Distribution Transformer Loss Research Articles

Design Algorithm for and Validation of Winding Losses in Large-Capacity Medium-Frequency Distribution Transformers

Design Algorithm for and Validation of Winding Losses in Large-Capacity Medium-Frequency Distribution Transformers

Reconfigurable topology assessment for efficient allocation of photovoltaic generators in unbalanced power delivery networks

The three-phase power delivery systems are reliable and efficient means of delivering electric power to customer node points. However, the unequal distribution of loads over the three phases leads to unbalance voltage between phases, resulting in increased power losses in distribution transformers and lines. It is highly challenging to install photovoltaic (PV) generators in unbalanced power distribution networks (PDNs) to restore power supply efficiency. This paper proposes dynamic reconfiguration (DR) in unbalanced PDNs to foster efficient integration of PV generators and improve system performance. Since the demand for electricity at residential node points and the PV-generated power varies with the season, this study proposes a seasonal DR of the PDNs. A framework with multiple objectives has been proposed for the DR-coupled PV deployment problem. A novel value-adaptive weight-aggregated (VAWA) grey-wolf optimizer (GWO) has been synthesized to contribute to system performance enhancement. The proposed strategy was validated and established through comparative investigations on an Indian 19-bus and modified IEEE 123-bus unbalanced radial distribution systems (URDSs).

Alternatif Penanganan Losses Akibat Ketidakseimbangan Beban Pada Trafo Distribusi

The unbalanced load on distribution transformers is mostly caused of connecting new customers and changing patterns of electricity usage in the community. The impact of the unbalanced load is the emergence of a current flowing on the transformer neutral conductor that causes energy losses. Load balancing of distribution transformers is commonly done by technicians, but the implementation takes quite a long time and is repeated, sometimes up to 4-5 times to get the appropriate results. This is more due to less valid customer phase data. Customer phase verification is needed to accelerate the load balancing process in an effort to reduce losses in distribution transformers. With valid customer phase data, just one stage of load balancing can reduce losses by 1.06%..

Evaluation method of power loss for distribution network fault handling based on distribution transformer outage and restoration information matrix

To solve the problem of power loss evaluation in distribution network fault handling, an evaluation method of power loss for distribution network fault handling based on distribution transformer outage and restoration information matrix is proposed. Firstly, the distribution transformer outage and restoration information matrix model is established to describe the distribution transformer, its outage time, its restoration time, its outage duration, and its lost power affected by the distribution network failure outage. Then the outage duration in the process of fault handling is discussed in four cases, and the detailed calculation formula is given. At last, the power loss of a single distribution transformer, the total power loss of distribution network failure, and the average power loss of a distribution transformer are described in detail by outage time and predicted power. The example analysis shows that this method can quickly estimate the power loss of a distribution transformer in the process of fault disposal, and the method is simple and reliable, which can meet the needs of field applications and has good promotion value.

Applications of Novel Heuristic Algorithms in Design Optimization of Energy-Efficient Distribution Transformer

Transformers are one of the crucial and expensive assets of a power grid. Reducing power losses in power and distribution transformers is important because it increases the efficiency of the transformer, which in turn reduces costs for the utility company and consumers. Losses in the transformer generate heat, which can reduce the lifespan of the transformer and require additional cooling. Additionally, reducing losses can help to decrease greenhouse gas emissions associated with the generation of electricity. This study presents an optimization method for transformer design problem using variables that have a great impact on the performance of a transformer. Due to the non-convex nature of the transformer design problems, the empirical methods fail to find the optimal solution and the design process is very tedious and time-consuming. Considering No Free Lunch (NFL) theorem, the design problem is solved using four novel heuristic optimization algorithms, the Firefly Optimization Algorithm (FA), Arithmetic Optimization Algorithm (AOA), Grey Wolf Optimization Algorithm (GWO), and Artificial Gorilla Troops Optimizer Algorithm (GTO) and the results are compared to an already manufactured 1000 kVA eco-friendly distribution transformer using the empirical methods. The outcome of the optimization shows that the suggested method along with the algorithms mentioned leads to a notable decrease in power losses by up to 3.5%, and a reduction in transformer weight by up to 8.3%. This leads to an increase in efficiency, decreased costs for materials, longer lifespan and a reduction in emissions. The developed model is capable of optimally designing oil-immersed distribution transformers with different power ratings and voltage levels.

