Transient instability is considered the most severe form of instability in power systems with grave socioeconomic repercussions if not prevented. Conventional methods, such as time domain simulations and direct methods impose limitations to fast on-line transient stability assessment in modern power systems. The development of phasor measurement units paved the way for transient stability assessment by means of artificial intelligence for pattern recognition and classification. Many classification algorithms have been reported in the literature for assessing transient stability. This paper aims to provide insights regarding which algorithm is more suitable for a given dataset for power system stability assessment. For this purpose, decision trees, support vector machines and artificial neural networks are investigated for their ability to address the binary stability classification problem in a comparative analysis for two datasets. The two datasets differ in terms of class distribution so that the impact of imbalanced datasets on classification accuracy could also be studied. The above datasets are created using MATLAB with two extension packages of MATPOWER and MATDYN to simulate different contingency scenarios in IEEE-9 bus test system.

This paper proposes a model for strategic maintenance scheduling of offshore wind farms (SMSOWF) in a deregulated power system. The objective of the model is to plan the maintenance schedules in a way to maximize the profit of the offshore wind farm. In addition, some network constraints, such as transmission lines capacity, and wind farm constraints, such as labor working shift, wave height limit and wake effect, as well as unexpected outages, are included in deterministic and stochastic studies. Moreover, the proposedmodel provides theability to incorporate information from condition monitoring systems. SMSOWF is formulated through a bi-level formulation and then transformed into a single-level through Karush–Kuhn–Tucker conditions. The model is validated through a test system, and the results demonstrate applicability, advantages and challenges of harnessing the full potential of the model.

Wind turbine anomaly or failure detection using machine learning techniques through supervisory control and data acquisition (SCADA) system is drawing wide attention from academic and industry While parameter selection is important for modelling a wind turbine’s condition, only a few papers have been published focusing on this issue and in those papers interconnections among sub-components in a wind turbine are used to address this problem. However, merely the interconnections for decision making sometimes is too general to provide a parameter list considering the differences of each SCADA dataset. In this paper, a method is proposed to provide more detailed suggestions on parameter selection based on mutual information. First, the copula is proven to be capable of simplifying the estimation of mutual information. Then an empirical copulabased mutual information estimation method (ECMI) is introduced for application. After that, a real SCADA dataset is adopted to test the method, and the results show the effectiveness of the ECMI in providing parameter selection suggestions when physical knowledge is not accurate enough.