Generator Reactive Power Output Research Articles

The robust curve-fitting based method for coordinated voltage regulation with distributed generator and OLTC in distribution network

The voltage violation in distribution networks may occur frequently due to the rising integration of flexible demands and supplies. In this paper, the reactive power of the inverter-based distributed generator (DG) and OLTC tap position are adjusted for voltage regulation in coordinated manner. Firstly, the reactive power adjustment for the voltage regulation has been carried out by use of the DG reactive power capacity. Afterwards, the tap position of the OLTC has been changed to regulate the bus voltage that has a voltage violation problem in the case of insufficient reactive power capacity of the DG inverter. The value of the distributed generator reactive power output that regulates the voltage on the bus that has a voltage violation is found with proposed curve-fitting method and tested with the Grid Search, Artificial Bee Colony methods. The proposed method derives mathematical expressions between DG reactive power output and bus voltages as quadratic equations by curve-fitting technique. Moreover, addition to inherent upper and lower reactive power limits of the DG inverter, they are determined by the proposed curve-fitting based technique while considering voltage constraints on all buses. Proposed method is much faster than other two methods because of its plain structure and gives adequate accurate results.

Electric Power PSS With Ramp-Rejection

The object of this paper is to demonstrate the performance of a Ramp-Rejecting electric Power Prefilter (RRPP), which is a modification of the Conventional electric Power Prefilter (CPP) for a speed-PSS. The RRPP can markedly reduce the undesirable swings in generator voltage and reactive power output due to ramping of turbine power. Like the Dual Input Prefilter (DIP) of the integral-of-accelerating-power (IAP) PSS the RRPP (i) significantly attenuates the torsional modes; and (ii) enhances modal damping. Significantly, the RRPP does not require an externally-estimated rotor-speed input signal - which is an important requirement of the DIP. For example, using generator terminal variables for the DIP it is difficult to accurately estimate a rotor-speed input that covers a wide range of operating conditions and modal frequencies.

Improved Real-Coded Genetic Algorithm for Reactive Power Dispatch

The present article establishes and suggested improved real-coded genetic algorithm (IRCGA) to solve various types of reactive power dispatch problems. The cornerstone of the primary human heritable chromosome operation is the Genetic algorithm (GA) which is a large number of evolutionary algorithms. GA has the capability to reach the global optimal solutions. Here IRCGA is introduced to improve the quality of the solution and raise the speed of convergence. The advanced IRCGA has been utilized to manage variables settings like generator terminal voltages, transformer taps and reactive power output of shunt VAR compensators to obtain minimum active power transmission loss, improved voltage profile, and voltage stability. IRCGA has been experimented on IEEE 30-bus, 57-bus and 118-bus test systems and 15 benchmark functions. The results obtained from the different test systems have been compared with other defined evolutionary methods and observed that the developed IRCGA gives better result.

Real‐time spatio‐temporal trend and level (T&L) filtering scheme for early detection of voltage instability

Discovering early signatures of voltage instability can enhance situational awareness and facilitate emergency control. Such signatures usually appear as decaying trends in load voltages and increase in reactive power output of generators. Discrete control actions such as frequent switching of load tap changer (LTC) and capacitor banks, that are otherwise unmonitored, can also be crucial indicators of a voltage problem. Hence, the authors propose a unified early warning scheme (EWS) for detecting voltage instability. To quantify and correlate spatial, temporal (spatio-temporal) trends and level changes, the authors use a multivariate, adaptive trend and level (T&L) filter. A unique feature of the proposed scheme is the signature authentication, by verifying simultaneity in T&L changes across multivariate time series. The authors show that, by extrapolating trends, limit violations in bus voltages and generator reactive power output can be estimated well in advance. Further, the level-change detection logic is shown to have the ability to detect frequent LTC-actions directly from the bus voltage time series. While the T&L filtering scheme is validated on field phasor measurement unit (PMU) datasets, EWS and emergency control is demonstrated on the classical 10-bus voltage stability test system.

