Power Loss Reduction Research Articles

Finite control set model predictive control of three-port converter for interfacing a PV–battery energy storage system to a three-phase stand-alone AC system

Abstract This paper proposes a multiport bidirectional non-isolated converter topology that provides advantages in terms of simultaneous multiple operations, single-stage conversion, high power density and reduced power losses due to the lower number of switches. The proposed multiport converter uses a centralized non-linear controller known as a finite control set model predictive controller to manage the flow of power between different ports. It deals with the parallel operation of photovoltaic and battery energy storage systems for stand-alone alternating current (AC) systems. The converter connects the lower voltage battery to the photovoltaic port using a bidirectional buck/boost converter and the photovoltaic port is linked to the stand-alone AC load through a three-phase full-bridge inverter. Each leg of the three-phase converter will act as a bidirectional direct current (DC)/DC converter as well as an inverter simultaneously. Only six switches manage the power transfer between all the connected ports of photovoltaic–battery energy storage system linked to the stand-alone AC load. The proposed multiport converter is mathematically modelled and controlled by a finite control set model predictive controller. The system is validated in simulation (1-kW rating) and experimental environment (200-W rating). The hardware prototype is developed in the laboratory and the controller is implemented on the field-programmable gate array board. Two independent case studies are carried out to validate the efficacy of the system. The first scenario is for a change in solar irradiance, while the second scenario is for a change in the output load.

A Novel Mixture-Devices-Based Submodule for MMC by Using Low on-State Voltage IGCT and High di/dt Ability IGBT

At present, insulated-gate bipolar transistor (IGBT) is widely used as the converter component in the submodule of modular multilevel converter (MMC) for its excellent performance in driving circuit and switching speed, but the further application is restricted by its high conduction loss and cost rate. Integrated gate-commutated thyristor (IGCT) is another converter device used in MMC with low conduction loss and cost. However, the d <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i</i> /d <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">t</i> capability of IGCT is limited so that a snubber circuit is required in the submodule for protection, which generates unwanted power loss for MMC. For better performance and reliability, this paper proposes a mixture-devices-based submodule (MS) for MMC based on the cooperation of parallel IGCT and IGBT devices. Double-pulse switching experiments for the MS are conducted to verify its effectiveness, and to get the data for the follow-up switching power loss calculation. The power loss of MMC based on IGBT, IGCT and mixture devices are calculated based on the model established in this paper. Furthermore, the comprehensive comparison for power loss among IGBT-based, IGCT-based and mixture-device-based MMCs are performed, proving the effectiveness of the mixture topology in terms of power loss reduction. Meanwhile, the proposed MS is cost-competitive.

Deep-Learning-Aided Voltage-Stability-Enhancing Stochastic Distribution Network Reconfiguration

Power distribution networks are approaching their voltage stability boundaries due to the severe voltage violations and the inadequate reactive power reserves caused by the increasing renewable generations and dynamic loads. In the broad endeavor to resolve this concern, we focus on enhancing voltage stability through stochastic distribution network reconfiguration (SDNR), which optimizes the (radial) topology of a distribution network under uncertain generations and loads. We propose a deep learning method to solve this computationally challenging problem. Specifically, we build a convolutional neural network model to predict the relevant voltage stability index from the SDNR decisions. Then we integrate this prediction model into successive branch reduction algorithms to reconfigure a radial network with optimized performance in terms of power loss reduction and voltage stability enhancement. Numerical results on two IEEE network models verify the significance of enhancing voltage stability through SDNR and the computational efficiency of the proposed method.

Effects of integration of distributed generation on reliability in distribution system

The reliability of power supply to consumers is the main factor that is to be considered while designing, planning and operation of a distribution network. This problem can be solved by incorporating distributed generation (DG) at the consumer side itself. In order to enhance service availability, power quality, reliability, and loss reduction, DG is positioned optimally at the consumer end within the system. The optimal DG location and size is to be calculated in such a way that the total loss in the system should be minimal and cost savings should be maximal. In this study, for the investigation of effects of DG on reliability of distribution system, a practical 11 KV feeder with 41-bus, was employed. The selected practical feeder is modelled using the power world simulator (PWS) software and reliability of the system is verified by calculating various reliability indices such as system average interruption frequency index (SAIFI), system average interruption duration index (SAIDI), customer average interruption duration index (CAIDI), average system utility index (ASUI) and average service availability index (ASAI). After applying proposed loss sensitivity factor (LSF) method, the active and reactive power loss reduction are 41.30 % and 38.64 % respectively, least bus voltage is enhanced to 0.9405 pu from 0.8978 pu and the reliability indices are also improved.

