Transmission Line Losses Research Articles

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.

A Developed Approach to Solve Economic and Emission Dispatch Problems Based on Moth-Flame Algorithm

The solution of Combined Economic and Emission dispatch (CEED) problems aims to minimize the operating fuel cost and emission levels simultaneously, while satisfying load demand and operational constraints. The CEED problem can be formulated as a nonlinear optimization problem with equality and inequality operating constraints. Moth-flame optimization algorithm (MFO) is one of the newest bio inspired optimization techniques. However, this paper proposes a modified moth-flame optimization algorithm (MMFO) to find the optimal solution of CEED problems. MMFO is mainly based on the concept of MFO with modification the path of moths in new spirals around the flame. The developed MMFO algorithm can be used to improve the convergence characteristics of CEED optimal solutions compared to other optimization algorithms. The performance of MMFO is compared with different optimization techniques. Valve point effect, environmental emissions, and transmission line losses are included in the CEED analysis to provide more practical results. MMFO is validated using different standard test systems. The simulated results show the effectiveness and the superiority of the developed MMFO against other well-known optimization techniques.

Multi-objective Mathematical Programming to Simultaneously Control SCUC and OUPFC to Improve Power System Controllability

ABSTRACT Security-Constrained Unit Commitment (SCUC) problem and subsequently employing Flexible AC Transmission Systems (FACTS) controllers including Optimal Unified Power Flow Controller (OUPFC) play a crucial role in power systems operation and control in terms of increasing the security of energy transmission networks. The principal objective of the SCUC problem is to acquire the minimum operating cost simultaneously maintaining the security of the system. In this paper, significant attention is paid particularly to simultaneously control SCUC and OUPFC, since energy loss reduction and transmission lines capacity release can be effectively achieved with incorporating SCUC and OUPFC, and subsequently setting the effective variables. In this regard, the SCUC problem with the presence of OUPFC is solved as Nonlinear Programming with Discontinuous Derivatives (DNLP) framework and the proposed algorithm is implemented on two IEEE 14-bus and IEEE 30-bus systems using General Algebraic Modeling System (GAMS) software in order to optimize the total fuel cost, power losses and cost of OUPFC installation as objective functions in the multi-objective framework. The simulation results show the capability of the proposed algorithm to simultaneously determine the optimal location of the OUPFC and its settings in the SCUC problem and to improve the controllability of the power system operation.

Energy exchange cooperative model in SDN-based interconnected multi-microgrids

With the expansion of the microgrid systems (MGs) exploitation, the development of multi-microgrid systems (MMGs) is rising. Along with the benefits offered by a microgrid, the combination of several microgrids and their synergies in energy supply augments system stability and reliability and reduces energy costs and losses in transmission lines. With the connection of all microgrids together in an interconnected multi-microgrid (IMMG) and the provision of interoperability and energy sharing in these grids, the perfect platform is created for optimal distribution of energy sharing by the appropriate strategies of an effective energy management system (EMS). The present paper proposes a Software-Defined Networking (SDN) based cooperative model for sharing energy among microgrids via a transactive energy framework (TE) in interconnected multi-microgrids. The proposed model utilizes incentive mechanisms to encourage microgrid collaboration to reduce the final energy supply cost of an IMMG. The current work introduces the energy sharing factor (ESF) that provides an energy exchange mechanism for the IMMG manager. The effectiveness of the proposed model is investigated by a cost reduction analysis that employs the microgrids dataset.

Power Flow Control in Transmission Line by Using UPFC

The power transfer capacity of electrical transmission lines is typically constrained by the capacity of large signals. Economic considerations such as the high cost of long lines and the income from the supply of additional power make it possible to pursue both economically and technically viable ways to increase the stability cap intensively. On the other hand, the production of efficient ways to use the full thermal capacity of transmission systems. The power industry has already begun to be affected by fast development in the field of power electronics. This is one direct consequence of the idea of aspects of FACTS, which has become possible due to the progress realized in power electronic devices. In theory, the FACTS devices can provide rapid control of real and Var power through transmission line. The UPFC is a FACTS family member with very appealing characteristics. Many parameters can be independently controlled by this unit. An alternative means of minimizing transmission system oscillations is given by this unit. The choice of input signals and the adopted control strategy for this system in order to dampen power oscillations in an efficient and robust manner are an important issue. In order to achieve the maximum desire effect in solving the first swing stability problem, the UPFC parameters can be regulated. In bulky power transmission systems with long transmission lines, this problem arises. A MATLAB Simulink Model with UPFC to test the efficiency of the electrical transmission system is considered in this paper. The main purpose of this analysis paper is to research different studies performed in the past to minimize UPFC transmission line losses.