Synchronizing Torque Impacts on Rotor Speed in Power Systems

Renewables are increasingly replacing power from conventional generators. Renewable power injected through power electronic converters lacks the fundamental electric torque components. Electric torque components have an important role in determining the behavior of conventional machines in the network. The influence of this factor becomes more notable in power systems with reduced inertia. Hence, questions arise on, how can synchronizing torque basically contribute to the rotor speed deviation and eventually the system frequency and if there is a potential for using the steady state synchronizing torque coefficient (STC) to achieve acceptable frequency operating points. This paper calculates the steady state STC matrix by using the multimachine Heffron-Philips model in conjunction with the network admitance matrix. Accordingly, it investigates the impact of the generator location and reactive power output on the STC matrix. It demonstrates how this impact manifests in the generator rotor speed deviation. Eventually, the significance of the STC from the system frequency perspective is assessed.

Multi-objective optimal reactive power dispatch using multi-objective differential evolution

This paper presents multi-objective differential evolution (MODE) to solve multi-objective optimal reactive power dispatch (MORPD) problem by minimizing active power transmission loss and voltage deviation and maximizing voltage stability while varying control variables such as generator terminal voltages, transformer taps and reactive power output of shunt VAR compensators. MODE has been tested on IEEE 30-bus, 57-bus and 118-bus systems. Numerical results for these three test systems have been compared with those acquired from strength pareto evolutionary algorithm 2 (SPEA 2).

Multi-Stage Optimization Based Automatic Voltage Control Systems Considering Wind Power Forecasting Errors

This paper proposes an automatic voltage control (AVC) system for power systems with limited continuous voltage control capability. The objective is to minimize the operational cost over a period, which consists of the power loss in the grid, the shunt switching cost, the transformer tap change cost, and the generator reactive power output cost. The problem is formulated in a multi-stage optimal reactive power flow (MORPF) framework, solved by the nonlinear programming techniques via a rolling process. The voltage uncertainty caused by wind power forecasting errors is considered in the optimal voltage/var dispatch by including a security margin in the voltage magnitude constraints. The robustness of the MORPF solution considering the voltage security margin is therefore improved. A platform is built for the offline investigations, employing the measurement data from the Danish national electricity control center, where study cases based on the western Danish power system demonstrate the superiority of the proposed AVC system in term of the cost minimization. Monte Carlo simulations are carried out to verify the proposed method on the robustness improvements.

Optimal Power Flow Using Firefly Algorithm with Consideration of FACTS Devices "UPFC"

This paper present solution of optimal power flow problem using a firefly algorithm (FA) with consideration of FACTS devices UPFC. The objective is to minimize the total fuel cost of generation and also maintain an acceptable system performance in terms of limits on generator real power and reactive power outputs, bus voltages and power flow of transmission lines. In order to maximize the relief of congestion in power system, to reduce the total system real power loss and improves the loadability of the system we propose also the optimization of the placement of FACTS devices in the power system (UPFC). The proposed method is tested on IEEE 30-bus system and the Algerian electrical network. The result of this method is compared with those obtained by biogeography based optimization (BBO), genetic algorithm (GA), and artificial bee colony (ABC) algorithm.

A hybrid multi-objective improved bacteria foraging algorithm for economic load dispatch considering emission

In this paper, a hybrid multiobjective improved bacterial foraging algorithm with fuzzy dominance sorting FSIBF is proposed and used to solve environmental friendly economic load dispatch problem. The fuzzy dominance-based sorting procedure is used to select the non-dominated solutions in the Pareto front. The proposed algorithm is applied to nonlinear constrained multiobjective environmental friendly economic load dispatch problem. In the proposed work, we have considered the standard IEEE 30-bus six-generator test system with fuel cost and emission as two conflicting objectives to be optimised simultaneously satisfying the system's operational constraints. The constraints imposed from the operation point of view are the limits on generator real power and reactive power outputs, bus voltages and power flow in the transmission lines. The proposed work also includes the effect of having non-smooth cost characteristics of the thermal generators which arises because of the valve point loading effect. The practical generator constraints such as ramp rate limits and prohibited operating zones are also incorporated in this work suitably.

