高压输电线路继电保护配置及其整定计算 杨雨昂
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高压输电线路继电保护配置及其整定计算
杨雨昂
高压
输电
线路
保护
配置
及其
计算
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高压输电线路继电保护配置及其整定计算 杨雨昂,高压输电线路继电保护配置及其整定计算,杨雨昂,高压,输电,线路,保护,配置,及其,计算
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华 北 电 力 大 学 科 技 学 院毕业设计(论文)任务书所在院系 电力工程系 专业班号 07k8 学生姓名 指导教师签名 徐玉琴 审批人签字 毕业设计(论文)题目 高压输电线路继电保护 配置及其整定计算 2011 年 2 月 21 日一、毕业设计(论文)主要内容1 熟悉220kV及以上高压电网的运行特点，了解高压输电线路对继电保护的要求以及继电保护的技术现状；2 研究220kV及以上输电线路主、后备保护的构成、工作原理；3 对某具体的220kV输电网络进行继电保护的选型配置，并进行整定计算，写出定值单；4 对220kV输电网络进行自动重合闸的选型配置，并进行整定计算；5 总结工作，撰写毕业设计（论文）。二、基本要求1 在教师指导下，独立完成上述各项工作内容，提高自己运用所学知识分析问题、解决问题的能力；2 完成220kV输电网络继电保护与自动重合闸装置的选型，并进行整定计算，得到合理的继电保护方案，符合电力行业标准；3 绘制规范的继电保护配置图；4 毕业设计论文要求内容充实、条理清楚、符合规范；5 培养自己认真负责、实事求是的科学态度和刻苦钻研的精神。三、设计(论文)进度序号设计项目名称完成时间备注1熟悉任务书内容与要求，通过调研、查阅文献，撰写文献综述，完成开题报告2011.3.282分析220kV及以上电压等级电网的运行特点、输电线路保护技术现状，进行保护选型2011.4.113相间保护整定计算（含保护构成与工作原理的掌握）20114254接地保护整定计算（含保护构成与工作原理的掌握）2011.5.23含2周生产实习5自动重合闸装置整定计算2011.6.16总结工作，完成论文撰写2011.6.12设计(论文)预计完成时间： 2011 年 6 月 12 日四、参考资料及文献1. 国家电网公司. DL/T559-94 220500kV电网继电保护装置运行整定规程. 北京：中国电力出版社，1995.2. 国家电网调度通信中心. DL 400-91. 继电保护与安全自动装置技术规程. 北京：中国电力出版社，2000.3. 崔家佩，孟庆炎等. 电力系统继电保护与安全自动装置整定计算，北京：中国电力出版社，1993年4. 张保会，尹项根 主编. 电力系统继电保护，北京：中国电力出版社，2005年5. 刘万顺. 电力系统故障分析，北京：中国电力出版社，1998年五、附件 电网主接线图与原始数据附录 电网主接线及原始数据1) 系统主接线图：图1 220kV电网主接线图2) 各元件原始数据：表1-1 发电机参数发电机型号PN(MW)UN(kv)cos XdF1 F2 F3QFNS-200-220015.750.850.146表1-2 变压器参数厂站编号型号SN(MVA)UK12%UK13%UK23%电压比(kv)E123SFPL-260000/220026014242/15.75G1SFPL-90000/2209019.512.46220/38.5/11F12SFPL-120000/22012021147220/121/38.5D12SFPL-120000/22012024.714.78.8220/121/10.5ABC站变压器参数与D相同表1-3 系统参数(SB=1000MVA，UB=UAV)系统编号X1minX1maxX0minX0maxA系统0.350.180.550.84B系统0.620.450.750.96表1-4 线路参数线路型号长度(KM)R1(/KM)X1(/KM)X0m(/KM)AB2LGJQ-400820.080.4170.65BDLGJQ-400520.080.417DELGJQ-400790.080.417EFLGJQ-400490.080.417BCLGJQ-300600.1070.427DFLGJQ-300420.1070.427FGLGJQ-300430.1070.427备注：X0=3X1表1-5 负荷分布线路 S(MVA)cosIfh(A)nLHAB2900.87281200/5BD1800.8452600/5DE2400.86021200/5EF3200.88031200/5BC2600.86531200/5DF2260.8567600/5FG1000.8251600/5断路器参数SW6-220：t合0.04 t分0.2电压互感器变比KVKV0.1KV运行方式最大运行方式各系统取最大方式，所有线路、发电机、变压器全部投入运行。最小运行方式各系统取最小方式，发电厂E停两台机组。Evolutionary Algorithm for Protection Relay Setting Coordination K. K. Li, C. W. So Hong Kong Polytechnic University Abstract- The protection relay setting coordination manages the protection relay operations to clear a system fadt i n several steps of contingence. Relays which are missoordinated will trip out unnecessary circuits resulting in electric supply interruption. The Time Coordination Method (TCM) which formulates the coordination of relay settings into a set of constraint equations and objective function i s developed to manage the relay settings. The protection system coordination is a highly constrained optimization problem and conventional methods fail in searching for the global optimum. This paper presents the application of Evolutionary Algorithm (EA) in optimizing the protection relay setting coordination in comparison with other intelligent methods. The result shows that Evolutionary Algorithm is an effective tool to search the optimum protection setting with maximum constraint satisfactions. I . INTRODUCTION The protection relay setting coordination manages the protection relay operations to clear a system fault in several steps of contingence. Relays which are mis-coordinated will trip out unnecessary circuits resulting in electric supply interruption. The Time Coordination Method (TCM) l is developed to manage the relay settings. It formulates the coordination of relay settings into a set of constraint equations and objective function, which are optimized by the Evolutionary Algorithm (EA). EA is a novel technique for solving highly constrained discrete optimization problems 2 such as protection relay coordination. This problem is difficult to be solved by conventional optimization technique such as linear programming or steeper descend gradient search 2. This paper presents the application of Evolutionary Algorithm on the protection relay setting Coordination. The results show that EA effectively searches for the optimum protection relay settings with maximum constraint satisfactions. 11. EVOLUTIONARY ALGORITHM Evolutionary Algorithm (EA) is one branch of the Evolutionary Computation. It can search for the optimum solution for a highly constrained problem. The f l o w chart for EA is shown in Fig 1. I Initialization I Generation I Objective Value Evaluation I 4 Yes End of EA Fig. 1 Evolutionary Algorithm Processes Flowing Diagram A. Initialization The initialization process of EA is similar to all Evolutionary Computational Methods such as Genetic Algorithm and Evolutionary Programming. It provides the starting points for the EA to search for the optimized solution. The greater number of points to start, the higher is the chance to search for the global optimum solution. The initialization of the TCM generates a set of relay settings and formulated a column vectorX,as shown in equation (1). X*= Where kj is the j setting in relay n. (1) Note For example, if RI is Inverse Definite Multiple Time Lag (IDMTL) Overcurrent (OC) Relay, RIsl is the Current Setting Multiplier (CSM) and RI, is the Time Multiplier (TM) of R,. 