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Abstract: In this paper, the performance of customer-owned distributed generation (DG) units is quantified from different perspectives through an uncertainty study. A Monte Carlo-based method is applied to assess the stochastic operation of the customer-owned DG units in the power distribution system. Several cases are studied to analyze the impact on system performance of using such generators, with the emphasis on benefits. The results of the studied cases show that proper operation of customer-owned DG units placed close to significant consumption centers offers several benefits which lead to significant energy savings and improvement in the performance indices while maintaining the cost-effectiveness. Furthermore, based on the energy demand, different electricity price scenarios considering a cost sensitivity analysis are performed to indicate how the variations in electricity price influence each scenario’s feasibility. It is concluded that implementation of a proper energy purchase policy, and allocating the benefits of DG units to the owners, improves the economic performance of their investments and encourages customer DG developers to connect DG to the distribution network. [Copyright &y& Elsevier]

In this work, voltage evaluation after power components energization such as transmission line, transformer and shunt reactor is analyzed using artificial neural network (ANN)-based approach. Throughout the initial phase of system restoration, unexpected overvoltage may happen due to nonlinear interaction between the unloaded transformer and the transmission system. Such an overvoltage might damage some equipment and delay power system restoration. In the cases of transformer and shunt reactor energization, ANN is trained with the worst case scenario of switching angle and remanent flux which reduce the number of required simulations for training ANN. Moreover, for achieving good generalization capability for developed ANN, equivalent parameters of the network are used as ANN inputs. The simulated results for a partial of 39-bus New England test system show that the proposed technique can estimate the peak values and duration of overvoltages during network switching with good accuracy. [ABSTRACT FROM AUTHOR]

This paper presents an intelligent approach to evaluate switching overvoltages during power equipment energization. Switching action is one of the most important issues in power system restoration schemes. This action may lead to overvoltages that can damage some equipment and delay power system restoration. In this work, transient overvoltages caused by power equipment energization are analyzed and estimated using artificial neural network (ANN)-based approach. Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD), and directed random search (DRS), were used to train the ANNs. In the cases of transformer and shunt reactor energization, ANNs are trained with the worst case scenario of switching angle and remanent flux which reduce the number of required simulations for training ANN. Also, for achieving good generalization capability for developed ANN, equivalent parameters of the network are used as ANN inputs. The simulated results for a partial of 39-bus New England test system, show that the proposed technique can estimate the peak values and duration of switching overvoltages with good accuracy and EDBD algorithm presents best performance. [ABSTRACT FROM AUTHOR]

Overvoltages caused by switching operation of power system equipments might damage some equipment and delay power system restoration. This paper presents a comparison between transmission line (TL) models for overvoltages study and investigates which TL model is most proper for every case study. Both simulation time and accuracy factors of TL models are considered for selecting best TL model. Various cases of switching of transformer, shunt reactor, capacitor bank, and transmission line are investigated and simulation results for a partial of 39-bus New England test system, show that the proposed TL model evaluation increase accuracy and reduce simulation time (accelerate power system restoration) properly. [ABSTRACT FROM AUTHOR]

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]

The shunt reactors located on both line terminals and substation bus-bars are commonly used on long extra high voltage (EHV) transmission systems for controlling voltage during load variations. In a small power system that appears in an early stage of a black start of a power system, an overvoltage could be caused by core saturation on the energization of a shunt reactor with residual flux. 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. A harmonic index has been introduced that it's minimum value is corresponding to the best-case switching time. In addition, in this paper an artificial neural network (ANN) is used to estimate the optimum switching instants for real time applications. ANN is trained with equivalent circuit parameters of the network, so that de-veloped 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 demon-strated. [ABSTRACT FROM AUTHOR]

