MULTI-BASIS WAVENET-BASED SPEED ESTIMATION IN DIRECT TORQUE CONTROLLED ASYNCHRONOUS MOTOR

This paper presents a proposed method for speed estimation of asynchronous motor in Direct Torque Control (DTC) system, based on a new architecture of multi-basis wavenet model. Such multi-basis model utilizes multi-set daughter wavelets. Firstly, the structure and training algorithm of the proposed method is discussed. The descent gradient method is used to fulfill both system structure and parameters initialization. Secondly, the proposed speed estimator and the DTC asynchronous motor are combined based on stator current signal and the motor speed is then estimated online with the operation of the system. Finally, the effectiveness of this method is proved by simulation carried out using Matlab/Simulink library and compared with the actual results obtained from the dynamic equations of the motor. The simulation results are obtained over the entire speed of starting, load conditions and motor braking. These results show that the proposed method is effective for speed estimation in DTC drives

Speed and Current Limiting Control Strategies for BLDC Motor Drive System: A Comparative Study

As a result of increasing the use of the brushless direct current (BLDC) motor in many life applications instead of the traditional motors, it is important to list and specify the more for its controlling methods. This paper presents a number of speed and current controlling methods as hysteresis band, variable dc-link bus voltage and pulse width modulation (PWM) controlling methods. These controlling methods have proportional integral derivative (PID) gains which are optimized by using particle swarm optimization (PSO) algorithm. By using fast Fourier transform (FFT) analysis to study the controller behavior from frequency analysis of the output signals and compute total harmonic distortion (THD), it can specify the more useful controlling method. The framework is modeled and fabricated by using Matlab/Simulink

Robotic dry cleaner for photovoltaic solar panels: an implemented design that evaluated in iraq's weather

Arabian desert areas are suffered from high mitigation in the produced photovoltaic (PV) power due to high dusty weather. This article presents a robotic cleaner that will significantly reduce the impact of dust on the installed PV systems in these areas. The proposed robotic cleaner is simple, low cost, standalone, self-powered, portable, and connected to the cloud. ESP32 used as a controller that manages the cleaning process and monitors its PV power production, the battery's state of charge, time of the day, and weather conditions. Thanks to the ESP32 features and its ability to connect to the cloud, as an internet of things (IoT), via the ThingSpeak website. All the electrical, mechanical, and electronic design aspects are presented and implemented in this article. The results show the effectiveness and performance enhancement due to periodic cleaning using the proposed robotic cleaner. The results also show that the total percentage of the monthly normalized accumulated losses for the two scheduled cleaning photovoltaic strings with a performance improvement of 15.54% for the weekly cleaned string (WCS) 83.04% for the never cleaned string (NCS) through the tested month

Wavelet Neural Networks for Speed Control of BLDC Motor

In the recent years, researchers have sophisticated the synthesis of neural networks depending on the wavelet functions to build the wavelet neural networks (WNNs), where the wavelet function is utilized in the hidden layer as a sigmoid function instead of conventional sigmoid function that is utilized in artificial neural network. The WNN inherits the features of the wavelet function and the neural network (NN), such as self-learning, self-adapting, time-frequency location, robustness, and nonlinearity. Besides, the wavelet function theory guarantees that the WNN can simulate the nonlinear system precisely and rapidly. In this chapter, the WNN is used with PID controller to make a developed controller named WNN-PID controller. This controller will be utilized to control the speed of Brushless DC (BLDC) motor to get preferable performance than the traditional controller techniques. Besides, the particle swarm optimization (PSO) algorithm is utilized to optimize the parameters of the WNN-PID controller. The modification for this method of the WNN such as the recurrent wavelet neural network (RWNN) was included in this chapter. Simulation results for all the above methods are given and compared

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Optimal PV Reconfiguration Under Partial Shading Based on White Shark Optimization

The main issue is that photovoltaic (PV) systems have their power output reduced due to partial shading. Less energy is produced by photovoltaic modules when partial shading causes an imbalance in the levels of irradiation. Array reconfigurations, both static and dynamic, are suggested as a strategy to improve power capture and reduce the impact of partial shading. Furthermore, it is recognized that irradiance fluctuations, snow, ice, and dust are environmental elements that impact the efficiency of PV arrays. Using white shark optimization (WSO), this research introduces a new method for optimizing the power reconfiguration of PV arrays. Minimizing the disparity in row currents and maximizing power production are the main goals of this WSO-based technique. Four different types of shade patterns are considered in the research: short wide (SW), long wide (LW), short narrow (SN), and long narrow (LN). In order to confirm that this method works, we ran simulations in MATLAB-Simulink and compared the outcomes to those of other configurations, such as Total Cross Tied (TCT), Butterfly Optimization Algorithm (BOA), Harris Hawks Optimization (HHO), and Flower Pollination Algorithm (FPA). Simulation results show the effectiveness of the WSO method in enhancing power extraction from the PV array, especially under partial shading conditions. Notably, the WSO method significantly increases the Global Maximum Power (GMP) output across different scenarios: by 26.22% in SW, 18.51% in LW, 10.95% in SN, and 10% in LN. This confirms the ability of this method to improve the PV power generation in diverse operating environments

