AU2022231757B2

AU2022231757B2 - Method for multi-adaptive optimal μPMU placement in micro-energy network

Info

Publication number: AU2022231757B2
Application number: AU2022231757A
Authority: AU
Inventors: Chen Fang; Yang FU; Lei GU; Jinsong Liu; Shu Liu; Shanshan SHI; Xiangjing SU; Shuxin TIAN; Xinchi WEI; Jie Wu
Original assignee: Shanghai University of Electric Power; State Grid Shanghai Electric Power Co Ltd; University of Shanghai for Science and Technology
Current assignee: Shanghai University of Electric Power; State Grid Shanghai Electric Power Co Ltd
Priority date: 2021-10-29
Filing date: 2022-07-14
Publication date: 2024-02-29
Anticipated expiration: 2042-07-14
Also published as: CN113991856A; CN113991856B; WO2023071300A1; AU2022231757A1

Abstract

The present disclosure relates to a method for multi-adaptive optimal micro phasor measurement unit (pPMU) placement in a micro-energy network, including: 1) obtaining network topology of a micro-energy network system, and establishing a mathematical model for pPMU placement in the micro-energy network; 2) by considering impact of zero-injection bus (ZIB), conventional measurement, single pPMU failure and single line failure factors on pPMU placement in the micro-energy network, establishing an optimized placement model under the impact of each factor with reference to the established mathematical model for pPMU placement in the micro-energy network; 3) calculating an optimized solution of the optimized placement model under the impact of each factor, to obtain a pPMU placement state set of the micro-energy network with a minimum configuration cost; and 4) further filtering the pPMU placement state set, to obtain a multi-adaptive optimal pPMU placement result of the micro-energy network, and summarizing a principle for optimal pPMU placement in the micro-energy network based on cases of all the influencing factors. Compared with the prior art, the present disclosure achieves advantages of improving accuracy of system state observability, considering impact of various factors, and ensuring safe and stable operation of the micro-energy network.

Description

METHOD FOR MULTI-ADAPTIVE OPTIMAL pPMU PLACEMENT IN MICRO-ENERGY NETWORK

TECHNICAL FIELD

[0001] The present disclosure relates to the technical field of configuration of power system measurement equipment, and in particular, to a method for multi-adaptive optimal micro phasor measurement unit (pPMU) placement in a micro-energy network.

BACKGROUND

[0002] With the increasingly prominent energy resource and environmental problems, vigorous development of renewable energy has become the way to deal with the increasingly serious energy and environmental problems. The micro-energy network has received wide attention due to its advantages of renewable energy consumption and high efficiency of energy use. The micro-energy network organically integrates various energy links, such as electricity, gas and heating/cooling, with users. Through the scientific dispatching of multiple energy sources in the system, it realizes efficient use of energy, satisfies users' tiered use of various types of energy, and provides safe and reliable social energy supply. The micro-energy network covers conversion, distribution and coordination of multiple energy sources such as electricity, natural gas and heat, and the coupling and interaction of various energy systems is a typical physical phenomenon of the micro-energy network. As the core and link of the micro-energy network, the monitoring and control of the power system is especially important to ensure safe and stable operation of the micro-energy network.

[0003] Power system monitoring is usually realized based on a supervisory control and data acquisition (SCADA) system, and the quantity measurement includes node voltage amplitude, branch current or power and other information. However, SCADA data collection does not have a uniform time scale. Therefore, it is difficult to process data of the whole station in a uniform time section; meanwhile, the SCADA system has a slow measurement refresh speed, which makes it difficult to analyze the system dynamically in real time. In contrast, a micro phasor measurement unit (pPMU) can make a timestamp on measurement data by a second pulse signal of the Global Positioning System (GPS), to ensure the synchronization of the phase measurement data. Then, the measurement data is transmitted to a dispatch center of the power system in real time through a wide area monitoring system, and pPMU data is converted into a unified time coordinate system by a master station system, to obtain synchronized phase measurement information of the system. The pPMU is small in size, high in accuracy, and capable of storing and exchanging a large amount of data, and allows real-time calculation and analysis of the data.

Therefore, the pPMU is suitable for monitoring and controlling the micro-energy network system, which is important to ensure the safe and stable operation of the micro-energy network.

