Ubiratan Holanda Bezerra

Impacts over the distribution grid from the adoption of distributed harmonic filters on low-voltage customers

This work presents a study of distributed passive harmonic filter design to minimize harmonic distortions caused by nonlinear loads in residential customers. The main objectives in this study are: 1) to improve the power factor, 2) to reduce current and voltage distortions to standard limits, and 3) to reduce electrical losses. In this sense, several measurements that were carried out in some domestic customers of the metropolitan distribution grid of Belem City, in the North of Brazil, have characterized as the main harmonic distribution current sources the third, fifth, and seventh harmonic components. According to the results obtained and considering the impacts caused in the distribution grid, a distributed filtering strategy is proposed using passive-tuned filters of low costs to be installed in the customers.

Simultaneous Fault Section Estimation and Protective Device Failure Detection Using Percentage Values of the Protective Devices Alarms

This paper proposes a new approach to fault diagnosis in electrical power systems, which presents an aspect little explored in the literature that is the protective device failure detection together with the fault section estimation, since the majority of the methodologies so far proposed to fault diagnosis are limited to the fault section estimation alone. The proposed methodology makes use of operation states of protective devices as well as information related to the protection philosophy. Initially, these data undergo a preprocessing step to convert the format of 0 and 1 to percentage values. The conversion to percentage values allows the use of artificial neural networks, whose numbers of inputs do not depend on the number of alarms of the protection philosophy, or the type of bus arrangement or the number of circuit breakers. This allows the same set of neural networks to be trained and applied in different power systems with different protection schemes and bus arrangements. The proposed system has five neural networks, each containing few neurons and requiring 30 μs to perform fault diagnosis. The proposed system was trained considering the IEEE 57-bus system, containing different selective protection schemes, and subsequently tested in the IEEE 14-bus, 30-bus, and 118-bus systems, and Eletronorte 230-kV real power system.

Use of Wavelet Transform and Generalized Regression Neural Network (GRNN) to the Characterization of Short-Duration Voltage Variation in Electric Power System.

This work presents the use of the wavelet transform and computational intelligence techniques to quantify voltage short-duration variation in electric power systems, with respect to time duration and magnitude. The wavelet transform is used to determine the event duration, as well as for obtaining a characteristic curve relating the signal norm as function of the number of cycles for a waveform without disturbance that is used as reference for the calculation of the magnitude of the event. A generalized regression neural network (GRNN) is used to interpolate not stored points of the characteristic curve. The method is part of a process to automate the post operation signal analysis in electric power systems, and it is used to quantify the voltage short-duration variation magnitude of previously selected signals. The method has been shown efficient, and some results obtained from the application of this method to power system real signals are presented.

Alternative energy sources in the Amazon

This paper discusses the evaluation of energy potential of palm oil for the generation of electricity in isolated communities. In Brazil, the energy sector culture has historically been directed almost exclusively toward major projects geared to meet the demands of those sectors of society that have the greatest economic and political influence. Prioritizing industrialization and an accelerated urbanization, they have oriented the national energy system toward centralized production of enormous blocks of energy adapted to meet major urban concentration consumption but incapable of satisfying the needs of a large part of the population that inhabits the rural areas. These small- and medium-sized communities are sometimes isolated from the developed urban centers and not connected to the conventional electricity networks. In this scenario, the Amazon region stands out due to its huge territorial extension and low demographic density, which is scattered among islands and other locations not easily accessible. As a rule, these areas lack electricity, and, when they do have it, supply is precarious and provided through fossil fuels for electricity production

Reactive power control of direct drive synchronous wind generators to enhance the Low Voltage Ride-Through capability

This paper explores the performance of alternative voltage control strategy applied to direct drive synchronous wind generators, more specifically with permanent magnetic (PMSG). The reactive power control of the grid-side converter is investigated for voltage control purposes. In Brazil, the Grid National Operator (ONS) requires that wind turbines stay connected to the grid during voltage dips, but does not stipulate yet the need of reactive power injection during faults in the electric grid. It just specifies the Low Voltage Ride-Through (LVRT) capability curve for voltage dips that the wind generators should follow to avoid the trip of the under-voltage relay. Criteria of the synchronous wind generators protection are evaluated starting from short-circuit simulations in a test grid with adoption of the Brazilian grid code, without reactive power injection, being compared with those of other countries that adopt reactive power injection curves.

