Improper placement of distributed generation (DG) units in power systems would not only lead to an increased power loss, but could also jeopardise the system operation. To avert these scenarios and tackle this optimisation problem, this study proposes an effective method to guide electric utility distribution companies (DISCOs) in determining the optimal size and best locations of DG sources on their power systems. The approach, taking into account the system constraints, maximises the system loading margin as well as the profit of the DISCO over the planning period. These objective functions are fuzzified into a single multi-objective function, and subsequently solved using genetic algorithm (GA). In the GA, a fuzzy controller is used to dynamically adjust the crossover and mutation rates to maintain the proper population diversity (PD) during GA's operation. This effectively overcomes the premature convergence problem of the simple genetic algorithm (SGA). The results obtained on IEEE 6-bus and 30-bus test systems with the proposed method are evaluated with the simulation results of the classical grid search algorithm, which confirm its robustness and accuracy. This study also demonstrates DG's economic viability relative to upgrading substation and feeder facilities, when the incremental cost of serving additional load is considered.

a b s t r a c tSingularities and uncertainties in arm configurations are the main problems in kinematics robot control resulting from applying robot model, a solution based on using Artificial Neural Network (ANN) is proposed here. The main idea of this approach is the use of an ANN to learn the robot system characteristics rather than having to specify an explicit robot system model.Despite the fact that this is very difficult in practice, training data were recorded experimentally from sensors fixed on each joint for a six Degrees of Freedom (DOF) industrial robot. The network was designed to have one hidden layer, where the input were the Cartesian positions along the X, Y and Z coordinates, the orientation according to the RPY representation and the linear velocity of the end-effector while the output were the angular position and velocities for each joint, In a free-of-obstacles workspace, off-line smooth geometric paths in the joint space of the manipulator are obtained.The resulting network was tested for a new set of data that has never been introduced to the network before these data were recorded in the singular configurations, in order to show the generality and efficiency of the proposed approach, and then testing results were verified experimentally.

Power systems employ measures of reliability indices to indicate the effectiveness a power system to perform its basic function of supplying electrical energy to its consumers. The amount of energy required in a generating system to ensure an adequate supply of electricity is determined using analytical and simulation techniques. This study focuses on reviewing the generation reliability assessment methods of power systems using Monte Carlo simulation (MCS) and variance reduction techniques (VRTs). MCS is a very flexible method for reliability assessment of the power systems, by the sequential process it can imitate the random nature of the system components and can be broadly classified into two, sequential and non-sequential simulations. A brief introduction to MCS is provided. Unlike analytical methods, MCS can be used to quantitatively estimate the system reliability in even the most complex system generating capacity situations. The major drawback of the MCS is that it requires more computational time to reach for converging with estimated the values of reliability indices. This paper presents an effective methods for accelerating MCS in power system reliability assessment. VRT used is to manipulate the way each sample of an MCS is defined in order to both preserve the randomness of the method and decrease the variance of the estimation. In addition, the study presents detailed descriptions of generation reliability assessment methods, in order to provide computationally efficient and precise methodologies based on the pattern simulation technique. These methodologies offer significantly improved computational ability during evaluations of power generation reliability.

One of the most current and widely discussed factors that could lead to the ultimate end of man's existence and the world at large is global warming. Global warming, described as the greatest environmental challenge in the 21st century, is the increase in the average global air temperature near the surface of the Earth, caused by the gases that trap heat in the atmosphere called greenhouse gases (GHGs). These gases are emitted to the atmosphere mostly as a result of human activities, and can lead to global climate change. The economic losses arising from climate change presently valued at $125 billion annually, has been projected to increase to $600 billion per year by 2030, unless critical measures are taken to reduce the spate of GHG emissions. Globally, the power generation sector is responsible for the largest share of GHG emissions today. The reason for this is that most power plants worldwide still feed on fossil fuels, mostly coal and consequently produce the largest amount of CO2 emitted into the atmosphere. Mitigating CO2 emissions in the power industry therefore, would significantly contribute to the global efforts to control GHGs. This paper gives a brief overview of GHGs, discusses the factors that aid global warming, and examines the expected devastating effects of this fundamental global threat on the entire planet. The study further identifies the key areas to mitigate global warming with a particular focus on the electric power industry.

Abstract:One of the most crucial prerequisites for effective wind power planning and operation in power systems is precise wind speed forecasting. Highly random fluctuations of wind influenced by the conditions of the atmosphere, weather and terrain result in difficulties of forecasting regardless of whether it is short-term or long-term. The current study has developed a method to model wind speed data predictions with dependence on seasonal wind variations over a particular time frame, usually a year, in the form of a Weibull distribution model with an artificial neural network (ANN). As a result, the essential dependencies between the wind speed and seasonal weather variation are exploited. The proposed model utilizes the ANN to predict the wind speed data, which has similar chronological and seasonal characteristics to the actual wind data. This model was applied to wind speed databases from selected sites in Malaysia, namely Mersing, Kudat, and Kuala Terengganu, to validate the proposed model. The results indicate that the proposed hybrid artificial neural network (HANN) model is capable of depicting the fluctuating wind speed during different seasons of the year at different locations.