[0004] The configuration of pPMU measurement devices in the micro-energy network can effectively improve the accuracy and real-time performance of the conventional SCADA system. Considering relatively high cost of the existing pPMU, it is unrealistic to install pPMUs in all buses. Moreover, the pPMU can measure voltage phases of the bus on which it is installed and neighboring buses. Therefore, the whole system can be observed through reasonable configuration of pPMU devices, to meet the state measurement requirements of the system. Therefore, the optimal placement method of pPMUs in a micro-energy network should be studied to minimize the configuration cost while ensuring the state of the micro-energy network system to be observable globally.

[0005] Research on optimal PMU placement in a power distribution network has been conducted, and the existing related literature studies include proposing a non-dominated sorting genetic algorithm to find a Pareto optimal solution for placement and obtain better measurement redundancy; or considering the effects of existing measurements, channel constraints and contingencies on PMU placement in the power distribution network; or proposing a PMU placement method based on complete/incomplete observability depth of a power system; or proposing a method for optimal PMU placement in a power distribution network for distribution feeders with high-accuracy fault location. Although lots of studies on PMU placement in the power distribution network have been proposed, none of the studies have considered optimized placement of pPMUs in a micro-energy networks or systematically summarized an optimal placement principle of pPMUs in the micro-energy network.

SUMMARY

[0006] An objective of the present disclosure is to provide a method for multi-adaptive optimal micro phasor measurement unit (pPMU) placement in a micro-energy network, to overcome the defects in the prior art.

[0007] The objective of the present disclosure can be achieved by the following technical solutions:

[0008] A method for multi-adaptive optimal pPMU placement in a micro-energy network includes the following steps:

[0009] Si: on the basis of obtaining network topology of a micro-energy network system, establishing a mathematical model of a pPMU placement problem of the micro-energy network by a topology observability method, with a minimum configuration cost of pPMUs as an objective and global observability of a micro-energy network power system as a constraint condition;

[0010] S2: performing analysis by considering impact of zero-injection bus (ZIB), conventional measurement, single pPMU failure and single line failure factors on pPMU placement in the micro-energy network, and establishing, with reference to the above basic model, an optimized placement model under the impact of each factor;

[0011] S3: calculating an optimized solution of each of the above optimized placement problems by an improved binary particle swarm optimization (BPSO) algorithm, to obtain a pPMU placement state set of the micro-energy network with the minimum configuration cost; and

[0012] S4: further filtering the pPMU placement state set based on a system observability redundancy index (SORI), to obtain a multi-adaptive optimal pPMU placement result of the micro-energy network, and summarizing a principle for optimal pPMU placement in the micro-energy network based on cases of all the influencing factors; and placing pPMUs in the micro-energy network according to the principle.

[0013] Si specifically includes the following content:

[0014] This step describes the mathematical model of the pPMU placement problem of the micro-energy network. By optimizing the quantity and locations of placed pPMUs, the configuration cost is minimized while global observability of the micro-energy network system is achieved. The topological analysis method is a common method for placement research. The method is based on the graph theory idea and requires only network topology related information, which is simple and easy to implement. The present disclosure adopts topological analysis to study pPMU placement in the micro-energy network. An objective function is to minimize the configuration cost of pPMUs, and the constraint condition is global observability of the micro-energy network system. A specific mathematical model for minimizing the configuration cost is as follows: N min CAx i~1 (1)

[0015] where ci is a cost factor; i is a system bus number; N is a total quantity of system buses; xi is a binary variable indicating whether to place a pPMU or not, which is specifically defined as follows:

[1 if p PMU is installed on bus i 0 Others (2)

[0016] The constraint condition is the global observability of the micro-energy network power system; when only impact factors of the power system are considered, the constraint condition is as follows: AX b (3)

[00171 where X= [X 1,...,XN ]T represents a PMU placement state of the micro-energy network power system; b = [1,..., 1 ]T , and b represents a measurement redundancy requirement of each node of the system; A is a binary connectivity matrix, which represents system network topology information, and an ai of the matrix is as follows: 1, ifi=j ori is directly connected to j = 0, Others (4)

[0018] O is defined as observability redundancy of bus i, to indicate the number of times that bus i is observed by a measurement device, expressed by a formula as follows: N

Oi, aijxj j-1 (5)

[0019] The global observability of the system means that any bus state of the system is observable; thus, the observability redundancy of each bus is required to be greater than or equal to 1, and the above equation (3) is equivalent to: 0, 1, Vi=1,2,- --,N (6)

[0020] According to the topology of the system, the bus measurement redundancy has its inherent upper limit constraint; therefore, it is unnecessary to add an additional upper limit constraint.