Economic Load Dispatch with the Proposed GA Algorithm for Large Scale System

Economic load dispatch (ELD) have been applied to obtain optimal fuel cost of generating units. Genetic Algorithm (GA) is a global search technique based on principles inspired from the genetic and evolution mechanism observed in natural biological systems. This paper presents a novel stochastic Genetic Algorithm approach to solve the Economic Load Dispatch problem considering various generator constraints and also conserves an acceptable system performance in terms of limits on generator real and reactive power outputs bus voltages, shunt capacitors/reactors, transformers tap-setting and power flow of transmission lines. The ELD problem in a power system is to determine the optimal combination of power outputs for all generating units which will minimize the total fuel cost while satisfying all practical constraints. To show its efficiency and effectiveness, the proposed GA algorithm is applied to some types of ED problems containing non-smooth cost functions of 13 and 40 generating units systems (large scale systems). The experimental results show that the proposed GA approach is comparatively capable of obtaining higher quality solution.

A Multiobjective Bacterial Foraging Algorithm to Solve the Environmental Economic Dispatch Problem

In this article, multi-objective bacterial foraging with non-dominated sorting procedure is applied to solve the non-linear constrained environmental/economic dispatch problem. In the proposed work, we have considered the standard IEEE 30-bus 6-generator test system with fuel cost and emission as two conflicting objectives to be optimized simultaneously. The limits on generator real power and reactive power outputs, bus voltages and power flow of transmission lines, ramp rate limits and prohibited operating zones are considered as the constraints. The proposed work also includes the effect of having non-smooth cost characteristics to mimic the valve point loading effect. The simulation result reveals that the proposed approach is a competitive one to the existing methods for finding the best optimal Pareto front of two conflicting objectives and has the better robustness.

Biogeography Based Optimization Approach for Solving Optimal Power Flow Problem

This paper presents the use of a novel evolutionary algorithm called Biogeography-based optimization (BBO) for the solution of the optimal power flow problem. The objective is to minimize the total fuel cost of generation and environmental pollution caused by fossil based thermal generating units and also maintain an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors and power flow of transmission lines. BBO searches for the global optimum mainly through two steps: Migration and Mutation. In the present work, BBO has been applied to solve the optimal power flow problems on IEEE 30-bus test system with six generating units to test the effectiveness of the proposed method. Satisfactory results obtained from the proposed method were compared to conventional and evolutionary optimization methods.

Dynamic estimation of rotor angle deviation of a generator in multi-machine power systems

This paper introduces a new approach to obtain the generator rotor angle difference in the multi-machine power system for transient stability studies. It aims to replace the center of inertia (COI) for on-line transient stability assessment. Compared with COI, the proposed method can give more reasonable estimations of rotor angle difference between the individual generator and the multi-machine power system. In addition, it allows generators to have their own references for evaluating transient stability, which is more reasonable and accurate than using COI. Moreover, the proposed technique calculates the rotor angle difference by using the local generator's active and reactive power output. This does not add any extra burden to the communication channel.

Economic power dispatch of power systems with pollution control using artificial bee colony optimization

This paper presents a solution for the emission-controlled economic dispatch (ECED) problem of medium-sized power systems via an artificial bee colony algorithm. The ECED problem, which accounts for the minimization of both the fuel cost and the emission, is a multiple objective function problem. The objective is to minimize the total fuel cost of the generation and environmental pollution caused by fossil-based thermal generating units and to also maintain an acceptable system performance in terms of the limits on the generator's real and reactive power outputs, bus voltages, shunt capacitors/reactors, and power flow of transmission lines. The proposed algorithm is validated on an IEEE 30-bus system with 6 generating units. The results of the proposed technique are compared with that of the particle swarm optimization technique. The proposed approach is also tested on the Algerian 59-bus network and compared with global optimization methods (fuzzy genetic algorithm and ant colony optimization). The results show that the approach proposed can converge to a near solution and obtain a competitive solution in a critical situation and within a reasonable time.

Smart Decentralized Control of DG for Voltage and Thermal Constraint Management

Active Network Management schemes are being developed to accommodate larger volumes of renewable generation within distribution networks. Approaches typically manage only single technical constraints or are highly complex with extensive sensing and communications needs that bring cost, deployment, and operational risks. This work offers an alternative, decentralized approach for real-time management of local voltage and thermal constraints that avoids extensive sensing and communications. It controls generator active and reactive power output to overcome voltage and thermal issues near the point of connection. Results from time-series analyses reveal its effectiveness in managing both constraints and allowing greater production. It represents a potentially effective and fast-to-deploy alternative to more complex, integrated solutions.