Power Swing Blocking Algorithm based on Real and Reactive Power Transient Stability

Power swing due to the disturbance in the power system is a cause of the untrue operation of protective elements. Previously, many blackouts have been perceived and recorded due to reactive power mismatch problems, active power mismatch problems, and system venerability. The power system stability is usually disturbed due to the sudden loss of transmission lines, generators, and transformers. The present work proposes the concept of apparent power transient stability due to the transient disturbance. By considering that concept, we propose a power swing blocking algorithm to prevent mal-operation of distance relay. The proposed algorithm uses the relative frequency between the sending end bus and system standard frequency with simultaneous measurement of voltage decay of the transmission line. We have implemented the proposed algorithm on a single machine infinite system (SMIB) and the IEEE-9 bus system to compare the power swing detection and blocking performance with mho type out of step relay in different power swing conditions. In the end, results show the accurate performance, simplicity, reliability, and correct co-ordination of the proposed relay with distance relay, which ultimately solved the issue of mal-operation of zone-3 distance relay during complex power swing conditions.

Investigation on Electromagnetic Properties of PLA/Carbon/Copper Antennas for Breast Cancer Detection

Breast cancer is the most common cancer of women in the globe. Microwave imaging has low power and longer wavelength signals to receive information about breast tissues and guarantees a safer and more accurate modality for regular breast control. So, the antenna is a crucial element in the microwave imaging system. This study involves analysis of the simulation results of the linear and planar microstrip array antennas in terms of high gain. In the series antenna design stage, the antenna models with high gain and directional radiation are aimed. 1x4 and 2x2 array antenna models are prepared with PLA/Carbon insulating material. Array antennas, three-dimensional full-wave electromagnetic structure analysis are designed using the CST (Computer Simulation Technology) Microwave Studio program. In the design phase, the transmission line losses of the array antennas are tried to be optimized by using T-junction and Wilkinson power divider techniques and impedance matching is realized. Also, the redundant areas in the turns are removed to minimize the undesirable reflections in the transmission line and to reduce the capacitive loading. Since the 1x4 array antenna performs better than the 2x2 array antenna, it was preferred for breast cancer simulation.

Dual-band substrate integrate waveguide slot antenna for 5G applications

The current fifth generation (5G) and millimeterwave technologies are urgently needed to provide huge bandwidth, dual-bands, multi bands, high gain and high directivity radiation pattern beams antennas. However, antennas at millimeterwave suffer from radiation loss and component loss with narrow bandwidth, especially when it is implemented using microstrip structures. Different transmission lines such as waveguide and substrate integrated waveguide (SIW) are studied and introduced for realizing antennas at millimeterwave bands. SIW structures are good candidate for implementation of antenna due to its property of low loss transmission line comprises the properties of microstrip and waveguide technology. However, SIW antennas and structures at millimeterwave have unwanted radiation losses comes from the vias holes. In addition, the vias separation distance is depended on waveguide size, which leads to a bigger size for massive antenna array network at proposed 26 GHz and 28 GHz. Hence, this paper proposed a dual band, compact size, and optimal SIW antenna structure to reduce the vias losses and provides higher bandwidth and gain at 26 GHz and 28 GHz. The proposed design is implemented using three slots to achieve the dual band property and increases the return loss. The proposed design is simulated and studied using CST software. The outcomes of dual-band response at 26 GHz and 28 GHz with a return loss more than 10 dB and a fractional bandwidth of 15% are achieved. A good gain around 8 dB is achieved. These results give a promising solution for realizing a full antenna array based on SIW technology at millimeterwave and 5G technology.

Transmission Line Galloping Prediction Based on GA-BP-SVM Combined Method

The paper proposes a method to construct a model for transmission line galloping prediction using machine learning algorithms to address the transmission line galloping problem, which can result in transmission line loss and pose a greater risk to the safety of electricity in society. First, to reduce sensor noise interference, a unilateral sliding time window is used for micro-meteorological data correction, and then gray correlation analysis method and the specific gravity method are used to obtain the influence weights of micro-meteorological elements. The galloping prediction models are constructed using six algorithms, with the GA-BP algorithm model and the SVM algorithm model having better prediction effects based on performance metrics. The GA-BP-SVM combined model is constructed on this basis, and all of its performance metrics are optimal. This model's prediction accuracy in both galloping and no galloping states reaches 95.5%; the probability of correct prediction when predicted as galloping reaches 95.1%; the probability that actual galloping can be predicted reaches 92.5%. The F1-score of the combined model reaches 0.938, which indicates that it has the best prediction effect. The prediction method described in the paper is accurate and practical, and operation and maintenance personnel can flexibly develop inspection strategies and anti-galloping measures based on the prediction results to ensure the safe and stable operation of transmission lines.