[0021] In the present disclosure, pPMUs are placed in the micro-energy network such that the state of the network is observable; the impact of a multi-energy system of the micro-energy network needs to be taken into account. A power system of the micro-energy network is particularly sensitive to the state monitoring of buses coupled with the multi-energy system and accessing renewable energy and an energy storage system; it is necessary to ensure high-accuracy real-time monitoring of the states of these buses. Moreover, the real-time monitoring requirement of buses in the case of a single pPMU failure also needs to be considered. Therefore, if bus i is a bus coupled with the multi-energy system and accessing the renewable energy and the energy storage system, it is called a coupled bus in the present disclosure, and the observability redundancy requirement thereof is as follows: 0 >2 (7)

[0022] In summary, the pPMU placement constraint condition of the power system of the micro-energy network can be expressed as follows: F 2, if bus i is a coupled bus 1, Others (8)

[0023] S2 specifically includes the following content:

[0024] In this step, the impact of factors such as the ZIB, conventional measurement, single pPMU failure and single line failure on the pPMU placement in the micro-energy network is taken into consideration, and modeling analyses of the influencing factors are as follows:

[0025] (1) ZIB impact

[0026] When at most one of the ZIB and its connected buses is unobservable, state information of the unobservable bus can be obtained by Kirchhoffs current law; therefore, the bus is also considered as observable. An auxiliary binary variable yij is introduced to simulate the impact of the ZIB on the observability redundancy of the bus, where the binary variable is expressed as follows: N 1, if bus j is a ZIB Y 0, O0thers1(9

[0027] where yij = 1 indicates that bus j is an observable ZIB, bus i is the only unobservable bus connected to bus j, and in this case, a state quantity of bus i can be obtained based on the above ZIB rule, thus achieving observability. Thus, the observability redundancy of bus i is jointly determined by the pPMU and the ZIB, and the formula is as follows: N N

0i= aijxj+ aiy, 1 =(10) N N

[0028] where J= is an effect of the pPMU, and J= represents an effect of the ZIB on the observability redundancy.

[0029] (2) Impact of conventional measurement

[0030] The micro-energy network system has been monitored by conventional SCADA measurements, and the conventional measurements also affect the configuration of pPMUs in the micro-energy network. The conventional measurements can be divided into the following three categories:

[0031] 2.1) Bus voltage measurement

[0032] The bus voltage measurement is a device that measures a phase of a bus voltage, and the observability redundancy of the bus with the bus voltage measurement is already 1, that is: r1, if the bus voltage measurement device is installed on bus i S o, Others (11)

[0033] Considering the impact of the bus voltage measurement, the observability redundancy of bus i is expressed as follows: N

0,=Zax,+z, (12)

[0034] 2.2) Tidal voltage measurement

[0035] When a voltage phase at one end of a branch is known, a voltage phase of a bus at the other end can be calculated based on the tidal voltage measurement in the branch, i.e. when bus i orj is measured by the pPMU: 1, if the tidal voltage measurement is installed on branch i-j z-_j + z'_, = 0, Others (13)

[0036] where zij and zji are binary variables that represent the impact of the tidal voltage measurement on the observability redundancy of the bus; when there is a tidal voltage measurement in branch i-j and at least one of the buses is measured by the pPMU, the observability redundancy of bus i and bus i is expressed as follows: N

, ajx.- j-1 +

(14) N

0, ajix, + zj (15)

[0037] 2.3) Power injection measurement

[0038] The power injection measurement is a device that measures power of a bus, and can provide an additional power balance equation for system state estimation. An auxiliary binary variable zi is introduced to simulate impact of the power injection measurement on the observability redundancy of the bus observations, where the auxiliary binary variable is expressed as follows: N [1, if the power injection measurement is installed on bus j _ 0, Others

[0039] where zi = 1 indicates that the observable bus j is the bus installed with the power injection measurement device, and bus i is the only unobservable bus connected to bus j. In this case, a state quantity of bus i can be calculated based on the power injection measurement, thus achieving observability. In this case, the observability redundancy of bus i is jointly determined by the pPMU and the power injection measurement, and the formula is as follows: N N

O,= ajxj+ aizi (17) N N

Ya xY aizy

[0040] where i=1 is an effect of the pPMU; j=1 is an effect of the power injection measurement on the observability redundancy of the bus.

