On-line voltage stability assessment using radial basis function network model with reduced input features

Devaraj, D.; Roselyn, J. Preetha

doi:10.1016/j.ijepes.2011.06.008

Cited by 82 publications

(31 citation statements)

References 16 publications

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“…Typical measures applied for the performance evaluation of ANNs are residual squared error (R 2 ) and mean square error (MSE) [23,26]. In this paper, R is used to measure the performance of the designed ANN.…”

Section: Evaluation Of Model Performancesmentioning

confidence: 99%

“…In [22], active and reactive powers of load buses were used in the ANN input vector. Reference [23] used voltage magnitude of load buses and active and reactive powers of load and generation buses. In [13], performances of different inputs of the ANN were compared and it was deduced that the best performance of ANN is attained using bus voltage phasors as the input vector.…”

Section: Introductionmentioning

confidence: 99%

“…This may become worse in N-2 contingencies. Research attempts in [20][21][22][23] have used an ANN to evaluate voltage stability in the normal condition and a separate ANN for contingency states. This may be inappropriate for a large power system with a huge number and different types of contingencies.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Power systems wide-area voltage stability assessment considering dissimilar load variations and credible contingencies

Shakerighadi

Aminifar

Afsharnia

2018

J. Mod. Power Syst. Clean Energy

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This paper reveals that the existing techniques have some deficiencies in the proper estimation of voltage stability margin (VSM) when applied to a power system with different load change scenarios. The problem gets worse when credible contingencies occur. This paper proposes a real-time wide-area approach to estimate VSM of power systems with different possible load change scenarios under normal and contingency operating conditions. The new method is based on an artificial neural network (ANN) whose inputs are bus voltage phasors captured by phasor measurement units (PMUs) and rates of change of active power loads. A new input feature is also accommodated to overcome the inability of trained ANN in prediction of VSM under N-1 and N-2 contingencies. With a new algorithm, the number of contingencies is reduced for the effective training of ANN. Robustness of the proposed technique is assured through adding a random noise to input variables. To deal with systems with a limited number of PMUs, a search algorithm is accomplished to identify the optimal placement of PMUs. The proposed method is examined on the IEEE 6-bus and the New England 39-bus test system. Results show that the VSM could be predicted with less than 1% error.

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Section: Evaluation Of Model Performancesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Power systems wide-area voltage stability assessment considering dissimilar load variations and credible contingencies

Shakerighadi

Aminifar

Afsharnia

2018

J. Mod. Power Syst. Clean Energy

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show abstract

“…However, the proposed algorithm is restricted to large system with large loads if better solutions are required. Besides that, [17] have used the VSI value as an indicator during optimization of reactive power in a network. In this study, the reactive power was found to be an important factor in the network stabilization by means of VSI indicator.…”

Section: Evaluation Of Voltage Stability By Using Voltage Stability Imentioning

confidence: 99%

“…One of the methods for such assessment is by use of Voltage Stability Index (VSI) which many researchers have attempted using in order to find the distance between initial loading points to the maximum loadbility point [3][4][5][6]. This index gives a measure of how far the present operating point is to voltage collapse point which is expressed in term of real or/and reactive power as well as the line parameters.…”

Section: Introductionmentioning

confidence: 99%

An Overview on Voltage Stability Indices as Indicators of Voltage Stability for Networks with Distributed Generations Penetration

Musa¹

2015

IJSTS

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Abstract:The increase in Distributed Generation (DG) penetration in distribution network can be used as a means of addressing the increasing load demand without the upgrade of transmission lines. However, this increasing demand despite the supplementary supply from DG sources can cause the system to operate at its maximum capacity or at point of voltage collapse. It therefore, becomes necessary to determine the maximum capacity limit of the system before voltage collapse occurs due to instability. This paper reviews the concept of voltage stability index (VSI) as an indicator of a weak bus that is closed to its maximum allowable limit or the most critical line to voltage collapse in a network. The review also evaluates various voltage stability indices including those originally developed for transmission systems. Subsequently, their adequacies are quantitatively compared and from the comparison, it is observed that the existing voltage stability indices would be inadequate for assessing the most sensitive bus or line on the verge of voltage collapse for modern distribution systems.

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Development of online demand response framework for smart grid infrastructure toward social welfare

Paramathma

Devaraj

Selvi

et al. 2021

Int Trans Electr Energ Syst

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Summary Due to continuous growth in the demand for electricity with unmatched generation and transmission capacity expansion, the resource management and rescheduling of load without affecting the welfare of the market participants are the major concerns of the power market. As the demand changes continuously, the peak load consumers are unaware of the bidding cost and penalty. The Artificial Neural Network (ANN) based online Demand Response (DR) connectivity scheme is proposed for the smart power networks to obtain the equilibrium demand. The optimally rescheduled load, percentage increase of peak load, and time are considered the ANN input. Bidding cost and penalty of the peak load consumer are considered as the output. The data required to develop the ANN are generated using the Genetic Algorithm (GA) to maximize social welfare as the objective. The optimum load curtailment is taken as the decision variable. In this proposed method, the Curtailment Index (CI) is calculated and incorporated to utilize DR connectivity properly. This adopted method is tested with IEEE 30 bus system, and the GA results for CI and bidding cost have been compared with Particle Swarm Optimization (PSO) methodology. The ANN predicted bidding cost results are compared with GA optimized bidding cost. The result shows the accuracy of ANN for online DR techniques with minimum testing Mean Square Error (MSE) value of 1.72 × 10−3 and the training period of 45.98 seconds.

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On-line voltage stability assessment using radial basis function network model with reduced input features

Cited by 82 publications

References 16 publications

Power systems wide-area voltage stability assessment considering dissimilar load variations and credible contingencies

Power systems wide-area voltage stability assessment considering dissimilar load variations and credible contingencies

An Overview on Voltage Stability Indices as Indicators of Voltage Stability for Networks with Distributed Generations Penetration

Development of online demand response framework for smart grid infrastructure toward social welfare

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