Power Losses In Transmission Lines Research Articles

Utilizing virtual power plants to support main grid for frequency regulation

In this paper, a new control architecture of virtual power plants (VPP) is proposed for the frequency regulation (FR) in main grid. Firstly, to address the power prediction of aggregation nodes (AN) with spatio-temporal correlation under VPP, a novel combined model based on graph convolutional networks and bi-directional long and short-term memory is established. Further, considering the effect of prediction uncertainty, a power shortage measurement method based on interval estimation is proposed to determine the effective power of AN. Secondly, a dispatch-based control strategy is introduced to enable VPP to respond to regulation commands from the main grid. Additionally, the influence of transmission line power loss on FR is considered, which can be eliminated by applying the developed power compensation design scheme based on proportional allocation. Finally, numerical simulations illustrate that the proposed method enhances prediction performance by 68.65%, taking mean absolute error as an example, while ensuring the accuracy and reliability of power prediction. Also, the adopted control strategy can effectively utilize VPP to provide FR services for the main grid. Moreover, the application results in a real scene are better than those of benchmark methods, which also demonstrates that the proposed approach has a high practical application potential in power system.

Optimal dispatch of combined heat and power generating units with prohibited operating zones using improved heap‐based optimizer

AbstractIn the present market scenario, energy cost has been increased and also restrictions have been faced in fossil fuels energy sources. This leads to evolvement of combined heat and power generating units having higher efficiency as compared to the existing systems. The optimal dispatch of CGUs involves a number of intricate constraints, making it a practical optimization problem. So, an improved heap‐based optimizer algorithm has been implemented in this work for solving optimal dispatch problem by accomplishing a proper balance between initial and final stages of global search and convergence, respectively. This problem provides optimal scheduling of heat and power generating units and seeks to minimize the overall fuel cost supply of combined units considering their operational limits. Here, three test systems, that is, 7‐units, 24‐units and 48 units system have been taken into consideration to verify the performance of proposed IHBO algorithm. The impact of prohibited zones on power dispatch has been analysed for both the system in an electricity market. In addition, the valve‐point loading effect, mutual dependency of produced heat and power of combined units and also transmission power losses in transmission lines have been studied. Comparisons among different methods have been made based on their respective solutions obtained and from that it manifests the robustness and superior performance of the proposed technique. Verification studies showed that impact of prohibited zones on generating units, increases the production cost with respect to without prohibited zones. However the proposed technique yields least production cost among other techniques.

Deep learning techniques for transmission line fault classification – A comparative study

Despite advancements in technology, power system faults leading to electric power interruption remain a significant issue. Efficient restoration of the power system relies on the swift classification and clearance of faults. Among the various types of faults in transmission lines, open circuit and short circuit faults are commonly encountered. This study specifically focuses on the analysis of five types of short circuit faults: line-to-line, line-to-ground, double line-to-ground, triple line, and triple line-to-ground faults. Faults can cause both power failure and power loss in transmission lines. Once a fault occurs, it is crucial to restore electricity supply promptly to prevent further losses. Therefore, the development of a system capable of accurately and swiftly detecting and removing faults is essential. Traditionally, categorizing transmission line faults required sophisticated mathematical modeling, intricate signal processing techniques, and expert knowledge to interpret the output signals. In this paper, an alternative approach is proposed, utilizing deep learning techniques for transmission line fault classification. Specifically, the paper employs techniques such as artificial neural network (ANN), long short-term memory (LSTM), with and without window regression (WR). By implementing these deep learning techniques, automatic feature extraction and signal processing are achieved, streamlining the fault classification process. The experimental results obtained from this study demonstrate an accuracy of 42.98% for ANN, 99.98% for LSTM, and 99.99% for LSTM-WR. These outcomes underscore the effectiveness of the deep learning techniques employed in accurately classifying transmission line faults, with LSTM and LSTM-WR outperforming ANN in terms of accuracy.

