SUBSTATION-BASED SELF-HEALING SOLUTION WITH ADVANCED FEATURES FOR CONTROL AND MONITORING OF DISTRIBUTION SYSTEMS

Transcription

1 SUBSTATION-BASED SELF-HEALING SOLUTION WITH ADVANCED FEATURES FOR CONTROL AND MONITORING OF DISTRIBUTION SYSTEMS Daniel Perez DUARTE João Carlos GUARALDO Henrique KAGAN Sinapsis Brazil Sinapsis Brazil Sinapsis - Brazil daniel.duarte@sinapsisenergia.com joao.guaraldo@sinapsisenergia.com henrique.kagan@sinapsisenergia.com Bruno Hideki NAKATA Paulo César PRANSKEVICIUS Argeu SUEMATSU Sinapsis Brazil AES Eletropaulo Brazil AES Eletropaulo - Brazil bruno.nakata@sinapsisenergia.com paulo.pranskevicius@aes.com argeu.suematsu@aes.com Marcel MARTINELLI AES Eletropaulo Brazil marcel.martinelli@aes.com Mayra Sayumi HOSHINA AES Eletropaulo Brazil mayra.hoshina@aes.com ABSTRACT In order to improve the reliability of the electric system, preventing economic losses to their customers and possible penalties applied by the electricity regulatory agency, the brazilian utility AES Eletropaulo constantly invests in new technologies applied in their electric grid. This paper aims to describe a substation-based selfhealing solution with advanced features for monitoring and control of the electric grid, developed as part of the utility s Smart Grid Project. A Self-healing solution performs the location and isolation of fault, and service restoration for feeder sections which were not directly affected by the fault. Some tasks that would take several to be done now can be completed in a few seconds, contributing significantly to the improvement of continuity indexes. The solution will be implemented in one of AES Eletropaulo s substations, Tambore, which has 14 feeders and 25 reclosers. INTRODUCTION Faults and outages have major economic and social impacts not only for the customers, but also for the power distribution company. While large energy customers like industries may suffer big losses even with short supply interruptions, the electric utility can be penalized according to the frequency and duration of interruptions. In Brazil, the continuity indexes used are SAIDI (System Average Interruption Duration Index) and SAIFI (System Average Interruption Frequency Index). Traditionally, the detection and location of a fault start with telephone calls from customers, which enable the grid operators to determine the approximate location of the fault and send field teams to this place. After determining the exact location, they execute the isolation of the fault and, if possible, the service restoration of healthy sections of the affected feeder by closing a normally open switch (tie switch). A self-healing solution can reduce significantly the time to restore the power supply to healthy sections of the feeder, as illustrated in figure 1. What would be done in more than two hours can now be completed in less than five. The times presented in figure 1 are based on AES Eletropaulo s electric grid. Customers Fault Report Occurs Outage Customers Fault Report Occurs Outage 1-5 Power Restored to Customers on Healthy Sections of Preparation and Travel Time Preparation and Travel Time Without Self- Healing Minutes Fault Location Fault Location With Self Healing Manual Switching Repair Time Power Restored to Customers on Healthy Sections of Repair Time Back to rmal Back to rmal Figure 1: Time Response for Outages with and without a self-healing solution [1] The self-healing solutions are divided in three main categories: Peer-to-peer: Intelligence distributed between field devices; Substation-based: Controller located in the substation level, sending commands to field devices; Centralized: Intelligence centralized in the control center, being able to control multiple substations and devices. As part of AES Eletropaulo s Smart Grid Project, it is being developed a substation-based self-healing solution with advanced features for control and monitoring of the electric grid. In the next sections, it will be presented the architecture and flowchart of the solution, also the software structure, hardware utilized, case study and conclusion. CIRED /5

