Average Power Loss Research Articles

The Low-Carbon Path of Active Distribution Networks: A Two-Stage Model from Day-Ahead Reconfiguration to Real-Time Optimization

The integration of renewable energy sources and distributed energy storage systems increasingly complicates the operation of distribution networks, while stringent carbon reduction targets demand low-carbon operational strategies. To address these complexities, this paper introduces a two-stage model for reconfiguring distribution networks and ensuring low-carbon dispatch. Initially, second-order cone programming is employed to minimize losses in the network. Subsequently, the outputs of renewable energy and energy storage systems are optimized using the mantis search algorithm (MSA) to achieve low-carbon dispatch, with the network’s carbon potential as the evaluation metric. The proposed model demonstrates a significant reduction in average active power loss by 34.85%, a decrease in daily carbon emissions by 509.97 kg, and a reduction in carbon emission costs by 17.24%, thereby markedly enhancing the economic and social benefits of grid operations.

Dynamic Optimization and Placement of Renewable Generators and Compensators to Mitigate Electric Vehicle Charging Station Impacts Using the Spotted Hyena Optimization Algorithm

The growing adoption of electric vehicles (EVs) offers notable benefits, including reduced maintenance costs, improved performance, and environmental sustainability. However, integrating EVs into radial distribution systems (RDSs) poses challenges related to power losses and voltage stability. The model accounts for hourly variations in demand, making it crucial to determine the optimal placement of electric vehicle charging stations (EVCSs) throughout the day. This study proposes a new approach that combines EVCSs, distribution static compensators (DSTATCOMs), and renewable distributed generation (RDG) from solar and wind sources, with a focus on dynamic analysis over 24 h. The spotted hyena optimization algorithm (SHOA) is employed to determine near-global optimum locations and sizes for RDG, DSTATCOMs, and EVCSs, aiming to minimize real power loss while meeting system constraints. The SHOA outperforms traditional methods due to its unique search mechanism, which effectively balances exploration and exploitation, allowing it to find superior solutions in complex environments. Simulations on an IEEE 34-bus RDS under dynamic load conditions validate the approach, demonstrating a reduction in average power loss from 180.43 kW to 72.04 kW, a 72.6% decrease. Compared to traditional methods under constant load conditions, the SHOA achieves a 77.0% reduction in power loss, while the BESA and PSO achieve reductions of 61.1% and 44.7%, respectively. These results underscore the effectiveness of the SHOA in enhancing system performance and significantly reducing real power loss.

Detailed Investigation of the Eddy Current and Core Losses in Coaxial Magnetic Gears through a Two-Dimensional Analytical Model

This work introduces a 2D model that calculates power losses in coaxial magnetic gears (CMGs). The eddy current losses of the magnets are computed analytically, whereas the core losses of the ferromagnetic segments are computed using an analytical–finite element hybrid model. The results were within 1.51% and 3.18% of those obtained from an FEA for the eddy current and core losses in the CMG for an indicative inner rotor speed of 2500 rpm. In addition, the significance of the circumferential magnet segmentation is demonstrated in the CMGs. Furthermore, a parametric investigation of the efficiency of the system for different applied external loads is carried out. Finally, a mesh sensitivity analysis is performed, along with the computation of the average power losses throughout one full period, resulting in an at least 80% reduction in computational costs with a negligible effect on accuracy. The developed model could be a valuable tool for the minimization of power losses in CMGs since it combines high accuracy with a low computational cost.

