Transformer Tap Research Articles

Single-phase grounding fault line selection method based on zero-sequence current increment

To address single-phase grounding fault line selection problem in small current grounding distribution network, the paper proposes a fault line selection method based on zero-sequence current (ZSC) increment. The method downshift grounding transformer (GT) tap step-by-step to amplify fault current, defines the line with largest ZSC variation as fault line. The results of typical non-effectively grounding 10 kV distribution network show the rationality and applicability of the proposed method.

Technique for modeling random disturbances of the electric arcs lengths for tuning a nonlinear P-controller of impedance

There is a common disadvantage for most modern electric mode control systems of electric arc furnaces and ladle-furnaces, such as HI-REG, Q-REG, ARCOS, Simelt, Melt Expert, Ferrotron, Meltnet, Arcelec, EMPERE, Digitarc. This disadvantage is associated with the absence of technical solutions aimed at complete linearization of the electric and hydraulic circuit of the furnace as a control object. As a result, the system cannot be tuned to the technical optimum over the entire range of working arc lengths. One of the ways to solve this problem is the use of a nonlinear adaptive P-impedance controller, which provides complete linearization of the electrical and hydraulic circuits using special linearizing characteristics. In order to achieve the technical effect, expressed in reducing the dispersion of electric arc currents on different melting stages, it is necessary to select the optimal value of the gain of the adaptive controller. In practice, this problem may be solved iteratively using a mathematical model that makes it possible to form random disturbances along the length of an electric arc, corresponding in their statistical characteristics to real disturbances. Obviously, the signal of the the electric arc current effective value is not stationary and ergodic throughout the entire melt due to constantly changing conditions (for example, furnace transformer tap, reactor tap, the setting of the control parameter, the stage of melting, etc.). As a result, the task of identifying stationary and ergodic sections as part of the electric arc current signal is of particular relevance. Within the framework of the research carried out, a method has been developed for modeling random disturbances of the length for optimal tuning of the impedance non-linear controller.The research was conducted under financial support of the RF Ministry of Science and High Education (the project No. FZRU-2020-0011).

Harmonics mitigated multi-objective energy optimization in PV integrated rural distribution network using modified TLBO algorithm

This paper presents an optimal operation of PV integrated distribution network to address the multiple issues such as harmonics, voltage variation, poor power factor, and power losses. A real time rural distribution network is modeled on ETAP (industrial software) to obtain un-optimized parameters and component settings, and, then the DHPF is embedded in TLBO algorithm to obtain the optimal parameters and component settings considering various constraints such as node voltage, conductor thermal loading (ampacity), transformer taps and inverter harmonic generation limits. The optimal parameters so obtained are validated again on real time distribution network and assessed in comparative reference context with the un-optimized base case. The results show that TLBO gives the best operation of the distribution network, particularly flat voltage profile, improved power factor, reduced power losses and mitigated harmonics.

Reactive Power Planning and Total Transfer Capability Enhancement using Facts and Capacitor Banks. (Dept. E)

In view of the continuous annual increase in demand, reactive power planning (RPP) is considered one of the most significant problems to address a major challenge of the secure power system operation. In this paper, a multi-objective genetic algorithm (MOGA) for RPP is proposed, with the goals of cost minimization of power losses, new reactive power (VAR) sources, and maximizing the Total Transfer Capacity (TTC). Different optimization factors are taken into account, including generator voltages, transformer tap changers, and various operating constraints. A fuzzy min-max approach is used to identify the optimum compromise option. Studies are being conducted to compare capacitor banks, flexible ac transmission systems (FACTS), or both as a new VAR support source to improve the system performance. Moreover, the optimal allocations of switchable VAR sources are not determined in advance; instead, they are treated as control variables to improve the techno-economic operation of the network. The effectiveness of the proposed algorithm is examined on the IEEE 30-bus test system where felicitous results have been acquired. From the results, the total annual cost is decreased from 3.671×106 $ before adding new VAR sources to a range between 2.02×106 and 2.486×106 $ depending on the selected type of VAR source. While the transfer capacity is increased from 458.37MW to a range between 483.084 and 539.055 MW.

New modified algorithm: θ-turbulent flow of water-based optimization.

