Transformer Tap Positions Research Articles

Equilibrium Optimizer Algorithm for Optimal Reactive Power Dispatch

Optimal Reactive Power Dispatch (ORPD) is a significant research area in terms of maintaining the reliability and safety of the power system and operating it more economically. ORPD problem can be formed from a variety of perspectives including the minimization of the active power losses and voltage deviation, and improving the voltage stability performance. The majority of methods so as to deal with ORPD problem is meta-heuristic techniques because of the complex, non-linear and non-convex nature of the problem. In this paper, a new physic-based meta-heuristic algorithm, Equilibrium Optimizer (EO), is proposed for ORPD problem to reach the optimal settings of control variables such as voltage magnitudes in PV buses, tap positions of transformers and reactive power support of shunt devices. The introduced algorithm is evaluated on IEEE 30-bus test system by using various objectives, and a comparison of the implemented method to other optimization techniques described in the literature is utilized to assess its efficacy. Simulation results and statistical indicators demonstrate that the EO algorithm validates its computational efficacy and robustness in handling the ORPD problem.

Automatic definition of topological corrective actions for post-contingency OPEX minimization

This paper presents a practical algorithm for the automatic definition of topological corrective actions (TCAs) to minimize the additional operating expenses required to mitigate the adverse effects of contingencies in the network. Contingency and sensitivity analyses are used to identify possible TCAs that comprise the disconnection of single branches, pre-defined switching actions on one or more circuit breakers (CB), and changing the tap position of phase-shifter transformers. A novel method is proposed for the identification of possible CB closing TCAs. For the TCAs that do not lead to uncontrolled cascade tripping, unit commitment (UC) calculations are eventually run to obtain the economically optimal amount of conventional generation redispatch, renewable energy curtailment and load shedding required to eliminate the residual overloadings after each possible TCA is applied. The proposed approach does not reformulate the UC optimal power flow problem but rather uses standard calculation functions available in industrial-grade software. The effectiveness of the proposed approach is shown for a reduced-order transmission system prepared to capture many post-contingency conditions found in practice.

A Novel Co-phase Traction Power Supply System in Electrified Railway

The electrified railway has power quality problems such as three phases seriously unbalanced, large harmonics, and reactive power. In addition, the neutral section exists at each power supply section, which restricts the development of the high-speed and heavy-haul railway. This paper proposes a novel co-phase traction power supply system (TPSS) based on a YNvd balanced transformer, which can not only solve the above problems but also save one single-phase coupling step-down transformer, thus reducing the cost and space of the whole system. In addition, the voltage level of the converter switching device can be reduced by the rational design of the transformer tap position, which enhances the reliability and feasibility of the system. The compensation principle and control strategy of the co-phase TPSS based on the novel YNvd balanced transformer are analyzed in detail in this paper, and the compensation current detection and integrated power flow controller (IPFC) control methods are given. Simulation and analysis show that the proposed topology structure and control method are correct and feasible.

A Multiobjective Artificial-Hummingbird-Algorithm-Based Framework for Optimal Reactive Power Dispatch Considering Renewable Energy Sources

This paper proposes a new artificial hummingbird algorithm (AHA)-based framework to investigate the optimal reactive power dispatch (ORPD) problem which is a critical problem in the capacity of power systems. This paper aims to improve the performance of power systems by minimizing two distinct objective functions namely active power loss in the transmission network and total voltage deviation at the load buses subjected to various constraints within multiobjective framework. The proposed AHA-based framework maps the inherent flight and foraging capabilities exhibited by hummingbirds in nature to determine the best settings for the control variables (i.e., voltages at generation buses, the tap positions of on-load tap-changing transformers (OLTCs) and the size of switchable shunt VAR compensators) to minimize the overall objective functions. A multiobjective optimal reactive power dispatch framework (MO-ORPD) considering renewable energy sources (RES) and load uncertainties is also proposed to minimize the individual objectives simultaneously. The competency and robustness of the proposed AHA-based framework is validated and tested on IEEE 14 bus and IEEE 39 bus test systems to solve the ORPD problem. Eventually, the results are compared with other well-known optimization techniques in the literature. Box plots and statistical tests using SPSS are performed and validated to justify the effectiveness of the proposed framework.

