Power Loss Reduction Research Articles

Pengaruh Efek Kontaminasi Isolator KeramikTerhadap Rugi DayaSaluran Udara Tegangan Tinggi

In meeting consumers' needs for electric power continuously, the reliability of electric power distribution is something that needs to be considered. One of the reliability parameters is the network's ability to distribute power from generators continuously, with the allowable voltage and frequency quality. For this reason, the presence of an insulator in the power distribution system is very important considering its function is to separate live conductors from their supports. If the isolation properties do not function, a voltage failure will occur so that power distribution will stop, resulting in low system reliability. From the data from the calculation results of 150 kV high voltage overhead line power losses due to flashovers on insulators installed in each fouling area, it is clear that increasing the contaminant value will make it easier for flashovers to occur which cause very large power losses and conversely, insulators that are clean make it difficult. the occurrence of flashover so as to reduce power losses on the transmission line. The appropriate insulator to use is the fog type, because the diameter is wider, increasing the propagation distance so that the insulator's ability to withstand greater voltage

Strategic planning of distribution network integrated with EV charging stations using fuzzy pareto optimality for performance improvement and grid-side emission reduction benefits

The increasing penetration of electric vehicles (EVs) in distribution systems requires efficient charging load management techniques for delivering high-quality power to consumers. In this paper, we proposed a strategic planning approach for distribution systems integrated with EV charging stations, employing fuzzy Pareto optimality to enhance system performance and minimize carbon emissions. Our approach involves a fuzzy Pareto heuristic network reconfiguration to minimize real power loss and improve voltage profiles. Additionally, Time of Use (ToU) pricing-based EV charging load scheduling is developed to minimize the annual energy cost. These objectives aim to improve minimum node voltage, reduce power losses, and maintain branch current constraints. To simulate the impact of EV charging loads, we aggregate residential, commercial, and industrial loads, along with EV charging loads at charging stations based on customer choice time and ToU pricing methods. The proposed reconfiguration approach is tested on 33 and 69-bus distribution systems with integrated EV charging stations, distributed generations (DGs), and shunt capacitors (SCs). The simulation results demonstrate the advantages of the reconfiguration algorithm in enhancing the performance of the distribution network in the presence of EV loads. Furthermore, we compare ToU pricing-based EV charging time zones with customer comfort-based EV charging, showcasing the advantages of the former in reducing emissions through real power loss reduction. Overall, our strategic planning approach not only improves distribution system performance but also offers economic benefits while minimizing carbon dioxide emissions.

Research on Q-Table Design for Maximum Power Point Tracking-Based Reinforcement Learning in PV Systems

Photovoltaic (PV) power generation is considered to be a clean energy source. Solar modules suffer from nonlinear behavior, which makes the maximum power point tracking (MPPT) technique for efficient PV systems particularly important. Conventional MPPT techniques are easy to implement but require fine tuning of their fixed step size. Unlike conventional MPPT, the MPPT based on reinforcement learning (RL-MPPT) has the potential to self-learn to tune step size, which is more adaptable to changing environments. As one of the typical RL algorithms, the Q-learning algorithm can find the optimal control strategy through the learned experiences stored in a Q-table. Thus, as the cornerstone of this algorithm, the Q-table has a significant impact on control ability. In this paper, a novel Q-table of reinforcement learning is proposed to maximize tracking efficiency with improved Q-table update technology. The proposed method discards the traditional MPPT idea and makes full use of the inherent characteristics of the Q-learning algorithm such as its fast dynamic response and simple algorithm principle. By establishing six kinds of Q-tables based on the RL-MPPT method, the optimal discretized state of a photovoltaic system is found to make full use of the energy of the photovoltaic system and reduce power loss. Therefore, under the En50530 dynamic test standard, this work compares the simulation and experimental results and their tracking efficiency using six kinds of Q-table, individually.