In this paper an intelligent-based approach is introduced to evaluate harmonic overvoltages during three-phase transformer energization. In a power system that appears in an early stage of a black start of a power system, an overvoltage could be caused by core saturation on the energization of a three-phase transformer with residual flux. Such an overvoltage might damage some equipment and delay power system restoration. A new approach based on worst case determination is proposed to reduce time-domain simulations. Also, an artificial neural network (ANN) has been used to estimate the temporary overvoltages (TOVs) due to three-phase transformer energization. Three learning algorithms, delta-bar-delta (DBD), extended delta-bar-delta (EDBD), and directed random search (DRS), were used to train the ANNs. ANN Training is performed based on equivalent circuit parameters of the network; thus trained ANN is applicable to every studied system. The developed ANN is trained with the worst case of the switching condition and remanent flux, and tested for typical cases. The simulated results for a partial of 39-bus New England test system, show that the proposed technique can estimate the peak values and durations of switching overvoltages with good accuracy and EDBD algorithm presents best performance. [ABSTRACT FROM AUTHOR]

One of the most important issues in power system restoration is overvoltages caused by transformer switching. These overvoltages might damage some equipment and delay power system restoration. This paper presents a radial basis function neural network (RBFNN) to study transformer switching overvoltages. To achieve good generalization capability for developed RBFNN, equivalent parameters of the network are added to RBFNN inputs. The developed RBFNN is trained with the worst-case scenario of switching angle and remanent flux and tested for typical cases. The simulated results for a partial of 39-bus New England test system show that the proposed technique can estimate the peak values and duration of switching overvoltages with good accuracy. [ABSTRACT FROM AUTHOR]

This paper presents an artificial intelligence application to measure switching overvoltages caused by shunt reactor energization by applying analytical rules. In a small power system that appears in an early stage of a black start of a power system, an overvoltage could be caused by core saturation on the energization of a reactor with residual flux. A radial basis function (RBF) neural network has been used to estimate the overvoltages due to reactor energization. Equivalent circuit parameters of network have been used as artificial neural network (ANN) inputs; thus, RBF neural network is applicable to every studied system. The developed ANN is trained with the worst case of the switching angle and remanent flux and tested for typical cases. The simulated results for a partial of 39-bus New England test system show that the proposed technique can measure the peak values and duration of switching overvoltages with good accuracy. [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]

Abstract: This paper presents a practical reactive power valuation method, for the purpose of the reactive power pricing, in which the VAr value is decomposed into two components. These components are basically different from those already employed in the literature. Using a security constrained optimization model, the proposed scheme allocates two specific amounts of MW transactions to each generator as a dynamic reactive power source. One of these allocated amounts represents the contribution of the VAr resource to the shipment of active power, while the other one represents the contribution to the protection of the system against voltage instability. The final VAr value is derived using the weighted sum of these associated components. Application of the proposed method has been illustrated using the IEEE 30-bus test system. The results of reactive power valuation for this sample network, in different operating conditions, are evaluated and discussed. [Copyright &y& Elsevier]

Abstract: This paper proposes a new adaptive control algorithm for a three-phase current-source shunt active power-line conditioner (APLC) operating under unbalanced and distorted network conditions. This control scheme aims at compensation of network’s reactive power, elimination of active power’s oscillating components, compensation of network current and voltage harmonic contents resulting in sinusoidal waveforms, and equilibrating the drawn power from the source evenly between the three-phases. Unlike many of the existing methods, the proposed strategy does not require any coordinate transformations or complicated calculations. The reference signals for the hysteresis-band current controlled voltage-source converter (HBCC-VSC) are generated by passing the measured current and voltage signals through two layers of modified adaptive notch filters (ANFs). To ensure superb performance and minimum total harmonic distortion (THD) level of the power system, parameters of the HBCC-VSC are obtained using differential evolution (DE) optimization algorithm. The proposed strategy is simple, easily implementable, and robust against uncertainty or variations of power system parameters and loads. The effectiveness of the proposed control scheme is validated by simulation results of a selected network under various load and power system conditions. [Copyright &y& Elsevier]