Feeder Switching Research Articles

Network Reconfiguration Framework for CO2 Emission Reduction and Line Loss Minimization in Distribution Networks Using Swarm Optimization Algorithms

This paper presents the development of a generic active distribution network (ADN) operation simulation framework that incorporates selected swarm optimization algorithms (SOAs) for the purpose of reducing CO2 emissions and line loss minimization through network reconfiguration (NR). The framework has been implemented in the ADN of Taipower. Network data, provided by the Distribution Mapping Management System and Distribution Dispatch Control Center (DDCC) of Taipower, were converted into an OpenDSS script to create ADN models. The SOA is integrated into the framework and utilized to determine the statuses of both four-way and two-way switches in the planning and operating stages, in accordance with the proposed multi-objective function and operational constraints. The weightings for these decisions can be customized by distribution operators to meet their specific requirements. In this paper, the weighting for line loss reduction is set to one for minimizing CO2 emissions. The numerical results demonstrate that the proposed ADN framework can recommend a feeder switching scheme to distribution operators, aiming to balance feeder loading and minimize the neutral line current. Finally, this approach leads to reduced line losses and minimizes CO2 emissions. In contrast to relying solely on historical operational experience, this generic ADN reconfiguration framework offers a systematic approach that can significantly contribute to reducing CO2 emissions and enhancing the operational efficiency of ADNs.

A novel fault location method for the active distribution network based on dynamic quantum genetic algorithm

The fault location of an active distribution network is a vital analysis to prevent major outages in the power system. Considering the influence of renewable distributed generations on fault characteristics, this paper proposes a novel location method based on a dynamic quantum genetic algorithm to solve for fault locations in active distribution networks. In the method, the fault current code is measured based on feeder terminal units. A universal switching function is presented to convert the feeder switch status into an uploaded fault current code. The fault location model is defined as an optimization problem that presents the evaluation objective function with an anti-false-positive factor. The dynamic quantum genetic algorithm is developed to locate the fault feeder according to the uploaded fault current code of the feeder terminal unit. The algorithm adopts dynamic rotating gate strategy and adaptive quantum crossover strategy to satisfy the requirements of quickness and accuracy for fault location. Moreover, the method avoids easily falling into a local optimum by integrating the discrete quantum mutation. The proposed fault location technique is tested and compared to other existing techniques on a 33-bus active distribution network. The simulation results show that the proposed fault location method can locate fault feeders accurately with fast computational times under conditions of single or multiple faults and with an information distortion of the feeder terminal unit.

Generalized Distribution Feeder Switching with Fuzzy Indexing for Energy Saving

The objective of this study is to analyze feeder loss minimization and load balance under given constrains. Effective methods are required for feeder switching/reconfiguration. Feeder switching is a mixed-integer large-scale combinatorial problem for optimization, not easily solvable with classical optimization techniques, especially involving a great number of switches. This paper proposes a fuzzy indexing algorithm for feeder switching, with membership functions defined for switches such as thermometers or indices. The optimal switches can be determined through fuzzy index operations. With membership functions defined, the developed method used numerical operations for indices instead of the “set” operation or the min-max operations of traditional fuzzy algorithms. The optimization problem becomes a simple numeric calculation instead of a large-scale sorting problem and is much faster than most algorithms. It greatly reduces the computation time and enhances efficiency, which is suitable for either planning or operation purposes. Many algorithms were tested with three typical examples chosen for illustration, including the “optimal” results with an exhausted search. It shows that the proposed algorithm is very effective and can balance the load to reduce the loss and costs in obtaining the solution.

Browse or browsing: Investigating goat preferences for feeding posture, feeding height and feed type.