Minimization of Electric Power Losses on 132 kV and 220 kV Uganda Electricity Transmission Lines

The classical minimization of power losses in transmission lines is dominated by artificial intelligence techniques, which do not guarantee global optimum amidst local minima. Revolutionary and evolutionary techniques are encumbered with sophisticated transformations, which weaken the techniques. Power loss minimization is crucial to the efficient design and operation of power transmission lines. Minimization of losses is one way to meet steady grid supply, especially at peak demand. Thus, this paper has presented a gradient technique to obtain optimal variables and values from the power loss model, which efficiently minimizes power losses by modifying the traditional power loss model that combines Ohm and Corona losses. Optimality tests showed that the unmodified model does not support the minimization of power losses on transmission lines as the Hessian matrix portrayed the maximization of power losses. However, the modified model is consistent with the gradient method of optimization, which yielded optimum variables and values from the power loss model developed in this study. The unmodified (modified) models for Bujagali-Kawanda 220 kV and Masaka West-Mbarara North 132 kV transmission lines in Uganda showed maximum power losses of 0.406 (0.391) and 0.452 (0.446) kW/km/phase respectively. These results indicate that the modified model is superior to the unmodified model in minimizing power losses in the transmission lines and should be implemented for the efficient design and operation of power transmission lines within and outside Uganda for the same transmission voltages.

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.

Single and Multi-Objective Optimal Power Flow Based on Hunger Games Search with Pareto Concept Optimization

In this study, a new meta-heuristic optimization method inspired by the behavioral choices of animals and hunger-driven activities, called hunger games search (HGS), is suggested to solve and formulate the single- and multi-objective optimal power flow problem in power systems. The main aim of this study is to optimize the objective functions, which are total fuel cost of generator, active power losses in transmission lines, total emission issued by fossil-fueled thermal units, voltage deviation at PQ bus, and voltage stability index. The proposed HGS approach is optimal and easy, avoids stagnation in local optima, and can solve multi-constrained objectives. Various single-and multi-objective (conflicting) functions were proposed simultaneously to solve OPF problems. The proposed algorithm (HGS) was developed to solve the multi-objective function, called the multi-objective hunger game search (MOHGS), by incorporating the proposed optimization (HGS) with Pareto optimization. The fuzzy membership theory is the function responsible to extract the best compromise solution from non-dominated solutions. The crowding distance is the strategies carried out to determine and ordering the Pareto non-dominated set. Two standard tests (IEEE 30 bus and IEEE 57 bus systems) are the power systems that were applied to investigate the performance of the proposed approaches (HGS and MOHGS) for solving single and multiple objective functions with 25 studied cases using MATLAB software. The numerical results obtained by the proposed approaches (HGS and MOHGS) were compared to other optimization algorithms in the literature. The numerical results confirmed the efficiency and superiority of the proposed approaches by achieving an optimal solution and giving the faster convergence characteristics in single objective functions and extracting the best compromise solution and well-distributed Pareto front solutions in multi-objective functions.

Investigation of optimal power flow solution techniques considering stochastic renewable energy sources: Review and analysis

Increased penetration of renewable energy sources (RESs) in power system networks poses several challenges in system planning and management due to their uncertain and non-dispatchable nature. Consequently, this paper presents a thorough and precise review of recent solution methodologies for solving the optimal power flow (OPF) problems incorporated with stochastic RESs based on multiple peer-reviewed research publications in reputed journals. The Teaching Learning Based Optimization algorithm has been discussed and implemented to solve the OPF problem considering solar photovoltaic, wind turbine, and tidal energy systems. Weibull, Lognormal, and Gumbel probability density functions representing the uncertainty associated with the availability of wind speed, solar irradiance, and tidal energy systems, respectively. The results obtained from the proposed technique validate its novelty regarding OPF problems like minimization of operating cost, power loss in transmission lines, enhancement of voltage profile, and voltage stability. The proposed solution technique for OPF problems is tested on a modified IEEE 30-bus test system. Thus, this study assists in understanding the OPF problem for new researchers concerned with this domain and also gives the idea of implementing nature-inspired optimization algorithms on a defined test system to solve the OPF problem.