The reactive power control of a power system is discussed under two types of variables: continuous variables (e.g., generator bus voltages) and discrete variables (e.g., transformer taps and the size of switched shunt capacitors). This paper proposes a novel and powerful algorithm, named turbulent flow of water-based optimization (TFWO) as well as a new improved version of this algorithm, called θ-TFWO, for optimal reactive power distribution (ORPD) to reduce losses. The proposed method is applied to two large-scale IEEE 57-bus systems. Furthermore, to demonstrate the competitive performance of the suggested algorithm, its performance was compared to that of several other algorithms, including biogeography-based optimization (BBO), social spider algorithm (SSA), and optics inspired optimization (OIO), in terms of solving the ORPD problem. The results confirmed the robustness and effectiveness of the proposed method as a powerful optimizer applicable to optimal reactive power distribution in power systems.

A Case History of Shore Power Transformer Taps Arcing During a No-Load Condition: Understanding the Cause of Arcing

Shore Power Transformers are used to connect berthing ships at ports to use shore power from the port facilities. It has been established that using shore power instead of power from on-board generators can minimize air pollution as ships use low-grade fuel for power generation. The process of switching off the onboard generators of berthing ships and connecting to shore power is called shore-to-ship power supplies (STSPSs). To comply with mandatory air-pollution requirements, in 2014, the Port of Oakland installed STSPS projects at various berths. In 2015 at Berth 37, a 7.5-megavolt-A (MVA), cast-coil transformer within an outdoor enclosure experienced an arcing of the primary no-load tap connections at 12.47 kV. The installation and testing of Berth 37 complied with the International Electrical Testing Association. It is a normal practice to leave the transformer energized when the STSPS operation is finished. The taps arcing occurred when the transformer was energized without any load. There was no power system transient event when taps arcing occurred.

Particle swarm optimisation for reactive power compensation on Oman 6 bus electrical grid

<span lang="EN-US">The consistent problem with operators and planners for power systems has been related to minimizing transmission losses. An important role is played by reactive power by keeping voltage stability and reliability in the system in order to support the transfer of real power. The optimal reactive power dispatch is associated with the problem of non-linear optimization along with many constraints. In this paper, a study is highlighted for an algorithm that optimizes reactive power with the help of particle swarm algorithm and compare the result with Newton-Raphson method. Reduction of system active power loss is the goal of the function in the projected algorithm. Here, the control variables identified are transformer tap positions, generator bus voltages, and shunt capacitor banks with switch. This projected algorithm is performed on Oman 6 bus electrical grid as oman electricity transmission company has an instability voltage issue in chosen 6 bus.</span>

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.

Voltage Constrained Reactive Power Planning Problem for Reactive Loading Variation Using Hybrid Harris Hawk Particle Swarm Optimizer

This study proposes the application of an efficient and hybrid meta-heuristic algorithm of Harris Hawk-Particle Swarm Optimizer (HHOPSO) for solving voltage constrained reactive power planning (VCRPP) problem. The prime objective of present work is diminishing the transmission loss and overall operating cost coupled with retention of voltage consistency and enhanced congestion management in transmission lines. The efficacy of the proposed algorithm is tested on standard IEEE 57 bus test system with optimal transformer tap settings, reactive generation control, generator voltage control and optimal control of Var sources. The best location of Var sources is determined by Voltage Collapse Proximity Indicator (VCPI) method. Simulation results are generated under normal and variable reactive power loading condition which makes the reactive power planning problem more challenging and validates the robustness of the proposed algorithm. The results of proposed algorithm when compared with the other optimization algorithms surfaced in contemporary state of art literature yields superior solution in maintaining diversity and solution optimality.

Use of a phase-shifting transformer for increasing the power transmission capacity, taking into account the mode of the adjacent network

In this research, we develop measures aimed at improving the efficiency of power systems by increasing their transmission capacity. To this end, a FACTS system based on the phase-shifting transformer with a thyristor switch developed at the Power Engineering Institute named after G.M. Krzhizhanovsky was used. The efficiency of the phaseshifting transformer under study for increasing the transmission capacity of power systems was determined by the maximum permissible cross-section flows of the Barnaul-Biysk node-2. The calculations were performed for normal and various post-accident schemes using the RastrWin3 software package. Such factors as the regulation of the taps of the phase-shifting transformer and various places of its installation were considered. For the section under consideration, the phase-shifting transformer increased the maximum permissible flow by 4–12%. The determining factor limiting the maximum permissible flow in the Barnaul-Biysk node-2 was found to be the current overload of the 110 kV lines of the adjacent network. The greatest effect of increasing the maximum permissible overflow was noted when the phase-shifting transformer was installed on the 220 kV line adjacent to the section, parallel to the 110 kV lines (which were overloaded when the mode became heavier), rather than on the 220 kV line included in the section. Similar calculations were performed for normal and post-accident schemes of an alternative option, which involved replacing wires and installing automatic equipment for limiting equipment overload on overloaded 110 kV lines. The obtained results show that the effect of increasing the transmission capacity for this option comprised 4%.