A Comprehensive Review and Analytical Comparison of Non-Isolated DC-DC Converters for Fuel Cell Applications

The use of renewable energy sources such as solar photovoltaic, wind, and fuel cells is becoming increasingly prevalent due to a combination of environmental concerns and technological advancements, as well as decreasing production costs. Power electronics DC-DC converters play a key role in various applications, including hybrid energy systems, hybrid vehicles, aerospace, satellite systems, and portable electronic devices. These converters are used to convert power from renewable sources to meet the demands of the load, improving the dynamic and steady-state performance of green generation systems. This study presents a comparison of the most commonly used non-isolated DC-DC converters for fuel cell applications. The important factors considered in the comparison include voltage gain ratio, voltage switch stress, voltage ripple, efficiency, cost, and ease of implementation. Based on the comparison results, the converters have been grouped according to voltage level applications, with low voltage applications being best served by converters such as DBC, DuBC, TLBC, 2-IBC, 1st M-IBC, PSOL, SEPIC, and 1st M-SEPIC owing to their lower cost, smaller size, and reduced switch stress. Medium voltage applications are best suited to converters such as TBC, 1st M-TLBC, 2nd M-TLBC, 4-IBC, 1st M-IBC, 2nd M-IBC, 1st M-PSOL, 2nd M-PSOL, 1st M-SEPIC, and 2nd M-SEPIC, which offer higher efficiency. Finally, high voltage applications are best served by converters such as TBC, 1st M-TBC, 2nd M-IBC, 3rd M-IBC, 3rd M-PSOL, 4th M-PSOL, 2nd M-SEPIC, 3rd M-SEPIC, and 4th M-SEPIC

Efficient Battery Cell Balancing Methods for Low-Voltage Applications: A Review

Battery balancing technologies are a crucial mech anism for the safe operation of electrochemical energy storage systems, such as lithium-ion batteries. Moreover, balancing be tween battery cells is essential for battery systems\u27 life. Without any balancing circuitry, individual cell voltages can reach their maximum/minimum battery voltage limit faster than others, posing safety hazards. Furthermore, battery capacity reduction can occur when overcharging/over-discharging individual cells. So far, many balancing methodologies have been proposed and discussed in available literature. This paper presents a review of different state-of-The-Art cell balancing methods suitable for low voltage applications. The required control complexity, switch stress, balancing speed, cost and circuit size are considered as key aspects. Typically, cell bypass techniques, such as passive balancing, have the lowest cost and require no complex control strategies. In contrast, cell-To-cell balancing techniques can significantly increase the energy efficiency compared to cell bypass balancers, but these come with higher system costs and control complexity

Detection and Diagnosis of Stator and Rotor Electrical Faults for Three-Phase Induction Motor via Wavelet Energy Approach

This paper presents a fault detection method in three-phase induction motors using Wavelet Packet Transform (WPT). The proposed algorithm takes a frame of samples from the three-phase supply current of an induction motor. The three phase current samples are then combined to generate a single current signal by computing the Root Mean Square (RMS) value of the three phase current samples at each time stamp. The resulting current samples are then divided into windows of 64 samples. Each resulting window of samples is then processed separately. The proposed algorithm uses two methods to create window samples, which are called non-overlapping window samples and moving/overlapping window samples. Non-overlapping window samples are created by simply dividing the current samples into windows of 64 samples, while the moving window samples are generated by taking the first 64 current samples, and then the consequent moving window samples are generated by moving the window across the current samples by one sample each time. The new window of samples consists of the last 63 samples of the previous window and one new sample. The overlapping method reduces the fault detection time to a single sample accuracy. However, it is computationally more expensive than the non-overlapping method and requires more computer memory. The resulting window samples are separately processed as follows: The proposed algorithm performs two level WPT on each resulting window samples, dividing its coefficients into its four wavelet subbands. Information in wavelet high frequency subbands is then used for fault detection and activating the trip signal to disconnect the motor from the power supply. The proposed algorithm was first implemented in the MATLAB platform, and the Entropy power Energy (EE) of the high frequency WPT subbands’ coefficients was used to determine the condition of the motor. If the induction motor is faulty, the algorithm proceeds to identify the type of the fault. An empirical setup of the proposed system was then implemented, and the proposed algorithm condition was tested under real, where different faults were practically induced to the induction motor. Experimental results confirmed the effectiveness of the proposed technique. To generalize the proposed method, the experiment was repeated on different types of induction motors with different working ages and with different power ratings. Experimental results show that the capability of the proposed method is independent of the types of motors used and their ages