The relay protection system in intelligent substations ensures the safety and stable operation of power grids. While redundancy configurations enhance reliability, excessive redundancy compromises economic efficiency. The reasonable redundancy of the relay protection system in the station needs to be considered. In this paper, a multiobjective optimization configuration model is proposed for the intelligent substation relay system considering both reliability and economy, where Markov state space is adopted to determine the reliability and the impact of economic factors such as equipment costs, operation and maintenance costs, and fault losses. In addition, the coupling relationship between reliability and economic factors are used. Then, the entropy weight method is adopted to transform the multiobjective optimization problem into a single objective one. An improved particle swarm optimization algorithm is designed to achieve the optimal configuration of the intelligent substation relay protection system. The proposed optimization configuration method is validated on an actual substation, and the benefits of the substation relay protection system under different redundant configurations are analyzed. The results indicate that the configuration should avoid no redundancy or excessive redundancy. This work provides a theoretical basis for relay protection configuration schemes of the intelligent substation in the future.

A compact single asymmetric coplanar waveguide feed (ACPW‐fed) dual circularly polarized microstrip antenna that operates at 1.8, 3.9, and 5.2 GHz in the entire operating frequency band 600 MHz–6 GHz for the radar detection of the improvised explosive devices (IEDs) carried by a person is introduced. The proposed novel quasi‐omnidirectional antenna consists of single sided rectangular ring microstrip patch antenna. L‐shaped slots are etched at the two opposite corners of the rectangular ring, introducing new resonance and circular polarization waves at the mid and upper bands, respectively. The achieved dual half‐rectangular ring patch antenna (DHRR‐patch) is loaded with strips of various shapes delicately placed at the center of the radiator, providing new resonance at the upper band and the improvement of the CP features. The matching technique designed based on CPW 50 Ω microstrip transmission line combined with the dual broad band matching techniques through quarter‐wave transformer in conjunction with open stubs and distributed lumped element method constitutes the novelty of the study. Based on quasi‐TEM mn (q‐TEM mn ) mode, ACPW‐fed and CP‐slots are employed to generate CP radiations at the q‐TEM 11 and q‐TEM 21 modes, respectively, while the ground plane width is optimized to enhance axial ratio bandwidth (AR‐BW). Input impedance and radiation pattern calculations of the conventional structure using transmission line and cavity model‐based q‐TEM 01 mode are conducted, respectively. Numerical experiments of the studied monolayer antenna are carried out using Advanced Design System (ADS) Version 2009 environment software employing internal one‐port option to excite the antenna. The prototype of the proposed antenna with a compact dimension (0.27 λ g × 0.38 λ g × 0.02 λ g at 1.8 GHz where λ g is the guided wavelength of the q‐TEM 01 mode) is fabricated on high loss laminate FR4 substrate of volume 43 × 38 × 1.6 (mm 3 ) and relative dielectric constant of 4.4 with simple laboratory‐based traditional printed circuit board (PCB) etching process. Measurement results show a fractional impedance bandwidth (FIBW) of 11.1%, 5.9%, and 7.1%, axial ratio (AR) of 4.6, 2.2, and 0.5 dB, and peak gain of 3.7, 4.7, and 6.1 dBic at 1.8, 4.0, and 5.2 GHz, respectively, demonstrating its suitability for IED detection applications. To verify the efficiency of the proposed model, measured results are compared with the simulated results and good agreement has been established.

Modern agricultural systems face increasing uncertainties, rare risks, and complex dynamics that make traditional decision‐making ineffective. This research presents an innovative digital twin–based framework for proactive decision‐making in such systems that simultaneously considers temporal dynamics, structural uncertainty, and conflicts between management objectives. The proposed framework, relying on matrix dynamic modeling and introducing an analytical early warning indicator, allows for the gradual identification of the system’s movement toward critical areas before a crisis occurs. Numerical and behavioral results show that the model is able to produce sustainable, interpretable, and adaptive proactive policies and maintain its structural resilience in the face of rare and severe shocks. This framework is not only theoretically coherent but also provides a practical tool from a managerial perspective for the transition from reactive to proactive and data‐driven management in agricultural systems.

Low‐frequency oscillations are created in the power system due to the interconnection of large electric networks with weak tie‐lines, heavy power exchanges, and sudden changes in load demand and generation, which must be damped quickly for stability. The proposed work consists of a power system stabilizer (PSS) tuned by three different optimization algorithms, which are the genetic algorithm, the moth‐flame algorithm, and a novel snake optimization algorithm (SOA) with the key features of exploitation and exploration. The first main objective is to optimally tune the controlling parameters of PSS, which could attain the best quick response and develop a robust power system achieved by SOA. The second novel contribution is the development of an advanced Heffron–Phillips model (AHPM), including the dynamics of d ‐axis internal voltage. The AHPM is designed with a higher‐order Synchronous Generator Model 1.1. There are only 6 K‐constants in the traditional model, whereas in AHPM, there are 10 K‐constants to represent the system dynamics. The integration of renewables into the grid creates new challenges. This AHPM is capable of meeting these challenges due to better mathematical modeling of the system. The damping ratio is (0.9830) with SOA‐based PSS.With AHPM, there is no need for the inclusion of FACTS devices with PSS to meet the stability issues. This next‐generation AHPM is an efficient and economical model. This model is developed using MATLAB R2020a.