Evaluation of Voltage Fluctuations Effect on Reconfiguration of Distribution Systems

Distribution system operators seriously restrict bus voltage deviations within a small range from the nominal voltage to ensure security aspects. The distribution network reconfiguration technique is often performed to improve these voltage levels, if not to reduce power losses. However, distribution systems feed various kinds of consumers, and their power demand dependency on bus voltages is different based on their load type. Modifying the network configuration changes the system's nodal voltage levels, and then the load power is also impacted after the reconfiguration. Only a few research works in the field of distribution system reconfiguration have included load power dependency to nodal voltages in their proposed models. However, these studies have not discussed whether adding voltage-depend loads to the reconfiguration problem is beneficial or only increases the computational efforts without substantial gains on the optimal solution. With the intention to fill this gap, this article develops a comprehensive analysis of how the change of load according to the voltage affects reconfiguration solutions. This investigation aims to help distribution system researchers and readers understand this phenomenon and highlight the benefits and drawbacks of taking it into account. Also, a robust reconfiguration model for considering load types and nodal voltage is proposed. The model is validated in different network models and sizes, where the obtained results are compared with other models presented in the literature. The results analysis indicates that voltage fluctuations impact the distribution network configuration through voltage effect on power losses and the system nodal loads. Also, load voltage-dependent models are more flexible than constant demand based-formulations in finding feasible solutions in more restricted voltage tolerances.

Volt–Var curve determination method of smart inverters by multi-agent deep reinforcement learning

Reactive power control of PV inverters can be applied to mitigate the voltage increase caused by reverse power flow and voltage fluctuations caused by PV output fluctuations in the distribution system. This paper focuses on the Volt–Var control of PV smart inverters to minimize power losses. It proposes a multi-agent type cooperative voltage control framework to optimize the blind band and slope of the VVC. The proposed method utilizes the optimized VVC to eliminate voltage deviations using only local information. The optimization problem is formed as a Markov decision process and solved using a deep reinforcement learning algorithm called multi-agent soft-actor critic, which achieves fast control that fully accounts for uncertainty while achieving global optimization. Therefore, the proposed method can contribute to reducing power loss, which is a system-wide problem, with only local information. Case studies were conducted using a small and medium-sized distribution network model with IEEE 33 buses and 12 demand cases. The comparison results demonstrate that the proposed method is superior to other benchmark methods in terms of minimizing power losses and mitigating voltage variations, as the proposed method can reduce power losses by about 9% while achieving avoidance of voltage deviations.

Distributed Energy Resource Deployment to Mitigate Adverse Effects of High EV Penetration in Unbalanced Distribution System by Analytical Sensitivity-Based Optimization

The rapid integration of electric vehicles (EVs) into distribution systems in response to environmental concerns poses challenges for power system operators. This article addresses the operational difficulties faced by unbalanced distribution systems (UDS) when incorporating three-phase EV loads. We propose a novel method for supporting EV integration and enhancing UDS operational indicators, including voltage unbalance, bus voltage stability, and bus voltage deviation. Our approach utilizes analytical loss sensitivity index (ALSI)-based meta-heuristic wild horse optimization for the optimal allocation of distributed energy resources (DERs) and capacitor bank during EV load connections. The ALSI is employed to identify potential DER locations, streamlining the computation process and reducing search space. Tested on a modified 123-bus UDS, the proposed method demonstrates a 76.71% reduction in total active power loss and a 96.43% reduction in total voltage deviation. The results underscore the effectiveness and applicability of the proposed approach. While showcasing promising outcomes, the article also discusses the potential limitations and implications for practical implementation.

