(1990). ENERGY FUNCTION ANALYSIS FOR POWER SYSTEM STABILITY. Electric Machines & Power Systems: Vol. 18, No. 2, pp. 209-210.

Energy function analysis for power system stability

Pythagorean fuzzy set, generalized by Yager, is a new tool to deal with vagueness considering the membership grade $$\mu $$
 and non-membership $$\nu $$
 satisfying the condition $$\mu ^2+\nu ^2\le 1$$
 . It can be used to characterize the uncertain information more sufficiently and accurately than intuitionistic fuzzy set. Pythagorean fuzzy set has attracted great attention of many scholars that have been extended to new types and these extensions have been used in many areas such as decision making, aggregation operators, and information measures. Because of such a growth, we present an overview on Pythagorean fuzzy set with aim of offering a clear perspective on the different concepts, tools and trends related to their extension. In particular, we provide two novel algorithms in decision making problems under Pythagorean fuzzy environment. It may be served as a foundation for developing more algorithms in decision making.

Pythagorean fuzzy set: state of the art and future directions

Increasing use of renewable energy sources, liberalized energy markets and most importantly, the integrations of various monitoring, measuring and communication infrastructures into modern power system network offer the opportunity to build a resilient and efficient grid However, it also brings about various threats of instabilities and security concerns in form of cyberattack, voltage instability, power quality (PQ) disturbance among others to the complex network The need for efficient methodologies for quicker identification and detection of these problems have always been a priority to energy stakeholders over the years In recent times, machine learning techniques (MLTs) have proven to be effective in numerous applications including power system studies In the literature, various MLTs such as artificial neural networks (ANN), Decision Tree (DT), support vector machines (SVM) have been proposed, resulting in effective decision making and control actions in the secured and stable operations of the power system Given this growing trend, this paper presents a comprehensive review on the most recent studies whereby MLTs were developed for power system security and stability especially in cyberattack detections, PQ disturbance studies and dynamic security assessment studies The aim is to highlight the methodologies, achievements and more importantly the limitations in the classifier(s) design, dataset and test systems employed in the reviewed publications A brief review of reinforcement learning (RL) and deep reinforcement learning (DRL) approaches to transient stability assessment is also presented Finally, we highlighted some challenges and directions for future studies

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A Review of Machine Learning Approaches to Power System Security and Stability

Large scale of plug-in electric vehicles(PEVs) integration will pose inevitable impacts on the planning and operation of power system in the future.The literature reviews were carried out on PEVs integration on power system including PEVs charging load modeling,simulation and calculation,PEVs' impacts on power system,and control and utilization of PEVs charging and discharging.Firstly,key factors when analyzing the charging load were pointed out.Additionally,the impacts of PEVs integration on generation expansion,grid operation,charging facilities and distribution system planning were discussed.The current research on coordinated charging of PEVs and "vehicle-to-grid(V2G)" operation were reviewed as well.Finally,possible future research areas were presented.

Impacts and Utilization of Electric Vehicles Integration Into Power Systems

This article reviews recent works applying machine learning (ML) techniques in the context of energy systems’ reliability assessment and control. We showcase both the progress achieved to date as well as the important future directions for further research, while providing an adequate background in the fields of reliability management and of ML. The objective is to foster the synergy between these two fields and speed up the practical adoption of ML techniques for energy systems reliability management. We focus on bulk electric power systems and use them as an example, but we argue that the methods, tools, etc. can be extended to other similar systems, such as distribution systems, microgrids, and multienergy systems.

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Recent Developments in Machine Learning for Energy Systems Reliability Management

Transient stability assessment is playing a vital role in modern power systems. For this purpose, machine learning techniques have been widely employed to find critical conditions and recognize transient behaviors based on massive data analysis. However, an ever increasing volume of data generated from power systems poses a number of challenges to traditional machine learning techniques, which are computationally intensive running on standalone computers. This paper presents a MapReduce based high performance neural network to enable fast stability assessment of power systems. Hadoop, which is an open-source implementation of the MapReduce model, is first employed to parallelize the neural network. The parallel neural network is further enhanced with HaLoop to reduce the computation overhead incurred in the iteration process of the neural network. In addition, ensemble techniques are employed to accommodate the accuracy loss of the parallelized neural network in classification. The parallelized neural network is evaluated with both the IEEE 68-node system and a real power system from the aspects of computation speedup and stability assessment.