Goats naturally browse different forages in various postures; this differs from typical farm practice, thus there are opportunities to improve goat welfare by understanding what and how they like to eat. We investigated if feeding preference was related to posture, feeder height relative to the ground, and type of feed. Sixteen adult, Saanen cross females participated in two experiments comparing a floor-level feeder (grazing posture; farm standard), with an elevated feeder (browsing posture; Exp1) and a platform-level feeder (raised, grazing posture; Exp2), when two forages (leaves, grass) were offered. Measurements included feed intake (g of DM/feeder), feeder switching frequency, first feeder visited, latency to visit first feeder and exploration and non-feeding activity time. Effects of posture (Exp1), height (Exp2) and feed type were analyzed. Type of feed affected preference for feeding posture and height. All goats consumed leaves over grass (Exp1: POP: 188 ± 6.52 g, GRA: 20.3 ± 7.19 g; Exp2: POP: 191 ± 6.15 g, GRA: 0.231 ± 6.91 g; P < 0.001), and the feeder containing leaves was often visited first (Exp 1: GRA/POP: 94% of visits, P < 0.001, POP/GRA: 53%, P = 0.724; Exp 2: GRA/POP: 91%, P < 0.001; POP/GRA: 69%, P = 0.041). When goats received only leaves, they consumed more from the floor-level (162 ± 22.2 g) vs. elevated level (102 ± 21.9 g) feeder (P = 0.039). When goats received only grass, there was no posture or height preference; however, they changed feeders more frequently (at least 4x (Exp1) and 2x (Exp2) more than other combinations; P > 0.01). Feed intake was negatively affected by exploring time (Exp1 only: r = -0.541; P < 0.001) and performing non-feeding activities (Exp1: r = -0.698; P < 0.001; Exp2: r = -0.673; P < 0.001). We did not identify a preference for elevated feeding posture; however, we suggest that our short test (compared to previous work) encouraged goats to make choices based on line-of-sight and also that the elevated feeder design (replicated from previous work) made leaf access harder. Nonetheless, we highlight that some goats actively used the elevated feeder; this coupled with the clear preference for leaves over grass, suggests that offering feed type and presentation diversity would allow individuals to express their natural feeding behavior more fully.

An optimization method for operational efficiency of ADN with four-port SSOP

The soft open point (SOP) replaces the traditional feeder switch based on controllable electronic converter, it provides real-time and accurate active and reactive power flow control. To address the optimization effects of SOP on the operating efficiency of active distribution network(ADN), this paper firstly establishes the access topology and mathematical model of four-port SOP with energy storage (SSOP) considering the operating constraints of four-port SSOP converter and DC-side energy storage device; secondly, an optimization model of the operating efficiency of ADN based on four-port SSOP is established, where power loss reduction, feeder load balancing and voltage profile improvement were taken as objectives. Semi-definite programming is used to simplify the model and convert nonlinear constrains into linear quantities. Case studies of a four-port SSOP in a ADN are conducted to verify the effectiveness and efficiency of the proposed method.

Restoration switching analysis in the integrated architecture for distribution network operation

Distribution network restoration is a recurrent issue of interest to power industry because electric distribution grids remain vulnerable against natural disasters and customers' expectations toward the continuity of electricity service are increasing in modern society. The increasing requirement for operation efficiency has encouraged the modernization of power distribution system by implementing new technologies of control and automation and data conditioning and processing. The integrated architecture of advanced distribution and outage management systems provides situational awareness of distribution networks. Fully automated restoration takes a central role with data from the distribution state estimation for achieving the feeder loading and switching plan able to reestablish the interrupted service. In active distribution networks, restoration switching analysis moves from a multi-objective model, where operator chooses the most suitable switching plan, to a single-objective optimization model for the fully automated restoration. The numerical analysis under a real-world substation is performed in three stages taking into account, firstly, the multi-objective solution and, then, the compromise solution under load forecast and, at last, the benefits of the automatic switching operation are verified.

Planning and Research of Distribution Feeder Automation with Decentralized Power Supply

The high penetration of distributed generation in distributed energy systems causes the variation of power loss and makes the power grid become more complicated, so this paper takes various types of optimal algorithms into account and simulates the feeder reconfiguration on the IEEE-33 system as well as the Taiwan power system. The simulation verifies linear population size reduction of successful history-based adaptive differential evolution (L-SHADE) and particle swarm optimization (PSO) fitness in different systems and provides the recommended location of distributed energy. The proposed method keeps the voltage bound of 0.95 to 1.03 p.u. of Taiwan regulation. In the IEEE-33 system, we achieved a 52.57% power loss reduction after feeder reconfiguration, and a 70.55% power loss reduction after the distributed generator was implemented and feeder reconfiguration. Under the variation of load demand and power generation of the Taiwan power system, we establish the system models by forecasting one-day load demand. Then, we propose a one-day feeder switch operation strategy by considering the switches’ operation frequency with the reduction of 83.3% manual operation and recommend feeder automation to achieve feeder power loss reduction, voltage profile improvement and get regional power grid resilient configuration.