Implementation of Early Warning System on SMART VU SCADA PT PLN (Persero) With LED Warning Lights

Electric power distribution is a crucial element in meeting the needs of the community, especially in the workshop and electronic equipment repair sector. The transmission process aims to optimize efficiency and reduce power losses. PT PLN (Persero) UP2D SUMBAR has a high commitment in responding to the needs of the community, related to the distribution of electricity, especially in the 20 KV distribution network. Although it already uses two Early Warning Systems connected to SMART VU SCADA, the DCC room alarm sound is disabled to minimize noise. Therefore, this research focuses on implementing an additional Early Warning System with LED warning lights. The research methodology includes literature study, initial testing (Experimental Method), system design, hardware design, second testing (successful/unsuccessful), direct testing in the DCC room, and analysis of results. It is expected that this research can improve alarm notifications, speed up dispatcher response time, and increase vigilance in handling disturbances in the 20 KV distribution network.

An experimental study on fire ignition in collector cable in wind power generation system caused by direct lightning strike

AbstractA separable connector is used for the power cables and apparatuses in renewable energy systems. The shield of the collector cable is sometimes an open circuit at one end to reduce power loss due to the shield current. However, this has a disadvantage from the viewpoint of lightning protection. The semiconductive part of the separable connector is grounded for safety. The ground potential rise due to a direct lightning strike to a wind tower sometimes damages the collector cables and power apparatuses in the wind tower. Combustion in a cable is one of the most serious types of lightning‐caused damage. This paper describes an experimental study on fire ignition in a collector cable caused by a ground potential rise due to a direct lightning strike to a receptor. The mechanism leading to fire ignition is discussed based on the experimental results. This paper also proposes a countermeasure against such fire ignition.

Optimal allocation of Combined DG and DSTATCOM for Improving Voltage Stability and Economical Benefits of Distribution System

In the vertically integrated power sector, the distribution system serves as the link between customers and the transmission network. Different load models, including industrial, residential, and commercial, are taken into account within the distribution systems. The loading pattern of these load models varies with a large range according to the peak hours. During peak load conditions, the voltage magnitude of some nodes exceeds the permissible limit of voltage value. It affects the voltage stability of the distribution system, leading to decreased reliability, heightened power losses, diminished voltage stability, and other safety concerns. Optimal placement of capacitors, applying DGs, using FACTS devices, and Distribution Network reconfigurations are possible to reduce the power loss. This paper focuses on reducing power loss and enhancing the voltage profile and stability of the system. It addresses the technical, economic, and environmental benefits of the distribution system through appropriate placement and sizing of DG and DSTATCOM units in a coordinated manner. The issue is approached as a Multi-Objective Optimization Problem (MOP) and is tackled through the application of the Group Teaching Optimization (GTO) algorithm, an innovative metaheuristic method. A numerical illustration utilizing an IEEE 69-node system is examined, and the simulated outcomes are presented in tabular form. These results are then juxtaposed with findings from comparable methodologies outlined in existing literature.

Identifying optical microscope images of CVD-grown two-dimensional MoS2 by convolutional neural networks and transfer learning.

In Complementary Metal-Oxide Semiconductor (CMOS) technology, scaling down has been a key strategy to improve chip performance and reduce power losses. However, challenges such as sub-threshold leakage and gate leakage, resulting from short-channel effects, contribute to an increase in distributed static power. Two-dimensional transition metal dichalcogenides (2D TMDs) emerge as potential solutions, serving as channel materials with steep sub-threshold swings and lower power consumption. However, the production and development of these 2-dimensional materials require some time-consuming tasks. In order to employ them in different fields, including chip technology, it is crucial to ensure that their production meets the required standards of quality and uniformity; in this context, deep learning techniques show significant potential. This research introduces a transfer learning-based deep convolutional neural network (CNN) to classify chemical vapor deposition (CVD) grown molybdenum disulfide (MoS2) flakes based on their uniformity or the occurrence of defects affecting electronic properties. Acquiring and labeling a sufficient number of microscope images for CNN training may not be realistic. To address this challenge, artificial images were generated using Fresnel equations to pre-train the CNN. Subsequently, accuracy was improved through fine-tuning with a limited set of real images. The proposed transfer learning-based CNN method significantly improved all measurement metrics with respect to the ordinary CNNs. The initial CNN, trained with limited data and without transfer learning, achieved 68% average accuracy for binary classification. Through transfer learning and artificial images, the same CNN achieved 85% average accuracy, demonstrating an average increase of approximately 17%. While this study specifically focuses on MoS2 structures, the same methodology can be extended to other 2-dimensional materials by simply incorporating their specific parameters when generating artificial images.