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A MapReduce Based High Performance Neural Network in Enabling Fast Stability Assessment of Power Systems

Total transfer capability (TTC) is an effective indicator to evaluate the transmission limit of the interconnected systems. However, due to the large-scale wind power integration, operation conditions of a power system may change rapidly, yielding time-varying characteristics of the TTC. As a result, the traditional time-consuming transient stability constrained TTC model is unable to assess the online transmission margin. In this paper, we propose an online measurement-based TTC estimator using the nonparametric analytics. It consists of three major components: the probabilistic data generation, the composite feature selection, and the group Lasso regression-based training scheme. Specifically, we present a probabilistic data generation approach to take into account the uncertainties of the day-ahead generation scheduling and to reduce the number of redundant or infeasible data. Then, the composite feature selection is used to reduce the dimension of the generated data and identify the features which are highly correlated with TTC. The features are determined by the maximal information coefficients and nonparametric independence screening approach. Finally, these selected features are trained by the group Lasso regression to learn the correlation between the TTC and the online measurements. Once real-time measurements are available, the TTC can be assessed immediately through the learned correlation relationship. Extensive numerical results carried out on the modified New England 39-bus test system demonstrate the feasibility of the proposed TTC estimator for online applications.

Online TTC Estimation Using Nonparametric Analytics Considering Wind Power Integration

In this paper, a two-stage Bayesian learning-based approach is proposed to enable online dynamic security assessment (DSA) and preventive control in power systems. To develop a reliable data mining-based model for DSA, the Bayesian neural network is learned by Bayes by Backprop to detect the security status in real-time and to inform the system operators with the confidence of the prediction. For dynamic insecurity prevention, the security-constrained optimal power flow (SCOPF) model incorporating the Bayesian neural network based security constraints is proposed, which is solved by the derivation-free Bayesian optimization. Case studies on the IEEE 39-bus system show that the Bayesian neural network is able to detect the security status of the power system correctly and reliably and is able to avoid the over-fitting problem compared with the traditional point estimation neural network. Also, Bayesian optimization is able to solve the non-analytical SCOPF model efficiently to mitigate the dynamic insecurity by preventive generation rescheduling.

A Bayesian Learning Based Scheme for Online Dynamic Security Assessment and Preventive Control

Online transient stability assessment (TSA) is of great necessity for fast awareness of transient instability caused by fault contingencies. In this paper, a non-parametric statistics based scheme is proposed for response-based online TSA. A critical clearing time-based stability margin index is defined as the predictive output and 14 kinds of severity indicators are proposed as input features for the TSA predictor. With no prior knowledge of the correlation structure, the non-parametric additive model is used as the basis of the predictor. To screen out the weakly correlated indicators and reduce the dimensionality of the input space, two-stage feature selection is fulfilled by non-parametric independence screening and group Lasso penalised regression successively. The predictor is then learnt by least-squares regression in the reduced multi-feature space. With phasor measurement unit measurements at generator buses, severity indicators can be computed in the real-time and fast evaluation of post-fault stability margin can be made by the offline-trained predictor. The effectiveness of the proposed non-parametric statistics based scheme is demonstrated in a modified New England 39-bus system and a practical 756-bus transmission system in China.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-gtd.2018.5802

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

A multi-indicator inference scheme is proposed in this paper to achieve an intuitive assessment of post-fault transient stability of power systems. The proposed scheme uses the fuzzy inference technique to classify the stability level as “safe,” “low-risk,” “high-risk,” and “danger.” A multi-criteria quality assessment method is first introduced. Several transient indicators are then proposed as assessment criteria. To select the effective indicators for assessment, correlation mining using univariate regression analysis is performed between each indicator and a critical clearance time (CCT)-based stability index. The fuzzy sets of indicators for different stability levels are then determined according to their correlations with the stability index. The weighting factors of indicators are also allocated according to their regression error in correlation mining. The proposed inference scheme is further demonstrated and its effectiveness is validated in case studies on IEEE 68-bus system and a 756-bus transmission system in China.

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Tingjian Liu

Papers

A MapReduce Based High Performance Neural Network in Enabling Fast Stability Assessment of Power Systems

Online TTC Estimation Using Nonparametric Analytics Considering Wind Power Integration

A Bayesian Learning Based Scheme for Online Dynamic Security Assessment and Preventive Control

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

Multi-indicator inference scheme for fuzzy assessment of power system transient stability