Modern low-voltage distribution lines, especially those linked with renewable energy sources, face technical hurdles like unaccounted and illegal electricity use, increased power losses, voltage ...control issues, and overheating. Tackling these challenges effectively requires continuously monitoring power flows and identifying problematic network spots. This study introduces a method involving ongoing energy flow monitoring from distribution transformers and other sources to end-users through auxiliary facilities. The algorithm seamlessly integrates with consumers’ existing smart power meters and supporting infrastructure, eliminating the need for extra equipment or data. Deployed in several distribution networks totaling about 40 GWh/year over two years, this diagnostic system showed promising results. It notably cut total power consumption by around 6% by detecting and mitigating illegal energy waste and addressing technical issues. Additionally, it reduced technical personnel involvement in operational tasks by approximately twentyfold, significantly enhancing network profitability overall.
The increasingly widespread occurrences of fast-changing loads, as in, for example, the charging of electrical vehicles and the stochastic output of PV generating facilities, are causing imbalances ...between generated and consumed power flows. The deviations in voltage cause noteworthy technical problems. The tap-changers in today’s transformers are slow-reacting and thus cannot effectively correct the imbalance. Tap-changers should be replaced by special appliances, installed in distribution lines, that can effectively estimate voltage RMS and refine imbalances during a fraction of the AC period, preferably less than half. This article suggests specially developed methods for RMS assessment based on approximating instantaneous voltage magnitudes using harmonics and correcting coefficients.
The requirement of RMS (voltage and current) measurements under a fraction of the AC period has become increasingly attractive in power systems. Some of these power applications are responsible for ...voltage stabilization in distribution lines when the voltage correction should be made in a short time, no more than one or two periods of the AC signal. Previously developed RMS correction applications must be validated in real-world situations characterized by an abrupt change (discontinuity) in voltage magnitude occurring even during a single AC period. Such circumstances can substantially influence the RMS estimation and, therefore, should be considered. This article suggests a mathematically based approach, validated in the laboratory, that improves the accuracy of a voltage RMS estimation for the appropriate measurement devices. It produces better results in cases where the RMS assessment should be done in a fraction of the AC period.
The influence of electrolyte velocity over the ion-exchange membrane surface on ion and vanadium redox batteries' conductivity was formalized and quantified. The increase in electrolyte velocity ...dramatically improves proton conductivity, resulting in improved battery efficiency. An analysis of conductivity was carried out using a math model considering diffusion and drift ion motion together with their mass transport. The model is represented by the system of partial differential together with algebraic equations describing the steady-state mode of dynamic behavior. The theoretical solution obtained was compared qualitatively with the experimental results that prove the correctness of the submitted math model describing the influence of the electrolyte flow on the resistance of the vanadium redox battery. The presented theoretical approach was employed to conduct a parametric analysis of flow batteries, aiming to estimate the impact of electrolyte velocity on the output characteristics of these batteries.
In this paper, a new hybrid TSA-PSO algorithm is proposed that combines tunicate swarm algorithm (TSA) with the particle swarm optimization (PSO) technique for efficient maximum power extraction from ...a photovoltaic (PV) system subjected to partial shading conditions (PSCs). The performance of the proposed algorithm was enhanced by incorporating the PSO algorithm, which improves the exploitation capability of TSA. The response of the proposed TSA-PSO-based MPPT was investigated by performing a detailed comparative study with other recently published MPPT algorithms, such as tunicate swarm algorithm (TSA), particle swarm optimization (PSO), grey wolf optimization (GWO), flower pollination algorithm (FPA), and perturb and observe (P&O). A quantitative and qualitative analysis was carried out based on three distinct partial shading conditions. It was observed that the proposed TSA-PSO technique had remarkable success in locating the maximum power point and had quick convergence at the global maximum power point. The presented TSA-PSO MPPT algorithm achieved a PV tracking efficiency of 97.64%. Furthermore, two nonparametric tests, Friedman ranking and Wilcoxon rank-sum, were also employed to validate the effectiveness of the proposed TSA-PSO MPPT method.
Currently, the Israeli energy industry faces the challenge of a considerable increase in solar electricity production. As a relatively isolated system, the significant expansion of solar electricity ...may cause problems with electricity quality. Electrical storage installation can resolve this problem. In Israel’s situation, the optimal solution could be the creation of a channel between the Mediterranean and the Dead Sea. The channel can solve three closely related problems: the increased production of desalinated water for domestic, industrial, and agricultural needs; the prevention of a permanent Dead Sea level decline and its imminent disappearance; the development of hydro-pumping electrical storage stations; and the creation of numerous PV facilities in the Negev area for national electricity generation. However, detailed analysis should be conducted for the estimation of the possible increase in solar electric generation with consideration of a stochastic PV outcome and the potential ability to use the Dead Sea for the brine discharge of electrical hydro-storage plants.
