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The optimal planning for distributed generations (DGs) associated with photovoltaics (PVs) in the utility-owned distribution system is crucial for increasing high penetration of renewables while against practical system operation constraints. Such PV-DG planning is categorized as a complicated mixed-integer nonlinear programming (MINLP) problem and is extremely difficult to solve by using conventional methods. In recent years, several bio-inspired metaheuristic algorithms have been proposed to tackle various complicated real-parameter optimization problems. This paper proposes a two-stage approach including a new bio-inspired algorithm, Coyote Optimization Algorithm (COA), to solve the large-scale MINLP PV-DG sizing problem considering different load levels. The objective function terms under consideration include the total system power loss and voltage regulator tap changes at different load levels while against limits of rms bus voltages, tap changes, and PV-DG constraints at each candidate bus. The proposed method is tested using the IEEE 123-bus unbalanced benchmark system and an actual utility distribution network. Results obtained are then compared with those obtained by a classic MINLP solver-based and four other bio-inspired methods. Moreover, results also show that the proposed method leads to lower loss, a minimum number of regulator tap changes, and higher PV penetration capacity among the compared methods and is suitable for solving the large-scale PV-DG planning problem in distribution systems.

Voltage sags are often manifested as the permanent or incipient faults occurred in the power system because of equipment malfunctions or failures. The incipient faults which are originally self-cleaning faults may repeatedly occur and gradually develop to a permanent fault after its first occurrence. The incipient fault detection is considered as a crucial task in predictive maintenance for power equipment such as transformers, circuit breakers, and underground cables. This paper proposes a hybrid method for incipient faults detection and classification. The proposed method firstly adopts two extraction methods and two feature measures to obtain seven peculiar features from voltage waveforms of abnormal phases recorded by power quality monitors at substations in a transmission network. Then, a feature selection method and the support vector machine combined with particle swarm optimization are applied to classify various types of incipient faults. Test results show that the proposed method contributes relatively accurate classification of incipient faults and can be employed as a useful tool for condition monitoring of major power equipment in the transmission network.

Voltage fluctuations caused by rapid reactive power consumption of loads and photovoltaic or wind generator output variations may produce flickers and cause undesired effects on electric power components and human eyes. This paper presents a hybrid approach for voltage fluctuations assessment by using a synchrosqueezing transform-based algorithm. The proposed method first gives characterization of voltage fluctuations through accurate extraction of the measured voltage envelope by the Hilbert transform. The synchrosqueezing transform and an unsupervised clustering method called mean shift are then applied to determine the number of frequency components and corresponding frequencies. It is followed by the implementation of the bandpass filters to detect the magnitude of each frequency component. The proposed hybrid method is tested by both simulations and field measurements. Results compared with other commonly seen methods show that the proposed method provides a more accurate assessment.

The papers in this special section examine modeling and simulation methods for the analysis and design of advanced energy conversion systems. Energy conversion lies aEnergyveral fronts including materials, conversion methods, power electronics, and controls have created new opportunities for efficient energy conversion from both the conventional and new sources of energy. Our urgent need to solve many critical problems with regards to the sustainability and security of our energy system as well as the ever-increasing environmental challenges facing humanity have further spurred unprecedented opportunities for creation of innovative solutions to address these challenges.

This paper presents a two-stage approach for planning photovoltaic solar distributed generation (PV-DG) units considering load variations by using biogeography-based optimization (BBO) method. The objective function components include the system power loss, voltage regulator tap changes, and quadratic penalty term of rms voltage profile at different load levels while against system operating constraints. The proposed method is tested using an IEEE 123-bus benchmark network and results are compared with two other nature-inspired methods to show its superiority.

In the past years, the applications of solar energy have grown significantly in electricity generation. However, the fluctuations of PV power output create different negative impacts on reliability, stability, and dispatch in the connecting grid. The exact PV power generation forecast is thus crucial to stabilize the operation of a power grid. This paper presents a radial basis function neural network with decoupling method for day-ahead PV power generation forecast. Results are compared with autoregressive integrated moving average (ARIMA), back propagation neural network (BPNN), and radial basis function neural network (RBFNN), and the actual measured PV power outputs. It shows that the proposed model leads to more accurate and the computational efficient forecast on PV output.

The frequency range between 2 and 150 kHz has recently gained significant attention, triggered by standardization needs and increased emission in this wide frequency range. This paper gives an overview of the state-of-the-art concerning these so-called supraharmonics, and noticeably indicates the research challenges associated with waveform distortion in this frequency range, with emphasis on the following aspects: emission; propagation; interference; measurements; standardization; modelling and simulation.

