Transformer Tap Positions Research Articles

A Machine Learning Approach for Coordinated Voltage and Reactive Power Control

Increasing penetration of renewable energy sources in form of distributed generators has brought many technical challenges to distribution networks. Among those, voltage and reactive power control should be revised and improved. Existing and new control resources should be coordinated based on real-time information and in closed loop. To achieve this, machine learning (ML) can be used to map the relationship between the selected network information and the desired control output. In this paper, setting of the shunt compensator operating in capacitive or inductive modes is coordinated with the tap position of substation transformer by the developed ML. Dataset emulating network behaviour during a year operation is constructed for training ML. A multi-class classification problem is formulated. Simulation results show satisfactory accuracy for some classes.

Security and optimization rule-based coordinated generator simultaneous tap changing dispatch scheme for transmission voltage regulation

To regulate transmission network voltage within statutory limits, transmission system operators (TSO) can issue instructions to the grid synchronized generators to simultaneously change their transformer tap positions, and these instructions are normally preceded by advance notice and executed at the required time. This operation action is known as “Simultaneous Tap Change Dispatch” (STCD).This paper presents a security and optimization rule-based generator Simultaneous Tap Change Dispatch (STCD) scheme, which aims to maintain system wide-area voltage stability through effective and fast voltage regulation, and to minimize the total amount of reactive power utilization; thus, indirectly reduces the line losses. Technical implementation of the proposed STCD is discussed, and the costs of MVar utilization by the STCD is compared with the employment of new shunt reactors. The proposed STCD scheme is validated in a DIgSILENT Power Factory platform to simulate a real regional system model in Great Britain (GB). And, the effectiveness of STCD scheme in transmission voltage regulation, generator MVar optimization, and operational security are verified by comparing to the baseline (a system with uncoordinated schemes).

Moth Search Optimization for Optimal DERs Integration in Conjunction to OLTC Tap Operations in Distribution Systems

In this paper, a newly developed moth search optimization (MSO) technique is introduced to solve the complex distributed energy resources (DER) integration problems of distribution systems. In order to overcome some of the limitations observed in the standard variant of MSO, minor corrections are also suggested. On the other hand, a new optimization problem is formulated for optimal deployment of dispatchable distributed generations and shunt capacitors while simultaneously optimizing the tap positions of on-load tap-changing transformers, already deployed in grid substations. The objective of this work is to minimize the cost of annual energy loss and node voltage deviations over multiple load levels. The proposed model is implemented and solved for two benchmark test distribution networks of 33 and 118 buses. The suggested corrections are also validated by comparing the performance of the proposed approach with standard MSO and other available optimization methods. The simulation results show that the developed model optimally utilizes the existing distribution system resources and generates higher deployment benefits at lesser DER penetration as compared to the planning model which ignores these resources.

The Adaptive Chaotic Symbiotic Organisms Search Algorithm Proposal for Optimal Reactive Power Dispatch Problem in Power Systems

DOI : 10.26650/electrica.2019.18008 This paper presents an adaptive chaotic symbiotic organisms search algorithm (A-CSOS) for finding the solution of optimal reactive power dispatch (ORPD) problem which is one of the main issues of power system planning and operations. The most important advantage of symbiotic organisms search algorithm (SOS) is that is not need any particular algorithm parameters. However, the SOS algorithm has some features to be enhanced, like falling into local minima and sluggish convergence. A-CSOS algorithm with adding new and improved features like adaptivity and chaos to conventional SOS algorithm is proposed to solve ORPD problem. The ORPD problem is mainly focused on minimization of transmission loss (Ploss) and total voltage deviation (TVD). To determine the optimal set points of control variables including generator bus voltages, tap positions of transformers, and reactive power outputs of shunt VAR compensators is very crucial for minimization to Ploss and TVD. The proposed algorithm is implemented on IEEE 30-bus test power systems for ascertaining the performance of A-CSOS algorithm on ORPD problem. The results showed that the proposed approach is up to 10.39% better than many of which the latest algorithms in literature and encourage the researchers to implement A-CSOS algorithm to ORPD problem. Cite this article as: Yalcin E, Taplamacioglu MC, Cam E. “The Adaptive Chaotic Symbiotic Organisms Search Algorithm Proposal for Optimal Reactive Power Dispatch Problem in Power Systems”, Electrica, 2019; 19(1): 37-47.

