Voltage Instability Prediction Research Articles

Analysis of Voltage Stability Index under Stressed Operating Conditions in Bus Power Systems

In stressed operating conditions, several types of voltage stability indices (VSI) are used for the assessment of voltage stability at specific operating points. The performance of various available VSIs are compared in this paper. The one generation unit tripped effects, single line to ground (SLG) fault and inductive loading variations occur in combinational format with such operating conditions. Voltage collapse occurs in the lines or nodes due to the stressed operating conditions (SOC). SLG fault, loading effects, power margin, line continency ranking, and line number are some of the performance parameters of VSI analysed in this paper. For utilization of reactive power compensation, the proper location can be chosen with the help of critical line and node analysis (CLNA) that makes use of VSIs. For any SOC, accurate voltage instability prediction is performed using VSI as per the simulation results. Under voltage collapse due to multiple causes, the voltage stability assessment of any specific line can be performed using this information.

Voltage Instability Prediction Using a Deep Recurrent Neural Network

This paper develops a new method for voltage instability prediction using a recurrent neural network with long short-term memory. The method is aimed to be used as a supplementary warning system for system operators, capable of assessing whether the current state will cause voltage instability issues several minutes into the future. The proposed method uses a long sequence-based network, where both real-time and historic data are used to enhance the classification accuracy. The network is trained and tested on the Nordic32 test system, where combinations of different operating conditions and contingency scenarios are generated using time-domain simulations. The method shows that almost all N-1 contingency test cases were predicted correctly, and N-1-1 contingency test cases were predicted with over 95 % accuracy only seconds after a disturbance. Further, the impact of sequence length is examined, showing that the proposed long sequenced-based method provides significantly better classification accuracy than both a feedforward neural network and a network using a shorter sequence.

Voltage instability prediction based on reactive power reserve of generating units and zone selection

In this study, a voltage instability prediction algorithm is proposed based on Thevenin impedance matching theorem. This algorithm consists of three important parts: an adaptive network zoning algorithm, the Thevenin-based voltage instability risk predictor (VIRP) and a third-order extrapolation technique. The zoning algorithm, based on the concept of electrical distance, is proposed to divide the main network into several zones after the occurrence of any large event. Each zone consists of some load buses and generating units have the most influences on their voltage stability. The proposed VIRP is defined similarly to the Thevenin indicator multiplied with a contribution factor. This factor is calculated for each zone, based on the lack of the reactive power reserve in its generating units, to increase the predictor value, especially in the proximity of the instability. So, VIRP could reduce the instability detection time with respect to the Thevenin indicator. The further reduction in the instability detection time is obtained by the well known cubic spline extrapolation technique. This function is applied to the values of VIRP to predict the instant of the instability. The proposed algorithm is tested on the modified New England 39-bus test system supplied with the voltage-source converter-high-voltage dc link.

Particle Swarm Optimization trained recurrent neural network for voltage instability prediction

Voltage instability is considered as a major problem that faces the power systems during its operation. Voltage instability prediction is necessary for avoiding voltage collapse. This paper investigates the performance of recurrent neural network (RNN) in voltage instability prediction. A recurrent neural network trained with Particle Swarm Optimization (PSO) is proposed in this paper. The proposed method is examined on 14-bus and 30-bus IEEE standard systems. These systems are simulated using MATLAB/Power System Toolbox program. Also, a detailed comparison between PSO algorithm and Backpropagation (BP) algorithm is discussed. The results proved the effectiveness of the proposed method.

Modified Thevenin‐based voltage instability indicator and load shedding approach for MCF connected network

Marine current farm (MCF) equipped with squirrel cage induction generators (SCIGs) influences on the voltage stability of the power network. Thevenin-based voltage instability risk indicator is a simple and local index that can be used for voltage instability prediction from a load or zero injection bus. This indicator is modified to evaluate the voltage stability from MCF bus. In addition, a Thevenin-power flow tracing-based load shedding approach is also presented for preventing voltage instability. The method compares two equivalent Thevenin impedances of the main network and MCF for total active and reactive power calculations. Then, the total values are used for load sharing and load shedding by power flow tracing algorithm. The proposed hybrid method is tested on IEEE 14-bus test system considering different contingencies. The results confirm the capability of the proposed indicator and load shedding approach in timely prediction and proper prevention of voltage instability problems, respectively.

