Voltage Collapse Proximity Index Research Articles

Performance of Various Voltage Stability Indices in a Stochastic Multiobjective Optimal Power Flow Using Mayfly Algorithm

The performance of voltage stability indices in the multiobjective optimal power flow of modern power systems is presented in this work. Six indices: the Voltage Collapse Proximity Index (VCPI), Line Voltage Stability Index (LVSI), Line Stability Index (Lmn), Fast Voltage Stability Index (FVSI), Line Stability Factor (LQP), and Novel Line Stability Index (NLSI) were considered as case studies on a modified IEEE 30-bus consisting of thermal, wind, solar and hybrid wind-hydro generators. A multiobjective evaluation using the multiobjective mayfly algorithm (MOMA) was performed in two operational scenarios: normal and contingency conditions, using the MATLAB–MATPOWER toolbox. Fuzzy Decision-Making technique was used to determine the best compromise solutions for each Pareto front. To evaluate the computational efficiency of the case studies, a preference selection index was used. The results indicate that VCPI and NLSI yielded the best-optimized system performance in minimizing generation costs, transmission loss reduction, and simulation time for normal and contingency conditions. The best-case studies also promoted the most scheduled reactive power generation from renewable energy sources (RES). On average, the VCPI index contributed the highest penetration level from RES (13.40%), while the Lmn index had the lowest. Overall, VCPI and Lmn index provided the best and worst average performance in both operating scenarios, respectively. Also, the MOMA algorithm demonstrated superior performance against the multiobjective harris hawks algorithm (MHHO), multiobjective Jaya algorithm (MOJAYA), multiobjective particle swarm algorithm (MOPSO), and nondominated sorting genetic algorithm III (NSGA-III) algorithms. In all, the proposed approach yields the lowest system cost and loss compared to other methods.

Optimal reactive power planning using oppositional grey wolf optimization by considering bus vulnerability analysis

AbstractPower system instability primarily results from the deviation of the frequency from its predefined rated value. This deviation causes voltage collapse, which further leads to sudden blackouts of the power system network. It is often triggered by a lack of reactive capacity. The solution to the reactive capacity problem can be obtained in two stages. In the first stage, the vulnerable buses, also known as ‘weak buses’, where voltage failure might occur are identified, and the Var compensating devices are mounted at those locations. The proposed approach utilizes three simple vulnerable bus detection methods: the fast voltage stability index, line stability index, and voltage collapse proximity index (VCPI). In the second stage, various optimization algorithms are implemented to determine the optimal setting of Var sources, such as particle swarm optimization, differential evolution, the whale optimization algorithm, the grasshopper optimization algorithm, the salp swarm algorithm, grey wolf optimization, and oppositional grey wolf optimization (OGWO). The results indicate that the best approach to poor bus recognition is the VCPI, and the OGWO technique provides a much less expensive system than other optimization strategies used for problems of optimal reactive power planning.

Multiobjective optimal power flow for static voltage stability margin improvement

Worldwide, utilities are aiming to increase the stability of modern power systems during system disturbances. Optimizing generation scheduling can improve system security in contingency and stressed conditions while lowering losses and generation costs. An efficient operating strategy for maintaining power system stability is proposed in this work. The paper focuses on incorporating a Voltage Collapse Proximity Index (VCPI) in the traditional optimal power flow problem for multiobjective optimization (MO). Different case studies are assessed to evaluate the impact on the control variables. A Preference Selection index (PSI) is utilized to determine the best-case study for optimal system operation. The effectiveness of the proposed approach is tested on standard IEEE 30-bus and IEEE 57-bus during normal, contingency, and stressed conditions using MATPOWER. During normal conditions, the MO voltage stability constrained optimal power flow (VSC-OPF) increases the system stability by 28.13 % higher than the single objective (SO) case. Furthermore, the transmission losses are lowered by 14.69% with the proposed MO approach. During line outage contingency conditions, the voltage stability enhancement and loss reduction are higher in the MO than in the SO case by 13.60% and 23.19%. However, the loss minimization and stability improvement during normal and contingency conditions come at a slightly higher generation cost of 5.05% in both systems. On the other hand, during stressed conditions, the SO performs better in voltage stability improvement (by 8.77%) and loss reduction (by 6.97%) than in the MO voltage stability constrained OPF. Additionally, PV Curve analysis for the two systems indicates that voltage stability in MO OPF problems provides a more significant margin enhancement of 9.00%, 118.95% in normal and contingency, respectively, higher than the SO case. However, the SO case increases the load margin by 12.36% more than the MO case in stressed conditions. Consequently, the PSI ranks the multiobjective optimization of the three objectives as the most optimal way for operating the systems in normal and line outage contingency conditions. However, during increased load conditions, the system performance is better if a singular objective function is considered. This is due to the lack of adequate reactive power generation during stressed conditions, and hence a singular objective focus is sufficient to assure system stability. Therefore, the proposed approach is an effective preventive control measure for possible voltage collapse in typical power systems. The resulting improvement also brings about a sufficient system stability margin, causing the system to become more secure.

