Electrical Variables Research Articles

Taguchi optimization of Wire EDM process parameters for machining LM5 aluminium alloy.

LM5 alloy is suitable for metal castings for marine and aesthetic uses due to its admirable resistance to corrosion. In order to make intricate shapes in the LM5 alloy, this study intends to assess the impact of Wire Electric Discharge Machining process variables, like Pulse on Time (Ton), Pulse off Time (Toff), Gap Voltage (GV) and Wire Feed (WF) on responses like Material Removal Rate (MRR), Surface Roughness (SR), and Kerf Width (Kw). The LM5 aluminium alloy plate was produced through stir casting process. SEM, EDAX and XRD images confirm the LM5 Al alloy's microstructure and crystal structure. WEDM studies were conducted using design of experiments approach based on L9 orthogonal array and analysed using Taguchi's Signal to Noise Ratio (S/N) analysis. Pulse on Time has the greatest statistical effects on MRR (68.25%), SR (79.46%) and kerf (81.97%). In order to assess the surface integrity of the WEDM machined surfaces, the SEM study on the topography was conducted using the optimum surface roughness process variables: Ton 110 μs, Toff 50 μs, GV 40 V, and WF 9 m/min. SEM images show the recast layer and its thickness. The average absolute error for MRR is 1.69%, SR is 3.89% and kerf is 0.88%, based on mathematical (linear regression) models. The Taguchi's Signal to Noise ratio analysis is the most appropriate for single objective optimization of responses.

Optimal Conversion of Food Packaging Waste to Liquid Fuel via Nonthermal Plasma Treatment: A Model-Centric Approach

The efficiency of employing a multifactorial approach to enhance the nonthermal plasma (NTP) chemical conversion of solid waste food packaging materials into liquid petroleum hydrocarbons was assessed for the first time in this study. The researchers adopted a hybrid approach which integrated the zero-dimensional (0-D) and response surface model (RSM) techniques. After their application, the researchers noted that these strategies significantly enhanced the model prediction owing to their accurate electrochemical description. Here, the researchers solved a set of equations to identify the optimisation dynamics. They also established experimental circumstances to determine the quantitative correlation among all process variables contributing to food plastic packaging waste degradation and the production of liquid fuels. The findings of the study indicate a good agreement between the numerical and experimental values. It was also noted that the electrical variables of NTP significantly influenced the conversion yield (Yconv%) of solid plastic packaging waste to liquid hydrocarbons. Similarly, after analysing the data, it was seen that factors like the power discharge rate (x1 ), discharge interval (x2), power frequency (x3), and power intensity (x4) could significantly affect the product yield. After optimizing the variables, the researchers observed a maximal Yconv% of approximately 86%. The findings revealed that the proposed framework could effectively scale up the plasma synergistic pyrolysis technology for obtaining the highest Yconv% of solid packaging plastic wastes to produce an aromatics-enriched oil. The researchers subsequently employed the precision of the constructed framework to upgrade the laboratory-scale procedures to industrial-scale processes, which showed more than 95% efficiency. The extracted oil showed a calorific value of 43,570.5 J/g, indicating that the liquid hydrocarbons exhibited properties similar to commercial diesel.

Digital twin-driven senseless cutting force monitoring and vibration stability control of a rotary ultrasonic machining system

Rotary ultrasonic machining (RUM) emerges as a superior technique for processing difficult-to-cut materials due to its capacity to improve machining efficiency and surface quality. The tool vibration amplitude and its stability in the machining process are critical to guarantee the performance of RUM. However, due to the lack of a reliable status monitoring and control method, the operator cannot identify and solve the problem of vibration loss in time, greatly restricting the wider industrial application of RUM. This study proposes a digital twin-driven method to monitor and control the status of the RUM system. A twin model of the RUM system is first established, which describes the relationship among the driving electrical variables, the vibration amplitude, and the external load when the system works at the resonant state. A frequency tracking algorithm using the electrical signals is used to make the RUM work at the resonant state, which ensures the twin model is always aligned with the physical RUM system. And the actual vibration amplitude and the external load can be derived in real time. Then, a voltage-adjusting method is proposed to make the actual vibration amplitude approach its idle value and the cutting force is updated. Applying the above route, the cutting force and vibration amplitude can be in-process monitored without extra sensors, and vibration stability can be improved. Finally, loading and drilling experiments are carried out to verify the efficacy of the proposed digital twin-driven method. The experimental results demonstrate that the proposed method can monitor the cutting force accurately (average deviation during the whole drilling process <10 %) without using any additional force sensor like dynamometer, and ensure the stability of vibration amplitudes under load (amplitude variation <10 %), enabling the RUM system to become intelligent.

