CN105160459A - Evaluation method for stable operation state of power system - Google Patents
Evaluation method for stable operation state of power system Download PDFInfo
- Publication number
- CN105160459A CN105160459A CN201510499499.XA CN201510499499A CN105160459A CN 105160459 A CN105160459 A CN 105160459A CN 201510499499 A CN201510499499 A CN 201510499499A CN 105160459 A CN105160459 A CN 105160459A
- Authority
- CN
- China
- Prior art keywords
- electric
- condition evaluation
- stable operation
- evaluation index
- electric system
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000011156 evaluation Methods 0.000 title claims abstract description 59
- 238000003062 neural network model Methods 0.000 claims abstract description 21
- 238000000034 method Methods 0.000 claims abstract description 16
- 238000012423 maintenance Methods 0.000 claims abstract description 7
- 230000003068 static effect Effects 0.000 claims description 16
- 230000007257 malfunction Effects 0.000 claims description 11
- 238000011835 investigation Methods 0.000 claims description 5
- 238000004458 analytical method Methods 0.000 claims description 3
- 230000006378 damage Effects 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 238000012502 risk assessment Methods 0.000 description 4
- 230000009897 systematic effect Effects 0.000 description 4
- 238000000605 extraction Methods 0.000 description 3
- 239000011159 matrix material Substances 0.000 description 3
- 230000006641 stabilisation Effects 0.000 description 2
- 238000011105 stabilization Methods 0.000 description 2
- 238000002940 Newton-Raphson method Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000015556 catabolic process Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 230000005684 electric field Effects 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000005284 excitation Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
Landscapes
- Supply And Distribution Of Alternating Current (AREA)
Abstract
The invention discloses an evaluation method for the stable operation state of a power system. The evaluation method comprises that (1) an operation state evaluation index system of the power system is established; (2) an index of the operation state evaluation index system are calculated; (3) a neural network model is established according to the operation state evaluation index system and solved to obtain an operation state value of the power system; and (4) the operate state value is compared with a preset stable operation threshold to obtain the stable operation level of the power system, and a state early warning or maintenance strategy is made according to the stable operation level. The power system is evaluated in real time by establishing the operation state evaluation index system of the power system and evaluating the index system according to the operation state of the power system; and use of the neural network model greatly simplifies the modeling process of the operation state evaluation index system of the power system, reduces the computational complexity, and is low in errors and more practical.
Description
Technical field
The present invention relates to power domain, the stable operation condition evaluation method of specifically a kind of electric system.
Background technology
Along with the development of electric system, system scale is increasing, and structure becomes increasingly complex, and serious harm may be brought to electric system in anything former capital, even brings significant damage to entire society.Therefore, Timeliness coverage affects safe operation of power system risk, takes counter-measure early, to guarantee safe operation of power system and very important to the reliable power supply of user.
For avoiding electric system generation power-off event, especially the massive blackout accident that causes of cascading failure, complete Operation of Electric Systems condition evaluation index system must be set up, and on this basis Modeling Calculation indices is carried out to electric system, this runs for guarantee power system safety and stability, and the accidents such as anti-locking system generation trend is out-of-limit, collapse of voltage and cascading failure are significant.
Existing Operation of Electric Systems condition evaluation method is comparatively complicated, cause its operational efficiency low, therefore, how concisely to pass judgment on the running status of electric system efficiently, operation maintenance personnel can be carried out with clearly defined objectively accordingly dynamically patrolling dimension according to its evaluation result, and becoming the art has a problem to be solved.
Summary of the invention
The object of the present invention is to provide a kind of stable operation condition evaluation method of simple and clear electric system efficiently.
For achieving the above object, the invention provides following technical scheme:
A stable operation condition evaluation method for electric system, comprises the following steps:
(1) Operation of Electric Systems condition evaluation index system is set up;
(2) index in Operation of Electric Systems condition evaluation index system is calculated;
(3) set up neural network model according to Operation of Electric Systems condition evaluation index system, and neural network model is solved, calculate the running status value of electric system;
(4) the stable operation threshold value comparing running status value and preset, draws the residing stable operation grade of electric system, and makes status early warning according to stable operation grade and provide Strategies of Maintenance.
As the further scheme of the present invention: described Operation of Electric Systems condition evaluation index system comprises: static system Voltage Instability risk indicator, the out-of-limit risk indicator of system voltage, system load flow overload risk indicator and system cascading failure risk indicator.