Solving the Optimal Reactive Power Dispatch Problem through a Python-DIgSILENT Interface

The Optimal Reactive Power Dispatch (ORPD) problem consists of finding the optimal settings of reactive power resources within a network, usually with the aim of minimizing active power losses. The ORPD is a nonlinear and nonconvex optimization problem that involves both discrete and continuous variables; the former include transformer tap positions and settings of reactor banks, while the latter include voltage magnitude settings in generation buses. In this paper, the ORPD problem is modeled as a mixed integer nonlinear programming problem and solved through two different metaheuristic techniques, namely the Mean Variance Mapping Optimization and the genetic algorithm. As a novelty, the solution of the ORPD problem is implemented through a Python-DIgSILENT interface that combines the strengths of both software. Several tests were performed on the IEEE 6-, 14-, and 39-bus test systems evidencing the applicability of the proposed approach. The results were contrasted with those previously reported in the specialized literature, matching, and in some cases improving, the reported solutions with lower computational times.

Fractional memetic computing paradigm for reactive power management involving wind-load chaos and uncertainties

Optimal reactive power dispatch (ORPD) with integration of renewable energy resources has a growing interest in research community due to its utmost requirements during the operation, planning and design of the modern electrical power networks. The objective of ORPD is to improve the performance of power network by means of reducing the losses in transmission line, improving the voltage profile, and decreasing the overall cost of operation through optimal tuning of the operational variables such as tap position of transformers, generator output voltages and capacitor banks. However, the nonlinear, non-stationary and complex nature of power network, presence of load uncertainties, and dynamic behavior of wind generation introduces a complex optimization task which cannot be readily solved in an efficient manner. In this research work, a new fractional memetic computing paradigm, i.e., the fractional particle swarm optimization gravitational search algorithm with entropy evolution (FPSOGSA-EE), is designed to solve the ORPD problems in power system adopting wind power plants (WPPs) and load uncertainties. The proposed optimization framework FPSOGSA-EE integrates the concept of fractional calculus and Shannon entropy to strengthen the optimization characteristics of canonical algorithm. The exhaustive experimentation endorse the efficacy of FPSOGSA-EE by providing minimum gauge of fitness evaluation function, namely, the line loss and voltage deviation index minimization, in IEEE 30 and 57 bus networks. The stability, consistency and reliability of proposed FPSOGSA-EE is ascertained through statistical interpretations by means of boxplots, probability measures for cumulative distribution function, and histogram illustrations.

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.

Modified distance protection for transmission line with hexagonal phase-shifting transformer

Phase-shifting transformers (PSTs) provide an economical solution to control the power flow in the electric grids. However, the PSTs vitiate the conventional protection system applied. This paper studies the impact of the delta-hexagonal phase-shifting transformer on the performance of the distance protection relays for the transmission lines. Modified distance protection formulas are derived for both phase and ground faults. The proposed method uses the local measurements at the relay point and does not require voltage or current measurements from a remote substation. Alternatively, the tap position of the phase-shifting transformer is the data needs to be shared between substations which can be accomplished using a simple communication method. The proposed method is evaluated on a simulated transmission line using MATLAB/Simulink packages. The results show the ability of the proposed method to compensate for the PST effect on the distance protection relays.

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 two-level over-voltage control strategy in distribution networks with high PV penetration

Over-voltage in low voltage feeders due to the increasing penetration level of photovoltaic (PV) is a crucial issue to be addressed. There is a need to use more advanced voltage control methods to satisfy the response time of the control system. In this paper, a two-level voltage control strategy is presented. In the first level, based on day-ahead PV production scenario, Both the on-load tap changer (OLTC) and the battery energy storage systems (ESSs) are applied to deal with over-voltage in the peak of PV generation and as well the voltage drop in the peak of demand. In this level, the batteries and the tap position of the feeder transformer are optimally set in order to improve the voltage profile for the entire planning horizon (next day) taking into account the uncertainties in the PV production. In the second level, based on the partitioning of the distribution network, reactive power compensation capability of PV inverters is employed to fine-tune the voltage profile for the next operating hour. To model the uncertainty pertaining to the output power of PV units, the parameters of beta distribution function are estimated for each hour time interval, and then Monte Carlo simulation method is used to generate daily scenarios. In order to reduce the complexities and computational burden, the linearized model of power flow equations and PV inverters have been implemented. A real and practical, 10 kV, 37-bus system is used to test the performance of the proposed method.