A data-driven multi-objective optimization framework for optimal integration planning of solid-state transformer fed energy hub in a distribution network

This study proposes an optimal planning technique for a solid-state transformer (SST) fed hybrid energy hub consisting of a wind turbine distributed generator (WTDG) and a battery energy storage system (BESS) in a distribution network (DN). The energy exchange within the energy hub and with the utility grid is regulated by an efficient energy management plan (EMP). The simultaneous planning of a WTDG-BESS energy hub in a DN fed through an SST is a challenging combinatorial optimization problem. It is not only about capacity allocation and placement, but also about charging and discharging dispatches of BESSs under dynamically altering system parameters with the intermittent nature of WTDG outputs and the operational limits of the system itself. As a result, simultaneous allocation becomes a mixed integer nonlinear programming (MINLP) problem, with the operational aspect acting as a limitation for the planning aspect and adding to the complexity of the problem. While the power flow takes into consideration the operational losses of the traditional distribution transformers (DT), the optimization problem reduces voltage deviations and energy losses of the DN. A 33-bus radial DN was used to test the proposed technique, and the outcomes of various case studies are included. The MINLP problem was addressed using the non-dominated sorted genetic algorithm (NSGA-II) in the MATLAB R2020a platform. The outcomes of the evaluation show an energy loss reduction (ELR) of 62%, while a voltage profile improvement (VPI) of 69% is obtained.

Study of Transformer Harmonic Loss Characteristic in Distribution Network Based on Field-Circuit Coupling Method

One of the primary causes of additional losses in dry-type distribution transformers is harmonic disturbances in the distribution network. It is critical to investigate the change law of trans-former losses under harmonic conditions. The effect of harmonics on transformer core losses and winding losses is first investigated in this paper. The field-circuit coupling method is then used to create a finite element model of a three-phase phase dry type distribution transformer. Finally, the relationship between core loss and harmonic voltage, winding loss and harmonic current is calculated and analyzed for each harmonic frequency. The AC resistance factor model is found to be more accurate than the conventional model in calculating transformer harmonic winding losses. This paper’s findings have significant theoretical implications for the analysis of harmonic losses and loss reduction in distribution networks.

Decarbonization of Distribution Transformers Based on Current Reduction: Economic and Environmental Impacts

Well-known industrial practice efficiency improvement techniques, such as reactive compensation, load balancing, and harmonic filtering, are used in this paper to reduce energy losses in distribution transformers, and therefore, to decrease carbon dioxide emissions and economic costs in the operation of these transformers. Load balancing is carried out by monitoring the values of the angles of the active and reactive components of the vector unbalanced power. Likewise, the application of Order 3/2020 of the Spanish National Markets and Competition Commission is described, in detail, for the calculation of the economic costs derived from the transformer energy losses caused by the load currents and the penalties due to transformer energy deliveries with capacitive power factors. Finally, all these improvement techniques are applied to determine savings in carbon dioxide emissions and costs on the electricity bill of an actual 1000 kVA distribution transformer that supplies a commercial and night-entertainment area. The results of this application case reveal that cost reductions due to energy loss savings are modest, but the reduction in carbon dioxide emissions and the savings in penalties for capacitive reactive supplies are significant.

Analysis of Technical Loss Calculation Using Load Curve Approach on 20 kV Distribution Network

Energy loss (losses) is the loss of a certain amount of energy generated when it is distributed to consumers so that it affects the profitability of the company concerned. The size of the losses from an electric power system shows the level of efficiency of the system, the lower the percentage of losses that occur the more efficient the system. Energy losses in the distribution network are generally divided into two, namely technical and non-technical losses. The calculation of technical losses in the 20 KV medium distribution network of PT PLN (Persero) Ulp Matang Glumpang Dua is carried out using the load curve approach method and using the help of the Microsoft Exel program, while the technical losses calculated are technical losses on the Medium Voltage Network and Distribution Transformer. From the results of the analysis of the calculation of technical losses in 2020, the total technical loss value at MG-01 Matang City is in the range of 13.8% to 20.8% which consists of the average technical loss in the Medium Voltage Network feeder of 0.02%. and the loss of Distribution Transformer by 17.6%.

