deepa

et al. 2021

Int Trans Electr Energ Syst

View full text Add to dashboard Cite

The global demand for electricity has visualized high growth with the rapid growth in population and economy. It thus becomes necessary to efficiently distribute electricity to households and industries in order to reduce power loss. Smart Grids (SG) have the potential to reduce such power losses during power distribution. Machine learning and artificial intelligence techniques have been successfully implemented on SGs to achieve enhanced accuracy in customer demand prediction. There exists a dire need to analyze and evaluate the various machine learning algorithms, thereby identify the most suitable one to be applied to SGs. In the present work, several state-of-the-art machine learning algorithms, namely Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Logistic Regression, Naive Bayes, Neural Networks, and Decision Tree classifier, have been deployed for predicting the stability of the SG. The SG dataset used in the study is publicly available collected from UC Irvine (UCI) machine learning repository. The experimentation results highlighted the superiority of the Decision Tree classification algorithm, which outperformed the other state of the art algorithms yielding 100% precision, 99.9% recall, 100% F1 score, and 99.96% accuracy.

show abstract

Towards Concise Models of Grid Stability

Cited by 99 publications

References 29 publications

A Stochastic Quasi-Newton Method for Large-Scale Nonconvex Optimization With Applications

A Stochastic Quasi-Newton Method for Large-Scale Nonconvex Optimization With Applications

Predicting Stability of a Decentralized Power Grid Linking Electricity Price Formulation to Grid Frequency Applying an Optimized Data-Matching Learning Network to Simulated Data

Comparative analysis of machine learning algorithms for prediction of smart grid stability ^†

Contact Info

Product

Resources

About

Towards Concise Models of Grid Stability

Cited by 99 publications

References 29 publications

A Stochastic Quasi-Newton Method for Large-Scale Nonconvex Optimization With Applications

A Stochastic Quasi-Newton Method for Large-Scale Nonconvex Optimization With Applications

Predicting Stability of a Decentralized Power Grid Linking Electricity Price Formulation to Grid Frequency Applying an Optimized Data-Matching Learning Network to Simulated Data

Comparative analysis of machine learning algorithms for prediction of smart grid stability †

Contact Info

Product

Resources

About

Comparative analysis of machine learning algorithms for prediction of smart grid stability ^†