Wind speed prediction with spatio–temporal correlation is among the most challenging tasks in wind speed prediction. In this paper, the problem of predicting wind speed for multiple sites ...simultaneously is investigated by using spatio–temporal correlation. This paper proposes a model for wind speed prediction with spatio–temporal correlation, i.e., the predictive deep convolutional neural network (PDCNN). The model is a unified framework, integrating convolutional neural networks (CNNs) and a multi-layer perceptron (MLP). Firstly, the spatial features are extracted by CNNs located at the bottom of the model. Then, the temporal dependencies among these extracted spatial features are captured by the MLP. In this way, the spatial and temporal correlations are captured by PDCNN intrinsically. Finally, PDCNN generates the predicted wind speed by using the learnt spatio–temporal correlations. In addition, three error indices are defined to evaluate the prediction accuracy of the model on the wind turbine array. Experiment results on real-world data show that PDCNN can capture the spatio–temporal correlation effectively, and it outperforms the conventional machine learning models, including multi-layer perceptron, support vector regressor, decision tree, etc.
•New method to solve convex and non-convex economic dispatch problems using MsEBBO.•MsEBBO is able to balance the global exploration and the local exploitation.•Considering valve-point effects, ramp ...rate limits, prohibited operating zones.•An effective repair technique for handling different constraints is proposed.•The sensitivity of MsEBBO to variations in population size is investigated.
Economic dispatch (ED) is an important task in power system operation. It is able to decrease the operating cost, save energy resources, and reduce environmental load. In this paper, a multi-strategy ensemble biogeography-based optimization (MsEBBO) based method for ED problems is proposed. BBO is a population-based meta-heuristic algorithm inspired by the science of biogeography and mainly consists of three components: migration model, migration operator, and mutation operator. It has good local exploitation ability but lacks satisfactory global exploration ability. To keep a proper balance between exploration and exploitation, MsEBBO has three extensions to BBO’s three components according to the no free lunch theorem. First, a nonlinear migration model based on sinusoidal curve is employed. Second, a backup migration operator through adopting a backup strategy to combine perturb operator and blended operator is presented. This operator can make the entire population fully exchange or share information and thus further strengthen the exploitation ability. Finally, both differential mutation and Lévy local search are embedded as mutation operator for MsEBBO using a similar backup strategy. Gaining from this mutation operator, MsEBBO can be accelerated to escape from local optima and perform efficient search within global range. Additionally, an effective repair technique is proposed to handle different constraints of ED problems. The performance of MsEBBO is tested on four ED problems with diverse complexities. Experimental results and comparisons with other recently reported ED solution methods confirm that MsEBBO is capable of yielding a good balance between exploration and exploitation, and obtaining competitive solution quality. Moreover, the sensitivity of MsEBBO to variations in population size is investigated as well.
Extensive adoption of Information and Communication Technologies makes power systems and communication systems more tightly coupled to form cyber‐physical power systems. It causes power systems to be ...growingly susceptible to cyber contingencies. Cyber contingencies may sabotage measurement availability, further disrupt the observability analysis in state estimation, and thus threaten the stable operation of power systems. This paper investigates the impact of cyber contingencies on measurement availability. A workflow is presented to achieve accurate and meticulous impact identification under intricate communication architectures with various cyber contingencies. And a set of indicators are proposed to quantitatively evaluate measurement availability. Case studies demonstrate that the proposed method is effective in identifying and assessing the impact of cyber contingencies on measurement availability. Meanwhile, the proposed workflow and indicators can also be employed to dynamically evaluate the system's resilience in protecting measurement transmission against hypothetical cyber contingencies, which could benefit the offline planning and online operation of cyber‐physical power systems
Biogeography-based optimization (BBO) is a powerful population-based algorithm inspired by biogeography and has been extensively applied to many science and engineering problems. However, its ...direct-copying-based migration and random mutation operators make BBO possess local exploitation ability but lack global exploration ability. To remedy the defect and enhance the performance of BBO, an enhanced BBO variant, called POLBBO, is developed in this paper. In POLBBO, a proposed efficient operator named polyphyletic migration operator can formally utilize as many as four individuals’ features to construct a new solution vector. This operator cannot only generate new features from more promising areas in the search space, but also effectively increase the population diversity. On the other hand, an orthogonal learning (OL) strategy based on orthogonal experimental design is employed. The OL strategy can quickly discover more useful information from the search experiences and efficiently utilize the information to construct a more promising solution, and thereby provide a systematic and elaborate reasoning method to guide the search directions of POLBBO. The proposed POLBBO is verified on a set of 24 benchmark functions with diverse complexities, and is compared with the basic BBO, five state-of-the-art BBO variants, five existing OL-based algorithms, and nine other evolutionary algorithms. The experimental results and comparisons demonstrate that the polyphyletic migration operator and the OL strategy can work together well and enhance the performance of BBO significantly in terms of the quality of the final solutions and the convergence rate.
•An enhanced BBO variant (POLBBO) is developed for solving global numerical optimization problems.•A polyphyletic migration operator is proposed to generate new features from more promising areas in the search space.•An OL strategy is employed to provide a systematic reasoning method to guide the search directions of POLBBO.•The polyphyletic migration operator and the OL strategy can work together well and enhance the performance of BBO significantly.•Five state-of-the-art BBO variants, five existing OL-based algorithms, and nine other evolutionary algorithms are employed to compare.