Distributed Generation in Electric Power Systems: An Overview and Important Issues

This paper discusses distributed generation (DG) in electric power systems. Various popular DG technologies that are currently used are also described, along with brief explanations of their working principles. It has been acknowledged that the integration of DG with renewable energy sources in power systems is increasing and will grow further. The main reason for this growth is the rising cost and environmental concerns of non-renewable energy sources (fossil fuels). Furthermore, DG offers some advantages, such as reducing power losses in transmission and distribution lines and improving power supply security. However, the increasing DG penetration brings technical implications for the power system to which the DG is connected. These critical issues are also highlighted in the present paper.

Optimal Probabilistic Allocation of Photovoltaic Distributed Generation: Proposing a Scenario-Based Stochastic Programming Model

The recent developments in the design, planning, and operation of distribution systems indicate the need for a modern integrated infrastructure in which participants are managed through the perceptions of a utility company in an economic network (e.g., energy loss reduction, restoration, etc.). The penetration of distributed generation units in power systems are growing due to their significant influence on the key attributes of power systems. As a result, the placement, type, and size of distributed generations have an essential role in reducing power loss and lowering costs. Power loss minimization, investment and cost reduction, and voltage profile improvement combine to form a conceivable goal function for distributed generation allocation in a constrained optimization problem, and they require a complex procedure to control them in the most appropriate way while satisfying network constraints. Such a complex decision-making procedure can be solved by adjusting the dynamic optimal power flow problem to the associated network. The purpose of the present work is to handle the distributed generation allocation problem for photovoltaic units, attempting to reduce energy and investment costs while accounting for generation unpredictability as well as load fluctuation. The problem is analyzed under various scenarios of solar radiation through a stochastic programming technique because of the intense uncertainty of solar energy resources. The formulation of photovoltaic distributed generation allocation is represented as a mixed-integer second-order conic programming problem. The IEEE 33-bus and real-world 136-bus distribution systems are tested. The findings illustrate the efficacy of the proposed mathematical model and the role of appropriate distributed generation allocation.

Distributed generator and capacitor‐embedded reconfiguration in three‐phase unbalanced distribution systems using teaching learning‐based optimization

AbstractIn the transmission and distribution networks, both active and reactive power play significant roles. While active power does the beneficial work, reactive power maintains the voltage that necessitates management from a system reliability perspective. The voltage variation from the nominal range may result in unintentional operation and early component failure. To maximize the amount of real power that can be transported over the power‐transmitting media, the system must also control reactive power flow. In a three‐phase unbalanced distribution system reinforced with distributed generator units (DGUs) and shunt capacitor units (SCUs), this paper suggests a teaching learning‐based optimization (TLBO) approach to be applied to combinatorial problems for simultaneous reduction of real power loss and net reactive power flow, enhance voltage stability index (VSI) and minimize aggregated voltage deviation index. A multiobjective formulation based on TLBO has been implemented to choose the ideal sizes and placements of DGUs and SCUs in large distribution networks. The case studies have been carried out on an IEEE 33‐bus standard system and a large 123‐bus unbalanced system. The analysis of the IEEE 33‐ and 123‐bus test systems reveals the economic efficiency of the suggested TLBO algorithm. Notably, by applying DG and capacitor simultaneously in the 33‐bus system, a significant reduction of 59.35%, 55.05%, and 50.76% (phases a, b, and c), and for the 123‐bus system, a significant reduction of 43.65%, 19%, and 48.65% (phases a, b, and c) in total active power losses have been achieved. The results suggest that the proposed method renders significant improvement in voltage stability and reduces power losses concerning the base case for both test systems.

