Global Positioning System (GPS) has become important and necessary in daily life. It is possible to reach any destination using GPS, which is included in many lands and marine applications. In this work, GPS was applied to a real navigation boat, integrated with the inertial navigation system (INS) device, and installed on the boat. The navigational devices were linked to the (mission planer) program, through which the results of the navigation process were shown. The system can provide better navigation performance accuracy and reliability due to the integration between GPS and INS. The data extracted from the navigation devices are processed using the Gaussian process (GP) prediction algorithm, to perform the GPS synchronization with the INS and predict the GPS cut-off values for specified periods. The prediction results of the GP algorithm are effective for the cut-off GPS data as the apparent error amount of the algorithm is low. In addition the inertial navigation system provides the location, speed, and position of the boat, but it contains a cumulative error that increases over time. On the other hand, the GPS better accuracy with a lower data rate than the INS, so the integration system between INS/GPS is necessary. It must be developed to overcome the negatives in both systems. Two types of integration were introduced and implemented herein: loosely and tightly. From the results obtained, one can see that the tight system is better at improving errors.
The optical properties of a grown gallium nitride (GaN) thin film on a porous silicon (P-Si) substrate was investigated. A Photo-electrochemical etching method was used to synthesize the Psi substrate, and a physical deposition method (pulsed laser deposition) of 1064 nm Q-switch Nd: YAG laser with a vacuum of 10−2
mbar was used to grow a thin layer of GaN on a prepared P-Si substrate. X-Ray diffraction displayed that GaN film has a high crystalline nature at the (002) plane. The photoluminescence of GaN film exhibited ultraviolet PL with a peak wavelength of 374 nm corresponding to GaN material and red PL with a peak wavelength of 730 nm corresponding to Psi substrate. The absorption coefficient of the P-Si substrate and grown GaN thin film was obtained from the absorption calculation of UV-Vis diffused spectroscopy at ambient temperature in the 230–1100 nm wavelength range. Extinction coefficients, optical energy gap, and refractive index of both the P-Si substrate and the grown GaN thin film have been determined, respectively. The direct optical energy gaps of both the P-Si substrate and grown GaN have also been determined using three methods: Plank’s relation with photoluminescence (PL) spectroscopy, Tauc'relation, and Kubulka-Munk argument with Uv-Vis diffused spectroscopy. It was observed that the optical energy gap of the P-Si substrate was 2.1 eV, while the grown GaN thin film had a multi-optical energy gap of 3.3 eV and 1.6 eV. A good agreement has been obtained between these mentioned methods.
Due to increased load demands, distribution systems suffer from high power losses, low voltage levels, high current, and low reliability. To solve these problems, integrate distributed generator units (DG) into the distribution system. DG units are among the most popular methods of improving distribution system reliability, power losses, and bus voltage improvement through the placement and selection of distributed generator units in an optimal location and size. This work proposed Enhanced Particle Swarm Optimization (EPSO) technology to find the optimum location and size of DG units to reduce power losses, improve bus voltage level, and employed the Transient Electricity Analyzer (ETAP) to evaluate the reliability of the distribution system network. ETAP is a programming tool for modeling, analysis, design, optimization, operation, and control of electrical power systems. These findings may be useful in conducting reliability assessments and correctly utilizing dispersed generation sources for future power system growth by power utilities and power producer companies. The proposed method was employed on the Iraqi distribution system (AL-Abasia distribution network (F10 feeder)). After adding three DG units to the distribution system, theer adding three DG units to the distribution system, the obtained simulation results showed a significant reduction in power losses, voltage levels, and reliability enhancement.
This work presents an Energy Management System (EMS) constructed on dissimilar power balance modes and dynamic grid power to activate a DC-link microgrid with a solar (PV-array) generator and battery storage. In addition, the option of requesting adjustable power from the grid to encounter load demand is also presented. Based on the availability of solar sources, battery state, and grid power, energy management offers the appropriate references for all modes. Six power balance options are defined based on power supply, storage system, and grid mobility to match the load requirement. The aims are to reduce energy usage and upsurge the life of the storage device. The microgrid is controlled to maintain a consistent DC-link voltage and manage the battery current depending on the mode of operation. Using MATLAB\SIMULINK software, the anticipated energy management system, which is based on power balancing modes, is tested under various scenarios. The simulation results demonstrated that the microgrid operated admirably, with seamless switching between power balance modes.