Optimized management of reactive power reserves of transmission grid-connected photovoltaic plants driven by an IoT solution

This paper presents a methodology for the analysis and simulation of the effect of operating large photovoltaic (PV) plants, in coordination, as static synchronous compensators (STATCOM). The goal is to improve voltage profiles at different load nodes and reduce power losses in transmission lines. The proposed approach takes into account the varying reactive power capacity in PV inverters, which depends on weather conditions.To implement the proposed method, proper Internet of Things (IoT) hardware and software solutions are required. In this context, the grid status and weather data need to be transmitted continuously, via wireless communication technology, to an edge computer. Based on the transmitted data, and using the system mathematical model, an optimization algorithm is then responsible for finding out the optimal reactive power setpoint for each plant in real time.The proposed method is implemented and tested successfully using MATLAB platform with the MATPOWER IEEE 30-bus test grid model. When only five 20 MW PV plants are connected to different locations in the grid with a penetration rate lower than 25 percent, the simulation shows the effectiveness of the optimal coordination of PV plants to deal with the effect on the transmission grid of instantaneous operation of multiple loads. In this context, a daily load profile of heat pumps, operating in winter scenario in multiple households, is approved. An improvement up to 68 percent in the global voltage profiles in the load buses for one-day scenario is achieved. Furthermore, total accumulated active and reactive power losses are reduced by 24.1 percent.

A Comparative Study of Compensate Schemes of Loop Power System Affecting to Total Power Losses

This paper presents the compensate schemes consisting of series scheme, shunt scheme and combine scheme of power system. The power system of Nakhonpathom Rajabhat University, which is the loop system while connected with 90% of rated transformer as the load, is chosen as case study. In addition, the Newton Raphson method is the power flow technique for simulation via ETAP programming. This paper focuses on comparing the total power losses in each scheme. Moreover, the suitable scheme is analyzed in term of voltage magnitude and total power losses in transmission line. The reactive power converting to compensate impedance is the technique that starts from 20 MVAr to 200 MVAr of both series and shunt compensate schemes. In term of combine compensation will be constant of 140 MVAr to 200 MVAr as the shunt compensate while the series compensate will be changed from 140 MVAr to 200 MVAr in each case. The simulation compares the total power losses of the original system without compensation and the new system compensated with three schemes. Nevertheless, the results show that the shunt compensation with reactive power start from 140 MVAr can decrease a total power loss of transmission line while the series compensation can only increase the voltage magnitude but the total power losses will be increased together. In addition, the combined compensation can increase the voltage magnitude and the total power losses. Therefore, this is the choice for the scheme for increasing the voltage magnitude and the total power losses. On the other hand, in terms of manufacturing there are many factors to determine such as sizing, insulator characteristic and installation.

Chaotic Particle Swarm Optimization for Solving Reactive Power Optimization Problem

The losses in electrical power systems are a great problem. Multiple methods have been utilized to decrease power losses in transmission lines. The proper adjusting of reactive power resources is one way to minimize the losses in any power system. Reactive Power Optimization (RPO) problem is a nonlinear and complex optimization problem and contains equality and inequality constraints. The RPO is highly essential in the operation and control of power systems. Therefore, the study concentrates on the Optimal Load Flow calculation in solving RPO problems. The Simple Particle Swarm Optimization (PSO) often falls into the local optima solution. To prevent this limitation and speed up the convergence for the Simple PSO algorithm, this study employed an improved hybrid algorithm based on Chaotic theory with PSO, called Chaotic PSO (CPSO) algorithm. Undeniably, this merging of chaotic theory in PSO algorithm can be an efficient method to slip very easily from local optima compared to Simple PSO algorithm due to remarkable behavior and high ability of the chaos. In this study, the CPSO algorithm was utilized as an optimization tool for solving the RPO problem; the main objective in this study is to decrease the power loss and enhance the voltage profile in the power system. The presented algorithm was tested on IEEE Node-14 system. The simulation implications for this system reveal that the CPSO algorithm provides the best results. It had a high ability to minimize transmission line losses and improve the system's voltage profile compared to the Simple PSO and other approaches in the literature.

