Recent Advances in Electrical & Electronic Engineering - Volume 16, Issue 3, 2023

Background: ADRC (active disturbance rejection controller) technology is a new practical technology that does not rely on the mathematical model of the controlled object and has strong robustness. It integrates the essence of modern control theory and classical proportion integral derivative theory and has good engineering application prospects. But with the research and development of the adaptive disturbance rejection controller, the problem of many parameters and the difficulty to adjust also arises. Objective: To act as a guide for future studies on the enhancement of ADRC parameter setting, to support the growth of ADRC technology, and to promote the effective use of the technology in other control fields. Methods: The basic concepts and principles of ADRC control, the research methodologies for ADRC parameter tuning, and the research progress for ADRC parameter tuning in each direction are all introduced in this paper. The benefits and drawbacks of each method are then compiled, and a potential course of future development is suggested. This information is expected to serve as a guide for future studies on the enhancement of ADRC parameter tuning. Conclusion: The parameter tuning of the ADRC is a complex adjustment process. At present, the mainstream parameter tuning methods include the empirical method, the bandwidth method, the intelligent algorithm tuning method, and the time scale tuning method. Among them, the empirical method demands the debugging personnel to adjust conform to the accumulated experience, and the adjustment process is cumbersome; The bandwidth method needs to go through a lot of tedious calculations to determine the control parameters according to the model of the system, and the generality is poor; and the use of intelligent algorithms to tune ADRC parameters has become the most widely used method for tuning parameters.

Background: Load forecasting is a crucial element in power utility business load forecasting and has influenced key decision-makers in the industry to predict future energy demand with a low error percentage to supply consumers with load-shedding-free and uninterruptible power. By applying the right technique, utility companies may save millions of dollars by using load prediction with a lower proportion of inaccuracy. Aims: This study paper aims to analyse the recently published papers (using the New York Independent System Operator's database) on load forecasting and find the most optimised forecasting method for electric load forecasting. Methods: An overview of existing electric load forecasting technology with a complete examination of multiple load forecasting models and an in-depth analysis of their MAPE benefits, challenges, and influencing factors is presented. The paper reviews hybrid models created by combining two or more predictive models, each offering better performance due to their algorithm's merits. Hybrid models outperform other machine learning (ML) approaches in accurately forecasting power demand. Results: Through the study, it is understood that hybrid methods show promising features. Deep learning algorithms were also studied for long-term forecasting. Conclusion: In the future, we can extend the study by extensively studying deep learning methods.

Background: Positioning accuracy is the most important index of the reconnaissance positioning system. Positioning accuracy involves many factors, such as the position, attitude and motion state of the flight platform, the pointing accuracy of the stable platform, and various coordinate transformations. A reasonable fusion strategy can guarantee stable positioning accuracy, so it is very important to study the precise fusion of machine vision, 3D geographic data and UAV airborne positioning information to realize the optimal combination of positioning data and complete the accurate and rapid positioning of the ground target. Methods: In this paper, a location model based on stereo vision and spatial information fusion method is proposed. It fully integrates visual information, satellite positioning information and spatial geographic information, greatly improves positioning accuracy, and through a real-time processing algorithm, significantly improves real-time positioning. Results: Through the related experiments, positioning accuracy and real-time ability of positioning could reach about three meters. Conclusion: The proposed real-time and high-precision positioning method of the ground target through UAV stereo vision and spatial information fusion is proposed showing significant improvement compared to other traditional methods.

Background: The need for electromagnetic target positioning has grown increasingly important in recent years, especially in the field of communication, navigation and other electromagnetic fields. Objective: The objective of this study is to find a method for electromagnetic target location in different geographical environment and improve the positioning accuracy of the electromagnetic target in specific geographical environments effectively. Methods: This paper has proposed an improved Mean-shift Algorithm that can be used in the rapid localization of electromagnetic objects under specific geographical environments. The paper also analyses different interference source power prediction algorithms and gives out the analysis results of the different algorithms. Parameter adjustments of mean shift algorithm are also given out, and the related process in detail is presented. Results: Through the suppression jamming source location experiment, the proposed method can be used in the rapid localization of electromagnetic objects under specific geographical environment, and its characteristics are better than other localization algorithms. Conclusion: The proposed algorithm is suitable for the location of the electromagnetic target in a specific geographical environment, and its accuracy is greatly improved compared with the traditional method.

