A new and compact four-pole wide-band planar filter-antenna design is proposed in this article. The effect of the dielectric material type on the characteristics of the design is also investigated and presented. The filter-antenna structure is formed by a fourth-order planar band-pass filter (BPF) cascaded with a monopole microstrip antenna. The designed filter-antenna operates at a centre frequency of 2.4 GHz and has a relatively wide-band impedance bandwidth of about 1.22 GHz and a fractional bandwidth (FBW) of about 50%. The effects of three different types of substrate material, which are Rogers RT5880, Rogers RO3003, and FR-4, are investigated and presented using the same configuration. The filter-antenna design is simulated and optimised using computer simulation technology (CST) software and is fabricated and measured using a Rogers RT5880 substrate with a height (h) of 0.81 mm, a dielectric constant of 2.2, and a loss tangent of 0.0009. The structure is printed on a compact size of 0.32 λ₀ × 0.30 λ₀, where λ₀ is the free-space wavelength at the centre frequency. A good agreement is obtained between the simulation and measurement performance. The designed filter-antenna with the achieved performance can find different applications for 2.4 GHz ISM band and 4G wireless communications.

The electrical energy from the sun can be extracted using solar photovoltaic (PV) modules. This energy can be maximized if the connected load resistance matches that of the PV panel. In search of the optimum matching between the PV and the load resistance, the maximum power point tracking (MPPT) technique offers considerable potential. This paper aims to show how the modelling process of an efficient PV system with a DC load can be achieved using a fuzzy neural network (FNN) controller. This is applied via an innovative methodology, which senses the irradiance and temperature of the PV panel and produces an optimal value of duty ration for the boost converter to obtain the MPPT. The coefficients of this controller have been refined based upon previous data sets using the irradiance and temperature. A gradient descent algorithm is employed to improve the parameters of the FNN controller to achieve an optimal response. The validity of the PV system using the MPPT technique based on the FNN controller is further demonstrated via a series of experimental tests at different ambient conditions. The simulation results show how the MPPT technique based on the FNN controller is more effective in maintaining the optimal power values compared with conventional techniques.

Switched reluctance motors (SRMs) have recently gained notable interest in industrial applications. Yet, their inherent torque ripple poses a significant impediment, limiting the adoption of SRM in high-performance applications. Instantaneous Torque Control (ITC) based on conventional Torque Sharing Function (TSF) is considered a promising technique for torque ripple reduction of SRMs at low speeds. However, the back electromotive force is increased with speed escalation, reducing torque tracking ability to its reference in the ITC based on conventional TSF, especially in the demagnetizing period. The high inductance value in this period opposes phase-current decaying, generating considerable torque ripple, making the ITC unsuitable at medium and high speeds. This paper proposes the indirect ITC based on an improved TSF to address the torque ripple over extended speed for SRM drive. The improved TSF relies on predicting the outgoing phase torque in the demagnetizing period and compensating for the difference between the predicting and reference torque in the next excitation phase by reshaping the incoming phase torque, minimizing the torque ripple. Moreover, simple analytical formulas are used to regulate the control angles to gain further improvements and extended speed. An accurate nonlinear motor model is created based on static magnetic characteristics estimated by the finite element analysis. Several simulated results are conducted to evaluate the effectiveness of the proposed controller. The proposed method offers higher torque ripple mitigation with the speed increase, decreasing the torque ripple by 10% to 45%. Furthermore, the proposed control reduces copper losses and enhances efficiency and torque/current ratio.

Prediction of solar irradiance plays an essential role in many energy systems. The objective of this paper is to present a low-cost solar irradiance meter based on artificial neural networks (ANN). A photovoltaic (PV) mathematical model of 50 watts and 36 cells was used to extract the short-circuit current and the open-circuit voltage of the PV module. The obtained data was used to train the ANN to predict solar irradiance for horizontal surfaces. The strategy was to measure the open-circuit voltage and the short-circuit current of the PV module and then feed it to the ANN as inputs to get the irradiance. The experimental and simulation results showed that the proposed method could be utilized to achieve the value of solar irradiance with acceptable approximation. As a result, this method presents a low-cost instrument that can be used instead of an expensive pyranometer.

This research proposes an Optimized Current chopping Control (CCC) approach for SRM drives. The goal is to implement a simple SRM drive that can effectively meet electric vehicle requirements, comprising minimized torque ripple to reduce vibrations and acoustic noises, maximized output torque to enhance vehicle acceleration, and improved efficiency, which contributes to extending the EV's battery life. Therefore, an optimization problem is formulated and solved offline, incorporating a CCC-based SRM drive model. The control variables for this optimization problem are the switching angles of the SRM. A multi-objective function is chosen to combine three performance indices: torque ripple, average torque, and efficiency. The Harris Hawks Optimization (HHO) method is utilized in this paper to solve the optimization problem and find the optimal switching angles based on the selected objective function. HHO demonstrates a strong search capability that can effectively handle the nonlinear magnetization characteristics of SRMs. Constraints on the switching angles are also included in the optimization problem to control the phase current's RMS value and power consumption. The optimized switching angles are applied to a current chopping control (CCC) strategy and an asymmetric half-bridge converter to implement the proposed HHO-based CCC drive. Moreover, to demonstrate the effectiveness of the proposed HHO-based CCC drive, a comparative analysis based on simulations and experimental measurements is presented against other CCC approaches for SRM drives, including modified particle swarm algorithm (MPSO)-based CCC drives and analytical-based CCC drives.

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.