Coupled Multiphysics Modeling of Lithium-Ion Batteries for Automotive Crashworthiness Applications

Abstract Considerable advances have been made in battery safety models, but achieving predictive accuracy across a wide range of conditions continues to be challenging. Interactions between dynamically evolving mechanical, electrical, and thermal state variables make model prediction difficult during mechanical abuse scenarios. In this study, we develop a physics-based modeling approach that allows for choosing between different mechanical and electrochemical models depending on the required level of analysis. We demonstrate the use of this approach to connect cell-level abuse response to electrode-level and particle-level transport phenomena. A pseudo-two-dimensional model and simplified single-particle models are calibrated to electrical–thermal cycling data and applied to mechanically induced short-circuit scenarios to understand how the choice of electrochemical model affects the model prediction under abuse scenarios. These models are implemented using user-defined subroutines on ls-dyna finite element software and can be coupled with existing automotive crash safety models.

Optimising energy efficiency enhancing NILM through high- resolution data analytics

This study introduces a novel approach to enhancing energy efficiency by building a high-resolution dataset for Non-Intrusive Load Monitoring, addressing the challenges of monitoring a wide range of devices. To achieve optimal energy efficiency, it is essential to have advanced monitoring of electrical variables. In this context, the second version of openZmeter is presented, which supports up to 4 devices, each capable of 160 measurements, including voltage, current and power harmonics on each channel. To carry out this purpose, the Non-Intrusive Load Monitoring Toolkit is adapted for the new openZmeter v2. The main objective of this study is to offer a new pragmatic approach to generating artificial intelligence models to optimise energy use, analysing the results through an exhaustive experimental analysis under various casuistry and conditions. Numerous comparative studies are provided using classical disaggregation algorithms with different requirements. Conclusively, the research emphasises the transformative potential of artificial intelligence for energy efficiency strategies, offering insights for scholars and practitioners.

A Time-Domain Perspective on the Structural and Electronic Response in Epitaxial Ferroelectric Thin Films on Silicon.

This operando study of epitaxial ferroelectric Pb(Zr0.48Ti0.52)O3 capacitors on silicon substrates studies their structural response via synchrotron-based time-resolved X-ray diffraction during hysteresis-loop measurements in the 2-200 kHz range. At high frequencies, the polarization hysteresis loop is rounded and the classical butterfly-like strain hysteresis acquires a flat dumbbell shape. We explain these observations from a time-domain perspective: The polarization and structural motion within the unit cell are coupled to the strain by the piezoelectric effect and limited by domain wall velocity. The solution of this coupled oscillator system is derived experimentally from the simultaneously measured electronic and structural data. The driving stress σFE(t) is calculated as the product of the measured voltage U(t) and polarization P(t). Unlike the electrical variables, σFE(t) and η(t) of the ferroelectric oscillate at twice the frequency of the applied electrical field. We model the measured frequency-dependent phase shift between η(t) and σFE(t).

Determination of light-independent shunt resistance in CIGS photovoltaic cells using a collection function-based model

Shunt resistance Rsh is a critical parameter for photovoltaic cells designed for low light indoor applications because it negatively affects the open circuit voltage, fill factor, and conversion efficiency. Standard CIGS cells are known to have low Rsh and are, therefore, unpromising candidates for indoor energy sources. In this paper, we extend the original work of Virtuani et al. by determining the electrical specifications of many CIGS cells with copper contents [Cu]/([Ga]+[In]) as low as 0.33 and gallium contents between 0.28 and 0.81. First, IV data are fitted by a standard single-diode electrical circuit model for each illumination, resulting in light-dependent parameters. Then, we use a procedure based on a single dataset of electrical variables, i.e., independent of light, corrected by the experimental collection function, which captures light-dependent physical mechanisms. In this way, we are able to correctly reproduce the illuminance dependence of the electrical response of the PV cell over three orders of magnitude, in particular with a fixed value of the shunt resistance. The highest Rsh is obtained with a low copper composition of 0.5, regardless of the gallium composition.