Experimental investigation of nonuniform PV soiling

Photovoltaic (PV) module soiling, i.e., the accumulation of dust on PV module surfaces, poses several challenges to PV system performance. Among these challenges, the nonuniform deposition of soiling across the module surface has received scarce attention. Soiling is directly associated with an overall performance loss, but can also potentially give rise to localised hotspots that can lead to long-term PV module failure. Therefore, addressing the issues arising from this nonuniformity is not only important for optimising energy production, but also for enhancing system reliability, and ensuring the long-term operation of relevant power generation systems. In this study, the impact of nonuniform soiling on PV performance is investigated experimentally by examining soil deposition on the upper surfaces of low-iron glass samples. Samples positioned at four different tilt angles were collected on a monthly basis over a one-year study period. Since the horizontal samples were found to represent the worst-case conditions, the most soiled sample at horizontal tilt was divided into four zones, each housing a single monocrystalline solar cell and examined further. The findings reveal that the soiled sample experiences an average transmittance deterioration of 13% relative to a clean sample, and a maximum (relative) spatial variation of 4% between the four zones. These optical losses affect the amount of sunlight received by the cells, resulting in a power deterioration of ∼6–7% per 5% drop in transmittance. The soiled sample experienced an average temperature rise of 2 °C, and an average power output (and efficiency) reduction of 30% relative to the clean sample, and a maximum (relative) spatial variation of 7% between the zones. The 30% average power loss measured in this nonuniform soiling case is more than double that which would be expected theoretically for a transmittance loss of 13% but from uniform soiling, so these results highlight the importance of addressing PV soiling for optimal PV performance, and of accounting for spatial soiling nonuniformity.

A Novel ANN-Based GMPPT Method for PV Systems Under Complex Partial Shading Conditions

In this paper, a new two-stage global maximum power point tracking (GMPPT) algorithm based on artificial neural network (ANN) is proposed. The novel ANN architecture is presented first, which requires fewer sampling points than other ANN-based GMPPT approaches, thereby reducing both the tracking time and power loss involved in the tracking. In addition, it does not require costly illuminance or temperature sensors and can be realized using a low-cost digital signal controller. According to the simulation results, the proposed method has the best performance among all methods in terms of tracking speed, tracking accuracy, and tracking loss for all the three tested shading patterns (SPs). The simulated results of 252 SPs show that the performance indexes (PIs) of the proposed method are the best among all the compared methods, which are: the average tracking time 0.18 seconds, average power loss 0.01 W. In addition, the proposed method can correctly predict the GMPP positions of all 252 SPs. Furthermore, the PIs of the proposed method are also the best among all the compared methods according to the experimental results, which are: the tracking speed 0.21 seconds, tracking accuracy 99.66%, and tracking loss 12.58 W, all the above are average values.

Improving the reliability of power distribution substations equipment for life extension by considering Condition Based Maintenance

Maintenance of aged power equipment is a proven technique for establishing systems reliability and longevity. It is extensively relevant when combined with Condition Based Maintenance (CBM) approaches for predicting power system infrastructure life, especially with imminent obsolescence. The research focuses on distribution companies with mixed aged power infrastructure, which continuously degradate. A significant setback in older systems is the scarcity of replacement parts leading to unplanned investment in substantial infrastructure due to unexpected failed components that are no longer in production. To guarantee excellent infrastructure health in older equipment, utility companies should consider applying Condition Based Maintenance (CBM) using Artificial Neural Network (ANN) to stabilize older equipment and restore malfunctioning assets. Studies have shown that the life span of older equipment can be extended when adequate maintenance strategies are put in place through the prediction of possible failures using equipment inspections and risk matrix. CBM's strategy improved the Key Performance Indicator (KPIs) System Average Interruption Duration Index (SAIDI) has improved by lowering the average monthly customers power interruptions by 19,15 % lesser time spent without electricity. The System Average Interruption Frequency Index (SAIFI) was improved by lowering the average monthly power interruptions frequency by 20,63% lesser outage frequency. The Distribution Supply Loss Index (DSLI) was improved by dropping the average monthly power losses by 7,5% power losses saved. The Customers Average Interruption Duration Index (CAIDI) was improved by lowering the average customer interruption by 36.1% improvement from the average period before the CBM optimization. The Average System Availability Index (ASAI) was improved by shortening the average annual interruption durations by 1,36%. The ASUI was improved by increasing the average duration of electrical power availability by 1.28%. The optimization of CBM was measured over 24 moths period.