Background: Distributed wind farms cover a wide area, the internal units are far apart, and the output correlation is low. With the gradual expansion of its development scale, accurate prediction of distributed wind power has become an important means to ensure the smooth operation of the power system. Objective: This paper proposes a decentralized wind power forecasting method based on cluster analysis and multi-model stacking ensemble learning. Methods: Firstly, the average output model of multiple units is established through cluster analysis to eliminate redundant and repetitive features; secondly, a feature complex model is established to construct multi-dimensional combined features to fully extract feature information; then, an improved Stacking integrated learning model is proposed. Sets and model sets, while reducing the amount of model calculation, give full play to the feature adaptability of different models. Results: The results of the proposed model are analyzed by comparison with traditional machine learning models such as SVM. Root Mean Square Error (RMSE) can be reduced by up to 6.3%. Conclusion: The results show that, compared with traditional single-model forecasting, the decentralized wind power forecasting method based on cluster analysis and multi-model stacking ensemble learning has higher forecasting accuracy.

Background: In recent years, wireless sensor network technology has been the center of attention in the international community. It has been a key element in e-health to monitor bodies. This technology enables new applications in different domains, including medical, physical training, etc. Methods: Some sensors in WSN (Wireless Sensor Nodes) capture physiological data such as temperature, heartbeat and moving distance. The ECG signal is obtained by an analog front-end chip ADS1292 and processed by an adaptive Kalman filter algorithm. The sensor LMT70 is used to gather the body temperature; the acceleration sensor MPU6050 is used to detect, capture, recognize and identify other body gestures and motions data and the STM32F103C8 microprocessor is used as the kernel control module. WSN sensor nodes provide wireless monitoring due to transceiver module NRF24L01for anybody, anywhere and anytime. Results: Each measurement result can meet physiological index demands. Conclusion: The system can be used to avoid sports accidents and injuries and to plan for future training and realized in LAB conveniently owing to the high cost-performance ratio.

Background: Superbuck converter is a strong nonlinear system. However, with the changes in parameters, Superbuck converter performance may be reduced due to nonlinear phenomena, such as bifurcation, chaos, etc. Current mode controlled Superbuck converters have been extensively applied in photovoltaic (PV) systems that require a stable output voltage. Superbuck converters, as nonlinear circuits, exhibit plentiful nonlinear phenomena, such as bifurcation and chaos. In this study, we analyze and control the nonlinear phenomena in the current mode controlled Superbuck converter. The piecewise switching model and discrete iteration mapping model of the Superbuck converter are established. The evolution process of the Superbuck converter from steady state to bifurcation and chaos is revealed when the reference current, input voltage, and load are used as bifurcation parameters, and the bifurcation forms of the converter are all period-doubling bifurcations. The stability criterion of Superbuck converter under current mode has been deduced from reference current by analyzing the stability of the converter. Through the resonant parametric perturbation (RPP) method, the chaos control of the Superbuck converter has been realized for the first time. The converter changes from a chaotic state to a stable single-cycle state about 0.4ms after control and the output voltage ripple is reduced from 3.905V to 0.679V, which effectively improves the output stability of the converter. Objective: This study aimed to analyze and control nonlinear phenomena in current-mode controlled Superbuck converters. Methods: Bifurcation diagram, time-domain waveform diagram, phase portrait and Poincare section are used as analytical methods for the nonlinear dynamic characteristics of Superbuck converters. The chaotic control method of the Superbuck converter is Resonant Parameter Perturbation (RPP). Results: After applying RPP control to the Superbuck converter in the chaotic state, it is converted into a single-cycle stable state in about 0.4ms. After the converter is controlled, the output current ripple is reduced from 0.71479A to 0.31A, and the output voltage ripple is reduced from 3.905V to 0.679V. Conclusion: With the increase in the reference current, the decrease in the input voltage, or the increase in the load, the system enters the chaotic state through the period-doubling bifurcation path. In this study, the chaos control of the Superbuck converter is carried out for the first time, and the control method used is the resonant parametric perturbation method. After RPP control, the converter is changed from an unstable, chaotic state to a stable period state in about 0.4ms.