A novel shadow dispersion approach for solar PV modules in array using chess board game methodology: An experimental study

In the global scenario, photovoltaic (PV) systems contribute much power compared to other renewable energy (RE) sources due to their benefits, e.g., environmental friendliness, low maintenance, and the small investment required for efficient energy harvest. However, it experiences performance limitations due to adverse climatic conditions, i.e., temperature and sun irradiance variations. Partial shadowing circumstances (PSCs) have a negative effect on PV modules, leading to a shift in irradiance and diminished PV performance in terms of power loss (PL), performance ratio (PR), and reduced fill factor (FF). In this paper, the modification of PV module interconnections in an array system shows the power loss reduction under PSCs. The chess board game theory (CBGT) based PV array reconfiguration technique shows efficient results in terms of shade dispersion on the entire PV array to diminish the shading effect. Novel CBGT configuration (9 × 9 size) shows minimum PL and higher global maximum power point (GMPP) compared to existing series–parallel (SP), total-cross-tied (TCT), and puzzle-based Su-Do-Ku (SDK) and puzzle game theory (PGT) PV configurations. The CBGT configuration achieves higher performance in power GMPP (315.8 W, 273.6 W, 232.8 W, and 262.1 W) under four shading instances experimentally.

Optimal location for an EVPL and capacitors in grid for voltage profile and power loss: FHO-SNN approach

In the context of a rapidly expanding electric vehicle market, this research investigates the ideal locations for Electric vehicle (EV) charging stations (EVCS) and capacitors in power grids to enhance voltage stability and reduce power losses, facilitating sustainable and efficient grid operation. Meeting the increasing demand for EV charging stations while simultaneously addressing grid reliability, voltage control, and power loss issues poses significant challenges. A hybrid approach for EV parking lot and optimal location of capacitors in voltage and power loss is proposed in this manuscript. The proposed hybrid method combines the Fire Hawk Optimizer and Spiking Neural Network, hence called the FHO-SNN technique. The objective of the proposed method is used to lessen the active-power-losses that improve system performance. The novelty of this proposed method is electric vehicle parking lot and optimal location of capacitors in voltage and lessens the power loss. The proposed system is done in the MATLAB, and it is validates their performance with existing methods, such as Arithmetic Optimization Algorithm (AOA), Seagull Optimization (SO), and Atomic Orbital Search (AOS). The proposed method shows low convergence time, which is 4 sec, number of feeders, and best function node is found compared with other existing methods. The optimization approach can enhance the stability and efficiency of electric grids by improving voltage profiles and reducing power losses. The implications of this research highlight the potential for optimizing the combination of electric vehicles and capacitors into the grid, resulting in a more sustainable, efficient, and resilient energy and transportation system.

A new robust modified capuchin search algorithm for the optimum amalgamation of DSTATCOM in power distribution networks

Very sensitive loads require the safe operation of electrical distribution networks, including hospitals, nuclear and radiation installations, industries used by divers, etc. To address this issue, the provided paper suggests an innovative method for evaluating the appropriate allocation of Distribution STATic COMpensator (DSTATCOM) to alleviate total power losses, relieve voltage deviation, and lessen capital annual price in power distribution grids (PDGs). An innovative approach, known as the modified capuchin search algorithm (mCapSA), has been introduced for the first time, which is capable of addressing several issues regarding optimal DSTATCOM allocation. Furthermore, the analytic hierarchy process method approach is suggested to generate the most suitable weighting factors for the objective function. In order to verify the feasibility of the proposed mCapSA methodology and the performance of DSTATCOM, it has been tested on two standard buses, the 33-bus PDG and the 118-bus PDG, with a load modeling case study based on real measurements and analysis of the middle Egyptian power distribution grid. The proposed mCapSA technique's accuracy is evaluated by comparing it to other 7 recent optimization algorithms including the original CapSA. Furthermore, the Wilcoxon sign rank test is used to assess the significance of the results. Based on the simulation results, it has been demonstrated that optimal DSTATCOM allocation contributes greatly to the reduction of power loss, augmentation of the voltage profile, and reduction of total annual costs. As a result of optimized DSTATCOM allocation in PDGs, distribution-level uncertainties can also be reduced.

