Manoel A. Carvalho

Application of wavelet and neural network models for wind speed and power generation forecasting in a Brazilian experimental wind park

The wind speed and wind generation forecasting are of extreme importance to aid in the planning studies and scheduled operation of hydrothermal and wind systems. This kind of generation is in the incipient phase in Brazil; however, the perspectives are mainly exciting aiming for increasing the potential of electricity generation. The use of wind power for producing electricity can create uncertainties in the generation. Therefore, the development of wind forecasting models is essential to integrate this kind of energy source with the generation system in an effective way. This work proposes the application of Artificial Neural Networks - ANN to produce a tool capable of accomplishing the wind speed forecasting. The ANN model is created using input data preprocessing by the Wavelet Transform - WT to extract important characteristics of the wind speed. Outputs of several ANNs show clearly the potential of the model based on WT compared with the others.

Recurrent neural networks solving a real large scale mid-term scheduling for power plants

This paper deals with an application of artificial neural network (ANN) to solve the operation planning problem of generation systems in the mid-term operation horizon. This problem is related to economic power dispatch that minimizes the overall production cost while satisfies the load demand. These kinds of problem are large scale optimization problems in which the complexity increases with the planning horizon and the accuracy of the system to be modeled. This paper considers the two-phase optimization neural network which solves linear and quadratic programming problems. These networks are based on the solution of a set of differential equations that are obtained from a transformation of an augmented Lagrange energy function. This network also provides the corresponding Lagrange multiplier associated with each constraint which is the marginal price. The results indicate that the developed ANN model provides optimal scheduling of hydro, thermal and wind power plant towards the minimal operation cost.

Combining Multiple Artificial Neural Networks Using Random Committee to Decide upon Electrical Disturbance Classification

An ANN-based automatic classifier for power system disturbance waveforms was developed. Actual voltage waveforms were applied in the training process. Signals were processed in two steps: i) decomposition through wavelet transformation up to the 5th decomposition level; ii) the resultant wavelet coefficients are processed via PCA, reducing the input space of the classifier to a much lower dimension. The classification was carried out using a combination of six MLPs with different architectures: five representing the first to fifth-level details, and one representing the fifth-level approximation. The RPROP algorithm was applied for training the networks. Network combination was formed using random committee which builds an ensemble of randomized base classifiers. Experimental results with real data indicate that the random committee is clearly an effective way to improve disturbance classification accuracy when compared with the simple average and the individual models.

Forecasting models of wind power in Northeastern of Brazil

Wind Power forecasting is extremely important to assist in planning and programming studies for the operation of wind power generation. Several studies have shown that the Brazilian wind potential can contribute significantly to the electricity supply, especially in the Northeast Brazil, where winds present an important feature of being complementary in relation to the flows of the San Francisco River. However, using wind power to generate electricity has some drawbacks, such as uncertainties in generation and some difficulty in planning and operation of the power system. This paper presents actual results of wind power forecasting for two parks in the region of northeastern Brazil with four different models. Models that perform power generation forecasting using the forecasted wind speeds and the wind power curve of the park are called Wind to Power (W2P) and models that perform power generation forecasting using the historical power generation of the park are called Power to Power (P2P). The models perform forecasting of wind power generation with 6 hours ahead, discretized by 10 minutes and with 5 days ahead, discretized by 30 minutes. Models that directly predict the wind power (P2P) got the best results. These models were more suitable for use in the power systems operation planning considering the wind parks analyzed in northeastern Brazil.

Wind forecasting and wind power generation: Looking for the best model based on artificial intelligence

Wind forecasting is extremely important to assist in planning and programming studies for the operation of wind power generation. Several studies have shown that the Brazilian wind potential can contribute significantly to the electricity supply, especially in the Northeast, where winds present an important feature of being complementary in relation to the flows of the San Francisco River. However, using wind power to generate electricity has some drawbacks, such as uncertainties in generation and some difficulty in planning and operation of the power system. This work proposes and develops models to forecast hourly average wind speeds and wind power generation based on Artificial Neural Networks, Fuzzy Logic and Wavelets. The models were adjusted for forecasting with variable steps up to twenty-four hours ahead. The gain of some of the developed models in relation to the reference models was of approximately 80% for forecasts in a period of one hour ahead. The results showed that a wavelet analysis combined with artificial intelligence tools provides more reliable forecasts than those obtained with the reference models, especially for forecasts in a period of 1 to 6 hours ahead.