[0041] (3) Impact of single pPMU failure

[0042] In the case of a single pPMU failure, if the micro-energy network system still needs to maintain global observability, the corresponding buses each need to be observed by at least two pPMUs simultaneously. This rule is applied to each bus; in this case, the observability redundancy constraint of the bus is transformed from formula (8) to be the following formula: N F3, if bus i is a coupled bus 0 1=$ax (18) j= 2, Others

[0043] (4) Impact of single line failure

[0044] A line failure leads to missing of a pPMU observable path, which reduces the system observability. The line failure causes the topology of the system to change, and the binary connectivity matrix A is thus changed, which affects the pPMU placement in the micro-energy network. A network topological element under the impact of the failure is as follows: 0, if branch 1 is a branch connecting buses i andj aOthers (19)

[0045] where I represents a faulty line, and bus i and bus j are buses at two ends of the line. In this case, the formula of the observability redundancy of bus i becomes: N

j-l (20)

[0046] Therefore, the constraint condition of pPMU placement in the micro-energy network becomes: N 2, if bus iis a coupled bus 0Y =a x1 (21) j= 1 , Others

[0047] S3 specifically includes the following content:

[0048] A particle swarm optimization (PSO) algorithm is based on the food search behavior of a flock of birds or fish. First, the particle swarm is initialized by random placement in a search space; then, the velocity and position of the particle swarm are updated through an iterative process to obtain a new optimized solution. In each iteration, each particle moves to an optimal position, which is obtained by combining best prior experience of the particle and best prior experience of all particles.

[0049] In a D-dimensional search space, the velocity and position arrays of particle i are given as follows: veli = {velii, veli 2 , ..., velid, ..., veliD and Pi =Pil i2' -- Pid PiD} . Update formulas for the velocity vei1 and position Pd of particle i are as follows: ve i=w. velid 1-r,(pbest,- - +y 2 - r(gbest, -JP) 22) pk+1 _ pk vek+1 id id id (23)

[0050] where w is an inertia weight factor; k is the current number of iterations; d is the dimensionality of the search space; $1 and $2 are learning factors;r 1 and r 2 are random numbers, which are uniformly distributed in [0, 1]in the k-th iteration; pbest is the best prior experience of the current particle, and then pbesti represents X with a minimum objective function of particle i in previous k iterations; gbest is best prior experience of all the particles, and then gbesti represents X with a minimum objective function of all the particles in the previous k iterations.

[0051] Considering that the decision variables in the optimal pPMU placement problem are 0 or 1, BPSO is more suitable for the optimal placement problem. BPSO mainly differs from PSO in that only the binary value 0 or1 is considered in the position array and an update process thereof. A Sigmoid function is used to update the position of the particle, and a value of the position depends on the velocity of the particle velid. The Sigmoid function is expressed by the following formula: S(velid) /(I+exp(-velid (24)

[0052] An update formula for position Pid is as follows: 1, if r < S(velid) d 0, Others (25)

[0053] where r 3 is a random number between 0 and 1.

[0054] Based on the above, a velocity limit threshold and linearly decaying inertia weight coefficient are introduced to improve the particle search capability and the BPSO algorithm. Setting the velocity limit threshold is essential to limit the particle search process; otherwise, the particles may accelerate uncontrollably and move to the outside of the search space; meanwhile, to control the search speed of the population, the inertia weight factor w needs to be adjusted appropriately to maintain the balance between exploitation and exploration. Therefore, the present disclosure introduces the linearly decaying inertia weight coefficient w w, which is expressed by the following formula:

W max wmi max (26)

[0055] where wk is an inertia weight coefficient of the k-th iteration; wmax and wmin are a maximum value and a minimum value of the inertia weight coefficient, which are set to 0.95 and 0.4, respectively; kmax is the maximum number of iterations, which is set to 1000.

[0056] Through optimization based on the improved BPSO algorithm, a pPMU placement state of the micro-energy network with a minimized objective function is obtained through the following steps:

[0057] step 1: inputting the binary connectivity matrix A of the network topology of the micro-energy network system;

[0058] step 2: setting BPSO parameters, including the number of iterations, a particle quantity, dimensionality, an inertia weight factor, learning factors, and a velocity threshold;

[0059] step 3: initializing particles, and if constraints are not satisfied, reinitializing the particles until the constraints are satisfied;

[0060] step 4: iteratively updating velocities and positions of the particles;

[0061] step 5: determining whether a termination condition is met, i.e., terminating an iteration process if the number of iterations reaches a specified upper limit; otherwise, returning to step 4; and

[0062] step 6: outputting a candidate solution set X with a minimum objective function.

