Neural network approach to voltage and reactive power control in power systems
14 Nov 2005-pp 228-233
TL;DR: A neural network based solution for voltage-VAR control is proposed with the aim to reduce the real power loss flowing in a power system and subsequently improve the voltage profile.
Abstract: Energy management engineers are focusing their interest in tapping maximum profit for their system from substation automation (SSA)/distribution automation (DA) Volt/Var control through fixed/switched capacitors, transformer taps and voltage set points are at different levels of research and implementation A neural network based solution for voltage-VAR control is proposed with the aim to reduce the real power loss flowing in a power system and subsequently improve the voltage profile The module consists of two networks The first network determines the control parameters ie, generator voltage, transformer taps and shunt capacitance for minimal power loss when the loads at the load buses are specified as inputs With the obtained parameters, a load flow program is run and power loss is noted and the system is checked for voltage violations In case of voltage violations, the voltages are fed to the second network, which gives dQ at different buses for voltage violation minimization These modules are successfully tested for different load patterns on a six-bus system
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TL;DR: In this article, the authors proposed a new intelligent approach to facilitate the implementation of the optimal power flow calculations to be utilized in various control centres, where the main advantage of the proposed intelligent system with real control centres is the possibility of controlling the system voltage profile in a tracking mode.
Abstract: The optimal power flow issue is one of the most important problems faced by dispatching engineers regarding large scale power systems. It is a particular mathematical approach of the global power system optimization problem that aims at determining the least control movements to keep power system at the most desired state. Thus, it represents a flexible and powerful tool, which can address a wide range of planning and operation studies. However, the complexity of optimal power flow increases dramatically with large-scale networks, which often discourages the utilization of this powerful tool in many applications. This paper proposes a new intelligent approach to facilitate the implementation of the optimal power flow calculations to be utilized in various control centres. A main advantage of the proposed intelligent systems with real control centres is the possibility of controlling the system voltage profile in a tracking mode. The simulation results using this intelligent system when applied to the IEEE 30-bus power network emphasize the validity and effectiveness of the proposed technique.
24 citations
Cites background from "Neural network approach to voltage ..."
...• OPF can also be used to minimize the total real power loss through reactive power dispatch [2], [4], [6], [7]....
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...The problems associated with the OPF have been discussed in much of the literature [2], [7], [8]....
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TL;DR: Artificial Neural Network, one of the Artificial Intelligence techniques, for the Volt / Var control in power distribution systems with dispersed generation (DG), shows promising results after testing.
Abstract: In this paper, Artificial Neural Network, one of the Artificial Intelligence (AI) techniques, for the Volt / Var control in power distribution systems with dispersed generation (DG) is proposed. Artificial neural networks have been considered due to their ability for real time control, simpler calculations and adaptability to different operating conditions. Neuro-controllers are much more effective, fast acting than conventional controllers. Neural network for controlling Step voltage regulator (SVR) with line rise compensation (LRC) /line drop compensation (LDC) function has been presented. The neural network based controller has been simulated for a radial distribution system with DG and the neuro-controller shows promising results after testing. Keywords Artificial Intelligence, Artificial neural network, Dispersed generation, Distribution system, Line drop compensation, Line rise compensation, Step Voltage regulator, Voltage / Reactive power control.
11 citations
Cites background from "Neural network approach to voltage ..."
...Keywords Artificial Intelligence, Artificial neural network, Dispersed generation, Distribution system, Line drop compensation, Line rise compensation, Step Voltage regulator, Voltage / Reactive power control....
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13 May 2014
TL;DR: In this article, a method for optimal distributed generation placement with goal of reducing active power system losses and voltage level regulation using an Artificial Neural Network (ANN) by simultaneous formulation for the determination process is presented.
Abstract: This paper presents a method for optimal Distributed Generation placement with goal of reducing active power system losses and voltage level regulation. Active power losses in radial distribution network are determined using an Artificial Neural Network (ANN) by simultaneous formulation for the determination process based on voltage level control and injected power. Adequate installed power of distributed generation and the appropriate terminal for distributed generation utilization are selected by means of a genetic algorithm (GA), performed in a distinct manner that fits the type of decision-making assignment. The training data for ANN is obtained by means of load flow simulation performed in DIgSILENT PowerFactory software on a part of the Croatian distribution network. The active power losses and voltage conditions are simulated for various operation scenarios in which the back propagation ANN model has been tested to predict the power losses and voltage levels for each system terminal, and GA is used to determine the optimal terminal for distributed generation placement.
9 citations
TL;DR: The paper analyzes the possibility of reducing active power losses in power system, constrained by regulated voltage levels, by implementing appropriate distributed generation capacity by developing hybrid methods based on artificial neural network and genetic algorithm.
Abstract: The paper analyzes the possibility of reducing active power losses in power system, constrained by regulated voltage levels, by implementing appropriate distributed generation capacity. The objectives of this paper were achieved by developing hybrid methods based on artificial neural network and genetic algorithm. Methods have been developed to determine the impact of different distributed generation power on all terminals in the observed system. The method that uses artificial neural network and genetic algorithm is applicable for radial distribution networks, and method using load flow and genetic algorithm is applicable to doubly-fed distribution network. For comparison purposes, additional method was developed that uses neural networks for the decision-making process. Data for training the neural network was obtained by power flow calculation in the DIgSILENT PowerFactory software on a part of Croatian distribution network. The same software was used as an analytical tool for checking the correctness of solutions obtained by optimization.