Analysis of Backup Protection Action of a Transformer Caused by Improper Implementation of Voltage Regulation Measures

It is an effective measure for power enterprises to improve the voltage level of the system by raising and lowering the tap position of the main transformer. For the parallel operation of the main transformer, the whole process of adjusting the tap must strictly meet the conditions of parallel operation. Aiming at a case of backup protection action of main transformer caused by improper implementation of voltage regulation measures, this paper analyzes the uneven load distribution and increase of reactive power loss caused by improper adjustment of tap, and the analysis results are verified by PSCAD.

Online Non-Iterative Estimation of Transmission Line and Transformer Parameters by SCADA Data

Utilization of abundant measurements provided by supervisory control and data acquisition (SCADA) system has attracted increasing attention. Real-time estimation of transmission line parameters, utilizing voltage and power flow measurements provided by remote terminal units (RTUs) located at two substations across the line, has been investigated, recently. This paper improves this approach by introducing a novel exact linear reformulation of the problem, which can be solved in closed form. The distributed-parameter model of long transmission lines is considered and its parameters are estimated in a non-iterative manner using RTU measurements. The method is also extended to estimate transformer series impedance and tap position by SCADA measurements, linearly. As such, the disadvantages associated with the previous iterative approach, e.g. concern over convergence, for transmission line parameters are avoided. Moreover, the novel technique for estimating transformer parameters allows to determine the tap position as well as updated transformer series impedance. Furthermore, a thorough analysis is presented to take the measurement accuracy into account. Simulation results for different transmission lines and transformers in the IEEE 118-bus test system are reported, where the result indicate successful performance of the proposed algorithms.

Controlling of Power Transformer Tap Positions (OLTC) Using Facts Devices

Controlling of Power Transformer Tap Positions (OLTC) Using Facts Devices

A novel hybrid optimization approach for reactive power dispatch problem considering voltage stability index

This paper proposes a novel, reliable, and effective hybrid approach based on the integration of the firefly algorithm (FA) and the adaptive particularly tunable fuzzy particle swarm optimization (APT-FPSO) method to address reactive power dispatch (RPD) problem, a crucial optimization problem in the operation of power systems. Similar to many other original meta-heuristic optimization techniques, the standard FA suffers from some severe drawbacks, most importantly being easily trapped into a locally optimal solution. In order to tackle these difficulties, in the current study, an improved version of fuzzy-based particle swarm optimization is utilized in the internal structure of the original FA. The developed hybrid approach, which is capable of avoiding premature convergence of the original FA by enhancing exploration and exploitation procedures, is employed to determine the optimum control variables (i.e., the voltage of generation buses, tap positions of tap-changer transformers, and reactive power output of shunt compensators) through optimizing three distinct objective functions consisting of total transmission real power loss, the voltage magnitude deviations as well as voltage stability index. To validate the accuracy and competency of the proposed hybrid approach, it is firstly used for solving several benchmark optimization functions and then applied to three test systems at different scales, consisting of IEEE 30-bus, IEEE 57-bus, and IEEE 118-bus power systems, for solving the RPD problem. Eventually, the results of the presented hybrid method will be compared to those obtained by other implemented swarm intelligence-based approaches. The statistical analysis of this research substantiates the robustness and effectiveness of the developed algorithm to handle sophisticated optimization problems, particularly the RPD problem.

A hybrid bacterial foraging-particle swarm optimization technique for solving optimal reactive power dispatch problem

<div data-canvas-width="397.27351844386203">This paper presents a hybrid evolutionary computation algorithm termed as hybrid bacterial foraging-particle swarm optimization (HBFPSO) algorithm, to optimal reactive power dispatch (ORPD) problem. HBFPSO algorithm merges velocity and position updating strategy of particle swarm optimization (PSO) algorithm and reproduction and elimination dispersal of bacterial foraging algorithm (BFA). The ORPD is solved for minimization of two objective functions; system real power loss and voltage stability L-index. The objective is minimized by optimally choosing the control variables; generator excitations, tap positions of on-load tap changing transformers and switched var compensators while satisfying their constraints and also the constraints of dependent variabl</div><div data-canvas-width="98.30049385204596">es; voltages of all load buses and reactive power generation of all generators. The proposed approach has been evaluated on a standard IEEE 30 bus test system and 24 bus EHV southern region equivalent Indian power system. The results offered by the proposed algorithm are compared with those offered by other evolutionary computation algorithms reported in the recent state of the art literature and the superiority of the proposed algorithm is demonstrated.</div>