[0063] S4 specifically includes the following content:

[0064] The system observability redundancy index (SORI) represents a total number of times that all buses in the micro-energy network power system are observed, to measure the overall system state measurement accuracy of the micro-energy network. After the pPMU placement state set of the micro-energy network is obtained based on the improved BPSO optimization algorithm, it is possible that the number of pPMUs is the same but the locations are inconsistent. Therefore, an optimal solution of the pPMU placement in the micro-energy network is selected by sorting the SORI values, to obtain a pPMU placement scheme with the highest system state measurement accuracy. The SORI is expressed by the following formula: N SORI= 0, i-1 (27)

[0065] The above scheme for optimal pPMU placement in the micro-energy network is applied to specific micro-energy network instances considering various typical influencing factors, to implement multi-adaptive optimal pPMU placement.

[0066] The method for multi-adaptive optimal pPMU placement in a micro-energy network provided by the present disclosure achieves at least the following beneficial effects compared with the prior art:

[0067] (1) In the method for multi-adaptive optimal pPMU placement in a micro-energy network proposed by the present disclosure, with a minimum configuration cost of pPMUs as an objective and global observability of a micro-energy network power system as a constraint condition, a mathematical model of pPMU placement in the micro-energy network is established, which ensures economical placement while satisfying the global observability of the system.

[0068] (2) The method for multi-adaptive optimal pPMU placement in a micro-energy network proposed by the present disclosure takes into account impact of typical factors such as ZIB, conventional measurement, single pPMUfailure and single line failure on the pPMU placement in the micro-energy network. A principle for multi-adaptive pPMU placement in the micro-energy network is summarized.

[0069] (3) In the method for multi-adaptive optimal pPMU placement in a micro-energy network proposed by the present disclosure, an optimized solution is calculated based on an improved BPSO algorithm, which increases the solution speed to a certain extent; in addition, an SORI is introduced to reflect the measurement accuracy of the system, which improves the accuracy of the system state measurement with the same configuration cost, thus obtaining an optimal pPMU placement scheme.

BRIEF DESCRIPTION OF THE DRAWINGS

[0070] FIG. 1 is a flowchart of an improved BPSO algorithm;

[0071] FIG. 2 is a schematic flowchart of a method according to the present disclosure; and

[0072] FIG. 3 is a diagram of a micro-energy network system modified from an IEEE33 node system in an embodiment.

DETAILED DESCRIPTION

[0073] The present disclosure will be described in detail below with reference to the drawings and specific embodiments. Apparently, the described examples are merely some rather than all of the examples of the present disclosure. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments of the disclosure without creative efforts shall fall within the protection scope of the disclosure.

[0074] Embodiment

[0075] The present disclosure relates to a method for multi-adaptive optimal pPMU placement in a micro-energy network, which mainly solves the following technical problems:

[0076] (1) Optimization of pPMU placement in a micro-energy network based on topology observability analysis. A mathematical model of optimized pPMU placement in the micro-energy network is established based on a topology observability method, to realize global observability of the micro-energy network. A corresponding mathematical model for placement is also established by considering impact of factors such as zero-injection bus (ZIB), conventional measurement, single pPMU failure and single line failure on pPMU placement.

[0077] (2) Efficient solution of optimal pPMU placement in the micro-energy network. A candidate set of pPMU placement solutions of the micro-energy network with a minimum objective function is obtained based on an improved BPSO algorithm (as shown in FIG. 1), and then an optimal pPMU placement scheme of the micro-energy network is obtained based on a SORI; finally, based on the above method and considering the impact of various typical factors, a principle for multi-adaptive optimal placement of pPMUs in the micro-energy network is concluded.

[0078] The overall technical solution of this implementation includes the study of the mathematical model for pPMU placement in the micro-energy network by the topology observability method, the consideration of impact of the above-mentioned typical factors on pPMU placement, the improved BPSO optimization algorithm used, and the introduction of the SORI concept.

[0079] Specifically, as shown in FIG. 2, the method for multi-adaptive optimal pPMU placement in a micro-energy network includes a data input step, a step of constructing a pPMU placement model of the micro-energy network, a step of optimized solution based on an improved BPSO algorithm, a step of filtering to obtain an optimal pPMU placement scheme, and a result output step.