The effective deployment of electrical energy has received attention because of its environmental implications. On the other hand, induction motors are the primary equipment used in many industries. ...Industrial facilities demand the maximum percentage of energy. This energy demand is determined by the operating circumstances imposed by the internal characteristics of the induction motor. Because internal parameters of an induction motor are not immediately measurable, they must be obtained through an identification process. This paper proposed an improved version of moth flame optimization (IMFO) for the efficient parameter estimation of induction motors. A steady-state equivalent circuit of the induction motor is employed for the simulation. The proposed technique handles the parameter estimation problem better than moth flame optimization (MFO), particle swarm optimization (PSO), the flower pollination algorithm (FPA), the tunicate swarm algorithm (TSA), and the sine cosine algorithm (SCA). The anticipated IMFO reduces the cost function by 49.38% as compared with the basic version of MFO.
One of the greatest challenges for widespread utilization of solar energy is the low conversion efficiency, motivating the needs of developing more innovative approaches to improve the design of ...solar energy conversion equipment. Solar cell is the fundamental component of a photovoltaic (PV) system. Solar cell's precise modelling and estimation of its parameters are of paramount importance for the simulation, design, and control of PV system to achieve optimal performances. It is nontrivial to estimate the unknown parameters of solar cell due to the nonlinearity and multimodality of search space. Conventional optimization methods tend to suffer from numerous drawbacks such as a tendency to be trapped in some local optima when solving this challenging problem. This paper aims to investigate the performance of eight state-of-the-art metaheuristic algorithms (MAs) to solve the solar cell parameter estimation problem on four case studies constituting of four different types of PV systems: R.T.C. France solar cell, LSM20 PV module, Solarex MSX-60 PV module, and SS2018P PV module. These four cell/modules are built using different technologies. The simulation results clearly indicate that the Coot-Bird Optimization technique obtains the minimum RMSE values of 1.0264E-05 and 1.8694E-03 for the R.T.C. France solar cell and the LSM20 PV module, respectively, while the wild horse optimizer outperforms in the case of the Solarex MSX-60 and SS2018 PV modules and gives the lowest value of RMSE as 2.6961E-03 and 4.7571E-05, respectively. Furthermore, the performances of all eight selected MAs are assessed by employing two non-parametric tests known as Friedman ranking and Wilcoxon rank-sum test. A full description is also provided, enabling the readers to understand the capability of each selected MA in improving the solar cell modelling that can enhance its energy conversion efficiency. Referring to the results obtained, some thoughts and suggestions for further improvements are provided in the conclusion section.
An enhanced version of the moth flame optimization algorithm is proposed in this paper for rapid and precise parameter extraction of solar cells. The proposed OBLVMFO algorithm’s novelty lies ...primarily in the improved search strategies, where two modifications are proposed to maintain a proper balance between exploration and exploitation. Firstly, an opposition-based learning mechanism is employed to initialize the search population for the purpose of enhancing the global search. Secondly, Lévy flight distribution is used to prevent the stagnation of solutions in local minima. The implementation of intelligent rules such as OBL and Lévy flight distribution significantly improves the performance of the standard MFO. The developed OBLVMFO performed adequately and is reliable in terms of RMSE compared to other methodologies such as MFO, ALO, SCA, MRFO, and WOA. The best optimized value of RMSE achieved by OBLVMFO is 6.060E−04, 1.3600E−05, and 7.0001E−06 for STE 4/100 (polycrystalline), LSM 20 (monocrystalline), and SS2018P (polycrystalline) PV modules, respectively. The experiments performed on the benchmark test function revealed that the OBLVMFO has a 61% faster convergence speed than the standard version of MFO, which improves solution accuracy. In addition to this, two non-parametric tests: Friedman ranking and Wilcoxon rank sum are performed for the validation.
•A novel OBLVMFO algorithm is proposed for parameter extraction of solar cell models.•Two modifications are anticipated to overcome the limitations of original MFO algorithm.•The performance of OBLVMFO is tested on standard benchmark functions and practical measured datasets.•The proposed OBLVMFO algorithm is 61% faster than standard version of MFO algorithm.
Widespread applications of AC motors fed by variable frequency drives in electrified vehicles have become a conventional technical solution. The flexibility of control, low cost, and high energy ...efficiency attract developers and engineers to apply these appliances in cars, railway trains, trams, etc. The distinctive characteristic of vehicles is a wide range of required rotation speed and torque. This circumstance means that the problems of the AC motor (nominal power, synchronous speed) and gearbox (transmission ratio) become non-trivial and necessitate optimal selection to ensure the best functionality of the entire driving system. This study proposes an approach for the optimal choice of a specific AC motor (nominal rating, synchronous speed) and the transmission ratio of the gearbox by analyzing the entire system’s losses. The optimal selection of an AC motor ensures maximum energy efficiency for a specific transportation driving cycle.
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