A novel hybrid centralised decentralised framework for electric vehicles coordination

AbstractHybrid Centralised‐Decentralised Electric Vehicle (EV) coordination policy in the urgent charging scenario is presented. First, a robust and complex optimisation problem considering several key features affecting EV coordination is formulated. Then, a solution strategy for the formulated problem is proposed by decomposing the formulated problem into an EV coordination and simple optimisation problem. The decentralised rule‐based EV coordination strategy works on the principle of direct load flattening and utilises practical EV aggregator‐customer interaction, customer behaviour, and temporospatial shifting of the EVs to flatten the load duration curve at the charging station. Then, the centralised optimisation problem is solved to minimise the operation cost, decrease the power loss, and decrease congestion in the grid. A comparison between uncoordinated and coordinated charging in the case study conducted on the IEEE 24 bus system shows that the proposed approach reduces the average EV load by 1283.26 kW/min, average power loss by 2.465 kW/min, and operation cost by 61.99 $/min during the peak hours.

Dual‐Objective Optimization for Maximizing Photovoltaic Output Power in a Two‐Stage Single‐Phase Grid‐Tied System

The effective utilization of photovoltaic systems (PVS) is significantly affected by partial shading (PS). Although total cross‐tied topology minimizes the power loss, the partial shading condition (PSC) decreases the output power generation and exhibits multiple power peaks in the Power Voltage characteristics. To address this issue, a dual‐objective optimization technique based on reduced search space equilibrium optimization (RSS‐EO) for a single‐phase, two‐stage grid‐connected PVS, is presented. The proposed technique accomplishes two objectives 1) maximizing power generation by reconfiguring the PV modules using a switching matrix based on irradiance equalization and 2) extracting the global maximum power (GMP) with faster convergence, negligible steady‐state oscillations, and reduced steady‐state error. The simulation and experimental results validate the effectiveness of the proposed RSS‐EO technique in efficiently distributing shade over the entire PV array and tracking GMP. The power enhancements achieved by RSS‐EO, particle swarm optimization, and Munkres are 23.03%, 20.02%, and 19.01%, respectively. Additionally, the proposed method exhibits an average mismatch power loss of 738 W, a fill factor of 58.1%, and a percentage power loss of 26.83%. Furthermore, the proposed algorithm is evaluated based on energy calculations and shows better performance by achieving the shortest payback period of 1.6 years.

Experimental investigation of soiling impact on PV module performance in Yanbu Al Sinaiyah, Saudi Arabia

For many years, photovoltaic (PV) modules have seen widespread use as one of the most effective solutions to meet the growing demand for clean energy. However, compared with other power generation technologies, PV modules still face operational challenges, especially in industrial cities and the harsh environmental conditions of arid coastal regions. This study examines the effects of dust, sand grains and soiling characterisation on the efficiency of PV modules under different environmental conditions. The researcher used PV modules of two different technologies (Mono-Si and Poly-Si), two different tilt angles (10° and 24°), and two different surface conditions (clean and soiled) for their experiments. The study was conducted over a period of two years in Yanbu, an industrial city on the western coast of the Kingdom of Saudi Arabia. It was the first study of its kind in this city. The main findings indicate that the more the PV module was able to capture solar radiation, the less it resisted soiling effect. The mono-Si module outperformed the poly-Si module by 13.7% on average for both clean and soiled modules over the period of the study. However, the losses in power output due to the impact of soiling on the poly-Si modules were less on average by 11% when compared to the mono-Si modules. The overall average power losses for both mono-Si and poly-Si modules were lower by around 40% when installed at the 24° tilt angle. The study of dust particles' morphology and chemical compositions, along with the collected environmental data, confirmed that the dust-induced shade effect could contribute to the soiling impact on PV modules.

Optimizing Resource Swap Functionality in IoE-Based Grids Using Approximate Reasoning Reward-Based Adjustable Deep Double Q-Learning

The Energy Router (ER) concept as a compact intelligent power electronic device has been proposed as the core of the Internet of Energy (IoE), maximizing energy efficiency, minimizing loss and costs, and addressing growing electricity demand. However, optimizing electricity routing in the residential sector has not been well investigated. Moreover, complex modeling of the energy components besides the uncertain environment made the conventional methods impotent in tackling these problems. Consequently, this research proposes a novel algorithm titled Approximate Reasoning Reward-based Adaptable Deep Double Q-Learning (A2R-ADDQL) that is introduced specially to optimize electricity routing in residential units. As a result, both overestimation and underestimation biases are reduced comparing other deep Q-Learning-based algorithms. Moreover, the sample complexity of the model is decreased due to utilizing a fuzzy approximate reasoning reward function. Ultimately, the proposed algorithm is assessed on a real-world dataset evaluating the findings in several benchmarks. The results indicate that the proposed model is unbiased while convergence speed is higher than other analyzed techniques. Additionally, monthly average cost and power loss are lowered by 24.9% and 29.1% more than other techniques. Finally, the proposed algorithm reduces greenhouse gases emission by 3.91 kg per month.