As the further scheme of the present invention: described step (2) middle finger target calculates, and comprising: the reliability data of investigation electric system under day-to-day operation state, ask for the probability of malfunction of each element; The reliability data of investigation electric system under inspecting state, asks for the probability of malfunction of each element.
As the further scheme of the present invention: described reliability data comprises forced outage rate, unplanned outage number of times, unplanned outage time, planned outage number of times and planned outage time; Described element comprises circuit, bus, transformer and isolating switch; And ask for the probability of malfunction of each element under day-to-day operation and inspecting state by markov component models.
As the further scheme of the present invention: in described step (2), the calculating of index, also comprises: utilize static electric voltage stability and cutting load to calculate to carry out Operation of Electric Systems condition evaluation index system middle finger target.
As the further scheme of the present invention: for system cascading failure risk indicator, Monte Carlo Analogue Method is adopted to analyze cascading failure realistic model; For system load flow overload risk indicator, the out-of-limit risk indicator of system voltage and static system Voltage Instability risk indicator, analytical method is adopted to analyze, respectively the probability that occurs of the impact that brings to system of the event such as N-1 and N-2 of computing system equipment and event.
As the further scheme of the present invention: also adopt venture worth theory to quantize system cascading failure risk, calculate loss and the probability of happening of cascading failure.
As the further scheme of the present invention: described step (3) comprising:
To evaluate the nodes of the input layer that the index quantity of Operation of Electric Systems condition evaluation index system is neural network model, the node of the output layer of setting neural network model is the running status value of electric system;
The start node number of the hidden layer of neural network model is set, and adopt method of trial and error solve error minimum time corresponding hidden layer nodes, determine the nodes of hidden layer;
According to formula
, solve the running status value of electric system, in formula, y is running status value, and g represents the function selected, and m is input layer number, and n is node in hidden layer, x
ifor input quantity, w
ijfor linking power, v between input layer with hidden layer
jfor the link power between hidden layer and output layer, b
jfor the biased power of hidden layer, b is the biased power of output layer, and e is error amount, and t is target output value.
Compared with prior art, the invention has the beneficial effects as follows:
The present invention affects the event of power system stability operation by research, the trend that may exist from electric system is out-of-limit, voltage out-of-limit, the event establishment Operation of Electric Systems condition evaluation index system of its security of operation of the harm such as Voltage Instability and cascading failure, and according to Operation of Electric Systems condition evaluation index system, Real-Time Evaluation is carried out to electric system, which kind of interval belonging to stable operation is judged according to its evaluation result, the running status of current Operation of Electric Systems condition evaluation index system is determined with this, operation maintenance personnel can carry out with clearly defined objectively accordingly dynamically patrolling dimension thus, promote the stability that Operation of Electric Systems condition evaluation index system is run, the probability of malfunction of effective reduction Operation of Electric Systems condition evaluation index system, ensure the safe and stable operation of electrical network.
The present invention, by neural network model, greatly simplifies the process of Operation of Electric Systems condition evaluation index system being constructed to research model, decrease calculated amount, and error is little, and closing to reality is applied.
Accompanying drawing explanation
Fig. 1 is the general flow chart of the stable operation condition evaluation method of electric system.
Embodiment
Below in conjunction with the embodiment of the present invention, be clearly and completely described the technical scheme in the embodiment of the present invention, obviously, described embodiment is only the present invention's part embodiment, instead of whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art, not making the every other embodiment obtained under creative work prerequisite, belong to the scope of protection of the invention.
Refer to Fig. 1, in the embodiment of the present invention, a kind of stable operation condition evaluation method of electric system, comprises the following steps:
(1) Operation of Electric Systems condition evaluation index system is set up.
This Operation of Electric Systems condition evaluation index system covers system load flow overload risk indicator, the out-of-limit risk indicator of system voltage, static system Voltage Instability risk indicator and system cascading failure risk indicator.These risk indicators have weighed the various event of failure probability of happening of system and breakdown loss quantitatively.
(2) index in Operation of Electric Systems condition evaluation index system is calculated.