2 ARCHITECTURE The architecture of the solution is described in figure 2: GIS Topology Communication Network GIS Main SCADA Control Center s Status (Open/ Close) Measures Grid Preparation State Estimation of the Grid Self-Healing Typical Load Curves Estimation: Current State and next 2 hours Self-Healing SCADA (Substation) Communication Network Figure 3: Flowchart Part 1 State Estimation of the Grid Substation Caption Information from field equipments Commands to open/close switches Geographic information from GIS Recloser Recloser Blocking commands from grid operator Alarms Figure 2: s architecture s Verifications, Optimization and Sequence of s When a fault occurs, before starting the optimization algorithm, the solution verifies some conditions like if there are new events (fault) on the grid, if the solution was blocked by the grid s operator or if any switches were blocked due to problems in their communication system. Through a supervisory control and data acquisition system (SCADA) located in the substation level, the solution acquires data (analogic measures and status) from field devices and, after running a optimization algorithm to determine the best sequence of operations for service restoration, it sends commands to the field devices using the same SCADA system. To obtain georeferenced information of equipment and model the electric grid in the solution, there is integration with a geographic information system (GIS). Also, the solution is integrated with the main SCADA system, located in the control center. SOLUTION S FLOWCHART State Estimation of the Grid With the information of grid s topology from the GIS system, the solution acquires analogic measures and status from field equipment and does a state estimation through power flow analysis. The state estimation is performed constantly, so when a fault occurs, it is possible to start the optimization algorithm with an estimation of the network immediately before the fault. Also, using typical load curves for each feeder, it is possible to forecast the state of the grid for the hours ahead. The solution uses this feature to obtain a sequence of maneuvers sustainable for the entire outage duration, not only for the moment of the fault. Outages Blocking Command Blocked switches Grid Prepared and event recorded Blocked? Optimization Sequence of s Self-Healing Start Event Log Optimization Parameters Figure 4: Flowchart part 2 Verificaitons, Optmization and Sequence of s The optimization algorithm starts with the identification of the switches that can participate in the service restoration for this particular fault. Then, the simplest solutions for the problem are chosen to start the modified genetic algorithm and improve its performance. To determine the optimal sequence of maneuvers for service restoration, the modified genetic algorithm considers the following variables in its objective function: Number of maneuvers Continuity indexes Supply for special customers Energy not supplied The obtained sequence of maneuvers must keep the network within its technical operating limits during the outage, respecting: Rated loading of equipment and sections; Voltage limits on delivery points. CIRED /5

3 Commands to With the obtained sequence, the solution starts sending commands to field equipment. Blocking Command Outages State of Operated Switches Command to execute Sequence Start i (1 i n) System Blocked? New Event? Execute END i = i + 1 i = N? confirmed? Stop Restart Optmization Self-Healing Consider Switch Blocked Figure 5: Flowchart Part 3 - Commands to At first, the solution verifies if there are any blocking commands performed by the grid s operator or if another fault occurred in the grid. If nothing happened, the solution sends the first operation command separately to the SCADA system. Before starting the procedures (verifications and command) for the second operation, the solution waits for the response from the field device about the first operation. This way, it is possible to stop the sequence and restart the optimization algorithm if an unexpected event occurs in the grid. Also, it is possible to exclude from the logic a switch with problems in its communication system. SOFTWARE The software for the solution is structured as a set of concurrent processes that communicate by sending and receiving messages. A message has the necessary information for the recipient process execute an operation. The software consists of the following processes: P_SCADAInterface: Process of interface with the SCADA system. Receives messages requesting data from field equipment or commands for switch operation. P_GisInterface: Performs the integration with the GIS system. Periodically, receives a GIS file and assembles the grid s topology in the software, sending it to the P_Topology process. P_Acquisition: According to pre-established timings, the process sends messages to the P_SCADAInterface process, requesting data from the SCADA System. P_Analysis: Receives messages from P_SCADA with data acquired from the SCADA system. If there are changes in any equipment status, sends a message to the P_Topology process. If it receives a message indicating an outage in the network, sends a message to the P_SelfHealing process. P_Topology: Performs periodically the state estimation of the network. After receiving a request, sends a copy of the grid to the P_SelfHealing process. P_SelfHealing: After receiving a message indicating an outage, the process requests a copy of the network and performs the optimization algorithm to determine the sequence of maneuvers to be executed. Finally, it sends the sequence to the P_ process. P_: Performs the management of maneuvers, sending one by one to the SCADA and executing the necessary checks, as described previously in the flowchart. P_Timer: Receives requests of timings from other processes and sends messages indicating the timeout of those timings. Figure 6 shows the cited processes and the exchanged messages between them. AQ1 P_Acquisition P_SCADAInterface AQ2 SCADA System P_Analysis AQ3 P_Topology ES1 SH1 P_GISInterface GIS System MA3 MA6 SH2 MA4 SH3 SH4 SH5 Figure 6: Software Structure MA2 MA5 P_ MA1 P_SelfHealing Also, table 1 shows the source, destination and a description of each message presented in figure 6. CIRED /5