A modified bacterial foraging algorithm based optimal reactive power dispatch

<p>This article describes an approach for optimal reactive power dispatch problem using a Modified Bacterial Foraging Algorithm. Modified bacterial foraging algorithm introduces a differential evolution operator in chemotaxis to overcome tumble failure in tumble step and accelerates the convergence speed of the original operator. In the new algorithm chaotic dynamics are used to generate initial population to have uniform distribution. The proposed new algorithm is applied to Optimal reactive power dispatch problem with two objective functions; minimization of real power loss and voltage stability L-index. The objective functions are minimized by optimally choosing the control variables such as generator excitations, tap positions of on-load tap changing transformers and switched var compensators. The proposed approach has been evaluated on an IEEE 30 bus standard test system. The performance of the proposed algorithm is compared with other evolutionary computation algorithms in the literature and the effectiveness of the proposed algorithm is demonstrated.</p><em><em><br /></em></em>

An integrated testbed for locally monitoring SCADA systems in smart grids

A testbed for evaluating if and how process-aware monitoring may increase the security of decentralized SCADA networks in power grids is presented. The testbed builds on the co-simulation framework Mosaik, and co-simulates in an integrated way, the power distribution network on different voltage levels, as well as the control network (Modbus/TCP). The existing simulators were extended to allow topology changes, and a controller (RTU) simulator connected to a SCADA server enabling remote control was implemented. Using the developed testbed, a recently proposed local monitoring approach was investigated. The results show that for so-called interlocks the proposed monitoring approach prevents the execution of 33.3% of the commands, that would result in an unsafe state of the power distribution grid. Furthermore, it is shown that unsafe transformer tap positions can also be avoided. To illustrate the relevance and importance of the proposed testbed, a detailed comparison of related work on process-aware intrusion detection approaches and testbeds combining (parts of) the control network and the power grid is provided.

The statistical interval estimation of the mean and the hypothesis testing of population proportions for transformer tap position estimation

Frequent changes in the modern electrical power systems necessitate network state estimating for energy management system. Efficient network state estimation depends on deriving a correct model of the power system. To best model, the precise values of system parameters should be known. Therefore, many studies have paid attention to estimate unknown network parameters. Among them, transformer tap position (TTP) is one of the most important parameters ignored in the power system. This paper estimates TTP based on the statistical interval estimation of the mean and the hypothesis testing of population proportions. We exploit statistical rules jointly with weighted least squares (WLS) method to efficiently obtain unknown TTPs. Because of the non-recursive form, this method does not suffer the calculation divergence problem and is also simpler than recursive methods such as Bayesian approach. The proposed approach is also compared with Seidel type Recursive Bayesian Approach (SRBA) in the IEEE 14-bus system to show its advantages. Furthermore, it is applied on a real power system.

Optimal reactive power dispatch using water wave optimization algorithm

This paper presents water wave optimization (WWO) algorithm to solve the optimal reactive power dispatch (ORPD) problem with the continuous and discrete control variables in power system. The ORPD problem is defined as a complex, discrete, constrained nonlinear combinatorial optimization problem. The WWO algorithm is utilized to find the optimized values of control variables such as generator voltages, tap positions of tap changing transformers and the amount of reactive compensation devices to achieve minimized value of active power losses. The WWO algorithm not only effectively avoids the shortcomings of local search and poor calculation accuracy, but also accelerates the convergence rate to find the global optimal solution. The WWO algorithm is implemented on standard IEEE 30-bus power system that is to verify the effectiveness and feasibility of the WWO algorithm to tackle with the ORPD problem. Compared with other algorithms, the WWO algorithm can find the set of the optimal solutions of control variables. The simulation experiment indicates that the WWO algorithm has better overall performance to reduce the real power losses.