Research on Fast Determination of Feeder Fault Based on Intelligent Alarm

According to the present situation that it is difficult for power dispatcher to identify and handle different types of feeder faults in time. This paper analyses the relationship between different types of feeder faults and 10kV bus grounding signal, 10kV bus voltage changes, bus grounding signal frequency, feeder switch trip signal, load change of fault feeder. On this basis, the mathematical calculation model based on the calculation of alarm signal and Telemetry data calculation is established. Then sends out intelligent alarm based on calculation results. It can quickly distinguish all kinds of faults, the accuracy of judgment is over 98%. It greatly improves the accuracy and efficiency of fault judgment and handling, also reduces user outage and risk of personal electric shock.

Hierarchical Distributed Voltage Optimization Method for HV and MV Distribution Networks

The increasing penetration of distributed photovoltaics (PVs) has brought great challenges to the security and stability of distribution networks (DNs), especially voltage violation. Current voltage optimization methods rarely consider the mutual voltage support between high voltage (HV) and medium voltage (MV) DNs, which may cause PV generation losses in extreme scenarios. Limited by the convergence conditions of common distributed optimization algorithms, mixed integer optimization problem can hardly be calculated in a distributed manner. This paper tries to exert the mutual voltage support capability of HV and MV DNs and realize optimal operation of continuous and discrete voltage regulation devices. Thus, a hierarchical distributed voltage optimization method for HV and MV DNs is proposed in this paper. It is assumed that HV and MV DNs are operated by different control systems and real-time communication between them is available. First, the voltage optimization models of HV and MV DNs are developed to minimize the total cost of voltage adjustment and network losses, which optimize the dispatch of on-load tap changers, reactive power compensators, feeder switches, and distributed PVs. Then the generalized Benders decomposition is applied to solve the mixed integer optimization model in a master-slave distributed manner and the accuracy of LinDistFlow equation is improved through the compensation of peak voltage and boundary power flow in MV DNs. Finally, the effectiveness of proposed method is demonstrated via simulation tests in Jinzhai DNs of China. The results of proposed optimization method are very close to those of global centralized optimization method.

Robust switching control for service restoration in smart grids considering switching malfunction by MAS

This study proposes an agent-based approach to have a reliable service restoration scheme, which is vital to restore as many loads as possible after a permanent fault. The proposed multi-agent system (MAS) has four different types of agents: feeder agents, zone agents, switch agents and DG agents. The agents can communicate and cooperate with each other in order to supply services to out-of-service customers. An artificial neural network has been considered to handle DG uncertainties. The restoration plan is built in a distributed manner, based on local data considering system conditions, operational constraints and fault location. The result of the proposed MAS is a switching sequence. After defining this sequence, the command for opening will be received by circuit breaker agents. In case of one switch failure, the plan cannot be implemented, and hence the restoration plan will fail. To have a reliable restoration plan, the main feeder agent has to handle malfunction in order to operate the system at an acceptable range without violating system constraints. Different loading conditions have been considered under different scenarios and the results of the proposed approach for each scenario have been compared. Simulation results show the efficiency of the proposed agent architecture.

A multiagent design for self-healing in electric power distribution systems

The out-of-service area after fault should berestored by changing the distribution system configuration by means of switching actions on the feeders. This paper proposes an agent-based approach for service restoration in smart distribution systems with distributed generations (DG). The proposed multi agent has four different types of agents: feeder agents, zone agents, switch agents and DG agents. The agents can communicate and cooperate with each other in order to supply services to out-of-service customers. An artificial neural network has been considered within DG agent to predict the DG generation. The restoration plan is built based on local data considering system conditions, operational constraints and fault location. Different loading conditions have been considered under different scenarios and the result of proposed multi agent approach for each scenario with and without DG have been compared. Simulation results show the efficiency of proposed agent architecture.

Optimal energy-emission management in hybrid AC-DC microgrids with vehicle-2-grid technology

This article focuses on the optimal operation management challenges in hybrid AC-DC microgrids considering optimal feeder switching, renewable energy sources, and plug-in electric vehicles. In comparison with the traditional hybrid AC-DC microgrid concept, the reconfigurable hybrid AC-DC microgrids provide more flexibility for better supporting consumers and reducing the operation costs through the remotely controlled switches. In addition, the reconfigurable structure of the hybrid microgrid along with the vehicle-to-grid technology supports the high penetration of plug-in electric vehicles by changing their role from only consuming loads into mobile storages. The proposed problem is prepared as a constrained multi-objective problem optimizing both cost and emission objectives. Due to the high complication and nonlinearity of the problem, an effective optimization algorithm called the theta-crow search algorithm is developed to solve the problem optimally. Also, a stochastic framework based on the point estimate method is used to model the high uncertainties of the problem. The high reliable and satisfying performance of the new method is shown on a test AC-DC microgrid.