Optimizing PV Sources and Shunt Capacitors for Energy Efficiency Improvement in Distribution Systems Using Subtraction-Average Algorithm

This work presents an optimal methodology based on an augmented, improved, subtraction-average-based technique (ASABT) which is developed to minimize the energy-dissipated losses that occur during electrical power supply. It includes a way of collaborative learning that utilizes the most effective response with the goal of improving the ability to search. Two different scenarios are investigated. First, the suggested ASABT is used considering the shunt capacitors only to minimize the power losses. Second, simultaneous placement and sizing of both PV units and capacitors are handled. Applications of the suggested ASAB methodology are performed on two distribution systems. First, a practical Egyptian distribution system is considered. The results of the simulation show that the suggested ASABT has a significant 56.4% decrease in power losses over the original scenario using the capacitors only. By incorporating PV units in addition to the capacitors, the energy losses are reduced from 26,227.31 to 10,554 kW/day with a high reduction of 59.75% and 4.26% compared to the initial case and the SABT alone, respectively. Also, the emissions produced from the substation are greatly reduced from 110,823.88 kgCO2 to 79,189 kgCO2, with a reduction of 28.54% compared to the initial case. Second, the standard IEEE 69-node system is added to the application. Comparable results indicate that ASABT significantly reduces power losses (5.61%) as compared to SABT and enhances the minimum voltage (2.38%) with a substantial reduction in energy losses (64.07%) compared to the initial case. For both investigated systems, the proposed ASABT outcomes are compared with the Coati optimization algorithm, the Osprey optimization algorithm (OOA), the dragonfly algorithm (DA), and SABT methods; the proposed ASABT shows superior outcomes, especially in the standard deviation of the obtained losses.

Kajian Teknis Dan Finansial Interkoneksi Pembangkit Listrik Tenaga Biogas Dari Palm Oil Mill Effluent (POME) Pada Sistem 20 KV Di Kabupaten Musi Rawas

Using renewable energy is one way to reduce global warming. Minister of Energy and Mineral Resources Regulation No. 21 of 2016 concerning Purchase of Electricity and Biogas Power. The government has supported efforts to develop electrical energy from biogas. PT Evans Lestari is an oil palm plantation company operating in Mura with a plantation area of 9,220 ha and a mill of 60 tons/hour. In order to fulfill PLN's requirements to offer electricity supply, it is necessary to carry out a technical assessment of the connection and an economic analysis. The connection of the EBT PLT to PLN's 20 kV distribution system has been regulated in Directors' Decree No. 0357.K/DIR/2014. The benefits obtained from using DG include reducing power losses and improving the voltage profile of the distribution system. Among the types of DG that are commonly used, one of them is the Bio Gas Power Plant (PTBG) fueled by liquid palm oil waste.

Investigation on the physical origins of laser effects on anisotropic magnetic properties of GO electrical steels by means of thermal annealing

Both conventional laser surface treatments (irradiation or scribing) and recently developed ablation process performed with UltraShort Pulse Lasers (USPL) can improve magnetic properties of Grain-Oriented Electrical Steels (GOES) along their Rolling Direction (RD). While pushing the study of laser treatments further, interesting power loss reductions could also be obtained along the Transverse Direction (TD) (or other directions) of GOES using the ablation process with adapted laser patterns. To better understand the physical origins of loss reductions obtained with the USPL ablation process, the effects of a thermal annealing on GOES laser treated with various patterns are investigated. Results differ depending on the excitation direction and in connection with the applied laser treatment and pattern, which provides an original insight into the origins of the modifications of magnetic properties by laser.