Ensuring Reliable Operation of Electricity Grid by Placement of FACTS Devices for Developing Countries

Flexible AC Transmission Systems (FACTS) are essential devices used for the efficient performance of modern power systems and many developing countries lack these devices. Due to the non-existence of these advanced technologies, the national grid remains weak and vulnerable to power stability issues that can jeopardize system stability. This study proposes novel research to solve issues of an evolving national grid through the installation of FACTS devices. FACTS devices play a crucial role in minimizing active power losses while managing reactive power flows to keep the voltages within their respective limits. Due to the high costs of FACTS, optimization must be done to discover optimal locations as well as ratings of these devices. However, due to the nonlinearity, it is a challenging task to find the optimal locations and appropriate sizes of these devices. Shunt VARs Compensators (SVCs) and Thyristor-Controlled Series Compensators (TCSCs) are the two FACTS devices considered for the study. Optimal locations for SVCs and TCSCs are determined by Voltage Collapse Proximity Index (VCPI) and Line Stability Index (Lmn), respectively. Particle Swarm Optimization (PSO) is employed to find the ideal rating for FACTS devices to minimize the system operating cost (cost due to active power loss and capital cost of FACTS devices). This technique is applied to IEEE (14 and 30) bus systems. Moreover, reliable operation of the electricity grid through the placement of FACTS for developing countries has also been analysed; Pakistan being a developing country has been selected as a case study. The planning problem has been solved for the present as well as for the forecasted power system. Consequently, in the current national network, 6.21% and 6.71% reduction in active and reactive power losses have been observed, respectively. Moreover, voltage profiles have been improved significantly. A detailed financial analysis covering the calculation of Operation Cost (OC) of the national grid before and after the placement of FACTS devices is carried out.

Multiobjective Optimal Power Flow for Static Voltage Stability Margin Improvement

The prevailing economic and environmental pressures have pushed the operation of power systems to close to stability limits. In such situations, utilities are aiming to increase the network’s stability during system disturbances. Optimized generation scheduling in contingency and stressed conditions can improve system security while lowering losses and generation costs. This paper focuses on incorporating Voltage Collapse Proximity Index in the conventional optimal power flow problem for multiobjective optimization. Multiobjective Particle Swarm Optimization (MOPSO) algorithm is utilized for its fast convergence and strong exploration and exploitation capabilities. The minimization of three competing objective functions of generation cost, power loss, and voltage stability is assessed in different case studies to evaluate the impact on the control variables. To select the best case study for optimal system operation a Preference Selection index (PSI) is utilized. The effectiveness of the proposed approach is tested on standard IEEE 30-bus and IEEE 57-bus during normal, contingency, and stressed conditions using MATPOWER. The results indicate that an average voltage stability improvement of 53.80%, 66.90%, and 54.15% is achieved in normal, contingency, and stressed system conditions respectively when the three objective functions are optimized simultaneously. Additionally, a possible voltage collapse is prevented during system disturbances.

Voltage Stability of Power System using PV Curve and PMU data

This paper describes a voltage stability indicator that can be used to determine the proximity to system collapse by power system operators. The proposed PV curve method to determine voltage instability is compared with the existing voltage collapse proximity index (VCPI). VCPI is indicative of critical transmission lines whereas the PV curve analysis is indicative of critical buses. Phasor Measurement Unit (PMU) data facilitates in quick calculation and presentation of the indices to the system operators. The drawbacks of VCPI and how PV curve method is better than VCPI are presented. The simulations have been carried out in Real Time Digital Simulator (RTDSTM) using the New England IEEE 39 bus system. The indices accurately quantify the closeness of the power system towards instability. A slope monitoring method has been used to take restorative actions for the system to return to secure operating conditions.

