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This paper introduces an approach to analyse transient overvoltages during capacitor banks switching based on artificial neural networks (ANN). Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS) were used to train the ANNs. The ANN training is based on equivalent parameters of the network and therefore, a trained ANN is applicable to every studied system. The developed ANN is trained with extensive simulated results and tested for typical cases. The new algorithms are presented and demonstrated for a partial 39-bus New England test system. The simulated results show the proposed technique can accurately estimate the peak values of switching overvoltages.

Transformer switching is one of the important stages during power system restoration. This switching can cause harmonic overvoltages that might damage some equipment and delay power system restoration. Core saturation on the energisation of a transformer with residual flux is a noticeable factor in harmonic overvoltages. This work uses artificial neural networks (ANN) in order to estimate the temporary overvoltages (TOVs) due to transformer energisation. In the proposed methodology, the Levenberg-Marquardt method is used to train the multilayer perceptron. The developed ANN is trained with the worst case of switching condition, and tested for typical cases. Simulated results for a partial 39-bus New England test system, show the proposed technique can accurately estimate the peak values and durations of switching overvoltages.

The paper presents a new approach that assists power system restoration planning with regards to control of temporary overvoltages (TOVs) due to transformer energization. The frequency-domain analysis in combination with analytical calculations is applied to find an optimum resonance condition. The analytical equations are based on sensitivity analysis for single frequencies and are extended to total sensitivity calculation. After an introduction of the phenomenon of overvoltages during power system restoration and harmonic resonance overvoltages, basic principles of their control are explained. Also, an algorithm is introduced for the results evaluation of harmonic resonance studies where it is necessary to perform simulations. The objective is to find a minimum time after which the simulation runs of temporary overvoltages can be terminated using short-time Fourier transform. Then the new algorithms are presented and demonstrated for a partial of 39-bus New England test system. Index Terms--Harmonic analysis, overvoltage protection, power system harmonics, power system restoration.

The compensation of MV distribution systems improve on the one hand the supply quality and imposes on the other hand higher constraints on earth fault detection and location strategies. Three new algorithms for the detection of permanent, resistive single phase earth faults are discussed. After a first implementation in the network, model testing showed the influence of important parameters on the algorithms accuracy, also for transient earth faults. Finally a novel strategy for fault location in compensated systems with a complex radial structure is outlined. Index Terms--Compensated distribution system, compensated grounding, DDA, DESIR, earth fault compensation, fault location, high resistive fault detection, Petersen coil, selection of the faulted feeder, sensitivity of the algorithms.

This paper presents a single-phase auto-reclosing study of a 500 kV Vietnamese transmission line. The success of the single-phase auto-reclosing depends on the extinction of the secondary arc. Therefore, the influences of several factors such as shunt and neutral reactors, faulted phase and location, capacitive and inductive coupling and series capacitor discharge on the extinction of secondary arc are investigated. This paper presents the influence of single-phase auto-reclosing on the transient stability of the Vietnam system. A new method in order to reduce the dead time for single-phase auto-reclosing is proposed. Index Terms--Recovery voltage, secondary arc current, single-phase auto-reclosing, stability.

One of the major preoccupations in the design of control systems in power systems is the evaluation of its stability robustness. Classical and modern control techniques use linear models for the controller synthesis. This introduces many uncertainties into the design and the robustness of the control and, for this reason, many control systems under certain conditions can not be operated properly. This paper presents the application of the structured singular value ([Mu]) analysis in order to model power system uncertainties and to evaluate the stability robustness. Moreover, this paper presents the calculation of a boundary function of stability depending on the control system. Robustness evaluation for different types of SVC controllers located in the interconnection of two power systems by a weak tie-line has been developed. KEYWORDS Power System Control, Power System Stability, and Power System Stability Robustness.

This paper proposed an artificial neural network (ANN)-based approach to mitigate harmonic overvoltages during transformer energization. Uncontrolled energization of large power transformers may result in magnetizing inrush current of high amplitude and switching overvoltages. The most effective method for the limitation of the switching overvoltages is controlled switching since the magnitudes of the produced transients are strongly dependent on the closing instants of the switch. We introduce a harmonic index that it's minimum value is corresponding to the best case switching time. Also, in this paper three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD) and directed random search (DRS) were used to train ANNs to estimate the optimum switching instants for real time applications. ANNs training is performed based on equivalent circuit parameters of the network. Thus, trained ANN is applicable to every studied system. To verify the effectiveness of the proposed index and accuracy of the ANN-based approach, two case studies are presented and demonstrated. [ABSTRACT FROM AUTHOR]

Uncontrolled energization of large power transformers may result in magnetizing inrush current of high amplitude and switching over-voltages. The most effective method for the limitation of the switching over-voltages is controlled switching since the magnitudes of the produced transients are strongly dependent on the closing instants of the switch. We introduce a harmonic index that its minimum value is corresponding to the best-case switching time. Also, this paper presents an Artificial Neural Network (ANN)-based approach to estimate the optimum switching instants for real time applications. In the proposed ANN, second order Levenberg-Marquardt method is used to train the multilayer perceptron. ANN training is performed based on equivalent circuit parameters of the network. Thus, trained ANN is applicable to every studied system. To verify the effectiveness of the proposed index and accuracy of the ANN-based approach, two case studies are presented and demonstrated. [ABSTRACT FROM AUTHOR]