Optimal Design and Control of SSSCs for TLs Considering Technical and Economic Indices Using GA and SAMPE-JAYA Algorithms

Improving transmission lines (TL) steady-state stability limit can be achieved by Static Synchronous Series Compensators (SSSC) that work as a converter-based compensator. The modification of the SSSC function to inject both active and reactive power into the TL improves its performance according to different indices. The TL improvement indices include the reduction of TL losses, the enhancement of dynamic stability margin and the improvement of voltage profile along the TL. The economic index is considered a vital factor in the design of SSSC devices installed in TLs. This paper proposes an objective function to design and control SSSC devices by considering various technical and economical indices. The formulation of the objective function requires power flow analysis in the compensated transmission line under different loading conditions and compensation levels. The proposed multi-objective function includes the transmission line losses, the dynamic stability span, and the cost of SSSC device. The Genetic Algorithm (GA) and Self-adaptive Multi-population Elitist Jaya Algorithm (SAMPE-JAYA) are proposed as optimization techniques to obtain the minimized value of the multi-objective function. This paper proposes the installation of two SSSC devices in TL that leads to the improvement of the design and control indexes. The proposed objective function and the optimization techniques are applied for tie-line (400kV, 400km) that connects between two networks.

Fault Detection and Protection Schemes for Distributed Generation Integrated to Distribution Network: Challenges and Suggestions

The use of distributed generation has received wide attention due to low maintenance costs, reduced transmission line losses and network congestion, as well as minimal impact on climate change and global warming. However, the distributed generation integrated to the distribution network introduces various protection problems that cannot be solved by conventional protection systems. These obstacles include the bi-directional power flow and the variation in the fault current level during the topology change. Thus, appropriate fault detection and protection scheme are strongly recommended to increase safety and reliability of the distributed network. This paper presents a comprehensive overview of the distributed network fault detection and protection strategies incorporated with distributed generation. This review also investigates the various fault detection approaches concerning types, communication methods, operation mode, constraints and benefits. Additionally, numerous island detection techniques are explored, focusing on generation types, parameters, cost and advantages. Moreover, the review outlines the various protection schemes highlighting categories, operation, constraints, strength and shortcomings. The key issues and challenges are discussed along with selective proposals for future research. All the highlighted viewpoints of this research will hopefully be beneficial to power system engineers and researchers for the advancement of distributed network fault and protection strategies for suitable operation and management of future distributed generation systems.

Optimal Location of FACTS Devices in Order to Simultaneously Improving Transmission Losses and Stability Margin Using Artificial Bee Colony Algorithm

It is not possible to use the full capacity of the transmission lines due to voltage limitations and stability issues. Therefore, compensators must be used to improve the transmission line capacity. One of the suggested ways for this purpose is to use flexible alternating current transmission system (FACTS) devices in the power system. The various capabilities of the FACTS devices have made it possible to set different targets to determine their optimal location and position. Some of the most important placement targets include increasing voltage stability, improving voltage profiles, reducing losses, increasing line capacity limit and reducing fuel costs for power plants through optimal power distribution. In this paper, optimal locating of FACTS devices to improve power system stability and transmission line losses reduction. Also, artificial bee colony algorithm is proposed for solving optimization problem. The artificial bee colony algorithm with high accuracy and high convergence speed is suitable for conducting FACTS placement studies. Finally, a comparison was made between the artificial bee colony algorithm and genetic algorithm (GA) and particle swarm optimization (PSO) algorithm. The results indicate that the artificial bee colony algorithm works better than the other algorithms in minimizing the fitness function.

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.

Power Quality and Reliability Considerations of Photovoltaic Distributed Generation

Worldwide energy consumption is increasing at a faster pace than energy generation because of enhanced industrialization, growing population and, improved living standards. Using the Distributed Generation (DG) near the end consumers can support the electrical grid stability and enhance the power system quality. The DG is consisting of a small-scale technology to generate electrical power near the end consumers, which can be renewable energy or generators. Solar Photovoltaic (PV) system is considered as one of the best renewable energy sources, due to its low running cost and low environmental affection comparing with traditional power plants. The proposed PVDG units are employing maximum power point tracking (MPPT) system to track the maximum power available in PV arrays. The proposed DG system used Particle Swarm Optimization (PSO) technique to optimally allocate the PVDG units for minimum cost, transmission line losses, and improved voltage profile of busbars under different operating conditions. This paper proposes a smart optimization technique employing PSO in the design, operation, and control of the proposed system. Also, the paper discusses the PSO algorithm for optimal DGs sizing and allocations. The simulation in this paper uses the IEEE-7 busbars system with an extension of IEEE-3 busbars as a new remote load. The simulation has been carried out by the power world simulator software to compare the voltage and the power losses with and without PVDGs connected to the system. The simulation results showed an acceptable reduction in generating cost, transmission line losses, and improvement of the busbars voltage profile which proves the success of the proposed system.