First investigate the reliability data of electric system under day-to-day operation state, ask for the probability of malfunction of each element; The reliability data of investigation electric system under inspecting state, asks for the probability of malfunction of each element.Wherein reliability data comprises forced outage rate, unplanned outage number of times, unplanned outage time, planned outage number of times and planned outage time; Described element comprises circuit, bus, transformer and isolating switch; And ask for the probability of malfunction of each element under day-to-day operation and inspecting state by markov component models.
Then utilize static electric voltage stability and cutting load to carry out the calculating of described index.
For system load flow overload risk indicator, voltage limit risk index and quiescent voltage unstability risk indicator, analytical method is adopted to analyze herein, respectively the probability that occurs of the impact that brings to system of the event such as N-1 and N-2 of computing system equipment and event.Adopt Monte Carlo Analogue Method to analyze cascading failure realistic model herein for system cascading failure risk indicator, and venture worth (VaR) theory applying financial field quantize, and calculates loss and the probability of happening of cascading failure to risk.
Continuous tide (CPF) is a kind of important method that power system steady state voltage stability is analyzed.Continuation Method finds the voltage path changed because of load growth by a kind of prediction-correcting scheme, progressively increase system loading until system reaches the static voltage stability limit.As shown in Figure 1, from basic load, predict the position of next operating point according to tangent line principle, this prediction correct by Newton-Raphson method, effectively eliminate the numerical problem that PV curve nose point place trend Jacobian matrix is unusual.The method introduces load parameter λ in power flow equation:
0≤λ≤λ
cr（1）
In formula, λ=0 represents that system loading is basic load, λ=λ
crexpression system reaches load during the static voltage stability limit.
Power flow equation is expressed as follows:
（2）
（3）
Wherein,
P
Gi＝P
Gi0（1+λk
Gi）（4）
P
Li＝P
Li0（1+λk
Li）（5）
Q
Li＝Q
Li0（1+λk
Li）（6）
In formula, k
li, k
giwhen being respectively load parameter λ change, the ratio of i-th bus load or generator output change, P
gi0the generator output on original state bus i, P
li0, Q
li0meritorious on original state bus i and load or burden without work.
By iterative computation, CPF accurately can obtain voltage stability limit point and than more complete PV curve.At limit point, λ=λ
cr, now system total load is:
（7）
Supposing the system load increase is that the power of whole load bus increases in proportion, i.e. k
li=1, then have:
（8）
（9）
In formula, P
l0it is the system total load of initial launch point.
Formula (9) matches with the definition of voltage stabilization load margin.The peak load parameter lambda obtained by CPF
crbe the load margin of system initial launch point.See from PV curve, if initial launch point is close to the static voltage stability limit point, then system loading nargin is very little, near limit point, and load margin convergence zero.Therefore, λ is passed through
crwith 0 compare the criterion that can be used as and judge certain state of system whether static voltage stability.
Described appraisal procedure comprises sets up POWER SYSTEM STATE probability model, thus utilization sets up described Risk Assessment Index System based on the static electric voltage stability of continuous tide.
Cutting load is one of Main Means of power system safety and stability emergency control, can prevent from causing systematic failures to expand because of equipment failure by rapid cut-out load.
For reducing load loss of outage as much as possible, need, under system safety operation constraint, how much to be optimized where reduction plans and reduction, this problem can be solved by setting up and solving minimum tangential load Optimized model.
In the present embodiment, minimum for optimization aim with system burden with power reduction total amount, to meet system power balance, Branch Power Flow, busbar voltage, generated output and system voltage stabilizes etc. for constraint condition, model is specific as follows:
Objective function:
（10）
In formula, N
lthe set of all load buses, C
ithe burden with power reduction of bus i, ω
iit is the weight factor of reflection load significance level.
Constraint condition:
Node power balance equation
（11）
（12）
In formula, i ∈ N
b; N
b, N
lall buses and the set of load bus respectively; P
gi, Q
githat generating bus i exerts oneself; Q
criit is the reactive power that on bus i, reactive-load compensation equipment injects; P
li, Q
liit is the load on bus i; G
ijand B
ijfor bus admittance matrix respective element, if there is FACTS element in system, then the real part of bus admittance matrix and the imaginary part function that will be FACTS element operational factor; δ
ijfor phase angle difference; According to power factor equal proportion reduction plans, the meritorious and load or burden without work reduction of bus i is C respectively
i and(Q
li/ P
li) C
i.