Improving Disturbance Classification by Combining Multiple Artificial Neural Networks

An ANN-based automatic classifier for power system disturbance waveforms was developed. Actual voltage waveforms were applied in the training process. Signals are processed in two steps: i) decomposition through wavelet transformation up to the 5th decomposition level; ii) the resultant wavelet coefficients are processed via PCA, reducing the input space of the classifier to a much lower dimension. The classification is carried out using a combination of 3 MLPs with different architectures. The RPROP algorithm is applied for training the networks. Network combination was formed and the final decision of the classifier corresponds to the combination output with the highest value. The results showed to be quite promising for five disturbance types tested so far: sags, swells, harmonics, oscillatory transients and interruptions, as well as in the particular case of no disturbance.

Combined artificial neural network and adaptive neuro-fuzzy inference system for improving a short-term electric load forecasting

The main topic in this work was the development of a hybrid intelligent system for the hourly load forecasting in a time period of 7 days ahead, using a combination of Artificial Neural Network and Adaptive Neuro-Fuzzy Inference System. The hourly load forecasting was accomplished in two steps: in the first one, two ANNs are used to forecast the total load of the day, where one of the networks forecasts the working days (Monday through Friday), and the other forecasts the Saturdays, Sundays and public holidays; in the second step, the ANFIS was used to give the hourly consumption rate of the load. The proposed system presented a better performance as against the system currently used by Energy Company of Pernambuco, named PREVER. The simulation results showed an hourly mean absolute percentage error of 2.81% for the year 2005.

Combining artificial neural networks and heuristic rules in a hybrid intelligent load forecast system

In this work, an Artificial Neural Network (ANN) is combined to Heuristic Rules producing a powerful hybrid intelligent system for short and mid-term electric load forecasting. The Heuristic Rules are used to adjust the ANN output to improve the system performance. The study was based on load demand data of Energy Company of Pernambuco (CELPE), which contain the hourly load consumption in the period from January-2000 until December-2004. The more critical period of the rationing in Brazil was eliminated from the data file, as well as the consumption of the holidays. For this reason, the proposed system forecasts a holiday as one Saturday or Sunday based on the specialists information. The result obtained with the proposed system is compared with the currently system used by CELPE to test its effectiveness. In addition, it was also compared to the result of the ANN acting alone.

On Efficient Implementation of Interior-Point Based Optimal Power Flows in Rectangular Coordinates

This paper deals with the efficient computational implementation of nonlinear optimal power flows representing complex bus voltages in rectangular coordinates and solution by primal-dual interior-point methods. Emphasis is given to the initialization of variables, the efficient assembling of Jacobian and Hessian matrices, sparse data structures, solution of the linear systems, and choice and setting of algorithm parameters. Although the discussions are based on the implementation of the minimum active power losses problem, they can be extended to several other optimal power flow models

Investigating the use of Echo State Networks for prediction of wind power generation

This paper presents the results of models created for prediction of wind power generation using Echo State Networks (ESN). An echo state network consist of a large, randomly connected neural network, the reservoir, which is driven by an input signal and projects to output units. ESN offer an intuitive methodology for using the temporal processing power of recurrent neural networks without the hassle of training them. The models perform forecasting of wind power generation with 6 hours ahead, discretized by 10 minutes and with 5 days ahead, discretized by 30 minutes. These models use ESNs with spectral radius greater than 1 and even then they can make predictions with good results. The forecast horizons presented here fall in medium-term forecasts, up to five days ahead, which is an appropriate horizon to subsidize the operation planning of power systems. Models that directly predict the wind power generation with ESNs showed promising results.