Lubrication Modeling of the Reciprocating Piston with High Lateral Load and Various Conditions in a Swash Plate-Type Piston Pump

Most asymmetrical lateral forces occur in the reciprocating piston mechanism, which is widely applied as a major component of power equipment. When this lateral force greatly acts on the piston, it comes into contact with the cylinder. To prevent this negative phenomenon, lubrication characteristic evaluation and control technology are necessary. In this study, a boundary lubrication model considering the elastic deformation of the contact surface was adopted to perform a lubrication analysis of a piston hydraulic pump widely used in the aviation and plant industries. The piston/cylinder mechanism was analyzed in terms of contact force, characteristic thickness, and power loss while varying various design and operating parameters (friction coefficient, clearance, profiling shape, operating speed, and pressure). In the overall bearing capacity to withstand the tilt of the piston, the bearing capacity ratio due to contact at the interface increased more steeply than the bearing capacity ratio in the fluid lubrication area. Profiling of the piston head played a positive role in reducing power loss but also increased piston tilt. This trend appeared more clearly as the head profiling degree of processing Increased. Lastly, the effects of variable operating speed and pressure were examined. High operating speed caused low contact force, and high operating pressure caused high contact force. Through this study, it was possible to predict the lubrication performance and power loss of reciprocating piston pumps used in the field more realistically through appropriate boundary lubrication modeling.

Reconfiguration of distribution network for improving power quality indexes with flexible lexicography method

Reconfiguration is a low-cost method to reduce power loss in distribution network (DN). Harmonics and unbalancing cause additional losses in DNs. Therefore, it is necessary to consider these two factors in distribution network reconfiguration (DNR). In this paper, the effect of reconfiguration in two harmonic polluted medium and large scale DN is evaluated. In addition, the effect of harmonic injection of distributed generation (DG) is studied. Moreover, the effect of reconfiguration in unbalanced DN in the presence of single-phase DGs is investigated, and it is concluded that distribution system operator (DSO) should consider network unbalance in DNR to more reduction of power losses. Furthermore, a new flexible lexicography method is introduced to solve multi-objective problems, especially in DNR. In the proposed method, the DSO determines the priority of each goal in comparison to the other goals, instead of using weighing factors of them. This method is effective for power quality goals which cannot be illustrated as cost function. In this paper, validation of the proposed method in IEEE 69 bus and 95 bus practical harmonic polluted DN and 25 bus unbalanced DN in presence of three-phase and single-phase PVs are investigated. Results show the effectiveness of the method in increasing the flexibility of DSO in DNR to improve power quality goals such as harmonics and unbalance, as well as reduce power losses.

Integration of Multiple Distributed Generation Sources in Radial Distribution System Using a Hybrid Evolutionary Programming-Firefly Algorithm

This paper presents an approach for the optimal integration of multiple distributed generation (DG) sources in a radial distribution system. The integration of DG sources poses various challenges such as can lead to higher power losses caused by reverse power flow, voltage exceeding secure limits, voltage stability, power quality, and economic operation. To address these challenges, a hybrid algorithm is proposed which combines the benefits of both Evolutionary Programming and Firefly Algorithm. The proposed hybrid Evolutionary - Firefly Algorithm is employed for the determination of the optimal size of the DG sources. The objective of the proposed algorithm is to minimize the total system power losses and improve the voltage profile. The algorithm considers various constraints including the DG capacity limits and voltage limits. A comprehensive case study is conducted on a radial distribution system to demonstrate the effectiveness of the proposed approach. The simulation results show that the hybrid algorithm can find the optimal size and location of DG sources while achieving the desired system performance. The integration of multiple DG sources leads to a significant reduction in power losses and improved voltage profile. Furthermore, the proposed approach provides a flexible framework for the optimal integration of DG sources in radial distribution systems, allowing for the accommodation of different types and capacities of DG sources. The proposed technique is tested on the IEEE Reliability Test systems, specifically the IEEE 69-bus. The combination of DG at bus 61 and bus 27 yields a loss reduction index of 94%.