Transformer Tap Position Estimation Improvement Using the Hypothesis Testing for a Population Mean

This article presents an efficient method for power transformer tap position estimation based on the Hypothesis Testing for a Population Mean. To have a good performance, energy management system requires a precise model of the modern power networks. This makes the parameter estimation to be a vital part, and the transformer tap position is a crucial parameter in the networks. Therefore, this article focuses on the transformer tap position estimation. To this aim, the power system states are estimated for all tap positions using the measurements and the weighted least-squares (WLS) method. Then, the estimated measurements are calculated by the estimated states. Finally, the mean of every measurement is compared with its estimated value by employing the Hypothesis Testing for a Population Mean, and the tap ratio with the largest correct responses is identified as the correct one. Simulations are done on both the IEEE 14-bus system and a power network of Iran, showing the reliability and efficiency of the proposed method over the most recent ones.

Integrated Strategies of Backtracking Search Optimizer for Solving Reactive Power Dispatch Problem

This paper proposes backtracking search optimizer (BSO) for solving the reactive power dispatch (RPD) problem. The RPD problem is highly nonlinear, nonconvex optimization problem and is consisting of both continuous and discrete control variables. It aims to find the optimal settings of the generator voltages, tap positions of tap changing transformers, and the amount of reactive compensation that able to optimize the transmission power losses. BSO has simple structure and single control parameter. It has two new tuned mutation and crossover operators that control the amplitude of the search-direction matrix and search space boundaries. In this paper, five diversified generation strategies of mutation factor of BSO to solve the RPD. The proposed BSO strategies are applied to the IEEE standard 14-, 30-, 57-bus test systems, and a real power system at West Delta region system as a part of the unified Egyptian network. Simulation results show that the BSO strategy, which almost generates mutation factor concentrated on the mean value of the normal distribution (BSO 4) assigns multidirections through the search space and supports the diversity of populations. To show the effectiveness and superiority of proposed BSO, the obtained results are compared with different algorithms reported in the literature.

Power loss estimation due to difference transformer tap changer position at interface

This paper presents a method to reduce the power loss during transmitting energy with different transformer setting at distribution area. Many engineers are not conscious that the transformer tap setting may affect high power loss during transmitting process. By controlling the tap changing, the system can avoid voltage unbalanced on distribution system. This research demonstrated through numerical studies using Power System Simulation for Engineering (PSS TM E) Software and Python Programming. The Python programming is to generate the load profile for time series by repetitively running the system in 24-hours for every minute. It is taking about 1440 times per day. This load profile is auto generate using 69 test bus system. The integrated simulation systems were simulated for different transformer tap position at 1.0 p.u and tap position at optimal.

Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm

<p>This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required to optimize the certain objective functions. The CSA algorithm has been developed from the inspiration that the obligate brood parasitism of some Cuckoo species lay their eggs in nests of other host birds which are of other species. The performance of CSA for solving the proposed optimal reactive power scheduling problem is examined on standard Ward Hale 6 bus, IEEE 30 bus, 57 bus, 118 bus and 300 bus test systems. The simulation results show that the proposed approach is more suitable, effective and efficient compared to other optimization techniques presented in the literature.</p>

Ordinal Optimization Technique for Three-Phase Distribution Network State Estimation Including Discrete Variables

This paper has discussed transformer tap position estimation with continuous and discrete variables in the context of three-phase distribution state estimation (SE). Ordinal optimization (OO) technique has been applied to estimate the transformer tap position for the first time in an unbalanced three-phase distribution network model. The results on 129 bus system model have demonstrated that the OO method can generate a reliable estimate for transformer exact tap position with discrete variables in distribution system SE and also in short period of time. In this paper, the node voltages and power losses are calculated for 129 bus network. It is also demonstrated that OO is much faster than other accurate methods such hybrid particle swarm optimization. The losses obtained with OO are much accurate. In view of this, OO performs better than weighted least square as it provides higher accuracy of the loss calculation. In a distribution network where about 5-6% of electricity generated is lost, accurate estimation of this loss has significant technical and commercial value. The authors believe the technique proposed will help realize those benefits.