Research of Islanding Operation and Fault Recovery Strategies of Distribution Network Considering Uncertainty of New Energy

With the problems of fault handling in the distribution network, few studies concern the correlation between islanding operation and fault recovery. Thus, this paper proposes an islanding operation and fault recovery strategy for the distribution network considering the uncertainty of new energy. Firstly, the objectives of the distribution network islanding division scheme and operation optimization are established. Combined with distribution network radiation constraints, islanding power supply capacity, safety constraints, and distributed generator (DG) operation constraints, a rolling optimization method is used to construct the distribution network islanding division and operation model. Secondly, in the fault recovery stage, considering the characteristics of the island operation stage, a node load weight value is designed. A distribution network fault recovery model is then constructed with the goals of ensuring greater load power supply recovery, improving electricity satisfaction, and reducing network losses and switching times. Thirdly, considering the randomness of intermittent DGs such as wind power and photovoltaics, an uncertainty model of intermittent DGs is constructed. A solution method for the distribution network islanding operation and fault recovery model considering uncertainty is proposed by combining scenario generation reduction methods and second-order cone programming theory. Finally, the proposed method’s feasibility and effectiveness are verified using the improved IEEE 33-node distribution network. The results show that in the islanding division stage, the node voltage consistently remains between 1.08 pu and 1.1 pu when new energy achieves up to 34.09% and 48.65%, and the line losses represent approximately 0.22% to 0.26% of the total load when the initial energy levels of the storage are at 50% and 80%. In the fault recovery stage, compared to the method without network reconstruction, the system shows significant power loss reductions of approximately 11.9%, 13.6%, and 14.2% in three respective cases.

Real power loss reduction by Protist and Otocyon megalotis optimization algorithms

Real power loss reduction by Protist and Otocyon megalotis optimization algorithms

Evaluation of antiparallel SiC Schottky diode in SiC MOSFET phase-leg configuration of synchronous rectifier

The MOSFET synchronous rectification (SR) is widely used to reduce the conduction loss during the freewheeling period. Due to the wide band gap of silicon carbide (SiC), the intrinsic body diode of SiC MOSFET exhibits a high voltage drop. Therefore, SiC Schottky diodes (SBD) and SiC MOSFETs are usually used in reverse parallel to reduce power loss. However, the increase of equivalent junction capacitance due to the addition of an external SiC SBD could bring larger turn-on current on opposite power transistor of the phase-leg. Furthermore, as the parasitic inductance associated with layout hinders the prompt transfer of current between SiC SBD and body diode, the external SiC SBD cannot be fully utilized, and it may deteriorate the overall performance, especially at heavy load. We comprehensively compare power losses when SiC SBD are antiparallel or not, at different working conditions, including different layout compactness, load current and dead time. It’s hard to get the effect of loss reduction loss when add antiparallel SiC SBD due to the parasitic inductance induced by the layout. The results can provide a guidance to properly select SiC SBD in a phase-leg configuration under SR mode for freewheeling during the dead time.

Integration of distributed generations and static var compensators with static synchronous compensators to reduce power losses

The necessity of power is growing in response to today's growing population. In order to reduce both real and reactive power losses, three devices have been combined in this research. The distributed generations (DGs) and static var compensator (SVC) with static synchronous compensator (STATCOM) play important role for reduction of power losses (both real and reactive power) and improvement of voltage profile. The research outcome presented here outlines that presents genetic algorithm (GA) may be utilised to improve power system performance and voltage profile (VP) of distribution networks by integrating DG, SVC and STATCOM planning properly. On a 37-bus distribution network, the proposed methodology has been applied. The results of this study are helpful to those focusing on the functionality of viable and upcoming electrical grids, as well as those working in the disciplines of DG, SVC and STATCOM integration for improved system performance.

Optimal Network Reconfiguration via Improved Whale Optimization Approach

Latterly, reduction of power loss in the distribution system is the objective of many researches due to its impact on total costs and voltage profiles. It can be handled by an optimal restructure of the Radial Distribution System (RDS). This article introduces an innovative approach to restructure of RDS by electing the optimal switches combination subject to the system operating constraints, which is Improved Whale Optimization Approach (IWOA). The suggested approach combines exploitation of WOA with exploration of Differential Evolution (DE), and thus it supplies a promising candidate solution. The suggested approach is tested on IEEE 33 and 69 bus RDS. The superiority of the suggested approach compared with other well-known approaches is verified through simulation results by examining total losses, cost and saving. Also, the impact of alterable loading is investigated to prove the effectiveness of the suggested IWOA.

