The Transient Stability Constrained Optimal Power Flow (TSCOPF) has become an important tool for power systems today. TSCOPF is a nonlinear optimisation problem, making its solution difficult, especially for small power systems. This paper presents a new optimisation method that incorporates Fitness-Distance Balance (FDB) with the Artificial Ecosystem Optimisation (AEO) algorithm to improve the solution quality in multi-dimensional and nonlinear optimisation problems. The proposed method, named the Fitness-Distance Balance Artificial Ecosystem Optimisation (FDBAEO), also has the capacity to solve the TSCOPF problem efficiently. In order to evaluate the proposed algorithm, it was tested on IEEE CEC benchmarks and on an IEEE 30-bus test system for the TSCOPF problem. Simulation results were compared with the basic AEO algorithm and other current meta-heuristic methods reported in the literature. The results showed that the proposed method was more effective in converging at the global optimum point in solving the TSCOPF problem compared to the other algorithms. This situation indicates that the design changes made in the decomposition phase of the AEO were more suitable for simulating the operation of the algorithm in the real world. The FDBAEO has exhibited a promising performance in solving both single-objective optimisation and constrained real-world engineering design problems.

In this study, symbiotic organisms search (SOS) algorithm is proposed to solve the dynamic economic dispatch with valve-point effects problem, which is one of the most important problems of the modern power system. Some practical constraints like valve-point effects, ramp rate limits and prohibited operating zones have been considered as solutions. Proposed algorithm was tested on five different test cases in 5 units, 10 units and 13 units systems. The obtained results have been compared with other well-known metaheuristic methods reported before. Results show that proposed algorithm has a good convergence and produces better results than other methods.

In this paper, a robust artificial intelligence (AI) algorithm is applied to overcome challenges at power density prediction especially at the installation process of wind power plant. This algorithm also explores relationships between the meteorological parameters and power density. Importance degree of parameters on power density is converted numerical weighting values independently from each other. Thus, the effects of the wind speed, the wind direction, the temperature, the damp, the pressure on power density could be modelled. Besides, experimental study shows that the prediction accuracy and stability of the applied method superior than traditional AI-based techniques.

This paper proposes the application of Symbiotic Organisms Search (SOS) Algorithm to solve the various Economic Load Dispatch (ELD) problems. Both classical ELD problem which has smooth fuel cost function and nonconvex ELD problem which has nonconvex and discontinuous fuel cost function due to considering of some practical constraints like valve point effects, ramp rate limits and prohibited generating zones have been solved in the study. Three different test cases have been used to show the efficiency and reliability of the proposed algorithm. 38-unit test system has been used for classical ELD and 3-unit and 15-unit test systems have been used for nonconvex ELD problem. Results have been compared to various heuristic methods reported before in the literature and they show that proposed algorithm converges to the global optimum in early iterations and can produce superior results than others in the solution of ELD problems which have both smooth and nonconvex and discontinuous fuel cost function.

In this study, a decision support system has been developed for land mine detection and classification. Data obtained from detector based magnetic anomaly have been used to classify the land mines. With this classification, it is decided that whether obtained data belongs to a land mine or not, and the type of mine. The meta-heuristic k-NN classifier (HKC) has been used in developed decision support system. Consequently, it is seen that decision support system detects the presence of mines and decides the type of mine with 100% success for measurements in a certain range, and the proposed classifying method shows much higher performance than traditional instance-based classification method.

In the classic ANN-based approaches, the synchronous motor parameters mostly could be modeled with n-hidden layered networks. It is an important challenge in driver software development is to realize complex mathematical models in real time environments and circuits. This paper presents an Adaptive Artificial Neural Network-based (AANN) method to easily model excitation current of synchronous motors. It has a simple network structure and less processing units (nodes) more than classic ANN. The main purpose of this method are to estimate the excitation current and also to assist designers to model excitation current easily and to develop complex driver software with low degree programming effort while improving the efficiency of classic ANN-based approach. In the adopted approach, the activation functions of nodes in the hidden layers of multilayered feed forward neural network have been determined by using a heuristic method. The experimental results have shown that the proposed method successfully creates single-hidden layered simple networks have less node number than classic ANN-based solutions and achieves the tasks in high estimation accuracies.

This paper presents a novel system is to develop to determine the suitability of a location for installation of solar power stations. Necessary data including speed and direction of wind, solar radiation and rainfall are received from a meteorology station, and data acquired are then converted to the labels. Finally, the labels are evaluated in a Naïve Bayes algorithm to determine the suitability of the location for the installation and axial structure of a Solar Power Plant. This helps to determine complicated calculations by means of the support system developed.

In this study, an intelligent decision making system which determines the compatibility of operating parameters has been designed for the travelling wave ultrasonic motor (TWUSM). The system designed converts input parameters and operating temperature into useful data by rule-based inference mechanism and these data are evaluated in naive Bayes classifier. The proposed decision making system gives effective results in the compatibility determination of operating parameters for speed stability of the TWUSM.

Among the renewable energy types, wind energy gets popularity in these days. To install a new wind energy turbine, measurement and evaluation of the meteorological data are quite important. In this paper, a novel system is developed for the installation of wind turbines. Firstly, necessary data including the speed and the direction of wind, the solar insolation, the ultraviolet radiation and the rainfall are received from a meteorology station, and then data acquired are converted into useful information using a rule-based inference mechanism. Finally, the useful information obtained are evaluated in a Naive Bayes algorithm. The power and the size of wind turbine are determined automatically using data measured. Thus, some complicated calculations including more than parameters related to fields where the large-sized wind turbines will be installed can easily be accomplished by means of the powerful decision support system developed.

Active power systems have been used in power system intensively. Selection of a suitable filter is very important to solve power quality problems perfectly. Since filter selection depend on a lot of parameters of power system, decision support systems can be employed in filter selection. In this paper, "An Intelligent Decision Support System, IDSS" is proposed. The system determines efficiency of three phase shunt active power filter for the power quality problems. The system developed converts the parameters obtained into the useful data at rule-based inference mechanism then the useful data are evaluated in Naive Bayes classifier. Thus a low cost decision support system has been developed to guide the users in the rate of suitability of three phase shunt active power for solution the power quality problems.

In this paper, a new user modeling approach has been developed to determine the knowledge status of user in Adaptive Educational Hypermedia System (AEHS). The approach is based on forming the domain model and determining relations among elements of that model to decide the knowledge status of user from domain model independently. The proposed method provides flexibility to individual learning or studying steps. In comparison to the literature, the proposed approach enables quick and powerful adaptation for matching instructional needs of users. The difficulties faced in developing such a system are purifying the gathered data, obtaining and evaluating useful data and providing a powerful adaptation effect in a short time.

In this study, an adaptive educational system based on AHAM reference was introduced to teach stepper motors being an important topic in the vocational education. In the system, a domain model for stepper motors was designed and a user overlay model was created based on the domain model. Naive Bayes Classifier (NBC), commonly used in adaptive educational hypermedia systems, was preferred in the application for modeling students in vocational education. Students were classified using NBC as "beginner", "intermediate" or "advanced" based on their knowledge level about the domain model. The system gathers data from the users (students) on-line as well as updates the user models continuously. Thus, the system got the on-line adaptability. Moreover, the system has such abilities as guiding and serving additional explanations according to the users' preferences and defects in the knowledge domain.