PSO Based Algorithm for Optimal PMU Placement in KPTC

PSO Based Algorithm for Optimal PMU positioning in KPTCA PSO Based dot Formation Algorithm for Optimal PMU post in KPTCLFirst A. Author, Designation, Organization, Second B. Author, and Third C. Author, Jr., Designation, OrganizationAbstract mightiness musical arrangement secernate estimation with the exclusive deployment of synchronous phasor measuring sticks demands that the remains moldiness be plump outly observable with PMUs only. Direct measuring rod of phase angles of watercourse and emf phasors ar now feasible by Phasor cadence Units (PMUs). To choose lesser make out of PMUs, the federal agencyment caper in any profits is considered as an optimization problem. This make headway-up presents a atom Swarm optimisation (PSO) ground clustering formation algorithmic program for best PMU side. The proposed algorithm clusters the mountaines into legion(predicate) sub groups and the gookimum connectivity good deal is selected as the header agglomer ate. The PMU is placed on the header lotbar to manage the connected mucklees for release brass observability. This paper analyses the proposed algorithm for the following three causasWithout PMU blemish,With single PMU loss, andZero Injection Bus.The manakin results for IEEE bus and the KPTCL bus dodgings ar presented and compared with the existing preliminaryes. The proposed results show that the method is fair to implement and run the true PMU frame.Index Terms IEEE Bus, Karnataka Power transmission frame Corporation Limited (KPTCL), Optimal PMU spot, Particle Swarm Optimization (PSO), Phasor Measurement Units (PMUs), and Power System State EstimationI. IntroductionPower utilities are facing numerous threats of security of operation due(p) to the over disquieted fountain engagement in the todays competitive condition market scenario. Phasor Measurement Unit (PMU) is an evaluating device which is used to measure the actual and voltage. It uses the Global P ositioning System (GPS) pulse to facilitate the synchronized measurements of very time phasors of currents and voltage. A force system is said to be recognisable when voltage phasors at all the buses are known. According to Ohms Law, if a PMU is placed at the bus, thus the neighboring buses also become observable. Obviously, when PMUs are placed at all the buses of the network, and the measurements for all the PMUs are communicated to the control units, and then the voltage phasors at all the buses would be known. This approach crumb form the traditional estimation to state measurement. PMUs are already installed in roughly(prenominal) utilities for various applications around the world such as state estimation, accommodative protection and system protection schemes. Other application fields intromit stability observeing, Wide Area Monitoring and Control (WAMC) and efficient system utilization.In the traditional power systems, the buses are remindered using the conventi onal measurements from voltage and current transformers and the data are forwarded to the Energy Management System (EMS) by means of the Supervisory Control and Data Acquisition (SCADA) system. It collects the real time measurements from the opposed Terminal Units (RTUs) placed in substations. This approaches are not able to monitor all the measurements across a roomy area power system because the data are not time-synchronized 1. PMUs are an essential stir up of happy grids and hence the rate of PMU knowledgeabilitys are increasing. In the emerging technology, the major ply need to be addressed is the military position of PMUs, which is influenced by the anticipated system applications. The major factor limiting the function of PMU elicitations are their woo and the conference facilities. Hence, the cost and communication constraints of PMUs have been motivated the researchers to identify the minimal PMU installation for the anticipated applications. Placing PMUs on all buses of the power system results a murder observability of the system. Since, a bus is observed if a PMU is placed on it or some of its neighboring buses, it is neither economical nor necessary to carry repose such installations. As a consequence, a problem called Optimal PMU Placement (OPP) problem has been occurs.The aim of this paper is to identify the optimum twist of PMUs to make the KPTCL topologically observable. Here, a PSO establish Clustering Algorithm is proposed to cluster the buses. The header bus is selected ground on the maximum connectivity among the buses. The header bus is placed with the PMU to monitor the other connected buses. The PMU placement strategy confirms the system observability during the normal on the job(p) retards and also the single PMU failures. The proposed method is found to be simple, fast and accurate in computation. The proposed method is applied on IEEE-6, IEEE-7, IEEE-9, IEEE-14, IEEE-30 bus systems and KPTCL power maps for 28 