0-7803-6338-8/00/$10.00()2000 IEEE 813 The dimension of X, is the summation of all protection relay settings in the TCM to be processed. Typically, EA requires pure random initialization. It can broaden the search area and increase the chance of searching out the global optimum solution. Unfortunately, protection setting coordination is a highly constrained problem. The pure random generated relay settings very often fail due to constraint violations 3. For example, a random generated relay settings may not satisfy the operation time margin between upstream and downstream relays l under fault conditions. Any insufficient relay operation time margin may cause unnecessary system supply interruption, which is classified as a constraint violation case. Those initialized relay settings with constraint violations will be discarded. Another set of relay settings will be generated and it will be tested against the constraint violations as before. The successful rate of a pure random initialized protection relay settings without any constraint violation may be calculated in the equation (2). N J U ) (21 N=gm Where n is the number of relays in the power system, m is the number of settings in relay n, N,(ij) is the number of setting steps of relay i setting j which satisfy the constraint violations, N , & ) is the number of settable steps of relay i setting j, N is the successfil rate of the protection relay settings without constraint violations. For example, if the total number of relays is 10. Each relay has two settings with 100 steps in each setting range. If the chance to satisfy the constraints is only 10% in each setting range, thus N = ( 1 O h OO)20 = 1 x 1 From equation 2, if the number of relays is increasing, the successful rate of the initialized relay settings without constraint violations will decrease and approaches to zero. To maximize the successful rate, the setting pusher technique is developed l to push the random generated protection settings from unfeasible solution region to feasible solution region. The processing of EA introduce the continuous improvement to relay settings in which some constraint violation cases are corrected to within the constraint violation limits. A small number of constraint violations is thus allowed at initialization stage. In the TCM, the maximum number of constraint violations is defmed. It counts the number of constraint violations for the initialized relay setting during constraint checking. If the checked number of constraint violations of the initialized relay settings is greater than the pre-defined value, it will be discarded. Otherwise, it will be put into the eligible pool for TCM process. The number of constraint violations will be reflected on the objective value. The initialization process will be terminated after the pre-defined number relay settings are initialized. B. Generation The EA is responsible for the generation of new relay settings. It is carried out by mutation, which is different from genetic algorithm 4 and evolutionary programming 5. For generation n of the k relay settings Xfi, the n+l generation of k relay settings X,+,F/ is generated by equation (3). X,FI = XfiI+ d w x f i I f l ( O Y 1 ) (3) ofiI=( onsin FI = JP - .)(xnrki) + Y where / 7 is the scale factor for EA mutation. y is the offset for EP mutation. (XJk-n is the objective value of the relay settings XJk-1. N(0,l) is the Gaussian normal distribution noise. PmJk is a mutation enabling matrix. a , is a step matrix. The step matrix 0 Jk is calculated before mutation process. This is generated from the objective value ( X , J of the protection setting XJk and each entry ,RI is independent of the others. The mutation enabling matrix Pmfi is designed to decrease the number of relay settings alternation in each mutation process. It is found that a larger number of relay setting altemation will result in a larger number of constraint violations. For the Genetic Algorithm, the single point crossover operator 6 may provide smooth relay setting alteration and introduce smaller number of constraint violations, but the speed of searching for the optimum relay setting is slow. If multi-point crossover operator is applied, the relay setting altemations in each generation is greater and will caused larger number of constraint violations. Consequently, the Genetic Algorithm fails in the Time Coordination Method application. At the end of generation, the new generated relay settings and the old relay settings will undergo a selection process to select the better relay settings for the next generation. The EA uses stochastic selection via a tournament 5. Each new generated relay settings face competition against a pre- selected number of opponents and receives a win if it is at least as good as its opponent in each encounter. Selection then eliminates those sets of relay settings with the least wills. 