Takaaki Sekiai

Auto Tuning Algorithm for Vigilance Parameter in the Adaptive Resonance Theory Model and its Application to Fault Diagnosis System of Thermal Power Plants

In order to operate thermal power plants safely, early detection of equipment failure signs is one of the most important issues. To detect the signs before an alarm is issued in the existing monitoring system, we developed a fault diagnosis system based on the Adaptive Resonance Theory (ART). The vigilance parameter, which is a design parameter in the ART model, was shown to influence the diagnosis accuracy. Fixing the value of the vigilance parameter also had problems: we needed to use time-consuming trial and error, and we needed to have empirical knowledge of the parameter tuning. In this paper, using simulations we demonstrated the relationship between the vigilance parameter and diagnosis accuracy. Furthermore, to overcome the problems of the vigilance parameter tuning, we have proposed an auto tuning algorithm to make the parameter the optimum value. The performance of the proposed algorithm was evaluated in several case studies using gas turbine plant data. The effectiveness of the proposed algorithm was confirmed by the obtained results.Copyright

A Search Space Reduction Method for Optimizing Sequential Control by Hypothetically Achievable Bound Estimation of the Objective Function

Metaheuristic methods such as genetic algorithm, simulated annealing, and artificial bee colony algorithm methods take much time to obtain an optimal solution, particularly when a large scale simulator is employed for estimating the state of the environment.In this paper, a search space reduction method for accelerating the optimization of sequential control systems is proposed. The proposed method estimates a hypothetical achievable bound of the objective function and uses it as the prior knowledge to reduce the search space. The hypothetical achievable bound is estimated using the fact that large scale plants consisting of multiple components are in many cases controlled in a sequential manner.The size of the search space reduction obtained by the proposed method is evaluated by an example problem that minimizes the start-up time of a thermal power plant. As a result, the size of the search space is reduced by 65%. The proposed method does not lose the optimality of the optimization method to be accelerated. In addition, this method is also applicable to optimization problems other than sequential control if the hypothetical achievable bound of the objective function is estimable without measuring the state of the environment or using the simulator.Copyright

A Robust and Flexible Control System to Reduce Environmental Effects of Thermal Power Plants

Regulations on environmental effects due to such issues as nitrogen oxide (NOx) and carbon monoxide (CO) emissions from thermal power plants have become stricter[1]; hence the need for compliance with these regulations has been increasing. To meet this need, several technologies with respect to fuel combustion, exhaust gas treatment and operational control have been developed[2-4]. The technologies for the fuel combustion and the exhaust gas treatment include a low NOx burner and an air quality control system, and they are capable of reducing impact on the environment as physical and chemical implementation methods. The operational control technology for the thermal power plants is constantly required to receive changes in operational conditions. It is difficult to realize operational control which responds to combustion properties. To overcome this issue, the operational control must be able to reduce NOx and CO emissions flexibly in accordance with such changes. Robustness is also required in such control because the measured NOx and CO data often include noise. Therefore, a robust and flexible plant control system is strongly desired to reduce environmental effects from thermal power plants efficiently. Several studies have proposed plant control technologies to reduce the environmental effects[4-10]. These technologies are classified into two types of methods: model based and non-model based methods. The former methods include an optimization algorithm and a numerical model to estimate plant properties using neural networks (NNs)[11,12] and multivariable model predictive control[13]. The optimization algorithm searches for optimal control signals to reduce NOx and CO emissions using the numerical model. The latter methods have no models and they generates the optimal control signals by fuzzy logic[14]. A fuzzy logic controller outputs the optimal control signals for multivariable inputs using fuzzy rule bases. The fuzzy rule bases are based on a priori knowledge of plant control, and they can be tuned by parameters. These technologies require the measured plant data for initial tuning of the model properties and the parameters of rules when the technologies are installed in plants. It usually takes some time to collect enough plant data. In addition, the search for control