Accurate forecasts of wind speed and wind power generation are essential for the effective operation of a wind farm. This paper presents an improved radial basis function neural network-based model with an error feedback scheme (IRBFNN-EF) for forecasting short-term wind speed and power of a wind farm, where an additional shape factor is included in the classic Gaussian basis function associated with each neuron in the hidden layer and a simple parameter initialization method is proposed to effectively find initial values of two key parameters of the basis function when performing neural network training. A wind farm near central Taiwan area connected to Taipower system is served as the measurement target. Provided with 24 h of input data at 10-min resolution (i.e. 6 × 24 input time steps) for training the proposed neural network, a look-ahead time up to 72 h (i.e. 6 × 72 forecasted output time steps) have been performed. Test cases for different months over 2014 are reported. Results obtained by the proposed model are compared with those obtained by four other artificial neural network-based forecasting methods. It shows that the proposed model leads to better accuracy for forecasting wind speed and wind power while the computational efficiency is maintained.

This paper presents an improved dynamic voltage-current model of an ac electric arc furnace (EAF) for multiple operation stages and for the study of propagations of voltage fluctuations in a power network associated with the EAF in operating stages. The EAF model is developed based on wavelet-transform and neural-network-based methods by training actual measured EAF current and voltage data. The proposed EAF model is then implemented in an actual 161-kV power system for simulations. The voltage fluctuations at other buses due to the EAF in operation are then assessed. Simulation and measured results show that the improved hybrid EAF model is accurate and is suitable for voltage fluctuation assessment when the actual measurements in the power network are limited or not available while the EAF is in operation or before the similar types of EAFs are to be installed in the system.

Penetration of grid connected renewable energy systems and smart loads based on power electronics technology has been increasing in low and medium voltage distribution. Power Electronics is a key technology for distribution networks which can transfer electrical power from renewable energy sources to grids or generate regulated frequency and/or voltage for different loads such as variable speed drives and battery chargers. New demands for a) cost and size reduction, b) performance and quality improvement and c) flexibility on power management have promoted power electronics applications extensively in industrial, commercial and residential sectors such as transportation, utility and home appliances in recent years.

This paper presents a study of the control strategies of three-phase grid-connected photovoltaic (PV) inverter to mitigate its harmonic current output and improve the power quality of the grid. The operation principle and topology of the PV inverter is analyzed by establishing the state-space mathematical model based on the adaptation of proportional resonant (PR) and quasi-proportional resonant (QPR) controllers. According to the characteristics of the PR ad QPR controllers, the theories for mitigating harmonic currents are reviewed. A three-phase ac microgrid with grid-connected inverter systems is then built on Matlab/Simulink and the performances of the inverter controllers for harmonic mitigations are verified by comparing the simulation results.

Forecasting wind speed or wind power generation is indispensable for the effective operation of a wind farm, and the optimal management of its revenue and risks. This paper proposes an improved radial basis function neural network structure for forecasting the wind power generation. Results are then compared with back propagation neural network (BPNN), BPNN with Levenberg-Marquardt (BPNN-LM), radial basis function neural network (RBFNN), and the actual measured wind power outputs. Test results show that the presented model can provide more accurate and stable time-horizons forecasting.

Solar power is a giant natural reactor that provides far more energy that the human needs. In recent years, there are many photovoltaic (PV) types of equipment installing in on-grid and off-grid systems. This paper proposed an enhanced grey theory system model with weight table for PV power output forecast. Actual measured PV power generation data had implemented the proposed scheme and the other original schemes including back propagation neural network (BPNN) model, radial basis function neural network (RBFNN) model, and classical grey theory system model. Results of studied case are based on the proposed scheme to compare with another original schemes. It demonstrates that more precise forecast is accomplished by using the enhanced grey theory system scheme with weight table.

In this paper, a stochastic approach is applied to determine the photovoltaic (PV) hosting capacity in a distribution feeder. The hosting capacity is determined accounting for the four voltage quality criteria: overvoltage, voltage deviation, dynamic voltage drop, and voltage unbalance. By applying the stochastic approach, a large number of potential PV deployment scenarios are simulated in a benchmark distribution feeder. While considering the unpredictability of future PV deployments, both sizes and locations are regarded as randomly generated factors. Simulations for assessments of voltage quality impacts are quantified and PV hosting capacities are statistically determined. In addition, smart inverter functions are included to improve voltage quality and enhance PV hosting capacity.

