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This paper present new technique namely Quantum-Inspired Evolutionary Programming (QIEP) to determine the optimal location and optimal amount of load to be shed for undervoltage load shedding schemes. This approach is based on the concept of quantum mechanics in the Evolutionary Programming (EP). Quantum-Inspired is implemented according to three l...
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Context 1
... shedding scheme was performed in the initial data of IEEE 33-bus distribution test system in order to improve the voltage profile. The results obtained for IEEE 33-bus distribution test system from the proposed technique are compared with previous technique developed in [18] as shown in Table 2. Multiobjective approaches have been presented in [18] to determine the optimal load shedding in IEEE 33-bus distribution test system. ...
Context 2
... objective of the optimisation is to minimise the sum of curtailed load and the voltage deviation index (VDI) using genetic algorithm (GA). From the results obtained in Table 2, it could be observed that total power loss, amount of shed load and cost of power interruption cost for QIEP are much lower as compared to [18]. The total power loss is reduced 70.74% from the base case using QIEP technique. ...
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Citations
... UFLS is the most promising load shedding strategy [4][5][6], as the UVLS requires positive value for the voltage deviation, which could occur in some normal operating conditions in the power system, as starting of large induction motors [3][4][5][6]. Different approaches are reported for implementing UFLS: traditional schemes [4][5][6][7][8], artificial intelligent techniques [9][10][11][12][13][14][15], and more recently meta-heuristic optimization algorithms [16][17][18][19][20][21][22][23][24][25][26][27][28][29]. ...
... Adaptive load shedding (ALS) method using Support Vector Machines (SVM) and Monte Carlo algorithm were investigated for UFLS in [23]. Different optimization techniques either single and/or multi objectives are reported for optimizing the parameters of UFLS relay in [24][25][26][27][28][29]. These optimization methods have the ability to reduce the amount of the dropped loads while maximizing lowest swing frequency. ...
... The objective function OF, (21), is considered simple and innovative. Equation (21) is subjected to number of constraints imposed on the different variables in functions g and f : These constraints include power flow limits, percentage of allowable load shedding, number of load shedding stages, and time delay of each stage, P minij < P ij P maxij (24) a min a a max (25) s min s s max (26) d min d d max (27) where P ij P minij and P maxij are active power in a transmission line between the buses i and j and its minimum and maximum limits, respectively. a, a min, and a max are percentage of allowable load shedding and its lower and upper limits, respectively. ...
Load shedding is an emergency strategy that mitigates substantial mismatch between the generation and the loads. It generally sustains system stability during/after severe disturbances. This article proposes robust, simple, and innovative Under-Frequency Load Shedding (UFLS) technique based on Multi-Objective Particle Swarm Optimization (MOPSO). MOPSO has the objectives of: minimizing the amount of the dropped load and maximizing the lowest swing frequency. The functionality and feasibility of the proposed MOPSO are corroborated via comprehensive comparison with traditional, adaptive, Single Objective PSO (SOPSO), and Genetic Algorithm (GA) UFLS schemes. IEEE 9-bus and 39-bus systems are used cases for examining the reliability, applicability, and viability of the proposed MOPSO. Different scenarios as: outage of single, multiple generating plants and load increase are applied in the test systems, while load shedding is executed via MOPSO, SOPOS, GA, traditional, and adaptive UFLS approaches. The DigSilent power factor software is used for simulating the test systems while subjected to the different disturbance levels. MATLAB is used for coding SOPSO, MOPSO, adaptive, and GA algorithms. The results show that MOPSO-based UFLS relay produces higher lowest swing frequency and lower amount of dropped load than SOPSO and GA. MOPSO requires less computation requirements than GA approach.
... In [15], a new voltage stability margin index considering load characteristics has been introduced in under-voltage centralized load shedding scheme. Quantum inspired evolutionary programming has been implemented in [16] for the optimal location and sizing of distributed generations (DGs) in radial distribution system. In [17], an optimal load shedding scheme have been proposed to monitor the load-generation unbalance in the plants with internal co-generation and to quickly initiate shedding of an optimal amount of load during a contingency. ...