A new parallel and decomposition approach to solve the medium- and low-voltage planning of large-scale power distribution systems

The world energy demand is increasing. Currently, most electricity generation sources are fossil fuels. In recent years, the global energy matrix for power generation is changing, in order to meet the demand with minimal environmental impacts. In this context, a new approach to solve the medium- (MV) and low- voltage (LV) planning of large-scale distribution systems via parallel computing and decomposition techniques is proposed. The mathematical formulation considers the allocation of substations, distribution transformers, MV and LV circuits, support structures, poles, renewable energy sources (RES), and energy storage sources (ESS). In addition, variable costs related to the operation of RES and ESS, power losses in cables, distribution transformers and substations, energy purchased from substations, and greenhouse gas emissions are also taken into account. System reliability and maintenance costs of these devices are also considered in the planning. To evaluate the new methodology performance, tests in a large-scale distribution system with 200 nodes in MV and 1672 nodes in LV is considered. Numerical results show the proposed methodology is able to find good solutions that guarantee the minimization of planning costs considering RES and ESS allocation. Furthermore, system reliability is improved by up to 22% and greenhouse gas emissions mitigation by up to 18%.

ALTERNATIF PENANGANAN LOSSES AKIBAT KETIDAKSEIMBANGAN BEBAN PADA TRAFO DISTRIBUSI


 
 
 The unbalanced load on distribution transformers is mostly caused of connecting new customers and changing patterns of electricity usage in the community. The impact of the unbalanced load is the emergence of a current flowing on the transformer neutral conductor that causes energy losses. Load balancing of distribution transformers is commonly done by technicians, but the implementation takes quite a long time and is repeated, sometimes up to 4-5 times to get the appropriate results. This is more due to less valid customer phase data. Customer phase verification is needed to accelerate the load balancing process in an effort to reduce losses in distribution transformers. With valid customer phase data, just one stage of load balancing can reduce losses by 1.06%.
 
 

Transient Finite Element Method for Computing and Analyzing the Effect of Harmonics on Hysteresis and Eddy Current Loss of Distribution Transformer

Transient Finite Element Method for Computing and Analyzing the Effect of Harmonics on Hysteresis and Eddy Current Loss of Distribution Transformer

Distributed Generation Approach for Single Step System Restoration During Cold Load Pickup (Dept.E)

When a system is restored after an extended outage, the demand is greater than that was before the outage. The increase in the demand is due to the cold load pickup (CLPU) condition. The attempt to restore such load simultaneously will cause an excessive loading on the network elements in addition to limits violation. For these reasons, step-by-step restoration was suggested to overcome such problem. However, this way of restoration requires a long time for the complete system to be back to service. In this paper, a particle swarm optimization (PSO) algorithm is introduced to find the optimal sizing and siting of distributed generation (DG) units for simultaneous single step restoration of transmission and distribution networks. The aim of the proposed DG allocation algorithm is to reduce the distribution transformer loss of life due to overloading and to reduce the lost load and hence the additional power demand caused by CLPU. The capacity of the DG required is determined on the basis of additional power demand and the load diversity preserved. The proposed algorithm is applied to the Egyptian 66 kV transmission network in the city of Alexandria including a 33-bus and a 69-bus, 11 kV primary distribution feeders.

Optimal Planning Method of Distribution Transformer Network for Seasonal Load

Taking into account the reliability of power supply, this paper proposes an optimal planning method for distribution transformer network adapting to seasonal load in order to improve the low efficiency of equipment utilization in rural coal-to-electricity conversion areas. In this method, a bi-level programming model of distribution transformer network is constructed to minimize the overall cost. Voronoi is used to plan the distribution transformer at the upper level, Prim algorithm is used to plan the low-voltage power supply line at the lower level, and genetic algorithm is used to optimize the solution (seek the optimal solution). Finally, the feasibility of this method is verified by the planning of distribution transformer in typical power supply areas. The analysis of examples shows that the distribution transformer loss can be reduced by establishing the low-voltage tie-line between distribution transformers and the transformation of operation mode, which also provides the basis for the load transfer under the low-voltage line fault.