Photovoltaic (PV) power output forecasting is a crucial issue in the PV power industry and power company. Nevertheless, the disadvantage of the existing PV power output forecasting models is that they often ignore weather fluctuation and uncertain information between the irradiation and ambient temperature caused by moving cloud, which leads to short of forecasting accuracy. This paper is to present an improved radial basis function neural network structure (RBFNN) with multi- parameters forecasting scheme to overcome above drawbacks. Result of the case study acquired by the proposed scheme is compared with two typical artificial neural network forecasting methods including radial basis function neural network (RBFNN) and back propagation neural network (BPNN). It demonstrations that the proposed model leads to better precision for forecasting PV power output.

In this paper, a DC-coupled hybrid microgrid is investigated and a simulation-based electricity analysis manner by using the OPAL-RT real-time simulation is developed for the discussion of the energy-saving issue on the microgrid. Methodologies include modelling, simulation mechanism, and the development of control strategies are presented, and two different electricity price rules are introduced to calculate the residential electricity that from the investigated microgrid based on the designed scenarios. Results show that the benefit of electricity saving provided by the operation of the microgrid system are reported.

Drastic variations of reactive power consumed by electric arc furnaces (EAFs) often lead to significant voltage fluctuations at the connecting network bus and yield noticeable flickers of lighting devices as well as cause malfunctions of the electrical equipment. If the flicker severity levels are predictable, corrective solutions such as controls of EAF electrodes and reactive power compensators can be developed to mitigate the voltage fluctuations. This paper presents a hybrid approach that combines an improved radial basis function neural network (IRBFNN) and Grey model for the forecast of flicker severity levels. Field measurements are used to train and implement the forecasting model. Test results of ΔV10, short-term flicker severity (Pst) and long-term flicker severity (Plt) obtained by the proposed and five other methods are then under comparisons. Results indicate that more accurate flicker forecast is obtained by adopting the proposed method.

The importance of short-term wind power forecasting is significantly increased because of the demand of green energy and large-scale integration of the wind power plants in the electric network. In this paper, a Gaussian mixture model (GMM)-based radial basis function neural network is proposed to forecast the short-term wind power generation. Actual measured wind power output data are adopted to implement the proposed model. Test results of wind power obtained by autoregressive integrated moving average (ARIMA), back propagation neural network (BPNN), radial basis function neural network (RBFNN), support vector regression (SVR), and the proposed method are then under comparisons. Simulated results show that the presented method leads to more accurate wind power forecasting.

With the advances of technologies, high quality of electric power is in demand. The power quality (PQ) disturbances occurred in the power system may cause voltage and current deviation from their nominals, which can result in serious damages or equipment malfunctions. Therefore, efficient detection and classification of PQ disturbances in a transmission system is of great importance for maintaining reliable system operation. This paper presents a hybrid approach to classify PQ disturbances by using support vector machine (SVM), discrete wavelet transform (DWT), and discrete Fourier transform (DFT) to extract features from a good number of actual measured PQ events occurred in a transmission system. Results show that the proposed method can provide fast and accurate classifications of the measured PQ events.

This paper proposes a discrete wavelet transform (DWT) and radial basis function neural network (RBFNN)-based method for modeling the dynamic voltage-current (v- i)characteristics of the ac electric arc furnace (EAF). The objective of the study is to develop a complete model of the EAF including different operation stages, and the model can be used as a harmonics and flicker source in its connected power system for the power-quality penetration or mitigation study, where the developed model can be embedded in the power system implemented by a commonly seen simulation tool, such as Matlab/Simulink. In the study, a combination of the DWT and the sequential RBFNN with parameters initialization algorithm is proposed to build the EAF v- i characteristics with enhanced lookup tables for different operation stages, where the field measurements of the EAF voltage and current are used to train the RBFNN for modeling the EAF load. Simulation results obtained by using the proposed model are compared with different measured data. It shows that the solution procedure accurately models the EAF dynamic v- i behavior. The proposed method also can be applied to model other highly nonlinear loads to assess the effectiveness of compensation devices or to perform relative penetration studies.

This paper presents a review with a concise description and analysis of the fundamentals, characteristics, analytical details, merits, and drawbacks associated with existing methods in frequency and time domain for harmonic analysis in practical power networks. The description and analysis are centered on methods developed in the harmonic domain, hybrid frequency-time domain, and time domain, respectively. Validation of the reviewed methods for harmonic analysis, against one of the widely accepted digital simulators, such as EMTP, EMTDC, or MATLAB/SIMULINK, is reported in the cited individual contributions.