Distinction of weak and strong AC grids for emerging hierarchical-infeed LCC-UHVDC systems is important for planning and operation departments. However, accuracy of earlier distinction methods is ...limited as they were developed by empirical reasoning without rigorous theoretical analysis. Hence in this letter, hierarchical-infeed interactive effective short-circuit ratio (HIESCR) index is first used for strength evaluation of HIDC systems with complex inter-inverter interactions considered. Boundary HIESCR (BHIESCR) is also introduced in the proposed distinction method of weak and strong AC grids. That is, weak (or strong) AC grids are, respectively, identified when HIESCR is less (or greater) than BHIESCR. Second, it is shown BHIESCR remains almost unchanged as 3.0 versus various system parameters and rated operation variables based on rigorous theoretical analysis. This salient feature makes the proposed method more accurate than earlier methods. Finally, the proposed method is validated by simulations based on the PSCAD/EMTDC program.
Taking transmission sections as the monitoring objects of power system security and stability level can largely improve the efficiency of analysis and control in power system operation. However, ...existing approaches for identifying transmission sections mainly depend on years of experience, which is not suitable for complicated and variable power systems with large scales. Thus, a novel method for automatic identification of transmission sections using complex network theory is proposed. The proposed method presents the fundamental conditions of transmission sections and identifies them from three levels: transmission lines, key transmission links and partition sections. First, based on the small-world characteristics of power grid, the index of transmission betweenness is presented to identify key transmission links from transmission lines. Then clustering algorithm of complex network is used to divide the power grid and to obtain partition sections from the key transmission links. Finally, the combinations of partition sections are selected and ranked as the transmission sections. Numerical results with CEPRI-36 system and a provincial system are provided to demonstrate the effectiveness and adaptability of the proposed method.
Accurate transient stability assessment (TSA) is a fundamental requirement for ensuring secure and stable operation of power systems. Tremendous efforts have been made to apply artificial ...intelligence approaches for TSA with phasor measurement unit data. However, many previous approaches may be failed to provide favorable accuracy due to the shallow architectures and error-prone hand-crafting features. This paper proposed a model for TSA, which is termed multi-branch stacked denoising autoencoder (MSDAE). This model is a unified framework integrating multiple stacked denoising autoencoders (SDAEs), one fusion layer, and one logistic regression (LR) layer. Initially, the SDAEs at the bottom of MSDAE extract features from multiple kinds of measurements respectively. Then, the extracted features are encoded into unified fusion features by the fusion layer. Finally, the LR layer performs TSA by using the fusion features. The depth of the architecture contributes to the remarkable ability for feature learning, while the width of the architecture (i.e., the multiple branches) enables MSDAE to deal with different kinds of measurements by a reasonable mechanism. In this way, MSDAE achieves feature extraction and classification intrinsically and simultaneously, namely, achieves TSA in an end-to-end manner. The results of experiments on IEEE 50-machine system demonstrate the superiority of the proposed model over the prior methods.
•A method for divisional fault diagnosis of large-scale power systems is proposed.•An overlapping network division method is proposed to divide a given power system.•The FRA can faster construct ...better-performing RBF neural networks.•Fuzzy integral can integrate historical experience and current state information.•The proposed method has strong fault tolerance and high diagnostic accuracy.
This paper proposes an effective method for fault diagnosis of large-scale power systems based on radial basis function (RBF) neural network (NN) and fuzzy integral. It aims at effectively diagnosing the tie lines which connect different adjacent sub-networks in the context of divisional fault diagnosis. First, an overlapping network division method is proposed to divide a large-scale power system into a desired number of eligible sub-networks. Then, for each sub-network, a local RBF NN diagnostic module which is constructed by an exhaustive search-assisted forward recursive algorithm is allocated. Finally, a Choquet fuzzy integral fusion module is constructed for any pair of connected sub-networks. When a fault occurs, local RBF NN diagnostic modules will be selectively triggered according to local alarm information. If it involves a tie line, the corresponding Choquet fuzzy integral fusion module will be triggered to fuse the diagnostic outputs derived from the adjacent sub-networks which are connected by the tie line. Case studies with a 14-bus power system are presented to evaluate the feasibility and efficiency of the proposed method under various complex fault scenarios. The diagnostic results demonstrate that this proposed method is efficient in identifying faults within local sub-networks as well as those on the tie lines with strong fault tolerance and high diagnostic accuracy.
The potential of utilizing doubly-fed induction generator (DFIG)-based wind farms to improve power system damping performance and to enhance small signal stability has been proposed by many ...researchers. However, the simultaneous coordinated tuning of a DFIG power oscillation damper (POD) with other damping controllers is rarely involved. A simultaneous robust coordinated multiple damping controller design strategy for a power system incorporating power system stabilizer (PSS), static var compensator (SVC) POD and DFIG POD is presented in this paper. This coordinated damping control design strategy is addressed as an eigenvalue-based optimization problem to increase the damping ratios of oscillation modes. Both local and inter-area electromechanical oscillation modes are intended in the optimization design process. Wide-area phasor measurement unit (PMU) signals, selected by the joint modal controllability/ observability index, are utilized as SVC and DFIG POD feedback modulation signals to suppress inter-area oscillation modes. The robustness of the proposed coordinated design strategy is achieved by simultaneously considering multiple power flow situations and operating conditions. The recently proposed Grey Wolf optimizer (GWO) algorithm is adopted to efficiently optimize the parameter values of multiple damping controllers. The feasibility and effectiveness of the proposed coordinated design strategy are demonstrated through frequency-domain eigenvalue analysis and nonlinear time-domain simulation studies in two modified benchmark test systems. Moreover, the dynamic response simulation results also validate the robustness of the recommended coordinated multiple damping controllers under various system operating conditions.
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