Machine learning-assisted prediction of the electronic features of a Schottky diode interlaid with PVP:BaTiO3 composite

This study employs two Machine Learning (ML) models to predict the electronic current and then analyze the main electronic variables of Schottky diodes (SDs), including leak current (I0), potential barrier height (ΦB0), ideality factor (n), series resistance (Rs), shunt resistance (Rsh), rectifying ratio (RR), and interface states density (Nss). The I-V characteristics are examined for both without and with an interlayer. The polyvinylpyrrolidone (PVP) polymer and BaTiO3 nanostructures are combined to form the nanocomposite interface. The ML algorithms that are employed include the Gaussian Process Regression (GPR) and Kernel Ridge Regression (KRR). The thermionic emission theory is used to gather training data for ML algorithms. Ultimately, the effectiveness of these ML methods in anticipating the electric characteristics of SDs is evaluated by contrasting the predicted and experimental findings in order to identify the optimal ML model. Whereas the GPR algorithm has given values that are closer to the actual values, the ML predictions of fundamental electric variables by practically both algorithms have the best level of agreement with the actual values. Also, the obtained findings indicate that when the nanocomposite interface is used, the amount of I0 and Nss for metal-semiconductor (MS) Schottky diodes reduces and φ B0 increases.

Load Flow Analysis of 10-Bus Test System

The importance of power flow analysis can’t be overstated. In the field of electrical power engineering, it is critical for both the utility and the consumer to understand a variety of electrical variables such as voltages and power flows inside power systems. This paper effectively applies Power World Simulator software to perform load flow analysis on a typical power system. The results may be applied to a much more complicated system with many loads and a range of power supply sources.

DEVELOPMENT OF AN ALGORITHM FOR ADAPTING A MATHEMATICAL MODEL OF THE PROCESS OF MIXING AND MELTING COPPER CONCENTRATES

The paper describes the information base of the system, which is formed by algorithms of centralized control, automated analytical control system and control data of material flows. The data processing algorithm according to a special program generates additional information necessary to solve the control tasks of the system and includes algorithms for processing analytical data information, as well as data for monitoring material flows, calculations of additional electric furnace variables, loading of charge into furnace bunkers and technological variables by department. The simulation model of the functioning of the complex "technological process – control system" is implemented in the form of a package of interconnected software modules. The package implementing the simulation model is divided into three complexes: a set of programs for "Data collection and processing"; a complex for "Optimal process control"; a set of programs for "optimal energy regime management".

Exact solution for the electrical response of a constant phase element with a series resistance to linear voltage sweep

A resistance Rs in series with a constant phase element (CPE) of frequency-dependent impedance given by the power law function Zc(s)=1/(Cαsα) is commonly used for the analysis of steady-state frequency response data exhibiting non-purely capacitive behavior. This is the case in most (bio)(electro)chemical systems including dielectrics, batteries, supercapacitors, capacitive deionization units, biological tissues and bioelectrodes. Passing to the time domain, the current, voltage and charge of the system are governed by differential equations with non-integer, fractional-order operators. The purpose of this study is to provide the exact analytical expressions for the electrical response of an Rs-CPE model under linear sweep voltammetry with the use of the Laplace transform method. The electrical variables are expressed in terms of special functions regularly encountered with fractional calculus such as the Fox’s H-function and the Mittag-Leffler function, and can be used for modeling non-ideal devices as well as extracting their characteristic parameters.

A chaotic memristive Hindmarsh-Rose neuron with hybrid offset boosting

In recent years, memristor has been widely introduced into neuronal models to simulate magnetically induced currents. However, there are relatively few studies on electric field stimulation of neuronal models. This paper presents a 4D memristive Hindmarsh-Rose (HR) neuron model that introduces a sinusoidal function memristor and electric field variables, which can produce heterogeneous multistability and homogeneous multistability. More interestingly, six modes of offset boosting can be triggered in the neuronal model by varying the electric field coupling coefficients, the first three of which are parameter-dependent two-dimensional offset boosting modes, and the second three are initial-value-dependent offset-boosting modes. Notably, homogeneous multistability can also combined with the offset-boosting, and then iterating cubic infinitely many attractors. This model presents a model where multiple offset boosting is freely controllable for the first time. Finally, the theoretical analysis is verified by digital circuits on a RISC-V platform.