814 C. Objective Value Evaluation The objective value evaluation is taking the key-roll in the TCM. It generates all system constraints according to the system configurations, fault types and fault locations 11. The constraint checking is playing the important part in the objective value evaluation. It checks the relay settings satisfaction in all constraints and counts the number of constraint violations. The number of constraint violations is a punishment to the relay settings as it is reflected in the objective value. The larger number of constraint violations scores higher objective value resulting in less chances to survive in the next generation. Systems Parameters EA SurvivalSize EA Offsety EA Mutation Factor EA ScaleFactor p D. Termination The termination of the EA process is similar to the other evolutionary computation methods, such as evolutionary programming, by applying the fixed number of generations. As EA introduce continuous improvement process, the occurrence of the global optimum solution cannot be predicted. Unlike Genetic Algorithm which generates offspring mainly by crossover operator, EA generates relay settings by mutation. It can get rid of the pre-mutual dominance which is the solution trapped in the local optimum. For some other optimization algorithms, the termination is by monitoring the difference of the objective values between two consecutive generations approaching to the pre-defined value. This technique fails in the TCM because the local optimum relay settings always last for several number of generations which satisfies the termination criteria but it is not the best optimum solution. Values 10 0 0.9 0.1 1 1 1 . SIMULATION The control parameter of EA are as follows: Number of generations - EA termination criteria. Population size - The number of sets of relay settings in each generation. Mutation Probability - To generate the mutation enabling matrix h J k . The TCM also has a set of control parameters to be set and are described in 11. To demonstrate the effectiveness of EA in Protection Setting Coordination, a typical distribution network with 8 circuits and each circuit is protected by a IDMTL Phase Fault / Earth Fault Overcurrent Relay as shown in Fig. 2 for study. The circuit parameters are listed in Table 1. 0 Line L571 L67 - B u s B7 IDMTL Phase Fault / Earth Fault Overcurrent Relay Fig. 2 Typical distribution network Table 1 Circuit Parameters Note : All values are per-unit U) at IOOMVAbase and all Lines are working at 1 1 kV. 815 Population No. of Objective Simulation Time per Generation for Pentium II The optimum solution among these simulation cases occurs in case 2 with the smallest objective value. The relay settings are shown in Table 4. The optimum relay settings can protect the system with fastest fault clearance time, maximum operation time margin and minimum number of constraint violations for all system conditions 13. Table 4 Optimum Protection Relay Settings Size 30 50 I00 Phase Fault I Earth Fault Line CSM I TM I CSM I TM Generations Value 350MHz 500 0.000670 0.912 sec 500 0.000650 I .728 sec 500 0.000730 3.563 sec The population size is the number of sets of relay settings in each generation to be processed. Obviously, a larger population size would use more computation power. Thus, case 1 is the fastest and the case 3 is the slowest. To examine the EA performance, all trails are recorded as shown in Fig. 3, 4, 5 and 6. Fig.3 shows the best, average and m a x i “ objective values recorded in each generation for the first 100 generations in case 1. From 21 to 93“ generations, it is found that the best objective values are improved significantly. Beyond 93“ generations, the improvements becomes less significant. When all individuals are improved, the better relay settings is prepared by EA and stored in several sets of relay settings. Eventually, the new best relay settings are generated. This improvement is carrying on for the first 300 generations as shown in Fig 4. In Fig 4,s and 6, the improvement becomes minimum, and the average and the best objective value becomes almost constant for the last 200 generations. Beyond 450 generations, the trend of improvement for both average and best objective values becomes flat. Typical effect also occurs in several other trials on the case. Therefore, 500 generations is selected to be the tetmination criteria. The Survival Size is controlled the tournament size and 10 is recommended by D.B. Fogel SI. The Offset and Scale Factor is set to 0 and 0.9 and they control the step matrix a & . The mutation enabling matrix Pm& is controlled by the Mutation Factor 0.1. The PmJk is generate in each EA generation by comparing the Mutation Factor and random numbers. The larger population size also allow more sets of protection settings survives in. each generation and the divergent effect is reflected on the maximum objective values in Fig 3, 4, 5 and 6. The divergent effect should be limited and specific to the problem. In case 2, the divergent effect is the minimum. V. CONCLUSION The Evolutionary Algorithm is successfully applied in the Time Coordination Method for protection setting coordination. The results show that the population size and the number of generations should be p
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