Impact of different DFIG wind turbines control modes on long-term voltage stability

This paper presents a comprehensive study showing the impacts of different doubly fed induction generator (DFIG) wind turbines control modes on long-term voltage stability. The study considers fixed voltage control and power factor control modes. The analyses also consider the dynamic models of Over Excitation Limiter (OEL) and On Load Tap Changers (OLTC) combined with static and dynamic loads using time domain simulations. A hypothetical network is used for the scenario of 20% load increase. The impact of each control strategy is studied and the resulting change in long-term system stability is quantified, as well as the interactions between OLTC and OEL equipments. The results show that the manner in which DFIGs are operated will have a significant impact on system stability.

Design of optimal PI controllers for doubly fed induction generators in wind turbines using genetic algorithm

This paper presents a design procedure based on evolutionary computation, more specifically on genetic algorithm (GA) to obtain optimal PI controllers to the static converter connected to the rotor of doubly fed induction generators (DFIGpsilas), in variable speed wind generation systems connected to the electrical grid. The converter of the DFIG has a protection system that monitors continuously the machine operation, emitting a blocking command when the limit of the rotor current is violated, due to transient disturbances occurring in the electrical grid. That implies in deactivating the DFIG control loops which affect negatively the system global controllability. This control action of the DFIG converter is accomplished by PI controllers, which gain adjustments are not a trivial task, due to the nonlinearities and the high complexity of the system. In this way an appropriate fitness function is derived to express the time domain evaluation of the DFIG with the objective to assure the DFIG continuous operation even under a fault condition and improve at the same time its transient behavior as compared with the formal methodology to design PI controllers using poles placement. The results obtained, confirm the efficiency of the proposed control design.

Alarm processing and fault diagnosis in power systems using Artificial Neural Networks and Genetic Algorithms

This work approaches relative aspects to the alarm processing problem and fault diagnosis in system level, having as purpose filter the alarms generated during a outage and identify the equipment under fault. A methodology was developed using Artificial Neural Networks (ANN) and Genetic Algorithms (GA) in order to resolve the problem. This procedure had as initiative explore the GA capacity to deal with combinatory problems, as well as the ANN processing speed and generalization capacity. Such strategy favors a fast and robust solution.

Decision support in power systems based on load forecasting models and influence analysis of climatic and socio-economic factors

This paper presents a decision support system for power load forecast and the learning of influence patterns of the socio-economic and climatic factors on the power consumption based on mathematical and computational intelligenge methods, with the purpose of defining the future power consumption of a given region, as well as to provide a mean for the analysis of correlations between the power consumption and these factors. Here we use a linear modelo of regression for the forecasting, also presenting a comparative analysis with neural networks, to prove its efectiveness; and also Bayesian networks for the learning of causal relationships from the data.

Genetic algorithms and treatment of multiple objectives in the allocation of capacitor banks in an electric power distribution system

Genetic Algorithm (GA) is a non-parametric optimization technique that is frequently used in problems of combinatory nature with discrete or continuous variables. Depending on the evaluation function used this optimization technique may be applied to solve problems containing more than one objective. In treating with multi-objective evaluation functions it is important to have an adequate methodology to solve the multiple objectives problem so that each partial objective composing the evaluation function is adequately treated in the overall optimal solution. In this paper the multi-objective optimization problem is treated in details and a typical example concerning the allocation of capacitor banks in a real distribution grid is presented. The allocation of capacitor banks corresponds to one of the most important problems related to the planning of electrical distribution networks. This problem consists of determining, with the smallest possible cost, the placement and the dimension of each capacitor bank to be installed in the electrical distribution grid with the additional objectives of minimizing the voltage deviations and power losses. As many other problems of planning electrical distribution networks, the allocation of capacitor banks is characterized by the high complexity in the search of the optimum solution. In this context, the GA comes as a viable tool to obtaining practical solutions to this problem. Simulation results obtained with a real electrical distribution grid are presented and demonstrate the effectiveness of the methodology used.