bus, 127 bus and 155 bus systems to verify the proposed algorithm surgery.The remaining part of the paper is organized as follows Section II involves the works connect to the existing algorithms for best PMU placement problem. Section III involves the description of the proposed PSO ground cluster formation algorithm for best PMU placement. Section IV involves the performance psychoanalysis of the proposed work. The paper is concluded in Section V.II. Related whole caboodleWith the number of PMUs estimated for installation in the near future, both the utilities and researchers are facial expression for the optimal solutions to their placement. The solutions for the optimal PMU placement problem endure be classified advertisement into two types mathematical and heuristic find out algorithms. Some of the existing works related to the optimal PMU placements are discussed. Integer computer programing is a mathematical program approach for solving an optimization problem having wh ole number design variables. Singh introduced an integer programming based methodology for the optimal placement of PMU. It reduces the cost of installation and facilitate the holy power system observability. The vigour scene buses model was used to further reduce the number of PMUs. Integer programming helps to fork over multiple results if the neighboring buses to zero shaft buses were not handled properly. The best results was selected based on the 2. Fan and Watson proposed a multi-channel PMU placement problem and their solution. Here, a near(a) relationship among the PMU placement problem and the classic combinatorial problem were identify 3.Roy et al proposed an optimal PMU placement approach for power system observability. Here, a three level optimal PMU placement method was formulated based on network connectivity information. set 1 and stage 2 of the algorithm iteratively estimate the less important bus locations to eliminate the PMUs and estimates where the PMUs were retained. The last stage reduces the number of PMUs using the pruning operation. The optimal set of PMU locations were regained for network observability 4. Manousakis and Korres intentional a weighted to the lowest degree squares algorithm for optimal PMU placement. A quadratic minimization problem with continuous decision factors were formulated subject to the nonlinear observability constraints. The optimal solution was started by an unconstrained nonlinear weighted least(prenominal) squares method 5. Mahari and Seyedi proposed a Binary Imperialistic Competition Algorithm (BICA) for optimal PMU placement. The zero injection bus was considered for all investigations to obtain the suitable answers. In addition to the traditional rules, new rule was also generated. It helps to reduce the number of PMUs placement 6.Tai et al proposed a Random Comp whiznt Outages (RCO) for optimal PMU placement for power system estimation. The optimal locations were chosen to reduce the sta te estimation and error covariance 7. Sodhi et al presented an optimal PMU placement method for complete topological and numerical observability of power system. A two stage PMU placement approach was proposed. Stage 1 identifies the minimum number of PMUs to make the system topologically observable. Stage 2 was proposed to identify if the resulted PMU placement yields to a full ranked measurement Jacobian. A sequential elimination algorithm was proposed to identify the optimal locations of supererogatory PMUs 8. An Exhaustive search is an optimization technique which systematically enumerates all possible candidates for the solution. It chosen the candidate which satisfy the constraints at the optimum quarry mold grade. It guaranteed the finding of the global optimum but it was not suitable for capacious scale systems with huge search space. Azizi et al proposed an optimal PMU placement by an equivalent linear formulation for exhaustive search. The state estimation was use ba sed on the complete linear placement 9.Fei et al 10 discussed an optimal PMU placement based on the limited exhaustive approach. An approximately optimal PMU placement (AOPP) was established in guild to identify the searching space. AOPP was deterministically retrieved by detailed power system state observability analysis. The notion of bus neighbor was defined to derive the searching space of limited exhaustive approach. The heuristic algorithms applied for optimal placements are Genetic algorithm, prohibited Search, Simulated Annealing, differential coefficient Evolution, Particle Swarm Optimization (PSO), Immune Algorithm, Iterated Local Search (ILS), Spanning maneuver Search (STS), Greedy Algorithm, Recursive Security N Algorithm, Decision point and Practical Heuristic Algorithm. Hajian et al introduced an optimal PMUs placement to maintain the network observability using a modified BPSO algorithm. An optimal measurement set was estimated to obtain the full network