Magnitude and duration are important characteristics when performing tracking of voltage variations. Accurate characterization of voltage variations relies much on the precise identification of frequencies of the measured signals. Although the Prony's method can provide high resolution for frequency estimation, the computational burden in the root-finding process is a crucial problem. A modified Prony-based solution procedure for voltage variation tracking is proposed in this paper. By providing a set of filters, the transfer polynomial with high estimation order in Prony's model can be efficiently reduced. The performance of the proposed method is validated by testing the generated and actual measured voltage signals. Results are compared with those obtained by typical Prony's method, namely, fast Fourier transform, adaptive linear neural network, and Kalman filtering. It shows that the proposed method is more accurate regardless of interferences of the power system frequency deviation, harmonics, and interharmonics, where both voltage variations and harmonics can be simultaneously detected.

SUMMARY This paper proposes an efficient method to obtain the equivalent fundamental positive-sequence voltage for determining the shunt active power filter (APF) reference compensation currents, where the source voltage magnitude and phase angle at the fundamental frequency are detected by a simplified adaptive linear combiner neural network. The APF reference current calculator is developed and is realized on the DSP board through processor-in-the-loop (PIL) simulation for validating the effectiveness of the proposed APF control strategy, where the source voltages and the load currents are both distorted and unbalanced. In addition, results obtained based on the true positive-sequence fundamental source current after APF compensation and another classic approach are included for comparison. It is shown that results obtained by the PIL simulations with the proposed and compared methods agree very well. The proposed APF reference compensation current strategy is simple and practical, which provides a cost-effective and computationally efficient manner for the APF controller implementation with PIL simulations. Copyright © 2013 John Wiley & Sons, Ltd.

The purpose of unit commitment (UC) for electric utilities is to determine the optimal thermal unit on/off statuses and their MW generations over the scheduled time horizon. The UC problem is formulated to minimize the total generation cost, while the load demand, reserve requirements, and unit constraints are satisfied. Among the UC constraints, an adequate provision of reserve is important to ensure the security of the power system and the frequency-regulating reserve is essential to bring the system frequency back to acceptable level following the loss of a sizable online unit within seconds. In this paper, the authors present and solve a mixed-integer linear programming (MILP)-based UC problem including the frequency-regulating reserve (FRR) constraints to determine the optimal FRR requirements and unit MW schedules for an isolated power system. Simulation results are then compared with those obtained by Lagrangian relaxation-based approach and by the current Taipower operation practice. It is observed that favorable reserve and unit MW schedules are obtained by the proposed method while the system security is maintained.

This paper describes the challenges and lessons learned when developing industrial-grade data translators aimed for the interfacing of power-flow programs with Electromagnetic Transients Program-type programs. It has been found that the greatest challenges to overcome include: the lack, in the databases used in power-flow programs, of vital pieces of information necessary to perform transient studies; inconsistency in the format of data files; the presence of data entry mistakes in very large databases; the validation of the translated data; and the analysis of the large amount of data that transient simulations provide. Several examples are presented to show the implemented solutions. Finally, recommendations based on experience are made to help future developers of interfacing tools.

Unit commitment (UC) is to determine the optimal unit status and generation level during each time interval of the scheduled period. The purpose of UC is to minimize the total generation cost while satisfying system demand, reserve requirements, and unit constraints. Among the UC constraints, an adequate provision of reserve is important to ensure the security of power system and the fast-response reserve is essential to bring system frequency back to acceptable level following the loss of an online unit within a few seconds. In this paper, the authors present and solve a UC problem including the frequency-based reserve constraints to determine the optimal FRR requirements and unit MW schedules. The UC problem is solved by using Lagrangian Relaxation-based approach and compared with the actual system schedules. It is observed that favorable reserve and unit MW schedules are obtained by the proposed method while the system security is maintained.

A microgrid is associated with a low voltage distribution power network and inherits small modular generation systems and loads that have certain coordinated functions to provide the solution to supply premium power to remote or specific areas. Similar to conventional power systems, the energy management of distributed generation resources (DERs) is carried out to minimize the operation cost and maximize benefit of installation of DERS in a microgrid. This paper presents the process of implementing the short-term DER scheduling function for a dc microgrid. The optimal scheduling results for two operation modes are then reported.