Solution to economic load dispatch using quasi-oppositional based CoDE by considering transmission line losses

The main objective of economic dispatch problem is to optimally allocate the load demand among the generating stations such that there is decrease in production cost of the power generation. In the present paper, a quasi-oppositional based composite differential evolution (QOCoDE) technique is proposed to economic load dispatch (ELD) problem. The suggested method provides optimal solutions by combining various strategies in generating trial vector with suitable control parameter settings. The proposed technique is applied on various generating units by considering fixed boundary conditions and transmission losses. The obtained results are compared with various evolutionary algorithms. The results demonstrate the capability of QOCoDE to provide optimal solutions when compared to other evolutionary techniques.

Distributed fixed step-size algorithm for dynamic economic dispatch with power flow limits

In this study, a discrete-time distributed algorithm is proposed for solving the dynamic economic dispatch problem with active power flow limits and transmission line loss. To avoid the communication burden and implement the algorithm in a favorably distributed manner, the splitting method is used to bypass the centralized updating of the algorithms parameters, which is unavoidable when implementing conventional Lagrangian methods. The use of a fixed step-size and distributed update enhances the applicability of the algorithm. The performance and effectiveness of the proposed distributed algorithm are verified via numerical studies on the IEEE 14-bus system.

Study on Line Loss Status Classification Based on Decision Tree

Line loss rate is the ratio of power loss to power supply in a certain period of time, and it is an important index to assess the technical economy of power grid. In recent years, with the increasing scale of power grid construction and the aging of power lines, the traditional monitoring methods with simple logic judgment can not meet the accurate judgment status of line loss, so it is urgent to effectively improve the lean management level of transmission and transformation line loss abnormality. In order to realize the automatic classification of line loss anomalies machine learning algorithm is proposed, the paper takes 1500 transmission line loss data as training samples, builds a line loss anomaly classification model based on decision tree, and realizes the accurate classification of line loss anomaly causes. Meanwhile, the paper uses KNN, support vector machine and decision tree algorithm to identify the anomaly for comparing the accuracy of different machine learning algorithms, and uses MATLAB software to verify the algorithm. The experimental results show that the comprehensive the recognition accuracy of decision tree for line loss anomaly is the highest, reaching 88.9%, 60% for support vector machine and 82% for KNN. This is of great significance to the realization of lean line loss management and has a high engineering value.

Research on the Causes of the Accurately Locating Line Loss Exceptions by Querying the Power Consumption Information Acquisition System Based on the Big Data Platform

The combination of a power user information collection system with a two-way interactive platform for power business has constructed intelligent power consumption big data for the energy flow, information flow, and business flow of the power grid and customers. Aiming at the problem that the current distribution network transmission line loss anomalies cannot be traced and difficult to locate, based on the data collected by the measurement automation system, through the management of station, line, transformer, and household data, the use of data mining and analysis technology will The factors of line loss are analyzed. Through the intelligent diagnosis model of line loss abnormality, it analyses the abnormal causes that affect line loss in real time, promotes the transformation of low-voltage line loss management from result management to process management, improves the power supply operation efficiency and management level, reduces the operating costs of enterprises, and improves the economic benefits of enterprises.

Impact Analysis of Seismic Events on Integrated Electricity and Natural Gas Systems

Seismic events can cause devastating impacts on both overground and underground energy system infrastructure. This paper proposes a methodology to evaluate the impact of seismic events on the security of integrated electricity and gas system, mainly focusing on pipelines leakage and connection loss of electricity transmission lines. A stochastic model is used to formulate the damage level based on earthquake severity. The seismic impact on the integrated system is classified according to the levels of pipe leak and electricity line failure. Load curtailment due to limited generation capacity and overloaded transmission lines is thereafter quantified. Seismic intensity is generated randomly based on Monte Carlo simulation so that a certain seismic intensity can be related to relevant load curtailment. An integrated energy system with a 30-busbar electricity system and a 6-node natural gas network is used to demonstrate the effectiveness of the proposed method. The results clearly illustrate damage consequences under seismic events in terms of both probability and severity levels. This work can inform resilience enhancement scheme design based on the vulnerability performance and impact of both systems.

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.