Voltage Deviation Index Research Articles

Optimal line flows based on voltage profile, power loss, cost and conductor temperature using coordinated controlled UPFC

In the context of flexible operation and bidirectional line flows in modern transmission systems, suitable line flows are essential for safe, secure and stable power systems. Improper line flows may deteriorate stability and longevity of transmission lines. To avoid line outage and faults, improvement in thermal margin of conductors by regulating line flows is a practical consideration. To maintain and control the line flows, the unified power flow controller (UPFC) is the most appropriate compensating device. Thus, in this study, line flows are considered as state variables to optimise several functions, like, voltage deviation index, active and reactive power losses, temperature of conductors and cost of UPFC. The UPFC is modelled with practical losses like switching, coupling and internal transmission losses. Suitable line flows are obtained for the 30-bus and 57-bus test systems by applying an adaptive-version of the wind-driven optimisation (AWDO) algorithm. The AWDO is designed using two innovative tuning parameters: a decaying friction coefficient to reduce the probability of stagnation, and an adaptive velocity function to maintain diversity. The results are compared among different cases of line flows, validating the necessity of the technical challenge. The performance of AWDO is compared with other techniques, suggesting its superiority over other methods.

A novel opposition-based tuned-chaotic differential evolution technique for techno-economic analysis by optimal placement of distributed generation

ABSTRACTOptimal placement of distributed generation (DG) units in a radial distribution network can reduce the system losses, improve the voltage profile of the network and thereby maximize the techno-economic benefits. The present problem considers various indices, namely the cost index, voltage deviation index and line flow capacity index, with several equality and inequality constraints, to analyse the techno-economic benefits. An opposition-based tuned-chaotic differential evolution (OTCDE) technique is proposed to avoid premature convergence. This technique uses an opposition-based scheme for initialization and logistic mapping to generate chaotic sequences within the specified range for the mutation factor, because wrong parameter selection may lead to premature convergence and even stagnation. The proposed algorithm has been applied on IEEE 33-bus, 69-bus and 118-bus radial distribution systems to verify its effectiveness. By several statistical analyses, it is observed that the proposed technique outperforms other newly proposed metaheuristic techniques.

Multi-objective Network Reconfiguration Considering V2G of Electric Vehicles in Distribution System with Renewable Energy

Abstract With the fluctuation and intermittence of distributed generation (DG) and electric vehicle (EV), it is important to ensure the safe and stable operation of distribution system. This paper presents a multi-objective approach of network reconfiguration considering vehicle-to-grid (V2G) electric vehicles in distribution system with renewable energy access. Firstly, the multi-objective function includes the network loss index, the voltage deviation index and the voltage stability margin index. The weight of each index is determined by the analytic hierarchy process (AHP). Secondly, the charging and discharging strategies of two kinds of electric vehicles are analyzed considering the renewable energy access. Finally, the improved genetic algorithm (GA) is used to solve this problem. The probabilistic statistical models are used to describe EV, and the Monte Carlo method is used to calculate the power. The proposed approach is verified on IEEE33 nodes system.

A fuzzy pragmatic DE–CSA hybrid approach for unbalanced radial distribution system planning with distributed generation

This paper presents a multi-objective planning approach for the optimal placement of distributed generation (DG) units in unbalanced radial distribution systems using a hybrid differential evolution (DE) and cuckoo search algorithm (CSA). In this planning optimization, the objective functions formulated are the minimization of: (i) total real power loss, (ii) maximum average voltage deviation index, (iii) total neutral current, and (iv) total cost. The total cost includes the cost of energy purchased from the grid and the capital investment and operational cost of DG units. These objective functions are aggregated using max–max and max–min analogies. Fuzzy set theory is used to model the uncertainties in load and generation of renewable DG units. Hence, these objective functions are found to be fuzzy sets. An appropriate defuzzification approach is used so as to compare and rank different solutions. A modified three-phase forward–backward sweep-based load flow algorithm including the DG model is used as the support subroutine of the proposed solution algorithm using the hybrid DE–CSA. The simulation results show that significant improvements in power loss, maximum average voltage deviation, system unbalance, and total annual energy cost are obtained due to the DG integration in unbalanced distribution networks. The results obtained with fuzzy-based modeling of load and generation are found to be superior as compared to the deterministic load and generation.