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.

Chaotic theory incorporated with PSO algorithm for solving optimal reactive power dispatch problem of power system

In this paper, the chaotic particle swarm optimization (CPSO) algorithm is combined with MATPOWER toolbox and used as an optimization tool for attaining solving the optimal reactive power dispatch (RPD) problem, by finding the optimal adjustment of reactive power control variables like a voltage of generator buses (VG), capacitor banks (QC) and transformer taps (Tap) while satisfying some of equality and inequality constraints at the same time. CPSO and Simple PSO algorithms will be checked in a large system such as IEEE node -118. CPSO and Simple PSO algorithms have been implemented and simulated in the MATLAB program, version (R2013b/m-file). Then compassion these results with the results obtained in the other algorithms in the literature like the comprehensive learning particle swarm optimization (CLPSO) algorithm. The simulation results confirm that the CPSO algorithm has high efficiency and ability in terms of decrease real power losses (P loss), and improve voltage profile compared with the obtained by using the simple (PSO) algorithm and (CLPSO) at light load.

Data Processing with Uncertain Transformer Taps.(Dept.E)

The ultimate function of the data processor for power system control is to obtain reliable data of the system's state and parameters. Existing techniques of data processing assumed that the transformer taps in the system are known. However, there are many uncertainties associated with an actual system. Among these uncertainties could be the actual positions of transformer taps. Therefore, modified algorithms with various methods and cases, using the weighted least squares approach and assuming no knowledge of the transformer tap positions, are presented and implemented in this paper Digital simulations have been performed and the results are presented.

Artificial neural network integrated with bio-inspired approach for optimal VAr management and voltage profile enhancement in grid system

ABSTRACT This article presents the design of an improved smart grid model with an improved optimal VAr strategy using a bio-inspired approach. The integration of an artificial neural network technique with bio-inspired algorithms is the novelty of this research work. The objective of the work is attained by integrating the artificial neural network with three different evolving techniques like the Big Bang-Big Crunch algorithm, the gravitational search algorithm, and bacterial foraging algorithm. The three different layers of artificial neural network reconcile with the power grid for enhancing the voltage profile generated by bio-inspired techniques. The effectiveness and reliability of the algorithms are examined on the standard IEEE 30 bus system. The sample test bus system comprises six Generators, four Transformer tap changers, and 2 Static Shunt VAr Compensator. Artificial intelligence technology is applied for enhancing the voltage profile by managing reactive power across the power system network. The results produced by artificial intelligence technique are more competent and outstanding than any other conservative optimization techniques. It is found that the real power loss across the network has been reduced from 5.744 MW to 4.234 MW, while the bus voltage deviation has been minimized from 1.475 p.u. to 0.0918 p.u. and the voltage profile has been improved from 0.968 to 1.0985 p.u. after integration of the artificial neural network. The optimal values of objective functions are attained by evaluating the training and testing performance of artificial neural network criteria such as coefficient of determination, mean absolute error, root mean square error, and mean absolute percentage error. The obtained results show the remarkable improvement in voltage profile after integrating artificial intelligence technology with a power grid system.

DDPG-Based Multi-Agent Framework for SVC Tuning in Urban Power Grid With Renewable Energy Resources

The uncertain nature of renewable energy resources (RERs) and fast demand response lead to recurring voltage violations in the power systems, which causes frequent transformer tap shifting and capacitor switching. Therefore, this paper resorts to the static var compensators (SVCs) to manage the bus voltages based on the multi-agent deep reinforcement learning (MA-DRL) algorithm. The proposed scheme includes several system agents and SVC agents to collaboratively adjust the injected reactive power to restrict the bus voltages within the normal range. All the agents are trained centrally and executed separately, which requires minimum communication cost. The IEEE 14-bus system, IEEE 300-bus system, and China 157-node urban power grid are used to verify the effectiveness of the proposed method.