... Usually these methods do not consider priorities for the loads to be shed, whereas, in distributed load shedding schemes priorities for the loads are being considered. Moreover, in the mathematical formulation of optimal load shedding schemes, reactive power of loads to be shed are not considered [13][14][15][16][17][18][19][20][21][22][23]. Also, the loads are considered to be independent of the system voltage, but in actual practice, the real and reactive power of the loads depends on the system voltage [1]. ...
During generation and overload contingencies in a power system, the system voltage and frequency will decline due to the deficiency of real and reactive powers. Consequently cascaded failures may occur which will lead to complete blackout of certain parts of the power system. Load shedding is considered as the ultimate step of emergency control action that is necessary to prevent a blackout in the power system. This paper proposes a memetic meta-heuristic algorithm known as shuffled frog leaping algorithm (SFLA) to find a solution for the steady state load shedding problem presented here. The optimum steady state load shedding problem uses squares of the difference between the
connected active and the reactive load and the supplied active and reactive power. The supplied active and reactive powers are treated as dependent variables modeled as functions of bus voltages only. The proposed algorithm is tested on IEEE 14 and 30 bus test systems. The viability of the proposed method is established by comparison with the other conventional methods
presented earlier in terms of solution quality and convergence properties.
... In [14] a new voltage stability margin index considering load characteristics has been introduced in under voltage centralized load shedding scheme. In [15] quantum inspired evolutionary programming has been implemented for the optimal location and sizing of DG in radial distribution system. In the present paper the optimal load shedding problem to minimize the sum of squares of the difference between the connected loads and the supplied power has been formulated. ...
... Currently only conventional methods have considered both real and reactive power loads to be shed. Evolutionary computation has been used already for different load shedding strategies that have considered shedding of real power loads only [12]–[15]. However there has been no work reported in the literature that considers both real and reactive power loads for shedding. Therefore in this work both real and reactive power loads are considered for shedding and solved by one of the meta-heuristic algorithms viz. ...
Real and reactive power deficiencies due to generation and overload contingencies in a power system may decline the system frequency and the system voltage. During these contingencies cascaded failures may occur which will lead to complete blackout of certain parts of the power system. Under such situations load shedding is considered as an emergency control action that is necessary to prevent a blackout in the power system by relieving overload in some parts of the system. The aim of this paper is to minimize the amount of load shed during generation and overload contingencies using a new meta-heuristic optimization algorithm known as artificial bee colony algorithm (ABC). The optimal solution for the problem of steady state load shedding is done by taking squares of the difference between the connected and supplied real and reactive power. The supplied active and reactive powers are treated as dependent variables modeled as functions of bus voltages only. The proposed algorithm is tested on IEEE 14, 30, 57, and 118 bus test systems. The applicability of the proposed method is demonstrated by comparison with the other conventional methods reported earlier in terms of solution quality and convergence properties. The comparison shows that the proposed algorithm gives better solutions and can be recommended as one of the optimization algorithms that can be used for optimal load shedding.
... In [15], a new voltage stability margin index considering load characteristics has been introduced in under-voltage centralized load shedding scheme. Quantum inspired evolutionary programming has been implemented in [16] for the optimal location and sizing of distributed generations (DGs) in radial distribution system. In [17], an optimal load shedding scheme has been proposed to monitor the load-generation unbalance in the plants with internal co-generation and to quickly initiate shedding of an optimal amount of load during a contingency. ...
... Usually these methods do not consider priorities for the loads to be shed, whereas, in distributed load shedding schemes priorities for the loads are being considered. Moreover , in the mathematical formulation of optimal load shedding schemes, reactive power of loads to be shed is not considered [13] [14] [15] [16] [17] [18] [19] [20] [21] [22]. Also, the loads are considered to be independent of the system voltage, but in actual practice, the real and reactive power of the loads depends on the system voltage [1]. ...