[0080] This embodiment takes a micro-energy network system modified from an IEEE33 node system as an example, as shown in FIG. 3. In the figure, bus 6 is a wind power system access bus; bus 15 is a photovoltaic system access bus; buses 11 and 27 are coupled with a natural gas system through cogeneration; bus 14 is coupled with a thermal system through an electric boiler unit; bus 11 is an energy storage system access bus. That is, system buses 6, 11, 14, 15 and 27 are all coupled buses. In this case, a constraint condition for pPMU placement in a power system of the micro-energy network IEEE33 node is expressed as follows:

[,>_2, i =6,11,14,15,27 = 1, Others (28)

[0081] According to the modeling analysis under the impact of the above factors, the constraint condition for pPMU placement in the micro-energy network under the impact of ZIB, conventional measurement and single line failure is obtained; with reference to formula (18), the constraint condition for pPMU placement in the micro-energy network under the impact of single pPMU failure can be obtained: N [ >3, i=6,11,14,15,27 0, =: aijx; (29) Zai ixi> 2 , Others

[0082] On this basis, optimal pPMU placement solutions of the micro-energy network under different scenarios are calculated through simulation by the BPSO optimization algorithm, including scenarios with different ZIB quantities, scenarios with different conventional measurement quantities, a single pPMU failure scenario, and a single line failure scenario, where simulation results are as follows

[0083] (1) Simulation result of impact of ZIB

[0084] Table 1 Impact of ZIB on pPMU placement in micro-energy network ZIB Quantity of SORI placement p[PMUs filtering None 14 2,5, 8, 11, 12, 14, 15, 17,21,24,26,27,30, 32 43 7, 17 14 2,3,6, 10, 12, 13, 15, 16,21,24,26,27,30,32 47

7,9, 17,23 13 2,5,10,12,14,15,16,21,24,26,27,29,32 44 3,7,9, 17, 13 2,6,10,12,13,14,16,21,24,26,27,29,32 47 19, 23,

[0085] According to Table 1, when the quantity of ZIBs in the system increases, the quantity of pPMUs implementing global observability of the power system of the micro-energy network decreases because the additional observation effect of ZIBs on the system increases; with the same quantity of pPMUs, the SORI value increases as the quantity of ZIBs increases, which improves the accuracy of system state estimation. When the quantity of ZIBs increases to a certain value, the observation effect of ZIBs on the system is saturated; therefore, the quantity of configured pPMUs does not change.

[0086] (2) Simulation result of impact of conventional measurement Table 2 Impact of conventional measurement on pPMU placement in micro-energy network Configuration of conventional Quantity of SORI measurement p[PMUs filtering 2,5,8, 11, 12, 14, 15, 17, None 14 43 21,24,26,27,30,32 Bus 4 is used for bus voltage measurement, and lines 8-9 are 2, 6, 7, 11, 12, 14, 15, 17, 14 46 used for tidal voltage 21,24,27,28,29,32 measurement. Buses 4 and 11 are used for bus voltage measurement, and 2, 6, 7, 11, 14, 15, 17, 21, 13 44 lines 8-9 are used for tidal 24,26,28,31,32 voltage measurement. Buses 4 and 11 are used for bus voltage measurement, lines 8-9, and 16-17 are used for 2, 6, 10, 12, 14, 15, 17, 21, 13 46 tidal voltage measurement, and 24,26,28,29,32 bus 23 is used for power injection measurement.

Buses 4, 11, and 27 are used for bus voltage measurement, lines 8-9, and 16-17 are used 2, 6, 10, 13, 14, 15, 17, 21, 12 43 for tidal voltage measurement, 25,26,29,32 and bus 23 is used for power injection measurement.

[0087] The analysis in Table 2 shows that when the quantity of conventional measurements increases, the quantity of pPMUs that achieve global observability of the micro-energy network decreases because the additional observation effect of the conventional measurements on the system increases.

[0088] (3) Simulation result of impact of single pPMU failure

[0089] Table 3 Impact of single pPMU failure on pPMU placement in micro-energy network Quantity of Quani ofPMU placement SORI filtering

2,5,8, 11, 12, 14, 15, 17,21,24,26,27,30, None 14 43 32 Single 2,4,5,7,10, 11,13, 15, 16,18,21,23,24, LPMU 18 25,26,27,29,32 failure

[0090] The analysis in Table 3 shows that in the case of a single pPMU failure in the micro-energy network, the number of pPMUs required for achieving global observability of the system increases because the measured observability redundancy of the bus decreases due to the pPMU failure.