An Electric Vehicle Charge Scheduling Approach Suited to Local and Supplying Distribution Transformers

Distribution networks with high electric vehicle (EV) penetration levels can experience transformer overloading and voltage instability issues. A charge scheduling approach is proposed to mitigate against these issues that suits smart home settings in residential areas. It comprises measurement systems located at distribution transformers that communicate directly with fuzzy logic controller (FLC) systems embedded within EV supply equipment (EVSE). This realizes a reduction in data processing requirements compared to more centralized control approaches, which is advantageous for distribution networks with large numbers of transformers and EV scheduling requests. A case study employing the proposed approach is presented. Realistic driver behavior patterns, EV types, and multivariate probabilistic modeling were used to estimate EV charging demands, daily travel mileage, and plug-in times. A Monte Carlo simulation approach was developed to obtain EV charging loads. The effectiveness of mitigation in terms of reducing distribution transformer peak load levels and losses, as well as improving voltage stability is demonstrated for a distribution network in Jakarta, Indonesia.

Active and Reactive Power Losses in Distribution Transformers

Active and Reactive Power Losses in Distribution Transformers

A Two-Loop Hybrid Method for Optimal Placement and Scheduling of Switched Capacitors in Distribution Networks

This paper presents a method to find the optimal size and place of the switched capacitors using a hybrid optimization algorithm. The objective function includes the active and reactive power of power plants, the capital and maintenance costs of capacitor banks, and the cost of active and reactive power losses in distribution lines and transformers. The impact of the load model on the optimal sizing and placement of switched capacitors is studied using three different scenarios: In the first scenario, all loads are voltage-dependent; in the second scenario, only a portion of loads are voltage-dependent; in the third scenario, all loads are voltage-independent. The proposed hybrid algorithm incorporates an outer and two inner optimization layers. The outer layer is executed by a genetic algorithm (GA), while the inner layer is performed by a GA, an exchange market algorithm (EMA), or a particle swarm optimization (PSO). The performance of GA-GA, GA-EMA, and GA-PSO hybrid schemes are compared on an IEEE 33-bus test system. Moreover, IEEE 33-bus and 69-bus networks are used to verify the effectiveness of proposed hybrid scheme against the gravitational search algorithm (GSA), a combination of PSO and GSA (PSOGSA), cuckoo search algorithm (CSA), teaching learning-based optimization (TLBO), and flower pollination algorithm (FPA). The results highlight the advantage of the proposed hybrid optimization scheme over the other optimization algorithms.

Charging coordination and load balancing of plug‐in electric vehicles in unbalanced low‐voltage distribution systems

Plug-in electric vehicles (PEVs) are gaining popularity as a solution to reduce greenhouse gas production. On the other hand, uncoordinated charging of PEVs is a reason for grid stress and increasing congestion, total power consumption (TPC) and distribution lines and transformers losses. Moreover, single-phase connection of electric vehicles in residential buildings causes line current imbalance and it has more negative effects on TPC and power losses. Thus, they are characterised as unbalanced loads with random locations, plug-in times, charging rates, and durations. Hence, a charging coordination algorithm is proposed to utilise the integration of PEVs in both operational modes (grid-to-vehicle, vehicle-to-grid) to guarantee power loss reduction, voltage profile maintaining and load current balancing. The introduced algorithm can perform an optimal calculation to determine proper phase for PEVs in distribution feeders and decline the unbalancing by deploying a new device called phase switcher which is in series with chargers in residential buildings. The phase switcher is connected to the smart load management centre which uses the proposed algorithm to determine the proper phase for connection or disconnection of PEVs and finding the appropriate numbers of PEVs. To show the effectiveness of the proposed algorithm, simulation results are provided.

Correction to: The Reduction Method of a Stray Load Loss of Distribution Transformer on the Bushing Flange Plate with STS

Due to an unfortunate oversight the author name of Jung-Kyu Jin has been misspelt. It should be read: Kyu-Jin Jung.

Linear Trend Application on the Estimated Age of Distribution Transformer Based on the Load Growth and Environmental Temperature

This research aimed to find the age loss of distribution transformer based on the load growth and ambient temperature to predict the remaining age of the transformer. This research used the remaining estimation age calculation based on load growth that was predicted using the linear trend analysis. The distribution transformer in this research was the BO043 200 kVA, installed in 2012, and was operated in Bolo Feeder, Woha District, Bima Regency. The results showed that the BO043 transformer was operating at the average ambient temperature of 28℃ with the optimum loading threshold of 92.77% from its power rating. The transformer would experience age reduction if the load given were above that value. The calculation results showed that the BO043 transformer had the estimated remaining age of 4 years from the standard 23 years with the 2019 load prediction of 83.39% and up to 115.94% load prediction in 2022.