Generation contingencies in a power system lead to under-frequency and low voltages owing to active and reactive power deficiencies. Load shedding is considered as a last alternative to avoid the cascaded tripping and blackout in power systems during generation contingencies. It is essential to optimize the amount of load to be shed in order to prevent excessive load shedding. To minimize load shedding, this paper proposes the implementation of music inspired optimization algorithm known as improved harmony search algorithm (IHSA). The optimal solution of steady state load shedding is carried out by squaring the difference between the connected and supplied power (active and reactive).
The proposed algorithm is tested on IEEE 14, 30 and 118 bus test systems. The viability of the proposed method in terms of solution quality and convergence properties is compared with the other conventional methods reported earlier.
... In [15], a new voltage stability margin index considering load characteristics has been introduced in under-voltage centralized load shedding scheme. Quantum inspired evolutionary programming has been implemented in [16] for the optimal location and sizing of distributed generations (DGs) in radial distribution system. In [17], an optimal load shedding scheme have been proposed to monitor the load-generation unbalance in the plants with internal co-generation and to quickly initiate shedding of an optimal amount of load during a contingency. ...
... Usually these methods do not consider priorities for the loads to be shed, whereas, in distributed load shedding schemes priorities for the loads are being considered. Moreover, in the mathematical formulation of optimal load shedding schemes, reactive power of loads to be shed are not considered [13][14][15][16][17][18][19][20][21][22][23]. Also, the loads are considered to be independent of the system voltage, but in actual practice, the real and reactive power of the loads depends on the system voltage [1]. ...
Load shedding is considered as a last alternative to avoid the cascaded tripping and blackout in power systems during generation contingencies. It is essential to optimize the amount of load to be shed in order to prevent excessive load shedding. To minimize load shedding, this paper proposes the implementation of nature inspired optimization algorithm known as glowworm swarm optimization (GSO) algorithm. The optimal solution of steady state load shedding is carried out by squaring the difference between the connected and supplied power (active and reactive). The proposed algorithm is tested on IEEE 14, 30, 57, 118 and Northern Regional Power Grid (NRPG)-(India) 246 bus test systems. The viability of the proposed method in terms of solution quality and convergence properties is compared with the conventional methods, namely, projected augmented Lagrangian method (PALM), gradient technique based on Kuhn–Tucker theorem (GTBKTT) and second order gradient technique (SOGT).
... The undervoltage load shedding is applied when the minimum voltage in the distribution systems falls below 0.90 p.u. by increasing loads at all buses simultaneously according to the load profile. Hundreds samples with different loading conditions are obtained from the QIEP technique for optimal load shedding developed in [12]. The technique will determine the optimal amount of load shedding based on multiobjective functions consists of power loss reduction, voltage profile improvement and cost minimization. ...
This paper presents new intelligent-based technique namely Quantum-Inspired Evolutionary Programming-Artificial Neural Network (QIEP-ANN) to predict the amount of load to be shed in a distribution systems during undervoltage load shedding. The proposed technique is applied to two hidden layers feedforward neural network with back propagation. The inputs to the ANN are the load buses and the minimum voltage while the outputs are the amount of load shedding. ANN is trained to perform a particular function by adjusting the values of the connections (weights) between elements, so that a particular input leads to a specific target output. The network is trained based on a comparison of the output and the target, until the network output matches the target. The parameters of ANN are optimally selected using Quantum-Inspired Evolutionary Programming (QIEP) optimization technique for accurate prediction. The QIEP-ANN is developed to search for the optimal training parameters such as number of neurons in hidden layers, the learning rate and the momentum rate. This method has been tested on IEEE 69-bus distribution test systems. The results show better prediction performance in terms of mean square error (MSE) and coefficients of determination (R2) as compared to classical ANN.
Voltage instability is characterized by loss of a stable operating point due to reactive power deficiency, which causes a drop of voltage profile in significant part of the system. The voltage is stable if the system can maintain its voltage within the acceptable limits when there is a change in load admittance. Voltage collapse may occur when the system is subjected to system fault(s); the occurrence of this phenomenon can be either slowly or drastically depending on the severity of the fault(s) [1]. Therefore, it is more accurate to analyze the system behavior dynamically with respect to voltage stability. This paper presents under voltage load shedding scheme using voltage stability indexes based on system behavior in dynamic environment.