[0091] (4) Simulation result of impact of single line failure

[0092] Table 4 Impact of single line failure onpPMU placement in micro-energy network Single line Quantity of SORI failure pPMUs filtering 2,5,8, 11, 12, 14, 15, 17,21,24,26,27,30, None 14 43 32 2,3,6,7, 10, 12, 14, 15, 17,21,24,26,28,29, 4-5 15 48 32 2,5,7,10, 12, 13,14, 15,16, 17,21,24,26, 14-15 16 47 28,30,32

[0093] As shown in Table 4, when a single line failure occurs in the micro-energy network, the quantity of pPMUs required for achieving global observability of the micro-energy network increases due to the reduction of pPMU observable paths. When a failure occurs on a line connected to a coupled bus, a larger quantity of pPMUs are added accordingly due to the higher measurement redundancy requirement of the coupled bus.

[0094] To sum up, a principle for typical multi-adaptive placement of pPMUs in the micro-energy network can be concluded from Tables 1, 2, 3, and 4. The specific content is shown in Table 5.

[0095] Table 5 Principle for typical pPMU placement in micro-energy network Key factors Specific content As the quantity of ZIBs increases, the quantity of pPMUs in the are added to the ZIB micro-energy network decreases; when 4 to 6 ZIBs system, the quantity of pPMUs for achieving full observability of the micro-energy network is reduced by 1. As the conventional measurements increase, the quantity of pPMUs in

Conventional the micro-energy network decreases; when 3 to 5 conventional measurements are added to the micro-energy network system, the measurement quantity of pPMUs for achieving full observability of the micro-energy network system is reduced by 1. Single pPMU A single pPMU failure increases the quantity ofpPMUs for achieving failure global observability of the micro-energy network. A single line failure increases the quantity of pPMUs for achieving

Singlelinefailure global observability of the micro-energy network, and a larger quantity of pPMUs are added when a line failure occurs on a line connected to a coupled bus.

[0096] In the method for multi-adaptive optimal pPMU placement in a micro-energy network proposed by the present disclosure, with a minimum configuration cost of pPMUs as an objective and global observability of a micro-energy network power system as a constraint condition, a mathematical model of pPMU placement in the micro-energy network is established, which ensures economical placement while satisfying the global observability of the system. Impact of typical factors such as ZIB, conventional measurement, single pPMU failure and single line failure on the pPMU placement in the micro-energy network is taken into consideration, to summarize a principle for multi-adaptive pPMU placement in the micro-energy network. In addition, in the present disclosure, an optimized solution is calculated based on an improved BPSO algorithm, which increases the solution speed to a certain extent; in addition, an SORI is introduced to reflect the measurement accuracy of the system, which improves the accuracy of the system state measurement with the same configuration cost, thus obtaining an optimal pPMU placement scheme.

[0097] The foregoing descriptions are only specific implementations of the present disclosure, but the protection scope of the present disclosure is not limited thereto. Any person skilled in art can easily conceive of various equivalent modifications or replacements within the technical scope disclosed in the present disclosure, and these equivalent modifications or replacements should fall within the protection scope of the present disclosure. Therefore, the protection scope of the present disclosure should be subject to the protection scope of the claims.

[0098] The reference to any prior art in this specification is not, and should not be taken as, an acknowledgement or any form of suggestion that such prior art forms part of the common general knowledge.

[0099] It will be understood that the terms "comprise" and "include" and any of their derivatives (e.g. comprises, comprising, includes, including) as used in this specification, and the claims that follow, is to be taken to be inclusive of features to which the term refers, and is not meant to exclude the presence of any additional features unless otherwise stated or implied.

Claims

CLAIMS: 1. A method for multi-adaptive optimal micro phasor measurement unit (PMU) placement in a micro-energy network, comprising: 1) obtaining network topology of a micro-energy network system, and establishing a mathematical model for pPMU placement in the micro-energy network; 2) by considering impact of zero-injection bus (ZIB), conventional measurement, single pPMU failure and single line failure factors on PMU placement in the micro-energy network, establishing an optimized placement model under the impact of each factor with reference to the mathematical model for pPMU placement in the micro-energy network established in step 1); 3) calculating an optimized solution of the optimized placement model under the impact of each factor, to obtain a pPMU placement state set of the micro-energy network with a minimum configuration cost; and 4) further filtering the pPMU placement state set, to obtain a multi-adaptive optimal PMU placement result of the micro-energy network, and summarizing a principle for optimal pPMU placement in the micro-energy network based on cases of all the influencing factors; and placing pPMUs in the micro-energy network according to the principle; wherein step 1) comprises: on the basis of obtaining the network topology of the micro-energy network system, establishing the mathematical model for pPMU placement in the micro-energy network by a topology observability method, with a minimum configuration cost of pPMUs as an objective and global observability of a micro-energy network power system as a constraint condition; wherein an expression of the minimum configuration cost of pPMUs is as follows: N min cx i=1