observab ility during normal conditions. After any PMU loss or single transmission line outage, the derived scheme in normal condition was modified. Observability analysis was carried out based on topological observability rules. A new rule was added to minimize the number of PMUs for complete system observability. A modified BPSO algorithm was used as an optimization tool to get the minimal number of PMUs and their corresponding locations 11.Sharma and Tyagi designed an optimal PMU placement approach based on Binary Particle Swarm Optimization (BPSO) with the conventional measurements. Quadratic programming was used in BPSO algorithm. A method for pseudo observability was introduced for depth one and depth two with and without zero injection measurements. It was tested on IEEE-7, IEEE-14, IEEE-30 and IEEE-57 bus system using BPSO technique 12. Peng et al formulated a multi object glass optimal PMU placement using a non-dominated variety differential evolution algorithm. It is an organic i ntegration of Pareto non-dominated sorting operation and the differential evolution algorithm. It enhances the one-on-one crowding apparatus and mutual mechanism 13. El-Zonkoly et al proposed an Improved Tabu Search (ITS) for complete observability and out of step prediction. The system was based on numerical observability and artificial intelligence. ITS was used to identify the optimal placement for the PMU to dungeon the system completely observable. A Predictive Out of Step (OOS) algorithm was proposed based on the observation of the voltage phase difference among the substations 14. Aminifar et al formulated an optimal PMU placement based on probabilistic cost or benefit analysis. The reduction of system risk cost was recognised as the benefit linked with the development of wide area measurement system 15.Das et al designed a simulation of wide area measurement system with optimal phasor measurement unit location. These measurements were mostly taken for every 4 to 10 secon ds offering a blotto state view of the power system behavior. It was implemented on IEEE sextette bus system 16. Jamuna and Swarup proposed a multi- physical object biogeography based optimization for optimal PMU placement. Here, the simultaneous optimization of the two conflicting objectives like minimization of the number of PMUs and maximisation of the measurement redundancy were performed. The Pareto optimal solution was obtained based on the non-dominated sorting and crowding distance. The compromised solution was selected based on the fuzzy based mechanism from the Pareto optimal solution 17. Ghosh et al made a reliability analysis of GIS aided optimal PMU location for smart operation. It investigate the impact of topological attributes on commissioning PMUs. Reliability was ensured through various PMU connectivity configuration 18. Peppanen et al proposed an optimal PMU placement with double star PSO 19. Abiri et al introduced an optimal PMU placement method for complete t opological observability of power system. A revised formulation for the optimal placement problem of the kinds of PMUs was presented 20.III. PSO Based Cluster Formation For Optimal PMU PlacementPower system observability is essential for identifying the real time monitoring and state estimation of the system. PMUs enable advanced solutions to existing utility problems and provide power system engineers a whole range of potential benefitsAccurate estimation of the power system state can be obtained at frequent intervals,Permitting dynamic phenomena to be observed from a chief location and suitable control actions are taken.Post disturbance analysis will be much alter for the PMU placement problem, which is obtained with the precise pictures of the system states through GPS synchronization.This section proposed a PSO based Optimal PMU Placement in power systems. The objective of this method is to provide the optimal placement of PMUs, which can make the system observable and to maxi mise the measurement redundancy of the system. Fig.1 shows the flow of the proposed method. Initially, the bus system is taken and from severally one bus is considered as a node. Each node connectivity is updated in the binary table. Here, we are considering the following three casesWithout PMU LossWith PMU LossZero InjectionsA. Particle Swarm Optimization Based Cluster Formation for Optimal PMU PlacementPSO is an optimization algorithm which facilitates a population based search turn in which individual are termed as particles. Here, the PSO algorithm is used to cluster the buses for optimal PMU placement. Each particle contains a PMU placement configuration for a power system. It represents that each particle is constructed by binary dimensions, such that each bus of the power system has a dimension which indicates the existence of a PMU in that bus, it is equal to 1, otherwise 0.Algorithm 1 PSO based Cluster FormationInput Connectivity