Recent Advances in Electrical & Electronic Engineering - Volume 14, Issue 2, 2021

Background: The growth of renewable energy sources is increasing in the world to meet the energy consumption demand. The major problem after the integration of renewable sources is islanding. The islanding is not safe for equipment and customers. As per Distributed Generation (DG) interconnection standards, it should be detected within 2 seconds. Objective: This paper presents the review of various islanding detection methods for increasing the stability of islanded DG. This will help future researchers in selecting the best islanding detection method with zero NDZ. Methods: The islanding detection methods are classified as local and remote techniques. The local techniques are again classified as active, passive and hybrid methods. Results: Each method is presented with their islanding detection time, power quality issues, Non- Detection Zone (NDZ), advantages and disadvantages. Conclusion: The fuzzy based artificial intelligence with Particle Swarm Optimization (PSO) passive methods have reduced the NDZ to zero and increases the stability of DG without degrading the power quality as active and passive methods.

Brain-computer Interface (BCI) systems, usually using signals taken from users' brain through electroencephalography (EEG), control various devices around and provide the user's command by interacting. Improving the quality of life of people with disabilities is the main goal of BCI systems. The importance of BCI-based smart home systems is further increasing as a smart home system directly affects the life of a disabled individual. On the other hand, few BCI systems can be run directly using smart home systems. The importance of the BCI-based smart home and the few existing systems require more work in this vital field. In addition, no reviews have described BCI systems in a smart home. In this study, we reviewed all the papers on BCI-based smart home systems published in the last 6 years. These studies investigated and evaluated BCI systems from nine different perspectives. In addition, all studies were examined in terms of signal processing methods. Finally, the problems and challenges of these systems were discussed and new solutions were proposed.

Background: Electricity price forecasting is still a challenging issue as it plays an essential role in balancing electricity generation and consumption. Probabilistic electricity price forecasting not only provides deterministic price forecasts but also effectively quantifies the uncertainty of electricity price. Methods: This paper introduces a new short-term electricity price forecasting approach called SASVQR, which is based on support vector quantile regression (SVQR) optimized by simulated annealing algorithm. In this study, SVQR is employed to obtain the conditional quantiles of the electricity under different quantile points, while the simulated annealing algorithm is applied to optimize each SVR model. Then the kernel density estimation takes these conditional quantiles as inputs and generates the probability density functions for future electricity prices. Results: The proposed algorithm is assessed in three datasets: the GEFCom 2014, two real electricity price datasets from the PJM market and the Singapore market. Three popular probabilistic forecasting criteria, namely prediction interval coverage probability (PICP), prediction interval normalized average width (PINAW), and coverage width-based criterion (CWC), are utilized to evaluate the numerical experiment results. It shows the promising forecasting performance, robustness, and effectiveness of SASVQR on different datasets. Conclusion: The SASVQR method can effectively forecast the short-term electricity price compared with other methods.

Background: Until now, a variety of proportional integral controllers and local controllers and predictive controller models have been used to stabilize VSC-HVDC transmission lines. One of the disadvantages of a linear model predictive control is that it is difficult and time consuming to adjust it for times when the disturbance entering the system is sudden. The goal is to monitor and take appropriate action to balance power and eliminate voltage or current that has exceeded the allowable limit. Methods: The way it works is that first, the DC current and the measured voltage of converter power source (VSC) is sup-plied based on various factors, and then the converters slowly begin to track the desired reference value, and this continues until the power sources are adjusted according to current and voltage limits. Results: To achieve this goal, this paper examines recent articles on the modeling of VSC-HVDC power transmission systems and a comprehensive model has been chosen that has not been published for several months since its publication. Next, to control the selected model, the combined control strategy of nonlinear predictive based on the Lyapunov function is used to ensure the stability of the system. Conclusion: The results of the implementation of this control strategy on the HVDC power transmission system model in MATLAB soft-ware express the ability of this controller to track the reference input values.

Background: In a hybrid multi-infeed direct current (HMIDC) system, the interaction between the AC and DC systems has an influence on the damping characteristics of the system, while a doubly-fed induction generator (DFIG) based wind turbine connected to the grid complicates the coupling between AC and DC. Objective: Based on the basic principles of wind power generation and low-frequency oscillation (LFO), a DFIG -based wind turbine is connected to the LCC-HVDC side and VSC-HVSC side of an HMIDC system to study the influence of the oscillation mode on a hybrid system with different wind power access locations, and two kinds of additional DC damping controllers have been designed to suppress the LFO. Moreover, the effects of different additional DC damping controllers on the suppression of the LFO in the system have also been compared. Methods: First, the total least squares- estimation of signal parameters via rotational invariance techniques (TLS-ESPRIT) was used to obtain the system’s oscillation mode. Second, the transfer function was determined by the Butterworth bandpass filter. Third, the LCC-HVDC and VSC-HVDC additional damping controllers were designed based on the H2/H∞ hybrid control theory. Results: The designed additional DC damping controller showed a good suppression effect on LFO in the HMIDC system and could meet the system damping deficit. The effect of the LCC-HVDC additional damping controller was observed to be better than that of VSC-HVDC. Conclusion: Through a simulation analysis on PSCAD/EMTDC simulation software, the effectiveness of the designed additional DC damping controller has been verified.