Minimizing Transmission Line Power Losses in Port Harcourt, Southern Nigeria: An Optimization-Based Approach

Minimizing Transmission Line Power Losses in Port Harcourt, Southern Nigeria: An Optimization-Based Approach

Optimization of Power Losses in Transmission Lines Using Metaheuristic Methods

Optimization of Power Losses in Transmission Lines Using Metaheuristic Methods

Model for 400 kV Transmission Line Power Loss Assessment Using the PMU Measurements

This paper presents an advanced model for monitoring losses on a 400 kV over-head transmission line (OHL) that can be used for measured data verification and loss assessment. Technical losses are unavoidable physical effects of energy transmission and can be reduced to acceptable levels, with a major share of technical losses on transmission lines being Joule losses. However, at 400 kV voltage levels, the influence of the electrical corona discharge effect and current leakage can have significant impact on power loss. This is especially visible in poor weather conditions, such as the appearance of fog, rain and snow. Therefore, loss monitoring is incorporated into exiting business process to provide transmission system operators (TSO) with the measure of losses and the accurate characterization of measured data. This paper presents an advanced model for loss characterization and assessment that uses phasor measurement unit (PMU) measurements and combines them with end-customer measurements. PMU measurements from the algorithm of differential protection are used to detect differential currents and angles, and this paper proposes further usage of these data for determining the corona losses. The collected data are further processed and used to calculate the amount of corona losses and provide accurate loss assessment and estimation. In each step of the model, cross verification of the measured and calculated data is performed in order to finally provide more accurate loss assessment which is incorporated into the current data acquisition and monitoring systems.

Minimizing Power Loss Using Modified Artificial Bee Colony Algorithm

Electrical energy losses are found in any part of the power system. In the power system, it is essential to minimize the real power loss in transmission lines. The voltage deviation at the load buses through controlling the reactive power flow is very important. This ensures the secured operation of power systems regarding voltage stability and the economics of the process due to loss minimization. In this paper, the Modified Artificial Bee Colony (MABC) algorithm is implemented to solve the power system's optimal reactive power flow problem. Generator bus voltages, transformer tap positions, and settings of switched shunt of compensators are used as decision variables to control the reactive power flow. These control variable values are adjusted for loss reduction. MABC algorithm is tested on the standard IEEE-30 bus test system. The results are compared with Firefly algorithm (FA) and Artificial Bee Colony (ABC) algorithm method to prove the effectiveness of the newest algorithm. The power loss results are quite productive, and the algorithm is the most efficient than the other methods such as ABC algorithm and FA algorithm. These results are produced by Matlab 2017b.

Multi-Mean Scout Particle Swarm Optimization (MMSCPSO) based Reactive Power Optimization in Large-Scale Power Systems

The particle swarm optimization (PSO) is a population-based algorithm belonging into metaheuristic algorithms and it has been used since many decades for handling and solving various optimization problems. However, it suffers from premature convergence and it can easily be trapped into local optimum. Therefore, this study presents a new algorithm called multi-mean scout particle swarm optimization (MMSCPSO) which solves reactive power optimization problem in a practical power system. The main objective is to minimize the active power losses in transmission line while satisfying various constraints. Control variables to be adjusted are voltage at all generator buses, transformer tap position and shunt capacitor. The standard PSO has a better exploitation ability but it has a very poor exploration ability. Consequently, to maintain the balance between these two abilities during the search process by helping particles to escape from the local optimum trap, modifications were made where initial population was produced by tent and logistic maps and it was subdividing it into sub-swarms to ensure good distribution of particles within the search space. Beside this, the idle particles (particles unable to improve their personal best) were replaced by insertion of a scout phase inspired from the artificial bee colony in the standard PSO. This algorithm has been applied and tested on IEEE 118-bus system and it has shown a strong performance in terms of active power loss minimization and voltage profile improvement compared to the original PSO Algorithm, whereby the MMSCPSO algorithm reduced the active power losses at 18.681% then the PSO algorithm reduced the active power losses at 15.457%. Hence, the MMSCPSO could be a better solution for reactive power optimization in large-scale power systems.