Background: Increase in the output current of inverter-based Distributed Generations (DGs), which are connected to an Upstream Grid (UG) and equipped with a droop controller, due to UG frequency and/or voltage magnitude drop is a conventional event. The first solution to limit the output currents is using current limiter circuits at the output side of the grid-connected droopcontrolled DG. Suppose the increasing output currents of this DG in continuous UG frequency drop exceeds their maximum. In that case, the current limiter circuits limit them to their maximum values. This limitation is based on the P-ω droop characteristic due to instability because the DG frequency will be greater than the UG frequency. This difference leads to sending and receiving power between DG and UG. Different methods have been presented in the literature to overcome this problem. Methods: This paper proposes a method based on a floating droop controller. In this method, in order to limit output currents strictly, Iod − ω and Ioq − v droop characteristics are used instead of P - ω and Q - v characteristics, respectively. In the proposed method, the droop curves are moved downward instead of increasing the droop coefficients to limit currents. Also, two transient supplementary signals are proposed to ensure that instantaneous currents do not overshoot in the transient state. Results: Small-signal stability analysis shows that the system remains stable under the proposed controller. Moreover, the proposed strategy performance is indicated by time-domain simulation results using MATLAB/Simulink software under different case studies. Conclusion: The proposed new method limits the output currents of grid-connected droopcontrolled DG without using current limiter circuits and performs a stable operation under the abnormal conditions of the grid.

Background: Ambient vibration is a promising source to provide low-power electronic devices with energy. The piezoelectric direct effect is widely used to generate energy from mechanical stress. Generating sufficient power in milliwatts is often obtained with input acceleration greater than 1g (9.81 m/s²) Aim: In this work, the author demonstrated that low acceleration (between 0.1g and 0.9 g) vibration sources can be utilized to generate a satisfactory level of electric power. Methods: A low pre-stress was introduced by fixing the piezo structure to the shaker using adhesive tape. The driving force was less than 0.06 N. The harvester was tested at a vibration frequency of 173 Hz. Results: The maximum power of 1.5 mW was achieved when the harvester resistance matched the load resistance value. At maximum harvested power, the efficiency was found to be 1.6. Conclusion: The results indicated that prestressed piezoceramics are a good candidate for vibration energy harvesting.

Background: Renewable energy systems are the most promising long-term sustainable energylong-term sustainable energy, with numerous advantages. Renewable energy systems that combine traditional and renewable resources are now cost-effective and have a low carbon impact. These systems are becoming more popular as a feasible option for rural areas, particularly when it comes to grid connectivity. Objective: This study discusses the ideal design and size for separated PV batteries and dieselbased renewable energy systems. South Sinai, Egypt, was chosen as the testing location for unusual environmental conditions, system specs, and daily energy requirement statistics to analyze the total energy required for a specific site. The load under investigation is a school complex in one of the governorate's cities. Methods: The complete system is simulated and tested using the MATLAB/Simulink program. Results: In this study the amount of energy required by the load is determined. The load power is compared with the system output power to justify the reliability of the system. The system output voltage is shown which is sinusoidal wave. The management control system between PV, diesel and battery is shown. Conclusion: The results show that the system is reliable; the control strategies always indicate the load requirement and minimize the diesel run time.