details of the given bus system1 Create binary table for the given buses asFor i = 1 to number of busFor j = 1 to number of busIf bus (i) connect to bus (j)Matrix element represent as 1ElseMatrix element represent as 0 finish IfEnd ForEnd For2 D= Sum (f(x))3 L = max (d)4 Calculate the bus companionship for Lth bus and place PMU on that bus5 update the binary table by eliminating the bus from binary table6 Initialize particles7 Position of particles = x and y coordinating points of bus location.8 speeding = random (number of buses)9 Check fitness for given position by using objective function.10 Minimum (F_Position)1112 Position = Position + Velocity13 For k = 1 to iterationIf Present_fitness Last_fitnessUpdate fitness valueEnd IfUpdate velocity and position.End For14 Find maximum (fitness_value), mf = max (fitness)15 Place PMU on that bus.16 Update binary table by eliminating the bus from binary table.17 grummet to Step 6 until binary table gets empty.18 If the PMU placed at only one bus,Check the nearest bus and made connection between them and update cluster.19 End IfThe proposed algorithm is applied on the three cases for optimal PMU placement.B. Case 1 Without PMU LossIn this case, the zero injection and the flow measurement are ignored. To formulate the constraint set, the binary connectivity matric is formed whose entries are defined in the following comparability (1)The matrix can be directly calculated from the bus admission matrix by converting the entries in the binary form.Consider the six bus systemThe binary table B is defined as (2)The constraints for this case is, (3)From the binary table, identify the maximum connectivity among the buses. The table shows the maximum connectivity is occurred in bus 3. Hence, bus 2, 3, 4, 5, and 6 are eliminated from the binary table.Then, the binary table can be updated as, (4)After performing the PSO based clustering algorithm, the PMU is placed on bus 1 and bus 3, which is shown in fig.3.C. Case 2 With loss of PMUIt is considered as each bus i s observable by single PMU and these PMUs are placed by the proposed clustering algorithm. Hence, the placement of PMUs are highly accepted but, if any disturbance occurred in power system or due to maintenance purpose any of the PMUs places is out from the system. If any of the PMU is disconnected, then some of the buses are connected to that PMUs are not remain observable. In order to overcome such unexpected PMU failures, a strategy is considered for single PMU loss. It can be achieved if all the buses are observed by at least two PMUs. These are operated as two sets,Primary set mount setIf suppose the PMU from primary set is not working properly, then the backup set will take the responsibility to observe the buses. To obtain the couple of PMUs, the constraint and objective function will remain resembling by only modifying the change in matrix f. In this case, the elements of f is equal to 2 instead of 1. It is defined as follows (5)This case place the PMU for monitoring the s ingle bus by two PMUs. Other than the objective function, the steps are same. The new constraint function can be constructed as follows (6)D. Case 3 Zero InjectionZero injection buses are the buses from that no current is passed into the system. Zero injection correspond to the transferring nodes in the system. If zero injection buses are also designed in the PMU placement problem, the entire number of PMUs are further minimized. Consider the following manakin for zero injection on six bus system where bus 2 is considered as the zero injection bus.Now, the constraint for zero injection bus can be written as follows, (7)From the above equation, it is set that the bus 3 has maximum connectivity. Hence, PMU is placed on the bus for entire system observability.IV. Performance AnalysisTo evaluate the performance of the proposed method, the optimal placement of PMU problem is solved for IEEE standard bus system and KPTCL 220 and 400 kV power systems. The KPTCL power buses are shown in f ig.6. The results of the proposed method for IEEE bus system is illustrated in table 2. Here, IEEE-6 bus, IEEE-7 bus, IEEE-9 bus, IEEE-14 bus, and IEEE-30 bus system are considered for evaluation. Table 2 provides results for the three cases of IEEE bus systems.We collect the data from the KPTCL 220 and 400 kV power system. Here, the PMU placement is obtained only for the case 1 (without PMU loss). Hence, we proposed an algorithm to obtain the PMU placement, which suits for all the three cases (with loss, without loss, zero injection bus). Table 3 provides the total number of PMU placement collected from the KPTCL. Whereas table 4 provides the proposed result for the given power system. The proposed method results for 28 bus, 127 bus and 155 bus system in all the three cases.V. Conclusion and Future WorkIn this paper, a PSO based cluster formation algorithm is proposed to solve the optimal PMU placement problem.