System cloud gray model limits
（13）
（14）
（15）
（16）
（17）
（18）
（19）
In formula,
with
bus i voltage magnitude bound respectively; N
lset of fingers, T
iwith
actual current and the rated current of branch road i respectively; N
g, N
crgenerating bus and the bus set having reactive-load compensation respectively;
,
,
,
that generating bus i injects bound that is meritorious and reactive power respectively;
,
it is the reactive power bound that i-th compensation point injects; L
ithe voltage stability index of load bus i,
for making system have certain voltage stability margin and the threshold value arranged.
The calculating of each index will be described in detail below.
Static system voltage stabilization risk assessment index calculate comprises:
11) systematic failures state set I is formed;
12) extraction system state successively from set I, enters next step and calculates;
13) carry out continuous tide calculating, judge whether system can keep static voltage stability in this state according to static system voltage stability criterion.If system voltage stabilizes, then jump to step 15); If unstability, this state is counted S set
1, enter next step and calculate;
14) utilize minimum tangential load Optimized model, generator Optimized Operation and cutting load are carried out to system, if optimize and revise rear system not need cutting load, then enter next step; If need cutting load, then this system state is counted S set
2, enter next step;
15) if the system state in set I is enumerated complete, then enter next step and calculate; Otherwise get back to step 12);
16) S is calculated
1, S
2in each accident condition probability and system risk value.
Voltage limit risk evaluation index calculates and comprises:
21) systematic failures state set I is formed;
22) extraction system state successively from set, enters next step and calculates;
23) carry out system load flow calculating, judge that whether every bar busbar voltage is out-of-limit.If do not have busbar voltage out-of-limit, then jump to step 5); Otherwise this system state is counted S set
1, enter next step and calculate;
24) carry out generated output to reschedule, if still there is voltage out-of-limit, then need cutting load, this system state is counted S set
2, then enter next step and calculate:
25) if all states in set I are enumerated complete, enter next step and calculate; Otherwise get back to step 22);
26) S is calculated
1, S
2in each accident condition probability P
jvalue-at-risk out-of-limit with system voltage.
Trend overload risk assessment index calculate comprises:
31) systematic failures state set I is formed;
32) extraction system state successively from set, enters next step and calculates;
33) carry out system load flow calculating, judge whether every bar Branch Power Flow transships.If do not have Branch Power Flow to transship, then jump to step 35); Otherwise this system state is counted S set
1, enter next step and calculate;
34) carry out generated output to reschedule, if still there is Branch Power Flow overload, then need cutting load, this system state is counted S set
2, statistics cutting load amount C, then enters next step and calculates:
35) if all system states of set I are enumerated complete, enter next step and calculate; Otherwise get back to step 2);
36) S is calculated
1, S
2in each accident condition probability P
jwith system load flow overload value-at-risk.
Risk assessment of cascading failure in power system index calculate comprises:
41) for kth time, the load level of certainty annuity and generator capacity;
42) solve trend, if trend convergence, enter next step; If trend does not restrain, call the continuous cutting system load of OPF until convergence, enter next step and calculate;
43) set fault branch set, the ratio for Branch Power Flow and rated capacity is greater than the heavily loaded branch road of α with probability β setting fault, and the branch road being less than α sets fault with probability γ, forms fault branch set L
1; For L
1in branch road set refusal with probability μ and disconnect, therefore can cause refusal disconnected branches adjacent legs fault, form fault branch set L
2; By L
1and L
2union obtains fault branch set L
outif, L
outfor sky then jumps to step 46), otherwise enter next step calculating;
44) by L
outmiddle branch road excision, determines whether isolated island and produces, if not, get back to step 42), otherwise enter next step calculating:
45) process islanding problem, return step 42);
46) add up kth time analogue system loss load, branch road and generator number, k=k+1, if k reaches maximum simulation times, terminates, otherwise returns step 41).
(3) set up neural network model according to Operation of Electric Systems condition evaluation index system, and neural network model is solved, calculate the running status value of electric system;
The nodes of the input layer being neural network model with the index quantity of described Operation of Electric Systems condition evaluation index system, the node of the output layer of setting neural network model is the running status value of electric system;
The start node number of the hidden layer of neural network model is set, and adopt method of trial and error solve error minimum time corresponding hidden layer nodes, determine the nodes of hidden layer;
According to formula
（20）
Solve the running status value of electric system, in formula, y is running status value, and g represents the function selected, and m is input layer number, and n is node in hidden layer.