Statistical method for on-line voltage collapse proximity estimation

This paper is aimed to propose a reliable method for estimating the voltage collapse proximity through a model obtained using statistical techniques. In the model building process a database is required, therefore the Voltage Collapse Proximity Index (VCPI) is used to obtain previous readings for different contingencies and loading conditions. This analytical proposal could be combined with existing equipment in the power system control centers for future on-line applications. The proposed method is applied to the IEEE 14-bus test system and the 190-bus Mexican equivalent. Results indicate that the proposed strategy is a reliable choice.

Experimental design for a large power system vulnerability estimation

This paper proposes a reliable experiment design for large power system vulnerability estimation. Assuming that in most cases security problems result in voltage instabilities, a strategy that allows a voltage collapse proximity estimation, known as vulnerability, is proposed. The premise is that power flow calculation is not required for vulnerability estimation, requiring only the bus voltage measurements; the approach is a promising tool for on-line implementation. The result is a model based on statistical techniques that analyze databases previously built, that contains voltage profiles for different contingencies and the corresponding calculated value for the Voltage Collapse Proximity Index (VCPI).

Sensitivity Analysis based Optimal Location and Tuning of Static VAR Compensator using Firefly Algorithm

This paper presents a new Meta heuristic optimization algorithm called firefly algorithm (FA) used to solve the multi objective optimal power flow to identify the optimal setting of Static VAR Compensator (SVC) and optimal location is identified by using sensitivity analysis when the system is operating under normal and overloaded conditions. Sensitivity analysis based Voltage collapse proximity Index is proposed for placing the SVC at appropriate location under normal and over loaded conditions. Once the location to install SVC is identified, the optimal allocation of SVC is determined through firefly algorithm based A multi-criterion objective function comprising of four objectives minimize total power loss, minimize total voltage magnitude deviations, minimize the fuel cost of total generation and minimize the branch loading to obtain the optimal power flow. Simulations have been implemented in MATLAB and the IEEE 14-bus, IEEE 30-bus and IEEE 57-bus systems have been used as a case study. The results we have obtained indicate that installing SVC can significantly enhance the voltage stability of power system. Also for the purpose of comparison the proposed technique was compared with another optimization technique namely Genetic Algorithm (GA). The results we have obtained indicate that FA is an easy to use, robust, and powerful optimization technique compared with GA.

Sensitivity Analysis Based Optimal Location and Tuning of Static Var Compensator Using Firefly Algorithm

This paper presents a new Meta heuristic optimization algorithm called firefly algorithm (FA) used to solve the multi objective optimal power flow to identify the optimal setting of Static VAR Compensator (SVC) and optimal location is identified by using sensitivity analysis when the system is operating under normal and overloaded conditions. Sensitivity analysis based Voltage collapse proximity Index is proposed for placing the SVC at appropriate location under normal and over loaded conditions. Once the location to install SVC is identified, the optimal allocation of SVC is determined through firefly algorithm based A multi-criterion objective function comprising of four objectives minimize total power loss, minimize total voltage magnitude deviations, minimize the fuel cost of total generation and minimize the branch loading to obtain the optimal power flow. Simulations have been implemented in MATLAB and the IEEE 14-bus, IEEE 30-bus and IEEE 57-bus systems have been used as a case study. The results we have obtained indicate that installing SVC can significantly enhance the voltage stability of power system. Also for the purpose of comparison the proposed technique was compared with another optimization technique namely Genetic Algorithm (GA). The results we have obtained indicate that FA is an easy to use, robust, and powerful optimization technique compared with GA.

Multiple regression models as identifiers of power system weak points

Multiple regression models designed to identify the weak points of a power system are presented. On the basis of the determined reliability indices (duration of loss of load DLOL, and demand-not-supplied DNS), and by taking into account the apparent power difference criterion SDC as a voltage collapse proximity index, one can define the mutual relationship between these indices and the states of the particular power system components. The regression coefficients of MRMs indicate participation of each power system component (generators, transmission lines, substations, etc.) with regard to DLOL, DNS and SDC. Such information helps take measures of increasing the power system reliability and security. Subject-related results obtained in the Bosnian utility are given.