Optimized Use of PV Distributed Generation in Voltage Regulation: A Probabilistic Formulation

This paper proposes a probabilistic optimiz-ation strategy for optimized use of Photovoltaic (PV) generators in distribution networks voltage regulation. Probabilistic PV output scenarios are incorporated in a constrained optimal load flow planning analysis for a given distribution network to minimize a voltage deviation index indicative of the quality of voltage regulation. The proposed strategy utilizes the PV generators as capacitors and inductors, whose reactive power compensation is monthly scheduled in coordination with the high-voltage/medium-voltage (HV/MV) substation on-load tap changer setting. The proposed strategy does not rely on a communication infrastructure nor does it comprise feedback voltage control. Physical solar irradiance and ambient temperature measurements are used to demonstrate the effectiveness of the proposed strategy on two detailed North-American MV distribution networks (25 kV and 12.66 kV). The results are validated by two distribution load flow calculation algorithms: backward/forward sweep in a MATLAB script; and the harmonic load flow solver in OpenDSS.

Coordinated Placement and Setting of FACTS in Electrical Network based on Kalai-smorodinsky Bargaining Solution and Voltage Deviation Index

To aid the decision maker, the optimal placement of FACTS in the electrical network is performed through very specific criteria. In this paper, a useful approach is followed; it is based particularly on the use of Kalai-Smorodinsky bargaining solution for choosing the best compromise between the different objectives commonly posed to the network manager such as the cost of production, total transmission losses (Tloss), and voltage stability index (Lindex). In the case of many possible solutions, Voltage Profile Quality is added to select the best one. This approach has offered a balanced solution and has proven its effectiveness in finding the best placement and setting of two types of FACTS namely Static Var Compensator (SVC) and Thyristor Controlled Series Compensator (TCSC) in the power system. The test case under investigation is IEEE-14 bus system which has been simulated in MATLAB Environment.

Multi-objective ant lion optimization algorithm to solve large-scale multi-objective optimal reactive power dispatch problem

PurposeIn the vast majority of published papers, the optimal reactive power dispatch (ORPD) problem is dealt as a single-objective optimization; however, optimization with a single objective is insufficient to achieve better operation performance of power systems. Multi-objective ORPD (MOORPD) aims to minimize simultaneously either the active power losses and voltage stability index, or the active power losses and the voltage deviation. The purpose of this paper is to propose multi-objective ant lion optimization (MOALO) algorithm to solve multi-objective ORPD problem considering large-scale power system in an effort to achieve a good performance with stable and secure operation of electric power systems.Design/methodology/approachA MOALO algorithm is presented and applied to solve the MOORPD problem. Fuzzy set theory was implemented to identify the best compromise solution from the set of the non-dominated solutions. A comparison with enhanced version of multi-objective particle swarm optimization (MOEPSO) algorithm and original (MOPSO) algorithm confirms the solutions. An in-depth analysis on the findings was conducted and the feasibility of solutions were fully verified and discussed.FindingsThree test systems – the IEEE 30-bus, IEEE 57-bus and large-scale IEEE 300-bus – were used to examine the efficiency of the proposed algorithm. The findings obtained amply confirmed the superiority of the proposed approach over the multi-objective enhanced PSO and basic version of MOPSO. In addition to that, the algorithm is benefitted from good distributions of the non-dominated solutions and also guarantees the feasibility of solutions.Originality/valueThe proposed algorithm is applied to solve three versions of ORPD problem, active power losses, voltage deviation and voltage stability index, considering large -scale power system IEEE 300 bus.

Exploring prospective benefits of electric vehicles for optimal energy conditioning in distribution networks

A potentially beneficial new opportunity is emerging around the exchange of energy between electric vehicles and the electrical energy grid, particularly as more low-carbon energy sources are connecting to the grid. Accordingly, this paper presents an optimization framework to activate the potential capabilities of electric vehicles equipped with bidirectional chargers for energy conditioning (including energy management and power quality improvement) of the future distribution networks. The proposed nonlinear optimization seeks to concurrently enhance the operation performance (using the network voltage deviation index) as well as power quality of the grid (using total harmonic distortion index). The proposed model is tested on a 33-bus distribution network to demonstrate its efficiency and performance.

Optimal reconfigurattion of distribution systems by considering switch and wind turbine placements to enhance reliability and efficiency

Reconfiguration and smart control of remote-controlled sectionalising switches in distribution networks are considered as the major solutions for loss mitigation, interruption time reduction and reliability improvement after events. That is because these automation tools bring about changes in the topology of the network, isolate the faulted regions, operate distributed generators for local load satisfaction and restore the un-faulted regions as rapidly as possible. Thus, a new solution methodology for solving the simultaneous optimal wind turbines (WTs)/switches placement as well as network reconfiguration is developed to enhance the distribution network's efficiency and reliability. Power losses, voltage deviation index, switch cost and reliability cost based on expected customer interruption cost are considered as the objective functions. The approach profits from a new multi-objective algorithm based on modified artificial bee colony for providing the best compromise solution. The presented framework is shown to provide superior results when applied to the IEEE 69-node test feeder. Finally, different scenarios based on feeder reconfiguration, switch placement and WT placement problems are constructed and presented in Section 5.