Optimized Reactive Power Supports Using Transformer Tap Stagger in Distribution Networks

In distribution networks, the operation of two parallel transformers in small different tap positions, i.e., known as tap stagger, can provide a means of absorbing reactive power. The aggregated VAr absorption from many pairs of parallel transformers could help mitigate the high voltages in the upstream transmission system during periods of low demand. Considering distribution networks as reactive power support providers, this paper presents an optimal control strategy for the tap stagger to minimize the introduced network losses, as well as the tap switching operations. A genetic algorithm-based solution procedure is developed to find the optimal dispatch for the transformer tap positions in order to achieve the VAr absorption required by the transmission system. The proposed method is compared with two alternative control approaches based on the rule-based method and the branch-and-bound algorithm. Two practical U.K. distribution networks are modeled from real data and used to demonstrate the effectiveness of the proposed control strategy under different operating conditions.

Using a new modified harmony search algorithm to solve multi-objective reactive power dispatch in deterministic and stochastic models

The optimal reactive power dispatch (ORPD) problem is a very important aspect in power system planning, and it is a highly non-linear, non-convex optimization problem because it consists of both the continuous and discrete control variables. Since a power system has an inherent uncertainty, this paper presents both the deterministic and stochastic models for the ORPD problem in multi-objective and single-objective formulations, respectively. The deterministic model considers three main issues in the ORPD problem including the real power loss, voltage deviation, and voltage stability index. However, in the stochastic model, the uncertainties in the demand and equivalent availability of shunt reactive power compensators have been investigated. To solve them, we proposed a new modified harmony search algorithm (HSA), implemented in single and multi-objective forms. Since, like many other general purpose optimization methods, the original HSA often traps into the local optima, an efficient local search method called chaotic local search (CLS) and a global search operator are proposed in the internal architecture of the original HSA algorithm to improve its ability in finding the best solution because the ORPD problem is very complex, with different types of continuous and discrete constrains, i.e. excitation settings of generators, sizes of fixed capacitors, tap positions of tap changing transformers, and amount of reactive compensation devices. Moreover, the fuzzy decision-making method is employed to select the best solution from the set of Pareto solutions. The proposed model is individually examined and applied on different test systems. The simulation results show that the proposed algorithm is suitable and effective for the reactive power dispatch problem compared to the other available algorithms.

Weighted elitism based Ant Lion Optimizer to solve optimum VAr planning problem

In this paper, a newly proposed Ant-Lion Optimizer (ALO) is applied to solve Optimal Reactive Power Dispatch (ORPD) problem of power system. The ORPD is a VAr planning problem and is a highly non linear, non convex, challenging optimization problem; usually devised as constrained Optimal Power Flow (OPF). This paper also proposes the improvement in the search capability of ALO. A novel weighted elitism concept is introduced in the elitism phase of the original ALO to improve the exploration property of the algorithm. The proposed Modified ALO (MALO) intelligently balances both exploration and exploitation, which enhances the hunting capability of ALO. Both ALO and MALO is used to determine the optimal settings of generator voltages, tap positions of tap changer transformers and VAr output of shunt capacitors to optimize objectives: like, the active power loss, total voltage deviation and voltage stability index. The proposed algorithm is programmed and simulated on MATLB and tested on IEEE-30 and 57 bus systems. The results are compared with ALO and other methods. The effectiveness of MALO is further verified by solving few benchmark mathematical functions. The numerical results reveal that, MALO gives better optimum solutions for the benchmark functions compare to original ALO and outperforms several other methods used in the literature to solve ORPD problems. The t-Test and parametric analysis confirms the robustness and consistency of the MALO over ALO and other similar methods.

