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This study proposes an intelligent traveling-wave (TW)-based protection algorithm for parallel transmission lines. In the proposed algorithm, Karenbauer's phase to modal transform is applied on three phase current signals. Teager energy operator is then applied on the modal components to extract TWs. First extracted TW of each modal component along with well-designed fuzzy systems are used for internal fault identification and fault type classification. In order to find the fault location, the time difference between the first and the second TWs and the TW propagation speed are utilised. Test signals are generated in PSCAD/EMTDC software and the algorithm is implemented in MATLAB. Results show that the proposed algorithm is an ultra-high-speed algorithm for the reliable protection of parallel transmission lines.

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Summary The cuckoo search (CS) algorithm is a simple and effective global optimization algorithm. It has been successfully applied to solve a wide range of real‐ world optimization problem. In this paper, a modified CS algorithm is applied to determine the feasible optimal solution of the economic load dispatch problem considering various generator constraints. In order to solve the problem of being trapped into the local optima, a new mutation scheme, inspired by the DE/current‐to‐gr_best/1, is incorporated in standard CS to enrich the searching behavior and solution quality and to avoid being trapped into local optimum. In order to verify the performance of our approach, the proposed approach is tested with two power system cases consisting of 3, 6, 15, and 40 thermal units with generator constraints. State‐of‐the‐art optimization techniques are compared with simulation results obtaining proposed method to illustrate the efficiency of the proposed method and to show that the improved CS could be used as a reliable tool for solving economic load dispatch problems with generator constraints. Copyright © 2014 John Wiley & Sons, Ltd.

In this paper, a bi‐level power management strategy is presented for an active distribution network (ADN) in the presence of virtual power plant (VPP). In the proposed strategy, VPP contains renewable energy source (RES), energy storage system (ESS) and electric vehicles (EVs) parking lot coordinated with VPP operator (VPPO), and there is a coordinated framework between VPPO and distribution system operator. The proposed power management strategy (PMS) is considered in the context of the bi‐level optimization problem. In the first level, the optimal harmonic power flow formulation of the ADN is considered by minimizing the summation of energy cost, voltage deviation function and voltage THD% as a normalized objective function. In the second level, the VPP is modeled according to the framework of day‐ahead energy and ancillary services markets. In the next step, the linear format of the proposed non‐linear original problem is obtained with an optimal solution and low calculation burden. Also, the Karush‐Kuhn‐Tucker condition is used to convert the proposed bi‐level PMS approach to a single‐level model, and the scenariobased stochastic programming models the uncertainty of load, energy price, RES power, and EVs demand. Finally, the proposed strategy is applied on the 33‐bus distribution network using GAMS software.

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