The efficiency of recovery and signal decoding efficacy at the receiver in end‐to‐end communications using linearly predicted coefficients are susceptible to errors, especially for highly compressed signals. In this paper, we propose a method to efficiently recover linearly predicted coefficients for high signal compression for end‐to‐end communications. Herein, the steepest descent algorithm is applied at the receiver to decode the affected linear predicted coefficients. This algorithm is used to estimate the unknown frequency, time, and phase. Subsequently, the algorithm facilitates down‐conversion, time and carrier recovery, equalization, and correlation processes. To evaluate the feasibility of the proposed method, parameters such as multipath interference, additive white Gaussian noise, timing, and phase noise are modeled as channel errors in signal compression using the software‐defined receiver. Our results show substantial recovery efficiency with noise variance between 0 and y × 10E − 3, where y lies between 0 and 10 using the modeled performance metrics of bit error rate, symbol error rate, and mean square error. This is promising for modeling software‐defined networks using highly compressed signals in end‐to‐end communications.

Major depressive disorder (MDD) is a common mental health illness in which the affected person experiences chronic sadness and loses interest in activities. Traditionally, MDD is diagnosed using clinical examinations and self‐report questionnaires, both of which are subjective and susceptible to error. Recent advances in electroencephalogram (EEG) analysis have combined physiological markers with machine learning (ML) and deep learning (DL) techniques, offering objective, noninvasive alternatives for MDD detection. Despite numerous studies leveraging ML and DL for EEG‐based MDD identification, there is a lack of comprehensive analysis that critically evaluates the strengths and limitations of these methods. This survey paper addresses this gap by analyzing 31 studies published between 2020 and 2024, focusing on critical aspects such as dataset size, electrode configurations, preprocessing techniques, feature engineering, and the ML/DL models employed. The paper presents a taxonomy that categorizes the methods used across various studies for each step of the EEG‐based depression detection pipeline and an analysis of the critical dimensions required for detecting depression based on the comprehensive analysis of dataset, suitable EEG electrode configuration, effective preprocessing techniques, and feature engineering for different model types. With such insights, this survey aims to guide future research and improve the accuracy and reliability of EEG‐based MDD detection.

The high‐impedance fault (HIF) occurring in medium voltage (MV) distribution networks is dangerous to livestock and personnel due to its arcing nature. The untimely detection of the fault can endanger lives and destroy equipment. Identifying the occurrence of HIF in a power system is a cumbersome task, as fault current falls within the normal current range. The paper analyses current signals from radial and mesh distribution networks and features extracted during HIF and non‐HIF conditions by using the local binary pattern (LBP), local neighbor gradient pattern (LNGP), local neighbor descriptive pattern (LNDP), and local gradient pattern (LGP). In the proposed algorithm, 1D signal analysis for HIF detection in the MV distribution system is performed for the first time for fault analysis. The Kruskal–Wallis test was carried out to get the best feature sets from the extracted features. HIF and non‐HIF were classified by bidirectional long short–term memory (Bi‐LSTM) for the selected feature sets. Among the four algorithms, LGP attains the best accuracy for both networks; hence, the paper recommends that LGP with Bi‐LSTM is more effective for detecting HIF occurrence.

This article presents a new generic core cell for designing the first‐order universal filter employed second‐generation voltage conveyor (VCII+). With appropriate admittance selection, the proposed core cell can derive four different filter types, namely, low‐pass, high‐pass, inverting all‐pass, and noninverting all‐pass functions and encompassing four basic modes: current mode (CM), voltage mode (VM), transimpedance mode (TIM), and transadmittance mode (TAM). The primary feature of the proposed filters is the ability to cascade the circuit easily, eliminating the need for an extra buffer. The proposed circuits were confirmed by 0.18‐μm TSMC CMOS process technology simulations. Numerous simulation results are included to verify the validity of the proposed circuits. Moreover, the experimental results of some proposed circuits from the commercial device built around AD844 agree well with the theory.

In this work, a proposed intelligent controller has been designed and implemented for a prototype of four stair‐step escalator. The required task of this controller is to manage the supplied power of driving motor of the escalator according to the applied load on the stair‐steps, which is represented by the number of persons standing on the stair‐steps. The proposed intelligent controller must realize the following objectives: adaptive consumption power, fast processing, high reliability, and low cost, which the contribution of this work. The backpropagation neural network (BPNN) was used in designing the software of the proposed controller for the following reasons: fast learning, fast processing, and capability of finding the optimal solution. The proposed neural network was designed using MATLAB software package; it involves three layers, they are input, hidden, and output layers; the input layer involves four neurons, while the hidden layer involves 10 neurons, and the output layer involves three neurons. After executing and testing the proposed controller, it was observed that the mean square error (MSE) value reached 8.68 × 10−18, the gradient reached 3.41 × 10−9, and there was fitting 100% between the desired and actual outputs, which indicates the high reliability and accuracy of the proposed controller. Finally, the software of the proposed controller was downloaded in field‐programmable gate array (FPGA) using ISE Design Suit software, as known, the main characteristics of FPGA are fast processing, small size, and low cost.