Be be by above formula vector representation with vector representation:
（21）
The excitation function of hidden layer and output layer, the algorithm accordingly by neural network solves model.In the middle of solution procedure, the number of nodes of input layer is three grades of index quantity in the assessment indicator system proposed in above table; Output layer node is set to 1, i.e. the stable value of electric system; Empirically, the start node number can fixing tentatively node in hidden layer is 10, subsequently by Hidden nodes corresponding when adopting the error of method of trial and error determination neural network model minimum.
(4) the stable operation threshold value comparing running status value and preset, draws the residing stable operation grade of electric system, and makes status early warning according to stable operation grade and provide Strategies of Maintenance.
Use the steady operational status of neural network model to electric system to solve, draw stable operation value, and itself and the stable operation threshold value in the assessment indicator system set up are compared, determine the traffic coverage that this electric system is current.
According to the actual O&M demand of electric field, the stable rank of electric system can be set to 6 ranks, be respectively I grade, II grade, III grade, IV grade, V grade, VI grade, represent stationary value with S, corresponding stable threshold can be arranged according to following table 1:
The stable level parameters of table 1 electric system
According to different steady state (SS)s, corresponding O&M strategy can be provided, realize the differentiation of electric system, lean O&M.
The present invention affects the event of power system stability operation by research, the trend that may exist from electric system is out-of-limit, voltage out-of-limit, the event establishment Operation of Electric Systems condition evaluation index system of its security of operation of the harm such as Voltage Instability and cascading failure, and according to Operation of Electric Systems condition evaluation index system, Real-Time Evaluation is carried out to electric system, which kind of interval belonging to stable operation is judged according to its evaluation result, the running status of current Operation of Electric Systems condition evaluation index system is determined with this, operation maintenance personnel can carry out with clearly defined objectively accordingly dynamically patrolling dimension thus, promote the stability that Operation of Electric Systems condition evaluation index system is run, the probability of malfunction of effective reduction Operation of Electric Systems condition evaluation index system, ensure the safe and stable operation of electrical network.
The present invention, by neural network model, greatly simplifies the process of Operation of Electric Systems condition evaluation index system being constructed to research model, decrease calculated amount, and error is little, and closing to reality is applied.
To those skilled in the art, obviously the invention is not restricted to the details of above-mentioned one exemplary embodiment, and when not deviating from spirit of the present invention or essential characteristic, the present invention can be realized in other specific forms.Therefore, no matter from which point, all should embodiment be regarded as exemplary, and be nonrestrictive, scope of the present invention is limited by claims instead of above-mentioned explanation, and all changes be therefore intended in the implication of the equivalency by dropping on claim and scope are included in the present invention.
In addition, be to be understood that, although this instructions is described according to embodiment, but not each embodiment only comprises an independently technical scheme, this narrating mode of instructions is only for clarity sake, those skilled in the art should by instructions integrally, and the technical scheme in each embodiment also through appropriately combined, can form other embodiments that it will be appreciated by those skilled in the art that.
Claims (8)
1. a stable operation condition evaluation method for electric system, is characterized in that, comprise the following steps:
(1) Operation of Electric Systems condition evaluation index system is set up;
(2) index in Operation of Electric Systems condition evaluation index system is calculated;
(3) set up neural network model according to Operation of Electric Systems condition evaluation index system, and neural network model is solved, calculate the running status value of electric system;
(4) the stable operation threshold value comparing running status value and preset, draws the residing stable operation grade of electric system, and makes status early warning according to stable operation grade and provide Strategies of Maintenance.
2. the stable operation condition evaluation method of electric system according to claim 1, it is characterized in that, described Operation of Electric Systems condition evaluation index system comprises: static system Voltage Instability risk indicator, the out-of-limit risk indicator of system voltage, system load flow overload risk indicator and system cascading failure risk indicator.
3. the stable operation condition evaluation method of electric system according to claim 1, is characterized in that, described step (2) middle finger target calculates, and comprising: the reliability data of investigation electric system under day-to-day operation state, asks for the probability of malfunction of each element; The reliability data of investigation electric system under inspecting state, asks for the probability of malfunction of each element.