The performance of antennas is critical to ensuring reliable wireless communication and robust data transmission. Unfortunately, antennas’ performance gets degraded when loaded with lossy materials. This paper presents the numerical and experimental evaluation of low-profile antennas’ performance when integrated with photovoltaic (PV) solar cells for potential use in smart grid and green power networks. Such integrated antennas can serve as a communication unit and sensors to monitor PV solar cells. For convenience, a microstrip patch antenna was used in this assessment study, where the antenna was designed, numerically simulated, and experimentally tested. After which, it was installed on top of a PV solar cell at different orientations. The antenna is designed to operate within the 2.45 GHz ISM band. Based on the results, the antenna performed well when placed at the middle of the PV solar cell with a peak gain of 2.58 dBi compared to other placements within the PV solar cell. Moreover, creating a small air gap between the antenna and the PV solar cell results in better performance. Based on the findings of this study, the antenna has satisfactory performance when integrated with PV cells, which is promising to deploy in many applications, including smart grid networks.
Inductance – Capacitance – Inductance (LCL) filter is a very attractive candidate for renewable energy system applications due to its high efficiency. High attenuation of the switching frequency harmonics, small size, low fee, and improving the overall harmonic distortion (THD). This paper presents how voltage is affected by increased loads or voltage sag. Therefore it is necessary to control it with certain controllers. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is used as an intelligent controller, the voltage constraint as training data for ANFIS obtained from PI. The filter works in a good connection between the inverter and the grid and rewords unwanted harmonics from using the inverter. The mathematical models for the LCL filter are investigated. The proposed approach shows more effective results than previous performance for voltage controlling and harmonic reduction. It gives overshoot (0.5%), steady state error (0.005), settling time (0.03 sec), rise time (0.005 sec), and improving THD 8.67% to 2.33% by comparing these results of ANFIS respectively with the results of PI which gave(3%),(0.01),(0.2sec)and( 0.02sec).
This paper proposed neural networks to continuously provide alternative constructed signals for vehicle and wheel speed sensors utilized for the Anti-Lock Braking System (ABS), which serves as the fault tolerant control method. These alternative constructed signals are used for two purposes. The first is to generate residual signals, and the second is to be adopted instead of isolated faulty signals. The residual signal is generated by extracting the difference between the alternative constructed signals and the corresponding actual signals. These residual signals serve as an indication of fault occurrence and to express that fault severity. Whenever a fault occurrence is detected and diagnosed in one of the sensor’s signals, the faulty signal is isolated and replaced by the corresponding constructed signal to maintain the system's normal behavior under a faulty condition. The range of data covered under the proposed estimating neural networks is huge, continuous in time, and not sampled. In this work, the range of the data lies between [50 to 120 km/h] when the braking is started. That cannot be performed by any available method. These models' training process is based on the Levenberg-Marquardt (LM) algorithm, implemented and tested by MATLAB/Simulink. The results show that these models can accurately map the measured data into the desired output through the best-fit functions. The fast response of the trained models makes them suitable for real-time alternative signals for fault-tolerant purposes for speed sensors during hard or panic braking.
Renewable energy sources (RESs), such as solar and wind power, offer new technologies for meeting the world's energy requirements. The distributed generator (DG) based on RESs has no rotational mass and damping effects compared to the traditional power system with synchronous generators (SG). However, the increasing penetration level of DG based on RESs causes low inertia, a dampening effect on the dynamic performance of the grid, and stability. A solution to improve the frequency stability of such a system is to provide virtual inertia by using virtual synchronous generators (VSG), which can be created by using short-term energy storage and a power inverter, and a suitable control mechanism. The VSG control mimics the dynamics of the rotation SG and enhances the power system's stability. This paper presents an overview of various topologies on virtual inertia, VSG concepts, control techniques, and VSG applications. Finally, the VSG challenges and future research will be discussed.
It is possible to represent the road map on the paper and study it using Dijkstra`s algorithm to find the shortest path on the real earth. Dijkstra`s Algorithms are used for calculating the shortest path from source to sink to enable query operations that follow. Dynamic shortest-route techniques are needed to accommodate the modifications within the underlying community topology. Every solution includes figuring out the nodes whose shortest routes could be impacted through the updates and producing a list of affected vertices and their updated shortest pathways. In this work, the advent of the retroactive priority queue information structure makes the Dijkstra algorithm dynamic. In this research, the stepping is changed forward in the shortest direction for 2 networks and two directions. That changed by locating the shortest static path, then circulating to the first node after the beginning node. At this node, the weights inside the segments directed from this node will exchange and cancel the vintage shortest direction and locate the new direction. Then flow to the next node in the new find shortest path, and repeat the operation until the end. The idea is that the best path can be constantly changed based on the latest data. These continuous changes are addressed in this paper, where the proposed system can find the best methods and update them automatically according to the variables.
