mohammad karkhaneh

In the smart grid era, load forecasting is the building block of a secure, reliable, and economic power system. Therefore, many researchers have spent a lot of time trying different methods to improve load forecasting accuracy. In recent years, one of the rather frequently used methods is the decomposition of load series into high and low-frequency components using wavelet transform, which reportedly has shown impressive results in some articles. In this paper, through several simulations, it’s demonstrated that despite some of the benefits of the wavelet transform, it can produce unrealistic results due to the border distortion problem. In fact, our work investigates the practical efficiency of wavelet transform in the load forecasting task from the viewpoint of a system operator who is forecasting the next day’s load profile every day. To this end, Multiple Linear Regression (MLR) and Artificial Neural Network (ANN) models are used with wavelet transform to conduct experiments on New York City electric load dataset.

Methane is one of the most widely used gases in industries with a high flammability potential. This study aimed to evaluate the efficiency of ventilation systems installed on methane valve pits based on hazardous areas classification.

This study was implemented in a steel industry in Qom Province in 2019. The tools used in this study were a DELTA OHM pitot tube (DO-2003) to measure wind speed, EPA Protocol for equipment leak emission estimates (U.S. Environmental Protection Agency) and IEC-60079-10 for evaluating the safety of ventilation of methane valve pits.

The methane LELm was about 0.0334 kg/m3, and the volume of the release area was approximately VZ = 0.053 m3. The expected leak emissions were within the Vz < 0.1 m3 range. The ventilation system embedded on methane distribution pipelines was not effective for openings with diameters of more than 0.3 mm and the volume of gas inside the valve pits would quickly exceed high ventilation border which might lead to a dangerous accumulation of gas in the valve pits.

Given that a very small opening or leak in gas transmission valves may lead to the formation of an explosive atmosphere, it is essential to monitor methane before entering the valve pit area and performing any operations on valve pits.

This paper addresses adaptive observer design problem for joint estimation of the states and unknown parameters for a class of nonlinear systems which satisfying one-sided Lipschitz and quadratic inner bounded conditions. It’s shown that the stability of the proposed observer is related to finding solutions to a quadratic inequality consists of state and parameter errors. A coordinate transformation is used to reformulate this inequality as a linear matrix inequality (LMI). Sufficient conditions that ensure the existence of adaptive observer are expressed in forms of LMIs، which are easily tractable via standard software algorithms. If the proposed conditions are satisfied، then the state estimation errors are guaranteed to converge to zero asymptotically while، the convergence of the parameters is guaranteed when a persistence of excitation condition is held. The effectiveness of the proposed method is shown by simulation for the joint estimation of states and parameters of a numerical system.