Long-term time-course of atrial sensing and incidence of atrial fibrillation in patients with VDD versus DDD pacemakers

Abstract Introduction VDD pacemakers (PM) theoretically have the same advantages with respect to maintaining atrioventricular (AV) synchrony as dual-chamber (DDD) pacemakers, with lower costs and simpler implantation procedure. However, their use is often limited by concerns about the maintenance of atrial sensing (AS) and the occurrence of atrial fibrillation (AF). Purpose We aimed to compare long-term atrial sensing stability, the incidence of AF and complications in VDD and DDD systems. Methods We retrospectively enrolled patients who underwent VDD or DDD PM implantation for AV block between 2014 and 2021 from 2 centers. Clinical, analytical and electrical variables at implantation, their evolution and events at follow-up (25.2±16.2 months) were collected, comparing the groups by Kaplan-Meier and Cox multivariate analysis. Results 487 patients who underwent PM implantation were included, 189 (38,8%) received a VDD PM and 298 (61,2%) a DDD PM. Patients with VDD were older and had a higher prevalence of risk factors and lower atrial voltage at implantation (Table). Complications at implantation and early complications were scarce and without significant differences in both groups. During follow-up, there was a reduction in atrial voltage in the VDD group (implant 1.4+0.8 mV; follow-up 1.2+0.9 mV; p&amp;lt;0.01), with stability in the DDD group (3.8+2.1 vs 3.9+2.2 mV). Total percentages of AS loss were 23.2% (VDD) vs 2.3% (DDD); p&amp;lt;0.01) (Figure 1). In the Cox multivariate analysis the risk of late AS loss was higher in the VDD group (HR=7.6; 95% CI 2.86-20.3; p&amp;lt;0.001) together with the AS values at implant (HR=0.72; 95% CI 0.52-1.00; p=0.052). AS loss was corrected with reprogramming in 23 patients, reintervention was needed in 1 (2%) and VVI was abandoned in 27 (53%). The risk of AF occurrence was lower in the VDD group (HR=0.39 CI 95% 0.18-0.87; p=0.021) (Figure 2). Conclusions In the long term, AS is significantly reduced in VDD MPs, leading to its loss in a quarter of VDD patients. It is usually correctable with reprogramming, rarely requiring abandonment to VVI. AS at implantation is crucial as it is directed related to the AS during follow-up. DDD PMs were independently associated with a higher incidence of AF, with no differences in the remaining complications.TableFigure

Radar-Enabled non-contact speed estimation for rotating electrical machinery

Electric motors play a pivotal role in contemporary industrial processes, prompting the necessity for their continuous monitoring through an array of sensors in various applications. Among the crucial parameters under scrutiny, the operational speed of electric motors holds a prominent position. Current methods for speed detection conventionally entail the utilization of physically connected sensors or the measurement of some electrical variables. Nonetheless, a non-contact speed measurement approach with a mobile device promises low cost, efficiency, and even safety in some specific cases. This research delves into the remote speed detection of electric motors employing Continuous Wave radar technology. To this end, phase extraction techniques, which have demonstrated robust performance in diverse application domains, have been assessed for their efficacy in motor speed detection. First, five distinct methods have been utilized to extract the phase of baseband signals obtained from a 24 GHz IQ-demodulated radar system directed toward an asynchronous motor. These methodologies have undergone comprehensive evaluation in experimental measurements and simulation encompassing ideal and noisy conditions. Complex Signal Demodulation, one of the phase extraction methods, exhibits a noteworthy superiority over alternative approaches in motor speed measurement experiments. These experiments have entailed three different load scenarios and encompassed ten distinct speeds, equally spaced between 500 and 1400 rpm. The Complex Signal Demodulation method has delivered exceptional results, successfully detecting motor speeds across thirty experiments with a mean absolute percentage error of merely 0.08. Radar-based non-contact speed detection holds immense promise, particularly in fault diagnosis and predictive maintenance.

Electromechanical coupling characteristics of multilayered piezoelectric quasicrystal plates in an elastic medium

AbstractQuasicrystals (QCs) have attracted tremendous attention of researchers for their unusual properties. In this paper, an exact electric‐elastic solution of the simply supported and multilayered three‐dimensional (3D) cubic piezoelectric quasicrystal (PQC) nanoplate with the nonlocal effect is derived. Based on the basic elasticity equation of 3D QCs, we construct the linear eigenvalue system in terms of the pseudo‐Stroh formalism, from which the general solutions of the extended displacements and stresses in any homogeneous layer can be obtained. The two‐parameter foundation model is utilized to simulate the interaction between the nanoplate and elastic medium. The propagator matrices are employed to connect the field variables at the upper interface to those at the lower interface of each layer. Based on the boundary conditions of the upper and lower surfaces of the laminate and foundation model, the solutions are employed to derive from the global propagator matrix. Compared with the conventional propagator matrix method, a new propagator method is reestablished to deal with numerical instabilities of the case of large aspect ratio and high‐order frequencies for QC laminates. Finally, typical numerical examples are presented to illustrate the influence of nonlocal parameters and elastic medium coefficients on phonon, phason, and electric variables of 3D PQC nanoplates.