Prospects for creating a refined model of leakage current losses due to contamination on insulator surfaces

This paper is devoted to identifying the prospects for creating a model of leakage current losses due to contamination on insulator surfaces. The aim of this study was to find mathematical models that would describe leakage current losses accurately enough to assess their importance for the integrated power system of Ukraine. The existing, widely used methodology for calculating leakage current losses was evaluated and found to be inappropriate because it is based solely on the insulator resistance with a constant value of m, which does not reflect the actual situation. Therefore, in order to improve the existing model, another one is proposed, which takes into account all atmospheric phenomena that affect the conductivity on the insulator surface, including fog, drizzle, rain and dew, which are recorded using gradations. It was found that the wind speed and humidity of the contamination layer on the insulator surface have a significant impact. The graphs of the average power loss rate for a single wetting in a garland of insulators under different climatic conditions are presented. The methodology was used to calculate the power loss rate in a 35 kV AC network and is presented in the table. The results show that it is possible to dynamically determine losses depending on climatic conditions on insulators at any point, depending on the available meteorological data. The methodology for determining the conductivity of the contamination layer on the insulator surface has been revised. It is shown how the resistance of contamination is represented, the method of its calculation, which allows further determining the conductivity of contamination, and the effect of precipitation on the cleaning of the insulator surface from contamination. It is established that under direct precipitation, insulator cleaning is insignificant, and the humidity of the contamination layer increases, which increases its conductivity. The conclusions show that the model has a high potential and the need to take into account dielectric losses.

Disperse Partial Shading Effect of Photovoltaic Array by Means of the Modified Complementary SuDoKu Puzzle Topology

This paper presents a novel modified Complementary SuDoKu puzzle (MC-SDKP) topology for the static reconfiguration of photovoltaic (PV) arrays. It was developed with the aim of enhancing the power output of a PV array which is exposed to partially shaded conditions (PSCs). To disperse patterns of both center shading and corner shading, the MC-SDKP technique modified and combined the Optimal SDKP and the Complementary SDKP (C-SDKP) topologies. An 8 × 8 PV array configured with the MC-SDKP topology was exposed to nine different shading patterns, and its performance was compared with that of the other four topologies. The results of the performance evaluation confirmed that, when configured according to the MC-SDKP, the PV array produced the highest average power output among all five topologies, with a 15.07% higher output on average than the total-cross tied. The PV array with the MC-SDKP topology also exhibited the lowest average power loss (1.34%). This study clearly established the effectiveness of the MC-SDKP topology at mitigating the effects of both center and corner shading. The advantages of the MC-SDKP reconfiguration technique are: an increase in extracted power, a reduction in current mismatch losses, an improvement in shade dispersion under conditions of center shading, and good scalability.

Simulation based analysis to study the effect of compression ring crown height in ring liner assembly on the performance of four stroke petrol engine

Among different piston rings of an Internal Combustion Engine (IC), the top compression ring-liner assembly shared a momentous amount of the total frictional losses, particularly at the Top Dead Center (TDC) and Bottom Dead Center (BDC) where boundary lubrication subsists. In the present study, the lubrication mechanism in piston ring cylinder liner conjunction for top compression ring using one dimensional Reynolds equation assuming axisymmetric contact was studied. The hydrodynamic and mixed lubrication regimes were taken during the study. Gumbel boundary conditions were used for Reynolds equation solution. For boundary pressure calculation, first, combustion chamber pressure variation was determined, then, interring pressure was calculated assuming orifice volume method by solving mass flow rate through crevice volumes using Runge-Kutta 4th order method. The Reynolds equation was solved using finite difference method. Whole solution process was repeated for different engine speed and crown height of top compression ring to study the effect of them in wear and power loss. First, the solution was found for 3000, 5000 and 7500 RPM keeping constant crown height of 10 µm. Average power loss was found to be increased with the increasing engine speed. There was increased in oil film thickness around mid-stroke with the increased engine speed, but there was no significant change in film thickness with the engine speed at the TDC and BDC positions. Then, the solution was again repeated for different RPM varying the crown height. The compression ring with lower crown height showed the better wear performance by increasing the minimum film thickness in critical zones of engine cycle for all RPM. The crown height of 5,7 and 9 µm gave the minimum power loss (77.89, 163.47 and 297.33 W) for 3000, 5000 and 7500 rpm. Moreover, the power loss in the range of 5-9 µm had no significant differences.