Robust Voltage Instability Predictor

This letter presents a robust voltage instability predictor using local noisy PMU measurements. A robust recursive least squares estimation is proposed to mitigate the impacts of gross PMU measurement errors on estimating the bus impedance and the Thevenin equivalent impedance used for voltage stability analysis. Numerical results on the IEEE 39-bus system validate the effectiveness and robustness of the proposed method.

POWER STABILITY MONITORING BASED ON VOLTAGE INSTABILITY PREDICTION APPROACH THROUGH WIDE AREA SYSTEM

Nowadays, power systems are being forced to operate closer to its security limit due to current economic growth and the difficulties to upgrade the existing grid infrastructure. With the sudden increment of power demand, voltage instability problem has become a main concern to the power system operator because voltage instability has led or crucially contributed to some major blackouts throughout the world. Hence, methods for early warning and early prevention are required to prevent the power system from a collapse and led to blackout. Real-time voltage instability predictor method is applied on one of the load areas in Tenaga Nasional Berhad (TNB) network in this study. Voltage instability predictor calculation is performed according to the real-time measurement provided by Phasor Measurement Units (PMUs). The voltage instability predictor calculates the power margin and makes comparison to the pre-set margin threshold. Last but not least, the stability condition of the system is based on the comparison between the margin and the pre-set threshold. The main perception is to ensure the margin index always stay positive and suitable early prevention should be implemented when the index is close to zero.

Fast online dynamic voltage instability prediction and voltage stability classification

In this study, a novel approach is proposed for fast prediction of dynamic voltage instability occurrence (as a short term phenomenon and/or a long term one) and voltage stability stiffness of the system, against load disturbances. The main contribution of this paper is in introducing a procedure for generating novel features to be applied to a pattern classifier, by which dynamic voltage stability status of a power system can be predicted. The proposed feature generation procedure only needs measured pre-disturbance variables and disturbance severity provided by phasor measurement units as inputs whereas a set of output variables are derived from an unconstrained power flow program. Since the proposed method does not need any measured post disturbance data, the prediction task can be performed just after the disturbance. Thus, corrective actions can be executed in a short time after the disturbance to inhibit voltage instability. Moreover as no measured post-disturbance data are needed, the proposed method can also be employed in preventive procedures for voltage stability enhancement and/or decreasing possibility of voltage instability occurrence. Training a decision tree based classifier with the proposed features and testing the method on a modified version of Nordic32 test system, the simulation results have demonstrated that the proposed method effectively predicts the status of dynamic voltage stability in the test system.

On-line dynamic voltage instability prediction based on decision tree supported by a wide-area measurement system

In this study, a new decision tree-based method is proposed to assess the small disturbance (limited to load disturbance) voltage stability of power systems by only using synchronised phasors (synchrophasors) measured by phasor measurement units in a wide-area measurement system. To properly predict the voltage instability considering various load increase directions as well as load increase step sizes, a mathematical morphology algorithm is used to extract appropriate features from synchronised measured voltage phasors during the few seconds just after the disturbance. These features along with the pre-disturbance ones are then applied to a trained decision tree to predict the voltage instability of the power system. The proposed method is applied and tested on the Nordic32 test system. To examine the efficiency and accuracy of the proposed method, the dynamic behaviours of on-load tap changing transformers as well as the stator current limiters of synchronous generators are considered. The simulation results demonstrate that this method effectively predicts the voltage instability of the power system during the first two seconds just after the disturbance, for the test system.

Studi Efektifitas Indikator Kestabilan Tegangan Pada Model Dinamis Sistem Tenaga Listrik

The paper presents the effectiveness of voltage stability indices for dynamic power system modelling study in providing information about the proximity of voltage collapse in power system. Four simple stability indices as Voltage Instability Predictor (VIP), Impedance Stability Index (ISI), Line Index (L index) and Voltage Collapse Prediction Index (VCPI) are compared using WSCC 9 bus test system. The comparison show the ISI and VCPI are much more reliable indicator, give the fast indication and fast time computation than L index for voltage collapse in dynamics voltage collapse prediction.