Optimal Volt/VAR control in distribution systems with prosumer DERs

This paper proposes an optimal Volt/VAR control method for distribution systems with proliferated prosumer distributed energy resources (P-DERs) with generation and consumption capabilities. The proposed method is developed based on a comprehensive mutual agreement between distribution system operator (DSO) and P-DERs to optimize the power loss and the cost of adjusting grid's voltage control assets by solving a multi-objective Volt/VAR control (MO-VVC) problem. In this paper, the proposed MO-VVC reduces the power loss by adjusting capacitor banks switches, tap position of on-load tap changer transformers, voltage regulators’ taps, and active and reactive power set-points of P-DERs. Three new sensitivity-based evolutionary algorithms are proposed to find the optimal solutions more accurately with reduced computational time. In addition, a payment system is proposed to calculate the financial benefits of participating parties. The performance of the proposed method is validated using the simulation results of the modified IEEE 34-bus test feeder with several P-DERs.

Hybrid DE/FFA algorithm applied for different optimal reactive power dispatch problems

ABSTRACT Security and economy of a power system are optimised by controlling of reactive power dispatch from synchronous generators and var sources like SVCs installed in the system. Generator bus voltages, transformer tap positions and SVC settings are the control parameters in optimal reactive power dispatch (ORPD) problem. Generally, population-based optimisation algorithms are used for optimising the values of control variables. In this work, a hybrid version of bio inspired algorithm of firefly optimisation algorithm (FFA) is suggested for reactive power optimisation. This proposed DE/FFA-based algorithm mimics the mating behaviour of fireflies. The exploration capability of basic FFA is poor and is enhanced by hybridising with the differential evolution algorithm (DE). The strength of this algorithm is tested by comparing its performance with that of the other bio inspired algorithms like Biogeography Based Optimisation (BBO). The new method is tested on the IEEE-14 bus, IEEE-30 bus and IEEE-57 bus test systems. The results obtained are much encouraging.

MULTI-OBJECTIVE OPTIMAL REACTIVE POWER DISPATCH USING DIFFERENTIAL EVOLUTION

Reactive power optimization is a major concern in the operation and control of power systems. In this paper a new multi-objective differential evolution method is employed to optimize the reactive power dispatch problem. It is the mixed–integer non linear optimization problem with continuous and discrete control variables such as generator terminal voltages, tap position of transformers and reactive power sources. The optimal VAR dispatch problem is developed as a nonlinear constrained multi objective optimization problem where the real power loss and fuel cost are to be minimized at the same time. A conventional weighted sum method is inflicted to provide the decision maker with a example and accomplishable Pareto-optimal set. This method underlines non-dominated solutions and at the same time asserts diversity in the non-dominated solutions. Thus this technique treats the problem as a true multi-objective optimization problem. The performance of the suggested differential evolution approach has been tested on the standard test system IEEE 30-bus.

Optimal reactive power dispatch for real power loss minimization and voltage stability enhancement using Artificial Bee Colony Algorithm

Reactive Power Dispatch (RPD) plays important role in power system reliability and security. This paper proposes the Optimal Reactive Power Dispatch (ORPD) for real power loss minimization, voltage deviation minimization and voltage stability enhancement using Artificial Bee Colony (ABC) Algorithm. ORPD is a mixed integer nonlinear optimization problem which includes both continuous and discrete control variables. The ABC algorithm is used to find the setting of control variables such as generator voltage magnitude, tap position of tap changing transformer and reactive power output of the compensation devices. The proposed algorithm is tested on IEEE 30 and 57 bus systems, Simulation results show that the proposed approach converges to better solutions and much faster than the earlier reported approaches in the literature. The optimization strategy is general and can be used to solve other power system optimization problems.