Simple Discontinuous Pulse-Width Modulation Scheme for the Loss Reduction of a Dual Inverter Fed an Open-End Winding Induction Motor

This paper presents a simple discontinuous pulse width modulation (DPWM) switching scheme to reduce the switching loss of a single DC-link dual two-level inverter fed an open-end winding (OW) induction motor (IM). The dual inverter system is attractive in many applications due to higher DC-link voltage utilization. Additionally, the OW motor has the potential fault-tolerant capability. However, the use of the dual inverter results in reduced system efficiency due to increased power losses in the inverter. Moreover, an OW motor inherently suffers from a zero-sequence current (ZSC) issue because the open neutral point of the stator windings provides the ZSC path. The ZSC causes negative effects in the system, such as an additional power loss and total harmonic distortion (THD). Thus, this work proposes an optimal DPWM scheme to reduce the inverter loss while suppressing the ZSC. Additionally, this work proposes a strategy to make the average power loss of each inverter equal. The performance of the proposed method is discussed in terms of switching power loss reduction and ZSC suppression. Simulation and experimental results are provided to verify the effectiveness of the proposed DPWM scheme.

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.

A Condition Evaluation Simplified Method for Traction Converter Power Module Based on Operating Interval Segmentation

In the actual operation of urban rail vehicles, it is essential to evaluate the condition of the traction converter IGBT modules. Considering the fixed line and the similarity of operation conditions between adjacent stations, this paper proposes an efficient and accurate simplified simulation method to evaluate IGBT conditions based on operating interval segmentation (OIS). Firstly, this paper proposes the framework for a condition evaluation method by segmenting operating intervals based on the similarity of average power loss between neighboring stations. The framework makes it possible to reduce the number of simulations to shorten the simulation time while ensuring the state trend estimation accuracy. Secondly, this paper proposes a basic interval segmentation model that uses the operating conditions as inputs to implement the segmentation of the line and is able to simplify the operation conditions of entire line. Finally, the simulation and analysis of the temperature and stress fields of IGBT modules based on segmented intervals completes the IGBT module condition evaluation and realizes the combination of lifetime calculation with actual operating conditions and internal stresses. The validity of the method is verified by comparing the interval segmentation simulation with actual test results. The results show that the method can effectively characterize the temperature and stress trends of traction converter IGBT modules in the whole line, which could support the fatigue mechanism and lifetime assessment reliability study of IGBT modules.

Electrostatic cleaning effect on the performance of PV modules in Jordan

This study investigates the effect of dust accumulation on photovoltaic modules performance and proposes a new photovoltaic cleaning method based on static electricity concepts. The results of the proposed cleaning method were compared with the results of three different conventional photovoltaic cleaning methods: manually -the reference-, naturally, and nano-coating. The economic feasibility of the proposed cleaning method was compared with the nan-coating cleaning method. For the mentioned purpose, a system of four photovoltaic modules was installed in Amman, the system was exposed to natural dust. Energy loss due to dust accumulation after two weeks of exposure to natural dust without cleaning or rain was found to be 5.93% for the naturally cleaned module. Electrostatic neutralization cleaning reduced energy loss to 4.56%. Nano-coating reduced energy loss to 2.33%. According to the literature on cleaning of PV modules, the average power loss of photovoltaic modules due to dust accumulation is extreme which could reach 1% per day. 1.37% enhancement in energy saving utilizing electrostatic neutralization cleaning is a highly appreciated improvement in the photovoltaic module's efficiency. From an economic perspective, nano-coating was about 3.8 times cheaper compared with electrostatic neutralization under the stated conditions in this study. Electrostatic neutralization has a promising techno-economic future for mega solar power plants in arid regions compared with nano-coating by developing a robust practical mechanism with less capital and running costs owing to nano-recoating barriers.

