Carolina M. Affonso

Comparative analysis between ROCOF and vector surge relays for distributed generation applications

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.

Long-Term Voltage Stability Analysis of Variable Speed Wind Generators

This paper presents the impacts caused by the integration of variable speed wind turbines on long-term voltage stability. The technologies used are fully rated converter (FRC) and doubly fed induction generator (DFIG) with two control strategies: grid-side converter (GSC) at unity power factor, which is usually adopted, and GSC controlling reactive power. Also, this paper considers wind turbines capability curves and its variable limits, since they are subject to several limitations that changes with the operating point and wind speed. This study also considers the dynamic models of over excitation limiter (OEL) and on-load tap changers (OLTC) combined with static and dynamic loads using time domain simulations. Different penetration levels of wind generation are analyzed. The results show that long-term voltage stability can be improved when GSC of DFIG is controlling reactive power. Moreover, the capability curve plays an important role in this analysis since reactive power is a key requirement to maintain voltage stability.

Planned islanding for Brazilian system reliability

This paper presents an impact study case of a new operational strategy regarding planned islanding operation of distributed generation. Traditionally, interconnection standards avoid islanding operation of distributed generation due to the concerns of equipment failure and safety issues. However, allowing the islanded operation of distributed generation may enhance reliability to final consumers and decrease outage cost by providing an alternative power source when there is an interruption in the upstream network. This paper performs dynamic studies in a Brazilian system which operates in the islanded mode during fault to supply critical loads. The analysis presented in this paper includes the behavior of the islanded system under load following and load rejection, faults, the load-frequency control and varying short-circuit level. The results show that this operational strategy can potentially bring many benefits to the distributed generator owner and customers.

Impact of distributed generation in steady state, voltage and transient stability — Real case

This paper investigates the influence of a synchronous distributed generation on a Brazilian real network. The distributed generation represents a small hydro power plant with total capacity of 30MW. The model of the network is developed in a professional computer software package. Simulations are carried out considering voltage dependency of static loads and different DG penetration levels. The technical aspects analyzed are steady-state voltage profile, electrical power losses, voltage and transient stability. Also, some adequacy aspects are analyzed, related to national and international standards. It was found that the distributed generation enhanced the overall system performance.

Pricing analysis in the Brazilian energy market: A decision tree approach

There is a general consensus that electricity price forecasting is an important task nowadays since power market players are interested in the maximization of profit and the minimization of risk. In this context, this paper proposes the use of Data-Mining techniques to predict the short-term electricity price in the Brazilian market. In Brazil, the market model adopted has unique characteristics with a centralized form of dispatch due to the predominance of hydro generation. To apply the proposed prediction model, all features of the Brazilian electricity market are considered, such as the transmission restrictions among geo-electrical regions and the price dependency with storage energy in reservoirs. In the proposed prediction model, the electricity price is the dependent variable and the monthly time series data sets from the Brazilian system (such as power load, stored energy and thermal generation) are the independent variables. First, clustering of the data samples is performed to group similar behavior of the attributes. After that, a decision tree algorithm is applied to extract if-then rules from database. The rules obtained allow the identification of attributes that most influence the short-term electricity price. Results show that the proposed model can be an attractive tool to all electricity market players to forecast the short-term electricity price and mitigate the risks in purchasing power.

Long-term voltage stability of distribution systems with induction generators

This paper presents an investigation about the longterm or small-disturbance voltage stability of distribution systems with induction generators by using time-domain nonlinear dynamic simulations. Results show that the presence of induction generators may decrease the system voltage stability margin. It was verified that in the maximum loading point, if the system loading is increased even more, then the induction generator accelerates to a high speed, becoming unstable and leading the system to a voltage collapse.

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.

Demand-Side Management to Improve Power System Security

This paper investigates the potential benefits provided by demand-side management programs applied in power system congested areas to improve voltage security. System congested areas are indicated by the expanded modal analysis technique from a new perspective of active power variations. The study simulates the relocation of the demand from congested areas during peak periods in response to price signals. A comprehensive study is carried out analyzing the performance of the proposed method. In addition, this work presents a contingency analysis to evaluate system performance under atypical conditions. These techniques are tested in a real-life Brazilian network. The results proved that this method can be an efficient strategy to improve system security and reliability preventing possible blackouts, also alleviating investments in the system, making more efficient usage of the energetic resources available

Energy price forecasting in the North Brazilian market using NN - ARIMA model and explanatory variables

This paper proposes a new hybrid approach for short-term energy price prediction. This approach combines ARIMA and NN models in a cascaded structure and uses explanatory variables. A two step procedure is applied. In the first step, the explanatory variables are predicted. In the second one, the energy prices are forecasted by using the explanatory variables prediction. The prediction time horizon is 12 weeks-ahead and is applied to the North Brazilian submarket, which adopts a cost-based model with unique characteristics of price behavior. The proposed strategy is compared with traditional techniques like ARIMA and NN and the results show satisfactory accuracy and good ability to predict spikes. Thus, the model can be an attractive tool to mitigate risks in purchasing power.

Soft sensor for fluoridated alumina inference in gas treatment centers

The Gas Treatment Center performs a key role in the aluminum smelting process, since it strongly influences the chemical and thermal stability of the electrolytic bath through fluoridated alumina. Therefore this variable should be considered to keep the bath chemistry under control. However, the fluorine concentration measurement in fluoridated alumina is very time-consuming and that information becomes available only after a while. By using Artificial Neural Network we developed a Soft Sensor capable to estimate the fluorine concentration in fluoridated alumina, and to provide that information to plant engineers in a timely manner. This paper discusses the methodology used and the results of an implemented Soft Sensor using Neural Networks on fluorine estimation in fluoridated alumina from a Gas Treatment Center in an important Brazilian Aluminum Smelter.