Optimal reconfigurattion of distribution systems by considering switch and wind turbine placements to enhance reliability and efficiency

Reconfiguration and smart control of remote-controlled sectionalising switches in distribution networks are considered as the major solutions for loss mitigation, interruption time reduction and reliability improvement after events. That is because these automation tools bring about changes in the topology of the network, isolate the faulted regions, operate distributed generators for local load satisfaction and restore the un-faulted regions as rapidly as possible. Thus, a new solution methodology for solving the simultaneous optimal wind turbines (WTs)/switches placement as well as network reconfiguration is developed to enhance the distribution network's efficiency and reliability. Power losses, voltage deviation index, switch cost and reliability cost based on expected customer interruption cost are considered as the objective functions. The approach profits from a new multi-objective algorithm based on modified artificial bee colony for providing the best compromise solution. The presented framework is shown to provide superior results when applied to the IEEE 69-node test feeder. Finally, different scenarios based on feeder reconfiguration, switch placement and WT placement problems are constructed and presented in Section 5.

Reactive Power Ancillary Service of Synchronous DGs in Coordination With Voltage Control Devices

This paper presents an optimal way to control steady-state voltage in distribution feeders using reactive power ancillary service that is provided by synchronous distributed generators (DGs). Based on load forecasts provided one day in advance, DG reactive power is dispatched on the hour in coordination with the switching operations of an on-load tap changer (OLTC) and shunt capacitors (SCs). This aims to reduce not only distribution line power losses but also the number of switching operations of the OLTC and the SCs, which affect the feeder voltage quality and switching device lifetime. For the reactive power dispatch, a mixed-integer nonlinear optimization problem is formulated using a multiobjective function and solved using a particle swarm optimization (PSO) algorithm. Modules using evolutionary and dynamic programming are incorporated into the PSO algorithm to be less susceptible to becoming trapped in local optima and have a better chance of reaching global optimum. Simulation case studies using small- and large-scale distribution networks were performed using MATLAB to demonstrate that the coordinated reactive power support of the DGs, OLTC, and SCs can achieve the objective effectively, resulting in improved voltage quality.

Combined Reconfiguration and Capacitor Placement for Distribution System Volt/Var Control through Opposition based Differential Evolution Algorithm

Power distribution systems must be very effective in power delivery. Utilities are continuously looking for recent technologies to enhance power delivery performance. The control of power loss is one of the most important issues directly related to system efficiency. Distribution system reconfiguration and optimal capacitor placement are the two most popular techniques adapted for the control of power loss. The techniques not only concentrate on power loss control but also control volt/var of the distribution system, and at the same time improve the system reliability and security. Former method is the process of changing the topology of distribution system by altering the open/closed status of switches to find a radial operating structure that minimizes the system real power loss while satisfying operating constraints. Later is the identification of optimal location and size of the capacitors with the objective of minimizing the power loss. This paper combines both reconfiguration and optimal capacitor placement for the effective optimization. Furthermore, it utilizes Opposition based Differential Evolution algorithm for efficient searching for the optimal solution. The effectiveness of the proposed approach is demonstrated by employing the feeder switching operation scheme to IEEE-33 bus Power Distribution systems. The proposed algorithm reduces the transmission loss and controls volt/var while satisfying power flow constraints.

Intelligent Protection Device of Dual-Core Feeder Switch Based on DSP

Feeder switch of mine guarantees safe operation of underground power supply system. In order to solve the poor stability and reliability of the current mining feeder switch, the article puts forward an dual-core intelligent protection device of feed switch based on DSP. The device to TMS320C6678 as control core of measure and control module collect electrical and deal with signal analysis in the grid. The DSP make instruction of protection operation. The equipment with ATmega128 as control core of management module complete the function of setting parameters and showing results of the measurement. The experimental results show that the device movement is stability , sensitivity. The equipment is high reliability and the scheme is feasible.