Optimal Distributed Generation Placement with Multiple Objectives Considering Probabilistic Load

In this paper, optimal DG sizing and siting has been obtained considering three objectives i.e. active power loss index (APLI), line loading index (LLI) and voltage deviation index (VDI). Multiple objective NSGA-II method along with fuzzy satisfying method (FSM) has been used to solve multiple objective optimization problem. Probabilistic load model has been considered in this study. A two point estimate (2PEM) method has been used carry out o obtain probabilistic load flow solution. The analysis has been carried out for IEEE 33 radial bus test system. The analysis has been carried out using coding in MATLAB.

An Efficient Metaheuristic Algorithm for Optimal Capacitor Allocation in Electric Distribution Networks

The capacitors are generally used to provide the compensation of reactive power in the electric distribution systems. This paper investigates a particular approach that highlights the influences which the connection of shunt capacitors may have over the solutions for voltage control in the medium voltage distribution networks. The problem of optimal capacitor allocation for minimization of bus voltage deviation index was solved by using the Bat algorithm in order to find the near-optimal solutions for the capacitor allocation problem in electric distribution systems. To demonstrate the feasibility of algorithm, a real distribution network was used and the results were compared with those obtained using genetic algorithm.

Analyzing the Effect of Distributed Generators on Economical and Technical Aspects of Distribution Systems

Objectives: This paper presents a two stage solution strategy to solve multi objective optimization problem in the presence of Distributed Generators (DG) in planning a primary distribution system. Methods/Statistical Analysis: Using this, the economical aspects such as net savings and the technical aspects such as voltage deviation and section current index objectives are optimized while satisfying system equality and in-equality constraints. The optimum number of DGs and its optimal locations are identified in a given system so as to enhance the system security by minimizing the system power losses. The proposed methodology uses fuzzy decision approach to select the best compromised solution from the total generation Pareto solution as per the operators' requirement. Findings: According to the analytical results, the proposed framework in the presence of DGs enhances the system performance more effectively. Application/Improvements: The feasibility and effectiveness of the proposed method are examined on standard 15-node, 33-node and 69-node test systems.

A new interactive sine cosine algorithm for loading margin stability improvement under contingency

In this paper, a new method called sine cosine algorithm is adapted in coordination with an interactive process to improve the power system security considering loading margin stability and faults at specified important branches. In this study, the loading margin stability is optimized in coordination with total cost, total power loss, total voltage deviation and voltage stability index. In order to locate the best loading margin stability, an initial global database containing suboptimized control variables is generated based on two indices named global and local critical reactive margin security related to generating units. The optimized loading margin stability is improved in coordination with the availability of reactive power of different shunt FACTS devices installed at particular locations. The robustness of the proposed planning strategy is validated on a small test system, the IEEE 30-Bus and to a large test system, the IEEE 118-Bus. Optimized results found confirmed clearly the improvement of loading margin stability at critical situations such as faults at specified branches.

Symbiotic Organisms Search Technique for SVC Installation in Voltage Control

<p>Increasing demand experienced by electric utilities in many parts of the world involving developing country is a normal phenomenon. This can be due to the urbanization process of a system network, which may lead to possible voltage decay at the receiving buses if no proper offline study is conducted. Unplanned load increment can push the system to operate closes to its instability point. Various compensation schemes have been popularly invented and proposed in power system operation and planning. This would require offline studies, prior to real system implementation. This paper presents the implementation of Symbiotic Organisms Search (SOS) algorithm for solving optimal static VAr compensator (SVC) installation problem in power transmission systems. In this study, SOS was employed to perform voltage control study in a transmission system under several scenarios via the SVC installation scheme. This realizes the feasibility of SOS applications in addressing the compensating scheme for the voltage control study. Minimum and maximum bound of the voltage at all buses have been considered as the inequality constraints as one of the aspects. A validation process conducted on IEEE 26-Bus RTS realizes the feasibility of SOS in performing compensation scheme without violating system stability. Results obtained from the optimization process demonstrated that the proposed SOS optimization algorithm has successfully reduced the total voltage deviation index and improve the voltage profile in the test system. Comparative studies have been performed with respect to the established evolutionary programming (EP) and artificial immune system (AIS) algorithms, resulting in good agreement and has demonstrated its superiority. Results from this study could be beneficial to the power system community in the planning and operation departments in terms of giving offline information prior to real system implementation of the corresponding power system utility.</p>

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.