Optimal Power Flow Solution of the Algerian Electrical Network using Differential Evolution Algorithm

This paper presents solution of optimal power flow (OPF) problem of a power system via differential evolution (DE) algorithm. The purpose of an electric power system is to deliver real power to the greatest number of users at the lowest possible cost all the time. So the objective is to minimize the total fuel cost of the generating units and also maintaining an acceptable system performance in terms of limits on generator reactive power outputs, bus voltages, static VAR compensator (SVC) parameters and overload in transmission lines. CPU times can be reduced by decomposing the problem in two subproblems, the first subproblem minimize the fuel cost of generation and the second subproblem is a reactive power dispatch so optimum bus voltages can be determined and reduce the losses by controlling tap changes of the transformers and the static VAR compensators (SVC). To verify the proposed approach and for comparison purposes, we perform simulations on the Algerian network with 114 buses, 175 branches (lines and transformers) and 15 generators. The obtained results indicate that DE is an easy to use, fast, robust and powerful optimization technique compared to the other global optimization methods such as PSO and GA.

Optimal Power Flow Solution of the Algerian Electrical Network using Differential Evolution Algorithm

This paper presents solution of optimal power flow (OPF) problem of a power system via Differential Evolution (DE) algorithm. The purpose of an electric power system is to deliver real power to the greatest number of users at the lowest possible cost all the time. So the objective is to minimize the total fuel cost of the generating units and also maintaining an acceptable system performance in terms of limits on generator reactive power outputs, bus voltages, Static VAR Compensator (SVC) parameters and overload in transmission lines. CPU times can be reduced by decomposing the problem in two subproblems, the first subproblem minimize the fuel cost of generation and the second subproblem is a reactive power dispatch so optimum bus voltages can be determined and reduce the losses by controlling tap changes of the transformers and the static Var Compensators (SVC). To verify the proposed approach and for comparison purposes, we perform simulations on the Algerian network with 114 buses, 175 branches (lines and transformers) and 15 generators. The obtained results indicate that DE is an easy to use, fast, robust and powerful optimization technique compared to the other global optimization methods such as PSO and GA.

전력계통 구조를 고려한 발전기 무효예비력 산정

This paper defines a generator reactive reserve considering power system network. Conventional generator reserve is calculated by the difference between the maximum reactive power output of generator and the current reactive power of generator. However, all generators could not affect on the whole power system. Thus, the effective generators should be selected by sensitivity analysis. The sensitivity depends on network configuration is the relation between generator reactive power outputs and reactive power loads. Using the sensitivity, the effective generator reactive reserve can be calculated.

Multiobjective Bees Algorithm for Optimal Power Flow Problem

This paper presents a multiobjective bees algorithm (MOBA) for solving the multiobjective optimal power flow. The multiobjective optimal power flow is to simultaneously minimize total fuel cost and environmental pollution of generation with considering various constraints i.e. limits on generator real and reactive power outputs, bus voltages, transformer tap-setting and power flow of transmission lines. The proposed multiobjective bees algorithm is developed by using principle of multiobjective optimization. A clustering algorithm is applied for multiobjective bees algorithm in order to manage the size of the Pareto-optimal set. The proposed approach has been tested on the standard IEEE 30-bus system. The multiobjective bees algorithm produces true and well-distributed Pareto-optimal fronts in a single run. The results show that multiobjective bees algorithm has effectiveness and potential for solving multiobjective optimal power flow problem.

An Unfixed Piecewise-Optimal Reactive Power-Flow Model and its Algorithm for AC-DC Systems

A novel AC-DC optimal reactive power flow (ORPF) model with the generator capability limits is proposed. The objective is to minimize the total cost of generator reactive and active power outputs paid by an independent transmission company. In the model, the generator operation area is divided into four parts with each one having a different reactive output cost function. The boundaries between the four parts change with operation states of generators and are unfixed. The model is an unfixed piecewise mixed-integer nonlinear programming problem. A heuristic approach is introduced into the interior point method to solve the unfixed piecewise problem. The numerical results of six test systems that range in size from 14 to 2572 buses demonstrate the effectiveness of the proposed method.