Tuning and real-time evaluation of a four-loop cascaded control scheme for a Combustion Turbogenerator

Development of control systems for power plants must demonstrate satisfactory real-time performance in both, the development and the target platforms, before going into actual operation. This paper presents the tuning procedure of a four-loop cascaded control scheme for a gas-turbine driven electric generator, and the real-time performance evaluation, against a conventional three-loop control scheme, when controlling speed (omega), terminal voltage (V), active power (P) and reactive power (Q). The cascaded-scheme considers parallel-cascaded and series-cascaded configurations using P. and VQ control couples for the turbine and the generator, respectively. The proposed control scheme, based on discrete-time PID controllers, is compared to a conventional P omega-V control scheme by means of real-time simulation experiments in a PC-based development platform. Simulations are carried out at the start-up, synchronization and generation stages. Results show that the four-loop P omega-VQ parallel-series scheme has better performance than the conventional P omega-V three-loop scheme

Technical assessment of small-scale wind power for residential use in Mexico: A Bayesian intelligence approach.

Nowadays, the global energy system is in a transition phase, in which the integration of renewable energy is among the main requirements for attenuating climate change. Wind power is a major alternative to supply clean energy; hence, its widespread penetration is being pursued in all end-use sectors. In particular, it is currently noteworthy to analyze the feasibility of deploying small-scale wind power technology to provide cleaner and cheaper energy in the residential sector. As a first step, a technical assessment must be carried out to provide crucial information to intensive energy consumers, providers of small-scale wind power technology, electric energy distribution utilities, and any other party, to help them decide whether or not to deploy small-scale wind turbines. With this aim, we propose to perform such an analysis using a suitable probabilistic paradigm to solve complex decision-making problems with uncertainty, namely Bayesian Intelligence, since wind resources and energy demands are intermittent variables, properly characterized by probability distribution functions. Then, the problem of determining the technical feasibility can be formulated as an investigation into whether or not small-scale wind turbine technology can produce enough energy to cover the excess demand of intensive energy residential consumers to get off high-priced tariffs. For this purpose, we introduce a novel model based on probabilistic reasoning to assess the suitability of small-scale wind turbine technology to produce the said energy, taking into consideration the availability of wind resources and the energy pricing structure. To demonstrate the usefulness and performance of the proposed model, we consider a case study of deploying 5 and 10 kW wind turbines and analyze the feasibility of their implementation in Mexico, where the energy pricing structure and scattered wind resource availability pose difficult challenges

Long-Term Estimation of Wind Power by Probabilistic Forecast Using Genetic Programming

Given the imminent threats of climate change, it is urgent to boost the use of clean energy, being wind energy a potential candidate. Nowadays, deployment of wind turbines has become extremely important and long-term estimation of the produced power entails a challenge to achieve good prediction accuracy for site assessment, economic feasibility analysis, farm dispatch, and system operation. We present a method for long-term wind power forecasting using wind turbine properties, statistics, and genetic programming. First, due to the high degree of intermittency of wind speed, we characterize it with Weibull probability distributions and consider wind speed data of time intervals corresponding to prediction horizons of 30, 25, 20, 15 and 10 days ahead. Second, we perform the prediction of a wind speed distribution with genetic programming using the parameters of the Weibull distribution and other relevant meteorological variables. Third, the estimation of wind power is obtained by integrating the forecasted wind velocity distribution into the wind turbine power curve. To demonstrate the feasibility of the proposed method, we present a case study for a location in Mexico with low wind speeds. Estimation results are promising when compared against real data, as shown by MAE and MAPE forecasting metrics