4. the stable operation condition evaluation method of electric system according to claim 3, is characterized in that, described reliability data comprises forced outage rate, unplanned outage number of times, unplanned outage time, planned outage number of times and planned outage time; Described element comprises circuit, bus, transformer and isolating switch; And ask for the probability of malfunction of each element under day-to-day operation and inspecting state by markov component models.
5. the stable operation condition evaluation method of electric system according to claim 3, it is characterized in that, in described step (2), the calculating of index, also comprises: utilize static electric voltage stability and cutting load to calculate to carry out Operation of Electric Systems condition evaluation index system middle finger target.
6. the stable operation condition evaluation method of electric system according to claim 5, is characterized in that, for system cascading failure risk indicator, adopts Monte Carlo Analogue Method to analyze cascading failure realistic model; For system load flow overload risk indicator, the out-of-limit risk indicator of system voltage and static system Voltage Instability risk indicator, analytical method is adopted to analyze, respectively the probability that occurs of the impact that brings to system of the event such as N-1 and N-2 of computing system equipment and event.
7. the stable operation condition evaluation method of electric system according to claim 6, is characterized in that, also adopts venture worth theory to quantize system cascading failure risk, calculates loss and the probability of happening of cascading failure.
8. the stable operation condition evaluation method of electric system according to claim 1, is characterized in that, described step (3) comprising:
To evaluate the nodes of the input layer that the index quantity of Operation of Electric Systems condition evaluation index system is neural network model, the node of the output layer of setting neural network model is the running status value of electric system;
The start node number of the hidden layer of neural network model is set, and adopt method of trial and error solve error minimum time corresponding hidden layer nodes, determine the nodes of hidden layer;
According to formula
, solve the running status value of electric system, in formula, y is running status value, and g represents the function selected, and m is input layer number, and n is node in hidden layer, x
ifor input quantity, w
ijfor linking power, v between input layer with hidden layer
jfor the link power between hidden layer and output layer, b
jfor the biased power of hidden layer, b is the biased power of output layer, and e is error amount, and t is target output value.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510499499.XA CN105160459A (en) | 2015-08-15 | 2015-08-15 | Evaluation method for stable operation state of power system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201510499499.XA CN105160459A (en) | 2015-08-15 | 2015-08-15 | Evaluation method for stable operation state of power system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN105160459A true CN105160459A (en) | 2015-12-16 |
Family
ID=54801310
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201510499499.XA Pending CN105160459A (en) | 2015-08-15 | 2015-08-15 | Evaluation method for stable operation state of power system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN105160459A (en) |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105356446A (en) * | 2016-01-07 | 2016-02-24 | 江苏省电力公司南京供电公司 | Risk evaluation method of electric power system network |
| CN106300365A (en) * | 2016-08-29 | 2017-01-04 | 东南大学 | A kind of static voltage stability control method based on air conditioner load |
| CN107179688A (en) * | 2017-06-21 | 2017-09-19 | 华北电力大学 | Consider the Power System Reliability Analysis method of Monte Carlo state sampling truncation |
| CN108336739A (en) * | 2018-01-15 | 2018-07-27 | 重庆大学 | A kind of Probabilistic Load Flow on-line calculation method based on RBF neural |
| CN110490378A (en) * | 2019-08-07 | 2019-11-22 | 中国南方电网有限责任公司 | The calculation method of Power Network Status Estimation precision based on cloud SCADA big data |
| CN110826228A (en) * | 2019-11-07 | 2020-02-21 | 国网四川省电力公司电力科学研究院 | Regional power grid operation quality limit evaluation method |
| CN111276967A (en) * | 2020-02-20 | 2020-06-12 | 中国电力科学研究院有限公司 | Elasticity capability evaluation method and device of power system |
| CN113991651A (en) * | 2021-10-27 | 2022-01-28 | 武汉大学 | Power grid static voltage stability margin probability prediction method considering new energy uncertainty |
-
2015
- 2015-08-15 CN CN201510499499.XA patent/CN105160459A/en active Pending
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105356446A (en) * | 2016-01-07 | 2016-02-24 | 江苏省电力公司南京供电公司 | Risk evaluation method of electric power system network |