Bacterial graphical user interface oriented by particle swarm optimization strategy for optimization of multiple type DFACTS for power quality enhancement in distribution system

This study proposes a graphical user interface (GUI) based on an enhanced bacterial foraging optimization (EBFO) to find the optimal locations and sizing parameters of multi-type DFACTS in large-scale distribution systems. The proposed GUI based toolbox, allows the user to choose between single and multiple DFACTS allocations, followed by the type and number of them to be allocated. The EBFO is then applied to obtain optimal locations and ratings of the single and multiple DFACTS. This is found to be faster and provides more accurate results compared to the usual PSO and BFO. Results obtained with MATLAB/Simulink simulations are compared with PSO, BFO and enhanced BFO. It reveals that enhanced BFO shows quick convergence to reach the desired solution there by yielding superior solution quality. Simulation results concluded that the EBFO based multiple DFACTS allocation using DSSSC, APC and DSTATCOM is preferable to reduce power losses, improve load balancing and enhance voltage deviation index to 70%, 38% and 132% respectively and also it can improve loading factor without additional power loss.

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.

LOCATION OF VOLTAGE SAG SOURCE BY USING ARTIFICIAL NEURAL NETWORK

Power quality (PQ) is a major concern for number of electrical equipment such as sophisticated electronics equipment, high efficiency variable speed drive (VSD) and power electronic controller. The most common power quality event is the voltage sag. The objective is to estimate the location of voltage sag source using ANN. In this paper, the multi-monitor based method is used. Based on the simulation results, the voltage deviation (VD) index of voltage sag is calculated and assigned as a training data for ANN. The Radial Basis Function Network (RBFN) is used due to its superior performances (lower training time and errors). The three types of performance analysis considered are coefficient of determination (R2), root mean square error (RMSE) and sum of square error (SSE). The RBFN is developed by using MATLAB software. The proposed method is tested on the CIVANLAR distribution test system and the Permas Jaya distribution network. The voltage sags are simulated using Power World software which is a common simulation tool for power system analysis. The asymmetrical fault namely line to ground (LG) fault, double line to ground (LLG) fault and line to line (LL) fault are applied in the simulation. Based on the simulation results of voltage sag analysis, the highest VD is contributed by LLG for both test systems. Based on the proposed RBFN results, the best performance analysis are R2, RMSE and SSE of 0.9999, 5.24E-04 and 3.90E-05, respectively. Based on the results, the highest VD shows the location of voltage sag source in that system. The proposed RBFN accurately identifies the location of voltage sag source for both test systems.

Optimal Placement of DG Based On Voltage Stability Index and Voltage Deviation Index

Objectives: In Modern world power production and its efficient usage defines the development of the country. In distribution system of an electrical network due to the influence of loads, voltage profile and losses are to be focussed a lot. In order to avoid the maximum losses and to maintain the voltage profile usage of Distribution Generator (DG) plays a major role in the network. Statistical Analysis: Since we have to use an appropriate place and size of DG, a measuring technique based with index through which optimal size and place can be identified and is implemented in the work. Findings: This Paper focuses on reducing power losses by accurately giving the location and size of DG based on two indices (a) Voltage Deviation Index (VDI) (b) Voltage Stability Index (VSI). Results such as power losses, location, size and cost of DG are obtained for both initial configuration and Feeder reconfigured state for IEEE 33 and 69 bus systems were listed. Applications: These analysis help us in installation of Different types of DG to present network where demand side management has to be followed.

Optimal sizing and sitting of DG with load models using soft computing techniques in practical distribution system

In the deregulated power market environment, distributed generation (DG) is an effective approach to manage performance, operation and control of the distribution system. Methods available in the literature for DG planning are often not able to simultaneously provide technical and economical benefits. Therefore an effective methodology is developed to improve the technical as well as economical benefits as compared with the existing approaches. This study reports the optimal installation of multi-DG in the standard 33-bus, 69-bus radial distribution systems and 54-bus practical radial distribution system. Several performance evaluation indices such as active and reactive power loss indices, voltage deviation index, reliability index and shift factor indices are used to develop a novel multi-objective function (MOF). A new set of equations is developed for representing different practical load models. A novel MOF has been solved to find optimal sizing and placement of DGs using genetic algorithm and particle swarm optimisation technique. The comparative result analysis is also discussed for both techniques. The result analysis reveals that system losses, energy not supplied, system MVA intakes are reduced, whereas available transfer capability, voltage profile, reliability and cost benefits are improved for the case with-DGs in the distribution system.