DEVELOPMENT OF ANN CONTROLLED UPFC BASED PROTECTION MODEL FOR NIGERIAN 330KV POWER LINES

This paper, proposed the use of artificial neural network (ANN) based UPFC for transmission network loss minimization. The overall effect of power losses on the system is a reduction in the quantity of power available to the consumers. Power loss leads to high cost of power generation, transmission and distribution. Unlike exiting up change and regulating transformer techniques for loss reduction, FACTS devices have fast switching capability and can be subjected to very free control algorithms for more optimal performance in loss reduction application in power systems. The neural network was modeled to output the firing angle to enable the FACTS device effectively control the adsorption and injection of reactive power for transmission loss reduction. The Nigerian 330KV power grid was used as a case study for the evaluation of the proposed power loss reduction system A digital model of the case study power system with the proposed neural network controlled UPFC integrated was created in the MATLAB/SIMULINK programming environment. Genetic algorithm was used for the optimal placement of the FACTS device in the MATLAB/SIMULINK model of the Nigerian 330KV transmission system. Simulation carried out involved alteration of power flow in order to cause different levels and distribution of losses in the network. With each variation, load flows was carried out to evaluate the distribution of losses and the active and reactive loss reduction achieved by the proposed system. The simulation and evaluation were carried out under two scenarios: (i) with the UPFC installed and (ii) without the UPFC installed. With each variation of the load at the bus, load flow is run to determine total system loss either with the UPFC installed or without the UPFC installed. Results obtained showed that the proposed system achieved an average active power loss reduction of 12.8416% and an average reactive power loss reduction of 21.82%.

Voltage regulation and power loss mitigation by optimal allocation of energy storage systems in distribution systems considering wind power uncertainty

Due to the ever increasing in the energy demand and the growth of the pollution problem, further interest in renewable energy becomes prominent, especially wind energy. However, the intermittent nature of the wind speed results in the system's insecurity problems. Integrating the energy storage system (ESS) optimally is the best so far solution. In this paper, a probabilistic discretizing method is derived and generalized to consider the uncertainties of the wind generators. Moreover, a mixed integer non-linear optimisation problem is constructed comprises three objective functions to be minimized simultaneously. The objectives comprising the total operation and planning cost of ESSs, the average voltage deviation and average power losses. The solving method is a hybrid algorithm incorporates the non-dominated sorting genetic algorithm (NSGAII), multi-objective particle swarm optimisation (MOPSO) and a decision-making algorithm. The aim is to determine the optimal location and sizing of ESSs considering their reactive power capability. 69 radial bus distribution system is adopted to demonstrate the efficacy of the proposed hybrid algorithm and performing some case studies. The simulation results verified the capability of ESSs to enhance the voltage profile and reduce the power losses, notably when reactive power capability is examined.

Optimal Deployment of Unified Power Flow Controller on Nigerian 330 kV Grid Using Artificial Bee Colony

Transmission line power loss minimization and voltage profile improvement are challenging problems in modern power systems due to continuous increase in load demand. In this paper, an optimal location and sizing of unified power flow controller (UPFC) to improve the performance of the updated 54-bus 330 kV Nigerian network was presented. Here, the maximum power loss bus was identified to be the most favorable location for fixing the UPFC under different loading conditions; slightly underloaded (90%), normal loading (100%) and slightly over loaded (110%). The optimum location was determined using the artificial bee colony (ABC) algorithm. The work was implemented in MATLAB® 2021b environment and its performance was evaluated based on the simulation results obtained. The simulation results revealed that, ABC proved to be robust based on the facts that all the voltage profile that violate ± 5% tolerance margin of the nominal voltage criteria were kept within the acceptable limit after the UPFC optimal deployment. Moreover, the proposed approach also recorded significant reduction in the overall active and reactive power loss in the entire network with a minimum percentage average active power loss reduction of 45.3% under different loading conditions.