MOSFET Selection for a 18ka modular power converter for HL-LHC inner triplet

The upgrade of the Large Hadron Collider (LHC) at CERN towards the High Luminosity-LHC (HL-LHC) presents different challenges, among them, the upgrade of the power converters associated to the inner triplet magnets to reach the required rated current of 18kA. In order to reach this current and to accomplish the stringent requirements associated to this kind of application, even under a fault scenario, redundancy and modularity are foreseen in the converter design. Consequently, the development of a 18kA/±10V power converter built from 10 parallel-connected sub-converters, each consisting of M modules, is carried out. Additionally, due to the converter being installed in an underground gallery, most of the heat must be extracted by using a water cooling system. Therefore, the reduction of power losses and an appropriate thermal design become key factors. These requirements, together with the large number of devices associated with the parallelization-based modular system, determine the choice of switching semiconductor devices as an important aspect in the design of the converter. This paper presents the process carried out for the choice of semiconductor devices based on analysis of power losses, thermal behavior and integration complexity. In this process, multiple variants in terms of semiconductor, number of modules, parallelization of devices, and switching frequency are considered. The proper operation of the selected device was verified on a 2kA sub-converter prototype based on the interconnection of 9 MOSFET based modules.

YSA ve Bukalemun Optimizasyon Algoritmalarına Dayalı DER'lerde Tekno Ekonomik için Pratik Radyal Dağıtım Besleyicisi

Distributed energy resources (DERs) are a better choice to meet load demand close to load centers. Optimal DER placement and DER ratings lead to power loss reduction, voltage profile improvement, environmental friendliness, dependability, and postponement of system changes. This study uses artificial neural networks and the Chameleon Optimization Algorithm to analyze the best integration of renewable energy sources and electric vehicles in distribution feeders to reduce power loss, regulate voltage levels, and decrease the cost and emissions under unpredictable load demand. In this study, the generated output power of the models is compared to solar photovoltaic generation systems and wind turbine generation systems. As a result, a fitness function with several objectives has been developed to reduce total active power loss while also reducing total cost and emissions generation. The study took into account the influence of EV charging/discharging behavior on the distribution system. The 28-bus rural distribution network in feeders is used to test the suggested methodology. Final analysis of the numerical results showed that the Artificial Neural Network and Chameleon Optimization Algorithms outperformed in terms of power loss (440.94 kw) and average purchase of real power (2224 kw), but these parameters do not favor the other optimization algorithms. This showed that the proposed strategy is both viable and effective.

Robust Distribution Network Reconfiguration in the Presence of Distributed Generation Under Uncertainty in Demand and Load Variations

In modern power systems, distributed generation (DG) plays an important role in energy losses reduction, but DG penetration in such networks is restricted by technical and investment limitations. Therefore, distribution network reconfiguration (DNR) and DG operation reduce power losses more efficiently than when only DG is utilized. Also, network reconfiguration mitigates improper DG placement in such systems which are subjected to high penetration of variable loads and DG. On the other hand, electrical energy demand is estimated by load forecasting methods and therefore is a time-variant and uncertain parameter. Moreover, DG power generation is uncertain and depends on variable load power. Consequently, models presented for the reconfiguration problem should be adequately robust against load and generation uncertainties. Thus, the present paper proposes an effective robust model for reconfiguring distribution networks with load and generation uncertainties. The proposed model is sufficiently simple for implementation and is accurate enough to find the optimal solutions in acceptable computational times. The main features of the proposed model are its high robustness against uncertain parameters, in which proposed configurations are not changed easily by small variations in DG power and load amounts, and its effectiveness, which means that uncertain parameters may change the obtained solutions.