Background: With the institutional reform of the power market and the need for demandside response, the requirements for load forecasting accuracy are getting higher and higher. In order to deeply explore the influencing factors of load forecasting accuracy and improve the forecasting accuracy of ultra-short-term load forecasting, a method for analyzing the influencing factors of ultra- short-term load forecasting considering time series characteristics is proposed in this paper. Firstly, based on the analysis of four different types of load characteristics, eight-time series characteristic parameters that can characterize the characteristics of the load curve and may be related to the prediction accuracy of the prediction model are extracted. These characteristic parameters include dispersion coefficient, slope, daily load rate, daily peak-valley difference, deviation, variance, skewness coefficient and kurtosis coefficient. Secondly, three kinds of load forecasting models are established, including the Autoregressive Integrated Moving Average model (ARIMA), grey system and support vector machine (SVM), and then forecast the load in Anchorage, Alaska, USA. The effects of eight time series features on the prediction accuracy of the three load forecasting models are analyzed. The results show that the discrete coefficient, slope difference, daily load rate and peak-valley difference greatly influence the load forecasting results and have different affects on different forecasting models. When the historical data is small, ARIMA model is suitable for shortterm load forecasting with small slope difference, large daily load rate and small daily peak-valley difference. The grey model is suitable for short-term load forecasting with small discrete coefficients of historical data. The SVM model is suitable for most short-term load forecasting when there is a lot of historical data. With the institutional reform of the power market and the need for demand-side response, the requirements for load forecasting accuracy are getting higher and higher. Objective: In order to deeply explore the influencing factors of load forecasting accuracy and improve the forecasting accuracy of ultra-short-term load forecasting, this paper proposes a method for analyzing the influencing factors of ultra-short-term load forecasting considering time series characteristics. Methods: Based on the analysis of four different load characteristics, 8 kinds of time series characteristics such as the dispersion coefficient, slope and daily load rate of the daily load curve are extracted. And three kinds of load forecasting models are established, including autoregressive integrated moving average model (ARIMA), grey system and support vector machine (SVM), and then forecast the load in Anchorage, Alaska, USA. The effects of these 8-time series features on the prediction accuracy of the three load forecasting models are analyzed. Conclusion: The results show that when there are few historical data, the ARIMA model is suitable for short-term load forecasting with a small slope difference, large daily load rate and small daily peak-to-valley difference characteristics. The gray system is suitable for short-term load forecasting with a small discrete coefficient of historical data. The SVM model is suitable for most short-term load forecasting with many historical data.

Background: Increasing the penetration of renewable energy sources has become necessary, especially in isolated microgrids. This increase leads to a decrease in the total inertia of the microgrids, which affects microgrid stability. Moreover, voltage and frequency control in lowinertia microgrids is more difficult and sensitive. Objective: Improve low inertia isolated microgrids' dynamic response and save the microgrid stability at different contingency and uncertainty conditions. Moreover, it allows for more penetration of renewable energy sources. Methods: Proposing different control strategies based on virtual inertia control. The first is a proportional- integral-derivative (PID) controller, and then, to allow for more tuning flexibility, a fractional- order proportional-integral-derivative (FOPID) controller is used. MATLAB ^TM/Simulink is used to compare the response of the isolated microgrid without virtual inertia control, with conventional virtual inertia control, PID-based virtual inertia control, and FOPID-based virtual inertia control. The PID and FOPID controllers’ parameters are tuned using the ant colony optimization (ACO) technique. Results: The proposed control techniques save the isolated microgrids' stability at different penetration levels of renewable energy sources and operating conditions. At the same time, the isolated microgrid without virtual inertia control or conventional virtual inertia control can not save its stability in many operating conditions. Conclusion: The proposed fractional-order proportional-integral-derivative (FOPID)-based virtual inertia control has proven its effectiveness in saving the isolated microgrid stability and gives the best controller response. FOPID-based virtual inertia control minimizes the frequency deviation with different disturbances and operating conditions.