Synchrophasors-Based Distributed Secondary Voltage/VAR Control via Cellular Network

The impact of the increasing connection of distributed generation to medium voltage feeders, with particular reference to photovoltaic (PV) units, justifies the investigation on secondary voltage/VAR control (VVC) schemes able to improve the utilization of available control resources and to reduce reactive power flows. This paper deals with a secondary VVC scheme based on a distributed multi-agent approach that requires only the estimation of the reactive power flows between the buses, where the PV units with reactive power control capability are connected. Phasor measurement units are used to get the relevant information. In general, distributed control approaches are expected to work adequately even by using communication infrastructures with lower performances than those required by centralized approaches. This paper addresses such an issue by the analysis of the distributed VVC performance when a shared cellular network is used for the cooperative adjustment of PV inverters reactive power outputs and of tap positions of transformers equipped with on-load tap changers. The analysis is carried out by using a specifically developed information and communications technology-power co-simulation platform. It is shown that the VVC scheme has adequate performances also in the presence of significant levels of background traffic and data loss.

Chaos embedded krill herd algorithm for optimal VAR dispatch problem of power system

Chaotic krill herd algorithm (KHA) (CKHA) is proposed in this paper to solve the optimal VAR dispatch problem of power system considering either minimization of real power loss or that of absolute value of total voltage deviation or improvements of voltage profile as an objective while satisfying all the equality and the inequality constraints of the power system network. Detailed studies of different chaotic maps are illustrated. Among these, Logistic map is considered in the proposed technique to improve the performance of the basic KHA. The performance of the proposed CKHA is implemented, successfully, on standard IEEE 14- and IEEE 118-bus test power systems in which the control of bus voltages, tap position of transformers and reactive power sources are involved. The results offered by the proposed CKHA are compared to other evolutionary optimization based techniques surfaced in the recent state-of-the-art literature. Simulation results indicate that the proposed CKHA approach yields better optimization efficacy over some other recent popular techniques in terms of results offered, effectiveness, quality of solution and convergence speed.

Coordinated Volt/Var Control in Distribution Systems with Distributed Generations Based on Joint Active and Reactive Powers Dispatch

One of the most significant control schemes in optimal operation of distribution networks is Volt/Var control (VVC). Owing to the radial structure of distribution systems and distribution lines with a small X/R ratio, the active power scheduling affects the VVC issue. A Distribution System Operator (DSO) procures its active and reactive power requirements from Distributed Generations (DGs) along with the wholesale electricity market. This paper proposes a new operational scheduling method based on a joint day-ahead active/reactive power market at the distribution level. To this end, based on the capability curve, a generic reactive power cost model for DGs is developed. The joint active/reactive power dispatch model presented in this paper motivates DGs to actively participate not only in the energy markets, but also in the VVC scheme through a competitive market. The proposed method which will be performed in an offline manner aims to optimally determine (i) the scheduled active and reactive power values of generation units; (ii) reactive power values of switched capacitor banks; and (iii) tap positions of transformers for the next day. The joint active/reactive power dispatch model for daily VVC is modeled in GAMS and solved with the DICOPT solver. Finally, the plausibility of the proposed scheduling framework is examined on a typical 22-bus distribution test network over a 24-h period.

Optimal Tap Setting of Voltage Regulation Transformers in Unbalanced Distribution Systems

In this paper, we propose a method to optimally set the tap position of voltage regulation transformers in distribution systems. We cast the problem as a rank-constrained semidefinite program (SDP), in which the transformer tap ratios are captured by 1) introducing a secondary-side “virtual” bus per transformer, and 2) constraining the values that these virtual bus voltages can take according to the limits on the tap positions. Then, by relaxing the non-convex rank-1 constraint in the rank-constrained SDP formulation, one obtains a convex SDP problem. The tap positions are determined as the ratio between the primary-side bus voltage and the secondary-side virtual bus voltage that result from the optimal solution of the relaxed SDP, and then rounded to the nearest discrete tap values. To efficiently solve the relaxed SDP, we propose a distributed algorithm based on the alternating direction method of multipliers (ADMM). We present several case studies with single- and three-phase distribution systems to demonstrate the effectiveness of the distributed ADMM-based algorithm, and compare its results with centralized solution methods.