This paper presents a comprehensive comparative analysis between rate-of-change-of-frequency (ROCOF) and vector-surge (VS) relays for distributed generation islanding detection. The analysis is based on the concepts of detection-time versus active power-imbalance curves and critical active power imbalance. Such curves are obtained through dynamic simulations. The performance of these devices considering different scenarios is determined and compared. Factors such as voltage-dependent loads, generator inertia constant, and multidistributed generator systems are analyzed. False operation of these relays due to faults in adjacent feeders is also addressed. Results show that ROCOF relays are more reliable to detect islanding than vector surge relays when the active power imbalance in the islanded system is small. However, ROCOF relays are more susceptible to false operation than VS relays.

Generation of electric energy through wind turbines is one of the practically inexhaustible alternatives of generation. It is considered a source of clean energy, but still needs a lot of research for the development of science and technologies that ensures uniformity in generation, providing a greater participation of this source in the energy matrix, since the wind presents abrupt variations in speed, density and other important variables. In wind-based electrical systems, it is essential to predict at least one day in advance the future values of wind behavior, in order to evaluate the availability of energy for the next period, which is relevant information in the dispatch of the generating units and in the control of the electrical system. This paper develops ultra-short, short, medium and long-term prediction models of wind speed, based on computational intelligence techniques, using artificial neural network models, Autoregressive Integrated Moving Average (ARIMA) and hybrid models including forecasting using wavelets. For the application of the methodology, the meteorological variables of the database of the national organization system of environmental data (SONDA), Petrolina station, from 1 January 2004 to 31 March 2017, were used. A comparison among results by different used approaches is also done and it is also predicted the possibility of power and energy generation using a certain kind of wind generator.

The performance characteristics of rate of change of frequency (ROCOF) relays for embedded generation islanding detection are investigated. The concepts of detection-time versus power-imbalance curve and critical power imbalance are proposed. These concepts are employed to determine the factors that may affect the islanding detection capability of ROCOF relays. The factors analysed are voltage-dependent loads, load power factor, generator inertia constant, feeder length and X/R ratio and multi-embedded generators. In addition, false operation due to faults on adjacent feeders is investigated. The results show that ROCOF relays may become ineffective for islanding detection if the power imbalance in the islanded system is smaller than 15%. In addition, these devices have a high risk of false operation.

Accurate solar radiation forecasting is essential to operate power systems safely under high shares of photovoltaic generation. This paper compares the performance of several machine learning algorithms for solar radiation forecasting using endogenous and exogenous inputs and proposes an ensemble feature selection method to choose not only the most related input parameters but also their past observations values. The machine learning algorithms used are: Support Vector Regression (SVR), Extreme Gradient Boosting (XGBT), Categorical Boosting (CatBoost) and Voting-Average (VOA), which integrates SVR, XGBT and CatBoost. The proposed ensemble feature selection is based on Pearson coefficient, random forest, mutual information and relief. Prediction accuracy is evaluated based on several metrics using a real database from Salvador, Brazil. Different prediction time-horizons are considered: 1 h, 2 h and 3 h ahead. Numerical results demonstrate that the proposed ensemble feature selection approach improves forecasting accuracy and that VOA performs better than the other algorithms in all prediction time horizons.

The uncoordinated charging of several plug-in electric vehicles in a parking garage can potentially overload distribution transformer reducing its lifetime. Attempting to mitigate this issue, this paper proposes a smart charging method to minimize electricity consumption costs and avoid transformer overloading, considering a charging station integrated with photovoltaic (PV) generation and battery energy storage system (BESS). The optimal coordination among all these elements is investigated assessing transformer hottest-spot temperature and loss-of-life, considering a case study with time-of-use rate and meteorological data from Texas, USA. In addition, an economic analysis is developed to evaluate the viability of the project, with a sensitivity study considering different charging fees and variation in the daily number of vehicles parked in the garage. The results show the proposed approach is feasible, yields tangible financial benefits and can preserve distribution transformer life.

This paper presents a methodology to improve the power system economical dispatch from a voltage stability margin perspective. The time horizon under discussion is the short-term operation planning. The proposed method is based on active/reactive power re-dispatch for normal operation, and also minimum load shedding strategies in case of critical contingencies. The actions are taken in the direction provided by modal participation factors computed for generator and load buses. The generators with negative impact on system margin, which are indicated by the modal index, are penalized with high costs on the objective function of the optimal power flow program used to run the re-dispatch process. Results of this work show a decrease on system losses and significant increase on voltage stability margin as well as on system reactive reserves. In addition, this work presents a study considering critical contingencies, for which is proposed an optimal load shedding strategy also based on modal participation factors to identify the most adequate buses for load shedding purposes. Finally, the proposed methodology is applied considering a typical hour-to-hour daily load curve, and the method presented very good performance since it considerably increases voltage stability margin for the insecure intervals.