The Optimal Configuration of Feeder Switches in DAS Based on Pareto Optimality Solution

In urban radial networks, the feeder is often complex with a main line, branches and even sub-branches. It is necessary to optimize the configuration of automation switching devices in distribution automation system (DAS) to reduce the impact range of power outage and improve the power supply reliability. The interruption frequency and the space-time relationship among the switching devices were ignored in the most of researches. This paper presents a new reliability planning scheme of switching devices in DAS which involves three types of circuit breakers and three types of load switches. The model which target is to get the minimal cost, the lowest average annual interruption frequency and the least interruption duration is built thereby. The constraint is the availability rate of electricity supply of load node. Based on the Pareto optimality theory, the multi-objective differential evolution algorithm is adopted to solve the Pareto front efficiently. The proposed algorithm can provide many optional choices and the final scheme can be chosen based on the situation. Finally, a practical example is used to illustrate the developed scheme.

Loss reduction in distribution system with hybrid fuzzy-opposition based differential evolution algorithm

ABSTRACTPower distribution systems must be very effective in power delivery. Utilities are continuously looking for recent technologies to enhance power delivery performance. The control of power loss is one of the most important issues directly related to system efficiency. Distribution system reconfiguration and optimal capacitor placement are the two most popular techniques adapted for the control of power loss. These techniques not only concentrate on power loss control but also control volt/volt-ampere reactive (VAR) of the distribution system, and at the same time improve the system reliability and security. This paper proposes a method to handle reconfiguration and capacitor placement simultaneously for the effective optimisation. Furthermore, it utilises opposition-based differential evolution (ODE) algorithm for efficient searching for the optimal solution. In order to consider the constraints along with the objective, heuristic fuzzy has been integrated with ODE. The effectiveness of the proposed approach is demonstrated by employing the feeder switching operation scheme to IEEE-33 bus power distribution systems. The proposed algorithm reduces the transmission loss and controls volt/VAR while satisfying power flow constraints.

DC Power Dynamic Management System in Digitalized Transformer Substation

In this paper, using current advanced computer and real-time Ethernet technologies to design a set of high performances’ DC power dynamic management system for digitalized transformer substation. In the system, the operation states and fault conditions of DC power system can be remote monitored and controlled, the state informations of the feeder switches are real time displayed, the state transformations of DC generatrices are remote controlled, the states of equalizing charge and floating charge of the battery charger are remote controlled and remote transformed, the parameters of the battery charger are remote set, etc. In addition, the operation modes of the whole DC power system in the transformer substation are shown by way of simulation map installed in the upper management machine. This dynamic management system greatly contribute to the construction of unwatched, intelligentized, digitalized transformer substation.

Network Reconfiguration of Unbalanced Distribution System through Hybrid Heuristic Technique

Electrical power distribution systems are critical links between the utility and customer. They are constructed by one of the three types: radial, open loop and network. They are usually arranged to be radial in operation to simplify over-current protection. Usually, distribution systems are designed to be most efficient at peak load demand. Utilities are constantly looking for newer technologies that enhance power delivery performance. One of the several important issues is the control of power loss. Several strategies can be employed to minimize power losses in a distribution network. They are inclusion of capacitor banks, phase balancing, feeder reconfiguration of distribution system, etc. Among these, feeder reconfiguration helps to operate the distribution system at minimum cost and at the same time improves the system reliability and security. Network reconfiguration is the process of changing the topology of distribution system by altering the open/closed status of switches to find a radial operating structure that minimizes the system real power loss while satisfying operating constraints. This paper proposes an efficient algorithm based on Plant Growth Simulation Algorithm (PGSA), Greedy and Fuzzy. The optimization approach based on PGSA provides detailed description on switch states for calculation. The inclusion of Greedy improves the efficiency of optimization by reducing the number of load flow execution. In addition, heuristic fuzzy has been incorporated to handle constraints along with objective. With the use of proposed algorithm, the system loss has been reduced convincingly, composes proper loading at the branches and make up buses voltage within the limit and which provides solution under different conditions such as normal and abnormal conditions of the system. Furthermore, the solution algorithm is implemented through J2EE (Java 2 Enterprise Edition) architecture to reduce software couplings and to achieve software reusability. The effectiveness of the proposed approach is demonstrated by employing the feeder switching operation scheme to unbalanced standard 25- bus distribution system and modified IEEE- 125 bus distribution system.