Numerical analysis of electrothermoconvection of a dielectric nanofluid in a heated cavity

A numerical analysis of electrohydrodynamic (EHD) flow and heat transfer of nanofluid in a heated rectangular cavity is presented. A two-dimensional (2D) rectangular cavity heated from the bottom is considered. An electric potential difference is applied vertically, with the bottom wall acting as a high-voltage electrode, and the top wall is grounded. TiO2-25 # transformer oil nanofluid with nanoparticle volume fraction ranging from 0–5% is considered. The numerical model for EHD flow and heat transfer of nanofluid is implemented in the finite-volume method (FVM) based numerical framework of OpenFOAM. A single-phase approach based on the effective properties is adopted to model the nanofluids. A two-way coupled EHD flow model is employed to consider mutual interactions of flow and electric field variables. The flow and heat transfer behavior of nanofluids in the presence of an electric field is quantified with reference to the key parameters, electric Rayleigh number (T), and the nanoparticle volume fraction ϕ. The addition of nanoparticles increased the viscosity and marginally reduced the natural convective flow and heat transfer. However, EHD flow induced by the electric field aided in overcoming the weak natural convection flow in nanofluids. Results confirm that nanofluids’ net effective heat transfer rates are notably increased in the presence of the electric field. For the parameters under consideration, combining electric fields with nanofluids led to a significant heat transfer enhancement of up to 32.3%. The present study showcases the feasibility of combining passive heat transfer enhancement using nanoparticles and active heat transfer enhancement using EHD flow.

Stability analysis for an ad-hoc model predictive control in DC/DC converters with a constant power load

This paper presents a stability analysis for a horizon-one continuous control set model predictive control (MPC) for DC/DC converters connected to a constant power loads (CPLs). Different MPC approaches have been previously presented, each tailored to a specific type of converter but without a formal stability proof, failing to ensure robust performance across various operating conditions. Unlike previous works in the scientific literature, our approach is general and applicable to any second-order DC/DC converter, including the Buck, Boost, Buck-Boost, and non-inverting Buck-Boost converters. A formal stability analysis was conducted using the proposed approach, initially for the open-loop system and subsequently for the closed-loop system. The trace-determinant or triangle criterion was employed for discrete-time systems in R2. Three analyses were conducted to evaluate the performance of the proposed MPC, showing the stability issues of DC/DC converters with CPLs in open loops. This work proposes solutions using the suggested technique. Numerical simulations utilizing the Boost and Buck-Boost converters demonstrate that, in open-loop operation, even minor perturbations in the CPL value can destabilize the behavior of electrical variables. In addition, simulations show that, for the Boost converter, the settling time was about 1.5ms. Meanwhile, in the Buck-Boost case, it was about 5ms. Standardized control indicators, such as the integral absolute error criterion (IAE), the integral of the time-multiplied absolute error criterion (ITAE), and the integral of the time-multiplied square error criterion (ITSE), confirm the effective performance of the proposed one-step MPC. The values range between 1.521×10−2 and 4.591×10−6 for the Boost converter, and between 6.607×10−3 and 3.366×10−6 for the Buck-Boost topology.

Tele-Trafficking of Virtual Data Storage Obtained from Smart Grid by Replicated Gluster in Syntose Environment

One of the most important developments in the energy industry is the evolution of smart grids, which record minute details of voltage levels, energy usage, and other critical electrical variables through General Packet Radio Service (GPRS)-enabled meters. This phenomenon creates an extensive dataset for the optimization of the grid system. However, the minute-by-minute energy details recorded by GPRS meters are challenging to store and manage in physical storage resources (old techniques lead to a memory shortage). This study investigates using the distributed file system, replicated Gluster, as a reliable storage option for handling and protecting the enormous volumes of data produced by smart grid components. This study performs two essential tasks. (1) The storage of virtual data received from GPRS meters and load flow analysis of SynerGee Electric 4.0 software from the smart grid (we have extracted electrical data from 16 outgoing feeders, distributed lines, in this manuscript). (2) Tele-trafficking is performed to check the performance of replicated Gluster (RG) for virtual data (electrical data received from the smart grid) storage in terms of User Datagram Protocol (UDP), Transmission Control Protocol (TCP), data flow, and jitter delays. This storage technique provides more opportuni11ty to analyze and perform smart techniques efficiently for future requirement, analysis, and load estimation in smart grids compared to traditional storage methods.