5 citations
Cites background from "Neural network approach to voltage ..."
...According to [2] automated distribution network, which represents prerequisite for smart-grid, must contain fast and accurate solution for power flow and current-voltage conditions control....
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TL;DR: The trained neural networks are capable of controlling the voltage, and reactive power in power systems is proved by the results with the high level of precision and speed.
Abstract: In order to minimize the power loss and to control the voltage in the power systems, the proposed momentum-based wavelet neural network and proposed momentum-based double wavelet neural network are proposed in this paper. The training data are obtained by using linear programming method by solving several abnormal conditions. The control variables considered are generator voltages and transformer taps, and the dependent variables are generator reactive powers and load bus voltages. The IEEE 14-bus system and IEEE 30-bus system are tested using the linear programming, Levenberg–Marquardt artificial neural network, proposed momentum-based wavelet neural network and proposed momentum-based double wavelet neural network to validate the effectiveness of the proposed MDWNN method. The trained neural networks are capable of controlling the voltage, and reactive power in power systems is proved by the results with the high level of precision and speed.
5 citations
References
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TL;DR: This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification and exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components.
Abstract: Artificial neural net models have been studied for many years in the hope of achieving human-like performance in the fields of speech and image recognition. These models are composed of many nonlinear computational elements operating in parallel and arranged in patterns reminiscent of biological neural nets. Computational elements or nodes are connected via weights that are typically adapted during use to improve performance. There has been a recent resurgence in the field of artificial neural nets caused by new net topologies and algorithms, analog VLSI implementation techniques, and the belief that massive parallelism is essential for high performance speech and image recognition. This paper provides an introduction to the field of artificial neural nets by reviewing six important neural net models that can be used for pattern classification. These nets are highly parallel building blocks that illustrate neural net components and design principles and can be used to construct more complex systems. In addition to describing these nets, a major emphasis is placed on exploring how some existing classification and clustering algorithms can be performed using simple neuron-like components. Single-layer nets can implement algorithms required by Gaussian maximum-likelihood classifiers and optimum minimum-error classifiers for binary patterns corrupted by noise. More generally, the decision regions required by any classification algorithm can be generated in a straightforward manner by three-layer feed-forward nets.
7,798 citations
"Neural network approach to voltage ..." refers background in this paper
...predicted load profile. Mamandur and Chenoweth [ 3 ] have presented...
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TL;DR: In this article, a particle swarm optimization (PSO) for reactive power and voltage control (volt/VAr control: VVC) considering voltage security assessment (VSA) is presented.
Abstract: Summary form only given, as follows. This paper presents a particle swarm optimization (PSO) for reactive power and voltage control (volt/VAr control: VVC) considering voltage security assessment (VSA). VVC can be formulated as a mixed-integer nonlinear optimization problem (MINLP). The proposed method expands the original PSO to handle a MINLP and determines an online VVC strategy with continuous and discrete control variables such as automatic voltage regulator (AVR) operating values of generators, tap positions of on-load tap changer (OLTC) of transformers, and the number of reactive power compensation equipment. The method considers voltage security using a continuation power now and a contingency analysis technique. The feasibility of the proposed method is demonstrated and compared with reactive tabu search (RTS) and the enumeration method on practical power system models with promising results.
1,340 citations
TL;DR: In this paper, a mathematical formulation of the optimal reactive power control (optimal VAR control) problem and results from tests of the algorithm are presented in order to minimize the real power losses in the system.
Abstract: A mathematical formulation of the optimal reactive power control (optimal VAR control) problem and results from tests of the algorithm are presented in this paper. The model minimizes the real power losses in the system. The constraints include the reactive power limits of the generators, limits on the load bus voltages, and the operating limits of the control variables, i.e., the transformer tap positions, generator terminal voltages and switchable reactive power sources. Real power economic dispatch is accomplished by standard techniques.
367 citations
TL;DR: In this article, an expert system using a two-stage artificial neural network is proposed to control in real-time multicap capacitors installed on a distribution system for a nonconforming load profile such that the system losses are minimized.
Abstract: An expert system using a two-stage artificial neural network is proposed to control in real time multicap capacitors installed on a distribution system for a nonconforming load profile such that the system losses are minimized. The required input data are directly obtained from online measurements which include the active and reactive line power flows, voltage magnitudes, and the current capacitor settings at certain buses. The optimum control does not involve any iteration procedure; therefore, it is computationally very efficient. Studies on a 30-bus distribution test system show the expert system to have quite satisfactory results. >
221 citations
TL;DR: In this paper, an artificial neural network (ANN) is designed to reach a preliminary dispatch schedule for the capacitor and load tap changer (LTC) positions for the 24 hours in the next day.
Abstract: Reactive power/voltage control in a distribution substation is investigated in this work. The purpose is to determine proper capacitor on/off status and suitable load tap changer (LTC) positions for the 24 hours in the next day. To reach this goal, an artificial neural network (ANN) is designed to reach a preliminary dispatch schedule for the capacitor and LTC. The inputs to the ANN are main transformer real power and reactive power and primary and secondary bus voltages and the outputs are the desired capacitor on/off status and LTC tap positions. The preliminary dispatch schedule is further refined by fuzzy dynamic programming in order to reach the final schedule. To demonstrate the effectiveness of the proposed method, reactive power/voltage control is performed on a distribution substation in Taipei, Taiwan. Results from the example show that a proper dispatch schedule for capacitor and LTC can be reached by the proposed method in a very short period.
106 citations