Multi-Timescale Coordinated Control With Optimal Network Reconfiguration Using Battery Storage System in Smart Distribution Grids

This paper focuses on the global optimality of the relaxed solutions of a multi-timescale co-optimization problem. The proposed co-optimization framework involves the multi-timescale co-optimization of distribution feeder reconfiguration with the optimal dispatch of traditional voltage regulating devices and utility-scale distributed energy resources. The on-load tap changer (OLTC) is scheduled on an hourly basis while the potential of fast-acting battery energy storage system and photovoltaic inverters is exploited by dispatching them on a 20-min basis. The optimal switching plan is computed on a daily basis. The proposed multi-timescale co-optimization model is formulated as a mixed-integer second-order cone program to achieve global optimum. The objective is to reduce power losses and improve load balancing among feeders. The proposed co-optimization framework satisfies the grid security constraints by employing the accurate DistFlow branch equations and exact linearization of the OLTC model. To ensure radiality, the limitation of widely used spanning tree constraints is addressed by combining them with single-commodity flow constraints. The simulation results demonstrate the feasibility (hence global optimality) of the relaxed solutions computed by the co-optimization framework.

Analysis of the Influence of Calculation Parameters on the Design of the Gearbox of a High-Power Wind Turbine

As wind turbine power requirements have evolved from the order of kilowatts (kWs) to the order of several megawatts (MWs), wind turbine components have been subjected to more demanding and critical operating conditions. The wind turbine must cope with higher wind loads due to larger blade sizes, which are also time-varying, and, ultimately, higher power levels. One of the challenges in the manufacture of high-power wind turbines lies in the gearbox and consists of achieving ever-greater power density without compromising efficiency, i.e., greater load capacity with lower weight (and production cost) and reduced power losses. Epicyclic geartrains are used to build the gearbox due to various advantages in relation to conventional gear systems, such as higher feasible gear ratios, higher efficiency, compactnesss, and lower weight. In this paper, several epicyclic geartrains with different structures will be analysed to reveal the influence that certain design parameters have on the size and weight of the gearbox components in the selected model and, therefore, of the gearbox itself. For this purpose, the theoretical model of the gearbox will be planned and the influence of the calculation parameters on the gearbox design will be analyzed following ISO 6336. Special emphasis is placed on the influence of the material used; the modulus and tooth width on the size and weight of the gearbox will be observed. Critical stresses are also calculated. The goal is to prepare the theoretical basis for an optimization process subject to geometric, kinematic, and dynamic constraints that will result in a gearbox as compact, energy-dense, and light as possible without compromising the service life of the components.

Simulasi Analisis Kualitas Daya Menggunakan Logika Fuzzy Untuk Meningkatkan Efisiensi Sistem Tenaga Listrik

This study aims to simulate power quality analysis using fuzzy logic to increase the efficiency of the power system. Poor power quality can cause disturbances in the electric power system and reduce the efficiency of the energy used. Therefore, improving power quality is one of the main focuses in the electric power industry.The fuzzy logic method is used in this study to evaluate power quality based on relevant parameters, such as voltage, current, frequency and harmonics. Fuzzy logic is able to overcome the uncertainty and complexity of data related to electric power systems, and provide more adaptive and intelligent solutions. In this study, simulations were carried out using special software that implements fuzzy logic to analyze power quality. The input data obtained from the electric power system is used to build a fuzzy logic model. Then, the simulation results are used to identify existing power quality problems and propose appropriate improvement strategies.The simulation results show that the use of fuzzy logic can significantly increase the efficiency of the power system by minimizing disturbances and optimizing power quality. In addition, fuzzy logic also provides the ability to predict the future conditions of the electric power system and take the necessary precautions. This research has important practical implications in the field of power systems. By using fuzzy logic in power quality analysis, power companies and grid operators can optimize power system operations, reduce power losses, improve energy efficiency, and ensure reliable electricity supply to consumers.