Forecasting of wind speed and wind power generation is indispensable for the effective operation of a wind farm and the optimal management of revenue and risks. Hybrid forecasting of time series data is considered to be a potentially effective alternative compared with the conventional single forecasting modeling approaches such as autoregressive integrated moving average (ARIMA) and artificial neural network (ANN). Hybrid forecasting typically consists of a classic prediction model for the linear component of a time series and a nonlinear forecast model for the nonlinear component. This paper presents a hybrid approach combining ARIMA and radial basis function neural network for forecasting wind speed and wind power. Results obtained by a case study show that the proposed method is suitable for short-term forecasting applications.

With the increasing use of non-linear loads, the harmonic problems have become of great concerns. One of the solutions to harmonics is the application of shunt active power filters (APFs). The APF reference compensation strategy proposed in this paper adopts the backpropagation neural network (BPNN) to train the control algorithm and calculate the reference compensation current under steady state and varying load levels. Simulation results obtained by using MATLAB/Simulink show that the proposed BPNN-based APF control strategy can effectively mitigate harmonic currents generated by the nonlinear load.

This paper presents the implementation of power quality (PQ) analysis platform that can access different venders' PQ meter data being translated to Power Quality Data Interchange Format (PQDIF) files and view the data through a LabVIEW-based graphical user interface. The implemented platform also can manage the retrieved PQ data in a customized database. Performance tests show that the implemented system can meet the PQ measurement requirements for smart grid applications.

The demand of renewable energy resources has become an important issue for green power generation in many countries. With the increasing photovoltaic generation in the distribution system, voltage fluctuation problems often present in the PV-connected grid affected by the moving cloud shadows. This paper presents a study to evaluate impacts on voltage quality due to cloud shadows movement over high-penetration PVs in a distribution power system in southern Taiwan. The moving-cloud shadow model is built under Matlab/Simulink incorporated with OpenDSS and is applied to show voltage fluctuations. Results show that the developed simulation tool is suitable for assessment of the PV output versus voltage changes due to cloud moving shadow impacts.

SUMMARY In this contribution, a control strategy in phase coordinates abc is proposed as the reference current generator to be used by the shunt active power filter (APF) compensation device. The development of the proposed reference current generator is described in detail. Its control design is kept simple, and transformation between different frameworks is not needed, thus allowing the control strategy to be applicable for both three-phase and single-phase electric systems maintaining theoretical and practical advantages. The proposed control strategy for the reference current generator is tested using Matlab/Simulink® with the incorporation of the Real-Time Workshop® for the simulation of the shunt APF compensation in real time. Based on the reported real-time application, it is possible to obtain simulation conditions that are close to the true real-time environment under study using only a single computer, giving accurate results with relatively low-cost hardware and providing a more complete evaluation of the applied control strategy. The dynamic behavior and performance of the shunt APF, using the proposed reference current calculator, have shown remarkable results. Copyright © 2012 John Wiley & Sons, Ltd.

Voltage stability improvement is an important issue in power system planning and operation. Voltage stability of a system depends on the network topology and settings of reactive compensation devices. This research first proposes an improved voltage stability index ( IVSI ) for network systems, and then presents an optimization method for reactive compensation devices settings. To solve the optimization problem, this work introduces the hybrid differential evolution (HDE) to determine tap settings of on-load tap changing (OLTC) transformers, excitation settings of generators or synchronous condensers (SCs), and locations with sizes of static var compensators (SVCs). The performance of this technique is verified using the IEEE 30-bus power flow test system. The results show that the proposed method using the improved voltage stability index and the optimization method is able to effectively enhance voltage stability of a system and reduce line losses.

This paper proposes an efficient backward and forward sweep algorithm for the three-phase power flow analysis of weakly meshed distribution system. In the backward sweep, the Kirchhoff's circuit laws are firstly used to calculate each line current and the upstream bus voltage of each line or a transformer branch. Next, the linear proportional principle is applied to find the ratios of the specified (or new) and the calculated bus voltages of the decomposed real and imaginary parts of the network. The compensation method is used to break meshes and to calculate the current injections at each end bus created by breaking the mesh. A useful bus indexing scheme is also proposed to determine the updated voltages at the junction and/or terminal busses for the six decomposed networks in the forward sweep. The procedure repeats and stops after the voltage mismatch of each pair of end busses of the opened mesh and the mismatch of the calculated and specified voltages at the substation bus are less than the predefined threshold value. The proposed method is tested by three IEEE benchmark systems with meshes, and with default system data and different system conditions. Results show that the proposed algorithm is efficient, accurate, and robust in comparing with the commonly used backward/forward sweep method for weakly meshed networks.