Autonomous vehicles (AV) are expected to improve, transform, and revolutionize ground transportation. Previous techniques are dependent on localization employing pricey inaccuracy Global Positioning sensor. Furthermore, the performance loss is caused by drifting errors of Simultaneous Localization and Mapping. Regarding categorizing and analyzing texture, cameras are much more accessible and practical. This work contributes to obtaining high accuracy for AV localization and reducing errors in predicted positions. Based on the light, accurate, and robust proposed Convolutional Neural Network (CNN), it will scale down the computational complexity and shorten the training time. Considering various weather and time of day conditions such as bright sunny, hard rain noon, and wet cloudy noon with a vision-only system Red, Green, and Blue (RGB) low-cost camera sensors. To check the positional accuracy of the CNN, RGB images are combined with depth images using the IHS method. The k-Mean technique evaluates the similarity between a specific image and all street images to obtain precise coordinates. The Simulation findings demonstrate the superiority of the suggested technique for different weather conditions, which has an accuracy of up to 94.74% and a Mean Squared Error MSE in a distance of 0 meters, as opposed to , where the MSE in the projected position is 4.8 meters. Another indication of the proposed method's effectiveness is that it yielded reliable results when its validity was tested on images from a dataset that had not been trained.
The Matrix Amplifier is a structure designed to increase the gain of the wideband distributed amplifiers. A matrix amplifier is used to improve the pass-band gain while preserving the dispersed design-wide characteristics to use the multiplicative gain mechanism. In this paper, the matrix distributed amplifier methodology is developed using differential cells instead of active amplifier cells to improve the wideband characteristics. Shunt capacitances are connected in the centerline to absorb the peaking impact at a cut-off frequency and reduce gain ripples. As an application of the ideas and concepts of matrix amplifiers, a modified step-by-step design of rows 4 and column 2 matrix amplifier is undertaken using a Quasi Differential amplifier. A Matrix differential amplifier using a shifted-second-tier structure technique is then built and tested in 0.18 µm Complementary Metal Oxide Semiconductors technology. The advantages gained from the proposed design are high gain, high bandwidth, low noise, and no need for balun circuits. The design and simulation results were achieved using ADS. The significant results show a high gain of 40 dB and a 33 GHz bandwidth. The noise figure is also 3.583, with S11, S22, and S12 being -10 dB, -10dB, and -40dB, respectively; the output power at 1-dB gain compression point is evaluated (P1dB) of +6.4 dBm, and the total DC power dissipation is 266mW. The cadence tools produced the layout design and specifications, although the chip size was 1.1mm2
With the development of communication systems, antennas of small size and high gain have become essential to keep up with the new challenges. The metamaterials made these challenges possible. In this paper, a new low-profile metamaterials-based array is designed. The array unit cell comprises a symmetric composite right left hand (CRLH) unit cell. A third-order Hilbert curve structure replaces the VIA, and aperiodic slots are introduced between the unit cells to enhance the overall performance. This design provides a significant improvement over the original design. CST MWS was used to stimulate the design. Gain and S11 are calculated to evaluate the antenna performance; a dual- bandwidth was achieved extended from (3.72 to 3.79) GHz and (6.99 to 8.55) GHz with maximum antenna gain equal to (5.28, 7.66) dBi, respectively. The antenna is characterized by its small size and high efficiency, making it suitable for Long-Term Evolution LTE, 5G, and satellite applications.