Background: In multi-level inverters (MLI), as the number of levels increases, there is a proportionate increase in the count of the semiconductor devices that are employed. Methods: An asymmetrical multi-level inverter topology using a bidirectional switch is presented, which employs a lesser number of power electronic devices to produce fifteen levels at the output voltage. The Nearest Level Modulation (NLM) technique is used to generate the switching pulses and reliability analysis is performed using Markov reliability methodology. The operating principle of the proposed MLI and its performance abilities are verified through MATLAB/Simulink and a prototype is developed to provide the experimental results. Results: Total Harmonic Distortion (THD) is computed for the proposed MLI for different types of loads in the simulation environment as well as in the developed hardware prototype. The fifteen level is achieved by using only 9 switches and 3 DC sources in comparison to the 28 switches and 6 DC sources required by the traditional cascaded H-bridge inverter. Conclusion: The simulation and hardware results confirm the suitability of the proposed fifteen level MLI as the total component count and the requirement of DC sources reduces considerably.

Background: The pulverizing system is an important part of the coal-fired unit; the safety and efficient operation of which are essential to improve the economy of the units. Due to the needs of industrial development, the pulverizing system has become increasingly complex, and it is challenging to design the optimal controllers based on traditional model-based methods. Objective: This paper proposes an improved intelligent data-driven control method to design the optimal controller for the pulverizing system, which does not need any information on the model. Methods: The proposed method is based on intelligent virtual reference feedback tuning and a new adaptive human learning optimization algorithm, in which adaptive human learning optimization is used to find the best values of the controller as well as the reference model to achieve the optimal control performance. The proposed method only needs a set of input and output data of the system and can avoid the influence of the model error. Results: The results of the CEC14 benchmark functions show that the proposed algorithm possesses a better searchability than the other five binary-coding optimization algorithms. Furthermore, the simulation results on the pulverizing system demonstrate that the presented method has advantages over different control methods, including the model-based PID methods, Z-N method and so on. Conclusion: The proposed method can easily and efficiently design the optimal controller without any model information, and it can save a lot of efforts and time for engineering applications. Therefore, it is a very promising control method.

Background: Conventional micro-grids operate autonomously in islanded mode but they always rely on the utility grid for BUS voltage support and power balance in the grid-connected mode. This results in non-seamless mode switching, alternating operation strategy and power exchange fluctuation problems. Methods: An AC/DC/AC converter is utilized as the interface between the micro-grid and the utility grid. This enables the two entities to have different voltages in the grid-connected mode. The microgrid exchanges predefined amount of power with the utility grid in the grid-connected mode. The power amount is estimated based on power forecasting of local generations and loads with the consideration of the Sate of Charge (SOC) of the battery, and is updated and broadcasted every 15 minutes. Results: A 100kVA AC micro-grid with a rotating generator, battery storage and solar arrays is built on Matlab/Simulink for investigation. Results indicate that the battery can effectively balance the power flow and mode switching hardly causes distortions. Conclusion: The proposed micro-grid can operate autonomously in both grid-connected and islanded mode without relying on the utility grid. Seamless switching between operation modes can be achieved naturally. Constant power is exchanged with the power grid, which benefits the powerdispatching algorithm of the power system.

Background: Ultrasound is widely used in the applications of underwater imaging. Capacitive micromachined ultrasonic transducer (CMUT) is a promising candidate for the traditional piezoelectric ultrasonic transducer. In underwater ultrasound imaging, better resolutions can be achieved with a higher frequency ultrasound. Therefore, a CMUT array for high-frequency ultrasound imaging is proposed in this work. Methods: Analytical methods were used to calculate the center frequency in water and the pull-in voltage for determining the operating point of CMUT. A finite element method model was developed to finalize the design parameters. The CMUT array was fabricated with a five-mask sacrificial release process. Results: The CMUT array owned an immersed center frequency of 2.6 MHz with a 6 dB fractional bandwidth of 123 %. The pull-in voltage of the CMUT array was 85 V. An underwater imaging experiment was carried out with the target of three steel wires. Conclusion: In this study, we have developed CMUT for high-frequency underwater imaging. The experiment showed that the CMUT could detect the steel wires with a diameter of 100 μm and the axial resolution was 0.582 mm, which was close to one wavelength of ultrasound in 2.6 MHz.

Background: Aiming at the problems of color distortion, low clarity, and poor visibility of underwater images caused by a complex underwater environment, a wavelet fusion method, UIPWF, for underwater image enhancement is proposed. Methods: First of all, an improved NCB color balance method is designed to identify and cut the abnormal pixels and balance the color of R, G, and B channels by an affine transformation. Then, the color correction map is converted to CIELab color space, and the L component is equalized with contrast limited adaptive histogram to obtain the brightness enhancement map. Finally, different fusion rules are designed for low-frequency and high-frequency components, the pixel level wavelet fusion of color balance image and brightness enhancement image is realized to improve the edge detail contrast on the basis of protecting the underwater image contour. Results: The experiments demonstrate that compared with the existing underwater image processing methods, UIPWF is highly effective in the underwater image enhancement task, improves the objective indicators greatly, and produces visually pleasing enhancement images with clear edges and reasonable color information. Conclusion: The UIPWF method can effectively mitigate the color distortion, improve the clarity and contrast, which is applicable for underwater image enhancement in different environments.

The last paragraph on page 149 is revised as follows: ACKNOWLEDGEMENTS The authors would like to extend their gratitude and sincere thanks to the IKG Punjab Technical University, Kapurthala for giving the opportunity to carry out the present research work. The original paragraph provided is mentioned below: ACKNOWLEDGEMENTS Declared none.