wherein ci is a cost factor; i is a system bus number; N is a total quantity of system buses; xi is a binary variable indicating whether to place a pPMU or not, which is defined as follows: 1 , if p PMU is installed on bus i Sto, Others wherein a constraint expression of the global observability of the micro-energy network power system is as follows: F 2, if bus i is a coupled bus 0' 11,Others wherein O is observability redundancy of bus i, to indicate the number of times that bus i is observed by a measurement device, expressed by a formula as follows:

=1.jX

wherein aj is an element of a binary connectivity matrix A representing system network topology information, and: 1, if i=j or i is directly connected to j t 0, Others wherein in step 3), the optimized solution of the optimized placement model under the impact of each factor is calculated by an improved binary particle swarm optimization (BPSO) algorithm, to obtain the PMU placement state set of the micro-energy network with the minimum configuration cost; wherein step 4) comprises: after the pPMU placement state set of the micro-energy network is obtained based on the improved BPSO optimization algorithm, selecting an optimal solution of the pPMU placement in the micro-energy network by sorting system observability redundancy index (SORI) values, to obtain a pPMU placement scheme with highest system state measurement accuracy, wherein a formula of SORI is as follows: N SORI= 0, i=1 , and applying the above scheme for optimal pPMU placement in the micro-energy network to specific micro-energy network instances considering various typical influencing factors, to implement multi-adaptive optimal pPMU placement.
2. The method for multi-adaptive optimal pPMU placement in a micro-energy network according to claim 1, wherein the calculating the optimized solution of the optimized placement model under the impact of each factor by an improved BPSO algorithm, to obtain the PMU placement state set of the micro-energy network with the minimum configuration cost comprises: 31) inputting the binary connectivity matrix A of the network topology of the micro-energy network system; 32) setting parameters of the improved BPSO algorithm, comprising the number of iterations, a particle quantity, dimensionality, an inertia weight factor, learning factors, and a velocity threshold; 33) initializing particles, and if constraints are not satisfied, reinitializing the particles until the constraints are satisfied; 34) iteratively updating velocities and positions of the particles; 35) determining whether a termination condition is met; if yes, terminating an iteration process; otherwise, returning to step 34); wherein the termination condition is that the number of iterations reaches a specified upper limit; and 36) outputting a candidate solution set X with a minimum objective function.
3. The method for multi-adaptive optimal pPMU placement in a micro-energy network according to claim 1, wherein in step 2), an expression of the optimized placement model under the impact of the ZIB factor is as follows: N 1, if bus j is a ZIB 0, Others wherein N is a total quantity of system buses; yij = 1 indicates that busj is an observable ZIB; bus i is the only unobservable bus connected to busj, and in this case, a state quantity of bus i is calculated based on a ZIB rule, thus achieving observability; therefore, the observability redundancy Oi of bus i is jointly determined by the pPMU and the ZIB, with a formula as follows: N N O,=Iaijxj+ j=1 Yaijyi j=1. N N

Za xZa,,y, wherein =1 is an effect of the pPMU, and /1 represents an effect of the ZIB on the observability redundancy.
4. The method for multi-adaptive optimal pPMU placement in a micro-energy network according to claim 1, wherein in step 2), an expression of the optimized placement model under the impact of the single pPMU failure factor is as follows: N F>3,ifbusiisacoupledbus ='axi>2, Others
5. The method for multi-adaptive optimal pPMU placement in a micro-energy network according to claim 1, wherein in step 2), an expression of the optimized placement model under the impact of the single line failure factor is as follows: 0, if branch 1 is a branch connecting buses i and j a = Ja , Others

wherein I represents a faulty line, bus i and bus j are buses at two ends of the line, and in this case, the observability redundancy of bus i is: N

O,=Za~x, j=1

wherein the constraint condition of pPMU placement in the micro-energy network becomes:

1 2, ifbusiisacoupledbus 'j J !1, Others