Electrical supply safety and quality represent targets that Iraqi power distribution companies always strive to meet. Load-side broken conductor fault LSBC is one of the greatest faults affecting both targets. Stand out since the magnitudes of the impact on the system are too small to activate the relevant system protection devices in Iraqi substation 33/11kV. Therefore, protection from LSBC faults has been one of the biggest challenges in the Iraqi electrical distribution system. In this context, the main aim of this article is to present a method for detecting LSBC faults by unbalanced three-phase currents faults measured in a 33/11kv distribution substation. Using computer simulations based on an actual distribution 11kV feeder model, this method was qualitatively tested. Then, a relationship between mathematical and simulation results was made. Finally, A comparison of the proposed method and recent literature was written. According to the obtained results of case studies, the protection devices in the Iraqi substations cannot efficiently sense the LSBC fault. The overcurrent relay is completely not sensitive to LSBC, and the neutral current fault relay is only sensitive (0-70)% of the different types of feeders under the study. While the proposed unbalance, the current method had been detected with 87% -93% of 11 kV feeders. The proposed techniques are applicable and compatible with the existing traditional protection of the overcurrent and earth fault protection system in the Iraqi 33/11kV substation.
In this paper, the software and hardware of a software-defined radio (SDR) platform are used to implement and verify the blind real-time sensing act of intelligent collaborative spectrum sensing based on a new theoretical formula for constructing denoised mixed features named MSKU3 and paired with an unsupervised machine learning K-Medoids algorithm. Two low-cost RTL-SDR dongle hardware receivers are used as two cooperative unlicensed secondary users to capture the radio frequency of a licensed primary user channel. A host personal computer is used as a fusion center to run GNU-Radio software signal processing blocks to implement the developed method, and a single Universal Software Radio Peripheral (USRP) N210 hardware transmitter based on FPGA is used to take up unoccupied desired radio frequency bandwidth. Two scenarios of signal-to-noise ratio levels have been adopted to verify and test the sensing performance of the developed system. The first one occurs when unlicensed secondary users have equal signal-to-noise ratio values. The second occurs when unlicensed secondary users have different signal-to-noise ratio values since each secondary user has their location. The experimental results of detecting action in terms of the probability of detection for the proposed method show that the theoretical and practical results are very close to each other.
The automotive industry is moving toward more environmentally friendly automobiles with greater range and performance than traditional vehicles as the effects of global warming worsen. Because of the positive impact, electric vehicles can have on reducing harmful emissions from the transportation sector, scientists have grown increasingly interested in the possibility of analyzing and simulating electric vehicles. In this study, we develop a non-linear dynamic concept of an electric vehicle by fusing kinetic and electrical components. Then we create a proportional Integral derivative (PID) controller to help it stay on course. To obtain optimal parameters for this controller by minimizing the error between the desired and actual output, Particle Swarm Optimization (PSO), and Multi-Verse Optimization (MVO) algorithm are used. The proposed controllers tested with linear and nonlinear trajectories to represent the electric vehicle's speed. The computation findings show that the proposed controller works perfectly, keeping up with the electric vehicle's speed quickly and precisely. In particular, the MVO-based proportional-integral-derivative (PID) controller is superior to the proportional-integral-derivative (PID) -based PSO method in terms of no steady-state error and smallest overshoot (0.05% with MVO while 0.25% with PSO) prevention for electric vehicle (EV) speed despite the better results of settling time and rising time obtained in PSO(0.767 And 0.211 s) respectively while these values were (0.807 and 0.215 s), respectively, in MVO. All works are performed in MATLAB (R2020a) /Simulink environment.
Several scientists have proposed using an underwater wireless optical communication system (UWOC) to deliver high-speed data services using the abundant optical spectrum. However, wireless optical signal propagation faces an antagonistic environment when using undersea channels due to various factors like scattering, absorption, turbulence, and optical link misalignment between the transmitter and the receiver. These factors will attenuate the optical signal and lead to degrading system performance. To reduce these factors impact on the communication system's performance, transmitted optical power (OTP) should be increased. Since the UWOC system is battery-powered, increasing OTP will consume more electrical power. Therefore, it is necessary to adjust OTP to a value commensurate with the underwater channel changes. So, an ANN model is proposed in this article for link adaptation, which can adjust the OTP level in tandem with the underwater channel conditions. Data for training, testing, and validation of the proposed system reliability was collected experimentally, and tap water was used as a transmission medium. Evaluation of the proposed model outcome demonstrates that reliable performance is achieved in predicting OTP needed in multiple scenarios. The MSE of the predicted OTP is(9.5×10-3,1.5×10-2, and 1.7×10-2) dBm in the training, testing, and validation stages, respectively. The regression values of the training, testing and validation sets are (0.9997,0.9990, and 0.9996). The results achieved by the proposed model prove it is reliable to be applied in UWOC systems.