Particle Swarm Optimization for Directional Overcurrent Relay Coordination with Distributed Generation

The Directional Overcurrent Relays (DOCRs) Coordination with Distributed Generation (DG) optimization problem is addressed in this study using the optimization method Particle Swarm Optimization (PSO). Changes in fault current, bus voltages, power flow, and reliability may result from DG integration. Thus, it might have an impact on the current protection coordination system. The formulation is built on a Mixed Integer Non-Linear Programming (MINLP) problem to address this DOCR issue. MATLAB was used to validate the technique on the IEEE-14 bus system, and Electrical Test Transient Analyzer Programming (ETAP) version 2021 software was used to model the test system. According to the simulation results, the suggested PSO with DG for Case 2 has reduced power loss by 6.24% and relay operating time by 46.79% when compared to PSO without the presence of DG.

Phase shift control of dual active bridge DC‐DC converter based on feedforward compensation and transient optimization strategy

SummaryIn the context of the dual active bridge (DAB) bidirectional DC‐DC converter using the traditional dual‐phase‐shift (DPS) control method, a transient DC bias occurs during power co‐direction and commutation conversion. This bias negatively impacts the converter's transient performance and can lead to harsh effects, such as hard switching problems, resulting in power loss and excessive current stress. In a closed‐loop control system, poor transient response may destroy the stability of the converter. In this paper, the transient‐optimized‐dual‐phase‐shift (TODPS) control strategy is proposed to eliminate the transient DC bias and to maintain soft switching during transient states. It reduces power loss, eliminates excessive current stress, and allows for smooth transitions between single‐phase‐shift (SPS) and DPS control strategies without introducing DC bias. A closed‐loop control system based on TODPS control is established with feed‐forward compensation to adjust the primary‐side current and modify transmission power. This adjustment enhances the transient performance of the DAB converter. The proposed strategy is versatile and applicable to various power conversion scenarios involving DAB converters. It enables quick switching of the magnitude and direction of the primary‐side current during transient states, resulting in a smooth transient effect without noticeable overshoot or oscillation. The experiment results verify the effectiveness of the proposed transient optimization strategy.

Optimal Design and Operation of Wind Turbines in Radial Distribution Power Grids for Power Loss Minimization

This research proposes a strategy to minimize the active power loss in the standard IEEE 85-node radial distribution power grid by optimizing the placement of wind turbines in the grid. The osprey optimization algorithm (OOA) and walrus optimization algorithm (WOA) are implemented to solve the problem. The two algorithms are validated in three study cases of placing two wind turbines (WTs) in the system for power loss reduction. Mainly, in Case 1, WTs can only produce active power, while in Case 2 and Case 3, WTs can supply both active and reactive power to the grid with different ranges of power factors. In Case 4, the best-applied methods between the two are reapplied to reach the minimum value of the total energy loss within one year. Notably, this case focuses on minimizing the total power loss for each hour in a day under load demand variations and dynamic power supply from WTs. On top of that, this case uses two different sets of actual wind power data acquired from the Global Wind Atlas for the two positions inherited from the previous case. Moreover, the utilization of wind power is also evaluated in the two scenarios: (1) wind power from WTs is fully used for all values of load demand, (2) and wind power from WTs is optimized for each load demand value. The results in the first three cases indicate that the WOA achieves better minimum, mean, and maximum power losses for the two cases than the OOA over fifty trial runs. Moreover, the WOA obtains an excellent loss reduction compared to the Base case without WTs. The loss of the base system is 224.3 kW, but that of Case 1, Case 2, and Case 3 is 115.6, 30.6 kW, and 0.097 kW. The placement of wind turbines in Case 1, Case 2, and Case 3 reached a loss reduction of 48.5%, 84.3%, and 99.96% compared to the Base case. The optimal placement of WTs in the selected distribution power grid has shown huge advantages in reducing active power loss, especially in Case 3. For the last study case, the energy loss in a year is calculated by WSO after reaching hourly power loss, the energy loss in a month, and the season. The results in this case also indicate that the optimization of wind power, as mentioned in Scenario 2, results in a better total energy loss value in a year than in Scenario 1. The total energy loss in Scenario 2 is reduced by approximately 95.98% compared to Scenario 1. So, WOA is an effective algorithm for optimizing the placement and determining the power output of wind turbines in distribution power grids to minimize the total energy loss in years.