Adaptive Congestion Control for Electric Vehicle Charging in the Smart Grid

This article proposes an adaptive control algorithm for plug-in electric vehicle charging without straining the power system. This control algorithm is decentralized and merely relies on congestion signals generated by sensors deployed across the network, e.g., distribution-level phasor measurement units. To dynamically adjust the parameter of this congestion control algorithm, we cast the problem as multi-agent reinforcement learning where each charging point is an independent agent which learns this parameter using an off-policy actor-critic deep reinforcement learning algorithm. Simulation results on a test distribution network with 33 primary distribution nodes, 1760 low voltage end nodes, and 500 electric vehicles corroborate that the proposed algorithm tracks the available capacity of the network in real-time, prevents transformer overloading and voltage limit violation problems for an extended period of time, and outperforms other decentralized feedback control algorithms proposed in the literature. These results also verify that our control method can adapt to changes in the distribution network such as transformer tap changes and feeder reconfiguration.

Application of Frequency Response Analysis Technique to Detect Transformer Tap Changer Faults

Power transformers are located in the electrical transmission and distribution networks where different voltage levels are needed. The turn ratio of the low voltage and high voltage windings is mechanically controlled by an on-load tap changer or de-energized tap changer. As the tap changer is the transformer’s only moving part, it is highly susceptible to mechanical failure and aging degradation. While some diagnostic tools have been used to determine the mechanical condition of tap changer contacts, not much attention was given to use the frequency response analysis to diagnose the transformer’s tap changers’ mechanical integrity. This paper is taking one step forward into maturing the application of the frequency response analysis (FRA) technique to detect transformer tap changer faults. In this regard, two common tap changer faults are created, and experimental testing for four FRA test configurations is conducted. For a better understanding of the tap changer fault mechanism, an electrical equivalent circuit model is proposed and designed using Simulink. The simulation and implementation of the equivalent circuits using MATLAB\R2018a.

Implementation and Optimal Sizing of TCSC for the Solution of Reactive Power Planning Problem Using Quasi-Oppositional Salp Swarm Algorithm

In this article, innovative algorithms named as salp swarm algorithm (SSA) and hybrid quasi-oppositional SSA (QOSSA) techniques have been proposed for finding the optimal coordination for the solution of reactive power planning (RPP). Quasi-oppositional based learning is a promising technique for improving convergence and is implemented with SSA as a new hybrid method for RPP. The proposed techniques are successfully implemented on standard test systems for deprecation of real power losses and overall cost of operation along with retention of bus voltages under acceptable limits. Optimal planning has been achieved by minimizing reactive power generation and transformer tap settings with optimal placement and sizing of TCSC. Identification of weakest branch in the power network is done for optimal TCSC placement and is tendered through line stability index method. Optimal TCSC placement renders a reduction in transmission loss by 8.56% using SSA and 8.82% by QOSSA in IEEE 14 bus system and 7.57% using SSA and 9.64% by QOSSA in IEEE 57 bus system with respect to base condition.

Optimal Corrective Rescheduling of Real and Reactive Powers for Power System Security Enhancement.(Dept.E)

This paper presents an optimal technique for solving the corrective rescheduling problem in p ower system. A successive dual linear programming approach is used with linearized p ower flow model. The system real and reactive power generations and transformer tap ratios are optimized within their operating limits so that no limit voilations of the line flow and bus voltage magnitudes in either the base case and contengency cases. The rest results on IEEE-30 bus test system show that the proposed technique can be successfully employed in conjunction with any fast security assessment scheme for a fast on-line security assessment and control.

Temperature dependent optimal power flow using chaotic whale optimization algorithm

AbstractThis work presents a novel, nature inspired evolutionary based approach, the chaotic whale optimization algorithm, to solve a temperature dependent optimal power flow problem of a power system. Whale optimization is inspired by the bubble‐net hunting strategy of the humpback whales; logistic chaotic maps are used to improve its performance. Whale optimization and our proposal are evaluated on three test systems namely, the IEEE 30‐bus test power system, the 2383‐bus Winter Peak Polish system and the 2736‐bus Summer Peak Polish system to give a solution to the temperature dependant optimal power flow of the power systems where control of generator bus voltages, transformer tap ratios and reactive power sources are involved. Minimization of total fuel cost is considered here as the objective function for this problem. The superiority and the effectiveness of the proposed algorithm technique have been exhibited in comparison to the other evolutionary optimization techniques identified in the recent literature.