| CN105356446B (en) * | 2016-01-07 | 2017-10-03 | 江苏省电力公司南京供电公司 | A kind of methods of risk assessment of power system network |
| CN106300365B (en) * | 2016-08-29 | 2018-11-06 | 东南大学 | A kind of static voltage stability control method based on air conditioner load |
| CN106300365A (en) * | 2016-08-29 | 2017-01-04 | 东南大学 | A kind of static voltage stability control method based on air conditioner load |
| CN107179688B (en) * | 2017-06-21 | 2020-06-26 | 华北电力大学 | Power system reliability analysis method considering Monte Carlo state sampling truncation |
| CN107179688A (en) * | 2017-06-21 | 2017-09-19 | 华北电力大学 | Consider the Power System Reliability Analysis method of Monte Carlo state sampling truncation |
| CN108336739A (en) * | 2018-01-15 | 2018-07-27 | 重庆大学 | A kind of Probabilistic Load Flow on-line calculation method based on RBF neural |
| CN108336739B (en) * | 2018-01-15 | 2021-04-27 | 重庆大学 | RBF neural network-based probability load flow online calculation method |
| CN110490378A (en) * | 2019-08-07 | 2019-11-22 | 中国南方电网有限责任公司 | The calculation method of Power Network Status Estimation precision based on cloud SCADA big data |
| CN110826228A (en) * | 2019-11-07 | 2020-02-21 | 国网四川省电力公司电力科学研究院 | Regional power grid operation quality limit evaluation method |
| CN111276967A (en) * | 2020-02-20 | 2020-06-12 | 中国电力科学研究院有限公司 | Elasticity capability evaluation method and device of power system |
| CN111276967B (en) * | 2020-02-20 | 2022-07-01 | 中国电力科学研究院有限公司 | Elasticity capability evaluation method and device of power system |
| CN113991651A (en) * | 2021-10-27 | 2022-01-28 | 武汉大学 | Power grid static voltage stability margin probability prediction method considering new energy uncertainty |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN105160459A (en) | Evaluation method for stable operation state of power system | |
| CN109936133B (en) | Power system vulnerability analysis method considering information and physics combined attack | |
| CN103366220A (en) | Evaluation method of operational risk of electric system | |
| Henneaux et al. | Benchmarking quasi-steady state cascading outage analysis methodologies | |
| CN103036230B (en) | A kind of Dynamic Equivalence of the alternating current-direct current series-parallel connection bulk power grid based on engineer applied | |
| Ranjbar et al. | A robust optimization method for co-planning of transmission systems and merchant distributed energy resources | |
| CN103985066A (en) | Static risk assessment method for power system based on hybrid power flow | |
| CN103310296B (en) | A kind of operation order Security Checking method based on disturbance assessment and trend analysis | |
| Safdarian et al. | A new formulation for power system reliability assessment with AC constraints | |
| Contreras-Jiménez et al. | Multi-state system reliability analysis of HVDC transmission systems using matrix-based system reliability method | |
| Rahimi et al. | Effect of communication time-delay attacks on the performance of automatic generation control | |
| CN106875127B (en) | Unified power flow controller reliability modeling and access power grid reliability assessment method thereof | |
| CN109886834B (en) | Power generation and transmission system reliability tracking algorithm based on maximum risk analysis | |
| CN113285452B (en) | Method for prejudging transient instability of power system and generating generator tripping control strategy | |
| CN113077075B (en) | New energy uncertainty electric power system safety risk prevention control method and device | |
| CN105514990A (en) | Power transmission line utilization rate improving platform and method taking economic benefits and safety into integrated consideration | |
| De Tuglie et al. | Real-time preventive actions for the enhancement of voltage-degraded trajectories | |
| Saberi et al. | A heuristic benders-decomposition-based algorithm for transient stability constrained optimal power flow | |
| Billinton et al. | Voltage stability considerations in composite power system reliability evaluation | |
| CN106779443B (en) | Operation risk determination method and device in power dispatching | |
| Faried et al. | Probabilistic technique for sizing FACTS devices for steady-state voltage profile enhancement | |
| Saberi et al. | A benders-decomposition-based transient-stability-constrained unit scheduling model utilizing cutset energy function method | |
| CN110649626B (en) | Receiving-end power grid layered optimization load shedding method and system | |
| CN110458330B (en) | Cascade section splitting optimization method and system based on grey correlation analysis method | |
| Voropai et al. | Smart technologies in emergency control of Russia's unified energy system |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| WD01 | Invention patent application deemed withdrawn after publication |
Application publication date: 20151216 |
|
| WD01 | Invention patent application deemed withdrawn after publication |