4 Table 1: Messages between processes Source Destination Description feeders and 25 automatic reclosers. The figure below shows the topology of the substation s feeders represented in the software. Data Acquisition from SCADA Topology Construction AQ1 P_Acquisition P_SCADAInterface Data Request (Status and Analogic Measures) AQ2 P_SCADAInterface P_Analysis Received Data from SCADA AQ3 P_Analysis P_Topology Information obtained from data analysis ES1 P_GISInterface P_Topology New topology received from GIS SH1 P_SCADAInterface P_Analysis Fault Indication Information to run the Self Healing SH2 P_Analysis P_SelfHealing Fault Indication SH3 P_Analysis P_ Fault Indication SH4 P_SelfHealing P_Topology Request for a copy of the prefault topology SH5 P_Topology P_SelfHealing Copy of the grid's topology Execution of Switch Operations The software also contains the following processes: P_Control: Manages the other processes; P_IHMServer: User Interface process; P_Log: Process that registers event logs; P_History: Registers operations of the solution. HARDWARE Figure 7: Hardware SEL-3355 The hardware chosen for the solution is the SEL-3355 computer from Schweitzer Engineering Laboratories. It offers high performance with Intel Core i7 Multicore Processor and 16 GB ECC RAM, Robust Hardware Design and Reliable Operation, which are important for operation at the substation level. CASE STUDY MA1 P_SelfHealing P_ Sequence of s Request for the status of the selfhealing MA2 P_ P_SCADAInterface solution MA3 P_SCADAInterface P_Analysis MA4 P_Analysis P_ MA5 P_ P_SCADAInterface Status of Self Healing Status of Self Healing Command to switch operation and request for switch status MA6 P_SCADAInterface P_Analysis Status of Operated Switch For the implementation of the solution, it was chosen the substation Tamboré of AES Eletropaulo, which has 14 Figure 8: Topology of Tamboré s feeders Simulations Initially, simulations were carried out to determine the impact of the self-healing solution in the continuity indexes of the substation s feeders. Table 2 shows the obtained results. Column SAIDI1 presents data of SAIDI for each feeder, considering the twelve months of In Brazil, only interruptions lasting more than three are considered in the calculation of SAIDI. Column SAIDI2 presents the expected values of SAIDI with the deployment of the self-healing solution, considering the same period and history of outage events. Table 2: Simulation Results SAIDI1 (hours) SAIDI2 (hours) Variation TAM 102 1,70 1,63 4,1% TAM 103 9,07 8,84 2,5% TAM 104 4,97 4,37 12,1% TAM 105 4,14 2,99 27,8% TAM 106 1,72 1,71 0,6% TAM 107 1,23 0,98 20,3% TAM 108 5,95 5,93 0,3% TAM 109 7,52 7,44 1,1% TAM 110 2,43 1,58 35,0% TAM 111 1,24 1,17 5,6% TAM 112 6,27 4,59 26,8% TAM 113 3,17 2,42 23,7% TAM 114 3,46 2,61 24,6% TAM 115 1,23 0,99 19,5% Results show that in 50% of the feeders (seven of fourteen), it is possible to reduce the DEC index by 20% or more. In substations with longer feeders and worse continuity indexes, it is possible to obtain even better results. CIRED /5

5 Deployment As of January 2015, the following parts of the solution are still in development: Software development and testing; Integration between the solution and the SCADA system; Deployment of telecommunication network in some regions. tests are expected to begin in May 2015 and the results will be available by the end of July CONCLUSION This paper presented the self-healing solution being developed within the Smart Grid Project of the brazilian utility AES Eletropaulo. The solution presents a substation-based architecture with advanced features to control and monitor the electric system. The self-healing solution performs advanced analyses as the ones performed by centralized solutions, without requiring the same communication infrastructure. To determine the best sequence of operations for service restoration, the solution uses a modified genetic algorithm that considers various parameters in its objective function. To ensure that the solution met the availability requirements of software that operates in real time, it was developed a structure with several processes that run in parallel and communicate by sending and receiving messages. In addition, the hardware chosen for the solution offers the required performance and reliability. The simulations show that the self-healing solution can significantly reduce the continuity indexes. By the end of the development and deployment of the solution, it will be possible to analyze the results and determine the exact benefits of the solution. REFERENCES [1] K. H. LaCommare, J. H. Eto, 2004, "Understanding the Cost of Power Interruptions to U.S. Electricity Consumers", Lawrence Berkeley National Laboratory. [2] Y. Kumar, B. Das, J. Sharma, 2006, "Genetic for Supply Restoration in Distribution System with Priority Customers", 9 th International Conference on Probabilistic Methods Applied to Power Systems.. [3] C. Angelo, 2013, "Technologies of the Self-Healing Grid", 22 nd International Conference on Electricity Distribution. CIRED /5