A new linear quadratic regulator model to mitigate frequency disturbances in the power system during cyber-attack

This paper proposes a new model integrating a linear quadratic regulator (LQR) controller to mitigate frequency disturbances in the power system during cyber-attack, called as linear quadratic regulator to mitigate frequency disturbances (LQRMFD). As we know, most of the existing models have a common problem with achieving significant performances in mitigating dynamic response parameters, such as frequency deviation and settling time. However, the key aspect of LQRMFD is to mitigate the above issues with remarkable performance improvements. An uncommon and stable power system model has been considered in LQRMFD first to reach such a goal. A numerical problem has been solved to derive a certain characteristic equation, where the Routh-Hurwitz array criterion is applied for determining the stability of such a power system. After that, a state-space equation is developed from the power system to activate the LQR controller. Thus, achieving diversity and eliminating the redundancy of the power system considered can be obtained in LQRMFD. To evaluate the performance of LQRMFD, a series of experiments was conducted using the MATLAB-Simulink tool. Rigorous comparisons were also made among the results of LQRMFD, self-implemented and existing models. Furthermore, a detailed analysis was reported among those models to find the performance improvement of LQRMFD in percentage

Improvement of voltage stability and loadability of power system employing the placement of unified power flow controller using artificial neural network

This paper proposes a voltage stability and loadability improvement model of power systems by incorporating the optimal placement of flexible alternating current transmission systems (FACTS) using an artificial neural network (ANN) called OPFANN. The key aspect of this model is to identify the weakest lines which having the most probability of voltage collapse utilized for placing FACTS devices. As installing a new power system network with rapidly increasing power demand cannot be possible, the operator usually operates the power system close to the stability limit. In this regard, continuous monitoring and improvement of system voltage stability and loadability of the existing system are vital issues for energy management systems nowadays. However, the proposed OPFANN introduces a more straightforward and faster scheme for voltage stability monitoring systems using ANN. Intelligent and reliable data samples have been designed to train the ANN based on two-line voltage stability indices (LVSI) techniques. Compared with other works, OPFANN effectively improves voltage stability and loadability at the load point by installing the unified power flow controller (UPFC) FACTS devices to the weakest lines. OPFANN can provide information on voltage collapse points using ANN and reduce the further computational cost of LVSI. Finally, OPFANN ensures faster and more secure operation of the power system

Artificial Neural Networks based Prediction of Insolation on Horizontal Surfaces for Bangladesh

AbstractIn this work, Artificial Neural Network (ANN) based model for predicting the solar radiation in Bangladesh has been developed. Standard multilayer, feed-forward, back-propagation neural networks with different architecture have been designed using MATLAB's Neural Network tool. The training and testing data of 64 different locations spread all over Bangladesh were obtained from the NASA surface meteorology and solar energy database. The input parameters for the network are: latitude, longitude, elevation, month, average daylight hours, mean earth temperature and relative humidity while the solar insolation on horizontal surfaces are the target parameters. The overall Mean Square Errors (MSE) during training 0.0029, regression value of 0.99707, small percentage of error (0.16% to 1.71%) in response to unknown input vectors indicate that the developed model can be used reliably for predicting insolation of locations where there is no direct irradiance measuring instruments

Mechanical transplanting and urea fertilizer deep placement into soil simultaneously with a walk behind type mechanical transplanter increased yield and benefit of wetland rice (Oryza sativa L.) cultivation in Bangladesh

AbstractA research was conducted to evaluate the BRRI developed rice transplanter cum prilled urea applicator (RTPUA) during Boro 2017-2018. Urea fertilizer deep placement mechanism successfully incorporated in the walk behind type rice transplanter (ARP-4UM). Engine power available at high rpm (more than 1800 rpm of the walking type rice transplanter) was conveyed to the applicator with the arrangement of a belt-pulley, worm gearing, shaft-bearing, chain-sprocket and bevel gear with engage-disengage facility resulting 22 rpm of the applicator main shaft considering transplanting speed of the transplanter. Impellor type mechanism was connected with the main shaft of the applicator to dispense the prilled urea fertilizer to the output channel. Field experiments were conducted on a silty loam soil in Gopalgonj, clay loam soil in Kushtia, silty clay loam soil in Gazipur and on a clay soil in Netrakona. Four transplanting and urea fertilizer treatments were T1 = Mechanical transplanting (MT) along with urea deep placement together (70% urea), T2= MT + hand broadcasting of urea (UHB) at three equal split, T3 = Hand transplanting (HT) and UHB at three equal split and T4 = Control (-N). In four locations, theoretical and actual field capacity of the rice transplanter was found higher to some extent without fertilizer deep placement mechanism during transplanting due to extra fertilizer re-filling time and slow of operation. Field capacity was found more in clay and clay loam soil. Average of four locations and three replications, actual field capacity of the rice transplanter was found 0.119 ha hr-1. In the field, saving percentage of urea fertilizer varied from 25.1 to 28.5% against the calibration of 30% of urea saving due to variation of the machine, operational speed and more penetration of the driving wheel in the field during operation etc. It was also observed that grain yield varied with the mode and rate of urea fertilizer application. Mechanical transplanting along with urea fertilizer deep placement (70% of recommended dose) gave significantly higher yield compared to manual transplanting and hand broadcasting of urea as well as higher benefit-cost ratio (BCR). Mechanized transplanting along with urea fertilizer deep placement in wet land rice establishment is thus a promising technology for rice farmers in Bangladesh as well as in Asia