Brief Analysis of the Location and Determination of Maximum Capacity of Distributed Generation in Electrical Systems Considering Demand Scenarios in Ecuador

This research focuses on evaluating the importance of the use of renewable sources through distributed generation and its implication in the operation of electrical systems given that its incorporation has a direct impact on the expansion of the capacity of the networks, the minimization losses, and the impact on end users, all supported by the growth of demand. Under this context, the study focuses on incorporating distributed generation (DG), taking scenarios of base, medium, and peak demand and the modeling of the network, and subsequently evaluating the service quality indices and operating costs in addition to the electrical variables of the system. For this purpose, the present work proposes an optimization model to be solved using the Matlab (2021b) computational program together with GAMS (37.1.0 Major release (11 November 2021)) and mixed-integer nonlinear programming, determining the optimal insertion and determination of the maximum capacity of distributed generators while complying with the technical restrictions of the system and applying optimal AC power flows. Localizing and determining maximum capacity for distributed generation (DG) in electrical systems are critical aspects of modern grid planning and operation. With the increasing penetration of renewable energy sources and the growing complexity of energy demand patterns, efficient integration of DG has become paramount for ensuring grid reliability and sustainability. In this context, the analysis of DG localization and capacity determination considering demand scenarios emerges as a critical area of research in electrical engineering. By employing advanced optimization techniques such as mixed-integer nonlinear programming (MINP), this research addresses the multidimensional challenges associated with DG deployment, including technical constraints, economic considerations, and environmental impacts. Understanding the contribution of this optimization approach to electrical engineering is fundamental for optimizing grid performance, enhancing renewable energy integration, and supporting the transition towards more resilient and sustainable energy systems. Consequently, investigating this optimization model represents a crucial step towards advancing the state-of-the-art in grid planning and facilitating the transition to a cleaner and more efficient energy future.

Investigation of hybrid CuPc-doped ZnO/p-silicon photodiodes for photonic and electronic applications

Copper phthalocyanine (CuPc) doped zinc oxide (ZnO) interlayered Al/p-Si Schottky barrier diodes (SBDs) were systematically fabricated utilizing spin coating technique. This study was undertaken to meticulously assess the influence of varying concentrations of CuPc on the intricate electrical and photodiode characteristics of these devices. The investigation involved the characterization of the current–voltage (I–V) characteristics configured with distinct different doping concentrations of CuPc such as 0.05 wt%,1 wt%, 2 wt%, under a wide range of voltages (± 5 V) and illumination irradiances. These measurements enabled the calculation of various critical electrical variables, such as the ideality factor (n), barrier height (ΦB), series resistance (Rs), shunt resistance (Rsh), interface states density (Nss) and their response under various illumination levels (between 10 and 100 mW/cm2) and under dark condition. An increase in the reverse current as the illumination increases suggested the potential utility of these SBDs as photodiodes, photosensors, or photodetectors. Notably, the linear dynamic range (LDR), a crucial factor for image sensors which obtained around 14 for all photodiodes. The photodiodes exhibited a good rectification ratio (RR) of approximately 104. The results obtained indicate that the rectifying properties of the structures can be controlled by CuPc doping. In addition, the results indicated that the presence of CuPc significantly influenced the values of n, ΦB, Rs/Rsh, and Nss. To further analyze the devices, capacitance–voltage (C–V) and conductance–voltage (G–V) measurements were carried out to determine parameters such as diffusion potential (VD), dopant acceptor atoms concentration (NA), Fermi energy level (EF), and width of depletion layer (WD) at both 1 kHz and 1 MHz. The measurements revealed that the capacitance values were higher at low frequencies compared to high frequencies, and this behavior was attributed to Nss. In summary, this study suggests that the manufactured photodiodes have the potential to be employed as photodiodes, sensors, or detectors in optical sensing applications, and their performance can be tailored by adjusting the concentration of CuPc in the ZnO interlayered structures. The discerned outcomes revealed the substantial influence of CuPc concentration on key electrical parameters, with conspicuous trends noted in the values of n, ΦB, Rs/Rsh, and Nss. Furthermore, the observed increase in the reverse current as the illumination level increases highlights the potential utility of these SBDs as sensitive photodiodes/sensors/detectors.