Fast Atom Search Algorithm for Reactive Power Optimization of Power Grid with High-Penetration Wind and Solar Energies

This paper constructs a reliable reactive power optimization (RPO) model of power grid with the controlled participation of high-penetration wind and solar energies and provides a novel fast atom search optimization (FASO) algorithm to reach a set of solutions to the RPO problem. The developed FASO algorithm owns prominent merits of high searching efficiency and premature convergence avoidance compared with the original atom search optimization (ASO) algorithm, which is applied to determine the optimal dispatch scheme including terminal voltage of generators, the capacity of static VAR compensator (SVC), reactive power output of wind and solar energies, and the tap ratio of transformers. There are two objective functions to be minimized for maintaining the safe and reliable operation of power grid, i.e., total power loss of transmission lines and total voltage deviation of nodes. Meanwhile, the regulation capacities of wind farms and photovoltaic (PV) stations are evaluated based on different weather conditions, i.e., wind speed and solar irradiation. Particularly, the reactive power outputs of wind and solar energies can be globally controlled to coordinate with other controllable units instead of a local self-control. Eventually, the extended IEEE 9-bus and IEEE 39-bus systems are introduced to test the performance of the FASO algorithm for RPO problem. It has been verified that FASO can not only meet the optimal regulation requirements of RPO but also obtain high-quality regulation schemes with the fastest convergence speed and highest convergence stability in contrast with else algorithms.

Impact of the penetration of distributed generation on optimal reactive power dispatch

Optimal reactive power dispatch (ORPD) is a complex and non-linear problem, and is one of the sub-problems of optimal power flow (OPF) in a power system. ORPD is formulated as a single-objective problem to minimize the active power loss in a transmission system. In this work, power from distributed generation (DG) is integrated into a conventional power system and the ORPD problem is solved to minimize transmission line power loss. It proves that the application of DG not only contributes to power loss minimization and improvement of system stability but also reduces energy consumption from the conventional sources. A recently proposed meta-heuristic algorithm known as the JAYA algorithm is applied to the standard IEEE 14, 30, 57 and 118 bus systems to solve the newly developed ORPD problem with the incorporation of DG. The simulation results prove the superiority of the JAYA algorithm over others. The respective optimal values of DG power that should be injected into the four IEEE test systems to obtain the minimum transmission line power losses are also provided.

Optimization of Wind-Thermal Economic-Emission Dispatch Problem using NSGA-III

Economic-emission dispatch (EED) of an electric power system can be considered as one of the most popular constrained multi-objective problems. In this paper the EED is formulated as a many-objective optimization problem with that consider the minimization of cost of thermal fuels, wind generation, greenhouse gas emission, and active power losses in transmission lines, satisfying physical and operational constraints of the system. To solve the EED problem with incorporate renewable power generations, a version of constrained many-objective optimization algorithm called non- dominated sorting genetic algorithm-III (NSGA-III) is proposed. The NSGA-III is based on reference points and explores the dominance relation criterion based on the constraints violation to select the new generation. To validate the efficiency and robustness of the proposed EED model and solution technique, results and analysis of the simulations with the IEEE-30 test system are presented.

Optimal Location of FACTS Devices for Active and Reactive Loadability Improvement Using KHA Algorithm

Optimal reactive power dispatch in power system is referred to minimizing the power loss in transmission lines and voltage deviations at the load bus by control of reactive power. This paper presents the comparison between particle swarm optimization (PSO) and an improved evolutionary krill herd algorithm (KHA) for computing optimal steady-state performance of power system by finding the optimal position for placement of flexible AC transmission system (FACTS). For this purpose thyristor controlled series compensator (TCSC), static VAR compensator (SVC). A comparative analysis between the two optimization techniques is done and KHA is found to give better results as compared to PSO. IEEE 30- bus system is considered for the optimal placement of FACTS controllers.

Swarm algorithms in dynamic optimization problem of reactive power compensation units control

Optimization of a power supply system is one of the main directions in power engineering research. The reactive power compensation reduces active power losses in transmission lines. In general, researches devoted to allocation and control of the compensation units consider this issue as a static optimization problem. However, it is dynamic and stochastic optimization problem that requires a real-time solution. To solve the dynamic optimization NP-hard problem, it is advisable to use Swarm Intelligence. This research deals with the problem of the compensation units power control as a dynamic optimization problem, considering the possible stochastic failures of the compensation units. The Particle Swarm Optimization and the Bees Algorithm were applied to solve it to compare the effectiveness of these algorithms in the dynamic optimization of a power supply system.